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Lozano PM, Bobb JF, Kapos FP, Cruz M, Mooney SJ, Hurvitz PM, Anau J, Theis MK, Cook A, Moudon AV, Arterburn DE, Drewnowski A. Residential Density Is Associated With BMI Trajectories in Children and Adolescents: Findings From the Moving to Health Study. AJPM Focus 2024; 3:100225. [PMID: 38682047 PMCID: PMC11046231 DOI: 10.1016/j.focus.2024.100225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
Introduction This study investigates the associations between built environment features and 3-year BMI trajectories in children and adolescents. Methods This retrospective cohort study utilized electronic health records of individuals aged 5-18 years living in King County, Washington, from 2005 to 2017. Built environment features such as residential density; counts of supermarkets, fast-food restaurants, and parks; and park area were measured using SmartMaps at 1,600-meter buffers. Linear mixed-effects models performed in 2022 tested whether built environment variables at baseline were associated with BMI change within age cohorts (5, 9, and 13 years), adjusting for sex, age, race/ethnicity, Medicaid, BMI, and residential property values (SES measure). Results At 3-year follow-up, higher residential density was associated with lower BMI increase for girls across all age cohorts and for boys in age cohorts of 5 and 13 years but not for the age cohort of 9 years. Presence of fast food was associated with higher BMI increase for boys in the age cohort of 5 years and for girls in the age cohort of 9 years. There were no significant associations between BMI change and counts of parks, and park area was only significantly associated with BMI change among boys in the age cohort of 5 years. Conclusions Higher residential density was associated with lower BMI increase in children and adolescents. The effect was small but may accumulate over the life course. Built environment factors have limited independent impact on 3-year BMI trajectories in children and adolescents.
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Affiliation(s)
- Paula Maria Lozano
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Jennifer F. Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Flavia P. Kapos
- Department of Orthopaedic Surgery and Duke Clinical Research Institute, Duke School of Medicine, Durham, North Carolina
- Center for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, Washington
| | - Maricela Cruz
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Stephen J. Mooney
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Harborview Injury Prevention & Research Center, University of Washington, Seattle, Washington
| | - Philip M. Hurvitz
- Urban Form Lab, Department of Urban Design and Planning, College of Built Environments, University of Washington, Seattle, Washington
- Center for Studies in Demography & Ecology, University of Washington, Seattle, Washington
| | - Jane Anau
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Mary Kay Theis
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Andrea Cook
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Anne Vernez Moudon
- Urban Form Lab, Department of Urban Design and Planning, College of Built Environments, University of Washington, Seattle, Washington
| | - David E. Arterburn
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Adam Drewnowski
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Center for Public Health Nutrition, University of Washington, Seattle, Washington
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Chubak J, Adler A, Bobb JF, Hawkes RJ, Ziebell RA, Pocobelli G, Ludman EJ, Zerr DM. A Randomized Controlled Trial of Animal-assisted Activities for Pediatric Oncology Patients: Psychosocial and Microbial Outcomes. J Pediatr Health Care 2024; 38:354-364. [PMID: 37930283 PMCID: PMC11066653 DOI: 10.1016/j.pedhc.2023.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 09/19/2023] [Accepted: 09/28/2023] [Indexed: 11/07/2023]
Abstract
INTRODUCTION Evidence about the effectiveness and safety of dog visits in pediatric oncology is limited. METHOD We conducted a randomized controlled trial (n=26) of dog visits versus usual care among pediatric oncology inpatients. Psychological functioning and microbial load from hand wash samples were evaluated. Parental anxiety was a secondary outcome. RESULTS We did not observe a difference in the adjusted mean present functioning score (-3.0; 95% confidence interval [CI], -12.4 to 6.4). The difference in microbial load on intervention versus control hands was -0.04 (95% CI, -0.60 to 0.52) log10 CFU/mL, with an upper 95% CI limit below the prespecified noninferiority margin. Anxiety was lower in parents of intervention versus control patients. DISCUSSION We did not detect an effect of dog visits on functioning; however, our study was underpowered by low recruitment. Visits improved parental anxiety. With hand sanitization, visits did not increase hand microbial levels. CLINICAL TRIAL REGISTRATION Clinicaltrials.gov NCT03471221.
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Rosenberg DE, Cruz MF, Mooney SJ, Bobb JF, Drewnowski A, Moudon AV, Cook AJ, Hurvitz PM, Lozano P, Anau J, Theis MK, Arterburn DE. Neighborhood built and food environment in relation to glycemic control in people with type 2 diabetes in the moving to health study. Health Place 2024; 86:103216. [PMID: 38401397 PMCID: PMC10957299 DOI: 10.1016/j.healthplace.2024.103216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/05/2024] [Accepted: 02/16/2024] [Indexed: 02/26/2024]
Abstract
OBJECTIVE To examine whether built environment and food metrics are associated with glycemic control in people with type 2 diabetes. RESEARCH DESIGN AND METHODS We included 14,985 patients with type 2 diabetes using electronic health records from Kaiser Permanente Washington. Patient addresses were geocoded with ArcGIS using King County and Esri reference data. Built environment exposures estimated from geocoded locations included residential unit density, transit threshold residential unit density, park access, and having supermarkets and fast food restaurants within 1600-m Euclidean buffers. Linear mixed effects models compared mean changes of HbA1c from baseline at 1, 3 (primary) and 5 years by each built environment variable. RESULTS Patients (mean age = 59.4 SD = 13.2, 49.5% female, 16.6% Asian, 9.8% Black, 5.5% Latino/Hispanic, 57.1% White, 20% insulin dependent, mean BMI = 32.7±7.7) had an average of 6 HbA1c measures available. Participants in the 1st tertile of residential density (lowest) had a greater decline in HbA1c (-0.42, -0.43, and -0.44 in years 1, 3, and 5 respectively) than those in the 3rd tertile (HbA1c = -0.37 at 1- and 3-years and -0.36 at 5-years; all p-values <0.05). Having any supermarkets within 1600 m of home was associated with a greater decrease in HbA1c at 1-year and 3-years compared to having none (all p-values <0.05). CONCLUSIONS Lower residential density and better proximity to supermarkets may benefit HbA1c control in people with people with type 2 diabetes. However, effects were small and indicate limited clinical significance.
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Affiliation(s)
| | - Maricela F Cruz
- Kaiser Permanente Washington Health Research Institute, USA.
| | | | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, USA.
| | | | | | - Andrea J Cook
- Kaiser Permanente Washington Health Research Institute, USA.
| | - Philip M Hurvitz
- University of Washington, Center for Studies in Demography and Ecology, USA.
| | - Paula Lozano
- Kaiser Permanente Washington Health Research Institute, USA.
| | - Jane Anau
- Kaiser Permanente Washington Health Research Institute, USA.
| | - Mary Kay Theis
- Kaiser Permanente Washington Health Research Institute, USA.
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Qiu H, Cook AJ, Bobb JF. Evaluating tests for cluster-randomized trials with few clusters under generalized linear mixed models with covariate adjustment: A simulation study. Stat Med 2024; 43:201-215. [PMID: 37933766 PMCID: PMC10872819 DOI: 10.1002/sim.9950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 10/04/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023]
Abstract
Generalized linear mixed models (GLMM) are commonly used to analyze clustered data, but when the number of clusters is small to moderate, standard statistical tests may produce elevated type I error rates. Small-sample corrections have been proposed for continuous or binary outcomes without covariate adjustment. However, appropriate tests to use for count outcomes or under covariate-adjusted models remains unknown. An important setting in which this issue arises is in cluster-randomized trials (CRTs). Because many CRTs have just a few clusters (eg, clinics or health systems), covariate adjustment is particularly critical to address potential chance imbalance and/or low power (eg, adjustment following stratified randomization or for the baseline value of the outcome). We conducted simulations to evaluate GLMM-based tests of the treatment effect that account for the small (10) or moderate (20) number of clusters under a parallel-group CRT setting across scenarios of covariate adjustment (including adjustment for one or more person-level or cluster-level covariates) for both binary and count outcomes. We find that when the intraclass correlation is non-negligible (≥ $$ \ge $$ 0.01) and the number of covariates is small (≤ $$ \le $$ 2), likelihood ratio tests with a between-within denominator degree of freedom have type I error rates close to the nominal level. When the number of covariates is moderate (≥ $$ \ge $$ 5), across our simulation scenarios, the relative performance of the tests varied considerably and no method performed uniformly well. Therefore, we recommend adjusting for no more than a few covariates and using likelihood ratio tests with a between-within denominator degree of freedom.
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Affiliation(s)
- Hongxiang Qiu
- Department of Epiidemiology and Biostatistics, Michigan State University, Michigan, United States
| | - Andrea J. Cook
- Biostatistics unit, Kaiser Permanente Washington Health Research Institute, Washington, United States
- Department of Biostatistics, University of Washington, Washington, United States
| | - Jennifer F. Bobb
- Biostatistics unit, Kaiser Permanente Washington Health Research Institute, Washington, United States
- Department of Biostatistics, University of Washington, Washington, United States
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Wartko PD, Bobb JF, Boudreau DM, Matthews AG, McCormack J, Lee AK, Qiu H, Yu O, Hyun N, Idu AE, Campbell CI, Saxon AJ, Liu DS, Altschuler A, Samet JH, Labelle CT, Zare-Mehrjerdi M, Stotts AL, Braciszewski JM, Murphy MT, Dryden D, Arnsten JH, Cunningham CO, Horigian VE, Szapocznik J, Glass JE, Caldeiro RM, Phillips RC, Shea M, Bart G, Schwartz RP, McNeely J, Liebschutz JM, Tsui JI, Merrill JO, Lapham GT, Addis M, Bradley KA, Ghiroli MM, Hamilton LK, Hu Y, LaHue JS, Loree AM, Murphy SM, Northrup TF, Shmueli-Blumberg D, Silva AJ, Weinstein ZM, Wong MT, Burganowski RP. Nurse Care Management for Opioid Use Disorder Treatment: The PROUD Cluster Randomized Clinical Trial. JAMA Intern Med 2023; 183:1343-1354. [PMID: 37902748 PMCID: PMC10616772 DOI: 10.1001/jamainternmed.2023.5701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/01/2023] [Indexed: 10/31/2023]
Abstract
Importance Few primary care (PC) practices treat patients with medications for opioid use disorder (OUD) despite availability of effective treatments. Objective To assess whether implementation of the Massachusetts model of nurse care management for OUD in PC increases OUD treatment with buprenorphine or extended-release injectable naltrexone and secondarily decreases acute care utilization. Design, Setting, and Participants The Primary Care Opioid Use Disorders Treatment (PROUD) trial was a mixed-methods, implementation-effectiveness cluster randomized clinical trial conducted in 6 diverse health systems across 5 US states (New York, Florida, Michigan, Texas, and Washington). Two PC clinics in each system were randomized to intervention or usual care (UC) stratified by system (5 systems were notified on February 28, 2018, and 1 system with delayed data use agreement on August 31, 2018). Data were obtained from electronic health records and insurance claims. An implementation monitoring team collected qualitative data. Primary care patients were included if they were 16 to 90 years old and visited a participating clinic from up to 3 years before a system's randomization date through 2 years after. Intervention The PROUD intervention included 3 components: (1) salary for a full-time OUD nurse care manager; (2) training and technical assistance for nurse care managers; and (3) 3 or more PC clinicians agreeing to prescribe buprenorphine. Main Outcomes and Measures The primary outcome was a clinic-level measure of patient-years of OUD treatment (buprenorphine or extended-release injectable naltrexone) per 10 000 PC patients during the 2 years postrandomization (follow-up). The secondary outcome, among patients with OUD prerandomization, was a patient-level measure of the number of days of acute care utilization during follow-up. Results During the baseline period, a total of 130 623 patients were seen in intervention clinics (mean [SD] age, 48.6 [17.7] years; 59.7% female), and 159 459 patients were seen in UC clinics (mean [SD] age, 47.2 [17.5] years; 63.0% female). Intervention clinics provided 8.2 (95% CI, 5.4-∞) more patient-years of OUD treatment per 10 000 PC patients compared with UC clinics (P = .002). Most of the benefit accrued in 2 health systems and in patients new to clinics (5.8 [95% CI, 1.3-∞] more patient-years) or newly treated for OUD postrandomization (8.3 [95% CI, 4.3-∞] more patient-years). Qualitative data indicated that keys to successful implementation included broad commitment to treat OUD in PC from system leaders and PC teams, full financial coverage for OUD treatment, and straightforward pathways for patients to access nurse care managers. Acute care utilization did not differ between intervention and UC clinics (relative rate, 1.16; 95% CI, 0.47-2.92; P = .70). Conclusions and Relevance The PROUD cluster randomized clinical trial intervention meaningfully increased PC OUD treatment, albeit unevenly across health systems; however, it did not decrease acute care utilization among patients with OUD. Trial Registration ClinicalTrials.gov Identifier: NCT03407638.
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Affiliation(s)
- Paige D Wartko
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Biostatistics, School of Public Health, University of Washington, Seattle
| | - Denise M Boudreau
- Kaiser Permanente Washington Health Research Institute, Seattle
- Now with Genentech Inc, South San Francisco, California
| | | | | | - Amy K Lee
- Kaiser Permanente Washington Health Research Institute, Seattle
- Now with Kaiser Permanente Washington, Renton
| | - Hongxiang Qiu
- Kaiser Permanente Washington Health Research Institute, Seattle
- Now with Department of Epidemiology and Biostatistics, Michigan State University, East Lansing
| | - Onchee Yu
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Noorie Hyun
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Abisola E Idu
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Cynthia I Campbell
- Division of Research, Kaiser Permanente Northern California, Oakland
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
| | - Andrew J Saxon
- Center of Excellence in Substance Addiction Treatment and Education, VA Puget Sound Health Care System, Seattle, Washington
| | - David S Liu
- National Institute on Drug Abuse Center for Clinical Trials Network, North Bethesda, Maryland
| | - Andrea Altschuler
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Jeffrey H Samet
- Boston University Schools of Medicine and Public Health, Boston Medical Center, Boston, Massachusetts
| | - Colleen T Labelle
- Boston University Schools of Medicine and Public Health, Boston Medical Center, Boston, Massachusetts
| | - Mohammad Zare-Mehrjerdi
- Department of Family and Community Medicine, UTHealth Houston McGovern Medical School, Houston, Texas
| | - Angela L Stotts
- Department of Family and Community Medicine, UTHealth Houston McGovern Medical School, Houston, Texas
| | - Jordan M Braciszewski
- Center for Health Policy and Health Services Research, Henry Ford Health, Detroit, Michigan
| | | | - Douglas Dryden
- MultiCare Health System, Tacoma, Washington
- Now with Mosaic Medical, Bend, Oregon
| | - Julia H Arnsten
- Montefiore Medical Center, Bronx, New York
- Albert Einstein College of Medicine, Bronx, New York
| | - Chinazo O Cunningham
- Albert Einstein College of Medicine, Bronx, New York
- Now with New York State Office of Addiction Services and Supports, New York
| | - Viviana E Horigian
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, Florida
| | - José Szapocznik
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, Florida
| | - Joseph E Glass
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Ryan M Caldeiro
- Mental Health and Wellness Department, Kaiser Permanente Washington, Renton
| | | | - Mary Shea
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Gavin Bart
- Department of Medicine, Hennepin Healthcare, Minneapolis, Minnesota
- University of Minnesota Medical School, Minneapolis
| | | | - Jennifer McNeely
- Department of Population Health, Grossman School of Medicine, New York University, New York
| | - Jane M Liebschutz
- Center for Research on Health Care, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Judith I Tsui
- Division of General Internal Medicine, Department of Medicine, University of Washington School of Medicine, Seattle
| | - Joseph O Merrill
- Division of General Internal Medicine, Department of Medicine, University of Washington School of Medicine, Seattle
| | - Gwen T Lapham
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, School of Public Health, University of Washington, Seattle
| | - Megan Addis
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Katharine A Bradley
- Kaiser Permanente Washington Health Research Institute, Seattle
- Division of General Internal Medicine, Department of Medicine, University of Washington School of Medicine, Seattle
- Kaiser Permanente Bernard J Tyson School of Medicine, Pasadena, California
| | - Megan M Ghiroli
- Montefiore Medical Center, Bronx, New York
- Albert Einstein College of Medicine, Bronx, New York
| | - Leah K Hamilton
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Yong Hu
- Center for Health Policy and Health Services Research, Henry Ford Health, Detroit, Michigan
| | | | - Amy M Loree
- Center for Health Policy and Health Services Research, Henry Ford Health, Detroit, Michigan
| | - Sean M Murphy
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | - Thomas F Northrup
- Department of Family and Community Medicine, UTHealth Houston McGovern Medical School, Houston, Texas
| | | | | | - Zoe M Weinstein
- Boston University Schools of Medicine and Public Health, Boston Medical Center, Boston, Massachusetts
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Lapham GT, Matson TE, Bobb JF, Luce C, Oliver MM, Hamilton LK, Bradley KA. Prevalence of Cannabis Use Disorder and Reasons for Use Among Adults in a US State Where Recreational Cannabis Use Is Legal. JAMA Netw Open 2023; 6:e2328934. [PMID: 37642968 PMCID: PMC10466162 DOI: 10.1001/jamanetworkopen.2023.28934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/06/2023] [Indexed: 08/31/2023] Open
Abstract
Importance Medical and nonmedical cannabis use and cannabis use disorders (CUD) have increased with increasing cannabis legalization. However, the prevalence of CUD among primary care patients who use cannabis for medical or nonmedical reasons is unknown for patients in states with legal recreational use. Objective To estimate the prevalence and severity of CUD among patients who report medical use only, nonmedical use only, and both reasons for cannabis use in a state with legal recreational use. Design, Setting, and Participants This cross-sectional survey study took place at an integrated health system in Washington State. Among 108 950 adult patients who completed routine cannabis screening from March 2019 to September 2019, 5000 were selected for a confidential cannabis survey using stratified random sampling for frequency of past-year cannabis use and race and ethnicity. Among 1688 respondents, 1463 reporting past 30-day cannabis use were included in the study. Exposure Patient survey-reported reason for cannabis use in the past 30 days: medical use only, nonmedical use only, and both reasons. Main Outcomes and Measures Patient responses to the Composite International Diagnostic Interview-Substance Abuse Module for CUD, corresponding to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition CUD severity (0-11 symptoms) were categorized as any CUD (≥2 symptoms) and moderate to severe CUD (≥4 symptoms). Adjusted analyses were weighted for survey stratification and nonresponse for primary care population estimates and compared prevalence of CUD across reasons for cannabis use. Results Of 1463 included primary care patients (weighted mean [SD] age, 47.4 [16.8] years; 748 [weighted proportion, 61.9%] female) who used cannabis, 42.4% (95% CI, 31.2%-54.3%) reported medical use only, 25.1% (95% CI, 17.8%-34.2%) nonmedical use only, and 32.5% (95% CI, 25.3%-40.8%) both reasons for use. The prevalence of CUD was 21.3% (95% CI, 15.4%-28.6%) and did not vary across groups. The prevalence of moderate to severe CUD was 6.5% (95% CI, 5.0%-8.6%) and differed across groups: 1.3% (95% CI, 0.0%-2.8%) for medical use, 7.2% (95% CI, 3.9%-10.4%) for nonmedical use, and 7.5% (95% CI, 5.7%-9.4%) for both reasons for use (P = .01). Conclusions and Relevance In this cross-sectional study of primary care patients in a state with legal recreational cannabis use, CUD was common among patients who used cannabis. Moderate to severe CUD was more prevalent among patients who reported any nonmedical use. These results underscore the importance of assessing patient cannabis use and CUD symptoms in medical settings.
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Affiliation(s)
- Gwen T. Lapham
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, University of Washington, Seattle
| | - Theresa E. Matson
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, University of Washington, Seattle
| | | | - Casey Luce
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Malia M. Oliver
- Kaiser Permanente Washington Health Research Institute, Seattle
| | | | - Katharine A. Bradley
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, University of Washington, Seattle
- Department of Medicine, University of Washington, Seattle
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Hallgren KA, Jack HE, Oliver M, Berger D, Bobb JF, Kivlahan DR, Bradley KA. Changes in alcohol consumption reported on routine healthcare screenings are associated with changes in depression symptoms. Alcohol Clin Exp Res 2023. [PMID: 37326806 DOI: 10.1111/acer.15075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/24/2023] [Accepted: 03/28/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND The Alcohol Use Disorders Identification Test-Consumption version (AUDIT-C) has been robustly validated as a point-in-time screen for unhealthy alcohol use, but less is known about the significance of changes in AUDIT-C scores from routine screenings over time. Unhealthy alcohol use and depression commonly co-occur, and changes in drinking often co-occur with changes in depression symptoms. We assess the associations between changes in AUDIT-C scores and changes in depression symptoms reported on brief screens completed in routine care. METHODS The study sample included 198,335 primary care patients who completed two AUDIT-C screens 11 to 24 months apart and the Patient Health Questionnaire-2 (PHQ-2) depression screen on the same day as each AUDIT-C. Both screening measures were completed as part of routine care within a large health system in Washington state. AUDIT-C scores were categorized to reflect five drinking levels at both time points, resulting in 25 subgroups with different change patterns. For each of the 25 subgroups, within-group changes in the prevalence of positive PHQ-2 depression screens were characterized using risk ratios (RRs) and McNemar's tests. RESULTS Patient subgroups with increases in AUDIT-C risk categories generally experienced increases in the prevalence of positive depression screens (RRs ranging from 0.95 to 2.00). Patient subgroups with decreases in AUDIT-C risk categories generally experienced decreases in the prevalence of positive depression screens (RRs ranging from 0.52 to 1.01). Patient subgroups that did not have changes in AUDIT-C risk categories experienced little or no change in the prevalence of positive depression screens (RRs ranging from 0.98 to 1.15). CONCLUSIONS As hypothesized, changes in alcohol consumption reported on AUDIT-C screens completed in routine care were associated with changes in depression screening results. Results support the validity and clinical utility of monitoring changes in AUDIT-C scores over time as a meaningful measure of changes in drinking.
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Affiliation(s)
- Kevin A Hallgren
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, Washington, USA
| | - Helen E Jack
- Division of General Internal Medicine, University of Washington, Seattle, Washington, USA
| | - Malia Oliver
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Douglas Berger
- Division of General Internal Medicine, University of Washington, Seattle, Washington, USA
- General Medicine Service VA Puget Sound, Seattle, Washington, USA
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Daniel R Kivlahan
- Center of Innovation for Veteran-Centered and Value-Driven Care, Health Services Research and Development, Veteran Affairs Puget Sound HealthCare System, Seattle, Washington, USA
| | - Katharine A Bradley
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, Washington, USA
- Division of General Internal Medicine, University of Washington, Seattle, Washington, USA
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Jack HE, Oliver MM, Berger DB, Bobb JF, Bradley KA, Hallgren KA. Association between clinical measures of unhealthy alcohol use and subsequent year hospital admissions in a primary care population. Drug Alcohol Depend 2023; 245:109821. [PMID: 36871376 PMCID: PMC10149294 DOI: 10.1016/j.drugalcdep.2023.109821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 02/27/2023]
Abstract
BACKGROUND Screening for unhealthy alcohol use in primary care may help identify patients at risk for negative health outcomes. AIMS This study examined the associations between 1) screening with the AUDIT-C (alcohol consumption) and 2) an Alcohol Symptom Checklist (symptoms of alcohol use disorder) and subsequent-year hospitalizations. METHODS This retrospective cohort study was conducted in 29 primary care clinics in Washington State. Patients were screened in routine care (10/1/2016-2/1/2019) with the AUDIT-C (0-12) and administered the Alcohol Symptom Checklist (0-11) if they had AUDIT-C score ≥ 7. All-cause hospitalizations were measured within 1 year of the AUDIT-C and Alcohol Symptom Checklist. AUDIT-C and Alcohol Symptom Checklist scores were categorized based on previously used cut-points. FINDINGS Of 305,376 patients with AUDIT-Cs, 5.3% of patients were hospitalized in the following year. AUDIT-C scores had a J-shaped relationship with hospitalizations, with risk for all-cause hospitalizations higher for patients with the AUDIT-C scores 9-12 (12.1%; 95% CI: 10.6-13.7%, relative to a comparison group of those with AUDIT-C scores 1-2 (female)/1-3 (male) (3.7%; 95% CI: 3.6-3.8%), adjusted for socio-demographics. Patients with AUDIT-C ≥ 7 and Alcohol Symptom Checklist scores reflecting severe AUD were at increased risk of hospitalization (14.6%, 95% CI: 11.9-17.9%) relative to those with lower scores. CONCLUSIONS Higher AUDIT-C scores were associated with higher incidence of hospitalizations except among people with low-level drinking. Among patients with AUDIT-C ≥ 7, the Alcohol Symptom Checklist identified patients at increased risk of hospitalization. This study helps demonstrate the potential clinical utility of the AUDIT-C and Alcohol Symptom Checklist.
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Affiliation(s)
- Helen E Jack
- Division of General Internal Medicine, Department of Medicine, University of Washington, 325 9th Ave, P.O. Box 359780, Seattle, WA 98104, USA.
| | - Malia M Oliver
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Suite 1600, Seattle, WA 98101, USA
| | - Douglas B Berger
- Division of General Internal Medicine, Department of Medicine, University of Washington, 325 9th Ave, P.O. Box 359780, Seattle, WA 98104, USA; General Medicine Service, Veteran Affairs Puget Sound Health Care System, 1660 S Columbian Way, Seattle, WA 98108, USA
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Suite 1600, Seattle, WA 98101, USA
| | - Katharine A Bradley
- Division of General Internal Medicine, Department of Medicine, University of Washington, 325 9th Ave, P.O. Box 359780, Seattle, WA 98104, USA; Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Suite 1600, Seattle, WA 98101, USA; Department of Health Systems and Population Health, University of Washington, 4060 E. Stevens Way NE, Seattle, WA 98195, USA
| | - Kevin A Hallgren
- Department of Psychiatry and Behavioral Sciences, University of Washington, 1959 NE Pacific Street, P.O. Box 356560, Seattle, WA 98195, USA
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DeBar LL, Bushey MA, Kroenke K, Bobb JF, Schoenbaum M, Thompson EE, Justice M, Zatzick D, Hamilton LK, McMullen CK, Hallgren KA, Benes LL, Forman DP, Caldeiro RM, Brown RP, Campbell NL, Anderson ML, Son S, Haggstrom DA, Whiteside L, Schleyer TKL, Bradley KA. A patient-centered nurse-supported primary care-based collaborative care program to treat opioid use disorder and depression: Design and protocol for the MI-CARE randomized controlled trial. Contemp Clin Trials 2023; 127:107124. [PMID: 36804450 PMCID: PMC10065939 DOI: 10.1016/j.cct.2023.107124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/14/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023]
Abstract
BACKGROUND Opioid use disorder (OUD) contributes to rising morbidity and mortality. Life-saving OUD treatments can be provided in primary care but most patients with OUD don't receive treatment. Comorbid depression and other conditions complicate OUD management, especially in primary care. The MI-CARE trial is a pragmatic randomized encouragement (Zelen) trial testing whether offering collaborative care (CC) to patients with OUD and clinically-significant depressive symptoms increases OUD medication treatment with buprenorphine and improves depression outcomes compared to usual care. METHODS Adult primary care patients with OUD and depressive symptoms (n ≥ 800) from two statewide health systems: Kaiser Permanente Washington and Indiana University Health are identified with computer algorithms from electronic Health record (EHR) data and automatically enrolled. A random sub-sample (50%) of eligible patients is offered the MI-CARE intervention: a 12-month nurse-driven CC intervention that includes motivational interviewing and behavioral activation. The remaining 50% of the study cohort comprise the usual care comparison group and is never contacted. The primary outcome is days of buprenorphine treatment provided during the intervention period. The powered secondary outcome is change in Patient Health Questionnaire (PHQ)-9 depression scores. Both outcomes are obtained from secondary electronic healthcare sources and compared in "intent-to-treat" analyses. CONCLUSION MI-CARE addresses the need for rigorous encouragement trials to evaluate benefits of offering CC to generalizable samples of patients with OUD and mental health conditions identified from EHRs, as they would be in practice, and comparing outcomes to usual primary care. We describe the design and implementation of the trial, currently underway. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT05122676. Clinical trial registration date: November 17, 2021.
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Affiliation(s)
- Lynn L DeBar
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States of America; The Center for Health Research, Kaiser Permanente Northwest, Portland, OR, United States of America.
| | - Michael A Bushey
- Indiana University School of Medicine, Department of Psychiatry, Indianapolis, IN, United States of America
| | - Kurt Kroenke
- Indiana University School of Medicine, Department of Medicine and Regenstrief Institute, Indianapolis, IN, United States of America
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States of America
| | - Michael Schoenbaum
- National Institute of Mental Health, Bethesda, MD, United States of America
| | - Ella E Thompson
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States of America
| | - Morgan Justice
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States of America
| | - Douglas Zatzick
- University of Washington, Harborview Medical Center, Seattle, WA, United States of America
| | - Leah K Hamilton
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States of America
| | - Carmit K McMullen
- The Center for Health Research, Kaiser Permanente Northwest, Portland, OR, United States of America
| | | | - Lindsay L Benes
- Montana State University, Bozeman, MT, United States of America
| | - David P Forman
- University of New Mexico, Albuquerque, NM, United States of America
| | - Ryan M Caldeiro
- Kaiser Permanente of Washington, Seattle, WA, United States of America
| | - Ryan P Brown
- Indiana University Health, Indianapolis, IN, United States of America
| | - Noll L Campbell
- Purdue University College of Pharmacy, West Lafayette, IN, United States of America
| | - Melissa L Anderson
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States of America
| | - Sungtaek Son
- University of Washington, Seattle, WA, United States of America
| | - David A Haggstrom
- VA HSR&D Center for Health Information and Communication, Indianapolis VA Medical Center, Indianapolis, IN, United States of America
| | - Lauren Whiteside
- University of Washington, Harborview Medical Center, Seattle, WA, United States of America
| | - Titus K L Schleyer
- Indiana University School of Medicine, Department of Psychiatry, Indianapolis, IN, United States of America
| | - Katharine A Bradley
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States of America; University of Washington, Seattle, WA, United States of America
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10
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Lee AK, Bobb JF, Richards JE, Achtmeyer CE, Ludman E, Oliver M, Caldeiro RM, Parrish R, Lozano PM, Lapham GT, Williams EC, Glass JE, Bradley KA. Integrating Alcohol-Related Prevention and Treatment Into Primary Care: A Cluster Randomized Implementation Trial. JAMA Intern Med 2023; 183:319-328. [PMID: 36848119 PMCID: PMC9972247 DOI: 10.1001/jamainternmed.2022.7083] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/24/2022] [Indexed: 03/01/2023]
Abstract
Importance Unhealthy alcohol use is common and affects morbidity and mortality but is often neglected in medical settings, despite guidelines for both prevention and treatment. Objective To test an implementation intervention to increase (1) population-based alcohol-related prevention with brief interventions and (2) treatment of alcohol use disorder (AUD) in primary care implemented with a broader program of behavioral health integration. Design, Setting, and Participants The Sustained Patient-Centered Alcohol-Related Care (SPARC) trial was a stepped-wedge cluster randomized implementation trial, including 22 primary care practices in an integrated health system in Washington state. Participants consisted of all adult patients (aged ≥18 years) with primary care visits from January 2015 to July 2018. Data were analyzed from August 2018 to March 2021. Interventions The implementation intervention included 3 strategies: practice facilitation; electronic health record decision support; and performance feedback. Practices were randomly assigned launch dates, which placed them in 1 of 7 waves and defined the start of the practice's intervention period. Main Outcomes and Measures Coprimary outcomes for prevention and AUD treatment were (1) the proportion of patients who had unhealthy alcohol use and brief intervention documented in the electronic health record (brief intervention) for prevention and (2) the proportion of patients who had newly diagnosed AUD and engaged in AUD treatment (AUD treatment engagement). Analyses compared monthly rates of primary and intermediate outcomes (eg, screening, diagnosis, treatment initiation) among all patients who visited primary care during usual care and intervention periods using mixed-effects regression. Results A total of 333 596 patients visited primary care (mean [SD] age, 48 [18] years; 193 583 [58%] female; 234 764 [70%] White individuals). The proportion with brief intervention was higher during SPARC intervention than usual care periods (57 vs 11 per 10 000 patients per month; P < .001). The proportion with AUD treatment engagement did not differ during intervention and usual care (1.4 vs 1.8 per 10 000 patients; P = .30). The intervention increased intermediate outcomes: screening (83.2% vs 20.8%; P < .001), new AUD diagnosis (33.8 vs 28.8 per 10 000; P = .003), and treatment initiation (7.8 vs 6.2 per 10 000; P = .04). Conclusions and Relevance In this stepped-wedge cluster randomized implementation trial, the SPARC intervention resulted in modest increases in prevention (brief intervention) but not AUD treatment engagement in primary care, despite important increases in screening, new diagnoses, and treatment initiation. Trial Registration ClinicalTrials.gov Identifier: NCT02675777.
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Affiliation(s)
- Amy K. Lee
- Kaiser Permanente Washington Health Research Institute, Seattle
- Mental Health and Wellness Department, Kaiser Permanente Washington, Seattle
| | | | - Julie E. Richards
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, School of Public Health, University of Washington, Seattle
| | - Carol E. Achtmeyer
- Veterans Affairs Puget Sound Health Care System, Health Services Research & Development, Denver-Seattle Center of Innovation for Veteran-Centered and Value-Driven Care, Seattle, Washington
| | - Evette Ludman
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Malia Oliver
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Ryan M. Caldeiro
- Mental Health and Wellness Department, Kaiser Permanente Washington, Seattle
| | - Rebecca Parrish
- Mental Health and Wellness Department, Kaiser Permanente Washington, Seattle
| | - Paula M. Lozano
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Gwen T. Lapham
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, School of Public Health, University of Washington, Seattle
| | - Emily C. Williams
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, School of Public Health, University of Washington, Seattle
- Veterans Affairs Puget Sound Health Care System, Health Services Research & Development, Denver-Seattle Center of Innovation for Veteran-Centered and Value-Driven Care, Seattle, Washington
| | - Joseph E. Glass
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, School of Public Health, University of Washington, Seattle
- Department of Psychiatry and Behavioral Sciences, School of Medicine, University of Washington, Seattle
| | - Katharine A. Bradley
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, School of Public Health, University of Washington, Seattle
- Department of Medicine, School of Medicine, University of Washington, Seattle
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11
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Chubak J, Pocobelli G, Ziebell RA, Hawkes RJ, Adler A, Bobb JF, Zerr DM. Effects of the COVID-19 Pandemic on Animal-Assisted Activities in Pediatric Hospitals. J Pediatr Health Care 2023; 37:173-178. [PMID: 36266165 PMCID: PMC9547756 DOI: 10.1016/j.pedhc.2022.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/23/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION The goal of this study was to document current hospital-based animal-assisted activities (AAA) practices. METHOD We contacted 20 hospitals and asked about their AAA programs, including COVID-19 precautions. RESULTS Eighteen of 20 hospitals responded. Before 2020, all offered either in-person only (n = 17) or both in-person and virtual AAA visits (n = 1). In early 2022, 13 provided in-person visits; the five hospitals that had not resumed in-person visits planned to restart. Most hospitals stopped group visits. Most required that patients and handlers be free of COVID-19 symptoms and that handlers be vaccinated and wear masks and eye protection. Most did not require COVID-19 vaccination for patients. None required handlers to test negative for COVID-19. DISCUSSION The COVID-19 pandemic impacted hospital-based pediatric AAA. Future studies should assess the effectiveness of virtual AAA and of precautions to prevent COVID-19 transmission between patients and AAA volunteers.
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Affiliation(s)
- Jessica Chubak
- Jessica Chubak, Senior Investigator, Kaiser Permanente Washington Health Research Institute, Seattle, WA.
| | - Gaia Pocobelli
- Gaia Pocobelli, Senior Collaborative Scientist, Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Rebecca A Ziebell
- Rebecca A. Ziebell, Manager, Data Reporting & Analytics, Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Rene J Hawkes
- Rene J. Hawkes, Project Manager, Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Amanda Adler
- Amanda Adler, Clinical Research Manager, Seattle Children's Hospital, Seattle, WA
| | - Jennifer F Bobb
- Jennifer F. Bobb, Associate Investigator, Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Danielle M Zerr
- Danielle M. Zerr, Professor and Division Chief of Pediatric Infectious Disease, Seattle Children's Hospital, Seattle, WA
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12
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Glass JE, Dorsey CN, Beatty T, Bobb JF, Wong ES, Palazzo L, King D, Mogk J, Stefanik-Guizlo K, Idu A, Key D, Fortney JC, Thomas R, McWethy AG, Caldeiro RM, Bradley KA. Study protocol for a factorial-randomized controlled trial evaluating the implementation, costs, effectiveness, and sustainment of digital therapeutics for substance use disorder in primary care (DIGITS Trial). Implement Sci 2023; 18:3. [PMID: 36726127 PMCID: PMC9893639 DOI: 10.1186/s13012-022-01258-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 12/22/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Experts recommend that treatment for substance use disorder (SUD) be integrated into primary care. The Digital Therapeutics for Opioids and Other SUD (DIGITS) Trial tests strategies for implementing reSET® and reSET-O®, which are prescription digital therapeutics for SUD and opioid use disorder, respectively, that include the community reinforcement approach, contingency management, and fluency training to reinforce concept mastery. This purpose of this trial is to test whether two implementation strategies improve implementation success (Aim 1) and achieve better population-level cost effectiveness (Aim 2) over a standard implementation approach. METHODS/DESIGN The DIGITS Trial is a hybrid type III cluster-randomized trial. It examines outcomes of implementation strategies, rather than studying clinical outcomes of a digital therapeutic. It includes 22 primary care clinics from a healthcare system in Washington State and patients with unhealthy substance use who visit clinics during an active implementation period (up to one year). Primary care clinics implemented reSET and reSET-O using a multifaceted implementation strategy previously used by clinical leaders to roll-out smartphone apps ("standard implementation" including discrete strategies such as clinician training, electronic health record tools). Clinics were randomized as 21 sites in a 2x2 factorial design to receive up to two added implementation strategies: (1) practice facilitation, and/or (2) health coaching. Outcome data are derived from electronic health records and logs of digital therapeutic usage. Aim 1's primary outcomes include reach of the digital therapeutics to patients and fidelity of patients' use of the digital therapeutics to clinical recommendations. Substance use and engagement in SUD care are additional outcomes. In Aim 2, population-level cost effectiveness analysis will inform the economic benefit of the implementation strategies compared to standard implementation. Implementation is monitored using formative evaluation, and sustainment will be studied for up to one year using qualitative and quantitative research methods. DISCUSSION The DIGITS Trial uses an experimental design to test whether implementation strategies increase and improve the delivery of digital therapeutics for SUDs when embedded in a large healthcare system. It will provide data on the potential benefits and cost-effectiveness of alternative implementation strategies. CLINICALTRIALS gov Identifier: NCT05160233 (Submitted 12/3/2021). https://clinicaltrials.gov/ct2/show/NCT05160233.
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Affiliation(s)
- Joseph E. Glass
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA
| | - Caitlin N. Dorsey
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA
| | - Tara Beatty
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA
| | - Jennifer F. Bobb
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA
| | - Edwin S. Wong
- grid.34477.330000000122986657Department of Health Systems and Population Health, School of Public Health, University of Washington, Box 351621, 3980 15th Ave. NE, Fourth Floor, Seattle, WA 98195 USA ,grid.418356.d0000 0004 0478 7015Department of Veterans Affairs, Health Services Research and Development, Center of Innovation, 1660 S Columbian Way, WA 98108 Seattle, USA
| | - Lorella Palazzo
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA
| | - Deborah King
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA
| | - Jessica Mogk
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA
| | - Kelsey Stefanik-Guizlo
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA
| | - Abisola Idu
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA
| | - Dustin Key
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA
| | - John C. Fortney
- grid.418356.d0000 0004 0478 7015Department of Veterans Affairs, Health Services Research and Development, Center of Innovation, 1660 S Columbian Way, WA 98108 Seattle, USA ,grid.34477.330000000122986657Department of Psychiatry and Behavioral Sciences, School of Medicine, University of Washington, Box 356560, 1959 NE Pacific Street, Seattle, WA 98195 USA
| | - Rosemarie Thomas
- Kaiser Permanente Washington Mental Health & Wellness Services, 1200 SW 27th St, Renton, WA 98057 USA
| | - Angela Garza McWethy
- Kaiser Permanente Washington Mental Health & Wellness Services, 1200 SW 27th St, Renton, WA 98057 USA
| | - Ryan M. Caldeiro
- Kaiser Permanente Washington Mental Health & Wellness Services, 1200 SW 27th St, Renton, WA 98057 USA
| | - Katharine A. Bradley
- grid.488833.c0000 0004 0615 7519Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 USA
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13
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Wartko PD, Qiu H, Idu AE, Yu O, McCormack J, Matthews AG, Bobb JF, Saxon AJ, Campbell CI, Liu D, Braciszewski JM, Murphy SM, Burganowski RP, Murphy MT, Horigian VE, Hamilton LK, Lee AK, Boudreau DM, Bradley KA. Baseline representativeness of patients in clinics enrolled in the PRimary care Opioid Use Disorders treatment (PROUD) trial: comparison of trial and non-trial clinics in the same health systems. BMC Health Serv Res 2022; 22:1593. [PMID: 36581845 PMCID: PMC9801668 DOI: 10.1186/s12913-022-08915-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 11/30/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Pragmatic primary care trials aim to test interventions in "real world" health care settings, but clinics willing and able to participate in trials may not be representative of typical clinics. This analysis compared patients in participating and non-participating clinics from the same health systems at baseline in the PRimary care Opioid Use Disorders treatment (PROUD) trial. METHODS This observational analysis relied on secondary electronic health record and administrative claims data in 5 of 6 health systems in the PROUD trial. The sample included patients 16-90 years at an eligible primary care visit in the 3 years before randomization. Each system contributed 2 randomized PROUD trial clinics and 4 similarly sized non-trial clinics. We summarized patient characteristics in trial and non-trial clinics in the 2 years before randomization ("baseline"). Using mixed-effect regression models, we compared trial and non-trial clinics on a baseline measure of the primary trial outcome (clinic-level patient-years of opioid use disorder (OUD) treatment, scaled per 10,000 primary care patients seen) and a baseline measure of the secondary trial outcome (patient-level days of acute care utilization among patients with OUD). RESULTS Patients were generally similar between the 10 trial clinics (n = 248,436) and 20 non-trial clinics (n = 341,130), although trial clinics' patients were slightly younger, more likely to be Hispanic/Latinx, less likely to be white, more likely to have Medicaid/subsidized insurance, and lived in less wealthy neighborhoods. Baseline outcomes did not differ between trial and non-trial clinics: trial clinics had 1.0 more patient-year of OUD treatment per 10,000 patients (95% CI: - 2.9, 5.0) and a 4% higher rate of days of acute care utilization than non-trial clinics (rate ratio: 1.04; 95% CI: 0.76, 1.42). CONCLUSIONS trial clinics and non-trial clinics were similar regarding most measured patient characteristics, and no differences were observed in baseline measures of trial primary and secondary outcomes. These findings suggest trial clinics were representative of comparably sized clinics within the same health systems. Although results do not reflect generalizability more broadly, this study illustrates an approach to assess representativeness of clinics in future pragmatic primary care trials.
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Affiliation(s)
- Paige D Wartko
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Ste 1600, Seattle, WA, 98101, United States.
| | - Hongxiang Qiu
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Ste 1600, Seattle, WA, 98101, United States
- Department of Biostatistics, University of Washington, 1705 NE Pacific Street, Seattle, WA, 98195, United States
- Department of Statistics and Data Science, University of Pennsylvania, 3451 Walnut St Philadelphia, Philadelphia, PA, 19104, United States
| | - Abisola E Idu
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Ste 1600, Seattle, WA, 98101, United States
| | - Onchee Yu
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Ste 1600, Seattle, WA, 98101, United States
| | - Jennifer McCormack
- The Emmes Company, 401 N Washington St #700, Rockville, MD, 20850, United States
| | - Abigail G Matthews
- The Emmes Company, 401 N Washington St #700, Rockville, MD, 20850, United States
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Ste 1600, Seattle, WA, 98101, United States
| | - Andrew J Saxon
- Center of Excellence in Substance Addiction Treatment and Education, VA Puget Sound Health Care System, 1660 S Columbian Way, Seattle, WA, 98108, United States
| | - Cynthia I Campbell
- Kaiser Permanente Northern California Division of Research, 2000 Broadway, Oakland, CA, 94612, United States
| | - David Liu
- National Institute on Drug Abuse Center for Clinical Trials Network, Three White Flint North, 11601 Landsdown Street, North Bethesda, MD, 20852, United States
| | | | - Sean M Murphy
- Department of Population Health Sciences, Weill Cornell Medical College, 1300 York Ave, New York, NY, 10065, United States
| | - Rachael P Burganowski
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Ste 1600, Seattle, WA, 98101, United States
| | - Mark T Murphy
- MultiCare Health System, 315 Martin Luther King Jr. Way, Tacoma, WA, 98415, United States
| | - Viviana E Horigian
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, 1120 NW 14th St, CRB 919, Miami, FL, 33136, United States
| | - Leah K Hamilton
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Ste 1600, Seattle, WA, 98101, United States
| | - Amy K Lee
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Ste 1600, Seattle, WA, 98101, United States
| | - Denise M Boudreau
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Ste 1600, Seattle, WA, 98101, United States
- Genentech, 1 DNA Way, South San Francisco, CA, 94080, United States
| | - Katharine A Bradley
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Ste 1600, Seattle, WA, 98101, United States
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14
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Braciszewski JM, Idu AE, Yarborough BJH, Stumbo SP, Bobb JF, Bradley KA, Rossom RC, Murphy MT, Binswanger IA, Campbell CI, Glass JE, Matson TE, Lapham GT, Loree AM, Barbosa-Leiker C, Hatch MA, Tsui JI, Arnsten JH, Stotts A, Horigian V, Hutcheson R, Bart G, Saxon AJ, Thakral M, Ling Grant D, Pflugeisen CM, Usaga I, Madziwa LT, Silva A, Boudreau DM. Sex Differences in Comorbid Mental and Substance Use Disorders Among Primary Care Patients With Opioid Use Disorder. Psychiatr Serv 2022; 73:1330-1337. [PMID: 35707859 PMCID: PMC9722542 DOI: 10.1176/appi.ps.202100665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
OBJECTIVE The authors sought to characterize the 3-year prevalence of mental disorders and nonnicotine substance use disorders among male and female primary care patients with documented opioid use disorder across large U.S. health systems. METHODS This retrospective study used 2014-2016 data from patients ages ≥16 years in six health systems. Diagnoses were obtained from electronic health records or claims data; opioid use disorder treatment with buprenorphine or injectable extended-release naltrexone was determined through prescription and procedure data. Adjusted prevalence of comorbid conditions among patients with opioid use disorder (with or without treatment), stratified by sex, was estimated by fitting logistic regression models for each condition and applying marginal standardization. RESULTS Females (53.2%, N=7,431) and males (46.8%, N=6,548) had a similar prevalence of opioid use disorder. Comorbid mental disorders among those with opioid use disorder were more prevalent among females (86.4% vs. 74.3%, respectively), whereas comorbid other substance use disorders (excluding nicotine) were more common among males (51.9% vs. 60.9%, respectively). These differences held for those receiving medication treatment for opioid use disorder, with mental disorders being more common among treated females (83% vs. 71%) and other substance use disorders more common among treated males (68% vs. 63%). Among patients with a single mental health condition comorbid with opioid use disorder, females were less likely than males to receive medication treatment for opioid use disorder (15% vs. 20%, respectively). CONCLUSIONS The high rate of comorbid conditions among patients with opioid use disorder indicates a strong need to supply primary care providers with adequate resources for integrated opioid use disorder treatment.
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Affiliation(s)
- Jordan M Braciszewski
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Abisola E Idu
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Bobbi Jo H Yarborough
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Scott P Stumbo
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Jennifer F Bobb
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Katharine A Bradley
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Rebecca C Rossom
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Mark T Murphy
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Ingrid A Binswanger
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Cynthia I Campbell
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Joseph E Glass
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Theresa E Matson
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Gwen T Lapham
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Amy M Loree
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Celestina Barbosa-Leiker
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Mary A Hatch
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Judith I Tsui
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Julia H Arnsten
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Angela Stotts
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Viviana Horigian
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Rebecca Hutcheson
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Gavin Bart
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Andrew J Saxon
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Manu Thakral
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Deborah Ling Grant
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Chaya Mangel Pflugeisen
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Ingrid Usaga
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Lawrence T Madziwa
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Angela Silva
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
| | - Denise M Boudreau
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit (Braciszewski, Loree); Kaiser Permanente Washington Health Research Institute (KPWHRI), Seattle (Idu, Bobb, Bradley, Glass, Matson, Lapham, Madziwa); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Yarborough, Stumbo); HealthPartners Institute and Department of Research, University of Minnesota, Minneapolis (Rossom); MultiCare Institute for Research and Innovation, MultiCare Health System, Tacoma, Washington (Murphy, Pflugeisen, Silva); Kaiser Permanente Colorado Institute for Health Research, Colorado Permanente Medical Group, Department of Health System Science, Bernard J. Tyson Kaiser Permanente School of Medicine, University of Colorado School of Medicine, Aurora (Binswanger); Kaiser Permanente Northern California Division of Research, Oakland (Campbell); Department of Health Systems and Population Health, University of Washington, Seattle (Lapham, Hutcheson); Washington State University Health Sciences Spokane, Spokane (Barbosa-Leiker); Department of Psychiatry and Behavioral Sciences and Addictions, Drug and Alcohol Institute, University of Washington, Seattle (Hatch); Department of Medicine, University of Washington and Harborview Medical Center, Seattle (Tsui); Albert Einstein College of Medicine, Montefiore Medical Center, New York City (Arnsten); Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston (Stotts); Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami (Horigian, Usaga); Hennepin Healthcare and Department of Medicine, University of Minnesota Medical School, Minneapolis (Bart); Veterans Affairs Puget Sound Health Care System, Seattle (Saxon); Manning College of Nursing and Health Sciences, University of Massachusetts, Boston (Thakral); Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena (Ling Grant); Genentech, Inc., San Francisco (Boudreau)
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15
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Matson TE, Lapham GT, Bobb JF, Oliver M, Hallgren KA, Williams EC, Bradley KA. Validity of the Single-Item Screen-Cannabis (SIS-C) for Cannabis Use Disorder Screening in Routine Care. JAMA Netw Open 2022; 5:e2239772. [PMID: 36318205 PMCID: PMC9627408 DOI: 10.1001/jamanetworkopen.2022.39772] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 09/16/2022] [Indexed: 11/05/2022] Open
Abstract
Importance Cannabis use is prevalent and increasing, and frequent use intensifies the risk of cannabis use disorder (CUD). CUD is underrecognized in medical settings, but a validated single-item cannabis screen could increase recognition. Objective To evaluate the Single-Item Screen-Cannabis (SIS-C), administered and documented in routine primary care, compared with a confidential reference standard measure of CUD. Design, Setting, and Participants This diagnostic study included a sample of adult patients who completed routine cannabis screening between January 28 and September 12, 2019, and were randomly selected for a confidential survey about cannabis use. Random sampling was stratified by frequency of past-year use and race and ethnicity. The study was conducted at an integrated health system in Washington state, where adult cannabis use is legal. Data were analyzed from May 2021 to March 2022. Exposures The SIS-C asks about frequency of past-year cannabis use with responses (none, less than monthly, monthly, weekly, daily or almost daily) documented in patients' medical records. Main Outcomes and Measures The Diagnostic and Statistical Manual, Fifth Edition (DSM-5) Composite International Diagnostic Interview-Substance Abuse Module (CIDI-SAM) for past-year CUD was completed on a confidential survey and considered the reference standard. The SIS-C was compared with 2 or more criteria on the CIDI-SAM, consistent with CUD. All analyses were weighted, accounting for survey design and nonresponse, to obtain estimates representative of the health system primary care population. Results Of 5000 sampled adult patients, 1688 responded to the cannabis survey (34% response rate). Patients were predominantly middle-aged (weighted mean [SD] age, 50.7 [18.1]), female or women (weighted proportion [SE], 55.9% [4.1]), non-Hispanic (weighted proportion [SE], 96.7% [1.0]), and White (weighted proportion [SE], 74.2% [3.7]). Approximately 6.6% of patients met criteria for past-year CUD. The SIS-C had an area under receiver operating characteristic curve of 0.89 (95% CI, 0.78-0.96) for identifying CUD. A threshold of less than monthly cannabis use balanced sensitivity (0.88) and specificity (0.83) for detecting CUD. In populations with a 6% prevalence of CUD, predictive values of a positive screen ranged from 17% to 34%, while predictive values of a negative screen ranged from 97% to 100%. Conclusions and Relevance In this diagnostic study, the SIS-C had excellent performance characteristics in routine care as a screen for CUD. While high negative predictive values suggest that the SIS-C accurately identifies patients without CUD, low positive predictive values indicate a need for further diagnostic assessment following positive results when screening for CUD in primary care.
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Affiliation(s)
- Theresa E. Matson
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle
- Health Services Research & Development Center for Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, Washington
| | - Gwen T. Lapham
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle
| | - Jennifer F. Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Biostatistics, University of Washington School of Public Health, Seattle
| | - Malia Oliver
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Kevin A. Hallgren
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle
| | - Emily C. Williams
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle
- Health Services Research & Development Center for Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, Washington
| | - Katharine A. Bradley
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle
- Department of Medicine, University of Washington School of Medicine, Seattle
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16
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Cruz M, Drewnowski A, Bobb JF, Hurvitz PM, Moudon AV, Cook A, Mooney SJ, Buszkiewicz JH, Lozano P, Rosenberg DE, Kapos F, Theis MK, Anau J, Arterburn D. Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study. Epidemiology 2022; 33:747-755. [PMID: 35609209 PMCID: PMC9378543 DOI: 10.1097/ede.0000000000001505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND Neighborhoods may play an important role in shaping long-term weight trajectory and obesity risk. Studying the impact of moving to another neighborhood may be the most efficient way to determine the impact of the built environment on health. We explored whether residential moves were associated with changes in body weight. METHODS Kaiser Permanente Washington electronic health records were used to identify 21,502 members aged 18-64 who moved within King County, WA between 2005 and 2017. We linked body weight measures to environment measures, including population, residential, and street intersection densities (800 m and 1,600 m Euclidian buffers) and access to supermarkets and fast foods (1,600 m and 5,000 m network distances). We used linear mixed models to estimate associations between postmove changes in environment and changes in body weight. RESULTS In general, moving from high-density to moderate- or low-density neighborhoods was associated with greater weight gain postmove. For example, those moving from high to low residential density neighborhoods (within 1,600 m) gained an average of 4.5 (95% confidence interval [CI] = 3.0, 5.9) lbs 3 years after moving, whereas those moving from low to high-density neighborhoods gained an average of 1.3 (95% CI = -0.2, 2.9) lbs. Also, those moving from neighborhoods without fast-food access (within 1600m) to other neighborhoods without fast-food access gained less weight (average 1.6 lbs [95% CI = 0.9, 2.4]) than those moving from and to neighborhoods with fast-food access (average 2.8 lbs [95% CI = 2.5, 3.2]). CONCLUSIONS Moving to higher-density neighborhoods may be associated with reductions in adult weight gain.
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Affiliation(s)
- Maricela Cruz
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Adam Drewnowski
- Center for Public Health Nutrition, 305 Raitt Hall, #353410, University of Washington, Seattle, WA, 98195-3410, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Jennifer F. Bobb
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Philip M Hurvitz
- Urban Form Lab, Department of Urban Design and Planning, College of Built Environments, University of Washington, 4333 Brooklyn Ave NE, Seattle, Washington 98195, USA
- Center for Studies in Demography and Ecology, University of Washington, Seattle, WA, 98195-3410, USA
| | - Anne Vernez Moudon
- Urban Form Lab, Department of Urban Design and Planning, College of Built Environments, University of Washington, 4333 Brooklyn Ave NE, Seattle, Washington 98195, USA
| | - Andrea Cook
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Stephen J. Mooney
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - James H. Buszkiewicz
- Center for Public Health Nutrition, 305 Raitt Hall, #353410, University of Washington, Seattle, WA, 98195-3410, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Paula Lozano
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - Dori E. Rosenberg
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - Flavia Kapos
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Mary Kay Theis
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - Jane Anau
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - David Arterburn
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
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17
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Hallgren KA, Matson TE, Oliver M, Witkiewitz K, Bobb JF, Lee AK, Caldeiro RM, Kivlahan D, Bradley KA. Practical Assessment of Alcohol Use Disorder in Routine Primary Care: Performance of an Alcohol Symptom Checklist. J Gen Intern Med 2022; 37:1885-1893. [PMID: 34398395 PMCID: PMC9198160 DOI: 10.1007/s11606-021-07038-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 07/13/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Alcohol use disorder (AUD) is highly prevalent but underrecognized and undertreated in primary care settings. Alcohol Symptom Checklists can engage patients and providers in discussions of AUD-related care. However, the performance of Alcohol Symptom Checklists when they are used in routine care and documented in electronic health records (EHRs) remains unevaluated. OBJECTIVE To evaluate the psychometric performance of an Alcohol Symptom Checklist in routine primary care. DESIGN Cross-sectional study using item response theory (IRT) and differential item functioning analyses of measurement consistency across age, sex, race, and ethnicity. PATIENTS Patients seen in primary care in the Kaiser Permanente Washington Healthcare System who reported high-risk drinking on the Alcohol Use Disorder Identification Test Consumption screening measure (AUDIT-C ≥ 7) and subsequently completed an Alcohol Symptom Checklist between October 2015 and February 2020. MAIN MEASURE Alcohol Symptom Checklists with 11 items assessing AUD criteria defined in the Diagnostic and Statistical Manual for Mental Disorders, 5th edition (DSM-5), completed by patients during routine medical care and documented in EHRs. KEY RESULTS Among 11,464 patients who screened positive for high-risk drinking and completed an Alcohol Symptom Checklist (mean age 43.6 years, 30.5% female), 54.1% reported ≥ 2 DSM-5 AUD criteria (threshold for AUD diagnosis). IRT analyses demonstrated that checklist items measured a unidimensional continuum of AUD severity. Differential item functioning was observed for some demographic subgroups but had minimal impact on accurate measurement of AUD severity, with differences between demographic subgroups attributable to differential item functioning never exceeding 0.42 points of the total symptom count (of a possible range of 0-11). CONCLUSIONS Alcohol Symptom Checklists used in routine care discriminated AUD severity consistently with current definitions of AUD and performed equitably across age, sex, race, and ethnicity. Integrating symptom checklists into routine care may help inform clinical decision-making around diagnosing and managing AUD.
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Affiliation(s)
- Kevin A Hallgren
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA.
| | - Theresa E Matson
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Malia Oliver
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Katie Witkiewitz
- Department of Psychology and Center on Alcohol, Substance Use, and Addictions, University of New Mexico, Albuquerque, NM, USA
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Amy K Lee
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Ryan M Caldeiro
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Daniel Kivlahan
- Center of Innovation for Veteran-Centered and Value-Driven Care, Health Services Research and Development, Veteran Affairs Puget Sound Health Care System, Seattle, WA, USA
| | - Katharine A Bradley
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Center of Innovation for Veteran-Centered and Value-Driven Care, Health Services Research and Development, Veteran Affairs Puget Sound Health Care System, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
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18
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Lapham GT, Matson TE, Carrell DS, Bobb JF, Luce C, Oliver MM, Ghitza UE, Hsu C, Browne KC, Binswanger IA, Campbell CI, Saxon AJ, Vandrey R, Schauer GL, Pacula RL, Horberg MA, Bailey SR, McClure EA, Bradley KA. Comparison of Medical Cannabis Use Reported on a Confidential Survey vs Documented in the Electronic Health Record Among Primary Care Patients. JAMA Netw Open 2022; 5:e2211677. [PMID: 35604691 PMCID: PMC9127557 DOI: 10.1001/jamanetworkopen.2022.11677] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 03/23/2022] [Indexed: 12/18/2022] Open
Abstract
Importance Patients who use cannabis for medical reasons may benefit from discussions with clinicians about health risks of cannabis and evidence-based treatment alternatives. However, little is known about the prevalence of medical cannabis use in primary care and how often it is documented in patient electronic health records (EHR). Objective To estimate the primary care prevalence of medical cannabis use according to confidential patient survey and to compare the prevalence of medical cannabis use documented in the EHR with patient report. Design, Setting, and Participants This study is a cross-sectional survey performed in a large health system that conducts routine cannabis screening in Washington state where medical and nonmedical cannabis use are legal. Among 108 950 patients who completed routine cannabis screening (between March 28, 2019, and September 12, 2019), 5000 were randomly selected for a confidential survey about cannabis use, using stratified random sampling for frequency of past-year use and patient race and ethnicity. Data were analyzed from November 2020 to December 2021. Exposures Survey measures of patient-reported past-year cannabis use, medical cannabis use (ie, explicit medical use), and any health reason(s) for use (ie, implicit medical use). Main Outcomes and Measures Survey data were linked to EHR data in the year before screening. EHR measures included documentation of explicit and/or implicit medical cannabis use. Analyses estimated the primary care prevalence of cannabis use and compared EHR-documented with patient-reported medical cannabis use, accounting for stratified sampling and nonresponse. Results Overall, 1688 patients responded to the survey (34% response rate; mean [SD] age, 50.7 [17.5] years; 861 female [56%], 1184 White [74%], 1514 non-Hispanic [97%], and 1059 commercially insured [65%]). The primary care prevalence of any past-year patient-reported cannabis use on the survey was 38.8% (95% CI, 31.9%-46.1%), whereas the prevalence of explicit and implicit medical use were 26.5% (95% CI, 21.6%-31.3%) and 35.1% (95% CI, 29.3%-40.8%), respectively. The prevalence of EHR-documented medical cannabis use was 4.8% (95% CI, 3.45%-6.2%). Compared with patient-reported explicit medical use, the sensitivity and specificity of EHR-documented medical cannabis use were 10.0% (95% CI, 4.4%-15.6%) and 97.1% (95% CI, 94.4%-99.8%), respectively. Conclusions and Relevance These findings suggest that medical cannabis use is common among primary care patients in a state with legal use, and most use is not documented in the EHR. Patient report of health reasons for cannabis use identifies more medical use compared with explicit questions about medical use.
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Affiliation(s)
- Gwen T. Lapham
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, University of Washington, Seattle
| | - Theresa E. Matson
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Systems and Population Health, University of Washington, Seattle
| | | | | | - Casey Luce
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Malia M. Oliver
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Udi E. Ghitza
- Center for the Clinical Trials Network, National Institute on Drug Abuse, National Institutes of Health, Bethesda, Maryland
| | - Clarissa Hsu
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Kendall C. Browne
- Center of Excellence in Substance Addiction Treatment and Education, Veteran Affairs Puget Sound Health Care System, Seattle, Washington
| | - Ingrid A. Binswanger
- Kaiser Permanente Colorado Institute for Health Research, Denver
- Colorado Permanente Medical Group, Denver
| | | | - Andrew J. Saxon
- Center of Excellence in Substance Addiction Treatment and Education, Veteran Affairs Puget Sound Health Care System, Seattle, Washington
| | - Ryan Vandrey
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - Rosalie Liccardo Pacula
- Price School of Public Policy, University of Southern California, Los Angeles
- Leonard D Schaeffer Center for Health Policy & Economics, University of Southern California, Los Angeles
| | - Michael A. Horberg
- Kaiser Permanente Mid-Atlantic Permanente Research Institute, North Bethesda, Maryland
| | - Steffani R. Bailey
- Department of Family Medicine, Oregon Health & Science University, Portland
| | - Erin A. McClure
- Medical University of South Carolina College of Medicine, Charleston
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19
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Devick KL, Bobb JF, Mazumdar M, Henn BC, Bellinger DC, Christiani DC, Wright RO, Williams PL, Coull BA, Valeri L. Bayesian kernel machine regression-causal mediation analysis. Stat Med 2022; 41:860-876. [PMID: 34993981 PMCID: PMC9150437 DOI: 10.1002/sim.9255] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 09/02/2021] [Accepted: 10/23/2021] [Indexed: 12/11/2022]
Abstract
Greater understanding of the pathways through which an environmental mixture operates is important to design effective interventions. We present new methodology to estimate natural direct and indirect effects and controlled direct effects of a complex mixture exposure on an outcome through a mediator variable. We implement Bayesian Kernel Machine Regression (BKMR) to allow for all possible interactions and nonlinear effects of (1) the co-exposures on the mediator, (2) the co-exposures and mediator on the outcome, and (3) selected covariates on the mediator and/or outcome. From the posterior predictive distributions of the mediator and outcome, we simulate counterfactuals to obtain posterior samples, estimates, and credible intervals of the mediation effects. Our simulation study demonstrates that when the exposure-mediator and exposure-mediator-outcome relationships are complex, BKMR-Causal Mediation Analysis performs better than current mediation methods. We applied our methodology to quantify the contribution of birth length as a mediator between in utero co-exposure to arsenic, manganese, and lead, and children's neurodevelopmental scores, in a prospective birth cohort in Bangladesh. Among younger children, we found a negative (adverse) association between the metal mixture and neurodevelopment. We also found evidence that birth length mediates the effect of exposure to the metal mixture on neurodevelopment for younger children. If birth length were fixed to its 75 t h percentile value, the harmful effect of the metal mixture on neurodevelopment is attenuated, suggesting nutritional interventions to help increase fetal growth, and thus birth length, could potentially block the harmful effect of the metal mixture on neurodevelopment.
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Affiliation(s)
- Katrina L. Devick
- Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, Arizona
| | - Jennifer F. Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Maitreyi Mazumdar
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Birgit Claus Henn
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts
| | - David C. Bellinger
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - David C. Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Robert O. Wright
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Paige L. Williams
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston Massachusetts
| | - Brent A. Coull
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Linda Valeri
- Department of Biostatistics, Columbia Mailman School of Public Health, New York New York
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20
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Richards JE, Boggs JM, Rowhani-Rahbar A, Kuo E, Betz ME, Bobb JF, Simon GE. Patient-Reported Firearm Access Prior to Suicide Death. JAMA Netw Open 2022; 5:e2142204. [PMID: 35006250 PMCID: PMC8749466 DOI: 10.1001/jamanetworkopen.2021.42204] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 11/11/2021] [Indexed: 11/29/2022] Open
Affiliation(s)
- Julie E. Richards
- Kaiser Permanente Washington Heath Research Institute, Seattle, Washington
- Department of Health Services, University of Washington, Seattle
| | | | - Ali Rowhani-Rahbar
- Department of Epidemiology, School of Public Health, University of Washington, Seattle
- Firearm Injury and Policy Research Program, Harborview Injury Prevention & Research Center, Seattle, Washington
| | - Elena Kuo
- Kaiser Permanente Washington Heath Research Institute, Seattle, Washington
| | - Marian E. Betz
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora
| | - Jennifer F. Bobb
- Kaiser Permanente Washington Heath Research Institute, Seattle, Washington
| | - Gregory E. Simon
- Kaiser Permanente Washington Heath Research Institute, Seattle, Washington
- Psychiatry and Behavioral Sciences, University of Washington, Seattle
- Kaiser Permanente Washington Department of Mental Health & Wellness, Seattle, Washington
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21
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Carrell DS, Cronkite DJ, Shea M, Oliver M, Luce C, Matson TE, Bobb JF, Hsu C, Binswanger IA, Browne KC, Saxon AJ, McCormack J, Jelstrom E, Ghitza UE, Campbell CI, Bradley KA, Lapham GT. Clinical documentation of patient-reported medical cannabis use in primary care: Toward scalable extraction using natural language processing methods. Subst Abus 2022; 43:917-924. [PMID: 35254218 PMCID: PMC9134865 DOI: 10.1080/08897077.2021.1986767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background: Most states have legalized medical cannabis, yet little is known about how medical cannabis use is documented in patients' electronic health records (EHRs). We used natural language processing (NLP) to calculate the prevalence of clinician-documented medical cannabis use among adults in an integrated health system in Washington State where medical and recreational use are legal. Methods: We analyzed EHRs of patients ≥18 years old screened for past-year cannabis use (November 1, 2017-October 31, 2018), to identify clinician-documented medical cannabis use. We defined medical use as any documentation of cannabis that was recommended by a clinician or described by the clinician or patient as intended to manage health conditions or symptoms. We developed and applied an NLP system that included NLP-assisted manual review to identify such documentation in encounter notes. Results: Medical cannabis use was documented for 16,684 (5.6%) of 299,597 outpatient encounters with routine screening for cannabis use among 203,489 patients seeing 1,274 clinicians. The validated NLP system identified 54% of documentation and NLP-assisted manual review the remainder. Language documenting reasons for cannabis use included 125 terms indicating medical use, 28 terms indicating non-medical use and 41 ambiguous terms. Implicit documentation of medical use (e.g., "edible THC nightly for lumbar pain") was more common than explicit (e.g., "continues medical cannabis use"). Conclusions: Clinicians use diverse and often ambiguous language to document patients' reasons for cannabis use. Automating extraction of documentation about patients' cannabis use could facilitate clinical decision support and epidemiological investigation but will require large amounts of gold standard training data.
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Affiliation(s)
- David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - David J Cronkite
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Mary Shea
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Malia Oliver
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Casey Luce
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Theresa E Matson
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Clarissa Hsu
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | | | - Kendall C Browne
- Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
| | - Andrew J Saxon
- Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
| | | | | | - Udi E Ghitza
- National Institutes of Health, National Institutes on Drug Abuse, Rockville, MD, USA
| | - Cynthia I Campbell
- Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
| | | | - Gwen T Lapham
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
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22
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Buszkiewicz JH, Bobb JF, Kapos F, Hurvitz PM, Arterburn D, Moudon AV, Cook A, Mooney SJ, Cruz M, Gupta S, Lozano P, Rosenberg DE, Theis MK, Anau J, Drewnowski A. Differential associations of the built environment on weight gain by sex and race/ethnicity but not age. Int J Obes (Lond) 2021; 45:2648-2656. [PMID: 34453098 PMCID: PMC8608695 DOI: 10.1038/s41366-021-00937-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 07/19/2021] [Accepted: 08/04/2021] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To explore the built environment (BE) and weight change relationship by age, sex, and racial/ethnic subgroups in adults. METHODS Weight trajectories were estimated using electronic health records for 115,260 insured Kaiser Permanente Washington members age 18-64 years. Member home addresses were geocoded using ArcGIS. Population, residential, and road intersection densities and counts of area supermarkets and fast food restaurants were measured with SmartMaps (800 and 5000-meter buffers) and categorized into tertiles. Linear mixed-effect models tested whether associations between BE features and weight gain at 1, 3, and 5 years differed by age, sex, and race/ethnicity, adjusting for demographics, baseline weight, and residential property values. RESULTS Denser urban form and greater availability of supermarkets and fast food restaurants were associated with differential weight change across sex and race/ethnicity. At 5 years, the mean difference in weight change comparing the 3rd versus 1st tertile of residential density was significantly different between males (-0.49 kg, 95% CI: -0.68, -0.30) and females (-0.17 kg, 95% CI: -0.33, -0.01) (P-value for interaction = 0.011). Across race/ethnicity, the mean difference in weight change at 5 years for residential density was significantly different among non-Hispanic (NH) Whites (-0.47 kg, 95% CI: -0.61, -0.32), NH Blacks (-0.86 kg, 95% CI: -1.37, -0.36), Hispanics (0.10 kg, 95% CI: -0.46, 0.65), and NH Asians (0.44 kg, 95% CI: 0.10, 0.78) (P-value for interaction <0.001). These findings were consistent for other BE measures. CONCLUSION The relationship between the built environment and weight change differs across demographic groups. Careful consideration of demographic differences in associations of BE and weight trajectories is warranted for investigating etiological mechanisms and guiding intervention development.
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Affiliation(s)
- James H Buszkiewicz
- Center for Public Health Nutrition, 305 Raitt Hall, #353410, University of Washington, Seattle, WA, USA.
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Flavia Kapos
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Philip M Hurvitz
- Urban Form Lab, Department of Urban Design and Planning, College of Built Environments, University of Washington, Seattle, WA, USA
- Center for Studies in Demography and Ecology, University of Washington, Raitt Hall, Seattle, WA, USA
| | - David Arterburn
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Anne Vernez Moudon
- Urban Form Lab, Department of Urban Design and Planning, College of Built Environments, University of Washington, Seattle, WA, USA
| | - Andrea Cook
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Stephen J Mooney
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Maricela Cruz
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Shilpi Gupta
- Center for Public Health Nutrition, 305 Raitt Hall, #353410, University of Washington, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Paula Lozano
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Dori E Rosenberg
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Mary Kay Theis
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Jane Anau
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Adam Drewnowski
- Center for Public Health Nutrition, 305 Raitt Hall, #353410, University of Washington, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
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23
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Buszkiewicz JH, Bobb JF, Hurvitz PM, Arterburn D, Moudon AV, Cook A, Mooney SJ, Cruz M, Gupta S, Lozano P, Rosenberg DE, Theis MK, Anau J, Drewnowski A. Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain. Int J Obes (Lond) 2021; 45:1914-1924. [PMID: 33976378 PMCID: PMC8592117 DOI: 10.1038/s41366-021-00836-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 04/23/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults. METHODS Weight trajectories over a 5-year period were obtained from electronic health records for 115,260 insured patients aged 18-64 years in the Kaiser Permanente Washington health care system. Home addresses were geocoded using ArcGIS. Built environment variables were population, residential unit, and road intersection densities captured using Euclidean-based SmartMaps at 800-m buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1600, and 5000-m buffers. Property values were a measure of socioeconomic status. Linear mixed effects models tested whether built environment variables at baseline were associated with long-term weight gain, adjusting for sex, age, race/ethnicity, Medicaid insurance, body weight, and residential property values. RESULTS Built environment variables at baseline were associated with differences in baseline obesity prevalence and body mass index but had limited impact on weight trajectories. Mean weight gain for the full cohort was 0.06 kg at 1 year (95% CI: 0.03, 0.10); 0.64 kg at 3 years (95% CI: 0.59, 0.68), and 0.95 kg at 5 years (95% CI: 0.90, 1.00). In adjusted regression models, the top tertile of density metrics and frequency counts were associated with lower weight gain at 5-years follow-up compared to the bottom tertiles, though the mean differences in weight change for each follow-up year (1, 3, and 5) did not exceed 0.5 kg. CONCLUSIONS Built environment variables that were associated with higher obesity prevalence at baseline had limited independent obesogenic power with respect to weight gain over time. Residential unit density had the strongest negative association with weight gain. Future work on the influence of built environment variables on health should also examine social context, including residential segregation and residential mobility.
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Affiliation(s)
- James H. Buszkiewicz
- Center for Public Health Nutrition, 305 Raitt Hall, #353410, University of Washington, Seattle, WA, 98195-3410, USA,Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Jennifer F. Bobb
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - Philip M Hurvitz
- Urban Form Lab, Department of Urban Design and Planning, College of Built Environments, University of Washington, 4333 Brooklyn Ave NE, Seattle, Washington 98195, USA,Center for Studies in Demography and Ecology, University of Washington, Seattle, WA, 98195-3410, USA
| | - David Arterburn
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - Anne Vernez Moudon
- Urban Form Lab, Department of Urban Design and Planning, College of Built Environments, University of Washington, 4333 Brooklyn Ave NE, Seattle, Washington 98195, USA
| | - Andrea Cook
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - Stephen J. Mooney
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Maricela Cruz
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - Shilpi Gupta
- Center for Public Health Nutrition, 305 Raitt Hall, #353410, University of Washington, Seattle, WA, 98195-3410, USA,Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Paula Lozano
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - Dori E. Rosenberg
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - Mary Kay Theis
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - Jane Anau
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave. Suite 1600, Seattle, WA, 98101, USA
| | - Adam Drewnowski
- Center for Public Health Nutrition, 305 Raitt Hall, #353410, University of Washington, Seattle, WA, 98195-3410, USA,Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
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24
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Richards JE, Kuo E, Stewart C, Bobb JF, Mettert KD, Rowhani-Rahbar A, Betz ME, Parrish R, Whiteside U, Boggs JM, Simon GE. Self-reported Access to Firearms Among Patients Receiving Care for Mental Health and Substance Use. JAMA Health Forum 2021; 2:e211973. [PMID: 35977197 PMCID: PMC8796974 DOI: 10.1001/jamahealthforum.2021.1973] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 06/14/2021] [Indexed: 12/03/2022] Open
Abstract
Question Did patients respond to a standard question about firearm access on a mental health questionnaire, and, if so, how did they respond? Findings In this cross-sectional study of 128 802 patients receiving care for mental health and substance use, 83% of primary care patients answered a standard question about firearm access and 21% reported access. In mental health clinics, 92% of patients answered the question and 15% reported access. Meaning In this study, most patients reported firearm access on standard questionnaires; this screening practice may improve efforts to identify and engage patients at risk of suicide in discussions about securing firearms. Importance Firearms are the most common method of suicide, one of the “diseases of despair” driving increased mortality in the US over the past decade. However, routine standardized questions about firearm access are uncommon, particularly among adult populations, who are more often asked at the discretion of health care clinicians. Because standard questions are rare, patterns of patient-reported access are unknown. Objective To evaluate whether and how patients self-report firearm access information on a routine mental health monitoring questionnaire and additionally to examine sociodemographic and clinical associations of reported access. Design, Setting, and Participants Cross-sectional study of patients receiving care for mental health and/or substance use in primary care or outpatient mental health specialty clinics of Kaiser Permanente Washington, an integrated health insurance provider and care delivery system. Main Outcomes and Measures Electronic health records were used to identify patients who completed a standardized self-reported mental health monitoring questionnaire after a single question about firearm access was added from January 1, 2016, through December 31, 2019. Primary analyses evaluated response (answered vs not answered) and reported access (yes vs no) among those who answered, separately for patients seen in primary care and mental health. These analyses also evaluated associations between patient characteristics and reported firearm access. Data analysis took place from February 2020 through May 2021. Results Among patients (n = 128 802) who completed a mental health monitoring questionnaire during the study period, 74.4% (n = 95 875) saw a primary care clinician and 39.3% (n = 50 631) saw a mental health specialty clinician. The primary care and mental health samples were predominantly female (63.1% and 64.9%, respectively) and White (75.7% and 77.0%), with a mean age of 42.8 and 51.1 years. In primary care, 83.4% of patients answered the question about firearm access, and 20.9% of patients who responded to the firearm question reported having access. In mental health, 91.8% of patients answered the question, and 15.3% reported having access. Conclusions and Relevance In this cross-sectional study of adult patients receiving care for mental health and substance use, most patients answered a question about firearm access on a standardized mental health questionnaire. These findings provide a critical foundation to help advance understanding of the utility of standardized firearm access assessment and to inform development of practice guidelines and recommendations. Responses to standard firearm access questions used in combination with dialogue and decision-making resources about firearm access and storage may improve suicide prevention practices and outcomes.
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Affiliation(s)
- Julie E. Richards
- Health Research Institute, Kaiser Permanente Washington, Seattle
- Department of Health Services, University of Washington, Seattle
| | - Elena Kuo
- Health Research Institute, Kaiser Permanente Washington, Seattle
| | | | - Jennifer F. Bobb
- Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Kayne D. Mettert
- Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Ali Rowhani-Rahbar
- Department of Epidemiology, University of Washington School of Public Health, Seattle
- Harborview Injury Prevention and Research Center, Seattle, Washington
| | - Marian E. Betz
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora
| | - Rebecca Parrish
- Department of Mental Health & Wellness, Kaiser Permanente Washington, Seattle
| | - Ursula Whiteside
- NowMattersNow.org, Seattle, Washington
- Psychiatry and Behavioral Sciences, University of Washington, Seattle
| | | | - Gregory E. Simon
- Health Research Institute, Kaiser Permanente Washington, Seattle
- Department of Mental Health & Wellness, Kaiser Permanente Washington, Seattle
- Psychiatry and Behavioral Sciences, University of Washington, Seattle
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Chen C, Warrington JA, Dominici F, Peng RD, Esty DC, Bobb JF, Bell ML. Temporal variation in association between short-term exposure to fine particulate matter and hospitalisations in older adults in the USA: a long-term time-series analysis of the US Medicare dataset. Lancet Planet Health 2021; 5:e534-e541. [PMID: 34390671 DOI: 10.1016/s2542-5196(21)00168-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 05/19/2021] [Accepted: 06/02/2021] [Indexed: 05/19/2023]
Abstract
BACKGROUND Short-term exposure to fine particulate matter (PM2·5) is associated with increased risk of hospital admissions and mortality, and health risks differ by the chemical composition of PM2·5. Policies to control PM2·5 could change its chemical composition and total mass concentration, leading to change in the subsequent health impact. However, there is little ence on whether associations between PM2·5 and health exhibit temporal variation. We investigated whether risks of hospitalisations from short-term exposure to PM2·5 varied over time in the USA. METHODS We did a time-series analysis using a national dataset comprising daily circulatory and respiratory hospitalisation rates of Medicare beneficiaries (age ≥65 years) and PM2·5 in 173 US counties from 1999 to 2016. We fitted modified quasi-Poisson models to estimate temporal trends of associations within a county, and pooled county-level estimates using Bayesian hierarchical modelling to generate an overall estimate. FINDINGS The study included 10 559 654 circulatory and 3 027 281 respiratory hospitalisations. We identified changes in the national average association between previous-day PM2·5 and respiratory hospitalisation over time, with a U-shape that is robust under stratification, linear, and non-linear models. The change in risk of respiratory hospitalisation per 10 μg/m3 increase in previous-day PM2·5 decreased from 0·75% (95% posterior credible interval 0·05 to 1·46) in 1999 to -0·28% (-0·79 to 0·23) in 2008, and then increased to 1·44% (0·00 to 2·91) in 2016. No statistically significant temporal change was observed for associations between same-day PM2·5 and circulatory hospitalisation. INTERPRETATION Hospitalisation risk from PM2·5 changes over time and has increased over the past 7 years in study, especially in northeastern USA. The temporal trend differs by cause of hospitalisation. This study emphasises the necessity of evaluating temporal heterogeneity in health impacts of PM2·5 and suggests caution in applying association estimates to a different time period. FUNDING US Environmental Protection Agency and Yale Institute for Biospheric Studies.
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Affiliation(s)
- Chen Chen
- School of the Environment, Yale University, New Haven, CT, USA.
| | - Jason A Warrington
- School of the Environment, Yale University, New Haven, CT, USA; School of Law, New York University, New York, USA
| | | | - Roger D Peng
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Daniel C Esty
- School of the Environment, Yale University, New Haven, CT, USA; Yale Law School, Yale University, New Haven, CT, USA
| | - Jennifer F Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Michelle L Bell
- School of the Environment, Yale University, New Haven, CT, USA
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26
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Boudreau DM, Lapham G, Johnson EA, Bobb JF, Matthews AG, McCormack J, Liu D, Campbell CI, Rossom RC, Binswanger IA, Yarborough BJ, Arnsten JH, Cunningham CO, Glass JE, Murphy MT, Zare M, Hechter RC, Ahmedani B, Braciszewski JM, Horigian VE, Szapocznik J, Samet JH, Saxon AJ, Schwartz RP, Bradley KA. Documented opioid use disorder and its treatment in primary care patients across six U.S. health systems. J Subst Abuse Treat 2021; 112S:41-48. [PMID: 32220410 DOI: 10.1016/j.jsat.2020.02.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 02/05/2020] [Accepted: 02/08/2020] [Indexed: 12/28/2022]
Abstract
BACKGROUND The United States is in the middle of an opioid overdose epidemic, and experts are calling for improved detection of opioid use disorders (OUDs) and treatment with buprenorphine or extended release (XR) injectable naltrexone, which can be prescribed in general medical settings. To better understand the magnitude of opportunities for treatment among primary care (PC) patients, we estimated the prevalence of documented OUD and medication treatment of OUD among PC patients. METHODS This cross-sectional study included patients with ≥2 visits to PC clinics across 6 healthcare delivery systems who were ≥16 years of age during the study period (fiscal years 2014-2016). Diagnoses, prescriptions, and healthcare utilization were ascertained from electronic health records and insurance claims (5 systems that also offer health insurance). Documented OUDs were defined as ≥1 International Classification of Diseases code for OUDs (active or remission), and OUD treatment was defined as ≥1 prescription(s) for buprenorphine formulations indicated for OUD or naltrexone XR, during the 3-year study period. The prevalence of documented OUD and treatment (95% confidence intervals) across health systems were estimated, and characteristics of patients by treatment status were compared. Prevalence of OUD and OUD treatment were adjusted for age, gender, and race/ethnicity. Combined results were also adjusted for site. RESULT Among 1,403,327 eligible PC patients, 54-62% were female and mean age ranged from 46 to 51 years across health systems. The 3-year prevalence of documented OUD ranged from 0.7-1.4% across the health systems. Among patients with documented OUD, the prevalence of medication treatment (primarily buprenorphine) varied across health systems: 3%, 12%, 16%, 20%, 22%, and 36%. CONCLUSION The prevalence of documented OUD and OUD treatment among PC patients varied widely across health systems. The majority of PC patients with OUD did not have evidence of treatment with buprenorphine or naltrexone XR, highlighting opportunities for improved identification and treatment in medical settings. These results can inform initiatives aimed at improving treatment of OUD in PC. Future research should focus on why there is such variation and how much of the variation can be addressed by improving access to medication treatment.
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Affiliation(s)
- Denise M Boudreau
- Kaiser Permanente Washington Health Research Institute, United States of America.
| | - Gwen Lapham
- Kaiser Permanente Washington Health Research Institute, United States of America
| | - Eric A Johnson
- Kaiser Permanente Washington Health Research Institute, United States of America
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, United States of America
| | | | | | - David Liu
- National Institute on Drug Abuse Center for Clinical Trials Network, United States of America
| | - Cynthia I Campbell
- Kaiser Permanente Northern California Division of Research, United States of America
| | | | - Ingrid A Binswanger
- Kaiser Permanente Colorado Institute for Health Research and Colorado Permanente Medical Group, United States of America
| | - Bobbi Jo Yarborough
- Kaiser Permanente Northwest Center for Health Research, United States of America
| | | | | | - Joseph E Glass
- Kaiser Permanente Washington Health Research Institute, United States of America
| | | | - Mohammad Zare
- University of Texas at Houston, United States of America
| | - Rulin C Hechter
- Kaiser Permanente Southern California Department of Research and Evaluation, United States of America
| | | | | | | | | | - Jeffrey H Samet
- Boston Medical Center, Boston University School of Medicine, United States of America
| | - Andrew J Saxon
- Veteran Affairs Puget Sound Health Care System, United States of America
| | | | - Katharine A Bradley
- Kaiser Permanente Washington Health Research Institute, United States of America
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Zhao Y, Naumova EN, Bobb JF, Claus Henn B, Singh GM. Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach. Am J Epidemiol 2021; 190:1353-1365. [PMID: 33521815 DOI: 10.1093/aje/kwab004] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 01/06/2021] [Accepted: 01/07/2021] [Indexed: 01/18/2023] Open
Abstract
The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1,928 healthy participants in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort (1985-2006), we explored the association between 12 dietary factors and 10-year predicted risk of atherosclerotic cardiovascular disease (ASCVD) using an innovative approach, Bayesian kernel machine regression (BKMR). Employing BKMR, we found that among women, unprocessed red meat was most strongly related to the outcome: An interquartile range increase in unprocessed red meat consumption was associated with a 0.07-unit (95% credible interval: 0.01, 0.13) increase in ASCVD risk when intakes of other dietary components were fixed at their median values (similar results were obtained when other components were fixed at their 25th and 75th percentile values). Among men, fruits had the strongest association: An interquartile range increase in fruit consumption was associated with -0.09-unit (95% credible interval (CrI): -0.16, -0.02), -0.10-unit (95% CrI: -0.16, -0.03), and -0.11-unit (95% CrI: -0.18, -0.04) lower ASCVD risk when other dietary components were fixed at their 25th, 50th (median), and 75th percentile values, respectively. Using BKMR to explore the complex structure of the total diet, we found distinct sex-specific diet-ASCVD relationships and synergistic interaction between whole grain and fruit consumption.
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28
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Pocobelli G, Dublin S, Bobb JF, Albertson-Junkans L, Andrade S, Cheetham TC, Salgado G, Griffin MR, Raebel MA, Smith D, Li DK, Pawloski PA, Toh S, Taylor L, Hua W, Horn P, Trinidad JP, Boudreau DM. Prevalence of prescription opioid use during pregnancy in eight US health plans during 2001-2014. Pharmacoepidemiol Drug Saf 2021; 30:1541-1550. [PMID: 34169607 DOI: 10.1002/pds.5312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 05/18/2021] [Accepted: 06/07/2021] [Indexed: 11/07/2022]
Abstract
PURPOSE To estimate prevalence of prescription opioid use during pregnancy in eight US health plans during 2001-2014. METHODS We conducted a cohort study of singleton live birth deliveries. Maternal characteristics were ascertained from health plan and/or birth certificate data and opioids dispensed during pregnancy from health plan pharmacy records. Prevalence of prescription opioid use during pregnancy was calculated for any use, cumulative days of use, and number of dispensings. RESULTS We examined prevalence of prescription opioid use during pregnancy in each health plan. Tennessee Medicaid had appreciably greater prevalence of use compared to the seven other health plans. Thus, results for the two groups were reported separately. In the seven health plans (n = 587 093 deliveries), prevalence of use during pregnancy was relatively stable at 9%-11% throughout 2001-2014. In Tennessee Medicaid (n = 256 724 deliveries), prevalence increased from 29% in 2001 to a peak of 36%-37% in 2004-2010, and then declined to 28% in 2014. Use for ≥30 days during pregnancy was stable at 1% in the seven health plans and increased from 2% to 7% in Tennessee Medicaid during 2001-2014. Receipt of ≥5 opioid dispensings during pregnancy increased in the seven health plans (0.3%-0.6%) and Tennessee Medicaid (3%-5%) during 2001-2014. CONCLUSION During 2001-2014, prescription opioid use during pregnancy was more common in Tennessee Medicaid (peak prevalence in late 2000s) compared to the seven health plans (relatively stable prevalence). Although a small percentage of women had opioid use during pregnancy for ≥30 days or ≥ 5 dispensings, they represent thousands of women during 2001-2014.
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Affiliation(s)
- Gaia Pocobelli
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Sascha Dublin
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | | | - Susan Andrade
- Meyers Primary Care Institute, Worcester, Massachusetts, USA
| | - T Craig Cheetham
- Kaiser Permanente Southern California, Department of Research & Evaluation, Pasadena, CA, Chapman University, School of Pharmacy, Irvine, CA, USA
| | - Gladys Salgado
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Marie R Griffin
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Marsha A Raebel
- Institute for Health Research, Kaiser Permanente Colorado, Colorado, USA
| | - David Smith
- Kaiser Permanente Center for Health Research, Portland, Oregon, USA
| | - De-Kun Li
- Kaiser Foundation Research Institute, Oakland, California, USA
| | | | - Sengwee Toh
- Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | | | - Wei Hua
- Food and Drug Administration, Silver Spring, Maryland, USA
| | - Pamela Horn
- Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Denise M Boudreau
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
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29
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Matson TE, Carrell DS, Bobb JF, Cronkite DJ, Oliver MM, Luce C, Ghitza UE, Hsu CW, Campbell CI, Browne KC, Binswanger IA, Saxon AJ, Bradley KA, Lapham GT. Prevalence of Medical Cannabis Use and Associated Health Conditions Documented in Electronic Health Records Among Primary Care Patients in Washington State. JAMA Netw Open 2021; 4:e219375. [PMID: 33956129 PMCID: PMC8103224 DOI: 10.1001/jamanetworkopen.2021.9375] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/18/2021] [Indexed: 12/11/2022] Open
Abstract
Importance Many people use cannabis for medical reasons despite limited evidence of therapeutic benefit and potential risks. Little is known about medical practitioners' documentation of medical cannabis use or clinical characteristics of patients with documented medical cannabis use. Objectives To estimate the prevalence of past-year medical cannabis use documented in electronic health records (EHRs) and to describe patients with EHR-documented medical cannabis use, EHR-documented cannabis use without evidence of medical use (other cannabis use), and no EHR-documented cannabis use. Design, Setting, and Participants This cross-sectional study assessed adult primary care patients who completed a cannabis screen during a visit between November 1, 2017, and October 31, 2018, at a large health system that conducts routine cannabis screening in a US state with legal medical and recreational cannabis use. Exposures Three mutually exclusive categories of EHR-documented cannabis use (medical, other, and no use) based on practitioner documentation of medical cannabis use in the EHR and patient report of past-year cannabis use at screening. Main Outcomes and Measures Health conditions for which cannabis use has potential benefits or risks were defined based on National Academies of Sciences, Engineering, and Medicine's review. The adjusted prevalence of conditions diagnosed in the prior year were estimated across 3 categories of EHR-documented cannabis use with logistic regression. Results A total of 185 565 patients (mean [SD] age, 52.0 [18.1] years; 59% female, 73% White, 94% non-Hispanic, and 61% commercially insured) were screened for cannabis use in a primary care visit during the study period. Among these patients, 3551 (2%) had EHR-documented medical cannabis use, 36 599 (20%) had EHR-documented other cannabis use, and 145 415 (78%) had no documented cannabis use. Patients with medical cannabis use had a higher prevalence of health conditions for which cannabis has potential benefits (49.8%; 95% CI, 48.3%-51.3%) compared with patients with other cannabis use (39.9%; 95% CI, 39.4%-40.3%) or no cannabis use (40.0%; 95% CI, 39.8%-40.2%). In addition, patients with medical cannabis use had a higher prevalence of health conditions for which cannabis has potential risks (60.7%; 95% CI, 59.0%-62.3%) compared with patients with other cannabis use (50.5%; 95% CI, 50.0%-51.0%) or no cannabis use (42.7%; 95% CI, 42.4%-42.9%). Conclusions and Relevance In this cross-sectional study, primary care patients with documented medical cannabis use had a high prevalence of health conditions for which cannabis use has potential benefits, yet a higher prevalence of conditions with potential risks from cannabis use. These findings suggest that practitioners should be prepared to discuss potential risks and benefits of cannabis use with patients.
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Affiliation(s)
- Theresa E. Matson
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Services, University of Washington, Seattle
| | | | | | | | - Malia M. Oliver
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Casey Luce
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Udi E. Ghitza
- Center for the Clinical Trials Network, National Institute on Drug Abuse, National Institutes of Health, Bethesda, Maryland
| | - Clarissa W. Hsu
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Cynthia I. Campbell
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Kendall C. Browne
- Center of Excellence in Substance Addiction Treatment and Education, Veteran Affairs Puget Sound Health Care System, Seattle, Washington
| | - Ingrid A. Binswanger
- Kaiser Permanente Colorado Institute for Health Research and Colorado Permanente Medical Group, Denver
| | - Andrew J. Saxon
- Center of Excellence in Substance Addiction Treatment and Education, Veteran Affairs Puget Sound Health Care System, Seattle, Washington
| | - Katharine A. Bradley
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Services, University of Washington, Seattle
| | - Gwen T. Lapham
- Kaiser Permanente Washington Health Research Institute, Seattle
- Department of Health Services, University of Washington, Seattle
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30
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Tsui JI, Akosile MA, Lapham GT, Boudreau DM, Johnson EA, Bobb JF, Binswanger IA, Yarborough BJH, Glass JE, Rossom RC, Murphy MT, Cunningham CO, Arnsten JH, Thakral M, Saxon AJ, Merrill JO, Samet JH, Bart GB, Campbell CI, Loree AM, Silva A, Stotts AL, Ahmedani B, Braciszewski JM, Hechter RC, Northrup TF, Horigian VE, Bradley KA. Prevalence and Medication Treatment of Opioid Use Disorder Among Primary Care Patients with Hepatitis C and HIV. J Gen Intern Med 2021; 36:930-937. [PMID: 33569735 PMCID: PMC8041979 DOI: 10.1007/s11606-020-06389-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 12/03/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND Hepatitis C and HIV are associated with opioid use disorders (OUD) and injection drug use. Medications for OUD can prevent the spread of HCV and HIV. OBJECTIVE To describe the prevalence of documented OUD, as well as receipt of office-based medication treatment, among primary care patients with HCV or HIV. DESIGN Retrospective observational cohort study using electronic health record and insurance data. PARTICIPANTS Adults ≥ 18 years with ≥ 2 visits to primary care during the study (2014-2016) at 6 healthcare systems across five states (CO, CA, OR, WA, and MN). MAIN MEASURES The primary outcome was the diagnosis of OUD; the secondary outcome was OUD treatment with buprenorphine or oral/injectable naltrexone. Prevalence of OUD and OUD treatment was calculated across four groups: HCV only; HIV only; HCV and HIV; and neither HCV nor HIV. In addition, adjusted odds ratios (AOR) of OUD treatment associated with HCV and HIV (separately) were estimated, adjusting for age, gender, race/ethnicity, and site. KEY RESULTS The sample included 1,368,604 persons, of whom 10,042 had HCV, 5821 HIV, and 422 both. The prevalence of diagnosed OUD varied across groups: 11.9% (95% CI: 11.3%, 12.5%) for those with HCV; 1.6% (1.3%, 2.0%) for those with HIV; 8.8% (6.2%, 11.9%) for those with both; and 0.92% (0.91%, 0.94%) among those with neither. Among those with diagnosed OUD, the prevalence of OUD medication treatment was 20.9%, 16.0%, 10.8%, and 22.3%, for those with HCV, HIV, both, and neither, respectively. HCV was not associated with OUD treatment (AOR = 1.03; 0.88, 1.21), whereas patients with HIV had a lower probability of OUD treatment (AOR = 0.43; 0.26, 0.72). CONCLUSIONS Among patients receiving primary care, those diagnosed with HCV and HIV were more likely to have documented OUD than those without. Patients with HIV were less likely to have documented medication treatment for OUD.
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Affiliation(s)
- Judith I Tsui
- University of Washington/Harborview Medical Center, Seattle, USA
| | - Mary A Akosile
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, STE 1600, Seattle, WA, 98101 (206) 948-1933, USA
| | - Gwen T Lapham
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, STE 1600, Seattle, WA, 98101 (206) 948-1933, USA
| | - Denise M Boudreau
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, STE 1600, Seattle, WA, 98101 (206) 948-1933, USA
| | - Eric A Johnson
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, STE 1600, Seattle, WA, 98101 (206) 948-1933, USA
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, STE 1600, Seattle, WA, 98101 (206) 948-1933, USA
| | - Ingrid A Binswanger
- Kaiser Permanente Colorado, Colorado Permanente Medical Group, and the University of Colorado School of Medicine, Aurora, USA
| | | | - Joseph E Glass
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, STE 1600, Seattle, WA, 98101 (206) 948-1933, USA
| | - Rebecca C Rossom
- HealthPartners Institute, University of Minnesota, Bloomington, USA
| | - Mark T Murphy
- MultiCare Institute for Research and Innovation, MultiCare Health System WA, Seattle, USA
| | - Chinazo O Cunningham
- Albert Einstein College of Medicine, Montefiore Medical Center, New York City, USA
| | - Julia H Arnsten
- Albert Einstein College of Medicine, Montefiore Medical Center, New York City, USA
| | - Manu Thakral
- College of Nursing and Health Sciences, University of Massachusetts Boston, Boston, USA
| | - Andrew J Saxon
- Center of Excellence in Substance Addiction Treatment and Education, VA Puget Sound Health Care System/University of Washington School of Medicine, Seattle, USA
| | - Joseph O Merrill
- University of Washington/Harborview Medical Center, Seattle, USA
| | | | - Gavin B Bart
- Hennepin Healthcare, University of Minnesota, Minneapolis, USA
| | - Cynthia I Campbell
- Division of Research, Kaiser Permanente Northern California, Oakland, USA
| | - Amy M Loree
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, USA
| | - Angela Silva
- MultiCare Institute for Research and Innovation, MultiCare Health System WA, Seattle, USA
| | - Angela L Stotts
- Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston (UTHealth), Houston, USA
| | - Brian Ahmedani
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, USA
| | - Jordan M Braciszewski
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, USA
- Department of Psychiatry, Henry Ford Health System, Detroit, USA
| | - Rulin C Hechter
- Department of Research and Evaluation, Kaiser Permanente Southern California, Oakland, USA
| | - Thomas F Northrup
- Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston (UTHealth), Houston, USA
| | - Viviana E Horigian
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Florida, USA
| | - Katharine A Bradley
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, STE 1600, Seattle, WA, 98101 (206) 948-1933, USA.
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31
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Campbell CI, Saxon AJ, Boudreau DM, Wartko PD, Bobb JF, Lee AK, Matthews AG, McCormack J, Liu DS, Addis M, Altschuler A, Samet JH, LaBelle CT, Arnsten J, Caldeiro RM, Borst DT, Stotts AL, Braciszewski JM, Szapocznik J, Bart G, Schwartz RP, McNeely J, Liebschutz JM, Tsui JI, Merrill JO, Glass JE, Lapham GT, Murphy SM, Weinstein ZM, Yarborough BJH, Bradley KA. PRimary Care Opioid Use Disorders treatment (PROUD) trial protocol: a pragmatic, cluster-randomized implementation trial in primary care for opioid use disorder treatment. Addict Sci Clin Pract 2021; 16:9. [PMID: 33517894 PMCID: PMC7849121 DOI: 10.1186/s13722-021-00218-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 01/15/2021] [Indexed: 01/10/2023] Open
Abstract
Background Most people with opioid use disorder (OUD) never receive treatment. Medication treatment of OUD in primary care is recommended as an approach to increase access to care. The PRimary Care Opioid Use Disorders treatment (PROUD) trial tests whether implementation of a collaborative care model (Massachusetts Model) using a nurse care manager (NCM) to support medication treatment of OUD in primary care increases OUD treatment and improves outcomes. Specifically, it tests whether implementation of collaborative care, compared to usual primary care, increases the number of days of medication for OUD (implementation objective) and reduces acute health care utilization (effectiveness objective). The protocol for the PROUD trial is presented here. Methods PROUD is a hybrid type III cluster-randomized implementation trial in six health care systems. The intervention consists of three implementation strategies: salary for a full-time NCM, training and technical assistance for the NCM, and requiring that three primary care providers have DEA waivers to prescribe buprenorphine. Within each health system, two primary care clinics are randomized: one to the intervention and one to Usual Primary Care. The sample includes all patients age 16–90 who visited the randomized primary care clinics from 3 years before to 2 years after randomization (anticipated to be > 170,000). Quantitative data are derived from existing health system administrative data, electronic medical records, and/or health insurance claims (“electronic health records,” [EHRs]). Anonymous staff surveys, stakeholder debriefs, and observations from site visits, trainings and technical assistance provide qualitative data to assess barriers and facilitators to implementation. The outcome for the implementation objective (primary outcome) is a clinic-level measure of the number of patient days of medication treatment of OUD over the 2 years post-randomization. The patient-level outcome for the effectiveness objective (secondary outcome) is days of acute care utilization [e.g. urgent care, emergency department (ED) and/or hospitalizations] over 2 years post-randomization among patients with documented OUD prior to randomization. Discussion The PROUD trial provides information for clinical leaders and policy makers regarding potential benefits for patients and health systems of a collaborative care model for management of OUD in primary care, tested in real-world diverse primary care settings. Trial registration # NCT03407638 (February 28, 2018); CTN-0074 https://clinicaltrials.gov/ct2/show/NCT03407638?term=CTN-0074&draw=2&rank=1
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Affiliation(s)
- Cynthia I Campbell
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, 3rd Floor, Oakland, CA, 94612, USA.
| | - Andrew J Saxon
- Center of Excellence in Substance Addiction Treatment and Education, VA Puget Sound Health Care System, 1660 S Columbian Way, Seattle, WA, 98108, USA
| | - Denise M Boudreau
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Seattle, WA, 98101, USA
| | - Paige D Wartko
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Seattle, WA, 98101, USA
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Seattle, WA, 98101, USA
| | - Amy K Lee
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Seattle, WA, 98101, USA
| | | | | | - David S Liu
- National Institute on Drug Abuse Center for Clinical Trials Network, Three White Flint North, 11601 Landsdown Street, North Bethesda, MD, 20852, USA
| | - Megan Addis
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Seattle, WA, 98101, USA
| | - Andrea Altschuler
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, 3rd Floor, Oakland, CA, 94612, USA
| | - Jeffrey H Samet
- Boston Medical Center/Boston University School of Medicine: Clinical Addiction Research & Education (CARE) Unit Crosstown Center, 801 Massachusetts Ave., 2nd Floor, Boston, MA, 02118, USA
| | - Colleen T LaBelle
- Boston Medical Center/Boston University School of Medicine: Clinical Addiction Research & Education (CARE) Unit Crosstown Center, 801 Massachusetts Ave., 2nd Floor, Boston, MA, 02118, USA
| | - Julia Arnsten
- Albert Einstein College of Medicine, Montefiore Medical Center, 3300 Kossuth Avenue, Bronx, NY, 10467, USA
| | - Ryan M Caldeiro
- Kaiser Permanente Washington, 9800 4th Ave. N.E., Seattle, WA, 98115, USA
| | - Douglas T Borst
- Kootenai Clinic Family Medicine, 1919 Lincoln Way, Suite 315, Coeur d Alene, ID, 83814, USA
| | - Angela L Stotts
- Department of Family & Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston School, 7000 Fannin Street, Houston, TX, 77030, USA
| | - Jordan M Braciszewski
- Department of Psychiatry, Center for Health Policy and Health Services Research, Henry Ford Health System, 2799 W Grand Blvd, Detroit, MI, 48202, USA
| | - José Szapocznik
- Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, 10th Floor, Miami, FL, 33136, USA
| | - Gavin Bart
- University of Minnesota/Hennepin Healthcare, 701 Park Avenue, Minneapolis, MN, 55415, USA
| | - Robert P Schwartz
- Friends Research Institute, 1040 Park Avenue, Suite 103, Baltimore, MD, 21201, USA
| | - Jennifer McNeely
- NYU Grossman School of Medicine, 180 Madison Ave., New York, NY, 10016, USA
| | - Jane M Liebschutz
- Division of General Internal Medicine, Center for Research On Health Care, University of Pittsburgh School of Medicine, 200 Lothrop Street, 933West, Pittsburgh, PA, 15213, USA
| | - Judith I Tsui
- University of Washington/Harborview Medical Center, 325 9th Ave, Seattle, WA, 98104, USA
| | - Joseph O Merrill
- University of Washington/Harborview Medical Center, 325 9th Ave, Seattle, WA, 98104, USA
| | - Joseph E Glass
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Seattle, WA, 98101, USA
| | - Gwen T Lapham
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Seattle, WA, 98101, USA
| | - Sean M Murphy
- Weill Cornell Medical College, 425 East 61st Street, Suite 301, New York, NY, 10065, USA
| | - Zoe M Weinstein
- Clinical Addiction Research & Education (CARE) Unit, Boston University School of Medicine, Crosstown Center, 801 Massachusetts Ave., 2nd Floor, Boston, MA, 02118, USA
| | - Bobbi Jo H Yarborough
- Kaiser Permanente Northwest, Center for Health Research, 3800 N. Interstate Avenue, Portland, OR, 97227-1098, USA
| | - Katharine A Bradley
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Seattle, WA, 98101, USA
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Williams EC, McGinnis KA, Rubinsky AD, Matson TE, Bobb JF, Lapham GT, Edelman EJ, Satre DD, Catz SL, Richards JE, Bryant KJ, Marshall BDL, Kraemer KL, Crystal S, Gordon AJ, Skanderson M, Fiellin DA, Justice AC, Bradley KA. Alcohol Use and Antiretroviral Adherence Among Patients Living with HIV: Is Change in Alcohol Use Associated with Change in Adherence? AIDS Behav 2021; 25:203-214. [PMID: 32617778 DOI: 10.1007/s10461-020-02950-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Alcohol use increases non-adherence to antiretroviral therapy (ART) among persons living with HIV (PLWH). Dynamic longitudinal associations are understudied. Veterans Aging Cohort Study (VACS) data 2/1/2008-7/31/16 were used to fit linear regression models estimating changes in adherence (% days with ART medication fill) associated with changes in alcohol use based on annual clinically-ascertained AUDIT-C screening scores (range - 12 to + 12, 0 = no change) adjusting for demographics and initial adherence. Among 21,275 PLWH (67,330 observations), most reported no (48%) or low-level (39%) alcohol use initially, with no (55%) or small (39% ≤ 3 points) annual change. Mean initial adherence was 86% (SD 21%), mean annual change was - 3.1% (SD 21%). An inverted V-shaped association was observed: both increases and decreases in AUDIT-C were associated with greater adherence decreases relative to stable scores [p < 0.001, F (4, 21,274)]. PLWH with dynamic alcohol use (potentially indicative of alcohol use disorder) should be considered for adherence interventions.
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Affiliation(s)
- Emily C Williams
- Health Services Research & Development (HSR&D), Center of Innovation for Veteran-Centered Value-Driven Care, Veteran Affairs (VA) Puget Sound Health Care System, 1660 S. Columbian Way, S-152, Seattle, WA, 98108, USA.
- Department of Health Services, University of Washington, Seattle, WA, USA.
| | - Kathleen A McGinnis
- Veterans Aging Cohort Study Coordinating Center, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Anna D Rubinsky
- Health Services Research & Development (HSR&D), Center of Innovation for Veteran-Centered Value-Driven Care, Veteran Affairs (VA) Puget Sound Health Care System, 1660 S. Columbian Way, S-152, Seattle, WA, 98108, USA
- Kidney Health Research Collaborative, University of California, San Francisco and VA San Francisco Health Care System, San Francisco, CA, USA
| | - Theresa E Matson
- Health Services Research & Development (HSR&D), Center of Innovation for Veteran-Centered Value-Driven Care, Veteran Affairs (VA) Puget Sound Health Care System, 1660 S. Columbian Way, S-152, Seattle, WA, 98108, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Gwen T Lapham
- Health Services Research & Development (HSR&D), Center of Innovation for Veteran-Centered Value-Driven Care, Veteran Affairs (VA) Puget Sound Health Care System, 1660 S. Columbian Way, S-152, Seattle, WA, 98108, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - E Jennifer Edelman
- Schools of Medicine and Public Health, Yale University, New Haven, CT, USA
| | - Derek D Satre
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Sheryl L Catz
- Betty Irene Moore School of Nursing, University of California at Davis, Sacramento, CA, USA
| | - Julie E Richards
- Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Kendall J Bryant
- National Institute On Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - Brandon D L Marshall
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Kevin L Kraemer
- Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Stephen Crystal
- Health Services Research, Rutgers University, New Brunswick, NJ, USA
| | - Adam J Gordon
- Division of Epidemiology, Department of Internal Medicine, Program for Addiction Research, Clinical Care, Knowledge and Advocacy (PARCKA), University of Utah School of Medicine, Salt Lake City, UT, USA
- Informatics, Decision-Enhancement, and Analytic Sciences Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Melissa Skanderson
- Veterans Aging Cohort Study Coordinating Center, VA Connecticut Healthcare System, West Haven, CT, USA
| | - David A Fiellin
- Veterans Aging Cohort Study Coordinating Center, VA Connecticut Healthcare System, West Haven, CT, USA
- Schools of Medicine and Public Health, Yale University, New Haven, CT, USA
| | - Amy C Justice
- Veterans Aging Cohort Study Coordinating Center, VA Connecticut Healthcare System, West Haven, CT, USA
- Schools of Medicine and Public Health, Yale University, New Haven, CT, USA
| | - Katharine A Bradley
- Health Services Research & Development (HSR&D), Center of Innovation for Veteran-Centered Value-Driven Care, Veteran Affairs (VA) Puget Sound Health Care System, 1660 S. Columbian Way, S-152, Seattle, WA, 98108, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Center of Excellence in Substance Abuse Treatment and Education (CESATE) VA Puget Sound Healthcare System-Seattle Division, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
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Zanobetti A, Coull BA, Luttmann-Gibson H, van Rossem L, Rifas-Shiman SL, Kloog I, Schwartz JD, Oken E, Bobb JF, Koutrakis P, Gold DR. Ambient Particle Components and Newborn Blood Pressure in Project Viva. J Am Heart Assoc 2020; 10:e016935. [PMID: 33372530 PMCID: PMC7955476 DOI: 10.1161/jaha.120.016935] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background Both elemental metals and particulate air pollution have been reported to influence adult blood pressure (BP). The aim of this study is to examine which elemental components of particle mass with diameter ≤2.5 μm (PM2.5) are responsible for previously reported associations between PM2.5 and neonatal BP. Methods and Results We studied 1131 mother‐infant pairs in Project Viva, a Boston‐area prebirth cohort. We measured systolic BP (SBP) and diastolic BP (DBP) at a mean age of 30 hours. We calculated average exposures during the 2 to 7 days before birth for the PM2.5 components—aluminum, arsenic, bromine, sulfur, copper, iron, zinc, nickel, vanadium, titanium, magnesium, potassium, silicon, sodium, chlorine, calcium, and lead—measured at the Harvard supersite. Adjusting for covariates and PM2.5, we applied regression models to examine associations between PM2.5 components and median SBP and DBP, and used variable selection methods to select which components were more strongly associated with each BP outcome. We found consistent results with higher nickel associated with significantly higher SBP and DBP, and higher zinc associated with lower SBP and DBP. For an interquartile range increase in the log Z score (1.4) of nickel, we found a 1.78 mm Hg (95% CI, 0.72–2.84) increase in SBP and a 1.30 (95% CI, 0.54–2.06) increase in DBP. Increased zinc (interquartile range log Z score 1.2) was associated with decreased SBP (−1.29 mm Hg; 95% CI, −2.09 to −0.50) and DBP (−0.85 mm Hg; 95% CI: −1.42 to −0.29). Conclusions Our findings suggest that prenatal exposures to particulate matter components, and particularly nickel, may increase newborn BP.
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Affiliation(s)
- Antonella Zanobetti
- Department of Environmental Health Harvard School of Public Health Boston MA
| | - Brent A Coull
- Department of Biostatistics Harvard School of Public Health Boston MA
| | | | - Lenie van Rossem
- Julius Center for Health Sciences and Primary Care University Medical Center UtrechtUtrecht University Utrecht the Netherlands
| | - Sheryl L Rifas-Shiman
- Division of Chronic Disease Research Across the Lifecourse Department of Population Medicine Harvard Medical School and Harvard Pilgrim Health Care Institute Boston MA
| | - Itai Kloog
- Department of Geography and Environmental Development Ben-Gurion University of the Negev Beer Sheva Israel
| | - Joel D Schwartz
- Department of Environmental Health Harvard School of Public Health Boston MA.,Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA
| | - Emily Oken
- Division of Chronic Disease Research Across the Lifecourse Department of Population Medicine Harvard Medical School and Harvard Pilgrim Health Care Institute Boston MA
| | - Jennifer F Bobb
- Biostatistics Unit Kaiser Permanente Washington Health Research Institute Seattle WA.,Department of Biostatistics University of Washington Seattle WA
| | - Petros Koutrakis
- Department of Environmental Health Harvard School of Public Health Boston MA
| | - Diane R Gold
- Department of Environmental Health Harvard School of Public Health Boston MA.,Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA
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34
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Cheetham TC, Dublin S, Pocobelli G, Bobb JF, Andrade S, Hechter RC, Portugal C, Munis M, Albertson-Junkans L, Salgado G, Wong L, Maarup TJ, Carroll K, Griffin MR, Raebel MA, Smith D, Li DK, Pawloski PA, Toh S, Taylor L, Hua W, Dinatale M, Ceresa C, Trinidad JP, Boudreau DM. Validity of diagnosis and procedure codes for identifying neural tube defects in infants. Pharmacoepidemiol Drug Saf 2020; 29:1489-1493. [PMID: 32929845 DOI: 10.1002/pds.5128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/24/2020] [Accepted: 08/31/2020] [Indexed: 11/10/2022]
Abstract
PURPOSE The use of validated criteria to identify birth defects in electronic healthcare databases can avoid the cost and time-intensive efforts required to conduct chart reviews to confirm outcomes. This study evaluated the validity of various case-finding methodologies to identify neural tube defects (NTDs) in infants using an electronic healthcare database. METHODS This analysis used data generated from a study whose primary aim was to evaluate the association between first-trimester maternal prescription opioid use and NTDs. The study was conducted within the Medication Exposure in Pregnancy Risk Evaluation Program. A broad approach was used to identify potential NTDs including diagnosis and procedure codes from inpatient and outpatient settings, death certificates and birth defect flags in birth certificates. Potential NTD cases were chart abstracted and confirmed by clinical experts. Positive predictive values (PPVs) and 95% confidence intervals (95% CI) are reported. RESULTS The cohort included 113 168 singleton live-born infants: 55 960 infants with opioid exposure in pregnancy and 57 208 infants unexposed in pregnancy. Seventy-three potential NTD cases were available for the validation analysis. The overall PPV was 41% using all diagnosis and procedure codes plus birth certificates. Restricting approaches to codes recorded in the infants' medical record or to birth certificate flags increased the PPVs (72% and 80%, respectively) but missed a substantial proportion of confirmed NTDs. CONCLUSIONS Codes in electronic healthcare data did not accurately identify confirmed NTDs. These results indicate that chart review with adjudication of outcomes is important when conducting observational studies of NTDs using electronic healthcare data.
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Affiliation(s)
- T Craig Cheetham
- Chapman University - School of Pharmacy, Irvine, California, USA
| | - Sascha Dublin
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Gaia Pocobelli
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Susan Andrade
- Meyers Primary Care Institute & University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Rulin C Hechter
- Kaiser Permanente Department of Research & Evaluation, Pasadena, California, USA
| | - Cecilia Portugal
- Kaiser Permanente Department of Research & Evaluation, Pasadena, California, USA
| | - Mercedes Munis
- Kaiser Permanente Department of Research & Evaluation, Pasadena, California, USA
| | | | - Gladys Salgado
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Lawrence Wong
- The Permanente Medical Group, Clinical Genetics, Oakland, California, USA
| | - Timothy J Maarup
- Southern California Permanente Medical Group, Genetics Department, Downey, California, USA
| | - Kecia Carroll
- Department of Pediatrics, Vanderbilt University Medical School, Nashville, Tennessee, USA
| | - Marie R Griffin
- Department of Health Policy, Vanderbilt University Medical School, Nashville, Tennessee, USA
| | - Marsha A Raebel
- Institute for Health Research, Kaiser Permanente Colorado, Denver, Colorado, USA
| | - David Smith
- Kaiser Permanente Northwest, Center for Health Research, Portland, Oregon, USA
| | - De-Kun Li
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | | | - Sengwee Toh
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Research Institute, Boston, Massachusetts, USA
| | - Lockwood Taylor
- CDER, Food and Drug Administration, Office of Surveillance and Epidemiology, Silver Spring, Maryland, USA
| | - Wei Hua
- CDER, Food and Drug Administration, Office of Surveillance and Epidemiology, Silver Spring, Maryland, USA
| | - Miriam Dinatale
- Division of Pediatric and Maternal Health, CDER, Food and Drug Administration, Silver Spring, Maryland, USA
| | - Carrie Ceresa
- Division of Pediatric and Maternal Health, CDER, Food and Drug Administration, Silver Spring, Maryland, USA
| | - James P Trinidad
- CDER, Food and Drug Administration, Office of Surveillance and Epidemiology, Silver Spring, Maryland, USA
| | - Denise M Boudreau
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
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35
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Bauer JA, Devick KL, Bobb JF, Coull BA, Bellinger D, Benedetti C, Cagna G, Fedrighi C, Guazzetti S, Oppini M, Placidi D, Webster TF, White RF, Yang Q, Zoni S, Wright RO, Smith DR, Lucchini RG, Claus Henn B. Associations of a Metal Mixture Measured in Multiple Biomarkers with IQ: Evidence from Italian Adolescents Living near Ferroalloy Industry. Environ Health Perspect 2020; 128:97002. [PMID: 32897104 PMCID: PMC7478128 DOI: 10.1289/ehp6803] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 06/03/2020] [Accepted: 08/04/2020] [Indexed: 05/04/2023]
Abstract
BACKGROUND Research on the health effects of chemical mixtures has focused mainly on early life rather than adolescence, a potentially important developmental life stage. OBJECTIVES We examined associations of a metal mixture with general cognition in a cross-sectional study of adolescents residing near ferromanganese industry, a source of airborne metals emissions. METHODS We measured manganese (Mn), lead (Pb), copper (Cu), and chromium (Cr) in hair, blood, urine, nails, and saliva from 635 Italian adolescents 10-14 years of age. Full-scale, verbal, and performance intelligence quotient (FSIQ, VIQ, PIQ) scores were assessed using the Wechsler Intelligence Scale for Children-III. Multivariable linear regression and Bayesian kernel machine regression (BKMR) were used to estimate associations of the metal mixture with IQ. In secondary analyses, we used BKMR's hierarchical variable selection option to inform biomarker selection for Mn, Cu, and Cr. RESULTS Median metal concentrations were as follows: hair Mn, 0.08 μ g / g ; hair Cu, 9.6 μ g / g ; hair Cr, 0.05 μ g / g ; and blood Pb, 1.3 μ g / dL . Adjusted models revealed an inverted U-shaped association between hair Cu and VIQ, consistent with Cu as an essential nutrient that is neurotoxic in excess. At low levels of hair Cu (10th percentile, 5.4 μ g / g ), higher concentrations (90th percentiles) of the mixture of Mn, Pb, and Cr (0.3 μ g / g , 2.6 μ g / dL , and 0.1 μ g / g , respectively) were associated with a 2.9 (95% CI: - 5.2 , - 0.5 )-point decrease in VIQ score, compared with median concentrations of the mixture. There was suggestive evidence of interaction between Mn and Cu. In secondary analyses, saliva Mn, hair Cu, and saliva Cr were selected as the biomarkers most strongly associated with VIQ score. DISCUSSION Higher adolescent levels of Mn, Pb, and Cr were associated with lower IQ scores, especially at low Cu levels. Findings also support further investigation into Cu as both beneficial and toxic for neurobehavioral outcomes. https://doi.org/10.1289/EHP6803.
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Affiliation(s)
- Julia A. Bauer
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Katrina L. Devick
- Division of Biomedical Statistics and Informatics, Mayo Clinic Arizona, Scottsdale, Arizona, USA
| | - Jennifer F. Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Brent A. Coull
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - David Bellinger
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Departments of Neurology and Psychiatry, Boston Children’s Hospital, Boston, Massachusetts, USA
- Departments of Neurology and Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Chiara Benedetti
- Department of Medical-Surgical Specialties, Radiological Science and Public Health, University of Brescia, Brescia, Italy
| | - Giuseppa Cagna
- Department of Medical-Surgical Specialties, Radiological Science and Public Health, University of Brescia, Brescia, Italy
| | - Chiara Fedrighi
- Department of Medical-Surgical Specialties, Radiological Science and Public Health, University of Brescia, Brescia, Italy
| | | | - Manuela Oppini
- Department of Medical-Surgical Specialties, Radiological Science and Public Health, University of Brescia, Brescia, Italy
| | - Donatella Placidi
- Department of Medical-Surgical Specialties, Radiological Science and Public Health, University of Brescia, Brescia, Italy
| | - Thomas F. Webster
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Roberta F. White
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA
- Department of Neurology, Boston University Medical School, Boston, Massachusetts, USA
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Silvia Zoni
- Department of Medical-Surgical Specialties, Radiological Science and Public Health, University of Brescia, Brescia, Italy
| | - Robert O. Wright
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Donald R. Smith
- Department of Microbiology and Environmental Toxicology, University of California, Santa Cruz, Santa Cruz, California, USA
| | - Roberto G. Lucchini
- Department of Medical-Surgical Specialties, Radiological Science and Public Health, University of Brescia, Brescia, Italy
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Birgit Claus Henn
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA
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Domingo-Relloso A, Grau-Perez M, Briongos-Figuero L, Gomez-Ariza JL, Garcia-Barrera T, Dueñas-Laita A, Bobb JF, Chaves FJ, Kioumourtzoglou MA, Navas-Acien A, Redon-Mas J, Martin-Escudero JC, Tellez-Plaza M. The association of urine metals and metal mixtures with cardiovascular incidence in an adult population from Spain: the Hortega Follow-Up Study. Int J Epidemiol 2020; 48:1839-1849. [PMID: 31329884 DOI: 10.1093/ije/dyz061] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2019] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The association of low-level exposure to metals and metal mixtures with cardiovascular incidence in the general population has rarely been studied. We flexibly evaluated the association of urinary metals and metal mixtures concentrations with cardiovascular diseases in a representative sample of a general population from Spain. METHODS Urine antimony (Sb), barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), molybdenum (Mo), vanadium (V) and zinc (Zn) were measured in 1171 adults without clinical cardiovascular diseases, who participated in the Hortega Study. Cox proportional hazard models were used for evaluating the association between single metals and cardiovascular incidence. We used a Probit extension of Bayesian Kernel Machine Regression (BKMR-P) to handle metal mixtures in a survival setting. RESULTS In single-metal models, the hazard ratios [confidence intervals (CIs)] of cardiovascular incidence, comparing the 80th to the 20th percentiles of metal distributions, were 1.35 (1.06, 1.72) for Cu, 1.43 (1.07, 1.90) for Zn, 1.51 (1.13, 2.03) for Sb, 1.46 (1.13, 1.88) for Cd, 1.64 (1.05, 2.58) for Cr and 1.31 (1.01, 1.71) for V. BKMR-P analysis was confirmatory of these findings, supporting that Cu, Zn, Sb, Cd, Cr and V are related to cardiovascular incidence in the presence of the other metals. Cd and Sb showed the highest posterior inclusion probabilities. CONCLUSIONS Urine Cu, Zn, Sb, Cd, Cr and V were independently associated with increased cardiovascular risk at levels relevant for the general population of Spain. Urine metals in the mixture were also jointly associated with cardiovascular incidence, with Cd and Sb being the most important components of the mixture.
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Affiliation(s)
- Arce Domingo-Relloso
- Area of Cardiometabolic and Renal Risk, Institute for Biomedical Research INCLIVA, Valencia, Spain.,Department of Statistics and Operational Research, University of Valencia, Valencia, Spain.,Department of Environmental Health Sciences, Columbia University, New York, NY, USA
| | - Maria Grau-Perez
- Area of Cardiometabolic and Renal Risk, Institute for Biomedical Research INCLIVA, Valencia, Spain.,Department of Statistics and Operational Research, University of Valencia, Valencia, Spain
| | | | | | | | | | - Jennifer F Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - F Javier Chaves
- Genotyping and Genetic Diagnosis Unit, Institute for Biomedical Research INCLIVA, Valencia, Spain.,CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Institute of Health Carlos III, Madrid, Spain
| | | | - Ana Navas-Acien
- Department of Environmental Health Sciences, Columbia University, New York, NY, USA
| | - Josep Redon-Mas
- Area of Cardiometabolic and Renal Risk, Institute for Biomedical Research INCLIVA, Valencia, Spain.,Department of Internal Medicine, Hospital Clínico de Valencia, Valencia, Spain.,CIBER Physiopathology of Obesity and Nutrition (CIBEROBN), Institute of Health CarlosIII, Madrid, Spain
| | | | - Maria Tellez-Plaza
- Area of Cardiometabolic and Renal Risk, Institute for Biomedical Research INCLIVA, Valencia, Spain.,Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Institute of Health Carlos III, Madrid, Spain.,Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Mooney SJ, Bobb JF, Hurvitz PM, Anau J, Theis MK, Drewnowski A, Aggarwal A, Gupta S, Rosenberg DE, Cook AJ, Shi X, Lozano P, Moudon AV, Arterburn D. Impact of Built Environments on Body Weight (the Moving to Health Study): Protocol for a Retrospective Longitudinal Observational Study. JMIR Res Protoc 2020; 9:e16787. [PMID: 32427111 PMCID: PMC7268006 DOI: 10.2196/16787] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 12/20/2019] [Accepted: 01/07/2020] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Studies assessing the impact of built environments on body weight are often limited by modest power to detect residential effects that are small for individuals but may nonetheless comprise large attributable risks. OBJECTIVE We used data extracted from electronic health records to construct a large retrospective cohort of patients. This cohort will be used to explore both the impact of moving between environments and the long-term impact of changing neighborhood environments. METHODS We identified members with at least 12 months of Kaiser Permanente Washington (KPWA) membership and at least one weight measurement in their records during a period between January 2005 and April 2017 in which they lived in King County, Washington. Information on member demographics, address history, diagnoses, and clinical visits data (including weight) was extracted. This paper describes the characteristics of the adult (aged 18-89 years) cohort constructed from these data. RESULTS We identified 229,755 adults representing nearly 1.2 million person-years of follow-up. The mean age at baseline was 45 years, and 58.0% (133,326/229,755) were female. Nearly one-fourth of people (55,150/229,755) moved within King County at least once during the follow-up, representing 84,698 total moves. Members tended to move to new neighborhoods matching their origin neighborhoods on residential density and property values. CONCLUSIONS Data were available in the KPWA database to construct a very large cohort based in King County, Washington. Future analyses will directly examine associations between neighborhood conditions and longitudinal changes in body weight and diabetes as well as other health conditions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/16787.
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Affiliation(s)
- Stephen J Mooney
- Department of Epidemiology, University of Washington, Seattle, WA, United States.,Harborview Injury Prevention & Research Center, University of Washington, Seattle, WA, United States
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
| | - Philip M Hurvitz
- Department of Urban Design and Planning, College of Built Environments, University of Washington, Seattle, WA, United States
| | - Jane Anau
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
| | - Mary Kay Theis
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
| | - Adam Drewnowski
- Department of Epidemiology, University of Washington, Seattle, WA, United States.,Center for Public Health Nutrition, University of Washington, Seattle, WA, United States
| | - Anju Aggarwal
- Department of Epidemiology, University of Washington, Seattle, WA, United States.,Center for Public Health Nutrition, University of Washington, Seattle, WA, United States
| | - Shilpi Gupta
- Department of Epidemiology, University of Washington, Seattle, WA, United States.,Center for Public Health Nutrition, University of Washington, Seattle, WA, United States
| | - Dori E Rosenberg
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
| | - Andrea J Cook
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
| | - Xiao Shi
- Department of Urban Design and Planning, College of Built Environments, University of Washington, Seattle, WA, United States
| | - Paula Lozano
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
| | - Anne Vernez Moudon
- Department of Urban Design and Planning, College of Built Environments, University of Washington, Seattle, WA, United States
| | - David Arterburn
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
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Sayre M, Lapham GT, Lee AK, Oliver M, Bobb JF, Caldeiro RM, Bradley KA. Routine Assessment of Symptoms of Substance Use Disorders in Primary Care: Prevalence and Severity of Reported Symptoms. J Gen Intern Med 2020; 35:1111-1119. [PMID: 31974903 PMCID: PMC7174482 DOI: 10.1007/s11606-020-05650-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 10/10/2019] [Accepted: 12/10/2019] [Indexed: 01/20/2023]
Abstract
BACKGROUND Most patients with substance use disorders (SUDs) never receive treatment and SUDs are under-recognized in primary care (PC) where patients can be treated or linked to treatment. Asking PC patients to directly report SUD symptoms on questionnaires might help identify SUDs but to our knowledge, this approach is previously untested. OBJECTIVE To describe the prevalence and severity of DSM-5 SUD symptoms reported by PC patients as part of routine care. DESIGN Cross-sectional study using secondary data. PARTICIPANTS A total of 241,265 adult patients who visited one of 25 PC sites in an integrated health system in Washington state and had alcohol, cannabis, or other drug use screening documented in their EHRs (March 2015-July 2018) were included in main analyses if they had a positive screen for high-risk substance use defined as AUDIT-C score 7-12 points, or report of past-year daily cannabis use or any other drug use. MAIN MEASURES The main outcome was number of SUD symptoms based on Diagnostic and Statistical Manual, 5th edition (DSM-5), reported on Symptom Checklists (0-11) for alcohol or other drugs: 2-3 mild; 4-5 moderate; 6-11 severe. RESULTS Of screened patients, 16,776 (5.7%) reported high-risk use of alcohol (2.4%), cannabis (3.9%), and/or other drugs (1.7%), and 65.0-69.9% of those completed Symptom Checklists. Of those with high-risk alcohol use, 52.5% (95% CI 50.9-54.0%) reported ≥ 2 symptoms consistent with mild-severe alcohol use disorders. Of those reporting daily cannabis use, 29.8% (28.6-30.9%) reported ≥ 2 symptoms consistent with mild-severe SUDs. Of those reporting any other drug use, 37.5% (35.7-39.3%) reported ≥ 2 symptoms consistent with mild-severe SUDs. CONCLUSIONS AND RELEVANCE Many PC patients who screened positive for high-risk substance use reported symptoms consistent with DSM-5 SUDs on self-report Symptom Checklists. Use of SUD Symptom Checklists could support PC providers in making SUD diagnoses and initiating discussions of substance use.
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Affiliation(s)
- Mikko Sayre
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
- Columbia-Bassett Program at Columbia University College of Physicians and Surgeons, Cooperstown, NY, USA.
| | - Gwen T Lapham
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Amy K Lee
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Malia Oliver
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Ryan M Caldeiro
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
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Bobb JF, Qiu H, Matthews AG, McCormack J, Bradley KA. Addressing identification bias in the design and analysis of cluster-randomized pragmatic trials: a case study. Trials 2020; 21:289. [PMID: 32293514 PMCID: PMC7092580 DOI: 10.1186/s13063-020-4148-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 02/06/2020] [Indexed: 02/03/2023] Open
Abstract
Background Pragmatic trials provide the opportunity to study the effectiveness of health interventions to improve care in real-world settings. However, use of open-cohort designs with patients becoming eligible after randomization and reliance on electronic health records (EHRs) to identify participants may lead to a form of selection bias referred to as identification bias. This bias can occur when individuals identified as a result of the treatment group assignment are included in analyses. Methods To demonstrate the importance of identification bias and how it can be addressed, we consider a motivating case study, the PRimary care Opioid Use Disorders treatment (PROUD) Trial. PROUD is an ongoing pragmatic, cluster-randomized implementation trial in six health systems to evaluate a program for increasing medication treatment of opioid use disorders (OUDs). A main study objective is to evaluate whether the PROUD intervention decreases acute care utilization among patients with OUD (effectiveness aim). Identification bias is a particular concern, because OUD is underdiagnosed in the EHR at baseline, and because the intervention is expected to increase OUD diagnosis among current patients and attract new patients with OUD to the intervention site. We propose a framework for addressing this source of bias in the statistical design and analysis. Results The statistical design sought to balance the competing goals of fully capturing intervention effects and mitigating identification bias, while maximizing power. For the primary analysis of the effectiveness aim, identification bias was avoided by defining the study sample using pre-randomization data (pre-trial modeling demonstrated that the optimal approach was to use individuals with a prior OUD diagnosis). To expand generalizability of study findings, secondary analyses were planned that also included patients newly diagnosed post-randomization, with analytic methods to account for identification bias. Conclusion As more studies seek to leverage existing data sources, such as EHRs, to make clinical trials more affordable and generalizable and to apply novel open-cohort study designs, the potential for identification bias is likely to become increasingly common. This case study highlights how this bias can be addressed in the statistical study design and analysis. Trial registration ClinicalTrials.gov, NCT03407638. Registered on 23 January 2018.
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Affiliation(s)
- Jennifer F Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA, 98101, USA. .,Department of Biostatistics, University of Washington, 1705 NE Pacific St, Seattle, WA, 98195, USA.
| | - Hongxiang Qiu
- Department of Biostatistics, University of Washington, 1705 NE Pacific St, Seattle, WA, 98195, USA
| | | | | | - Katharine A Bradley
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA, 98101, USA.,Department of Health Services, University of Washington, 1959 NE Pacific St, Seattle, WA, 98195, USA.,Department of Medicine, University of Washington, 1959 NE Pacific St, Seattle, WA, 98195, USA
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40
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Lapham G, Boudreau DM, Johnson EA, Bobb JF, Matthews AG, McCormack J, Liu D, Samet JH, Saxon AJ, Campbell CI, Glass JE, Rossom RC, Murphy MT, Binswanger IA, Yarborough BJH, Bradley KA, Ahmedani B, Amoroso PJ, Arnsten JH, Bart G, Braciszewski JM, Cunningham CO, Hechter RC, Horigian VE, Liebschutz JM, Loree AM, Matson TE, McNeely J, Merrill JO, Northrup TF, Schwartz RP, Stotts AL, Szapocznik J, Thakral M, Tsui JI, Zare M. Prevalence and treatment of opioid use disorders among primary care patients in six health systems. Drug Alcohol Depend 2020; 207:107732. [PMID: 31835068 PMCID: PMC7158756 DOI: 10.1016/j.drugalcdep.2019.107732] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 10/25/2019] [Accepted: 11/11/2019] [Indexed: 11/30/2022]
Abstract
BACKGROUND The U.S. experienced nearly 48,000 opioid overdose deaths in 2017. Treatment of opioid use disorder (OUD) with buprenorphine is a recommended part of primary care, yet little is known about current U.S. practices in this setting. This observational study reports the prevalence of documented OUD and OUD treatment with buprenorphine among primary care patients in six large health systems. METHODS Adults with ≥2 primary care visits during a three-year period (10/1/2013-9/30/2016) in six health systems were included. Data were obtained from electronic health record and claims data, with measures, assessed over the three-year period, including indicators for documented OUD from ICD 9 and 10 codes and OUD treatment with buprenorphine. The prevalence of OUD treatment was adjusted for age, gender, race/ethnicity, and health system. RESULTS Among 1,368,604 primary care patients, 13,942 (1.0 %) had documented OUD, and among these, 21.0 % had OUD treatment with buprenorphine. For those with documented OUD, the adjusted prevalence of OUD treatment with buprenorphine varied across demographic and clinical subgroups. OUD treatment was lower among patients who were older, women, Black/African American and Hispanic (compared to white), non-commercially insured, and those with non-cancer pain, mental health disorders, greater comorbidity, and more opioid prescriptions, emergency department visits or hospitalizations. CONCLUSIONS Among primary care patients in six health systems, one in five with an OUD were treated with buprenorphine, with disparities across demographic and clinical characteristics. Less buprenorphine treatment among those with greater acute care utilization highlights an opportunity for systems-level changes to increase OUD treatment.
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Affiliation(s)
- Gwen Lapham
- Kaiser Permanente Washington Health Research Institute, United States; University of Washington, Department of Health Services, United States.
| | - Denise M Boudreau
- Kaiser Permanente Washington Health Research Institute.,University of Washington Department of Pharmacy
| | | | | | | | | | - David Liu
- National Institute on Drug Abuse Center for Clinical Trials Network
| | - Jeffrey H Samet
- Boston University & Boston Medical Center Department of Medicine, Division of General Internal Medicine
| | - Andrew J Saxon
- Veteran Affairs Puget Sound Health Care System Center of Excellence in Substance Abuse Treatment and Education.,University of Washington Department of Psychiatry and Behavioral Sciences
| | | | - Joseph E Glass
- Kaiser Permanente Washington Health Research Institute.,University of Washington Department of Psychiatry and Behavioral Sciences
| | | | - Mark T Murphy
- Multicare Health System MultiCare Tacoma Central Family Medicine
| | - Ingrid A Binswanger
- Kaiser Permanente Colorado Institute for Health Research.,Colorado Permanente Medical Group, Denver, Colorado
| | | | - Katharine A Bradley
- Kaiser Permanente Washington Health Research Institute.,University of Washington Department of Health Services.,University of Washington Department of Medicine
| | - Brian Ahmedani
- Henry Ford Health System Center for Health Policy & Health Services Research
| | - Paul J Amoroso
- Multicare Health System MultiCare Institute for Research and Innovation
| | - Julia H Arnsten
- Montefiore Medical Center Department of Medicine.,Montefiore Medical Center Division of General Internal Medicine.,Albert Einstein College of Medicine Department of Medicine, Division of General Internal Medicine
| | | | | | - Chinazo O Cunningham
- Montefiore Medical Center Department of Medicine.,Albert Einstein College of Medicine Department of Medicine, Division of General Internal Medicine
| | - Rulin C Hechter
- Kaiser Permanente Southern California Department of Research and Evaluation
| | - Viviana E Horigian
- University of Miami Miller School of Medicine, Department of Public Health Sciences
| | - Jane M Liebschutz
- University of Pittsburgh School of Medicine Division of General Internal Medicine, Center for Research on Health Care
| | - Amy M Loree
- Henry Ford Health System Center for Health Policy & Health Services Research
| | | | - Jennifer McNeely
- New York University School of Medicine, Department of Population Health and Department of Medicine, Division of General Internal Medicine and Clinical Innovation
| | | | - Thomas F Northrup
- McGovern Medical School at The University of Texas Health Science Center at Houston
| | | | - Angela L Stotts
- McGovern Medical School at The University of Texas Health Science Center at Houston
| | - José Szapocznik
- University of Miami Miller School of Medicine, Department of Public Health Sciences
| | - Manu Thakral
- University of Massachusetts Boston College of Nursing and Health Sciences, Boston, MA, USA
| | - Judith I Tsui
- University of Washington Division of General Internal Medicine
| | - Mohammad Zare
- McGovern Medical School at The University of Texas Health Science Center at Houston
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Williams EC, Bobb JF, Lee AK, Ludman EJ, Richards JE, Hawkins EJ, Merrill JO, Saxon AJ, Lapham GT, Matson TE, Chavez LJ, Caldeiro R, Greenberg DM, Kivlahan DR, Bradley KA. Effect of a Care Management Intervention on 12-Month Drinking Outcomes Among Patients With and Without DSM-IV Alcohol Dependence at Baseline. J Gen Intern Med 2019:10.1007/s11606-019-05261-7. [PMID: 31432438 DOI: 10.1007/s11606-019-05261-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 04/24/2019] [Accepted: 06/18/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND The CHOICE care management intervention did not improve drinking relative to usual care (UC) for patients with frequent heavy drinking at high risk of alcohol use disorders. Patients with alcohol dependence were hypothesized to benefit most. We conducted preplanned secondary analyses to test whether the CHOICE intervention improved drinking relative to UC among patients with and without baseline DSM-IV alcohol dependence. METHODS A total of 304 patients reporting frequent heavy drinking from 3 VA primary care clinics were randomized (stratified by DSM-IV alcohol dependence, sex, and site) to UC or the patient-centered, nurse-delivered, 12-month CHOICE care management intervention. Primary outcomes included percent heavy drinking days (%HDD) using 28-day timeline follow-back and a "good drinking outcome" (GDO)-abstaining or drinking below recommended limits and no alcohol-related symptoms on the Short Inventory of Problems at 12 months. Generalized estimating equation binomial regression models (clustered on provider) with interaction terms between dependence and intervention group were fit. RESULTS At baseline, 59% of intervention and UC patients had DSM-IV alcohol dependence. Mean drinking outcomes improved for all subgroups. For participants with dependence, 12-month outcomes did not differ for intervention versus UC patients (%HDD 37% versus 38%, p = 0.76 and GDO 16% versus 16%, p = 0.77). For participants without dependence, %HDD did not differ between intervention (41%) and UC (31%) patients (p = 0.12), but the proportion with GDO was significantly higher among UC participants (26% versus 13%, p = 0.046). Neither outcome was significantly modified by dependence (interaction p values 0.19 for %HDD and 0.10 for GDO). CONCLUSIONS Among participants with frequent heavy drinking, care management had no benefit relative to UC for patients with dependence, but UC may have had benefits for those without dependence. TRIAL REGISTRATION ClinicalTrials.gov NCT01400581.
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Affiliation(s)
- Emily C Williams
- Health Services Research & Development (HSR&D), Center of Innovation for Veteran-Centered Value-Driven Care, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, WA, USA.
- Department of Health Services, University of Washington, Seattle, USA.
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | - Amy K Lee
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | - Evette J Ludman
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | - Julie E Richards
- Department of Health Services, University of Washington, Seattle, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | - Eric J Hawkins
- Health Services Research & Development (HSR&D), Center of Innovation for Veteran-Centered Value-Driven Care, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, WA, USA
- Center of Excellence in Substance Abuse Treatment and Education (CESATE), VA Puget Sound Health Care System, Seattle, USA
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA
| | | | - Andrew J Saxon
- Center of Excellence in Substance Abuse Treatment and Education (CESATE), VA Puget Sound Health Care System, Seattle, USA
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA
| | - Gwen T Lapham
- Department of Health Services, University of Washington, Seattle, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | - Theresa E Matson
- Health Services Research & Development (HSR&D), Center of Innovation for Veteran-Centered Value-Driven Care, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
- Center of Excellence in Substance Abuse Treatment and Education (CESATE), VA Puget Sound Health Care System, Seattle, USA
| | | | - Ryan Caldeiro
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
- Kaiser Permanente Washington, Seattle, USA
| | | | - Daniel R Kivlahan
- Health Services Research & Development (HSR&D), Center of Innovation for Veteran-Centered Value-Driven Care, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, WA, USA
- Center of Excellence in Substance Abuse Treatment and Education (CESATE), VA Puget Sound Health Care System, Seattle, USA
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA
| | - Katharine A Bradley
- Health Services Research & Development (HSR&D), Center of Innovation for Veteran-Centered Value-Driven Care, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
- Department of Medicine, University of Washington, Seattle, USA
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Pettigrew SM, Pan WK, Berky A, Harrington J, Bobb JF, Feingold BJ. In urban, but not rural, areas of Madre de Dios, Peru, adoption of a Western diet is inversely associated with selenium intake. Sci Total Environ 2019; 687:1046-1054. [PMID: 31412442 DOI: 10.1016/j.scitotenv.2019.05.484] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 05/26/2019] [Accepted: 05/31/2019] [Indexed: 06/10/2023]
Abstract
Road development has been a major driver of the transition from traditional to calorie-dense processed 'Western' diets in lower and middle-income countries. The paving of the Interoceanic Highway (IOH) facilitated rapid development to the Madre de Dios (MDD) region in the Peruvian Amazon. As traditional foods such as Brazil nuts and fish are known to be rich in the essential micronutrient selenium, people further along the nutrition transition to a Western diet may have lower selenium (Se) intake. To test this hypothesis, in 2014 the Investigacion de Migracion, Ambiente, y Salud (IMAS Study) (Migration, Environment, and Health Study) collected household surveys from 310 households in 46 communities along the IOH and nails for Se analysis from 418 adults. Principal component analysis of 25 commonly consumed food items identified a factor resembling Western diet, which was used to calculate household Western diet weighted sum factor scores (WSFS). WSFS means were interpolated into a 10 km buffer around the IOH using inverse distance weighting. Western diet adoption was higher in urban compared to rural areas (p < 0.0001), and geographic variation was observed between mining and agricultural areas. Mean nail Se was 730 ng/g, SD 198 ng/g (range: 200-1390 ng/g). Generalized estimating equation (GEE) models assessed the association between food consumption and nail Se. Household chicken consumption was positively associated with Se in rural areas only. Urban/rural status modified the effect of western diet adoption on nail Se, and Se was inversely associated with WSFS in urban areas only. Conclusion: In urban, but not rural, areas of Madre de Dios, Peru, adoption of a Western diet is inversely associated with selenium intake. As the essential micronutrient selenium is a vital part of antioxidant proteins, lower intake could compound the chronic health effects that may result from transition to a calorie-dense diet.
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Affiliation(s)
- Stacy M Pettigrew
- Department of Environmental Health Sciences, State University of New York at Albany, School of Public Health, 1 University Place, Rensselaer, NY 12144, United States of America
| | - William K Pan
- Global Health Institute, Duke University, Durham, NC 27710, USA; Nicholas School of the Environment, Duke University, Durham, NC 27710, USA
| | - Axel Berky
- Nicholas School of the Environment, Duke University, Durham, NC 27710, USA
| | - James Harrington
- Analytical Sciences Department, Research Triangle Institute, East Cornwallis Road, Post Office Box 12194, Research Triangle Park, NC 27709-2194, United States of America
| | - Jennifer F Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, #1600, Seattle, WA 98101, United States of America
| | - Beth J Feingold
- Department of Environmental Health Sciences, State University of New York at Albany, School of Public Health, 1 University Place, Rensselaer, NY 12144, United States of America.
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Richards JE, Bobb JF, Lee AK, Lapham GT, Williams EC, Glass JE, Ludman EJ, Achtmeyer C, Caldeiro RM, Oliver M, Bradley KA. Integration of screening, assessment, and treatment for cannabis and other drug use disorders in primary care: An evaluation in three pilot sites. Drug Alcohol Depend 2019; 201:134-141. [PMID: 31212213 PMCID: PMC6642904 DOI: 10.1016/j.drugalcdep.2019.04.015] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 04/15/2019] [Accepted: 04/16/2019] [Indexed: 11/18/2022]
Abstract
BACKGROUND This pilot study evaluated whether use of evidence-based implementation strategies to integrate care for cannabis and other drug use into primary care (PC) as part of Behavioral Health Integration (BHI) increased diagnosis and treatment of substance use disorders (SUDs). METHODS Patients who visited the three pilot PC sites were eligible. Implementation strategies included practice coaching, electronic health record decision support, and performance feedback (3/2015-4/2016). BHI introduced annual screening for past-year cannabis and other drug use, a Symptom Checklist for DSM-5 SUDs, and shared decision-making about treatment options. Main analyses tested whether the proportions of PC patients diagnosed with, and treated for, new cannabis or other drug use disorders (CUDs and DUDs, respectively), differed significantly pre- and post-implementation. RESULTS Of 39,599 eligible patients, 57% and 59% were screened for cannabis and other drug use, respectively. Among PC patients reporting daily cannabis use (2%) or any drug use (1%), 51% and 37%, respectively, completed an SUD Symptom Checklist. The proportion of PC patients with newly diagnosed CUD increased significantly post-implementation (5 v 17 per 10,000 patients, p < 0.0001), but not other DUDs (10 vs 13 per 10,000, p = 0.24). The proportion treated for newly diagnosed CUDs did not increase post-implementation (1 vs 1 per 10,000, p = 0.80), but did for those treated for newly diagnosed other DUDs (1 vs 3 per 10,000, p = 0.038). CONCLUSIONS A pilot implementation of BHI to increase routine screening and assessment for SUDs was associated with increased new CUD diagnoses and a small increase in treatment of new other DUDs.
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Affiliation(s)
- Julie E Richards
- Kaiser Permanente Washington Health Research Institute, Seattle USA; Department of Health Services, University of Washington, Seattle USA.
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle USA
| | - Amy K Lee
- Kaiser Permanente Washington Health Research Institute, Seattle USA
| | - Gwen T Lapham
- Kaiser Permanente Washington Health Research Institute, Seattle USA; Department of Health Services, University of Washington, Seattle USA
| | - Emily C Williams
- Kaiser Permanente Washington Health Research Institute, Seattle USA; Department of Health Services, University of Washington, Seattle USA; VA Puget Sound, Health Services Research and Development Center of Innovation for Veteran-Centered and Value-Driven Care, Seattle, WA USA
| | - Joseph E Glass
- Kaiser Permanente Washington Health Research Institute, Seattle USA; Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle USA
| | - Evette J Ludman
- Kaiser Permanente Washington Health Research Institute, Seattle USA
| | - Carol Achtmeyer
- Kaiser Permanente Washington Health Research Institute, Seattle USA; VA Puget Sound Health Care System, Center of Excellence in Substance Abuse Treatment and Education, Seattle, USA
| | - Ryan M Caldeiro
- Kaiser Permanente Washington, Mental Health and Wellness, Seattle USA
| | - Malia Oliver
- Kaiser Permanente Washington Health Research Institute, Seattle USA
| | - Katharine A Bradley
- Kaiser Permanente Washington Health Research Institute, Seattle USA; Department of Health Services, University of Washington, Seattle USA; VA Puget Sound, Health Services Research and Development Center of Innovation for Veteran-Centered and Value-Driven Care, Seattle, WA USA; Department of Medicine, University of Washington, Seattle USA
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Nelson JC, Ulloa-Pérez E, Bobb JF, Maro JC. Leveraging the entire cohort in drug safety monitoring: part 1 methods for sequential surveillance that use regression adjustment or weighting to control confounding in a multisite, rare event, distributed data setting. J Clin Epidemiol 2019; 112:77-86. [PMID: 31108199 DOI: 10.1016/j.jclinepi.2019.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 03/01/2019] [Accepted: 04/04/2019] [Indexed: 10/26/2022]
Abstract
OBJECTIVE Study designs involving self-controlled or exposure-matched samples are commonly used to monitor postmarket vaccine and drug safety, and they use a subset of the available larger cohort. This article overviews group sequential methods designed for observational data safety monitoring that use the whole exposed and unexposed cohorts by implementing regression adjustment or weighting to control confounding. METHODS We summarize what is known about the performance of "whole cohort" methods in multisite health plan data networks such as the Sentinel System of the Food and Drug Administration, where outcomes are rare, individual-level patient data cannot be pooled across sites, site heterogeneity is large, and data are dynamically updated over time. RESULTS Group sequential estimation and testing methods that use regression or weighting can flexibly handle electronic health care data's unpredictability, including an uncertain rate of new product uptake, variable composition of the population over time, and data changes due to dynamic administrative updates. Regression and weighting methods generally have higher power, faster signal detection, and fewer practical challenges compared with some design-based confounder adjustment methods. CONCLUSION Group sequential regression adjustment and weighting approaches are feasible and underused in practice. They leverage more information than designs that involved sampling and increase power to detect rare adverse effects without increasing bias.
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Affiliation(s)
- Jennifer C Nelson
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Ernesto Ulloa-Pérez
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jennifer F Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Judith C Maro
- Department of Population Medicine, Harvard Medical School, Boston, MA, USA; Harvard Pilgrim Health Care Institute, Boston, MA, USA
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Liu SH, Bobb JF, Lee KH, Gennings C, Claus Henn B, Bellinger D, Austin C, Schnaas L, Tellez-Rojo MM, Hu H, Wright RO, Arora M, Coull BA. Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures. Biostatistics 2019; 19:325-341. [PMID: 28968676 DOI: 10.1093/biostatistics/kxx036] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 07/21/2017] [Indexed: 11/14/2022] Open
Abstract
The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.
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Affiliation(s)
- Shelley H Liu
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA
| | - Jennifer F Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Seattle, WA 98101, USA
| | - Kyu Ha Lee
- Epidemiology and Biostatistics Core, The Forsyth Institute, 245 First Street, Cambridge, MA 02142, USA
| | - Chris Gennings
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 17 E. 102nd Street, New York, NY 10029, USA
| | - Birgit Claus Henn
- Department of Environmental Health, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA
| | - David Bellinger
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA
| | - Christine Austin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 17 East 102nd Street, New York, NY 10029, USA
| | - Lourdes Schnaas
- Division of Research in Community Interventions, National Institute of Perinatology, Montes Urales 800, Lomas Virreyes, CP 11000, CDMX, México
| | - Martha M Tellez-Rojo
- Center for Nutrition and Health Research, National Institute of Public Health, Universidad No. 655 Colonia Santa María, Ahuacatitlán, Cerrada Los Pinos y Caminera, C.P. 62100, Cuernavaca, Morelos, México
| | - Howard Hu
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON, M5T 3M7, Canada
| | - Robert O Wright
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 17 E. 102nd Street, New York, NY 10029, USA
| | - Manish Arora
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 17 E. 102nd Street, New York, NY 10029, USA
| | - Brent A Coull
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA
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Shortreed SM, Cook AJ, Coley RY, Bobb JF, Nelson JC. Challenges and Opportunities for Using Big Health Care Data to Advance Medical Science and Public Health. Am J Epidemiol 2019; 188:851-861. [PMID: 30877288 DOI: 10.1093/aje/kwy292] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 12/20/2018] [Indexed: 12/14/2022] Open
Abstract
Methodological advancements in epidemiology, biostatistics, and data science have strengthened the research world's ability to use data captured from electronic health records (EHRs) to address pressing medical questions, but gaps remain. We describe methods investments that are needed to curate EHR data toward research quality and to integrate complementary data sources when EHR data alone are insufficient for research goals. We highlight new methods and directions for improving the integrity of medical evidence generated from pragmatic trials, observational studies, and predictive modeling. We also discuss needed methods contributions to further ease data sharing across multisite EHR data networks. Throughout, we identify opportunities for training and for bolstering collaboration among subject matter experts, methodologists, practicing clinicians, and health system leaders to help ensure that methods problems are identified and resulting advances are translated into mainstream research practice more quickly.
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Affiliation(s)
- Susan M Shortreed
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Andrea J Cook
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - R Yates Coley
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Jennifer F Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Jennifer C Nelson
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
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Marcum ZA, Walker R, Bobb JF, Sin MK, Gray SL, Bowen JD, McCormick W, McCurry SM, Crane PK, Larson EB. Reply to: Comment on: Serum Cholesterol and Incident Alzheimer's Disease: Findings From the Adult Changes in Thought Study. J Am Geriatr Soc 2019; 67:1303-1305. [PMID: 30893465 DOI: 10.1111/jgs.15879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 02/25/2019] [Indexed: 11/28/2022]
Affiliation(s)
- Zachary A Marcum
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, Washington
| | - Rod Walker
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Mo-Kyung Sin
- College of Nursing, Seattle University, Seattle, Washington
| | - Shelly L Gray
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, Washington
| | | | - Wayne McCormick
- Department of Medicine, Division of Gerontology and Geriatric Medicine, University of Washington, Seattle, Washington
| | - Susan M McCurry
- School of Nursing, University of Washington, Seattle, Washington
| | - Paul K Crane
- Department of Medicine, Division of General Internal Medicine, University of Washington, Seattle, Washington
| | - Eric B Larson
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
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Liu SH, Bobb JF, Henn BC, Gennings C, Schnaas L, Tellez-Rojo M, Bellinger D, Arora M, Wright RO, Coull BA. Bayesian varying coefficient kernel machine regression to assess neurodevelopmental trajectories associated with exposure to complex mixtures. Stat Med 2018; 37:4680-4694. [PMID: 30277584 PMCID: PMC6522130 DOI: 10.1002/sim.7947] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 07/14/2018] [Accepted: 07/27/2018] [Indexed: 11/09/2022]
Abstract
Exposure to environmental mixtures can exert wide-ranging effects on child neurodevelopment. However, there is a lack of statistical methods that can accommodate the complex exposure-response relationship between mixtures and neurodevelopment while simultaneously estimating neurodevelopmental trajectories. We introduce Bayesian varying coefficient kernel machine regression (BVCKMR), a hierarchical model that estimates how mixture exposures at a given time point are associated with health outcome trajectories. The BVCKMR flexibly captures the exposure-response relationship, incorporates prior knowledge, and accounts for potentially nonlinear and nonadditive effects of individual exposures. This model assesses the directionality and relative importance of a mixture component on health outcome trajectories and predicts health effects for unobserved exposure profiles. Using contour plots and cross-sectional plots, BVCKMR also provides information about interactions between complex mixture components. The BVCKMR is applied to a subset of data from PROGRESS, a prospective birth cohort study in Mexico city on exposure to metal mixtures and temporal changes in neurodevelopment. The mixture include metals such as manganese, arsenic, cobalt, chromium, cesium, copper, lead, cadmium, and antimony. Results from a subset of Programming Research in Obesity, Growth, Environment and Social Stressors data provide evidence of significant positive associations between second trimester exposure to copper and Bayley Scales of Infant and Toddler Development cognition score at 24 months, and cognitive trajectories across 6-24 months. We also detect an interaction effect between second trimester copper and lead exposures for cognition at 24 months. In summary, BVCKMR provides a framework for estimating neurodevelopmental trajectories associated with exposure to complex mixtures.
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Affiliation(s)
- Shelley H. Liu
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jennifer F. Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Birgit Claus Henn
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts
| | - Chris Gennings
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lourdes Schnaas
- Division for Research in Community Interventions, National Institute of Perinatology, Mexico, Mexico
| | - Martha Tellez-Rojo
- Center for Research in Nutrition and Health, National Institute of Public Health, Cuernavaca, Mexico
| | - David Bellinger
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Manish Arora
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Robert O. Wright
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Brent A. Coull
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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Yitshak-Sade M, Bobb JF, Schwartz JD, Kloog I, Zanobetti A. The association between short and long-term exposure to PM 2.5 and temperature and hospital admissions in New England and the synergistic effect of the short-term exposures. Sci Total Environ 2018; 639:868-875. [PMID: 29929325 PMCID: PMC6051434 DOI: 10.1016/j.scitotenv.2018.05.181] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Revised: 04/25/2018] [Accepted: 05/15/2018] [Indexed: 05/04/2023]
Abstract
BACKGROUND Particulate matter < 2.5 μm in diameter (PM2.5) and heat are strong predictors of morbidity, yet few studies have examined the effects of long-term exposures on non-fatal events, or assessed the short and long-term effect on health simultaneously. OBJECTIVE We jointly investigated the association of short and long-term exposures to PM2.5 and temperature with hospital admissions, and explored the modification of the associations with the short-term exposures by one another and by temperature variability. METHODS Daily ZIP code counts of respiratory, cardiac and stroke admissions of adults ≥65 (N = 2,015,660) were constructed across New-England (2001-2011). Daily PM2.5 and temperature exposure estimates were obtained from satellite-based spatio-temporally resolved models. For each admission cause, a Poisson regression was fit on short and long-term exposures, with a random intercept for ZIP code. Modifications of the short-term effects were tested by adding interaction terms with temperature, PM2.5 and temperature variability. RESULTS Associations between short and long-term exposures were observed for all of the outcomes, with stronger effects of long-term exposures to PM2.5. For respiratory admissions, the short-term PM2.5 effect (percent increase per IQR) was larger on warmer days (1.12% versus -0.53%) and in months of higher temperature variability (1.63% versus -0.45%). The short-term temperature effect was higher in months of higher temperature variability as well. For cardiac admissions, the PM2.5 effect was larger on colder days (0.56% versus -0.30%) and in months of higher temperature variability (0.99% versus -0.56%). CONCLUSIONS We observed synergistic effects of short-term exposures to PM2.5, temperature and temperature variability. Long-term exposures to PM2.5 were associated with larger effects compared to short-term exposures.
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Affiliation(s)
- Maayan Yitshak-Sade
- Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jennifer F Bobb
- Biostatistics Unit, Kaiser Permanent Washington Health Research Institute, Seattle, WA, USA
| | - Joel D Schwartz
- Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Itai Kloog
- Department of Geography and Environmental Development, Faculty of Humanities and Social Sciences, Ben-Gurion University, Beer-Sheva, Israel
| | - Antonella Zanobetti
- Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Marcum ZA, Walker R, Bobb JF, Sin MK, Gray SL, Bowen JD, McCormick W, McCurry SM, Crane PK, Larson EB. Serum Cholesterol and Incident Alzheimer's Disease: Findings from the Adult Changes in Thought Study. J Am Geriatr Soc 2018; 66:2344-2352. [PMID: 30289959 DOI: 10.1111/jgs.15581] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To evaluate associations between high-density lipoprotein cholesterol (HDL) and non-HDL-C levels at specific ages and subsequent Alzheimer's disease (AD) risk. DESIGN Prospective population-based cohort study. SETTING Adult Changes in Thought (ACT) Study. PARTICIPANTS Individuals aged 65 and older with no dementia at ACT Study entry. We identified separate, partially overlapping subcohorts of ACT participants who were eligible for each age band-specific analysis (50-59, n = 1,088; 60-69, n = 2,852; 70-79, n = 2,344; 80-89, n = 537). MEASUREMENTS Exposure consisted of clinical measures of total cholesterol (TC) and HDL-C from laboratory data during a given age band. Outcomes of incident AD were assessed post-age band using standard research diagnostic criteria. Statistical analyses used adjusted Cox proportional hazards regression models for each exposure and outcome pair within an age band. Cholesterol exposures were modeled using cubic splines. RESULTS For non-HDL-C, we found a statistically significant association with AD risk in the 60 to 69 (omnibus p = .005) and 70 to 79 (omnibus p = .04) age bands, suggesting a potential U-shaped relationship (greater risk at low and high levels). For example, in people aged 60 to 69, those with an average non-HDL-C level of 120 mg/DL had a 29% greater AD hazard (hazard ratio (HR)=1.29, 95% confidence interval (CI)=1.04-1.61) than those with an average non-HDL-C level of 160 mg/dL, whereas those with an average non-HDL-C level of 210 mg/dL had a 16% greater hazard (HR=1.16, 95% CI=1.01-1.33). We did not find a statistically significant association between HDL-C and AD risk. CONCLUSION People with low (120 mg/dL) and high (210 mg/dL) non-HDL-C levels during their 60s and 70s had modestly higher risk of AD than those with intermediate (160 mg/dL) levels. The extreme age bands (50s and 80s) had small sample sizes. J Am Geriatr Soc 66:2344-2352, 2018.
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Affiliation(s)
- Zachary A Marcum
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, Washington
| | - Rod Walker
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington
| | - Jennifer F Bobb
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington
| | - Mo-Kyung Sin
- Seattle University, College of Nursing, Seattle, Washington
| | - Shelly L Gray
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, Washington
| | | | - Wayne McCormick
- Department of Medicine, Division of Gerontology and Geriatric Medicine, University of Washington, Seattle, Washington
| | - Susan M McCurry
- School of Nursing, University of Washington, Seattle, Washington
| | - Paul K Crane
- Department of Medicine, Division of General Internal Medicine, University of Washington, Seattle, Washington
| | - Eric B Larson
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington
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