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Perrin EM, Skinner AC, Sanders LM, Rothman RL, Schildcrout JS, Bian A, Barkin SL, Coyne-Beasley T, Delamater AM, Flower KB, Heerman WJ, Steiner MJ, Yin HS. The Injury Prevention Program to Reduce Early Childhood Injuries: A Cluster Randomized Trial. Pediatrics 2024; 153:e2023062966. [PMID: 38557871 PMCID: PMC11035157 DOI: 10.1542/peds.2023-062966] [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] [Accepted: 02/16/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND AND OBJECTIVES The American Academy of Pediatrics designed The Injury Prevention Program (TIPP) in 1983 to help pediatricians prevent unintentional injuries, but TIPP's effectiveness has never been formally evaluated. We sought to evaluate the impact of TIPP on reported injuries in the first 2 years of life. METHODS We conducted a stratified, cluster-randomized trial at 4 academic medical centers: 2 centers trained their pediatric residents and implemented TIPP screening and counseling materials at all well-child checks (WCCs) for ages 2 to 24 months, and 2 centers implemented obesity prevention. At each WCC, parents reported the number of child injuries since the previous WCC. Proportional odds logistic regression analyses with generalized estimating equation examined the extent to which the number of injuries reported were reduced at TIPP intervention sites compared with control sites, adjusting for baseline child, parent, and household factors. RESULTS A total of 781 parent-infant dyads (349 TIPP; 432 control) were enrolled and had sufficient data to qualify for analyses: 51% Hispanic, 28% non-Hispanic Black, and 87% insured by Medicaid. Those at TIPP sites had significant reduction in the adjusted odds of reported injuries compared with non-TIPP sites throughout the follow-up (P = .005), with adjusted odds ratios (95% CI) of 0.77 (0.66-0.91), 0.60 (0.44-0.82), 0.32 (0.16-0.62), 0.26 (0.12-0.53), and 0.27 (0.14-0.52) at 4, 6, 12, 18, and 24 months, respectively. CONCLUSIONS In this cluster-randomized trial with predominantly low-income, Hispanic, and non-Hispanic Black families, TIPP resulted in a significant reduction in parent-reported injuries. Our study provides evidence for implementing the American Academy of Pediatrics' TIPP in routine well-child care.
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Affiliation(s)
- Eliana M. Perrin
- Department of Pediatrics, Johns Hopkins University Schools of Medicine and Nursing, Baltimore, Maryland
| | - Asheley C. Skinner
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Lee M. Sanders
- Departments of Pediatrics and Health Policy, Stanford University School of Medicine, Palo Alto, California
| | | | | | | | - Shari L. Barkin
- Department of Pediatrics, Virginia Commonwealth University, Richmond, Virginia
| | - Tamera Coyne-Beasley
- Departments of Pediatrics and Internal Medicine, University of Alabama-Birmingham, Birmingham, Alabama
| | - Alan M. Delamater
- Department of Pediatrics, University of Miami Miller School of Medicine, Miami, Florida
| | - Kori B. Flower
- Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | | | - Michael J. Steiner
- Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - H. Shonna Yin
- Departments of Pediatrics and Population Health, New York University Grossman School of Medicine, New York, New York
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Duh-Leong C, Perrin EM, Heerman WJ, Schildcrout JS, Wallace S, Mendelsohn AL, Lee DC, Flower KB, Sanders LM, Rothman RL, Delamater AM, Gross RS, Wood C, Yin HS. Prenatal Risks to Healthy Food Access and High Birthweight Outcomes. Acad Pediatr 2024; 24:613-618. [PMID: 37659601 PMCID: PMC10904668 DOI: 10.1016/j.acap.2023.08.017] [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: 01/18/2023] [Revised: 08/22/2023] [Accepted: 08/25/2023] [Indexed: 09/04/2023]
Abstract
OBJECTIVE Infants with high birthweight have increased risk for adverse outcomes at birth and across childhood. Prenatal risks to healthy food access may increase odds of high birthweight. We tested whether having a poor neighborhood food environment and/or food insecurity had associations with high birthweight. METHODS We analyzed cross-sectional baseline data in Greenlight Plus, an obesity prevention trial across six US cities (n = 787), which included newborns with a gestational age greater than 34 weeks and a birthweight greater than 2500 g. We assessed neighborhood food environment using the Place-Based Survey and food insecurity using the US Household Food Security Module. We performed logistic regression analyses to assess the individual and additive effects of risk factors on high birthweight. We adjusted for potential confounders: infant sex, race, ethnicity, gestational age, birthing parent age, education, income, and study site. RESULTS Thirty-four percent of birthing parents reported poor neighborhood food environment and/or food insecurity. Compared to those without food insecurity, food insecure families had greater odds of delivering an infant with high birthweight (adjusted odds ratios [aOR] 1.96, 95% confidence intervals [CI]: 1.01, 3.82) after adjusting for poor neighborhood food environment, which was not associated with high birthweight (aOR 1.35, 95% CI: 0.78, 2.34). Each additional risk to healthy food access was associated with a 56% (95% CI: 4%-132%) increase in high birthweight odds. CONCLUSIONS Prenatal risks to healthy food access may increase high infant birthweight odds. Future studies designed to measure neighborhood factors should examine infant birthweight outcomes in the context of prenatal social determinants of health.
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Affiliation(s)
- Carol Duh-Leong
- NYU Grossman School of Medicine (C Duh-Leong, RS Gross, and HS Yin), Division of General Pediatrics, Department of Pediatrics, New York, NY.
| | - Eliana M Perrin
- Johns Hopkins University (EM Perrin), Division of General Pediatrics, Department of Pediatrics, Schools of Medicine and Nursing, Baltimore, Md
| | - William J Heerman
- Vanderbilt University Medical Center (WJ Heerman and S Wallace), Department of Pediatrics, Nashville, Tenn
| | - Jonathan S Schildcrout
- Vanderbilt University Medical Center (JS Schildcrout), Department of Biostatistics, Nashville, Tenn
| | - Shelby Wallace
- Vanderbilt University Medical Center (WJ Heerman and S Wallace), Department of Pediatrics, Nashville, Tenn
| | - Alan L Mendelsohn
- NYU Grossman School of Medicine (AL Mendelsohn), Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, New York, NY
| | - David C Lee
- Ronald O. Perelman Department of Emergency Medicine (DC Lee), NYU Grossman School of Medicine, New York, NY
| | - Kori B Flower
- University of North Carolina at Chapel Hill (KB Flower), Division of General Pediatrics and Adolescent Medicine, UNC School of Medicine, Chapel Hill, NC
| | - Lee M Sanders
- Stanford University School of Medicine (LM Sanders), Division of General Pediatrics, Palo Alto, Calif
| | - Russell L Rothman
- Vanderbilt University Medical Center (RL Rothman), Institute of Medicine and Public Health, Nashville, Tenn
| | - Alan M Delamater
- University of Miami Miller School of Medicine (AM Delamater), Department of Pediatrics, Miami, Fla
| | - Rachel S Gross
- NYU Grossman School of Medicine (C Duh-Leong, RS Gross, and HS Yin), Division of General Pediatrics, Department of Pediatrics, New York, NY
| | - Charles Wood
- Duke University School of Medicine (C Wood), Department of Pediatrics, Division of General Pediatrics and Adolescent Health, Durham, NC
| | - Hsiang Shonna Yin
- NYU Grossman School of Medicine (C Duh-Leong, RS Gross, and HS Yin), Division of General Pediatrics, Department of Pediatrics, New York, NY
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Trochez RJ, Barrett JB, Shi Y, Schildcrout JS, Rick C, Nair D, Welch SA, Kumar AA, Bell SP, Kripalani S. Vulnerability to functional decline is associated with noncardiovascular cause of 90-day readmission in hospitalized patients with heart failure. J Hosp Med 2024; 19:386-393. [PMID: 38402406 DOI: 10.1002/jhm.13316] [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: 10/16/2023] [Revised: 01/29/2024] [Accepted: 02/05/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Hospital readmission is common among patients with heart failure. Vulnerability to decline in physical function may increase the risk of noncardiovascular readmission for these patients, but the association between vulnerability and the cause of unplanned readmission is poorly understood, inhibiting the development of effective interventions. OBJECTIVES We examined the association of vulnerability with the cause of readmission (cardiovascular vs. noncardiovascular) among hospitalized patients with acute decompensated heart failure. DESIGNS, SETTINGS, AND PARTICIPANTS This prospective longitudinal study is part of the Vanderbilt Inpatient Cohort Study. MAIN OUTCOME AND MEASURES The primary outcome was the cause of unplanned readmission (cardiovascular vs. noncardiovascular). The primary independent variable was vulnerability, measured using the Vulnerable Elders Survey (VES-13). RESULTS Among 804 hospitalized patients with acute decompensated heart failure, 315 (39.2%) experienced an unplanned readmission within 90 days of discharge. In a multinomial logistic model with no readmission as the reference category, higher vulnerability was associated with readmission for noncardiovascular causes (relative risk ratio [RRR] = 1.36, 95% confidence interval [CI]: 1.06-1.75) in the first 90 days after discharge. The VES-13 score was not associated with readmission for cardiovascular causes (RRR = 0.94, 95% CI: 0.75-1.17). CONCLUSIONS Vulnerability to functional decline predicted noncardiovascular readmission risk among hospitalized patients with heart failure. The VES-13 is a brief, validated, and freely available tool that should be considered in planning care transitions. Additional work is needed to examine the efficacy of interventions to monitor and mitigate noncardiovascular concerns among vulnerable patients with heart failure being discharged from the hospital.
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Affiliation(s)
- Ricardo J Trochez
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jennifer B Barrett
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Yaping Shi
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Chelsea Rick
- Department of Medicine, Division of Geriatric Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Devika Nair
- Department of Medicine, Division of Nephrology & Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sarah A Welch
- Department of Physical Medicine & Rehabilitation, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Veterans Affairs, Geriatric Research Education and Clinical Center(GRECC), Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - Anupam A Kumar
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Susan P Bell
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sunil Kripalani
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Section of Hospital Medicine, Department of Medicine, Division of General Internal Medicine & Public Health, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Nair D, Schildcrout JS, Shi Y, Trochez R, Nwosu S, Bell SP, Mixon AS, Welch SA, Goggins K, Bachmann JM, Vasilevskis EE, Cavanaugh KL, Rothman RL, Kripalani SB. Patient-reported predictors of postdischarge mortality after cardiac hospitalization. J Hosp Med 2024. [PMID: 38560772 DOI: 10.1002/jhm.13336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Adults hospitalized for cardiovascular events are at high risk for postdischarge mortality. Screening of psychosocial risk is prioritized by the Joint Commission. We tested whether key patient-reported psychosocial and behavioral measures could predict posthospitalization mortality in a cohort of adults hospitalized for a cardiovascular event. METHODS We conducted a prospective cohort study to test the prognostic utility of validated patient-reported measures, including health literacy, social support, health behaviors and disease management, and socioeconomic status. Cox survival analyses of mortality were conducted over a median of 3.5 years. RESULTS Among 2977 adults hospitalized for either acute coronary syndrome or acute decompensated heart failure, the mean age was 53 years, and 60% were male. After adjusting for demographic, clinical, and other psychosocial factors, mortality risk was greatest among patients who reported being unemployed (hazard ratio [HR]: 1.99, 95% confidence interval [CI]): 1.30-3.06), retired (HR: 2.14, 95% CI: 1.60-2.87), or unable to work due to disability (HR: 2.36, 95% CI: 1.73-3.21), as compared to those who were employed. Patient-reported perceived health competence (PHCS-2) and exercise frequency were also associated with mortality risk after adjusting for all other variables (HR: 0.86, 95% CI: 0.73-1.00 per four-point increase in PHCS-2; HR: 0.86, 95% CI: 0.77-0.96 per 3-day increase in exercise frequency, respectively). CONCLUSIONS Patient-reported measures of employment status, perceived health competence, and exercise frequency independently predict mortality after a cardiac hospitalization. Incorporating these brief, valid measures into hospital-based screening may help with prognostication and targeting patients for resources during post-discharge transitions of care.
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Affiliation(s)
- Devika Nair
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Yaping Shi
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Ricardo Trochez
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sam Nwosu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Susan P Bell
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Amanda S Mixon
- Section of Hospital Medicine, Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Veterans Affairs, Geriatric Research Education and Clinical Center (GRECC), Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - Sarah A Welch
- Department of Veterans Affairs, Geriatric Research Education and Clinical Center (GRECC), Tennessee Valley Healthcare System, Nashville, Tennessee, USA
- Department of Physical Medicine and Rehabilitation, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kathryn Goggins
- Vanderbilt Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Section of Hospital Medicine, Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Justin M Bachmann
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Kerri L Cavanaugh
- Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Russell L Rothman
- Institute of Medicine and Public Health, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sunil B Kripalani
- Vanderbilt Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Section of Hospital Medicine, Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Clinical Quality and Implementation Research, VUMC, Nashville, Tennessee, USA
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5
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Di Gravio C, Schildcrout JS, Tao R. Efficient designs and analysis of two-phase studies with longitudinal binary data. Biometrics 2024; 80:ujad010. [PMID: 38364804 PMCID: PMC10871867 DOI: 10.1093/biomtc/ujad010] [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: 02/16/2023] [Revised: 08/23/2023] [Accepted: 11/09/2023] [Indexed: 02/18/2024]
Abstract
Researchers interested in understanding the relationship between a readily available longitudinal binary outcome and a novel biomarker exposure can be confronted with ascertainment costs that limit sample size. In such settings, two-phase studies can be cost-effective solutions that allow researchers to target informative individuals for exposure ascertainment and increase estimation precision for time-varying and/or time-fixed exposure coefficients. In this paper, we introduce a novel class of residual-dependent sampling (RDS) designs that select informative individuals using data available on the longitudinal outcome and inexpensive covariates. Together with the RDS designs, we propose a semiparametric analysis approach that efficiently uses all data to estimate the parameters. We describe a numerically stable and computationally efficient EM algorithm to maximize the semiparametric likelihood. We examine the finite sample operating characteristics of the proposed approaches through extensive simulation studies, and compare the efficiency of our designs and analysis approach with existing ones. We illustrate the usefulness of the proposed RDS designs and analysis method in practice by studying the association between a genetic marker and poor lung function among patients enrolled in the Lung Health Study (Connett et al, 1993).
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Affiliation(s)
- Chiara Di Gravio
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, xUnited Kingdom
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, United Kingdom
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, United Kingdom
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Tian Y, Shepherd BE, Li C, Zeng D, Schildcrout JS. Analyzing clustered continuous response variables with ordinal regression models. Biometrics 2023; 79:3764-3777. [PMID: 37459181 PMCID: PMC10792095 DOI: 10.1111/biom.13904] [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: 07/17/2022] [Accepted: 06/28/2023] [Indexed: 12/21/2023]
Abstract
Continuous response data are regularly transformed to meet regression modeling assumptions. However, approaches taken to identify the appropriate transformation can be ad hoc and can increase model uncertainty. Further, the resulting transformations often vary across studies leading to difficulties with synthesizing and interpreting results. When a continuous response variable is measured repeatedly within individuals or when continuous responses arise from clusters, analyses have the additional challenge caused by within-individual or within-cluster correlations. We extend a widely used ordinal regression model, the cumulative probability model (CPM), to fit clustered, continuous response data using generalized estimating equations for ordinal responses. With the proposed approach, estimates of marginal model parameters, cumulative distribution functions , expectations, and quantiles conditional on covariates can be obtained without pretransformation of the response data. While computational challenges arise with large numbers of distinct values of the continuous response variable, we propose feasible and computationally efficient approaches to fit CPMs under commonly used working correlation structures. We study finite sample operating characteristics of the estimators via simulation and illustrate their implementation with two data examples. One studies predictors of CD4:CD8 ratios in a cohort living with HIV, and the other investigates the association of a single nucleotide polymorphism and lung function decline in a cohort with early chronic obstructive pulmonary disease.
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Affiliation(s)
- Yuqi Tian
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee
| | - Bryan E. Shepherd
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee
| | - Chun Li
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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Lutz MR, Orr CJ, Shonna Yin H, Heerman WJ, Flower KB, Sanders LM, Rothman RL, Schildcrout JS, Bian A, Kay MC, Wood CT, Delamater AM, Perrin EM. TV Time, Especially During Meals, is Associated with Less Healthy Dietary Practices in Toddlers. Acad Pediatr 2023:S1876-2859(23)00370-4. [PMID: 37802249 DOI: 10.1016/j.acap.2023.09.019] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 09/23/2023] [Accepted: 09/30/2023] [Indexed: 10/08/2023]
Abstract
BACKGROUND While several studies examine the relationship between screen time and dietary practices in children and teenagers, there is limited research in toddlers. This study evaluates the association between television (TV) exposure and dietary practices in two-year-old children. METHODS We conducted a cross-sectional, secondary data analysis from the Greenlight Intervention Study. Toddlers' daily TV watching time, mealtime TV, and dietary practices were assessed by caregiver report at the 24-month well child visit. Separate regression models were used and adjusted for sociodemographic/household characteristics and clinic site. RESULTS 532 toddlers were included (51% Latino; 30% non-Latino Black; 59% ≤$20,000 annual household income). Median daily TV watching time was 42 minutes [IQR: 25, 60]; 25% reported the TV was "usually on" during mealtimes. After adjustment, toddlers who watched more TV daily had higher odds of consuming sugar-sweetened beverages (SSB), fast food, and more junk food; those watching less TV had higher odds of consuming more fruits/vegetables. Those with the TV "usually on" during mealtimes were more likely to consume SSB [aOR 3.72 (95%CI 2.16-6.43)], fast food [aOR 2.83 (95%CI 1.54-5.20)], and more junk food [aOR 4.25 (95%CI 2.71-6.65)]. CONCLUSIONS Among toddlers from primarily minoritized populations and of lower socioeconomic status, those who watched more TV daily and usually had the TV on during meals had significantly less healthy dietary practices, even after adjusting for known covariates. This study supports the current American Academy of Pediatrics screen time guidelines and underscores the importance of early counseling on general and mealtime TV.
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Affiliation(s)
- Melissa R Lutz
- Department of Pediatrics, Johns Hopkins University School of Medicine.
| | - Colin J Orr
- Department of Pediatrics, University of North Carolina at Chapel Hill.
| | - H Shonna Yin
- Department of Pediatrics and Population Health, New York University Grossman School of Medicine.
| | | | - Kori B Flower
- Department of Pediatrics, University of North Carolina at Chapel Hill.
| | - Lee M Sanders
- Departments of Pediatrics and Health Policy, Stanford University.
| | - Russell L Rothman
- Department of Pediatrics, Vanderbilt University Medical Center; Department of Internal Medicine, Vanderbilt University Medical Center.
| | | | - Aihua Bian
- Department of Biostatistics, Vanderbilt University Medical Center.
| | - Melissa C Kay
- Duke Center for Childhood Obesity Research and Duke Global Digital Health Science Center, Duke University School of Medicine, and Duke Global Health Institute.
| | - Charles T Wood
- Department of Pediatrics, Duke University School of Medicine.
| | - Alan M Delamater
- Mailman Center for Child Development, University of Miami Miller School of Medicine.
| | - Eliana M Perrin
- Department of Pediatrics, Johns Hopkins University School of Medicine; Johns Hopkins School of Nursing.
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Nair D, Schildcrout JS, Shi Y, Trochez R, Nwosu S, Bell SP, Mixon AS, Welch SA, Goggins K, Bachmann JM, Vasilevskis EE, Cavanaugh KL, Rothman RL, Kripalani SB. Patient-reported predictors of post-discharge mortality after cardiac hospitalization. medRxiv 2023:2023.10.02.23296460. [PMID: 37873096 PMCID: PMC10593012 DOI: 10.1101/2023.10.02.23296460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background Adults hospitalized for cardiovascular events are at high risk for post-discharge mortality. Hospital-based screening of health-related psychosocial risk factors is now prioritized by the Joint Commission and the National Quality Forum to achieve equitable, high-quality care. We tested our hypothesis that key patient-reported psychosocial and behavioral measures could predict post-hospitalization mortality in a cohort of adults hospitalized for a cardiovascular event. Methods This was a prospective cohort of adults hospitalized at Vanderbilt University Medical Center. Validated patient-reported measures of health literacy, social support, disease self-management, and socioeconomic status were used as predictors of interest. Cox survival analyses of mortality were conducted over a median 3.5-year follow-up (range: 1.25 - 5.5 years). Results Among 2,977 adults, 1,874 (63%) were hospitalized for acute coronary syndrome and 1,103 (37%) were hospitalized for acute decompensated heart failure; 60% were male; and the mean age was 53 years. After adjusting for demographic, clinical, and other psychosocial factors, mortality risk was greatest among patients who reported being unable to work due to disability (Hazard Ratio (HR) 2.36, 95% Confidence Interval (CI): 1.73-3.21), who were retired (HR 2.14, 95% CI 1.60-2.87), and who reported unemployment (HR 1.99, 95% CI 1.30-3.06) as compared to those who were employed. Patient-reported measures of disease self-management, perceived health competence and exercise frequency, were also associated with mortality risk after full covariate adjustment (HR 0.86, 95% CI 0.73-1.00 per four-point increase), (HR 0.86, 95% CI 0.77-0.96 per three-day change), respectively. Conclusions Patient-reported measures of employment status independently predict post-discharge mortality after a cardiac hospitalization. Measure of disease self-management also have prognostic modest utility. Hospital-based screening of psychosocial risk is increasingly prioritized in legislative policy. Incorporating brief, valid measures of employment status and disease self-management factors may help target patients for psychosocial, financial, and rehabilitative resources during post-discharge transitions of care.
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Kay MC, Pankiewicz AR, Schildcrout JS, Wallace S, Wood CT, Shonna Yin H, Rothman RL, Sanders LM, Orr C, Delamater AM, Flower KB, Perrin EM. Early Sweet Tooth: Juice Introduction During Early Infancy is Related to Toddler Juice Intake. Acad Pediatr 2023; 23:1343-1350. [PMID: 37150479 PMCID: PMC10592660 DOI: 10.1016/j.acap.2023.04.009] [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: 08/15/2022] [Revised: 04/18/2023] [Accepted: 04/24/2023] [Indexed: 05/09/2023]
Abstract
OBJECTIVE To assess if 100% fruit juice intake prior to 6 months is associated with juice and sugar-sweetened beverage (SSB) intake at 24 months and whether this differs by sociodemographic factors. METHODS We used longitudinal data from infants enrolled in the control (no obesity intervention) arm of Greenlight, a cluster randomized trial to prevent childhood obesity which included parent-reported child 100% fruit juice intake at all well child checks between 2 and 24 months. We studied the relationship between the age of juice introduction (before vs after 6 months) and juice and SSB intake at 24 months using negative binomial regression while controlling for baseline sociodemographic factors. RESULTS We report results for 187 participants (43% Hispanic, 39% non-Hispanic Black), more than half (54%) of whom had reported 100% fruit juice intake before 6 months. Average 100% fruit juice intake at 24 months was greater than the recommended amount (of 4 oz) and was 8.2 oz and 5.3 oz for those who had and had not, respectively, been introduced to juice before 6 months. In adjusted models, early introduction of juice was associated with a 43% (95% confidence interval: 5%-96%) increase in juice intake at 24 months. CONCLUSIONS 100% fruit juice intake exceeding recommended levels at 6 and 24 months in this diverse cohort was prevalent. Introducing 100% fruit juice prior to 6 months may put children at greater risk for more juice intake as they age. Further research is necessary to determine if early guidance can reduce juice intake.
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Affiliation(s)
- Melissa C Kay
- Department of Pediatrics (MC Kay, AR Pankiewicz, and CT Wood), Duke University, Durham, NC.
| | - Aaron R Pankiewicz
- Department of Pediatrics (MC Kay, AR Pankiewicz, and CT Wood), Duke University, Durham, NC.
| | - Jonathan S Schildcrout
- Department of Biostatistics (JS Schildcrout), Vanderbilt University Medical Center, Nashville, Tenn.
| | - Shelby Wallace
- Division of General Pediatrics (S Wallace and RL Rothman), Vanderbilt University Medical Center, Nashville, Tenn.
| | - Charles T Wood
- Department of Pediatrics (MC Kay, AR Pankiewicz, and CT Wood), Duke University, Durham, NC.
| | - H Shonna Yin
- Departments of Pediatrics and Population Health (H Shonna Yin), New York University Grossman School of Medicine.
| | - Russell L Rothman
- Division of General Pediatrics (S Wallace and RL Rothman), Vanderbilt University Medical Center, Nashville, Tenn.
| | - Lee M Sanders
- Department of Pediatrics (LM Sanders), Stanford University, Calif.
| | - Colin Orr
- General Pediatrics and Adolescent Medicine (C Orr and KB Flower), University of North Carolina, Chapel Hill.
| | - Alan M Delamater
- Department of Pediatrics (AM Delamater), University of Miami, Coral Gables, Fla.
| | - Kori B Flower
- General Pediatrics and Adolescent Medicine (C Orr and KB Flower), University of North Carolina, Chapel Hill.
| | - Eliana M Perrin
- Department of Pediatrics (EM Perrin), Johns Hopkins University Schools of Medicine and Nursing, Baltimore, Md.
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10
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Guzauskas GF, Garbett S, Zhou Z, Schildcrout JS, Graves JA, Williams MS, Hao J, Jones LK, Spencer SJ, Jiang S, Veenstra DL, Peterson JF. Population Genomic Screening for Three Common Hereditary Conditions : A Cost-Effectiveness Analysis. Ann Intern Med 2023; 176:585-595. [PMID: 37155986 DOI: 10.7326/m22-0846] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [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] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND The cost-effectiveness of screening the U.S. population for Centers for Disease Control and Prevention (CDC) Tier 1 genomic conditions is unknown. OBJECTIVE To estimate the cost-effectiveness of simultaneous genomic screening for Lynch syndrome (LS), hereditary breast and ovarian cancer syndrome (HBOC), and familial hypercholesterolemia (FH). DESIGN Decision analytic Markov model. DATA SOURCES Published literature. TARGET POPULATION Separate age-based cohorts (ages 20 to 60 years at time of screening) of racially and ethnically representative U.S. adults. TIME HORIZON Lifetime. PERSPECTIVE U.S. health care payer. INTERVENTION Population genomic screening using clinical sequencing with a restricted panel of high-evidence genes, cascade testing of first-degree relatives, and recommended preventive interventions for identified probands. OUTCOME MEASURES Incident breast, ovarian, and colorectal cancer cases; incident cardiovascular events; quality-adjusted survival; and costs. RESULTS OF BASE-CASE ANALYSIS Screening 100 000 unselected 30-year-olds resulted in 101 (95% uncertainty interval [UI], 77 to 127) fewer overall cancer cases and 15 (95% UI, 4 to 28) fewer cardiovascular events and an increase of 495 quality-adjusted life-years (QALYs) (95% UI, 401 to 757) at an incremental cost of $33.9 million (95% UI, $27.0 million to $41.1 million). The incremental cost-effectiveness ratio was $68 600 per QALY gained (95% UI, $41 800 to $88 900). RESULTS OF SENSITIVITY ANALYSIS Screening 30-, 40-, and 50-year-old cohorts was cost-effective in 99%, 88%, and 19% of probabilistic simulations, respectively, at a $100 000-per-QALY threshold. The test costs at which screening 30-, 40-, and 50-year-olds reached the $100 000-per-QALY threshold were $413, $290, and $166, respectively. Variant prevalence and adherence to preventive interventions were also highly influential parameters. LIMITATIONS Population averages for model inputs, which were derived predominantly from European populations, vary across ancestries and health care environments. CONCLUSION Population genomic screening with a restricted panel of high-evidence genes associated with 3 CDC Tier 1 conditions is likely to be cost-effective in U.S. adults younger than 40 years if the testing cost is relatively low and probands have access to preventive interventions. PRIMARY FUNDING SOURCE National Human Genome Research Institute.
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Affiliation(s)
- Gregory F Guzauskas
- The CHOICE Institute, Department of Pharmacy, University of Washington, Seattle, Washington (G.F.G., S.J.)
| | - Shawn Garbett
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee (S.G., J.S.S.)
| | - Zilu Zhou
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee (Z.Z., J.A.G.)
| | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee (S.G., J.S.S.)
| | - John A Graves
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee (Z.Z., J.A.G.)
| | - Marc S Williams
- Department of Genomic Health, Geisinger, Danville, Pennsylvania (M.S.W.)
| | - Jing Hao
- Department of Genomic Health and Department of Population Health Sciences, Geisinger, Danville, Pennsylvania (J.H.)
| | - Laney K Jones
- Department of Population Health Sciences and Heart Institute, Geisinger, Danville, Pennsylvania (L.K.J.)
| | - Scott J Spencer
- Institute for Public Health Genetics, University of Washington, Seattle, Washington (S.J.S.)
| | - Shangqing Jiang
- The CHOICE Institute, Department of Pharmacy, University of Washington, Seattle, Washington (G.F.G., S.J.)
| | - David L Veenstra
- The CHOICE Institute, Department of Pharmacy, and Institute for Public Health Genetics, University of Washington, Seattle, Washington (D.L.V.)
| | - Josh F Peterson
- Department of Biomedical Informatics and Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee (J.F.P.)
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11
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Maronge JM, Schildcrout JS, Rathouz PJ. Model misspecification and robust analysis for outcome-dependent sampling designs under generalized linear models. Stat Med 2023; 42:1338-1352. [PMID: 36757145 PMCID: PMC10883476 DOI: 10.1002/sim.9673] [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: 08/29/2022] [Revised: 12/19/2022] [Accepted: 01/13/2023] [Indexed: 02/10/2023]
Abstract
Outcome-dependent sampling (ODS) is a commonly used class of sampling designs to increase estimation efficiency in settings where response information (and possibly adjuster covariates) is available, but the exposure is expensive and/or cumbersome to collect. We focus on ODS within the context of a two-phase study, where in Phase One the response and adjuster covariate information is collected on a large cohort that is representative of the target population, but the expensive exposure variable is not yet measured. In Phase Two, using response information from Phase One, we selectively oversample a subset of informative subjects in whom we collect expensive exposure information. Importantly, the Phase Two sample is no longer representative, and we must use ascertainment-correcting analysis procedures for valid inferences. In this paper, we focus on likelihood-based analysis procedures, particularly a conditional-likelihood approach and a full-likelihood approach. Whereas the full-likelihood retains incomplete Phase One data for subjects not selected into Phase Two, the conditional-likelihood explicitly conditions on Phase Two sample selection (ie, it is a "complete case" analysis procedure). These designs and analysis procedures are typically implemented assuming a known, parametric model for the response distribution. However, in this paper, we approach analyses implementing a novel semi-parametric extension to generalized linear models (SPGLM) to develop likelihood-based procedures with improved robustness to misspecification of distributional assumptions. We specifically focus on the common setting where standard GLM distributional assumptions are not satisfied (eg, misspecified mean/variance relationship). We aim to provide practical design guidance and flexible tools for practitioners in these settings.
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Affiliation(s)
- Jacob M. Maronge
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, TX, USA
| | | | - Paul J. Rathouz
- Department of Population Health, Dell Medical School at the University of Texas at Austin, TX, USA
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12
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Self WH, Shotwell MS, Gibbs KW, de Wit M, Files DC, Harkins M, Hudock KM, Merck LH, Moskowitz A, Apodaca KD, Barksdale A, Safdar B, Javaheri A, Sturek JM, Schrager H, Iovine N, Tiffany B, Douglas IS, Levitt J, Busse LW, Ginde AA, Brown SM, Hager DN, Boyle K, Duggal A, Khan A, Lanspa M, Chen P, Puskarich M, Vonderhaar D, Venkateshaiah L, Gentile N, Rosenberg Y, Troendle J, Bistran-Hall AJ, DeClercq J, Lavieri R, Joly MM, Orr M, Pulley J, Rice TW, Schildcrout JS, Semler MW, Wang L, Bernard GR, Collins SP. Renin-Angiotensin System Modulation With Synthetic Angiotensin (1-7) and Angiotensin II Type 1 Receptor-Biased Ligand in Adults With COVID-19: Two Randomized Clinical Trials. JAMA 2023; 329:1170-1182. [PMID: 37039791 PMCID: PMC10091180 DOI: 10.1001/jama.2023.3546] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [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: 12/21/2022] [Accepted: 02/24/2023] [Indexed: 04/12/2023]
Abstract
Importance Preclinical models suggest dysregulation of the renin-angiotensin system (RAS) caused by SARS-CoV-2 infection may increase the relative activity of angiotensin II compared with angiotensin (1-7) and may be an important contributor to COVID-19 pathophysiology. Objective To evaluate the efficacy and safety of RAS modulation using 2 investigational RAS agents, TXA-127 (synthetic angiotensin [1-7]) and TRV-027 (an angiotensin II type 1 receptor-biased ligand), that are hypothesized to potentiate the action of angiotensin (1-7) and mitigate the action of the angiotensin II. Design, Setting, and Participants Two randomized clinical trials including adults hospitalized with acute COVID-19 and new-onset hypoxemia were conducted at 35 sites in the US between July 22, 2021, and April 20, 2022; last follow-up visit: July 26, 2022. Interventions A 0.5-mg/kg intravenous infusion of TXA-127 once daily for 5 days or placebo. A 12-mg/h continuous intravenous infusion of TRV-027 for 5 days or placebo. Main Outcomes and Measures The primary outcome was oxygen-free days, an ordinal outcome that classifies a patient's status at day 28 based on mortality and duration of supplemental oxygen use; an adjusted odds ratio (OR) greater than 1.0 indicated superiority of the RAS agent vs placebo. A key secondary outcome was 28-day all-cause mortality. Safety outcomes included allergic reaction, new kidney replacement therapy, and hypotension. Results Both trials met prespecified early stopping criteria for a low probability of efficacy. Of 343 patients in the TXA-127 trial (226 [65.9%] aged 31-64 years, 200 [58.3%] men, 225 [65.6%] White, and 274 [79.9%] not Hispanic), 170 received TXA-127 and 173 received placebo. Of 290 patients in the TRV-027 trial (199 [68.6%] aged 31-64 years, 168 [57.9%] men, 195 [67.2%] White, and 225 [77.6%] not Hispanic), 145 received TRV-027 and 145 received placebo. Compared with placebo, both TXA-127 (unadjusted mean difference, -2.3 [95% CrI, -4.8 to 0.2]; adjusted OR, 0.88 [95% CrI, 0.59 to 1.30]) and TRV-027 (unadjusted mean difference, -2.4 [95% CrI, -5.1 to 0.3]; adjusted OR, 0.74 [95% CrI, 0.48 to 1.13]) resulted in no difference in oxygen-free days. In the TXA-127 trial, 28-day all-cause mortality occurred in 22 of 163 patients (13.5%) in the TXA-127 group vs 22 of 166 patients (13.3%) in the placebo group (adjusted OR, 0.83 [95% CrI, 0.41 to 1.66]). In the TRV-027 trial, 28-day all-cause mortality occurred in 29 of 141 patients (20.6%) in the TRV-027 group vs 18 of 140 patients (12.9%) in the placebo group (adjusted OR, 1.52 [95% CrI, 0.75 to 3.08]). The frequency of the safety outcomes was similar with either TXA-127 or TRV-027 vs placebo. Conclusions and Relevance In adults with severe COVID-19, RAS modulation (TXA-127 or TRV-027) did not improve oxygen-free days vs placebo. These results do not support the hypotheses that pharmacological interventions that selectively block the angiotensin II type 1 receptor or increase angiotensin (1-7) improve outcomes for patients with severe COVID-19. Trial Registration ClinicalTrials.gov Identifier: NCT04924660.
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Affiliation(s)
- Wesley H. Self
- Vanderbilt Institute for Clinical and Translational Research, Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Matthew S. Shotwell
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kevin W. Gibbs
- Department of Medicine, Wake Forest University, Winston-Salem, North Carolina
| | - Marjolein de Wit
- Department of Medicine, Virginia Commonwealth University, Richmond
| | - D. Clark Files
- Department of Medicine, Wake Forest University, Winston-Salem, North Carolina
| | - Michelle Harkins
- Department of Internal Medicine, University of New Mexico, Albuquerque
| | | | - Lisa H. Merck
- Department of Emergency Medicine, Virginia Commonwealth University Health System, Richmond
| | - Ari Moskowitz
- Department of Medicine, Montefiore Medical Center, Bronx, New York
| | | | - Aaron Barksdale
- Department of Emergency Medicine, University of Nebraska Medical Center, Omaha
| | - Basmah Safdar
- Department of Emergency Medicine, Yale University, New Haven, Connecticut
| | - Ali Javaheri
- Department of Medicine, Washington University, St Louis, Missouri
| | | | - Harry Schrager
- Department of Medicine, Tufts School of Medicine, Newton-Wellesley Hospital, Newton, Massachusetts
| | - Nicole Iovine
- Department of Medicine, University of Florida, Gainesville
| | | | - Ivor S. Douglas
- Department of Medicine, Denver Health Medical Center, Denver, Colorado
| | - Joseph Levitt
- Department of Medicine, Stanford University, Stanford, California
| | | | - Adit A. Ginde
- Department of Emergency Medicine, School of Medicine, University of Colorado, Aurora
| | - Samuel M. Brown
- Department of Pulmonary/Critical Care Medicine, Intermountain Medical Center, Murray, Utah
| | - David N. Hager
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Katherine Boyle
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Abhijit Duggal
- Department of Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Akram Khan
- Department of Medicine, Oregon Health & Science University, Portland
| | - Michael Lanspa
- Department of Pulmonary/Critical Care Medicine, Intermountain Medical Center, Murray, Utah
| | - Peter Chen
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Michael Puskarich
- Department of Emergency Medicine, University of Minnesota, Minneapolis
| | - Derek Vonderhaar
- Department of Medicine, Ochsner Medical Center, New Orleans, Louisiana
| | | | - Nina Gentile
- Department of Emergency Medicine, Temple University, Philadelphia, Pennsylvania
| | - Yves Rosenberg
- National Heart, Lung, and Blood Institute, Bethesda, Maryland
| | - James Troendle
- National Heart, Lung, and Blood Institute, Bethesda, Maryland
| | - Amanda J. Bistran-Hall
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Josh DeClercq
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Robert Lavieri
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Meghan Morrison Joly
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Michael Orr
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jill Pulley
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Todd W. Rice
- Vanderbilt Institute for Clinical and Translational Research, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Matthew W. Semler
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Li Wang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Gordon R. Bernard
- Vanderbilt Institute for Clinical and Translational Research, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Sean P. Collins
- Vanderbilt Institute for Clinical and Translational Research, Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Healthcare System, Nashville
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13
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Maronge JM, Tao R, Schildcrout JS, Rathouz PJ. Generalized case-control sampling under generalized linear models. Biometrics 2023; 79:332-343. [PMID: 34586638 PMCID: PMC9358725 DOI: 10.1111/biom.13571] [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: 07/10/2020] [Revised: 08/17/2021] [Accepted: 09/14/2021] [Indexed: 12/01/2022]
Abstract
A generalized case-control (GCC) study, like the standard case-control study, leverages outcome-dependent sampling (ODS) to extend to nonbinary responses. We develop a novel, unifying approach for analyzing GCC study data using the recently developed semiparametric extension of the generalized linear model (GLM), which is substantially more robust to model misspecification than existing approaches based on parametric GLMs. For valid estimation and inference, we use a conditional likelihood to account for the biased sampling design. We describe analysis procedures for estimation and inference for the semiparametric GLM under a conditional likelihood, and we discuss problems with estimation and inference under a conditional likelihood when the response distribution is misspecified. We demonstrate the flexibility of our approach over existing ones through extensive simulation studies, and we apply the methodology to an analysis of the Asset and Health Dynamics Among the Oldest Old study, which motives our research. The proposed approach yields a simple yet versatile solution for handling ODS in a wide variety of possible response distributions and sampling schemes encountered in practice.
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Affiliation(s)
- Jacob M. Maronge
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan S. Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Paul J. Rathouz
- Department of Population Health, Dell Medical School at the University of Texas at Austin, Austin, Texas, USA
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14
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Lyons GC, Kay MC, Duke NN, Bian A, Schildcrout JS, Perrin EM, Rothman RL, Yin HS, Sanders LM, Flower KB, Delamater AM, Heerman WJ. Social Support and Breastfeeding Outcomes Among a Racially and Ethnically Diverse Population. Am J Prev Med 2023; 64:352-360. [PMID: 36460526 PMCID: PMC9974778 DOI: 10.1016/j.amepre.2022.10.002] [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: 06/02/2022] [Revised: 09/28/2022] [Accepted: 10/03/2022] [Indexed: 12/05/2022]
Abstract
INTRODUCTION Social support is a modifiable social determinant of health that shapes breastfeeding outcomes and may contribute to racial and ethnic breastfeeding disparities. This study characterizes the relationship between social support and early breastfeeding. METHODS This is a cross-sectional analysis of baseline data collected in 2019-2021 for an RCT. Social support was measured using the Enhancing Recovery in Coronary Heart Disease Social Support Instrument. Outcomes, collected by self-report, included (1) early breastfeeding within the first 21 days of life, (2) planned breastfeeding duration, and (3) confidence in meeting breastfeeding goals. Each outcome was modeled using proportional odds regression, adjusting for covariates. Analysis was conducted in 2021-2022. RESULTS Self-reported race and ethnicity among 883 mothers were 50% Hispanic, 17% Black, 23% White, and 10% other. A large proportion (88%) of mothers were breastfeeding. Most breastfeeding mothers (82%) planned to breastfeed for at least 6 months, with more than half (58%) planning to continue for 12 months or more. Most women (65%) were confident or very confident in meeting their breastfeeding duration goal. In adjusted models, perceived social support was associated with planned breastfeeding duration (p=0.042) but not with early breastfeeding (p=0.873) or confidence in meeting breastfeeding goals (p=0.427). Among the covariates, maternal depressive symptoms were associated with lower breastfeeding confidence (p<0.001). CONCLUSIONS The associations between perceived social support and breastfeeding outcomes are nuanced. In this sample of racially and ethnically diverse mothers, social support was associated with longer planned breastfeeding duration but not with early breastfeeding or breastfeeding confidence.
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Affiliation(s)
| | - Melissa C Kay
- Division of General Pediatrics and Adolescent Health, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina
| | - Naomi N Duke
- Division of General Pediatrics and Adolescent Health, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina
| | - Aihua Bian
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Eliana M Perrin
- Department of Pediatrics, Schools of Medicine and Nursing, Johns Hopkins University, Baltimore, Maryland
| | - Russell L Rothman
- Institute of Medicine and Public Health, Vanderbilt University Medical Center, Nashville, Tennessee
| | - H Shonna Yin
- Department of Pediatrics, New York University School of Medicine, New York, New York; Department of Population Health, New York University School of Medicine, New York, New York
| | - Lee M Sanders
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Kori B Flower
- Division of General Pediatrics and Adolescent Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Alan M Delamater
- Department of Pediatrics, University of Miami Miller School of Medicine, Miami, Florida
| | - William J Heerman
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee
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15
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Heerman WJ, Perrin EM, Yin HS, Schildcrout JS, Delamater AM, Flower KB, Sanders L, Wood C, Kay MC, Adams LE, Rothman RL. The Greenlight Plus Trial: Comparative effectiveness of a health information technology intervention vs. health communication intervention in primary care offices to prevent childhood obesity. Contemp Clin Trials 2022; 123:106987. [PMID: 36323344 DOI: 10.1016/j.cct.2022.106987] [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: 04/01/2022] [Revised: 10/17/2022] [Accepted: 10/26/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND The first 1000 days of a child's life are increasingly recognized as a critical window for establishing a healthy growth trajectory to prevent childhood obesity and its associated long-term comorbidities. The purpose of this manuscript is to detail the methods for a multi-site, comparative effectiveness trial designed to prevent childhood overweight and obesity from birth to age 2 years. METHODS This study is a multi-site, individually randomized trial testing the comparative effectiveness of two active intervention arms: 1) the Greenlight intervention; and 2) the Greenlight Plus intervention. The Greenlight intervention is administered by trained pediatric healthcare providers at each well-child visit from 0 to 18 months and consists of a low health literacy toolkit used during clinic visits to promote shared goal setting. Families randomized to Greenlight Plus receive the Greenlight intervention plus a health information technology intervention, which includes: 1) personalized, automated text-messages that facilitate caregiver self-monitoring of tailored and age-appropriate child heath behavior goals; and 2) a web-based, personalized dashboard that tracks child weight status, progress on goals, and electronic Greenlight content access. We randomized 900 parent-infant dyads, recruited from primary care clinics across six academic medical centers. The study's primary outcome is weight for length trajectory from birth through 24 months. CONCLUSIONS By delivering a personalized and tailored health information technology intervention that is asynchronous to pediatric primary care visits, we aim to achieve improvements in child growth trajectory through two years of age among a sample of geographically, socioeconomically, racially, and ethnically diverse parent-child dyads.
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Affiliation(s)
- William J Heerman
- Vanderbilt University Medical Center, Department of Pediatrics, 2200 Children's Way, Suite 2404, Nashville, TN 37232, United States of America.
| | - Eliana M Perrin
- Johns Hopkins University, Department of Pediatrics, Schools of Medicine and Nursing, 200 N. Wolfe St, Rubenstein Building-2071, Baltimore, MD 21287, United States of America.
| | - H Shonna Yin
- New York University School of Medicine, Departments of Pediatrics and Population Health, 550 First Avenue, New York, NY 10016, United States of America.
| | - Jonathan S Schildcrout
- Vanderbilt University Medical Center, Department of Biostatistics, 1161 21st Ave S # D3300, Nashville, TN 37232, United States of America.
| | - Alan M Delamater
- University of Miami Miller School of Medicine, Department of Pediatrics, 1601 NW 12(th) Ave., Miami, FL 33136, United States of America.
| | - Kori B Flower
- University of North Carolina at Chapel Hill, Division of General Pediatrics and Adolescent Medicine, 231 MacNider Building, CB# 7225, 321 S. Columbia Street, UNC School of Medicine, Chapel Hill, NC 27599-7225, United States of America.
| | - Lee Sanders
- Stanford University School of Medicine, United States of America.
| | - Charles Wood
- Duke University School of Medicine, Department of Pediatrics, Division of General Pediatrics and Adolescent Health, 3116 N. Duke St., Durham, NC 27704, United States of America.
| | - Melissa C Kay
- Duke University School of Medicine, Department of Pediatrics, Division of General Pediatrics and Adolescent Health, 3116 N. Duke St., Durham, NC 27704, United States of America.
| | - Laura E Adams
- Vanderbilt University Medical Center, Department of Pediatrics, 2200 Children's Way, Suite 2404, Nashville, TN 37232, United States of America.
| | - Russell L Rothman
- Vanderbilt University Medical Center, Institute of Medicine and Public Health, 1161 21st Ave S # D3300, Nashville, TN 37232, United States of America.
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16
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Borinstein SC, Agamasu D, Schildcrout JS, Bastarache L, Bagheri M, Davis LK, Roden DM, Michael Stein C, Van Driest SL, Mosley JD. Frequency of benign neutropenia among Black versus White individuals undergoing a bone marrow assessment. J Cell Mol Med 2022; 26:3628-3635. [PMID: 35642720 PMCID: PMC9258701 DOI: 10.1111/jcmm.17346] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 01/01/2023] Open
Abstract
Healthy individuals in the United States identified as having Black race have lower neutrophil counts, on average, than individuals identified as having White race, which could result in more negative diagnostic evaluations for neutropenia. To test this hypothesis, the proportion of evaluations where the final diagnosis was clinically insignificant neutropenia for Black and White individuals who underwent an evaluation by a haematologist that included a bone marrow (BM) biopsy to investigate neutropenia was assessed. 172 individuals without prior haematological diagnoses who underwent a haematological evaluation to investigate neutropenia. Individuals diagnosed with clinically insignificant neutropenia between Black and White individuals were compared using a propensity-score-adjusted logistic regression. Of 172 individuals, 42 (24%) were classified as Black race, 86 (50%) were males, and the 79 (46%) were over 18 years old. A BM biopsy did not identify pathology in 95% (40 of 42) of Black individuals and 68% (89 of 130) of White Individuals. Black individuals (25 of 42 [60%]) received a final diagnosis of clinically insignificant neutropenia, compared to White individuals (12 of 130 [9%]) (adjusted odds ratio =7.9, 95% CI: 3.1 - 21.1). We conclude that black individuals were more likely to receive a diagnosis of clinically insignificant neutropenia after haematological assessment.
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Affiliation(s)
- Scott C Borinstein
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Minoo Bagheri
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lea K Davis
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - C Michael Stein
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sara L Van Driest
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan D Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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17
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Schildcrout JS, Harrell FE, Heagerty PJ, Haneuse S, Gravio CD, Garbett S, Rathouz PJ, Shepherd BE. Model-assisted analyses of longitudinal, ordinal outcomes with absorbing states. Stat Med 2022; 41:2497-2512. [PMID: 35253265 PMCID: PMC9232888 DOI: 10.1002/sim.9366] [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] [Received: 08/06/2021] [Revised: 02/09/2022] [Accepted: 02/16/2022] [Indexed: 10/07/2023]
Abstract
Studies of critically ill, hospitalized patients often follow participants and characterize daily health status using an ordinal outcome variable. Statistically, longitudinal proportional odds models are a natural choice in these settings since such models can parsimoniously summarize differences across patient groups and over time. However, when one or more of the outcome states is absorbing, the proportional odds assumption for the follow-up time parameter will likely be violated, and more flexible longitudinal models are needed. Motivated by the VIOLET Study (Ginde et al), a parallel-arm, randomized clinical trial of Vitamin D 3 in critically ill patients, we discuss and contrast several treatment effect estimands based on time-dependent odds ratio parameters, and we detail contemporary modeling approaches. In VIOLET, the outcome is a four-level ordinal variable where the lowest "not alive" state is absorbing and the highest "at-home" state is nearly absorbing. We discuss flexible extensions of the proportional odds model for longitudinal data that can be used for either model-based inference, where the odds ratio estimator is taken directly from the model fit, or for model-assisted inferences, where heterogeneity across cumulative log odds dichotomizations is modeled and results are summarized to obtain an overall odds ratio estimator. We focus on direct estimation of cumulative probability model (CPM) parameters using likelihood-based analysis procedures that naturally handle absorbing states. We illustrate the modeling procedures, the relative precision of model-based and model-assisted estimators, and the possible differences in the values for which the estimators are consistent through simulations and analysis of the VIOLET Study data.
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Affiliation(s)
- Jonathan S. Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, U.S.A
| | - Frank E. Harrell
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, U.S.A
| | - Patrick J. Heagerty
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA U.S.A
| | - Sebastien Haneuse
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, U.S.A
| | - Chiara Di Gravio
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, U.S.A
| | - Shawn Garbett
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, U.S.A
| | - Paul J. Rathouz
- Department of Population Health, Dell Medical Center, University of Texas, Austin Texas, U.S.A
| | - Bryan E. Shepherd
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA U.S.A
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18
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Gravio CD, Tao R, Schildcrout JS. Design and analysis of two-phase studies with multivariate longitudinal data. Biometrics 2022. [PMID: 35014029 DOI: 10.1111/biom.13616] [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: 06/03/2021] [Revised: 11/03/2021] [Accepted: 12/10/2021] [Indexed: 11/27/2022]
Abstract
Two-phase studies are crucial when outcome and covariate data are available in a first phase sample (e.g., a cohort study), but costs associated with retrospective ascertainment of a novel exposure limit the size of the second phase sample, in whom the exposure is collected. For longitudinal outcomes, one class of two-phase studies stratifies subjects based on an outcome vector summary (e.g., an average or a slope over time) and oversamples subjects in the extreme value strata while undersampling subjects in the medium value stratum. Based on the choice of the summary, two-phase studies for longitudinal data can increase efficiency of time-varying and/or time-fixed exposure parameter estimates. In this manuscript, we extend efficient, two-phase study designs to multivariate longitudinal continuous outcomes, and we detail two analysis approaches. The first approach is a multiple imputation analysis that combines complete data from subjects selected for phase two with the incomplete data from those not selected. The second approach is a conditional maximum likelihood analysis that is intended for applications where only data from subjects selected for phase two are available. Importantly, we show that both approaches can be applied to secondary analyses of previously conducted two-phase studies. We examine finite sample operating characteristics of the two approaches and use the Lung Health Study (Connett et al., 1993) to examine genetic associations with lung function decline over time. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Chiara Di Gravio
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, 37232, U.S.A
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, 37232, U.S.A.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, 37232, U.S.A
| | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, 37232, U.S.A
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19
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Van Driest SL, Abul-Husn NS, Glessner JT, Bastarache L, Nirenberg S, Schildcrout JS, Eswarappa MS, Belbin GM, Shaffer CM, Mentch F, Connolly J, Shi M, Stein CM, Roden DM, Hakonarson H, Cox NJ, Borinstein SC, Mosley JD. Association Between a Common, Benign Genotype and Unnecessary Bone Marrow Biopsies Among African American Patients. JAMA Intern Med 2021; 181:1100-1105. [PMID: 34180972 PMCID: PMC8239990 DOI: 10.1001/jamainternmed.2021.3108] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
IMPORTANCE Up to two-thirds of African American individuals carry the benign rs2814778-CC genotype that lowers total white blood cell (WBC) count. OBJECTIVE To examine whether the rs2814778-CC genotype is associated with an increased likelihood of receiving a bone marrow biopsy (BMB) for an isolated low WBC count. DESIGN, SETTING, AND PARTICIPANTS This retrospective genetic association study assessed African American patients younger than 90 years who underwent a BMB at Vanderbilt University Medical Center, Mount Sinai Health System, or Children's Hospital of Philadelphia from January 1, 1998, to December 31, 2020. EXPOSURE The rs2814778-CC genotype. MAIN OUTCOMES AND MEASURES The proportion of individuals with the CC genotype who underwent BMB for an isolated low WBC count and had a normal biopsy result compared with the proportion of individuals with the CC genotype who underwent BMB for other indications and had a normal biopsy result. RESULTS Among 399 individuals who underwent a BMB (mean [SD] age, 41.8 [22.5] years, 234 [59%] female), 277 (69%) had the CC genotype. A total of 35 patients (9%) had clinical histories of isolated low WBC counts, and 364 (91%) had other histories. Of those with a clinical history of isolated low WBC count, 34 of 35 (97%) had the CC genotype vs 243 of 364 (67%) of those without a low WBC count history. Among those with the CC genotype, 33 of 34 (97%) had normal results for biopsies performed for isolated low WBC counts compared with 134 of 243 individuals (55%) with biopsies performed for other histories (P < .001). CONCLUSIONS AND RELEVANCE In this genetic association study, among patients of African American race who had a BMB with a clinical history of isolated low WBC counts, the rs2814778-CC genotype was highly prevalent, and 97% of these BMBs identified no hematologic abnormality. Accounting for the rs2814778-CC genotype in clinical decision-making could avoid unnecessary BMB procedures.
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Affiliation(s)
- Sara L Van Driest
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Noura S Abul-Husn
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joseph T Glessner
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.,The Center for Applied Genomics, the Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Sharon Nirenberg
- Department of Scientific Computing, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Meghana S Eswarappa
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Gillian M Belbin
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Christian M Shaffer
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Frank Mentch
- The Center for Applied Genomics, the Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - John Connolly
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Mingjian Shi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - C Michael Stein
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Dan M Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hakon Hakonarson
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Nancy J Cox
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Scott C Borinstein
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
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20
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Hish AJ, Wood CT, Howard JB, Flower KB, Yin HS, Rothman RL, Delamater AM, Sanders LM, Bian A, Schildcrout JS, Perrin EM. Infant Television Watching Predicts Toddler Television Watching in a Low-Income Population. Acad Pediatr 2021; 21:988-995. [PMID: 33161116 PMCID: PMC8096856 DOI: 10.1016/j.acap.2020.11.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 06/22/2020] [Revised: 10/26/2020] [Accepted: 11/01/2020] [Indexed: 12/30/2022]
Abstract
OBJECTIVE This study examines the development of active television (TV) watching behaviors across the first 2 years of life in a racially and ethnically diverse, low-income cohort and identifies caregiver and child predictors of early TV watching. METHODS We used longitudinal data from infants enrolled in the active control group (N = 235; 39% Latino; 29% Black; 15% White) of Greenlight, a cluster randomized multisite trial to prevent childhood obesity. At preventive health visits from 2 months to 2 years, caregivers were asked: "How much time does [child's first name] spend watching television each day?" Proportional odds models and linear regression analyses were used to assess associations among TV introduction age, active TV watching amount at 2 years, and sociodemographic factors. RESULTS Sixty-eight percent of children watched TV by 6 months, and 88% by 2 years. Age of TV introduction predicted amount of daily active TV watching at 2 years, with a mean time of 93 minutes if starting at 2 months; 64 minutes if starting at 4 or 6 months; and 42 minutes if starting after 6 months. Factors predicting earlier introduction included lower income, fewer children in household, care away from home, male sex, and non-Latino ethnicity of child. CONCLUSIONS Many caregivers report that their infants actively watch TV in the first 6 months of life. Earlier TV watching is related to sociodemographic factors yet predicts more daily TV watching at 2 years even controlling those factors. Interventions to limit early TV watching should be initiated in infancy.
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Affiliation(s)
| | - Charles T Wood
- Department of Pediatrics, Duke University (CT Wood, JB Howard, and EM Perrin), Vienna, Austria; Duke Center for Childhood Obesity Research, Duke University School of Medicine (CT Wood, JB Howard, and EM Perrin), Durham, NC
| | - Janna B Howard
- Department of Pediatrics, Duke University (CT Wood, JB Howard, and EM Perrin), Vienna, Austria; Duke Center for Childhood Obesity Research, Duke University School of Medicine (CT Wood, JB Howard, and EM Perrin), Durham, NC
| | - Kori B Flower
- Division of General Pediatrics and Adolescent Medicine, Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine (KB Flower)
| | - H Shonna Yin
- Department of Pediatrics, School of Medicine/Bellevue Hospital Center, New York University (HS Yin), New York, NY
| | - Russell L Rothman
- Center for Health Services Research, Vanderbilt University Medical Center (RL Rothman), Nashville, Tenn
| | - Alan M Delamater
- University of Miami School of Medicine (AM Delamater), Miami, Fla
| | - Lee M Sanders
- Department of Pediatrics, Center for Policy, Outcomes and Prevention, Stanford University (LM Sanders), Stanford, Calif
| | - Aihua Bian
- Department of Biostatistics, Vanderbilt University School of Medicine (A Bian and JS Schildcrout), Nashville, Tenn
| | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University School of Medicine (A Bian and JS Schildcrout), Nashville, Tenn
| | - Eliana M Perrin
- Department of Pediatrics, Duke University (CT Wood, JB Howard, and EM Perrin), Vienna, Austria; Duke Center for Childhood Obesity Research, Duke University School of Medicine (CT Wood, JB Howard, and EM Perrin), Durham, NC.
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21
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Mitani AA, Mercaldo ND, Haneuse S, Schildcrout JS. Survey design and analysis considerations when utilizing misclassified sampling strata. BMC Med Res Methodol 2021; 21:145. [PMID: 34247586 PMCID: PMC8273975 DOI: 10.1186/s12874-021-01332-8] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 06/15/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A large multi-center survey was conducted to understand patients' perspectives on biobank study participation with particular focus on racial and ethnic minorities. In order to enrich the study sample with racial and ethnic minorities, disproportionate stratified sampling was implemented with strata defined by electronic health records (EHR) that are known to be inaccurate. We investigate the effect of sampling strata misclassification in complex survey design. METHODS Under non-differential and differential misclassification in the sampling strata, we compare the validity and precision of three simple and common analysis approaches for settings in which the primary exposure is used to define the sampling strata. We also compare the precision gains/losses observed from using a disproportionate stratified sampling scheme compared to using a simple random sample under varying degrees of strata misclassification. RESULTS Disproportionate stratified sampling can result in more efficient parameter estimates of the rare subgroups (race/ethnic minorities) in the sampling strata compared to simple random sampling. When sampling strata misclassification is non-differential with respect to the outcome, a design-agnostic analysis was preferred over model-based and design-based analyses. All methods yielded unbiased parameter estimates but standard error estimates were lowest from the design-agnostic analysis. However, when misclassification is differential, only the design-based method produced valid parameter estimates of the variables included in the sampling strata. CONCLUSIONS In complex survey design, when the interest is in making inference on rare subgroups, we recommend implementing disproportionate stratified sampling over simple random sampling even if the sampling strata are misclassified. If the misclassification is non-differential, we recommend a design-agnostic analysis. However, if the misclassification is differential, we recommend using design-based analyses.
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Affiliation(s)
- Aya A Mitani
- Division of Biostatistics, University of Toronto Dalla Lana School of Public Health, Toronto, Canada.
| | | | - Sebastien Haneuse
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA
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22
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Hicks JK, El Rouby N, Ong HH, Schildcrout JS, Ramsey LB, Shi Y, Tang LA, Aquilante CL, Beitelshees AL, Blake KV, Cimino JJ, Davis BH, Empey PE, Kao DP, Lemkin DL, Limdi NA, Lipori GP, Rosenman MB, Skaar TC, Teal E, Tuteja S, Wiley LK, Williams H, Winterstein AG, Van Driest SL, Cavallari LH, Peterson JF. Opportunity for Genotype-Guided Prescribing Among Adult Patients in 11 US Health Systems. Clin Pharmacol Ther 2021; 110:179-188. [PMID: 33428770 PMCID: PMC8217370 DOI: 10.1002/cpt.2161] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [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: 09/24/2020] [Accepted: 12/24/2020] [Indexed: 12/11/2022]
Abstract
The value of utilizing a multigene pharmacogenetic panel to tailor pharmacotherapy is contingent on the prevalence of prescribed medications with an actionable pharmacogenetic association. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has categorized over 35 gene-drug pairs as "level A," for which there is sufficiently strong evidence to recommend that genetic information be used to guide drug prescribing. The opportunity to use genetic information to tailor pharmacotherapy among adult patients was determined by elucidating the exposure to CPIC level A drugs among 11 Implementing Genomics In Practice Network (IGNITE)-affiliated health systems across the US. Inpatient and/or outpatient electronic-prescribing data were collected between January 1, 2011 and December 31, 2016 for patients ≥ 18 years of age who had at least one medical encounter that was eligible for drug prescribing in a calendar year. A median of ~ 7.2 million adult patients was available for assessment of drug prescribing per year. From 2011 to 2016, the annual estimated prevalence of exposure to at least one CPIC level A drug prescribed to unique patients ranged between 15,719 (95% confidence interval (CI): 15,658-15,781) in 2011 to 17,335 (CI: 17,283-17,386) in 2016 per 100,000 patients. The estimated annual exposure to at least 2 drugs was above 7,200 per 100,000 patients in most years of the study, reaching an apex of 7,660 (CI: 7,632-7,687) per 100,000 patients in 2014. An estimated 4,748 per 100,000 prescribing events were potentially eligible for a genotype-guided intervention. Results from this study show that a significant portion of adults treated at medical institutions across the United States is exposed to medications for which genetic information, if available, should be used to guide prescribing.
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Affiliation(s)
- J. Kevin Hicks
- Department of Individualized Cancer Management, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Nihal El Rouby
- Department of Pharmacotherapy & Translational Research, University of Florida, Gainesville, FL
- James Winkle College of Pharmacy, University of Cincinnati, Cincinnati, OH
| | - Henry H. Ong
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN
| | | | - Laura B. Ramsey
- Department of Pediatrics, College of Medicine, University of Cincinnati, Divisions of Research in Patient Services and Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Yaping Shi
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Leigh Anne Tang
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Christina L. Aquilante
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Aurora, CO
| | | | | | - James J. Cimino
- Informatics Institute, University of Alabama at Birmingham, Birmingham, AL
| | - Brittney H. Davis
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL
| | - Philip E. Empey
- Department of Pharmacy & Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA
| | - David P. Kao
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
| | | | - Nita A. Limdi
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL
| | - Gloria P. Lipori
- University of Florida Health and University of Florida Health Sciences Center, Gainesville, FL
| | - Marc B. Rosenman
- Indiana University School of Medicine, Indianapolis, IN
- Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL
| | - Todd C. Skaar
- Indiana University School of Medicine, Indianapolis, IN
| | | | - Sony Tuteja
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Laura K. Wiley
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
| | | | - Almut G. Winterstein
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL
| | - Sara L. Van Driest
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Larisa H. Cavallari
- Department of Pharmacotherapy & Translational Research, University of Florida, Gainesville, FL
| | - Josh F. Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
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23
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Sanders LM, Perrin EM, Yin HS, Delamater AM, Flower KB, Bian A, Schildcrout JS, Rothman RL. A Health-Literacy Intervention for Early Childhood Obesity Prevention: A Cluster-Randomized Controlled Trial. Pediatrics 2021; 147:peds.2020-049866. [PMID: 33911032 PMCID: PMC8086006 DOI: 10.1542/peds.2020-049866] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/27/2021] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Children who become overweight by age 2 have greater risk of long-term obesity and health problems. The study aim was to assess the effectiveness of a primary care-based intervention on the prevalence of overweight at age 24 months. METHODS In a cluster-randomized trial, sites were randomly assigned to the Greenlight intervention or an attention-control arm. Across 4 pediatric residency clinics, we enrolled infant-caregiver dyads at the 2-month well-child visit. Inclusion criteria included parent English- or Spanish-speaking and birth weight ≥1500 g. Designed with health-literacy principles, the intervention included a parent toolkit at each well-child visit, augmented by provider training in clear-health communication. The primary outcome was proportion of children overweight (BMI ≥85th percentile) at age 24 months. Secondary outcomes included weight status (BMI z score). RESULTS A total of 459 intervention and 406 control dyads were enrolled. In total, 49% of all children were overweight at 24 months. Adjusted odds for overweight at 24 months (treatment versus control) was 1.02 (95% confidence interval [CI]: 0.63 to 1.64). Adjusted mean BMI z score differences (treatment minus control) were -0.04 (95% CI: -0.07 to -0.01), -0.09 (95% CI: -0.14 to -0.03), -0.19 (-0.33 to -0.05), -0.20 (-0.36 to -0.03), -0.16 (95% CI: -0.34 to 0.01), and 0.00 (95% CI -0.21 to 0.21) at 4, 6, 12, 15, 18, and 24 months, respectively. CONCLUSIONS The intervention resulted in less weight gain through age 18 months, which was not sustained through 24 months. Clinic-based interventions may be beneficial for early weight gain, but greater intervention intensity may be needed to maintain positive effects.
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Affiliation(s)
- Lee M. Sanders
- Division of General Pediatrics, Department of Pediatrics, Center for Policy, Outcomes and Prevention, Stanford University, Stanford, California
| | - Eliana M. Perrin
- Division of Primary Care and Duke Center for Childhood Obesity Research, Department of Pediatrics, Medical Center, Duke University, Durham, North Carolina
| | - H. Shonna Yin
- Department of Pediatrics and Population Health, School of Medicine, New York University and Department of Pediatrics, Bellevue Hospital Center, New York City, New York
| | - Alan M. Delamater
- Department of Pediatrics, School of Medicine, University of Miami, Miami, Florida
| | | | - Aihua Bian
- Division of General Pediatrics and Adolescent Medicine, Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Jonathan S. Schildcrout
- Division of General Pediatrics and Adolescent Medicine, Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Russell L. Rothman
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee; and
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24
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Graves J, Garbett S, Zhou Z, Schildcrout JS, Peterson J. Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation. Med Decis Making 2021; 41:453-464. [PMID: 33733932 DOI: 10.1177/0272989x21995805] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We discuss tradeoffs and errors associated with approaches to modeling health economic decisions. Through an application in pharmacogenomic (PGx) testing to guide drug selection for individuals with a genetic variant, we assessed model accuracy, optimal decisions, and computation time for an identical decision scenario modeled 4 ways: using 1) coupled-time differential equations (DEQ), 2) a cohort-based discrete-time state transition model (MARKOV), 3) an individual discrete-time state transition microsimulation model (MICROSIM), and 4) discrete event simulation (DES). Relative to DEQ, the net monetary benefit for PGx testing (v. a reference strategy of no testing) based on MARKOV with rate-to-probability conversions using commonly used formulas resulted in different optimal decisions. MARKOV was nearly identical to DEQ when transition probabilities were embedded using a transition intensity matrix. Among stochastic models, DES model outputs converged to DEQ with substantially fewer simulated patients (1 million) v. MICROSIM (1 billion). Overall, properly embedded Markov models provided the most favorable mix of accuracy and runtime but introduced additional complexity for calculating cost and quality-adjusted life year outcomes due to the inclusion of "jumpover" states after proper embedding of transition probabilities. Among stochastic models, DES offered the most favorable mix of accuracy, reliability, and speed.
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Affiliation(s)
- John Graves
- Department of Health Policy, Vanderbilt University School of Medicine Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shawn Garbett
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zilu Zhou
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University School of Medicine Vanderbilt University Medical Center, Nashville, TN, USA
| | - Josh Peterson
- Department of Biomedical Informatics, Vanderbilt University School of Medicine Vanderbilt University Medical Center, Nashville, TN, USA
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25
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Kostelanetz S, Di Gravio C, Schildcrout JS, Roumie CL, Conway D, Kripalani S. Should We Implement Geographic or Patient-Reported Social Determinants of Health Measures In Cardiovascular Patients. Ethn Dis 2021; 31:9-22. [PMID: 33519151 DOI: 10.18865/ed.31.1.9] [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] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Objectives To compare patient-reported social determinants of health (SDOH) to the Brokamp Area Deprivation Index (ADI), and evaluate the association of patient-reported SDOH and ADI with mortality in patients with cardiovascular disease (CVD). Design Prospective cohort. Setting Academic medical center. Participants Adults with acute coronary syndrome (ACS) and/or acute exacerbation of heart failure (HF) hospitalized between 2011 and 2015. Methods Patient-reported SDOH included: income range, education, health insurance, and household size. ADI was calculated using census tract level variables of poverty, median income, high school completion, lack of health insurance, assisted income, and vacant housing. Primary Outcome All-cause mortality, up to 5 years follow-up. Results The sample was 60% male, 84% White, and 93% insured; mean patient-reported household income was $48,000 (SD $34,000). ADI components were significantly associated with corresponding patient-reported variables. In age, sex, and race adjusted Cox regression models, ADI was associated with mortality for ACS (HR 1.23, 95% CI 1.06, 1.42), but not HF (HR 1.09, 95% CI .99, 1.21). Mortality models for ACS improved with consideration of social determinants data (C-statistics: base demographic model=.612; ADI added=.644; patient-reported SDOH added=.675; both ADI and patient-reported SDOH added=.689). HF mortality models improved only slightly (C-statistics: .600, .602, .617, .620, respectively). Conclusions The Brokamp ADI is associated with mortality in hospitalized patients with CVD. In the absence of available patient-reported data, hospitals could implement the Brokamp ADI as an approximation for patient-reported data to enhance risk stratification of patients with CVD.
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Affiliation(s)
- Sophia Kostelanetz
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Chiara Di Gravio
- Department of Biostatistics, Vanderbilt University, Nashville, TN
| | | | - Christianne L Roumie
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Douglas Conway
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN
| | - Sunil Kripalani
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
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26
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Tao R, Mercaldo ND, Haneuse S, Maronge JM, Rathouz PJ, Heagerty PJ, Schildcrout JS. Two-wave two-phase outcome-dependent sampling designs, with applications to longitudinal binary data. Stat Med 2021; 40:1863-1876. [PMID: 33442883 DOI: 10.1002/sim.8876] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 06/29/2020] [Revised: 12/07/2020] [Accepted: 12/25/2020] [Indexed: 12/26/2022]
Abstract
Two-phase outcome-dependent sampling (ODS) designs are useful when resource constraints prohibit expensive exposure ascertainment on all study subjects. One class of ODS designs for longitudinal binary data stratifies subjects into three strata according to those who experience the event at none, some, or all follow-up times. For time-varying covariate effects, exclusively selecting subjects with response variation can yield highly efficient estimates. However, if interest lies in the association of a time-invariant covariate, or the joint associations of time-varying and time-invariant covariates with the outcome, then the optimal design is unknown. Therefore, we propose a class of two-wave two-phase ODS designs for longitudinal binary data. We split the second-phase sample selection into two waves, between which an interim design evaluation analysis is conducted. The interim design evaluation analysis uses first-wave data to conduct a simulation-based search for the optimal second-wave design that will improve the likelihood of study success. Although we focus on longitudinal binary response data, the proposed design is general and can be applied to other response distributions. We believe that the proposed designs can be useful in settings where (1) the expected second-phase sample size is fixed and one must tailor stratum-specific sampling probabilities to maximize estimation efficiency, or (2) relative sampling probabilities are fixed across sampling strata and one must tailor sample size to achieve a desired precision. We describe the class of designs, examine finite sampling operating characteristics, and apply the designs to an exemplar longitudinal cohort study, the Lung Health Study.
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Affiliation(s)
- Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Nathaniel D Mercaldo
- Departments of Radiology and Neurology, Massachusetts General Hospital and Harvard University, Boston, Massachusetts, USA
| | - Sebastien Haneuse
- Department of Biostatistics, Harvard University, Boston, Massachusetts, USA
| | - Jacob M Maronge
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Paul J Rathouz
- Department of Population Health, University of Texas, Austin, Texas, USA
| | - Patrick J Heagerty
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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27
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McGee G, Perkins NJ, Mumford SL, Kioumourtzoglou MA, Weisskopf MG, Schildcrout JS, Coull BA, Schisterman EF, Haneuse S. Methodological Issues in Population-Based Studies of Multigenerational Associations. Am J Epidemiol 2020; 189:1600-1609. [PMID: 32608483 DOI: 10.1093/aje/kwaa125] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [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: 10/16/2019] [Revised: 06/23/2020] [Accepted: 06/23/2020] [Indexed: 12/25/2022] Open
Abstract
Laboratory-based animal research has revealed a number of exposures with multigenerational effects-ones that affect the children and grandchildren of those directly exposed. An important task for epidemiology is to investigate these relationships in human populations. Without the relative control achieved in laboratory settings, however, population-based studies of multigenerational associations have had to use a broader range of study designs. Current strategies to obtain multigenerational data include exploiting birth registries and existing cohort studies, ascertaining exposures within them, and measuring outcomes across multiple generations. In this paper, we describe the methodological challenges inherent to multigenerational studies in human populations. After outlining standard taxonomy to facilitate discussion of study designs and target exposure associations, we highlight the methodological issues, focusing on the interplay between study design, analysis strategy, and the fact that outcomes may be related to family size. In a simulation study, we show that different multigenerational designs lead to estimates of different exposure associations with distinct scientific interpretations. Nevertheless, target associations can be recovered by incorporating (possibly) auxiliary information, and we provide insights into choosing an appropriate target association. Finally, we identify areas requiring further methodological development.
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28
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Ramsey LB, Ong HH, Schildcrout JS, Shi Y, Tang LA, Hicks JK, El Rouby N, Cavallari LH, Tuteja S, Aquilante CL, Beitelshees AL, Lemkin DL, Blake KV, Williams H, Cimino JJ, Davis BH, Limdi NA, Empey PE, Horvat CM, Kao DP, Lipori GP, Rosenman MB, Skaar TC, Teal E, Winterstein AG, Owusu Obeng A, Salyakina D, Gupta A, Gruber J, McCafferty-Fernandez J, Bishop JR, Rivers Z, Benner A, Tamraz B, Long-Boyle J, Peterson JF, Van Driest SL. Prescribing Prevalence of Medications With Potential Genotype-Guided Dosing in Pediatric Patients. JAMA Netw Open 2020; 3:e2029411. [PMID: 33315113 PMCID: PMC7737091 DOI: 10.1001/jamanetworkopen.2020.29411] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [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] [Indexed: 12/17/2022] Open
Abstract
IMPORTANCE Genotype-guided prescribing in pediatrics could prevent adverse drug reactions and improve therapeutic response. Clinical pharmacogenetic implementation guidelines are available for many medications commonly prescribed to children. Frequencies of medication prescription and actionable genotypes (genotypes where a prescribing change may be indicated) inform the potential value of pharmacogenetic implementation. OBJECTIVE To assess potential opportunities for genotype-guided prescribing in pediatric populations among multiple health systems by examining the prevalence of prescriptions for each drug with the highest level of evidence (Clinical Pharmacogenetics Implementation Consortium level A) and estimating the prevalence of potential actionable prescribing decisions. DESIGN, SETTING, AND PARTICIPANTS This serial cross-sectional study of prescribing prevalences in 16 health systems included electronic health records data from pediatric inpatient and outpatient encounters from January 1, 2011, to December 31, 2017. The health systems included academic medical centers with free-standing children's hospitals and community hospitals that were part of an adult health care system. Participants included approximately 2.9 million patients younger than 21 years observed per year. Data were analyzed from June 5, 2018, to April 14, 2020. EXPOSURES Prescription of 38 level A medications based on electronic health records. MAIN OUTCOMES AND MEASURES Annual prevalence of level A medication prescribing and estimated actionable exposures, calculated by combining estimated site-year prevalences across sites with each site weighted equally. RESULTS Data from approximately 2.9 million pediatric patients (median age, 8 [interquartile range, 2-16] years; 50.7% female, 62.3% White) were analyzed for a typical calendar year. The annual prescribing prevalence of at least 1 level A drug ranged from 7987 to 10 629 per 100 000 patients with increasing trends from 2011 to 2014. The most prescribed level A drug was the antiemetic ondansetron (annual prevalence of exposure, 8107 [95% CI, 8077-8137] per 100 000 children). Among commonly prescribed opioids, annual prevalence per 100 000 patients was 295 (95% CI, 273-317) for tramadol, 571 (95% CI, 557-586) for codeine, and 2116 (95% CI, 2097-2135) for oxycodone. The antidepressants citalopram, escitalopram, and amitriptyline were also commonly prescribed (annual prevalence, approximately 250 per 100 000 patients for each). Estimated prevalences of actionable exposures were highest for oxycodone and ondansetron (>300 per 100 000 patients annually). CYP2D6 and CYP2C19 substrates were more frequently prescribed than medications influenced by other genes. CONCLUSIONS AND RELEVANCE These findings suggest that opportunities for pharmacogenetic implementation among pediatric patients in the US are abundant. As expected, the greatest opportunity exists with implementing CYP2D6 and CYP2C19 pharmacogenetic guidance for commonly prescribed antiemetics, analgesics, and antidepressants.
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Affiliation(s)
- Laura B. Ramsey
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio
- Divisions of Research in Patient Services and Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Henry H. Ong
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Yaping Shi
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Leigh Anne Tang
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - J. Kevin Hicks
- Department of Individualized Cancer Management, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Nihal El Rouby
- Department of Pharmacotherapy and Translational Research, University of Florida, Gainesville
- James Winkle College of Pharmacy, University of Cincinnati, Cincinnati, Ohio
| | - Larisa H. Cavallari
- Department of Pharmacotherapy and Translational Research, University of Florida, Gainesville
| | - Sony Tuteja
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | | | | | - Daniel L. Lemkin
- Department of Emergency Medicine, University of Maryland, Baltimore
| | - Kathryn V. Blake
- Center for Pharmacogenomics and Translational Research, Nemours Children’s Health System, Jacksonville, Florida
| | - Helen Williams
- Nemours Research Institute, Nemours Children’s Health System, Jacksonville, Florida
| | | | | | - Nita A. Limdi
- Department of Neurology, University of Alabama at Birmingham
| | - Philip E. Empey
- Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Christopher M. Horvat
- Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - David P. Kao
- Department of Medicine, School of Medicine, University of Colorado, Aurora
| | - Gloria P. Lipori
- University of Florida Health and University of Florida Health Sciences Center, Gainesville
| | - Marc B. Rosenman
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis
- Department of Pediatrics, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
| | - Todd C. Skaar
- Department of Medicine, Indiana University School of Medicine, Indianapolis
| | | | - Almut G. Winterstein
- Department of Pharmaceutical Outcomes and Policy and Center for Drug Evaluation and Safety, University of Florida, Gainesville
| | - Aniwaa Owusu Obeng
- The Charles Bronfman Institute for Personalized Medicine, Departments of Medicine and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Daria Salyakina
- Personalized Medicine Initiative, Nicklaus Children’s Health System, Miami, Florida
| | - Apeksha Gupta
- Personalized Medicine Initiative, Nicklaus Children’s Health System, Miami, Florida
| | - Joshua Gruber
- Personalized Medicine Initiative, Nicklaus Children’s Health System, Miami, Florida
| | | | - Jeffrey R. Bishop
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis
| | - Zach Rivers
- Department of Pharmaceutical Care and Health Systems, University of Minnesota College of Pharmacy, Minneapolis
| | - Ashley Benner
- Clinical and Translational Science Institute, University of Minnesota, Minneapolis
| | - Bani Tamraz
- School of Pharmacy, University of California, San Francisco
| | | | - Josh F. Peterson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Sara L. Van Driest
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee
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White RO, Chakkalakal RJ, Wallston KA, Wolff K, Gregory B, Davis D, Schlundt D, Trochez KM, Barto S, Harris LA, Bian A, Schildcrout JS, Kripalani S, Rothman RL. The Partnership to Improve Diabetes Education Trial: a Cluster Randomized Trial Addressing Health Communication in Diabetes Care. J Gen Intern Med 2020; 35:1052-1059. [PMID: 31919724 PMCID: PMC7174470 DOI: 10.1007/s11606-019-05617-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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 12/12/2019] [Indexed: 11/29/2022]
Abstract
BACKGROUND Effective type 2 diabetes care remains a challenge for patients including those receiving primary care in safety net settings. OBJECTIVE The Partnership to Improve Diabetes Education (PRIDE) trial team and leaders from a regional department of health evaluated approaches to improve care for vulnerable patients. DESIGN Cluster randomized controlled trial. PATIENTS Adults with uncontrolled type 2 diabetes seeking care across 10 unblinded, randomly assigned safety net clinics in Middle TN. INTERVENTIONS A literacy-sensitive, provider-focused, health communication intervention (PRIDE; 5 clinics) vs. standard diabetes education (5 clinics). MAIN MEASURES Participant-level primary outcome was glycemic control [A1c] at 12 months. Secondary outcomes included select health behaviors and psychosocial aspects of care at 12 and 24 months. Adjusted mixed effects regression models were used to examine the comparative effectiveness of each approach to care. KEY RESULTS Of 410 patients enrolled, 364 (89%) were included in analyses. Median age was 51 years; Black and Hispanic patients represented 18% and 25%; 96% were uninsured, and 82% had low annual income level (< $20,000); adequate health literacy was seen in 83%, but numeracy deficits were common. At 12 months, significant within-group treatment effects occurred from baseline for both PRIDE and control sites: adjusted A1c (- 0.76 [95% CI, - 1.08 to - 0.44]; P < .001 vs - 0.54 [95% CI, - 0.86 to - 0.21]; P = .001), odds of poor eating (0.53 [95% CI, 0.33-0.83]; P = .01 vs 0.42 [95% CI, 0.26-0.68]; P < .001), treatment satisfaction (3.93 [95% CI, 2.48-6.21]; P < .001 vs 3.04 [95% CI, 1.93-4.77]; P < .001), and self-efficacy (2.97 [95% CI, 1.89-4.67]; P < .001 vs 1.81 [95% CI, 1.1-2.84]; P = .01). No significant difference was observed between study arms in adjusted analyses. CONCLUSIONS Both interventions improved the participant's A1c and behavioral outcomes. PRIDE was not more effective than standard education. Further research may elucidate the added value of a focused health communication program in this setting.
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Affiliation(s)
- Richard O White
- Division of Community Internal Medicine, Mayo Clinic, Jacksonville, FL, USA.
| | - Rosette James Chakkalakal
- Department of General Internal Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kenneth A Wallston
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kathleen Wolff
- School of Nursing, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Becky Gregory
- Vanderbilt Diabetes Research and Training Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dianne Davis
- Vanderbilt Diabetes Research and Training Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - David Schlundt
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - Karen M Trochez
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shari Barto
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Laura A Harris
- Mid-Cumberland Regional Office, Tennessee Department of Health , Nashville, TN, USA
| | - Aihua Bian
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | | | - Sunil Kripalani
- Department of General Internal Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA.,Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Russell L Rothman
- Department of General Internal Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA.,Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA
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30
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Schildcrout JS, Haneuse S, Tao R, Zelnick LR, Schisterman EF, Garbett SP, Mercaldo ND, Rathouz PJ, Heagerty PJ. Two-Phase, Generalized Case-Control Designs for the Study of Quantitative Longitudinal Outcomes. Am J Epidemiol 2020; 189:81-90. [PMID: 31165875 DOI: 10.1093/aje/kwz127] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [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: 11/01/2018] [Revised: 05/06/2019] [Accepted: 05/14/2019] [Indexed: 01/30/2023] Open
Abstract
We propose a general class of 2-phase epidemiologic study designs for quantitative, longitudinal data that are useful when phase 1 longitudinal outcome and covariate data are available but data on the exposure (e.g., a biomarker) can only be collected on a subset of subjects during phase 2. To conduct a study using a design in the class, one first summarizes the longitudinal outcomes by fitting a simple linear regression of the response on a time-varying covariate for each subject. Sampling strata are defined by splitting the estimated regression intercept or slope distributions into distinct (low, medium, and high) regions. Stratified sampling is then conducted from strata defined by the intercepts, by the slopes, or from a mixture. In general, samples selected with extreme intercept values will yield low variances for associations of time-fixed exposures with the outcome and samples enriched with extreme slope values will yield low variances for associations of time-varying exposures with the outcome (including interactions with time-varying exposures). We describe ascertainment-corrected maximum likelihood and multiple-imputation estimation procedures that permit valid and efficient inferences. We embed all methodological developments within the framework of conducting a substudy that seeks to examine genetic associations with lung function among continuous smokers in the Lung Health Study (United States and Canada, 1986-1994).
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Affiliation(s)
| | - Sebastien Haneuse
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Leila R Zelnick
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Enrique F Schisterman
- Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
| | - Shawn P Garbett
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Paul J Rathouz
- Department of Population Health, Dell Medical School, University of Texas, Austin, Texas
| | - Patrick J Heagerty
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
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31
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Mercaldo ND, Brothers KB, Carrell DS, Clayton EW, Connolly JJ, Holm IA, Horowitz CR, Jarvik GP, Kitchner TE, Li R, McCarty CA, McCormick JB, McManus VD, Myers MF, Pankratz JJ, Shrubsole MJ, Smith ME, Stallings SC, Williams JL, Schildcrout JS. Enrichment sampling for a multi-site patient survey using electronic health records and census data. J Am Med Inform Assoc 2019; 26:219-227. [PMID: 30590688 DOI: 10.1093/jamia/ocy164] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 11/15/2018] [Indexed: 11/14/2022] Open
Abstract
Objective We describe a stratified sampling design that combines electronic health records (EHRs) and United States Census (USC) data to construct the sampling frame and an algorithm to enrich the sample with individuals belonging to rarer strata. Materials and Methods This design was developed for a multi-site survey that sought to examine patient concerns about and barriers to participating in research studies, especially among under-studied populations (eg, minorities, low educational attainment). We defined sampling strata by cross-tabulating several socio-demographic variables obtained from EHR and augmented with census-block-level USC data. We oversampled rarer and historically underrepresented subpopulations. Results The sampling strategy, which included USC-supplemented EHR data, led to a far more diverse sample than would have been expected under random sampling (eg, 3-, 8-, 7-, and 12-fold increase in African Americans, Asians, Hispanics and those with less than a high school degree, respectively). We observed that our EHR data tended to misclassify minority races more often than majority races, and that non-majority races, Latino ethnicity, younger adult age, lower education, and urban/suburban living were each associated with lower response rates to the mailed surveys. Discussion We observed substantial enrichment from rarer subpopulations. The magnitude of the enrichment depends on the accuracy of the variables that define the sampling strata and the overall response rate. Conclusion EHR and USC data may be used to define sampling strata that in turn may be used to enrich the final study sample. This design may be of particular interest for studies of rarer and understudied populations.
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Affiliation(s)
- Nathaniel D Mercaldo
- Department of Radiology, Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Kyle B Brothers
- Department of Pediatrics, University of Louisville, Louisville, Kentucky, USA
| | - David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Ellen W Clayton
- Center for Biomedical Ethics and Society, Vanderbilt University, Nashville, Tennessee, USA
| | - John J Connolly
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Ingrid A Holm
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Carol R Horowitz
- Department of Population Health Science and Policy, Ichan School of Medicine at Mt. Sinai, New York, New York, USA
| | - Gail P Jarvik
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Terrie E Kitchner
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA
| | - Rongling Li
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, Maryland, USA
| | - Catherine A McCarty
- Department of Family Medicine and Biobehavioral Health, University of Minnesota Medical School, Duluth, Minnesota, USA
| | | | - Valerie D McManus
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA
| | - Melanie F Myers
- Division of Human Genetics, Cincinnati Children's Hospital, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Joshua J Pankratz
- Department of Information Technology, Mayo Clinic, Rochester, Minnesota, USA
| | - Martha J Shrubsole
- Vanderbilt Epidemiology Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Maureen E Smith
- Center for Genetic Medicine, Northwestern University, Chicago, Illinois, USA
| | - Sarah C Stallings
- Division of Geriatric Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Janet L Williams
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, USA
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32
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Kim C, Lehmann CU, Hatch D, Schildcrout JS, France DJ, Chen Y. Provider Networks in the Neonatal Intensive Care Unit Associate with Length of Stay. IEEE Conf Collab Internet Comput 2019; 2019:127-134. [PMID: 32637942 PMCID: PMC7339831 DOI: 10.1109/cic48465.2019.00024] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
We strive to understand care coordination structures of multidisciplinary teams and to evaluate their effect on post-surgical length of stay (PSLOS) in the Neonatal Intensive Care Unit (NICU). Electronic health record (EHR) data were extracted for 18 neonates, who underwent gastrostomy tube placement surgery at the Vanderbilt University Medical Center NICU. Based on providers' interactions with the EHR (e.g. viewing, documenting, ordering), provider-provider relations were learned and used to build patient-specific provider networks representing the care coordination structure. We quantified the networks using standard network analysis metrics (e.g., in-degree, out-degree, betweenness centrality, and closeness centrality). Coordination structure effectiveness was measured as the association between the network metrics and PSLOS, as modeled by a proportional-odds, logistical regression model. The 18 provider networks exhibited various team compositions and various levels of structural complexity. Providers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those, who treated higher PSLOS patients (P = 0.0294). In the NICU, improved dissemination of information may be linked to reduced PSLOS. EHR data provides an efficient, accessible, and resource-friendly way to study care coordination using network analysis tools. This novel methodology offers an objective way to identify key performance and safety indicators of care coordination and to study dissemination of patient-related information within care provider networks and its effect on care. Findings should guide improvements in the EHR system design to facilitate effective clinical communications among providers.
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Affiliation(s)
- Cindy Kim
- Department of Mathematics, Vanderbilt University, Nashville, TN
| | | | - Dupree Hatch
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN
| | | | - Daniel J France
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN
| | - You Chen
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
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33
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Presley CA, White RO, Bian A, Schildcrout JS, Rothman RL. Factors associated with antidepressant use among low-income racially and ethnically diverse patients with type 2 diabetes. J Diabetes Complications 2019; 33:107405. [PMID: 31405797 PMCID: PMC6736726 DOI: 10.1016/j.jdiacomp.2019.07.002] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 06/26/2019] [Accepted: 07/09/2019] [Indexed: 01/28/2023]
Abstract
OBJECTIVE Depression is common in patients with type 2 diabetes and associated with poor diabetes-related outcomes. We evaluated the factors associated with antidepressant use in a low-income, racially and ethnically diverse sample of patients with type 2 diabetes. RESEARCH DESIGN AND METHODS We performed a cross-sectional study of baseline data from participants in a cluster randomized trial evaluating a health literacy intervention for diabetes care in safety net clinics. Depressive symptoms were measured by the Center for Epidemiological Studies Depression Scale (CES-D); antidepressant use was abstracted from medication lists. Multivariable mixed effects logistic regression was used to evaluate the relationship between antidepressant use and race/ethnicity adjusting for depressive symptoms, age, gender, income, and health literacy. RESULTS Of 403 participants, 58% were non-Hispanic White, 18% were non-Hispanic Black, and 24% were Hispanic. Median age was 51 years old; 60% were female, 52% of participants had a positive screen for depression, and 18% were on antidepressants. Black and Hispanic participants were significantly less likely to be on an antidepressant compared with white participants, adjusted odds ratios 0.31(95% CI: 0.12 to 0.80) and 0.26 (95% CI: 0.10 to 0.74), respectively. CONCLUSIONS In this vulnerable population with type 2 diabetes, we found a high prevalence of depressive symptoms, and a small proportion of participants were on an antidepressant. Black and Hispanic participants were significantly less likely to be treated with an antidepressant. Our findings suggest depression may be inadequately treated in low-income, uninsured patients with type 2 diabetes, especially racial and ethnic minorities.
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Affiliation(s)
- Caroline A Presley
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, 2525 West End Ave, Suite 450, Nashville, TN 37203, United States of America; Geriatric Research Education and Clinical Center, VA Tennessee Valley Healthcare System, 1310 24th Avenue South, Nashville, TN 37212, United States of America.
| | - Richard O White
- Division of Community Internal Medicine, Mayo Clinic, Cannaday Building, 3 West 4500 San Pablo Road, Jacksonville, FL 32224, United States of America.
| | - Aihua Bian
- Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End, Suite 1100, Nashville, TN 37203, United States of America.
| | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End, Suite 1100, Nashville, TN 37203, United States of America; Department of Anesthesiology, Vanderbilt University Medical Center, 1211 Medical Center Dr, Nashville, TN 37232, United States of America.
| | - Russell L Rothman
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, 2525 West End Ave, Suite 450, Nashville, TN 37203, United States of America.
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34
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Shi Y, Graves JA, Garbett SP, Zhou Z, Marathi R, Wang X, Harrell FE, Lasko TA, Denny JC, Roden DM, Peterson JF, Schildcrout JS. A Decision-Theoretic Approach to Panel-Based, Preemptive Genotyping. MDM Policy Pract 2019; 4:2381468319864337. [PMID: 31453360 PMCID: PMC6699004 DOI: 10.1177/2381468319864337] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [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: 12/31/2018] [Accepted: 06/01/2019] [Indexed: 12/22/2022] Open
Abstract
We discuss a decision-theoretic approach to building a panel-based, preemptive
genotyping program. The method is based on findings that a large percentage of
patients are prescribed medications that are known to have pharmacogenetic
associations, and over time, a substantial proportion are prescribed additional
such medication. Preemptive genotyping facilitates genotype-guided therapy at
the time medications are prescribed; panel-based testing allows providers to
reuse previously collected genetic data when a new indication arises. Because it
is cost-prohibitive to conduct panel-based genotyping on all patients, we
describe a three-step approach to identify patients with the highest anticipated
benefit. First, we construct prediction models to estimate the risk of being
prescribed one of the target medications using readily available clinical data.
Second, we use literature-based estimates of adverse event rates, variant allele
frequencies, secular death rates, and costs to construct a discrete event
simulation that estimates the expected benefit of having an individual’s genetic
data in the electronic health record after an indication has occurred. Finally,
we combine medication prescription risk with expected benefit of genotyping once
a medication is indicated to calculate the expected benefit of preemptive
genotyping. For each patient-clinic visit, we calculate this expected benefit
across a range of medications and select patients with the highest expected
benefit overall. We build a proof of concept implementation using a cohort of
patients from a single academic medical center observed from July 2010 through
December 2012. We then apply the results of our modeling strategy to show the
extent to which we can improve clinical and economic outcomes in a cohort
observed from January 2013 through December 2015.
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Affiliation(s)
- Yaping Shi
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - John A Graves
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Shawn P Garbett
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Zilu Zhou
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ramya Marathi
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Xiaoming Wang
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Frank E Harrell
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Thomas A Lasko
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
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Ierodiakonou D, Coull BA, Zanobetti A, Postma DS, Boezen HM, Vonk JM, McKone EF, Schildcrout JS, Koppelman GH, Croteau-Chonka DC, Lumley T, Koutrakis P, Schwartz J, Gold DR, Weiss ST. Pathway analysis of a genome-wide gene by air pollution interaction study in asthmatic children. J Expo Sci Environ Epidemiol 2019; 29:539-547. [PMID: 31028280 PMCID: PMC10730425 DOI: 10.1038/s41370-019-0136-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [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: 06/09/2018] [Revised: 11/23/2018] [Accepted: 03/08/2019] [Indexed: 05/05/2023]
Abstract
OBJECTIVES We aimed to investigate the role of genetics in the respiratory response of asthmatic children to air pollution, with a genome-wide level analysis of gene by nitrogen dioxide (NO2) and carbon monoxide (CO) interaction on lung function and to identify biological pathways involved. METHODS We used a two-step method for fast linear mixed model computations for genome-wide association studies, exploring whether variants modify the longitudinal relationship between 4-month average pollution and post-bronchodilator FEV1 in 522 Caucasian and 88 African-American asthmatic children. Top hits were confirmed with classic linear mixed-effect models. We used the improved gene set enrichment analysis for GWAS (i-GSEA4GWAS) to identify plausible pathways. RESULTS Two SNPs near the EPHA3 (rs13090972 and rs958144) and one in TXNDC8 (rs7041938) showed significant interactions with NO2 in Caucasians but we did not replicate this locus in African-Americans. SNP-CO interactions did not reach genome-wide significance. The i-GSEA4GWAS showed a pathway linked to the HO-1/CO system to be associated with CO-related FEV1 changes. For NO2-related FEV1 responses, we identified pathways involved in cellular adhesion, oxidative stress, inflammation, and metabolic responses. CONCLUSION The host lung function response to long-term exposure to pollution is linked to genes involved in cellular adhesion, oxidative stress, inflammatory, and metabolic pathways.
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Affiliation(s)
- Despo Ierodiakonou
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Brent A Coull
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Antonella Zanobetti
- Environmental Epidemiology and Risk Program, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Dirkje S Postma
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Pulmonology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - H Marike Boezen
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Judith M Vonk
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Edward F McKone
- Department of Respiratory Medicine, St. Vincent University Hospital, Dublin, Ireland
| | - Jonathan S Schildcrout
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, United States
| | - Gerard H Koppelman
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Pediatric Pulmonology and Pediatric Allergology-Beatrix Children Hospital, University of Groningen, University Medical Center, Groningen, The Netherlands
| | - Damien C Croteau-Chonka
- Channing Division of Network Medicine, Brigham and Women's Hospital, Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Thomas Lumley
- Department of Biostatistics, University of Auckland, Auckland, New Zealand
| | - Petros Koutrakis
- Environmental Epidemiology and Risk Program, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Joel Schwartz
- Environmental Epidemiology and Risk Program, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Diane R Gold
- Environmental Epidemiology and Risk Program, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Channing Division of Network Medicine, Brigham and Women's Hospital, Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Department of Medicine, Harvard Medical School, Boston, MA, United States
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Mayberry LS, Schildcrout JS, Wallston KA, Goggins K, Mixon AS, Rothman RL, Kripalani S. Health Literacy and 1-Year Mortality: Mechanisms of Association in Adults Hospitalized for Cardiovascular Disease. Mayo Clin Proc 2018; 93:1728-1738. [PMID: 30414733 PMCID: PMC6299453 DOI: 10.1016/j.mayocp.2018.07.024] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [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: 04/20/2018] [Revised: 07/03/2018] [Accepted: 07/10/2018] [Indexed: 01/08/2023]
Abstract
OBJECTIVE To test theorized patient-level mediators in the causal pathway between health literacy (HL) and 1-year mortality in adults with cardiovascular disease (CVD). PATIENTS AND METHODS A total of 3000 adults treated at Vanderbilt University Hospital from October 11, 2011, through December 18, 2015, for acute coronary syndrome or acute decompensated heart failure (ADHF) participated in the Vanderbilt Inpatient Cohort Study. Participants completed a bedside-administered survey and consented to health record review and longitudinal follow-up. Multivariable mediation models examined the direct and indirect effects of HL (a latent variable with 4 indicators) with 1-year mortality after discharge (dichotomous). Hypothesized mediators included social support, health competence, health behavior, comorbidity index, type of CVD diagnosis, and previous-year hospitalizations. RESULTS Of the 2977 patients discharged from the hospital (60% male; mean age, 61 years; 83% non-Hispanic white, 37% admitted for ADHF), 17% to 23% had inadequate HL depending on the measure, and 10% (n=304) died within 1 year. The total effect of lower HL on 1-year mortality (adjusted odds ratio [AOR]=1.31; 95% CI, 1.01-1.69) was decomposed into an indirect effect (AOR=1.50; 95% CI, 1.35-1.67) via the mediators and a nonsignificant direct effect (AOR=0.87; 95% CI, 0.66-1.14). Each SD decrease in HL was associated with an absolute 3.2 percentage point increase in the probability of 1-year mortality via mediators admitted for ADHF, comorbidities, health behavior, health competence, and previous-year hospitalizations (listed by contribution to indirect effect). CONCLUSION Patient-level factors link low HL and mortality. Health competence and health behavior are modifiable mediators that could be targeted by interventions post hospitalization for CVD.
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Affiliation(s)
- Lindsay S Mayberry
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Center for Health Behavior and Health Education, Vanderbilt University Medical Center, Nashville, TN; Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN; Center for Effective Health Communication, Vanderbilt University Medical Center, Nashville, TN.
| | | | - Kenneth A Wallston
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN; Center for Effective Health Communication, Vanderbilt University Medical Center, Nashville, TN; School of Nursing, Vanderbilt University, Nashville, TN
| | - Kathryn Goggins
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN; Center for Effective Health Communication, Vanderbilt University Medical Center, Nashville, TN; Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Nashville, TN
| | - Amanda S Mixon
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN; Geriatric Research Education and Clinical Center, VA Tennessee Valley Healthcare System, Nashville, TN
| | - Russell L Rothman
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN; Center for Effective Health Communication, Vanderbilt University Medical Center, Nashville, TN
| | - Sunil Kripalani
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN; Center for Effective Health Communication, Vanderbilt University Medical Center, Nashville, TN; Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Nashville, TN
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Antommaria AHM, Brothers KB, Myers JA, Feygin YB, Aufox SA, Brilliant MH, Conway P, Fullerton SM, Garrison NA, Horowitz CR, Jarvik GP, Li R, Ludman EJ, McCarty CA, McCormick JB, Mercaldo ND, Myers MF, Sanderson SC, Shrubsole MJ, Schildcrout JS, Williams JL, Smith ME, Clayton EW, Holm IA. Parents' attitudes toward consent and data sharing in biobanks: A multisite experimental survey. AJOB Empir Bioeth 2018; 9:128-142. [PMID: 30240342 DOI: 10.1080/23294515.2018.1505783] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
BACKGROUND The factors influencing parents' willingness to enroll their children in biobanks are poorly understood. This study sought to assess parents' willingness to enroll their children, and their perceived benefits, concerns, and information needs under different consent and data-sharing scenarios, and to identify factors associated with willingness. METHODS This large, experimental survey of patients at the 11 eMERGE Network sites used a disproportionate stratified sampling scheme to enrich the sample with historically underrepresented groups. Participants were randomized to receive one of three consent and data-sharing scenarios. RESULTS In total, 90,000 surveys were mailed and 13,000 individuals responded (15.8% response rate). 5737 respondents were parents of minor children. Overall, 55% (95% confidence interval 50-59%) of parents were willing to enroll their youngest minor child in a hypothetical biobank; willingness did not differ between consent and data-sharing scenarios. Lower educational attainment, higher religiosity, lower trust, worries about privacy, and attitudes about benefits, concerns, and information needs were independently associated with less willingness to allow their child to participate. Of parents who were willing to participate themselves, 25% were not willing to allow their child to participate. Being willing to participate but not willing to allow one's child to participate was independently associated with multiple factors, including race, lower educational attainment, lower annual household income, public health care insurance, and higher religiosity. CONCLUSIONS Fifty-five percent of parents were willing to allow their youngest minor child to participate in a hypothetical biobank. Building trust, protecting privacy, and addressing attitudes may increase enrollment and diversity in pediatric biobanks.
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Affiliation(s)
- Armand H Matheny Antommaria
- a Ethics Center , Cincinnati Children's Hospital Medical Center.,b Department of Pediatrics , University of Cincinnati College of Medicine
| | | | - John A Myers
- c Department of Pediatrics , University of Louisville
| | - Yana B Feygin
- c Department of Pediatrics , University of Louisville
| | | | | | | | | | - Nanibaa' A Garrison
- h Treuman Katz Center for Pediatric Bioethics , Seattle Children's Hospital and Research Institute.,i Department of Pediatrics (Bioethics) , University of Washington
| | - Carol R Horowitz
- j Department of Population Health Science and Policy , Icahn School of Medicine at Mount Sinai
| | - Gail P Jarvik
- k Departments of Medicine (Medical Genetics) and Genome Sciences , University of Washington
| | - Rongling Li
- l Division of Genomic Medicine , National Human Genome Research Institute
| | | | | | | | | | - Melanie F Myers
- b Department of Pediatrics , University of Cincinnati College of Medicine.,q Division of Human Genetics , Cincinnati Children's Hospital Medical Center
| | - Saskia C Sanderson
- r Department of Genetics and Genomic Sciences , Icahn School of Medicine at Mount Sinai
| | | | | | | | | | - Ellen Wright Clayton
- w Center for Biomedical Ethics and Society, Vanderbilt University Medical Center
| | - Ingrid A Holm
- x Division of Genetics and Genomics and the Manton Center for Orphan Diseases Research , Boston Children's Hospital.,y Department of Pediatrics , Harvard Medical School
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Abstract
The use of outcome-dependent sampling with longitudinal data analysis has previously been shown to improve efficiency in the estimation of regression parameters. The motivating scenario is when outcome data exist for all cohort members but key exposure variables will be gathered only on a subset. Inference with outcome-dependent sampling designs that also incorporates incomplete information from those individuals who did not have their exposure ascertained has been investigated for univariate but not longitudinal outcomes. Therefore, with a continuous longitudinal outcome, we explore the relative contributions of various sources of information toward the estimation of key regression parameters using a likelihood framework. We evaluate the efficiency gains that alternative estimators might offer over random sampling, and we offer insight into their relative merits in select practical scenarios. Finally, we illustrate the potential impact of design and analysis choices using data from the Cystic Fibrosis Foundation Patient Registry.
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Affiliation(s)
- Leila R Zelnick
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | | | - Patrick J Heagerty
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
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Sterling MR, Safford MM, Goggins K, Nwosu SK, Schildcrout JS, Wallston KA, Mixon AS, Rothman RL, Kripalani S. Numeracy, Health Literacy, Cognition, and 30-Day Readmissions among Patients with Heart Failure. J Hosp Med 2018; 13:145-151. [PMID: 29455228 PMCID: PMC5836748 DOI: 10.12788/jhm.2932] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [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] [Indexed: 01/03/2023]
Abstract
BACKGROUND Numeracy, health literacy, and cognition are important for chronic disease management. Prior studies have found them to be associated with poorer selfcare and worse clinical outcomes, but limited data exists in the context of heart failure (HF), a condition that requires patients to monitor their weight, fluid intake, and dietary salt, especially in the posthospitalization period. OBJECTIVE To examine the relationship between numeracy, health literacy, and cognition with 30-day readmissions among patients hospitalized for acute decompensated HF (ADHF). DESIGN, SETTING, PATIENTS The Vanderbilt Inpatient Cohort Study is a prospective longitudinal study of adults hospitalized with acute coronary syndromes and/or ADHF. We studied 883 adults hospitalized with ADHF. MEASUREMENTS During their hospitalization, a baseline interview was performed in which demographic characteristics, numeracy, health literacy, and cognition were assessed. Through chart review, clinical characteristics were determined. The outcome of interest was 30-day readmission to any acute care hospital. To examine the association between numeracy, health literacy, cognition, and 30-day readmissions, multivariable Poisson (log-linear) regression was used. RESULTS Of the 883 patients admitted for ADHF, 23.8% (n = 210) were readmitted within 30 days; 33.9% of the study population had inadequate numeracy skills, 24.6% had inadequate/marginal literacy skills, and 53% had any cognitive impairment. Numeracy and cognition were not associated with 30-day readmissions. Though (objective) health literacy was associated with 30-day readmissions in unadjusted analyses, it was not in adjusted analyses. CONCLUSIONS Numeracy, health literacy, and cognition were not associated with 30-day readmission among this sample of patients hospitalized with ADHF.
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Affiliation(s)
- Madeline R Sterling
- Department of Medicine, Weill Cornell Medical College, New York, New York, USA.
- Division of General Internal Medicine, Weill Cornell Medical College, New York, New York, USA
| | - Monika M Safford
- Department of Medicine, Weill Cornell Medical College, New York, New York, USA
- Division of General Internal Medicine, Weill Cornell Medical College, New York, New York, USA
| | - Kathryn Goggins
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Effective Health Communication, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sam K Nwosu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Amanda S Mixon
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
- Department of Veterans Affairs, Tennessee Valley Healthcare System Geriatric Research Education and Clinical Center (GRECC), Nashville, Tennessee, USA
| | - Russell L Rothman
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Sunil Kripalani
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Effective Health Communication, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
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Schildcrout JS, Schisterman EF, Aldrich MC, Rathouz PJ. Outcome-related, Auxiliary Variable Sampling Designs for Longitudinal Binary Data. Epidemiology 2017; 29:58-66. [PMID: 29068841 DOI: 10.1097/ede.0000000000000765] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Epidemiologists have long used case-control and related study designs to enhance variability of response and information available to estimate exposure-disease associations. Less has been done for longitudinal data. METHODS We discuss an epidemiological study design and analysis approach for longitudinal binary response data. We seek to gain statistical efficiency by oversampling relatively informative subjects for inclusion into the sample. In this methodological demonstration, we develop this concept by sampling repeatedly from an existing cohort study to estimate the relationship of chronic obstructive pulmonary disease to past-year smoking in a panel of baseline smokers. To account for oversampling, we describe a sequential offsetted regressions approach for valid inferences in this setting. RESULTS Targeted sampling can lead to increased statistical efficiency when combined with sequential offsetted regressions. Efficiency gains are degraded with increased prevalence of the disease response variable, with decreased association between the sampling variable and the response, and with other design and analysis parameters, providing guidance to those wishing to use these types of designs in the future. CONCLUSIONS These designs hold promise for efficient use of resources in longitudinal cohort studies.
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Schildcrout JS, Denny JC, Roden DM. On the Potential of Preemptive Genotyping Towards Preventing Medication-Related Adverse Events: Results from the South Korean National Health Insurance Database. Drug Saf 2017; 40:1-2. [PMID: 27873192 DOI: 10.1007/s40264-016-0476-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA. .,Department of Anesthesiology, Vanderbilt University School of Medicine, Nashville, TN, USA.
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.,Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.,Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.,Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA
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Luzum JA, Pakyz RE, Elsey AR, Haidar CE, Peterson JF, Whirl-Carrillo M, Handelman SK, Palmer K, Pulley JM, Beller M, Schildcrout JS, Field JR, Weitzel KW, Cooper-DeHoff RM, Cavallari LH, O’Donnell PH, Altman RB, Pereira N, Ratain MJ, Roden DM, Embi PJ, Sadee W, Klein TE, Johnson JA, Relling MV, Wang L, Weinshilboum RM, Shuldiner AR, Freimuth RR. The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Outcomes and Metrics of Pharmacogenetic Implementations Across Diverse Healthcare Systems. Clin Pharmacol Ther 2017; 102:502-510. [PMID: 28090649 PMCID: PMC5511786 DOI: 10.1002/cpt.630] [Citation(s) in RCA: 102] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 01/11/2017] [Indexed: 12/23/2022]
Abstract
Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real-world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene-drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.
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Affiliation(s)
- Jasmine A. Luzum
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI, USA
- Center for Pharmacogenomics, College of Medicine, Ohio State University, Columbus, OH, USA
| | - Ruth E. Pakyz
- Program for Personalized and Genomic Medicine, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Amanda R. Elsey
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA
| | - Cyrine E. Haidar
- Department of Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Josh F. Peterson
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | | | - Samuel K. Handelman
- Center for Pharmacogenomics, College of Medicine, Ohio State University, Columbus, OH, USA
| | - Kathleen Palmer
- Program for Personalized and Genomic Medicine, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Jill M. Pulley
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Marc Beller
- Office of Research Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Jonathan S. Schildcrout
- Department of Statistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Julie R. Field
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Kristin W. Weitzel
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA
| | - Rhonda M. Cooper-DeHoff
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA
| | - Larisa H. Cavallari
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA
| | - Peter H. O’Donnell
- Center for Personalized Therapeutics, University of Chicago, Chicago, IL, USA
| | - Russ B. Altman
- Stanford University School of Medicine, Palo Alto, California, USA
| | - Naveen Pereira
- Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Mark J. Ratain
- Center for Personalized Therapeutics, University of Chicago, Chicago, IL, USA
| | - Dan M. Roden
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Peter J. Embi
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, USA
| | - Wolfgang Sadee
- Center for Pharmacogenomics, College of Medicine, Ohio State University, Columbus, OH, USA
- Department of Cancer Biology and Genetics, College of Medicine, Ohio State University, Columbus, OH, USA
| | - Teri E. Klein
- Stanford University School of Medicine, Palo Alto, California, USA
| | - Julie A. Johnson
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA
| | - Mary V. Relling
- Department of Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Liewei Wang
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Richard M. Weinshilboum
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Alan R. Shuldiner
- Program for Personalized and Genomic Medicine, School of Medicine, University of Maryland, Baltimore, MD, USA
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Sanderson SC, Brothers KB, Mercaldo ND, Clayton EW, Antommaria AHM, Aufox SA, Brilliant MH, Campos D, Carrell DS, Connolly J, Conway P, Fullerton SM, Garrison NA, Horowitz CR, Jarvik GP, Kaufman D, Kitchner TE, Li R, Ludman EJ, McCarty CA, McCormick JB, McManus VD, Myers MF, Scrol A, Williams JL, Shrubsole MJ, Schildcrout JS, Smith ME, Holm IA. Public Attitudes toward Consent and Data Sharing in Biobank Research: A Large Multi-site Experimental Survey in the US. Am J Hum Genet 2017; 100:414-427. [PMID: 28190457 DOI: 10.1016/j.ajhg.2017.01.021] [Citation(s) in RCA: 132] [Impact Index Per Article: 18.9] [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: 09/23/2016] [Accepted: 01/11/2017] [Indexed: 12/16/2022] Open
Abstract
Individuals participating in biobanks and other large research projects are increasingly asked to provide broad consent for open-ended research use and widespread sharing of their biosamples and data. We assessed willingness to participate in a biobank using different consent and data sharing models, hypothesizing that willingness would be higher under more restrictive scenarios. Perceived benefits, concerns, and information needs were also assessed. In this experimental survey, individuals from 11 US healthcare systems in the Electronic Medical Records and Genomics (eMERGE) Network were randomly allocated to one of three hypothetical scenarios: tiered consent and controlled data sharing; broad consent and controlled data sharing; or broad consent and open data sharing. Of 82,328 eligible individuals, exactly 13,000 (15.8%) completed the survey. Overall, 66% (95% CI: 63%-69%) of population-weighted respondents stated they would be willing to participate in a biobank; willingness and attitudes did not differ between respondents in the three scenarios. Willingness to participate was associated with self-identified white race, higher educational attainment, lower religiosity, perceiving more research benefits, fewer concerns, and fewer information needs. Most (86%, CI: 84%-87%) participants would want to know what would happen if a researcher misused their health information; fewer (51%, CI: 47%-55%) would worry about their privacy. The concern that the use of broad consent and open data sharing could adversely affect participant recruitment is not supported by these findings. Addressing potential participants' concerns and information needs and building trust and relationships with communities may increase acceptance of broad consent and wide data sharing in biobank research.
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Affiliation(s)
- Saskia C Sanderson
- Department of Behavioural Science and Health, University College London, London WC1E 6BT, UK; Great Ormond Street Hospital, London WC1N 3JH, UK; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Kyle B Brothers
- Department of Pediatrics, University of Louisville, Louisville, KY 40202, USA.
| | | | - Ellen Wright Clayton
- Center for Biomedical Ethics and Society, Vanderbilt University, Nashville, TN 37203, USA
| | | | - Sharon A Aufox
- Center for Genetic Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Murray H Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Diego Campos
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | | | - John Connolly
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Pat Conway
- Essentia Institute of Rural Health, Duluth, MN 55805, USA
| | - Stephanie M Fullerton
- Department of Bioethics and Humanities, University of Washington, Seattle, WA 98195, USA
| | - Nanibaa' A Garrison
- Treuman Katz Center for Pediatric Bioethics, Seattle Children's Research Institute, Seattle, WA 98101, USA; Department of Pediatrics, Division of Bioethics, University of Washington, Seattle, WA 98101, USA
| | - Carol R Horowitz
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Gail P Jarvik
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - David Kaufman
- Division of Genomics and Society, National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Terrie E Kitchner
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Rongling Li
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD 20892, USA
| | | | | | | | - Valerie D McManus
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Melanie F Myers
- Genetic Counseling Graduate Program, Cincinnati Children's Hospital Medical Center and University of Cincinnati, Cincinnati, OH 45229, USA
| | - Aaron Scrol
- Group Health Research Institute, Seattle, WA 98101, USA
| | - Janet L Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Martha J Shrubsole
- Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | | | - Maureen E Smith
- Center for Genetic Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Ingrid A Holm
- Division of Genetics and Genomics and the Manton Center for Orphan Diseases Research, Boston Children's Hospital, Boston, MA 02115, USA
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Bachmann JM, Goggins KM, Nwosu SK, Schildcrout JS, Kripalani S, Wallston KA. Perceived health competence predicts health behavior and health-related quality of life in patients with cardiovascular disease. Patient Educ Couns 2016; 99:2071-2079. [PMID: 27450479 PMCID: PMC5525151 DOI: 10.1016/j.pec.2016.07.020] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 06/07/2016] [Accepted: 07/13/2016] [Indexed: 05/17/2023]
Abstract
OBJECTIVE Evaluate the effect of perceived health competence, a patient's belief in his or her ability to achieve health-related goals, on health behavior and health-related quality of life. METHODS We analyzed 2063 patients hospitalized with acute coronary syndrome and/or congestive heart failure at a large academic hospital in the United States. Multivariable linear regression models investigated associations between the two-item perceived health competence scale (PHCS-2) and positive health behaviors such as medication adherence and exercise (Health Behavior Index) as well as health-related quality of life (5-item Patient Reported Outcome Information Measurement System Global Health Scale). RESULTS After multivariable adjustment, perceived health competence was highly associated with health behaviors (p<0.001) and health-related quality of life (p<0.001). Low perceived health competence was associated with a decrease in health-related quality of life between hospitalization and 90days after discharge (p<0.001). CONCLUSIONS Perceived health competence predicts health behavior and health-related quality of life in patients hospitalized with cardiovascular disease as well as change in health-related quality of life after discharge. PRACTICE IMPLICATIONS Patients with low perceived health competence may be at risk for a decline in health-related quality of life after hospitalization and thus a potential target for counseling and other behavioral interventions.
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Affiliation(s)
- Justin M Bachmann
- Department of Medicine, Vanderbilt University Medical Center, Nashville, USA.
| | - Kathryn M Goggins
- Department of Medicine, Vanderbilt University Medical Center, Nashville, USA.
| | - Samuel K Nwosu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, USA.
| | | | - Sunil Kripalani
- Department of Medicine, Vanderbilt University Medical Center, Nashville, USA.
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Smith ME, Sanderson SC, Brothers KB, Myers MF, McCormick J, Aufox S, Shrubsole MJ, Garrison NA, Mercaldo ND, Schildcrout JS, Clayton EW, Antommaria AHM, Basford M, Brilliant M, Connolly JJ, Fullerton SM, Horowitz CR, Jarvik GP, Kaufman D, Kitchner T, Li R, Ludman EJ, McCarty C, McManus V, Stallings S, Williams JL, Holm IA. Conducting a large, multi-site survey about patients' views on broad consent: challenges and solutions. BMC Med Res Methodol 2016; 16:162. [PMID: 27881091 PMCID: PMC5122167 DOI: 10.1186/s12874-016-0263-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [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: 05/13/2016] [Accepted: 11/11/2016] [Indexed: 11/28/2022] Open
Abstract
Background As biobanks play an increasing role in the genomic research that will lead to precision medicine, input from diverse and large populations of patients in a variety of health care settings will be important in order to successfully carry out such studies. One important topic is participants’ views towards consent and data sharing, especially since the 2011 Advanced Notice of Proposed Rulemaking (ANPRM), and subsequently the 2015 Notice of Proposed Rulemaking (NPRM) were issued by the Department of Health and Human Services (HHS) and Office of Science and Technology Policy (OSTP). These notices required that participants consent to research uses of their de-identified tissue samples and most clinical data, and allowing such consent be obtained in a one-time, open-ended or “broad” fashion. Conducting a survey across multiple sites provides clear advantages to either a single site survey or using a large online database, and is a potentially powerful way of understanding the views of diverse populations on this topic. Methods A workgroup of the Electronic Medical Records and Genomics (eMERGE) Network, a national consortium of 9 sites (13 separate institutions, 11 clinical centers) supported by the National Human Genome Research Institute (NHGRI) that combines DNA biorepositories with electronic medical record (EMR) systems for large-scale genetic research, conducted a survey to understand patients’ views on consent, sample and data sharing for future research, biobank governance, data protection, and return of research results. Results Working across 9 sites to design and conduct a national survey presented challenges in organization, meeting human subjects guidelines at each institution, and survey development and implementation. The challenges were met through a committee structure to address each aspect of the project with representatives from all sites. Each committee’s output was integrated into the overall survey plan. A number of site-specific issues were successfully managed allowing the survey to be developed and implemented uniformly across 11 clinical centers. Conclusions Conducting a survey across a number of institutions with different cultures and practices is a methodological and logistical challenge. With a clear infrastructure, collaborative attitudes, excellent lines of communication, and the right expertise, this can be accomplished successfully.
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Affiliation(s)
- Maureen E Smith
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, 645 N. Michigan Avenue, Chicago, IL, 60611, USA.
| | - Saskia C Sanderson
- Icahn School of Medicine at Mount Sinai, New York, NY, USA.,University College London, London, UK
| | - Kyle B Brothers
- University of Louisville School of Medicine, Louisville, KY, USA
| | - Melanie F Myers
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | - Sharon Aufox
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, 645 N. Michigan Avenue, Chicago, IL, 60611, USA
| | - Martha J Shrubsole
- Vanderbilt University Medical Center and Vanderbilt University, Nashville, TN, USA
| | | | - Nathaniel D Mercaldo
- Vanderbilt University Medical Center and Vanderbilt University, Nashville, TN, USA
| | | | - Ellen Wright Clayton
- Vanderbilt University Medical Center and Vanderbilt University, Nashville, TN, USA
| | | | - Melissa Basford
- Vanderbilt University Medical Center and Vanderbilt University, Nashville, TN, USA
| | | | - John J Connolly
- The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | | | | | - Dave Kaufman
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Terri Kitchner
- Marshfield Clinic Research Foundation, Marshfield, WI, USA
| | - Rongling Li
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | - Sarah Stallings
- Vanderbilt University Medical Center and Vanderbilt University, Nashville, TN, USA
| | | | - Ingrid A Holm
- Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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Mixon AS, Goggins K, Bell SP, Vasilevskis EE, Nwosu S, Schildcrout JS, Kripalani S. Preparedness for hospital discharge and prediction of readmission. J Hosp Med 2016; 11:603-9. [PMID: 26929109 PMCID: PMC5003753 DOI: 10.1002/jhm.2572] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [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: 11/24/2015] [Revised: 01/26/2016] [Accepted: 02/02/2016] [Indexed: 11/06/2022]
Abstract
BACKGROUND, OBJECTIVE Patients' self-reported preparedness for discharge has been shown to predict readmission. It is unclear what differences exist in the predictive abilities of 2 available discharge preparedness measures. To address this gap, we conducted a comparison of these measures. DESIGN, SETTING, PATIENTS Adults hospitalized for cardiovascular diagnoses were enrolled in a prospective cohort. MEASUREMENTS Two patient-reported preparedness measures assessed during postdischarge calls: the 11-item Brief Prescriptions, Ready to re-enter community, Education, Placement, Assurance of safety, Realistic expectations, Empowerment, Directed to appropriate services (B-PREPARED) and the 3-item Care Transitions Measure (CTM-3). Cox proportional hazard models analyzed the relationship between preparedness and time to first readmission or death at 30 and 90 days, adjusted for readmission risk using the administrative database-derived Length of stay, Acuity, Comorbidity, and Emergency department use (LACE) index and other covariates. RESULTS Median preparedness scores were: B-PREPARED 21 (interquartile range [IQR] 18-22) and CTM-3 77.8 (IQR 66.7-100). In individual Cox models, a 4-point increase in B-PREPARED score was associated with a 16% decrease in time to readmission or death at 30 and 90 days. A 10-point increase in CTM-3 score was not associated with readmission or death at 30 days, but was associated with a 6% decrease in readmission or death at 90 days. In models with both preparedness scores, B-PREPARED retained an association with readmission or death at both 30 and 90 days. However, neither preparedness score was as strong a predictor as the LACE index when all were included in the model predicting 30- and 90-day readmission or death. CONCLUSION The B-PREPARED score was more strongly associated with readmission or death than the more widely adopted CTM-3, but neither predicted readmission as well as the LACE index. Journal of Hospital Medicine 2016;11:603-609. © 2016 Society of Hospital Medicine.
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Affiliation(s)
- Amanda S. Mixon
- Department of Veterans Affairs, Tennessee Valley Healthcare System Geriatric Research Education and Clinical Center (GRECC), 1310 24 avenue South, Nashville, TN 37212-2637
- Section of Hospital Medicine, Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, D-3100, Medical Center North, Nashville, TN 37232-2358
- Center for Health Services Research, Vanderbilt University Medical Center, Medical Center East, Suite 6000, Nashville, Tennessee 37232-8300
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Medical Center East, Suite 6000, Nashville, Tennessee 37232-8300
| | - Kathryn Goggins
- Center for Health Services Research, Vanderbilt University Medical Center, Medical Center East, Suite 6000, Nashville, Tennessee 37232-8300
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Medical Center East, Suite 6000, Nashville, Tennessee 37232-8300
| | - Susan P. Bell
- Center for Quality Aging, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 350, Nashville, TN, 37203-1425
| | - Eduard E. Vasilevskis
- Department of Veterans Affairs, Tennessee Valley Healthcare System Geriatric Research Education and Clinical Center (GRECC), 1310 24 avenue South, Nashville, TN 37212-2637
- Section of Hospital Medicine, Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, D-3100, Medical Center North, Nashville, TN 37232-2358
- Center for Health Services Research, Vanderbilt University Medical Center, Medical Center East, Suite 6000, Nashville, Tennessee 37232-8300
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Medical Center East, Suite 6000, Nashville, Tennessee 37232-8300
- Center for Quality Aging, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 350, Nashville, TN, 37203-1425
| | - Samuel Nwosu
- Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End, Ste. 11000 Nashville, TN 37203
| | - Jonathan S. Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End, Ste. 11000 Nashville, TN 37203
| | - Sunil Kripalani
- Section of Hospital Medicine, Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, D-3100, Medical Center North, Nashville, TN 37232-2358
- Center for Health Services Research, Vanderbilt University Medical Center, Medical Center East, Suite 6000, Nashville, Tennessee 37232-8300
- Center for Clinical Quality and Implementation Research, Vanderbilt University Medical Center, Medical Center East, Suite 6000, Nashville, Tennessee 37232-8300
- Center for Quality Aging, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 350, Nashville, TN, 37203-1425
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Schildcrout JS, Shi Y, Danciu I, Bowton E, Field JR, Pulley JM, Basford MA, Gregg W, Cowan JD, Harrell FE, Roden DM, Peterson JF, Denny JC. A prognostic model based on readily available clinical data enriched a pre-emptive pharmacogenetic testing program. J Clin Epidemiol 2016; 72:107-15. [PMID: 26628336 PMCID: PMC4779720 DOI: 10.1016/j.jclinepi.2015.08.028] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [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: 10/06/2014] [Revised: 07/06/2015] [Accepted: 08/31/2015] [Indexed: 12/27/2022]
Abstract
OBJECTIVES We describe the development, implementation, and evaluation of a model to pre-emptively select patients for genotyping based on medication exposure risk. STUDY DESIGN AND SETTING Using deidentified electronic health records, we derived a prognostic model for the prescription of statins, warfarin, or clopidogrel. The model was implemented into a clinical decision support (CDS) tool to recommend pre-emptive genotyping for patients exceeding a prescription risk threshold. We evaluated the rule on an independent validation cohort and on an implementation cohort, representing the population in which the CDS tool was deployed. RESULTS The model exhibited moderate discrimination with area under the receiver operator characteristic curves ranging from 0.68 to 0.75 at 1 and 2 years after index dates. Risk estimates tended to underestimate true risk. The cumulative incidences of medication prescriptions at 1 and 2 years were 0.35 and 0.48, respectively, among 1,673 patients flagged by the model. The cumulative incidences in the same number of randomly sampled subjects were 0.12 and 0.19, and in patients over 50 years with the highest body mass indices, they were 0.22 and 0.34. CONCLUSION We demonstrate that prognostic algorithms can guide pre-emptive pharmacogenetic testing toward those likely to benefit from it.
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Affiliation(s)
- Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University School of Medicine, 2525 West End Ave, Suite 1100, Nashville, TN 37203, USA; Department of Anesthesiology, Vanderbilt University School of Medicine, 1211 21st Avenue South, Nashville, TN 37212, USA.
| | - Yaping Shi
- Department of Biostatistics, Vanderbilt University School of Medicine, 2525 West End Ave, Suite 1100, Nashville, TN 37203, USA
| | - Ioana Danciu
- Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, 2525 West End Ave, Nashville, TN, 37203, USA
| | - Erica Bowton
- Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, 2525 West End Ave, Nashville, TN, 37203, USA
| | - Julie R Field
- Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, 2525 West End Ave, Nashville, TN, 37203, USA
| | - Jill M Pulley
- Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, 2525 West End Ave, Nashville, TN, 37203, USA
| | - Melissa A Basford
- Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, 2525 West End Ave, Nashville, TN, 37203, USA
| | - William Gregg
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 1475, Nashville, TN 37203, USA; Department of Medicine, Vanderbilt University School of Medicine, 1161 21st Avenue South, Nashville, TN 37232, USA
| | - James D Cowan
- Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, 2525 West End Ave, Nashville, TN, 37203, USA
| | - Frank E Harrell
- Department of Biostatistics, Vanderbilt University School of Medicine, 2525 West End Ave, Suite 1100, Nashville, TN 37203, USA
| | - Dan M Roden
- Department of Medicine, Vanderbilt University School of Medicine, 1161 21st Avenue South, Nashville, TN 37232, USA; Department of Pharmacology, Vanderbilt University School of Medicine, 1285 Medical Research Building IV, Nashville, TN 37232-0575, USA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 1475, Nashville, TN 37203, USA; Department of Medicine, Vanderbilt University School of Medicine, 1161 21st Avenue South, Nashville, TN 37232, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 1475, Nashville, TN 37203, USA; Department of Medicine, Vanderbilt University School of Medicine, 1161 21st Avenue South, Nashville, TN 37232, USA
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Billings FT, Hendricks PA, Schildcrout JS, Shi Y, Petracek MR, Byrne JG, Brown NJ. High-Dose Perioperative Atorvastatin and Acute Kidney Injury Following Cardiac Surgery: A Randomized Clinical Trial. JAMA 2016; 315:877-88. [PMID: 26906014 PMCID: PMC4843765 DOI: 10.1001/jama.2016.0548] [Citation(s) in RCA: 167] [Impact Index Per Article: 20.9] [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] [Indexed: 12/19/2022]
Abstract
IMPORTANCE Statins affect several mechanisms underlying acute kidney injury (AKI). OBJECTIVE To test the hypothesis that short-term high-dose perioperative atorvastatin would reduce AKI following cardiac surgery. DESIGN, SETTING, AND PARTICIPANTS Double-blinded, placebo-controlled, randomized clinical trial of adult cardiac surgery patients conducted from November 2009 to October 2014 at Vanderbilt University Medical Center. INTERVENTIONS Patients naive to statin treatment (n = 199) were randomly assigned 80 mg of atorvastatin the day before surgery, 40 mg of atorvastatin the morning of surgery, and 40 mg of atorvastatin daily following surgery (n = 102) or matching placebo (n = 97). Patients already taking a statin prior to study enrollment (n = 416) continued taking the preenrollment statin until the day of surgery, were randomly assigned 80 mg of atorvastatin the morning of surgery and 40 mg of atorvastatin the morning after (n = 206) or matching placebo (n = 210), and resumed taking the previously prescribed statin on postoperative day 2. MAIN OUTCOMES AND MEASURES Acute kidney injury defined as an increase of 0.3 mg/dL in serum creatinine concentration within 48 hours of surgery (Acute Kidney Injury Network criteria). RESULTS The data and safety monitoring board recommended stopping the group naive to statin treatment due to increased AKI among these participants with chronic kidney disease (estimated glomerular filtration rate <60 mL/min/1.73 m2) receiving atorvastatin. The board later recommended stopping for futility after 615 participants (median age, 67 years; 188 [30.6%] were women; 202 [32.8%] had diabetes) completed the study. Among all participants (n = 615), AKI occurred in 64 of 308 (20.8%) in the atorvastatin group vs 60 of 307 (19.5%) in the placebo group (relative risk [RR], 1.06 [95% CI, 0.78 to 1.46]; P = .75). Among patients naive to statin treatment (n = 199), AKI occurred in 22 of 102 (21.6%) in the atorvastatin group vs 13 of 97 (13.4%) in the placebo group (RR, 1.61 [0.86 to 3.01]; P = .15) and serum creatinine concentration increased by a median of 0.11 mg/dL (10th-90th percentile, -0.11 to 0.56 mg/dL) in the atorvastatin group vs by a median of 0.05 mg/dL (10th-90th percentile, -0.12 to 0.33 mg/dL) in the placebo group (mean difference, 0.08 mg/dL [95% CI, 0.01 to 0.15 mg/dL]; P = .007). Among patients already taking a statin (n = 416), AKI occurred in 42 of 206 (20.4%) in the atorvastatin group vs 47 of 210 (22.4%) in the placebo group (RR, 0.91 [0.63 to 1.32]; P = .63). CONCLUSIONS AND RELEVANCE Among patients undergoing cardiac surgery, high-dose perioperative atorvastatin treatment compared with placebo did not reduce the risk of AKI overall, among patients naive to treatment with statins, or in patients already taking a statin. These results do not support the initiation of statin therapy to prevent AKI following cardiac surgery. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00791648.
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Affiliation(s)
- Frederic T Billings
- Department of Anesthesiology, Vanderbilt University School of Medicine, Nashville, Tennessee2Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Patricia A Hendricks
- Department of Anesthesiology, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Jonathan S Schildcrout
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Yaping Shi
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Michael R Petracek
- Department of Cardiac Surgery, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - John G Byrne
- Department of Cardiac Surgery, Harvard University School of Medicine, Boston, Massachusetts
| | - Nancy J Brown
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
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Peterson JF, Field JR, Unertl KM, Schildcrout JS, Johnson DC, Shi Y, Danciu I, Cleator JH, Pulley JM, McPherson JA, Denny JC, Laposata M, Roden DM, Johnson KB. Physician response to implementation of genotype-tailored antiplatelet therapy. Clin Pharmacol Ther 2016; 100:67-74. [PMID: 26693963 DOI: 10.1002/cpt.331] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 11/20/2015] [Accepted: 12/17/2015] [Indexed: 01/07/2023]
Abstract
Physician responses to genomic information are vital to the success of precision medicine initiatives. We prospectively studied a pharmacogenomics implementation program for the propensity of clinicians to select antiplatelet therapy based on CYP2C19 loss-of-function variants in stented patients. Among 2,676 patients, 514 (19.2%) were found to have a CYP2C19 variant affecting clopidogrel metabolism. For the majority (93.6%) of the cohort, cardiologists received active and direct notification of CYP2C19 status. Over 12 months, 57.6% of poor metabolizers and 33.2% of intermediate metabolizers received alternatives to clopidogrel. CYP2C19 variant status was the most influential factor impacting the prescribing decision (hazard ratio [HR] in poor metabolizers 8.1, 95% confidence interval [CI] [5.4, 12.2] and HR 5.0, 95% CI [4.0, 6.3] in intermediate metabolizers), followed by patient age and type of stent implanted. We conclude that cardiologists tailored antiplatelet therapy for a minority of patients with a CYP2C19 variant and considered both genomic and nongenomic risks in their clinical decision-making.
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Affiliation(s)
- J F Peterson
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.,Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - J R Field
- Institute of Clinical and Translational Research, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - K M Unertl
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - J S Schildcrout
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.,Department of Anesthesiology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - D C Johnson
- Department of Pharmacy, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Y Shi
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - I Danciu
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.,Institute of Clinical and Translational Research, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - J H Cleator
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.,Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - J M Pulley
- Institute of Clinical and Translational Research, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - J A McPherson
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - J C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.,Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - M Laposata
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - D M Roden
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.,Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - K B Johnson
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.,Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
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White RO, Chakkalakal RJ, Presley CA, Bian A, Schildcrout JS, Wallston KA, Barto S, Kripalani S, Rothman R. Perceptions of Provider Communication Among Vulnerable Patients With Diabetes: Influences of Medical Mistrust and Health Literacy. J Health Commun 2016; 21:127-134. [PMID: 27662442 PMCID: PMC5540358 DOI: 10.1080/10810730.2016.1207116] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Patient-provider communication is modifiable and is linked to diabetes outcomes. The association of communication quality with medical mistrust is unknown. We examined these factors within the context of a low-literacy/numeracy-focused intervention to improve diabetes care, using baseline data from diverse patients enrolled in a randomized trial of a health communication intervention. Demographics, measures of health communication (Communication Assessment Tool [CAT], Interpersonal Processes of Care survey [IPC-18]), health literacy (Short Test of Functional Health Literacy in Adults), depression, medical mistrust, and glycemic control were ascertained. Adjusted proportional odds models were used to test the association of mistrust with patient-reported communication quality. The interaction effect of health literacy on mistrust and communication quality was also assessed. A total of 410 patients were analyzed. High levels of mistrust were observed. In multivariable modeling, patients with higher mistrust had lower adjusted odds of reporting a higher CAT score (adjusted odds ratio [AOR] = 0.67, 95% confidence interval [CI] [0.52, 0.86], p = .003) and higher scores on the Communication (AOR = 0.69, 95% CI [0.55, 0.88], p = .008), Decided Together (AOR = 0.74, 95% CI [0.59, 0.93], p = .02), and Interpersonal Style (AOR = 0.69, 95% CI [0.53, 0.90], p = .015) subscales of the IPC-18. We observed evidence of an interaction effect of health literacy for the association between mistrust and the Decided Together subscale of the IPC-18 such that patients with higher mistrust and lower literacy perceived worse communication relative to mistrustful patients with higher literacy. In conclusion, medical mistrust was associated with poorer communication with providers in this public health setting. Patients' health literacy level may vary the effect of mistrust on interactional aspects of communication. Providers should consider the impact of mistrust on communication with vulnerable diabetes populations and focus efforts on mitigating its influence.
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Affiliation(s)
- Richard O. White
- Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Jacksonville, Florida, USA
| | - Rosette J. Chakkalakal
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Caroline A. Presley
- School of Public Health, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Aihua Bian
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan S. Schildcrout
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Shari Barto
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sunil Kripalani
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Russell Rothman
- Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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