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Moon KA, Poulsen MN, Bandeen-Roche K, Hirsch AG, DeWalle J, Pollak J, Schwartz BS. Community profiles in northeastern and central Pennsylvania characterized by distinct social, natural, food, and physical activity environments and their relation to type 2 diabetes. Environ Epidemiol 2024; 8:e328. [PMID: 39170821 PMCID: PMC11338261 DOI: 10.1097/ee9.0000000000000328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 07/15/2024] [Indexed: 08/23/2024] Open
Abstract
Background Understanding geographic disparities in type 2 diabetes (T2D) requires approaches that account for communities' multidimensional nature. Methods In an electronic health record nested case-control study, we identified 15,884 cases of new-onset T2D from 2008 to 2016, defined using encounter diagnoses, medication orders, and laboratory test results, and frequency-matched controls without T2D (79,400; 65,069 unique persons). We used finite mixture models to construct community profiles from social, natural, physical activity, and food environment measures. We estimated T2D odds ratios (OR) with 95% confidence intervals (CI) using logistic generalized estimating equation models, adjusted for sociodemographic variables. We examined associations with the profiles alone and combined them with either community type based on administrative boundaries or Census-based urban/rural status. Results We identified four profiles in 1069 communities in central and northeastern Pennsylvania along a rural-urban gradient: "sparse rural," "developed rural," "inner suburb," and "deprived urban core." Urban areas were densely populated with high physical activity resources and food outlets; however, they also had high socioeconomic deprivation and low greenness. Compared with "developed rural," T2D onset odds were higher in "deprived urban core" (1.24, CI = 1.16-1.33) and "inner suburb" (1.10, CI = 1.04-1.17). These associations with model-based community profiles were weaker than when combined with administrative boundaries or urban/rural status. Conclusions Our findings suggest that in urban areas, diabetogenic features overwhelm T2D-protective features. The community profiles support the construct validity of administrative-community type and urban/rural status, previously reported, to evaluate geographic disparities in T2D onset in this geography.
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Affiliation(s)
- Katherine A. Moon
- Department of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | | | - Karen Bandeen-Roche
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | | | - Joseph DeWalle
- Department of Population Health Sciences, Geisinger, Danville, PA
| | - Jonathan Pollak
- Department of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - Brian S. Schwartz
- Department of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
- Department of Population Health Sciences, Geisinger, Danville, PA
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McAlexander TP, Ryan V, Uddin J, Kanchi R, Thorpe L, Schwartz BS, Carson A, Rolka DB, Adhikari S, Pollak J, Lopez P, Smith M, Meeker M, McClure LA. Associations between PM 2.5 and O 3 exposures and new onset type 2 diabetes in regional and national samples in the United States. ENVIRONMENTAL RESEARCH 2023; 239:117248. [PMID: 37827369 DOI: 10.1016/j.envres.2023.117248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/07/2023] [Accepted: 09/09/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND Exposure to particulate matter ≤2.5 μm in diameter (PM2.5) and ozone (O3) has been linked to numerous harmful health outcomes. While epidemiologic evidence has suggested a positive association with type 2 diabetes (T2D), there is heterogeneity in findings. We evaluated exposures to PM2.5 and O3 across three large samples in the US using a harmonized approach for exposure assignment and covariate adjustment. METHODS Data were obtained from the Veterans Administration Diabetes Risk (VADR) cohort (electronic health records [EHRs]), the Reasons for Geographic and Racial Disparities in Stroke (REGARDS) cohort (primary data collection), and the Geisinger health system (EHRs), and reflect the years 2003-2016 (REGARDS) and 2008-2016 (VADR and Geisinger). New onset T2D was ascertained using EHR information on medication orders, laboratory results, and T2D diagnoses (VADR and Geisinger) or report of T2D medication or diagnosis and/or elevated blood glucose levels (REGARDS). Exposure was assigned using pollutant annual averages from the Downscaler model. Models stratified by community type (higher density urban, lower density urban, suburban/small town, or rural census tracts) evaluated likelihood of new onset T2D in each study sample in single- and two-pollutant models of PM2.5 and O3. RESULTS In two pollutant models, associations of PM2.5, and new onset T2D were null in the REGARDS cohort except for in suburban/small town community types in models that also adjusted for NSEE, with an odds ratio (95% CI) of 1.51 (1.01, 2.25) per 5 μg/m3 of PM2.5. Results in the Geisinger sample were null. VADR sample results evidenced nonlinear associations for both pollutants; the shape of the association was dependent on community type. CONCLUSIONS Associations between PM2.5, O3 and new onset T2D differed across three large study samples in the US. None of the results from any of the three study populations found strong and clear positive associations.
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Affiliation(s)
- Tara P McAlexander
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA.
| | - Victoria Ryan
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Jalal Uddin
- Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Rania Kanchi
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Lorna Thorpe
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Brian S Schwartz
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - April Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, 39213, USA
| | - Deborah B Rolka
- Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Samrachana Adhikari
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Jonathan Pollak
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Priscilla Lopez
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Megan Smith
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Melissa Meeker
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Leslie A McClure
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
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Uddin J, Zhu S, Adhikari S, Nordberg CM, Howell CR, Malla G, Judd SE, Cherrington AL, Rummo PE, Lopez P, Kanchi R, Siegel K, De Silva SA, Algur Y, Lovasi GS, Lee NL, Carson AP, Hirsch AG, Thorpe LE, Long DL. Age and sex differences in the association between neighborhood socioeconomic environment and incident diabetes: Results from the diabetes location, environmental attributes and disparities (LEAD) network. SSM Popul Health 2023; 24:101541. [PMID: 38021462 PMCID: PMC10665656 DOI: 10.1016/j.ssmph.2023.101541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Objective Worse neighborhood socioeconomic environment (NSEE) may contribute to an increased risk of type 2 diabetes (T2D). We examined whether the relationship between NSEE and T2D differs by sex and age in three study populations. Research design and methods We conducted a harmonized analysis using data from three independent longitudinal study samples in the US: 1) the Veteran Administration Diabetes Risk (VADR) cohort, 2) the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort, and 3) a case-control study of Geisinger electronic health records in Pennsylvania. We measured NSEE with a z-score sum of six census tract indicators within strata of community type (higher density urban, lower density urban, suburban/small town, and rural). Community type-stratified models evaluated the likelihood of new diagnoses of T2D in each study sample using restricted cubic splines and quartiles of NSEE. Results Across study samples, worse NSEE was associated with higher risk of T2D. We observed significant effect modification by sex and age, though evidence of effect modification varied by site and community type. Largely, stronger associations between worse NSEE and diabetes risk were found among women relative to men and among those less than age 45 in the VADR cohort. Similar modification by age group results were observed in the Geisinger sample in small town/suburban communities only and similar modification by sex was observed in REGARDS in lower density urban communities. Conclusions The impact of NSEE on T2D risk may differ for males and females and by age group within different community types.
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Affiliation(s)
- Jalal Uddin
- Department of Epidemiology, University of Alabama at Birmingham, School of Public Health, Birmingham, AL, USA
- Department of Community Health and Epidemiology, Dalhousie University, Faculty of Medicine, Halifax, Canada
| | - Sha Zhu
- Department of Epidemiology, University of Alabama at Birmingham, School of Public Health, Birmingham, AL, USA
| | - Samrachana Adhikari
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Cara M. Nordberg
- Department of Population Health Sciences, Geisinger, Danville, PA, USA
| | - Carrie R. Howell
- Department of Medicine, Division of Preventive Medicine, University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA
| | - Gargya Malla
- Department of Epidemiology, University of Alabama at Birmingham, School of Public Health, Birmingham, AL, USA
- Department of Internal Medicine, University of Arizona, Tucson, AZ, USA
| | - Suzanne E. Judd
- Department of Biostatistics, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Andrea L. Cherrington
- Department of Medicine, Division of Preventive Medicine, University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA
| | - Pasquale E. Rummo
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Priscilla Lopez
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Rania Kanchi
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Karen Siegel
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- Emory Global Diabetes Research Center, Emory University, Atlanta, GA, USA
| | - Shanika A. De Silva
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Yasemin Algur
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Gina S. Lovasi
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
- Urban Health Collaborative, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Nora L. Lee
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Lorna E. Thorpe
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - D. Leann Long
- Department of Biostatistics, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
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Lee DC, Orstad SL, Kanchi R, Adhikari S, Rummo PE, Titus AR, Aleman JO, Elbel B, Thorpe LE, Schwartz MD. Demographic, social and geographic factors associated with glycaemic control among US Veterans with new onset type 2 diabetes: a retrospective cohort study. BMJ Open 2023; 13:e075599. [PMID: 37832984 PMCID: PMC10582880 DOI: 10.1136/bmjopen-2023-075599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/07/2023] [Indexed: 10/15/2023] Open
Abstract
OBJECTIVES This study evaluated whether a range of demographic, social and geographic factors had an influence on glycaemic control longitudinally after an initial diagnosis of diabetes. DESIGN, SETTING AND PARTICIPANTS We used the US Veterans Administration Diabetes Risk national cohort to track glycaemic control among patients 20-79-year old with a new diagnosis of type 2 diabetes. PRIMARY OUTCOME AND METHODS We modelled associations between glycaemic control at follow-up clinical assessments and geographic factors including neighbourhood race/ethnicity, socioeconomic, land use and food environment measures. We also adjusted for individual demographics, comorbidities, haemoglobin A1c (HbA1c) at diagnosis and duration of follow-up. These factors were analysed within strata of community type: high-density urban, low-density urban, suburban/small town and rural areas. RESULTS We analysed 246 079 Veterans who developed a new type 2 diabetes diagnosis in 2008-2018 and had at least 2 years of follow-up data available. Across all community types, we found that lower baseline HbA1c and female sex were strongly associated with a higher likelihood of within-range HbA1c at follow-up. Surprisingly, patients who were older or had more documented comorbidities were more likely to have within-range follow-up HbA1c results. While there was variation by community type, none of the geographic measures analysed consistently demonstrated significant associations with glycaemic control across all community types.
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Affiliation(s)
- David C Lee
- Emergency Medicine, NYU Grossman School of Medicine, New York City, New York, USA
- Population Health, NYU Grossman School of Medicine, New York City, New York, USA
| | - Stephanie L Orstad
- Population Health, NYU Grossman School of Medicine, New York City, New York, USA
- Medicine, NYU Grossman School of Medicine, New York City, New York, USA
| | - Rania Kanchi
- Population Health, NYU Grossman School of Medicine, New York City, New York, USA
| | - Samrachana Adhikari
- Population Health, NYU Grossman School of Medicine, New York City, New York, USA
| | - Pasquale E Rummo
- Population Health, NYU Grossman School of Medicine, New York City, New York, USA
| | - Andrea R Titus
- Population Health, NYU Grossman School of Medicine, New York City, New York, USA
| | - Jose O Aleman
- Medicine, NYU Grossman School of Medicine, New York City, New York, USA
- Veterans Affairs, VA New York Harbor Healthcare System, New York City, New York, USA
| | - Brian Elbel
- Population Health, NYU Grossman School of Medicine, New York City, New York, USA
- Wagner Graduate School of Public Service, NYU, New York City, New York, USA
| | - Lorna E Thorpe
- Population Health, NYU Grossman School of Medicine, New York City, New York, USA
| | - Mark D Schwartz
- Population Health, NYU Grossman School of Medicine, New York City, New York, USA
- Veterans Affairs, VA New York Harbor Healthcare System, New York City, New York, USA
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Poulsen MN, Nordberg CM, Troiani V, Berrettini W, Asdell PB, Schwartz BS. Identification of opioid use disorder using electronic health records: Beyond diagnostic codes. Drug Alcohol Depend 2023; 251:110950. [PMID: 37716289 PMCID: PMC10620734 DOI: 10.1016/j.drugalcdep.2023.110950] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND We used structured and unstructured electronic health record (EHR) data to develop and validate an approach to identify moderate/severe opioid use disorder (OUD) that includes individuals without prescription opioid use or chronic pain, an underrepresented population. METHODS Using electronic diagnosis grouper text from EHRs of ~1 million patients (2012-2020), we created indicators of OUD-with "tiers" indicating OUD likelihood-combined with OUD medication (MOUD) orders. We developed six sub-algorithms with varying criteria (multiple vs single MOUD orders, multiple vs single tier 1 indicators, tier 2 indicators, tier 3 and 4 indicators). Positive predictive values (PPVs) were calculated based on chart review to determine OUD status and severity. We compared demographic and clinical characteristics of cases identified by the sub-algorithms. RESULTS In total, 14,852 patients met criteria for one of the sub-algorithms. Five sub-algorithms had PPVs ≥0.90 for any severity OUD; four had PPVs ≥0.90 for moderate/severe OUD. Demographic and clinical characteristics differed substantially between groups. Of identified OUD cases, 31.3% had no past opioid analgesic orders, 79.7% lacked evidence of chronic prescription opioid use, and 43.5% lacked a chronic pain diagnosis. DISCUSSION Incorporating unstructured data with MOUD orders yielded an approach that adequately identified moderate/severe OUD, identified unique demographic and clinical sub-groups, and included individuals without prescription opioid use or chronic pain, whose OUD may stem from illicit opioids. Findings show that incorporating unstructured data strengthens EHR algorithms for identifying OUD and suggests approaches limited to populations with prescription opioid use or chronic pain exclude many individuals with OUD.
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Affiliation(s)
- Melissa N Poulsen
- Department of Population Health Sciences, Geisinger, Danville, PA, USA.
| | - Cara M Nordberg
- Department of Population Health Sciences, Geisinger, Danville, PA, USA.
| | - Vanessa Troiani
- Department of Autism and Developmental Medicine, Geisinger, Lewisburg, PA, USA.
| | - Wade Berrettini
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
| | - Patrick B Asdell
- Department of Family Medicine, Summa Health, Barberton, OH, USA.
| | - Brian S Schwartz
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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Algur Y, Rummo PE, McAlexander TP, De Silva SSA, Lovasi GS, Judd SE, Ryan V, Malla G, Koyama AK, Lee DC, Thorpe LE, McClure LA. Assessing the association between food environment and dietary inflammation by community type: a cross-sectional REGARDS study. Int J Health Geogr 2023; 22:24. [PMID: 37730612 PMCID: PMC10510199 DOI: 10.1186/s12942-023-00345-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/06/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Communities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors. OBJECTIVE This cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US. METHODS Using baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003-2007), we calculated participants' dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant's census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together. RESULTS Participants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was - 0.004 (SD = 2.5; min = - 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values < 0.01); no significant associations were present in higher density urban or rural areas. CONCLUSIONS The relative availability of fast-food restaurants was associated with higher DIS among participants residing in suburban/small town and lower density urban community types, suggesting that these communities might benefit most from interventions and policies that either promote restaurant diversity or expand healthier food options.
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Affiliation(s)
- Yasemin Algur
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Nesbitt Hall, 3215 Market Street, Philadelphia, PA, 19104, USA.
| | - Pasquale E Rummo
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Tara P McAlexander
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Nesbitt Hall, 3215 Market Street, Philadelphia, PA, 19104, USA
| | - S Shanika A De Silva
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Nesbitt Hall, 3215 Market Street, Philadelphia, PA, 19104, USA
| | - Gina S Lovasi
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Nesbitt Hall, 3215 Market Street, Philadelphia, PA, 19104, USA
| | - Suzanne E Judd
- Department of Biostatistics, The University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Victoria Ryan
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Nesbitt Hall, 3215 Market Street, Philadelphia, PA, 19104, USA
| | - Gargya Malla
- Department of Epidemiology, The University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Alain K Koyama
- Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - David C Lee
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
- Department of Emergency Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Lorna E Thorpe
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Leslie A McClure
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Nesbitt Hall, 3215 Market Street, Philadelphia, PA, 19104, USA
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Wu H, Lin W, Li Y. Health education in the management of chronic diseases among the elderly in the community with the assistance of a Mask R-CNN model. Am J Transl Res 2023; 15:4629-4638. [PMID: 37560230 PMCID: PMC10408518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/28/2023] [Indexed: 08/11/2023]
Abstract
OBJECTIVE To analyze the role of health education in the management of chronic diseases in older people in the community and the countermeasures. METHODS After establishing a community health management model for chronic diseases of the elderly based on references, a prospective study was conducted on 120 elderly patients with chronic diseases registered in Xinyang Zhongxing Community Health Service Center, Xixiangtang District, Nanning City from January 2019 to June 2020. The lottery method was used to divide all patients into observation and control groups. Patients in the control group received conventional chronic disease health management, while the observation group received an additional community-based chronic disease health education model for the elderly on the basis of care given to the control group. The change in chronic disease prevention knowledge mastering, medical compliance behavior score, anxiety and depression score, and quality of life score before and after the intervention were compared. RESULTS After intervention, the awareness rates of patients in the observation group on the clinical manifestations, diagnostic criteria, high-risk behaviors, susceptible population and preventive measures of chronic diseases were significantly higher than that in the control group (all P<0.05), the scores of diet, exercise and lifestyle were significantly higher than those in the control group (all P<0.05), and the scores of depression and anxiety were significantly lower than those in the control group (all P<0.05). The scores of mental function, physical function and social function were significantly higher than those of control group (all P<0.05). CONCLUSION Health education intervention play an important role in community management of chronic diseases in elderly patients. It effectively improves patients' understanding of the disease and enhances their compliance to medical advice, while reducing patients' anxiety, depression mood and improving their quality of life.
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Affiliation(s)
- Hanzhou Wu
- Department of General Medicine, Ruikang Hospital Affiliated to Guangxi University of Traditional Chinese MedicineNanning 530011, Guangxi Zhuang Autonomous Region, China
| | - Weiying Lin
- Department of General Medicine, Nanning Xixiangtang District Xinyang Zhongxing Community Health Service CenterNanning 530005, Guangxi Zhuang Autonomous Region, China
| | - Yikang Li
- Department of General Medicine, Nanning Xingning District Xingdong Community Health Service CenterNanning 530010, Guangxi Zhuang Autonomous Region, China
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8
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Moon KA, Nordberg CM, Orstad SL, Zhu A, Uddin J, Lopez P, Schwartz MD, Ryan V, Hirsch AG, Schwartz BS, Carson AP, Long DL, Meeker M, Brown J, Lovasi GS, Adhikari S, Kanchi R, Avramovic S, Imperatore G, Poulsen MN. Mediation of an association between neighborhood socioeconomic environment and type 2 diabetes through the leisure-time physical activity environment in an analysis of three independent samples. BMJ Open Diabetes Res Care 2023; 11:11/2/e003120. [PMID: 36858436 PMCID: PMC9980357 DOI: 10.1136/bmjdrc-2022-003120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/14/2023] [Indexed: 03/03/2023] Open
Abstract
INTRODUCTION Inequitable access to leisure-time physical activity (LTPA) resources may explain geographic disparities in type 2 diabetes (T2D). We evaluated whether the neighborhood socioeconomic environment (NSEE) affects T2D through the LTPA environment. RESEARCH DESIGN AND METHODS We conducted analyses in three study samples: the national Veterans Administration Diabetes Risk (VADR) cohort comprising electronic health records (EHR) of 4.1 million T2D-free veterans, the national prospective cohort REasons for Geographic and Racial Differences in Stroke (REGARDS) (11 208 T2D free), and a case-control study of Geisinger EHR in Pennsylvania (15 888 T2D cases). New-onset T2D was defined using diagnoses, laboratory and medication data. We harmonized neighborhood-level variables, including exposure, confounders, and effect modifiers. We measured NSEE with a summary index of six census tract indicators. The LTPA environment was measured by physical activity (PA) facility (gyms and other commercial facilities) density within street network buffers and population-weighted distance to parks. We estimated natural direct and indirect effects for each mediator stratified by community type. RESULTS The magnitudes of the indirect effects were generally small, and the direction of the indirect effects differed by community type and study sample. The most consistent findings were for mediation via PA facility density in rural communities, where we observed positive indirect effects (differences in T2D incidence rates (95% CI) comparing the highest versus lowest quartiles of NSEE, multiplied by 100) of 1.53 (0.25, 3.05) in REGARDS and 0.0066 (0.0038, 0.0099) in VADR. No mediation was evident in Geisinger. CONCLUSIONS PA facility density and distance to parks did not substantially mediate the relation between NSEE and T2D. Our heterogeneous results suggest that approaches to reduce T2D through changes to the LTPA environment require local tailoring.
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Affiliation(s)
- Katherine A Moon
- Department of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Cara M Nordberg
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania, USA
| | - Stephanie L Orstad
- Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA
- Department of Medicine, Division of General Internal Medicine and Clinical Innovation, New York University Grossman School of Medicine, New York, NY, USA
| | - Aowen Zhu
- Department of Epidemiology, The University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
| | - Jalal Uddin
- Department of Epidemiology, The University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
| | - Priscilla Lopez
- Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA
| | - Mark D Schwartz
- Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA
- The Department of Veterans Affairs, New York Harbor Healthcare System, New York, NY, USA
| | - Victoria Ryan
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, Pennsylvania, USA
| | - Annemarie G Hirsch
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania, USA
| | - Brian S Schwartz
- Department of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - D Leann Long
- Department of Biostatistics, University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
| | - Melissa Meeker
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, Pennsylvania, USA
| | - Janene Brown
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, Pennsylvania, USA
| | - Gina S Lovasi
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, Pennsylvania, USA
- The Urban Health Collaborative, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Samranchana Adhikari
- Department of Medicine, Division of General Internal Medicine and Clinical Innovation, New York University Grossman School of Medicine, New York, NY, USA
| | - Rania Kanchi
- Department of Medicine, Division of General Internal Medicine and Clinical Innovation, New York University Grossman School of Medicine, New York, NY, USA
| | - Sanja Avramovic
- Department of Health Administration and Policy, George Mason University, Fairfax, Virginia, USA
| | - Giuseppina Imperatore
- Surveillance, Epidemiology, Economics, and Statistics Branch, Division of Diabetes Translation, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA
| | - Melissa N Poulsen
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania, USA
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9
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Campione JR, Ritchie ND, Fishbein HA, Mardon RE, Johnson MC, Pace W, Birch RJ, Seeholzer EL, Zhang X, Proia K, Siegel KR, McKeever Bullard K. Use and Impact of Type 2 Diabetes Prevention Interventions. Am J Prev Med 2022; 63:603-610. [PMID: 35718629 PMCID: PMC10015596 DOI: 10.1016/j.amepre.2022.04.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/22/2022] [Accepted: 04/05/2022] [Indexed: 11/01/2022]
Abstract
INTRODUCTION RCTs have found that type 2 diabetes can be prevented among high-risk individuals by metformin medication and evidence-based lifestyle change programs. The purpose of this study is to estimate the use of interventions to prevent type 2 diabetes in real-world clinical practice settings and determine the impact on diabetes-related clinical outcomes. METHODS The analysis performed in 2020 used 2010‒2018 electronic health record data from 69,434 patients aged ≥18 years at high risk for type 2 diabetes in 2 health systems. The use and impact of prescribed metformin, lifestyle change program, bariatric surgery, and combinations of the 3 were examined. A subanalysis was performed to examine uptake and retention among patients referred to the National Diabetes Prevention Program. RESULTS Mean HbA1c values declined from before to after intervention for patients who were prescribed metformin (-0.067%; p<0.001) or had bariatric surgery (-0.318%; p<0.001). Among patients referred to the National Diabetes Prevention Program lifestyle change program, the type 2 diabetes postintervention incidence proportion was 14.0% for nonattendees, 12.8% for some attendance, and 7.5% for those who attended ≥4 sessions (p<0.001). Among referred patients to the National Diabetes Prevention Program lifestyle change program, uptake was low (13% for 1‒3 sessions, 15% for ≥4 sessions), especially among males and Hispanic patients. CONCLUSIONS Findings suggest that metformin and bariatric surgery may improve HbA1c levels and that participation in the National Diabetes Prevention Program may reduce type 2 diabetes incidence. Efforts to increase the use of these interventions may have positive impacts on diabetes-related health outcomes.
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Affiliation(s)
| | - Natalie D Ritchie
- Office of Research, Denver Health and Hospital Authority, Denver, Colorado
| | | | | | | | | | | | | | - Xuanping Zhang
- Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Krista Proia
- Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Karen R Siegel
- Centers for Disease Control and Prevention, Atlanta, Georgia
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10
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Associations of four indexes of social determinants of health and two community typologies with new onset type 2 diabetes across a diverse geography in Pennsylvania. PLoS One 2022; 17:e0274758. [PMID: 36112581 PMCID: PMC9480999 DOI: 10.1371/journal.pone.0274758] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 09/04/2022] [Indexed: 11/19/2022] Open
Abstract
Evaluation of geographic disparities in type 2 diabetes (T2D) onset requires multidimensional approaches at a relevant spatial scale to characterize community types and features that could influence this health outcome. Using Geisinger electronic health records (2008–2016), we conducted a nested case-control study of new onset T2D in a 37-county area of Pennsylvania. The study included 15,888 incident T2D cases and 79,435 controls without diabetes, frequency-matched 1:5 on age, sex, and year of diagnosis or encounter. We characterized patients’ residential census tracts by four dimensions of social determinants of health (SDOH) and into a 7-category SDOH census tract typology previously generated for the entire United States by dimension reduction techniques. Finally, because the SDOH census tract typology classified 83% of the study region’s census tracts into two heterogeneous categories, termed rural affordable-like and suburban affluent-like, to further delineate geographies relevant to T2D, we subdivided these two typology categories by administrative community types (U.S. Census Bureau minor civil divisions of township, borough, city). We used generalized estimating equations to examine associations of 1) four SDOH indexes, 2) SDOH census tract typology, and 3) modified typology, with odds of new onset T2D, controlling for individual-level confounding variables. Two SDOH dimensions, higher socioeconomic advantage and higher mobility (tracts with fewer seniors and disabled adults) were independently associated with lower odds of T2D. Compared to rural affordable-like as the reference group, residence in tracts categorized as extreme poverty (odds ratio [95% confidence interval] = 1.11 [1.02, 1.21]) or multilingual working (1.07 [1.03, 1.23]) were associated with higher odds of new onset T2D. Suburban affluent-like was associated with lower odds of T2D (0.92 [0.87, 0.97]). With the modified typology, the strongest association (1.37 [1.15, 1.63]) was observed in cities in the suburban affluent-like category (vs. rural affordable-like–township), followed by cities in the rural affordable-like category (1.20 [1.05, 1.36]). We conclude that in evaluating geographic disparities in T2D onset, it is beneficial to conduct simultaneous evaluation of SDOH in multiple dimensions. Associations with the modified typology showed the importance of incorporating governmentally, behaviorally, and experientially relevant community definitions when evaluating geographic health disparities.
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11
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McAlexander TP, Malla G, Uddin J, Lee DC, Schwartz BS, Rolka DB, Siegel KR, Kanchi R, Pollak J, Andes L, Carson AP, Thorpe LE, McClure LA. Urban and rural differences in new onset type 2 diabetes: Comparisons across national and regional samples in the diabetes LEAD network. SSM Popul Health 2022; 19:101161. [PMID: 35990409 PMCID: PMC9385670 DOI: 10.1016/j.ssmph.2022.101161] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 01/25/2023] Open
Abstract
Introduction Geographic disparities in diabetes burden exist throughout the United States (US), with many risk factors for diabetes clustering at a community or neighborhood level. We hypothesized that the likelihood of new onset type 2 diabetes (T2D) would differ by community type in three large study samples covering the US. Research design and methods We evaluated the likelihood of new onset T2D by a census tract-level measure of community type, a modification of RUCA designations (higher density urban, lower density urban, suburban/small town, and rural) in three longitudinal US study samples (REGARDS [REasons for Geographic and Racial Differences in Stroke] cohort, VADR [Veterans Affairs Diabetes Risk] cohort, Geisinger electronic health records) representing the CDC Diabetes LEAD (Location, Environmental Attributes, and Disparities) Network. Results In the REGARDS sample, residing in higher density urban community types was associated with the lowest odds of new onset T2D (OR [95% CI]: 0.80 [0.66, 0.97]) compared to rural community types; in the Geisinger sample, residing in higher density urban community types was associated with the highest odds of new onset T2D (OR [95% CI]: 1.20 [1.06, 1.35]) compared to rural community types. In the VADR sample, suburban/small town community types had the lowest hazard ratios of new onset T2D (HR [95% CI]: 0.99 [0.98, 1.00]). However, in a regional stratified analysis of the VADR sample, the likelihood of new onset T2D was consistent with findings in the REGARDS and Geisinger samples, with highest likelihood of T2D in the rural South and in the higher density urban communities of the Northeast and West regions; likelihood of T2D did not differ by community type in the Midwest. Conclusions The likelihood of new onset T2D by community type varied by region of the US. In the South, the likelihood of new onset T2D was higher among those residing in rural communities.
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Affiliation(s)
- Tara P. McAlexander
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Gargya Malla
- Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jalal Uddin
- Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - David C. Lee
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
- Department of Emergency Medicine, NYU Grossman School of Medicine, New York, NY, USA
| | - Brian S. Schwartz
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Deborah B. Rolka
- Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Karen R. Siegel
- Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rania Kanchi
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Jonathan Pollak
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Linda Andes
- Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, 39213, USA
| | - Lorna E. Thorpe
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Leslie A. McClure
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
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12
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Hirsch AG, Nordberg CM, Bandeen-Roche K, Pollak J, Poulsen MN, Moon KA, Schwartz BS. Urban-Rural Differences in Health Care Utilization and COVID-19 Outcomes in Patients With Type 2 Diabetes. Prev Chronic Dis 2022; 19:E44. [PMID: 35862512 PMCID: PMC9336194 DOI: 10.5888/pcd19.220015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Introduction Two studies in Pennsylvania aimed to determine whether community type and community socioeconomic deprivation (CSD) 1) modified associations between type 2 diabetes (hereinafter, diabetes) and COVID-19 hospitalization outcomes, and 2) influenced health care utilization among individuals with diabetes during the COVID-19 pandemic. Methods The hospitalization study evaluated a retrospective cohort of patients hospitalized with COVID-19 through 2020 for COVID-19 outcomes: death, intensive care unit (ICU) admission, mechanical ventilation, elevated D-dimer, and elevated troponin level. We used adjusted logistic regression models, adding interaction terms to evaluate effect modification by community type (township, borough, or city census tract) and CSD. The utilization study included patients with diabetes and a clinical encounter between 2017 and 2020. Autoregressive integrated moving average time-series models evaluated changes in weekly rates of emergency department and outpatient visits, hemoglobin A1c (HbA1c) laboratory tests, and antihyperglycemic medication orders from 2018 to 2020. Results In the hospitalization study, of 2,751 patients hospitalized for COVID-19, 1,020 had diabetes, which was associated with ICU admission and elevated troponin. Associations did not differ by community type or CSD. In the utilization study, among 93,401 patients with diabetes, utilization measures decreased in March 2020. Utilization increased in July, and then began to stabilize or decline through the end of 2020. Changes in HbA1c tests and medication order trends during the pandemic differed by community type and CSD. Conclusion Diabetes was associated with selected outcomes among individuals hospitalized for COVID-19, but these did not differ by community features. Utilization trajectories among individuals with diabetes during the pandemic were influenced by community type and CSD and could be used to identify individuals at risk of gaps in diabetes care.
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Affiliation(s)
- Annemarie G Hirsch
- Department of Population Health Sciences, Geisinger, 100 N Academy Ave, Danville, PA 17822. .,Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Cara M Nordberg
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania
| | - Karen Bandeen-Roche
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Jonathan Pollak
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Melissa N Poulsen
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania
| | - Katherine A Moon
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Brian S Schwartz
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania.,Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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13
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Thorpe LE, Adhikari S, Lopez P, Kanchi R, McClure LA, Hirsch AG, Howell CR, Zhu A, Alemi F, Rummo P, Ogburn EL, Algur Y, Nordberg CM, Poulsen MN, Long L, Carson AP, DeSilva SA, Meeker M, Schwartz BS, Lee DC, Siegel KR, Imperatore G, Elbel B. Neighborhood Socioeconomic Environment and Risk of Type 2 Diabetes: Associations and Mediation Through Food Environment Pathways in Three Independent Study Samples. Diabetes Care 2022; 45:798-810. [PMID: 35104336 PMCID: PMC9016733 DOI: 10.2337/dc21-1693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 01/05/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We examined whether relative availability of fast-food restaurants and supermarkets mediates the association between worse neighborhood socioeconomic conditions and risk of developing type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS As part of the Diabetes Location, Environmental Attributes, and Disparities Network, three academic institutions used harmonized environmental data sources and analytic methods in three distinct study samples: 1) the Veterans Administration Diabetes Risk (VADR) cohort, a national administrative cohort of 4.1 million diabetes-free veterans developed using electronic health records (EHRs); 2) Reasons for Geographic and Racial Differences in Stroke (REGARDS), a longitudinal, epidemiologic cohort with Stroke Belt region oversampling (N = 11,208); and 3) Geisinger/Johns Hopkins University (G/JHU), an EHR-based, nested case-control study of 15,888 patients with new-onset T2D and of matched control participants in Pennsylvania. A census tract-level measure of neighborhood socioeconomic environment (NSEE) was developed as a community type-specific z-score sum. Baseline food-environment mediators included percentages of 1) fast-food restaurants and 2) food retail establishments that are supermarkets. Natural direct and indirect mediating effects were modeled; results were stratified across four community types: higher-density urban, lower-density urban, suburban/small town, and rural. RESULTS Across studies, worse NSEE was associated with higher T2D risk. In VADR, relative availability of fast-food restaurants and supermarkets was positively and negatively associated with T2D, respectively, whereas associations in REGARDS and G/JHU geographies were mixed. Mediation results suggested that little to none of the NSEE-diabetes associations were mediated through food-environment pathways. CONCLUSIONS Worse neighborhood socioeconomic conditions were associated with higher T2D risk, yet associations are likely not mediated through food-environment pathways.
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Affiliation(s)
- Lorna E Thorpe
- Department of Population Health, New York University Grossman School of Medicine, New York, NY
| | - Samrachana Adhikari
- Department of Population Health, New York University Grossman School of Medicine, New York, NY
| | - Priscilla Lopez
- Department of Population Health, New York University Grossman School of Medicine, New York, NY
| | - Rania Kanchi
- Department of Population Health, New York University Grossman School of Medicine, New York, NY
| | - Leslie A McClure
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA
| | | | - Carrie R Howell
- Division of Preventive Medicine, University of Alabama at Birmingham School of Medicine, Birmingham, AL
| | - Aowen Zhu
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL
| | - Farrokh Alemi
- Department of Health Administration and Policy, George Mason University, Fairfax, VA
| | - Pasquale Rummo
- Department of Population Health, New York University Grossman School of Medicine, New York, NY
| | - Elizabeth L Ogburn
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - Yasemin Algur
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA
| | - Cara M Nordberg
- Department of Population Health Sciences, Geisinger, Danville, PA
| | | | - Leann Long
- Department of Biostatistics, University of Alabama at Birmingham School of Public Health, Birmingham, AL
| | - April P Carson
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL
| | - Shanika A DeSilva
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA
| | - Melissa Meeker
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA
| | - Brian S Schwartz
- Department of Population Health Sciences, Geisinger, Danville, PA
- Department of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - David C Lee
- Department of Population Health, New York University Grossman School of Medicine, New York, NY
- Department of Emergency Medicine, New York University Grossman School of Medicine, New York, NY
| | - Karen R Siegel
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA
| | - Giuseppina Imperatore
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA
| | - Brian Elbel
- Department of Population Health, New York University Grossman School of Medicine, New York, NY
- New York University Wagner Graduate School of Public Service, New York, NY
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14
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Uddin J, Malla G, Long DL, Zhu S, Black N, Cherrington A, Dutton GR, Safford MM, Cummings DM, Judd SE, Levitan EB, Carson AP. The association between neighborhood social and economic environment and prevalent diabetes in urban and rural communities: The Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. SSM Popul Health 2022; 17:101050. [PMID: 35295743 PMCID: PMC8919294 DOI: 10.1016/j.ssmph.2022.101050] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 01/24/2022] [Accepted: 02/17/2022] [Indexed: 11/24/2022] Open
Abstract
Objective The association between neighborhood disadvantage and health is well-documented. However, whether these associations may differ across rural and urban areas is unclear. This study examines the association between a multi-item neighborhood social and economic environment (NSEE) measure and diabetes prevalence across urban and rural communities in the US. Methods This study included 27,159 Black and White participants aged ≥45 years at baseline (2003-2007) from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. Each participant's residential address was geocoded. NSEE was calculated as the sum of z-scores for six US Census tract variables (% of adults with less than high school education; % of adults unemployed; % of households earning <$30,000 per year; % of households in poverty; % of households on public assistance; and % of households with no car) and within strata of community type (higher density urban, lower density urban, suburban/small town, and rural). NSEE was categorized as quartiles, with higher NSEE quartiles reflecting more disadvantage. Prevalent diabetes was defined as fasting blood glucose ≥126 mg/dL or random blood glucose ≥200 mg/dL or use of diabetes medication at baseline. Multivariable adjusted Poisson regression models were used to estimate prevalence ratios (PR) and 95% confidence intervals (CI) for the association between NSEE and prevalent diabetes across community types. Results The mean age was 64.8 (SD=9.4) years, 55% were women, 40.7% were non-Hispanic Black adults. The overall prevalence of diabetes was 21% at baseline and was greatest for participants living in higher density urban areas (24.5%) and lowest for those in suburban/small town areas (18.5%). Compared with participants living in the most advantaged neighborhood (NSEE quartile 1, reference group), those living in the most disadvantaged neighborhoods (NSEE quartile 4) had higher diabetes prevalence in crude models. After adjustment for sociodemographic factors, the association remained statistically significant for moderate density community types (lower density urban quartile 4 PR=1.50, 95% CI=1.29, 1.75; suburban/small town quartile 4 PR=1.54, 95% CI=1.24, 1.92). These associations were also attenuated and of smaller magnitude for those living in higher density urban and rural communities. Conclusion Participants living in the most disadvantaged neighborhoods had a higher diabetes prevalence in each urban/rural community type and these associations were only partly explained by individual-level sociodemographic factors. In addition to addressing individual-level factors, identifying neighborhood characteristics and how they operate across urban and rural settings may be helpful for informing interventions that target chronic health conditions.
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Affiliation(s)
- Jalal Uddin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Gargya Malla
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - D. Leann Long
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sha Zhu
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Andrea Cherrington
- Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, AL, USA
| | - Gareth R. Dutton
- Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, AL, USA
| | - Monika M. Safford
- Department of Medicine, Weill Medical College of Cornell University, New York, NY, USA
| | - Doyle M. Cummings
- Department of Family Medicine and Public Health, East Carolina University, Greenville, NC, USA
| | - Suzanne E. Judd
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Emily B. Levitan
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, 350 West Woodrow Wilson Avenue, Suite 701, Jackson, MS 39213, USA
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15
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Zhu Y, Cheng K, Wang H, Xu Z, Zhang R, Cheng W, Wang Y, Lyu W. Exercise Adherence and Compliance and Its Related Factors Among Elderly Patients with Type 2 Diabetes in China: A Cross-Sectional Study. Patient Prefer Adherence 2022; 16:3329-3339. [PMID: 36568916 PMCID: PMC9785139 DOI: 10.2147/ppa.s374120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 11/01/2022] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To explore exercise adherence and compliance as well as its related factors among elderly patients with type 2 diabetes mellitus (T2DM) to provide a basis for clinical intervention strategies. PATIENTS AND METHODS The present study was a cross-sectional study of 205 elderly patients with T2DM who regularly visited a Shanghai community health center from August 2020 to July 2021. Exercise adherence and compliance was measured using an exercise adherence and compliance questionnaire, and potential correlates were explored using multiple linear regression analysis. RESULTS The mean total score of the exercise adherence and compliance questionnaire was 16.72±5.08. The stepwise regression results revealed that exercise adherence and compliance was positively correlated with self-monitoring (F=3.510, P=0.005), exercise knowledge (r=0.784, P<0.001), exercise willingness (r=0.556, P<0.001), professional support (r=0.426, P<0.001), and self-efficiency (r=0.5, P<0.001). There was a negative correlation between hypoglycemia and exercise adherence and compliance (F=-3.672, P<0.001). CONCLUSION Low exercise adherence and compliance was related to low glucose self-monitoring frequency, increased hypoglycemia, less exercise knowledge, less exercise willingness, less professional support, and less self-efficiency. When developing exercise instructions adapted to the cognitive and volitional needs of diabetic patients, it is essential to focus on their daily self-management habits and extrinsic motivation to improve exercise adherence and compliance.
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Affiliation(s)
- Yingyi Zhu
- School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Kangyao Cheng
- School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Hui Wang
- School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Ziwei Xu
- School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Ruiyu Zhang
- School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Wenjie Cheng
- School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Yan Wang
- Nursing Department, Tangzhen Community Healthcare Center, Shanghai, People’s Republic of China
- Correspondence: Yan Wang, Tangzhen Community Healthcare Center, 75 Middle Chuangxin Road, Shanghai, 201203, People’s Republic of China, Tel +86 13816514677, Email
| | - Weibo Lyu
- School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
- Weibo Lyu, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201210, People’s Republic of China, Tel +86 13661498053, Email
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16
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Hirsch AG, Nordberg CM, Chang A, Poulsen MN, Moon KA, Siegel KR, Rolka DB, Schwartz BS. Association of community socioeconomic deprivation with evidence of reduced kidney function at time of type 2 diabetes diagnosis. SSM Popul Health 2021; 15:100876. [PMID: 34377762 PMCID: PMC8327153 DOI: 10.1016/j.ssmph.2021.100876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/09/2021] [Accepted: 07/15/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND While there are known individual-level risk factors for kidney disease at time of type 2 diabetes diagnosis, little is known regarding the role of community context. We evaluated the association of community socioeconomic deprivation (CSD) and community type with estimated glomerular filtration rate (eGFR) when type 2 diabetes is diagnosed. METHODS This was a retrospective cohort study of 13,144 adults with newly diagnosed type 2 diabetes in Pennsylvania. The outcome was the closest eGFR measurement within one year prior to and two weeks after type 2 diabetes diagnosis, calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equation. We used adjusted multinomial regression models to estimate associations of CSD (quartile 1, least deprivation) and community type (township, borough, city) with eGFR and used adjusted generalized estimating equation models to evaluate whether community features were associated with the absence of diabetes screening in the years prior to type 2 diabetes diagnosis. RESULTS Of the participants, 1279 (9.7%) had hyperfiltration and 1377 (10.5%) had reduced eGFR. Women were less likely to have hyperfiltration and more likely to have reduced eGFR. Black (versus White) race was positively associated with hyperfiltration when the eGFR calculation was corrected for race but inversely associated without the correction. Medical Assistance (ever versus never) was positively associated with reduced eGFR. Higher CSD and living in a city were each positively associated (odds ratio [95% confidence interval]) with reduced eGFR (CSD quartiles 3 and 4 versus quartile 1, 1.23 [1.04, 1.46], 1.32 [1.11, 1.58], respectively; city versus township, 1.38 [1.15, 1.65]). These features were also positively associated with the absence of a type 2 diabetes screening measure. CONCLUSIONS In a population-based sample, more than twenty percent had hyperfiltration or reduced eGFR at time of type 2 diabetes diagnosis. Individual- and community-level factors were associated with these outcomes.
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Affiliation(s)
- Annemarie G. Hirsch
- Department of Population Health Sciences, Geisinger, Danville, PA, USA
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Cara M. Nordberg
- Department of Population Health Sciences, Geisinger, Danville, PA, USA
| | - Alexander Chang
- Department of Population Health Sciences, Geisinger, Danville, PA, USA
| | | | - Katherine A. Moon
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Karen R. Siegel
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Deborah B. Rolka
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Brian S. Schwartz
- Department of Population Health Sciences, Geisinger, Danville, PA, USA
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Poulsen MN, Schwartz BS, Dewalle J, Nordberg C, Pollak JS, Silva J, Mercado CI, Rolka DB, Siegel KR, Hirsch AG. Proximity to freshwater blue space and type 2 diabetes onset: the importance of historical and economic context. LANDSCAPE AND URBAN PLANNING 2021; 209:10.1016/j.landurbplan.2021.104060. [PMID: 34737482 PMCID: PMC8563019 DOI: 10.1016/j.landurbplan.2021.104060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Salutogenic effects of living near aquatic areas (blue space) remain underexplored, particularly in non-coastal and non-urban areas. We evaluated associations of residential proximity to inland freshwater blue space with new onset type 2 diabetes (T2D) in central and northeast Pennsylvania, USA, using medical records to conduct a nested case-control study. T2D cases (n=15,888) were identified from diabetes diagnoses, medication orders, and laboratory test results and frequency-matched on age, sex, and encounter year to diabetes-free controls (n=79,435). We calculated distance from individual residences to the nearest lake, river, tributary, or large stream, and residence within the 100-year floodplain. Logistic regression models adjusted for community socioeconomic deprivation and other confounding variables and stratified by community type (townships [rural/suburban], boroughs [small towns], city census tracts). Compared to individuals living ≥1.25 miles from blue space, those within 0.25 miles had 8% and 17% higher odds of T2D onset in townships and boroughs, respectively. Among city residents, T2D odds were 38-39% higher for those living 0.25 to <0.75 miles from blue space. Residing within the floodplain was associated with 16% and 14% higher T2D odds in townships and boroughs. A post-hoc analysis demonstrated patterns of lower residential property values with nearer distance to the region's predominant waterbody, suggesting unmeasured confounding by socioeconomic disadvantage. This may explain our unexpected findings of higher T2D odds with closer proximity to blue space. Our findings highlight the importance of historic and economic context and interrelated factors such as flood risk and lack of waterfront development in blue space research.
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Affiliation(s)
| | - Brian S Schwartz
- Department of Population Health Sciences, Geisinger, Danville, PA
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD
| | - Joseph Dewalle
- Department of Population Health Sciences, Geisinger, Danville, PA
| | - Cara Nordberg
- Department of Population Health Sciences, Geisinger, Danville, PA
| | - Jonathan S Pollak
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jennifer Silva
- Paul H. O'Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN
| | - Carla I Mercado
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA
| | - Deborah B Rolka
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA
| | - Karen Rae Siegel
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA
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18
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Poulsen MN, Schwartz BS, Nordberg C, DeWalle J, Pollak J, Imperatore G, Mercado CI, Siegel KR, Hirsch AG. Association of Greenness with Blood Pressure among Individuals with Type 2 Diabetes across Rural to Urban Community Types in Pennsylvania, USA. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18020614. [PMID: 33450813 PMCID: PMC7828293 DOI: 10.3390/ijerph18020614] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 01/05/2021] [Accepted: 01/08/2021] [Indexed: 01/25/2023]
Abstract
Greenness may impact blood pressure (BP), though evidence is limited among individuals with type 2 diabetes (T2D), for whom BP management is critical. We evaluated associations of residential greenness with BP among individuals with T2D in geographically diverse communities in Pennsylvania. To address variation in greenness type, we evaluated modification of associations by percent forest. We obtained systolic (SBP) and diastolic (DBP) BP measurements from medical records of 9593 individuals following diabetes diagnosis. Proximate greenness was estimated within 1250-m buffers surrounding individuals’ residences using the normalized difference vegetation index (NDVI) prior to blood pressure measurement. Percent forest was calculated using the U.S. National Land Cover Database. Linear mixed models with robust standard errors accounted for spatial clustering; models were stratified by community type (townships/boroughs/cities). In townships, the greenest communities, an interquartile range increase in NDVI was associated with reductions in SBP of 0.87 mmHg (95% CI: −1.43, −0.30) and in DBP of 0.41 mmHg (95% CI: −0.78, −0.05). No significant associations were observed in boroughs or cities. Evidence for modification by percent forest was weak. Findings suggest a threshold effect whereby high greenness may be necessary to influence BP in this population and support a slight beneficial impact of greenness on cardiovascular disease risk.
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Affiliation(s)
- Melissa N. Poulsen
- Department of Population Health Sciences, Geisinger, Danville, PA 17822, USA; (B.S.S.); (C.N.); (J.D.); (A.G.H.)
- Correspondence:
| | - Brian S. Schwartz
- Department of Population Health Sciences, Geisinger, Danville, PA 17822, USA; (B.S.S.); (C.N.); (J.D.); (A.G.H.)
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA;
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Cara Nordberg
- Department of Population Health Sciences, Geisinger, Danville, PA 17822, USA; (B.S.S.); (C.N.); (J.D.); (A.G.H.)
| | - Joseph DeWalle
- Department of Population Health Sciences, Geisinger, Danville, PA 17822, USA; (B.S.S.); (C.N.); (J.D.); (A.G.H.)
| | - Jonathan Pollak
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA;
| | - Giuseppina Imperatore
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA; (G.I.); (C.I.M.); (K.R.S.)
| | - Carla I. Mercado
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA; (G.I.); (C.I.M.); (K.R.S.)
| | - Karen R. Siegel
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA; (G.I.); (C.I.M.); (K.R.S.)
| | - Annemarie G. Hirsch
- Department of Population Health Sciences, Geisinger, Danville, PA 17822, USA; (B.S.S.); (C.N.); (J.D.); (A.G.H.)
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