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Examining the Drivers of Racial/Ethnic Disparities in Non-Adherence to Antihypertensive Medications and Mortality Due to Heart Disease and Stroke: A County-Level Analysis

Author

Listed:
  • Macarius M. Donneyong

    (College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA)

  • Michael A. Fischer

    (General Internal Medicine at Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, USA)

  • Michael A. Langston

    (Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA)

  • Joshua J. Joseph

    (College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA)

  • Paul D. Juarez

    (Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA)

  • Ping Zhang

    (Division of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA)

  • David M. Kline

    (Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA)

Abstract

Background: Prior research has identified disparities in anti-hypertensive medication (AHM) non-adherence between Black/African Americans (BAAs) and non-Hispanic Whites (nHWs) but the role of determinants of health in these gaps is unclear. Non-adherence to AHM may be associated with increased mortality (due to heart disease and stroke) and the extent to which such associations are modified by contextual determinants of health may inform future interventions. Methods: We linked the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014–2016) and the 2016 County Health Ranking (CHR) dataset to investigate the associations between AHM non-adherence, mortality, and determinants of health. A proportion of days covered (PDC) with AHM < 80%, was considered as non-adherence. We computed the prevalence rate ratio (PRR)—the ratio of the prevalence among BAAs to that among nHWs—as an index of BAA–nHW disparity. Hierarchical linear models (HLM) were used to assess the role of four pre-defined determinants of health domains—health behaviors, clinical care, social and economic and physical environment—as contributors to BAA–nHW disparities in AHM non-adherence. A Bayesian paradigm framework was used to quantify the associations between AHM non-adherence and mortality (heart disease and stroke) and to assess whether the determinants of health factors moderated these associations. Results: Overall, BAAs were significantly more likely to be non-adherent: PRR = 1.37, 95% Confidence Interval (CI):1.36, 1.37. The four county-level constructs of determinants of health accounted for 24% of the BAA-nHW variation in AHM non-adherence. The clinical care (β = −0.21, p < 0.001) and social and economic (β = −0.11, p < 0.01) domains were significantly inversely associated with the observed BAA–nHW disparity. AHM non-adherence was associated with both heart disease and stroke mortality among both BAAs and nHWs. We observed that the determinants of health, specifically clinical care and physical environment domains, moderated the effects of AHM non-adherence on heart disease mortality among BAAs but not among nHWs. For the AHM non-adherence-stroke mortality association, the determinants of health did not moderate this association among BAAs; the social and economic domain did moderate this association among nHWs. Conclusions: The socioeconomic, clinical care and physical environmental attributes of the places that patients live are significant contributors to BAA–nHW disparities in AHM non-adherence and mortality due to heart diseases and stroke.

Suggested Citation

  • Macarius M. Donneyong & Michael A. Fischer & Michael A. Langston & Joshua J. Joseph & Paul D. Juarez & Ping Zhang & David M. Kline, 2021. "Examining the Drivers of Racial/Ethnic Disparities in Non-Adherence to Antihypertensive Medications and Mortality Due to Heart Disease and Stroke: A County-Level Analysis," IJERPH, MDPI, vol. 18(23), pages 1-15, December.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12702-:d:693362
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    References listed on IDEAS

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    1. Paul D. Juarez & Mohammad Tabatabai & Robert Burciaga Valdez & Darryl B. Hood & Wansoo Im & Charles Mouton & Cynthia Colen & Mohammad Z. Al-Hamdan & Patricia Matthews-Juarez & Maureen Y. Lichtveld & D, 2020. "The Effects of Social, Personal, and Behavioral Risk Factors and PM 2.5 on Cardio-Metabolic Disparities in a Cohort of Community Health Center Patients," IJERPH, MDPI, vol. 17(10), pages 1-19, May.
    2. Kindig, D.A. & Stoddart, G., 2003. "What is population health?," American Journal of Public Health, American Public Health Association, vol. 93(3), pages 380-383.
    3. Morenoff, Jeffrey D. & House, James S. & Hansen, Ben B. & Williams, David R. & Kaplan, George A. & Hunte, Haslyn E., 2007. "Understanding social disparities in hypertension prevalence, awareness, treatment, and control: The role of neighborhood context," Social Science & Medicine, Elsevier, vol. 65(9), pages 1853-1866, November.
    4. Leonhard Knorr‐Held & Nicola G. Best, 2001. "A shared component model for detecting joint and selective clustering of two diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 73-85.
    5. Anders Skrondal & Sophia Rabe‐Hesketh, 2007. "Latent Variable Modelling: A Survey," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 712-745, December.
    6. Diez-Roux, A.V., 1998. "Bringing context back into epidemiology: Variables and fallacies in multilevel analysis," American Journal of Public Health, American Public Health Association, vol. 88(2), pages 216-222.
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    1. Xiao Chang & Kai Wang & Yuting Wang & Houmian Tu & Guiping Gong & Haifeng Zhang, 2022. "Medication Literacy in Chinese Patients with Stroke and Associated Factors: A Cross-Sectional Study," IJERPH, MDPI, vol. 20(1), pages 1-12, December.

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