IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i23p12702-d693362.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/23/12702/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/23/12702/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Win Wah & Arul Earnest & Charumathi Sabanayagam & Ching-Yu Cheng & Marcus Eng Hock Ong & Tien Y Wong & Ecosse L Lamoureux, 2015. "Composite Measures of Individual and Area-Level Socio-Economic Status Are Associated with Visual Impairment in Singapore," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-12, November.
    2. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "GLLAMM Manual," U.C. Berkeley Division of Biostatistics Working Paper Series 1160, Berkeley Electronic Press.
    3. Bolte, Gabriele, 2018. "Epidemiologische Methoden und Erkenntnisse als eine Grundlage für Stadtplanung und gesundheitsfördernde Stadtentwicklung," Forschungsberichte der ARL: Aufsätze, in: Baumgart, Sabine & Köckler, Heike & Ritzinger, Anne & Rüdiger, Andrea (ed.), Planung für gesundheitsfördernde Städte, volume 8, pages 118-134, ARL – Akademie für Raumentwicklung in der Leibniz-Gemeinschaft.
    4. Shelley H. Liu & Yitong Chen & Jordan R. Kuiper & Emily Ho & Jessie P. Buckley & Leah Feuerstahler, 2024. "Applying Latent Variable Models to Estimate Cumulative Exposure Burden to Chemical Mixtures and Identify Latent Exposure Subgroups: A Critical Review and Future Directions," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 482-502, July.
    5. Spielman, Seth E. & Yoo, Eun-hye, 2009. "The spatial dimensions of neighborhood effects," Social Science & Medicine, Elsevier, vol. 68(6), pages 1098-1105, March.
    6. Karen Minyard & Tina A. Smith & Richard Turner & Bobby Milstein & Lori Solomon, 2018. "Community and programmatic factors influencing effective use of system dynamic models," System Dynamics Review, System Dynamics Society, vol. 34(1-2), pages 154-171, January.
    7. Arpino, Bruno & Varriale, Roberta, 2009. "Assessing the quality of institutions’ rankings obtained through multilevel linear regression models," MPRA Paper 19873, University Library of Munich, Germany.
    8. Bigoni, Maria & Fort, Margherita, 2013. "Information and learning in oligopoly: An experiment," Games and Economic Behavior, Elsevier, vol. 81(C), pages 192-214.
    9. Dusan Paredes Araya & Tomothy M Komarek, 2013. "Spatial Income Inequality in Chile and the Rol of Spatial Labor Sorting," Documentos de Trabajo en Economia y Ciencia Regional 46, Universidad Catolica del Norte, Chile, Department of Economics, revised Apr 2013.
    10. Melissa D. Olfert & Rebecca L. Hagedorn & Makenzie L. Barr & Oluremi A. Famodu & Jessica M. Rubino & Jade A. White, 2018. "eB4CAST: An Evidence-Based Tool to Promote Dissemination and Implementation in Community-Based, Public Health Research," IJERPH, MDPI, vol. 15(10), pages 1-13, September.
    11. Seth E Spielman & Eun-Hye Yoo & Crystal Linkletter, 2013. "Neighborhood Contexts, Health, and Behavior: Understanding the Role of Scale and Residential Sorting," Environment and Planning B, , vol. 40(3), pages 489-506, June.
    12. Zanetta Gant & Larry Gant & Ruiguang Song & Leigh Willis & Anna Satcher Johnson, 2014. "A Census Tract–Level Examination of Social Determinants of Health among Black/African American Men with Diagnosed HIV Infection, 2005–2009—17 US Areas," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-7, September.
    13. Byzalov, Dmitri & Basu, Sudipta, 2024. "The misuse of regression-based x-Scores as dependent variables," Journal of Accounting and Economics, Elsevier, vol. 77(2).
    14. Miller, Charlotte E. & Vasan, Ramachandran S., 2021. "The southern rural health and mortality penalty: A review of regional health inequities in the United States," Social Science & Medicine, Elsevier, vol. 268(C).
    15. Ben Cave & Ryngan Pyper & Birgitte Fischer-Bonde & Sarah Humboldt-Dachroeden & Piedad Martin-Olmedo, 2021. "Lessons from an International Initiative to Set and Share Good Practice on Human Health in Environmental Impact Assessment," IJERPH, MDPI, vol. 18(4), pages 1-23, February.
    16. Roger Tourangeau & J. Michael Brick & Sharon Lohr & Jane Li, 2017. "Adaptive and responsive survey designs: a review and assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 203-223, January.
    17. Carter, Virginia & Derudder, Ben & Henríquez, Cristián, 2021. "Assessing local governments’ perception of the potential implementation of biophilic urbanism in Chile: A latent class approach," Land Use Policy, Elsevier, vol. 101(C).
    18. Myers, Douglas J. & Kriebel, David & Karasek, Robert & Punnett, Laura & Wegman, David H., 2007. "The social distribution of risk at work: Acute injuries and physical assaults among healthcare workers working in a long-term care facility," Social Science & Medicine, Elsevier, vol. 64(4), pages 794-806, February.
    19. infocede, 2001. "Desnutrición infantil en Colombia: inequidades y determinantes," Documentos CEDE 20100, Universidad de los Andes, Facultad de Economía, CEDE.
    20. Martin Gaechter & Peter Schwazer & Engelbert Theurl, 2012. "Stronger Sex but Earlier Death: A Multi-level Socioeconomic Analysis of Gender Differences in Mortality in Austria," DANUBE: Law and Economics Review, European Association Comenius - EACO, issue 1, pages 1-23, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12702-:d:693362. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.