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Determinants of COVID-19 Outcome as Predictors of Delayed Healthcare Services among Adults ≥50 Years during the Pandemic: 2006–2020 Health and Retirement Study

Author

Listed:
  • Hind A. Beydoun

    (Department of Research Programs, Fort Belvoir Community Hospital, Fort Belvoir, VA 22060, USA)

  • May A. Beydoun

    (Laboratory of Epidemiology and Population Sciences, National Institute on Aging Intramural Research Program, Baltimore, MD 21225, USA)

  • Brook T. Alemu

    (Health Sciences Program, School of Health Sciences, Western Carolina University, Cullowhee, NC 28723, USA)

  • Jordan Weiss

    (Department of Demography, University of California Berkeley, Berkeley, CA 94720, USA)

  • Sharmin Hossain

    (Laboratory of Epidemiology and Population Sciences, National Institute on Aging Intramural Research Program, Baltimore, MD 21225, USA)

  • Rana S. Gautam

    (Department of Sociology and Human Services, University of North Georgia, Dahlonega, GA 30597, USA)

  • Alan B. Zonderman

    (Laboratory of Epidemiology and Population Sciences, National Institute on Aging Intramural Research Program, Baltimore, MD 21225, USA)

Abstract

Background: The coronavirus disease 19 (COVID-19) was declared a global pandemic on 11 March 2020. To date, a limited number of studies have examined the impact of this pandemic on healthcare-seeking behaviors of older populations. This longitudinal study examined personal characteristics linked to COVID-19 outcomes as predictors of self-reported delayed healthcare services attributed to this pandemic, among U.S. adults, ≥50 years of age. Methods: Secondary analyses were performed using cross-sectional data (1413 participants) and longitudinal data (2881 participants) from Health and Retirement Study (HRS) (2006–2018) linked to the 2020 HRS COVID-19 Project (57% female, mean age: 68 years). Demographic, socioeconomic, lifestyle and health characteristics were evaluated in relation to delayed overall, surgical and non-surgical healthcare services (“Since March 2020, was there any time when you needed medical or dental care, but delayed getting it, or did not get it at all?” and “What type of care did you delay”) using logistic regression and Ensemble machine learning for cross-sectional data as well as mixed-effects logistic modeling for longitudinal data. Results: Nearly 32.7% delayed healthcare services, 5.8% delayed surgical services and 31.4% delayed non-surgical services. Being female, having a college degree or higher and 1-unit increase in depression score were key predictors of delayed healthcare services. In fully adjusted logistic models, a history of 1 or 2 cardiovascular and/or metabolic conditions (vs. none) was associated with 60–70% greater odds of delays in non-surgical services, with distinct findings for histories of hypertension, cardiovascular disease, diabetes and stroke. Ensemble machine learning predicted surgical better than overall and non-surgical healthcare delays. Conclusion: Among older adults, sex, education and depressive symptoms are key predictors of delayed healthcare services attributed to the COVID-19 pandemic. Delays in surgical and non-surgical healthcare services may have distinct predictors, with non-surgical delays more frequently observed among individuals with a history of 1 or 2 cardiovascular and/or metabolic conditions.

Suggested Citation

  • Hind A. Beydoun & May A. Beydoun & Brook T. Alemu & Jordan Weiss & Sharmin Hossain & Rana S. Gautam & Alan B. Zonderman, 2022. "Determinants of COVID-19 Outcome as Predictors of Delayed Healthcare Services among Adults ≥50 Years during the Pandemic: 2006–2020 Health and Retirement Study," IJERPH, MDPI, vol. 19(19), pages 1-24, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12059-:d:923426
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    References listed on IDEAS

    as
    1. Yi-Chang Chou & Yung-Feng Yen & Dachen Chu & Hsiao-Yun Hu, 2021. "Impact of the COVID-19 Pandemic on Healthcare-Seeking Behaviors among Frequent Emergency Department Users: A Cohort Study," IJERPH, MDPI, vol. 18(12), pages 1-10, June.
    2. Sinisi Sandra E. & Polley Eric C & Petersen Maya L & Rhee Soo-Yon & van der Laan Mark J., 2007. "Super Learning: An Application to the Prediction of HIV-1 Drug Resistance," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-26, February.
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