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

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.

Predictors of Covid-19 level of concern among older adults from the health and retirement study.

The purpose of this longitudinal study is to construct a prediction model for Covid-19 level of concern using established Covid-19 socio-demographic, lifestyle and health risk characteristics and to examine specific contributions of obesity-related cardiometabolic health characteristics as predictors of Covid-19 level of concern among a representative sample of U.S. older adults. We performed secondary analyses of existing data on 2872 2006-2020 Health and Retirement Study participants and examined 19 characteristics in relation to the outcome of interest using logistic regression and machine learning algorithms. In mixed-effects ordinal logistic regression models, a history of diabetes, stroke as well as 1-2 cardiometabolic risk factors and/or chronic conditions were associated with greater Covid-19 level of concern, after controlling for confounders. Female sex, birth cohort, minority race, Hispanic ethnicity and total wealth as well as depressive symptoms were associated with higher level of Covid-19 concern, and education was associated with lower level of Covid-19 concern in fully adjusted mixed-effects ordinal logistic regression models. The selected socio-demographic, lifestyle and health characteristics accounted for < 70% of the variability in Covid-19 level of concern based on machine learning algorithms. Independent risk factors for Covid-19 level of concern among U.S. older adults include socio-demographic characteristics and depressive symptoms. Advanced research is needed to identify relevant predictors and elucidate underlying mechanisms of observed relationships.

Socio-demographic, lifestyle and health characteristics as predictors of self-reported Covid-19 history among older adults: 2006-2020 Health and Retirement Study.

BACKGROUND: To identify key socio-demographic, lifestyle, and health predictors of self-reported coronavirus disease 2019 (Covid-19) history, examine cardiometabolic health characteristics as predictors of self-reported Covid-19 history and compare groups with and without a history of Covid-19 on trajectories in cardiometabolic health and blood pressure measurements over time, among United States (U.S.) older adults. METHODS: Nationally representative longitudinal data on U.S. older adults from the 2006-2020 Health and Retirement Study were analyzed using logistic and mixed-effects logistic regression models. RESULTS: Based on logistic regression, number of household members (OR=1.26, 95% CI: 1.05, 1.52), depressive symptoms score (OR = 1.21, 95% CI: 1.04, 1.42) and number of cardiometabolic risk factors or chronic conditions ("1-2" vs "0") (OR = 0.27, 95% CI: 0.11, 0.67) were significant predictors of self-reported Covid-19 history. Based on mixed-effects logistic regression, several statistically significant predictors of Covid-19 history were identified, including female sex (OR = 3.06, 95% CI: 1.57, 5.96), other race (OR = 5.85, 95% CI: 2.37, 14.43), Hispanic ethnicity (OR = 2.66, 95% CI: 1.15, 6.17), number of household members (OR = 1.25, 95% CI: 1.10, 1.42), moderate-to-vigorous physical activity (1-4 times per month vs never) (OR = 0.38, 95% CI: 0.18, 0.78) and number of cardiometabolic risk factors or chronic conditions ("1-2" vs "0") (OR = 0.34, 95% CI: 0.19, 0.60). CONCLUSIONS: Number of household members, depressive symptoms and number of cardiometabolic risk factors or chronic conditions may be key predictors for self-reported Covid-19 history among U.S. older adults. In-depth analyses are needed to confirm preliminary findings.

COVID-19 Pandemic Impact on Trajectories in Cardiometabolic Health, Physical Activity, and Functioning Among Adults from the 2006-2020 Health and Retirement Study.

BACKGROUND: The aim of this study was to evaluate the impact of the COVID-19 pandemic on trajectories in cardiometabolic health, physical activity, and functioning among U.S. older adults, overall and according to selected baseline sociodemographic characteristics. METHODS: We performed secondary analyses using longitudinal data on 1,372 participants from the 2006-2020 Health and Retirement Study. Pre-post COVID-19 pandemic onset was examined in relation to body mass index (BMI), number of cardiometabolic risk factors and/or chronic conditions, physical activity, Activities of Daily Living (ADL), and Instrumental Activities of Daily Living (IADL) using mixed-effects regression models and group-based trajectory models. RESULTS: The COVID-19 pandemic was associated with significantly increased BMI (ß = 1.39, 95% confidence interval [CI]: 0.74, 2.03). Furthermore, the odds of having at least one cardiometabolic risk factor and/or chronic disease increased pre-post COVID-19 onset (odds ratio 1.16, 95% CI: 1.00, 1.36), whereas physical functioning worsened pre-post COVID-19 onset (ADL: ß = 1.11, 95% CI: 0.94, 1.28; IADL: ß = 0.59, 95% CI: 0.46, 0.73). The pre-post COVID-19 period (2018-2020) showed a stable group of trajectories, with low, medium and high levels of the selected health indicators. Health disparities according to sex, race/ethnicity, educational level, work status, and total wealth are highlighted. CONCLUSIONS: The COVID-19 pandemic onset appears to worsen cardiometabolic health and physical functioning among U.S. older adults, with clusters of individuals defined by selected sociodemographic characteristics experiencing distinct trajectories pre-post COVID-19 pandemic onset.