key: cord-0996360-shaao8al
authors: Beydoun, Hind A.; Beydoun, May A.; Hossain, Sharmin; Alemu, Brook T.; Gautam, Rana S.; Weiss, Jordan; Zonderman, Alan B.
title: Socio-demographic, lifestyle and health characteristics as predictors of self-reported Covid-19 history among older adults: 2006-2020 Health and Retirement Study
date: 2022-03-12
journal: Am J Infect Control
DOI: 10.1016/j.ajic.2022.02.021
sha: 0a072e8c90a555e36b4455fbc8e9323122b7d186
doc_id: 996360
cord_uid: shaao8al

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 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’ versus ‘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’ versus ‘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.

The World Health Organization labeled the coronavirus disease 2019 triggered by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a public health emergency of international concern on January 30 th , 2020 (1) and as an infectious disease of pandemic magnitude on March 11 th , 2020 (2) (3) (4) (5) (6) . The ongoing Covid-19 pandemic has affected over 200 countries with approximately 131 million confirmed cases and 2.85 million fatalities by April 2021 worldwide, including 30.8 million confirmed cases and 555,000 fatalities in the United States alone (3, (6) (7) (8) (9) (10) (11) . The clinical presentation, course and prognosis of can range from asymptomatic to mild, moderate and severe symptomatology, potentially leading to hospitalization, intensive care unit (ICU) admission and death (4, 6, 11) . Accumulating evidence from observational studies and meta-analyses has identified high-risk groups who may be more likely than others to experience the detrimental health consequences of Covid-19 (4, 6, (11) (12) (13) .

The SARS-CoV-2 belongs to a family of coronaviruses (CoVs) which are positive-and single-stranded enveloped RNA viruses (14, 15) . Several emerging infectious diseases identified in recent years had CoVs as their etiology including the 2002 severe acute respiratory syndrome coronavirus (SARS-CoV) which was responsible for 8,098 cases and 774 deaths in 26 countries (14, 16) and the 2012 Middle East respiratory syndrome-Coronavirus (MERS-Cov) which was responsible for 2,449 cases and 845 deaths in 27 countries (1, 9, 16) . Research has established that individuals who are older, immunocompromised as well as those with cardiometabolic health problems or cardiorespiratory dysfunction are more susceptible to SARS-CoV, MERS-Cov and SARS-CoV-2 infection and their disease-specific complications (9) . A common feature of SARS-CoV and SARS-CoV-2 is their use of the angiotensin-converting enzyme 2 (ACE2) as a mechanism for cell entry causing the anti-inflammatory arm of renin−angiotensin−aldosterone system (RAAS), namely the ACE2-Angiotensin-(1-7)−Mas receptor (MasR) pathway, to be blocked, potentially leading to excessive inflammatory response (15, 16) . The discovery that ACE2 plays a central role in SARS-CoV-2 infection and Covid-19 prognosis resulted in a controversy surrounding use of antihypertensive medicationsespecially angiotensin converting enzyme inhibitors (ACEi) and angiotensin II type I receptor blockers (ARB) which target the RAAS (16) . The ubiquity of ACE2, which can be expressed by a wide variety of cell and tissue types, has reinforced the idea that Covid-19 is not merely a respiratory condition while supporting the concept that cardiometabolic health, especially hypertension, is a key predictor of Covid-19 prognosis (16, 17) . While ACE2 plays an important role in regulating the RAAS, evidence suggests that individuals diagnosed with hypertension express higher levels of ACE2 and are therefore more susceptible than others to deleterious effects of Covid-19 (11) . A knowledge gap exists as to whether long-term exposure to biomarkers of hypertension, namely, systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP) can predict Covid-19.

Previously conducted epidemiologic studies have contributed to a better understanding of the host characteristics that may influence susceptibility to Covid-19 infection and/or detrimental Covid-19 related outcomes for the purpose of risk stratification (7) (8) (9) . High-risk groups who are overrepresented among Covid-19 cases include men (6) (7) (8) 18) , older adults (6, 7, 15, 16) , minorities (3, 7, 12) , obese individuals (11, 18) and those with pre-existing chronic conditions (6, 7, 15, 16) , including hypertension (3, 6) , diabetes (3, 11, 14) , coronary artery disease (3, 6, 11, 19) , cerebrovascular disease (2, 3, 6, 11) , arrhythmias (11, 13) , heart failure (3, 6, 11, 20) , chronic kidney disease (12, 18, 20) , chronic respiratory disease (11, 20) , cancer (12, 20) and multimorbidities. (12, 18 ) These risk factors have been linked to a wide range of Covid-19 outcomes, including severity (3, 7, 9, 21, 22) , hospitalization (2, 4, 11) , Intensive Care Unit (ICU) admission (3, 6, 10, 11, 16) , mechanical ventilation (3, 10, 11, 23) and mortality (3, 7, 9, 16) , with evidence overwhelmingly generated using convenience samples within clinical settings.

The purpose of this cohort study involving a nationally representative sample of U.S. older adults from the 2006-2020 Health and Retirement Study (HRS) is three-fold: [1] To identify key socio-demographic, lifestyle and health predictors of self-reported Covid-19 history; [2] To examine cardiometabolic health characteristics as predictors of self-reported Covid-19 history; [3] To compare groups with and without self-reported Covid-19 history on trajectories in cardiometabolic health and blood pressure measurements over time. The population-based nature and longitudinal design of the HRS enhance our ability to generalize study findings to older adults in the United States, while establishing a temporal relationship between cardiometabolic risk factors and Covid-19 outcomes.

Initiated in 1992, the HRS is an ongoing, nationally representative longitudinal study of community-dwelling U.S. adults over the age of 50 and their spouses of any age. The HRS was designed to study economic well-being, labor force participation, health and family composition among older adults through biennial surveys administered by telephone or face-to-face interviews. Although the HRS only interviews community-dwelling adults in their baseline surveys, respondents who enter long-term care facilities are also retained. Multistage probability sampling of U.S. households within geographical strata was performed whereby African Americans, Hispanics, and residents of Florida were over-sampled. Response rates at baseline and follow-up waves were >80% for all HRS interviews. Written informed consent was provided by all participants and the University of Michigan's Institutional Review Board approved study protocols. The HRS is sponsored by the National Institute on Aging (grant number U01AG009740) and the Social Security Administration. Details of HRS procedures were reported elsewhere (24, 25) . This study was conducted in accordance with the Declaration of Helsinki and received a determination of research not involving human subjects at our institution.

The original HRS study consists of participants from whom data were collected in 1992, Of these characteristics, smoking and physical exercise are established Covid-19 risk factors.

2006-2020 HRS data were extracted on self-rated health and depressive symptoms. Self-rated health was evaluated using a single item (''would you say your health is excellent, very good, good, fair, or poor?'') and dichotomized as "excellent/very good/good" versus "fair/poor". Symptoms of depression were assessed using a modified 8-item Center for Epidemiological Studies Depression Scale (CES-D) and total CES-D score was calculated with higher scores indicating worse symptoms of depression (25, 27) . Neither self-rated health nor symptoms of depression are established Covid-19 risk factors although both of these characteristics have been linked to morbidity or mortality risks. conditions was determined using a series of standard questions focused on physiciandiagnosed hypertension, diabetes, heart disease (heart attack, coronary heart disease, angina, congestive heart failure and/or other heart problems) and stroke. We further categorized the number of obesity-related cardiometabolic risk factors and chronic conditions as '0', '1-2' and '≥ 3' (25, 27).

Measures & Biomarkers Booklet" which describes standard procedures used for measuring blood pressure, breathing, head strength, balance tests (with 30 seconds full-tandem), balance tests (with 60 seconds full-tandem), walking test, height, weight, waist circumference (WC), among others, and for collecting biological 

Complete subject analyses were conducted using Stata release 16 (StataCorp19. Stata Statistical Software; Release 16. College Station, TX, USA: StataCorp LLC) while taking into account complex sampling design and using the preliminary HRS Covid-19 project weight variable CVWGTR to ensure national representativeness of estimates. Whereas categorical data were summarized using frequencies and percentages, continuous data were summarized by calculating measures of central tendency (mean, median) and dispersion (standard error (SEM), interquartile range), as appropriate. Furthermore, we examined bivariate associations using uncorrected Chi-square and design-based F-tests and performed predictive modeling using logistic and mixed-effects logistic regression modeling for binary outcomes. First, we described socio-demographic, lifestyle and health characteristics at the latest HRS wave of data according to self-reported Covid-19 history. Second, we constructed binary logistic regression models for associations of cardiometabolic health and blood pressure measurements at the latest HRS wave of data with self-reported Covid-19 history, before and after controlling for socio-demographic, lifestyle and health characteristics that were significantly related to self-reported Covid-19 history in bivariate analyses at α=0.20. Third, we displayed trajectories in cardiometabolic health and blood pressure measurements over time after stratifying by self-reported Covid-19 history.

Specifically, we applied Locally Weighted Scatterplot Smoothing (LOWESS) using default STATA settings including a bandwidth of 0.8. The LOWESS is a non-parametric strategy for fitting a smooth curve to data points to find the optimally shaped curve without making any assumptions with respect to a theoretical distribution. Fourth, we constructed mixed-effects binary logistic regression models for repeated measures of cardiometabolic health characteristics, SBP, DBP, and MAP as predictors of self-reported Covid-19 history, before and after controlling for socio-demographic, lifestyle and health characteristics that were significantly related to selfreported Covid-19 history in bivariate analyses at α=0.20. Finally, we constructed logistic and mixed-effects logistic regression models for key predictors of self-reported Covid-19 history. We performed two-sided statistical tests while assuming an alpha level of 0.05.

As shown in Figure 1 , 17 and a one-unit increase in depressive symptoms score was associated with nearly 20% increased odds of Covid-19 history (OR=1.23, 95% CI: 1.00, 1.50). Although sex, race, ethnicity, smoking status and physical activity were associated with Covid-19 history at α=0.2, no clear trend was observed with other socio-demographic, lifestyle and health characteristics. Furthermore, age and birth cohort were strongly correlated and could potentially cause multicollinearity if included simultaneously in regression models. Accordingly, sex, age, race, ethnicity, number of household members, smoking status, physical activity, and depressive symptoms score were considered in the final analyses as described in Table 4 . Whereas those without a Covid-19 history had gradually increasing prevalence of these cardiometabolic health characteristics, trends among those with a history of Covid-19 were either U-shaped or J-shaped. Figures B.1-B 

To our knowledge, this is the first study to analyze data from a nationally representative sample of U.S. older adults for evaluating the longitudinal relationship between established Covid-19 prognostic factors and Covid-19 infection, symptoms and/or outcomes in a community setting. Nearly 1% of study participants self-reported a history of Covid-19 and multiple logistic regression models suggested that the number of household members and depressive symptoms score were consistently associated with increased likelihood of a history of Covid-19 whereas number of cardiometabolic risk factors or chronic conditions was consistently associated with a decreased likelihood of a history of Covid-19. Despite the fact that immunosenescence is known to mediate the impact of aging on Covid-19 susceptibility (13), chronological age did not predict Covid-19 history among study participants after controlling for confounders. This study finding could be attributed to homogeneity of the population in terms of chronological age distribution. Although individual and household-level data linking depressive symptoms and overcrowding to Covid-19 history may be limited, the global literature has established that the Covid-19 pandemic itself may be associated with a greater level of depressive symptoms among adults (28) (29) (30) and that its trajectory may be influenced by overcrowding (31-33) potentially worsening health disparities according to race, ethnicity and socioeconomic status (12) . Whereas depressive symptoms may be manifestations of stress, which in turn, could adversely affect immune response against microorganisms, including SARS-CoV-2, overcrowding may increase the opportunity for SARS-CoV-2 exposure potentially leading to a greater frequency of Covid-19 as the number of household members residing with older adults increases.

This study found an inverse relationship between the number of cardiometabolic risk factors or chronic conditions and Covid-19 history. This finding is in contrast to multiple systematic reviews and meta-analyses that have linked various Covid-19 outcomes to obesity and its associated cardiometabolic disorders (1, 6, 34). However, it may be explained by the fact that study participants who completed 2020 HRS surveys are survivors of the Covid-19 pandemic who were asked to self-report Covid-19 infection, symptoms and/or outcomes used to define Covid-19 history. Therefore, it is likely that study participants with pre-existing health problems linked to worse Covid-19 outcomes may have taken precautions to reduce their exposure to Covid-19 and were, therefore, less likely than others to experience Covid-19. Shortly after onset of the Covid-19 pandemic, it was widely reported in the media that older adults with cardiometabolic risk may be more severely affected by the disease. It is therefore likely that the message has reached HRS participants. It is worth noting that, a year later, the emphasis on vaccinating the most vulnerable populations also highlighted the necessity for some individuals to take precautions.

Inconsistent with the published literature (7, 10, 16) , this study found a significant association between Covid-19 history and number of cardiometabolic features, but not with BMI categories or specific cardiometabolic features or blood pressure measurements. In a prospective (7). The absence of a significant association between specific cardiometabolic health characteristics and Covid-19 history may be attributed to sample size limitations or to prevalence-incidence bias whereby survivors of the Covid-19 pandemic were surveyed and those who self-reported a history of Covid-19 differed from those who did not have the opportunity to participate in the Covid-19 project as a result of death or disability. On the other hand, the distinct trends in diabetes, hypertension and heart disease as well as blood pressure measurements between those with and without a Covid-19 history necessitate further evaluation.

The HRS is a large, nationally representative study with > 20 years of longitudinal data covering several cohorts and it includes a wide range of socio-demographic, lifestyle and healthrelated markers. Nevertheless, study findings need to be interpreted with caution and in light of several limitations. First, the linkage of 2006-2018 HRS with 2020 HRS Covid-19 project data and missing information on key variables yielded analytic samples that were much smaller than the full HRS sample potentially leading to selection bias. When comparing potentially studyeligible HRS participants who were included in the study sample to those excluded from the study sample, differences were observed according to sex, but not according to birth cohort, race, ethnicity and level of education (Table C.1). Small sample sizes may also explain the wide confidence intervals around measures of association and the disparate findings of '1-2' vs. '0' and '≥3' vs. '0' cardiometabolic conditions in relation to Covid-19 history. A larger sample size is needed to clarify these findings, especially that more compromised adults from the HRS may not have taken part in the 2020 Covid-19 project. Second, the majority of HRS data were selfreported, potentially leading to non-differential misclassification and measures of association that are biased towards the null value. Furthermore, the method of survey administration varied between the 2006-2018 HRS waves (face-to-face) versus the 2020 HRS wave (telephone), potentially affecting the quality of survey data. Unlike previously conducted studies, Covid-19 history was defined on the basis of self-reported infection, symptoms and/or outcomes, and did not incorporate data obtained from electronic health records or the National Death Index. Third, cross-sectional and longitudinal data analyses have been conducted using observational HRS data and, as such, the estimated relationships are prone to confounding bias and cannot be deemed causal. Given the observational design, there is a potential for reverse causality or endogeneity bias, especially when interpreting the correlation between depressive symptoms and Covid-19 history. Finally, this study involves secondary analysis of existing HRS data and topics consistently covered by the 2006-2020 waves of HRS data may or may not have yielded the most relevant predictors of Covid-19 history.

In conclusion, we identified number of household members, depressive symptoms, and number of cardiometabolic risk factors or chronic conditions as key predictors for self-reported 

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intake (9) . In-depth analyses and larger sample sizes are needed to confirm these preliminary findings.

The manuscript was supported in part by XXX in XXX, XXX.