Judd, Suzanne E.

ImportanceIdentifying factors associated with resilience during the COVID-19 pandemic can inform targeted interventions and resource allocation for groups disproportionately affected by systemic inequities.ObjectiveTo examine factors associated with self-reported resilience during the COVID-19 pandemic in racially and ethnically diverse, community-dwelling US adults.Design, Setting, and ParticipantsThis cross-sectional study was conducted as part of the Collaborative Cohort of Cohorts for COVID-19 Research (C4R) study, which assessed the associations of the pandemic with self-reported resilience of participants from 14 established US prospective cohorts since January 2021. This report includes participants who responded to the self-reported resilience question on C4R questionnaires. Data was initially analyzed from October 2023 to May 2024, with updated analyses performed from August 2024 to April 2025.ExposureRace and ethnicity, behavior factors, health conditions, and social determinants of health measurements accessed before and during the COVID-19 pandemic through cohort visits and C4R questionnaires.Main Outcomes and MeasuresSelf-reported resilience was collected via 1 question (from the Brief Resilience Scale) in C4R questionnaires, “I tend to bounce back quickly after hard times.” Participants who answered agree or strongly agree were classified as resilient, and those who reported neutral, disagree, or strongly disagree were classified as not resilient. Modified Poisson regression was performed to estimate prevalence ratios (PRs) and access multivariable-adjusted associations with resilience.ResultsOf 31 045 participants (18 672 [60%] women; 10 746 [34.6%] aged <65 years), 1185 (3.8%) identified as American Indian, 6728 (21.7%) as Black, 293 (0.9%) as East Asian, 6311 (20.3%) as Hispanic, 565 (1.8%) as South Asian, and 15 961 (51.3%) as White; a total of 23 103 participants (74.4%) self-identified as resilient. Compared with White participants, Black and Hispanic participants had higher prevalence of self-reported resilience (adjusted PR [aPR], 1.04; 95% CI, 1.02-1.06; aPR, 1.08; 95% CI, 1.06-1.11; respectively) and American Indian and East Asian participants had lower prevalence (aPR, 0.90; 95% CI, 0.86-0.94; aPR, 0.76; 95% CI, 0.68-0.84; respectively). Higher education, being married or living as married, higher income, and overweight were also associated with higher prevalence of resilience. Being female, having diabetes, and being unemployed were associated with lower prevalence of self-reported resilience. Compared with participants with public insurance only, participants with private insurance had higher prevalence of resilience (aPR, 1.07; 95% CI, 1.03-1.10). COVID-19 vaccination and infection statuses were not significantly associated with resilience. Modification analyses showed important racial and ethnic differences in how factors such as hypertension, marital status, and insurance status were associated with resilience.Conclusions and RelevanceIn this cross-sectional study of 31 045 adults, self-reported resilience varied by race, ethnicity, and sociodemographic factors. These findings highlight the complex interplay of individual and social factors in shaping the perception of resilience.

ImportancePersistent symptoms and disability following SARS-CoV-2 infection, known as post–COVID-19 condition or “long COVID,” are frequently reported and pose a substantial personal and societal burden.ObjectiveTo determine time to recovery following SARS-CoV-2 infection and identify factors associated with recovery by 90 days.Design, Setting, and ParticipantsFor this prospective cohort study, standardized ascertainment of SARS-CoV-2 infection was conducted starting in April 1, 2020, across 14 ongoing National Institutes of Health–funded cohorts that have enrolled and followed participants since 1971. This report includes data collected through February 28, 2023, on adults aged 18 years or older with self-reported SARS-CoV-2 infection.ExposurePreinfection health conditions and lifestyle factors assessed before and during the pandemic via prepandemic examinations and pandemic-era questionnaires.Main Outcomes and MeasuresProbability of nonrecovery by 90 days and restricted mean recovery times were estimated using Kaplan-Meier curves, and Cox proportional hazards regression was performed to assess multivariable-adjusted associations with recovery by 90 days.ResultsOf 4708 participants with self-reported SARS-CoV-2 infection (mean [SD] age, 61.3 [13.8] years; 2952 women [62.7%]), an estimated 22.5% (95% CI, 21.2%-23.7%) did not recover by 90 days post infection. Median (IQR) time to recovery was 20 (8-75) days. By 90 days post infection, there were significant differences in restricted mean recovery time according to sociodemographic, clinical, and lifestyle characteristics, particularly by acute infection severity (outpatient vs critical hospitalization, 32.9 days [95% CI, 31.9-33.9 days] vs 57.6 days [95% CI, 51.9-63.3 days]; log-rank P < .001). Recovery by 90 days post infection was associated with vaccination prior to infection (hazard ratio [HR], 1.30; 95% CI, 1.11-1.51) and infection during the sixth (Omicron variant) vs first wave (HR, 1.25; 95% CI, 1.06-1.49). These associations were mediated by reduced severity of acute infection (33.4% and 17.6%, respectively). Recovery was unfavorably associated with female sex (HR, 0.85; 95% CI, 0.79-0.92) and prepandemic clinical cardiovascular disease (HR, 0.84; 95% CI, 0.71-0.99). No significant multivariable-adjusted associations were observed for age, educational attainment, smoking history, obesity, diabetes, chronic kidney disease, asthma, chronic obstructive pulmonary disease, or elevated depressive symptoms. Results were similar for reinfections.Conclusions and RelevanceIn this cohort study, more than 1 in 5 adults did not recover within 3 months of SARS-CoV-2 infection. Recovery within 3 months was less likely in women and those with preexisting cardiovascular disease and more likely in those with COVID-19 vaccination or infection during the Omicron variant wave.

Background: Administrative data such as Medicare data are commonly used for health services research and comparative effectiveness studies. These databases are readily available and a good source of long-term data on healthcare utilization and health outcomes in real-world settings. Outcomes are captured using diagnostic codes, but the evidence on the validity of these codes is scarce in contemporary Medicare data. Methods: We linked the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study data using SSN, date of birth and sex to 2003-2009 Medicare claims. In addition to the events captured by REGARDS phone interviews, medical charts were pursued for Medicare-identified strokes not previously identified in REGRADS. Events were adjudicated by stroke specialists using the retrieved medical charts. Using all the adjudicated strokes as gold standard, we calculated the sensitivity, specificity, PPV and NPV of inpatient stroke algorithms with ICD-9-CM codes [430, 431, 433.x1, 434.x1, 436] in the primary discharge diagnosis. Results: We successfully linked 15,089 REGARDS participants with mean age of 69.3, 52% female and 37% Black. We adjudicated 457 strokes, of which 48 were identified by claims only and not by interviews. The tested algorithms had high specificity (99.8-100%), PPV (88.6-90.5%) and NPV (98.9-99.9%), but low sensitivity (58.6-67.4%) (Table). Conclusions: High specificity and PPV of our algorithms to identify strokes in contemporary Medicare populations support their use in etiological and comparative effectiveness studies. While our inpatient Medicare algorithms identified 12% extra cases of strokes, their usefulness for estimating stroke incidence or related healthcare utilizations is limited by their low sensitivity. Linking cohorts to Medicare data is feasible and should be considered to increase the completeness of follow-up. Further studies are needed to evaluate more sensitive Medicare algorithms.

Background: Stroke symptoms in the general population are common (≈18% of adults 45+) and are associated with future stroke. These symptoms may also identify a broad spectrum of clinically-undetected vascular conditions. Methods: Stroke symptoms (sudden weakness, numbness, unilateral or general loss of vision, and loss of ability to communicate or understand) and time to self-reported hospitalization for any cardiovascular (CVD) reason (heart, stroke, or TIA) or stroke were assessed in the REGARDS Study (a national, population-based, longitudinal study of 30,239 African-American and white adults > 45 years enrolled 2003-2007). The symptom-hospitalization association was assessed by proportional hazards analysis in persons stroke/TIA-free at baseline, with adjustment for demographics (age, race and sex), further adjustment for SES (income and education), CVD risk factors (smoking, hypertension, diabetes, dyslipidemia, atrial fibrillation and heart disease), and “perceived health status.” Results: Over an average follow-up of 5.6 years, presence of any stroke symptom was associated with greater risk of hospitalization for stroke (11% versus 6%; HR = 1.68; 95% CI: 1.52 - 1.85), a risk only modestly attenuated by adjustment for SES (HR = 1.59; 95% CI: 1.44 - 1.76), risk factors (HR = 1.44; 95% CI: 1.29 - 1.60), and perceived health (HR = 1.37; 95% CI: 1.23 - 1.53). However, presence of any stroke symptom was more strongly related to hospitalization for CVD (42% vs. 24%; HR = 1.92; 95 CI: 1.82 - 2.03), only modestly mediated by adjustment for SES (HR = 1.82; 95% CI: 1.73 - 1.93), risk factors (HR = 1.66; 95% CI: 1.57 - 1.76), and perceived health (HR = 1.57; 95% CI: 1.48 - 1.66). Conclusions: Stroke symptoms appear to be a marker not only for stroke risk, but also for general CVD. Simple questions can identify 18% of the general population that have over a 40% chance of hospitalization for CVD over a 5-year horizon. Implications for potential intervention to reduce risk are profound.

Background: Levels of the pro-inflammatory cytokines interleukin (IL)-6 and IL-8 and the anti-inflammatory cytokine IL-10 are altered in acute stroke patients compared to controls. However, results for IL-10 are inconsistent (higher in stroke patients compared to controls in some studies and decreased in others) and very few studies have examined associations of these biomarkers with risk of incident stroke. Methods: We examined associations of baseline levels of IL-6, IL-8 and IL-10 with stroke risk factors and risk of incident stroke in 1,572 white and black men and women from the REGARDS Study; an observational cohort of 30,239 adults followed for 5 years. Among those without prebaseline stroke, stroke cases (n=592, 53% men, 59% white, mean age 70 years) were compared to a cohort random sample (n=980, 44% men, 58% white, mean age 65 years). We used Cox proportional hazards models to examine associations of biomarkers with incident stroke. Hazard ratios (HR; 95% confidence intervals) for highest compared to lowest quartile for each biomarker are presented. Results: Baseline IL-6 was significantly higher in incident stroke cases compared to the cohort sample (p<0.001) while IL-8 and IL-10 levels did not vary significantly (p>0.05 for both). Adjusting for age, sex and race, IL-6 was higher in blacks compared to whites and higher in current smokers compared to never/former smokers (all p≤0.01). IL-6 was inversely associated with physical activity, alcohol use and education (all p≤0.01). IL-8 and IL-10 did not vary significantly with the risk factors examined. Adjusting for age, sex and race, the HR for risk of incident stroke in those in the highest compared to lowest quartile of IL-6 was 2.4 (1.6-3.4). HRs for IL-8 and IL-10 were 1.5 (1.0-2.1) and 1.4 (1.0-1.9), respectively. Adjusting for Framingham stroke risk factors and history of coronary heart disease, only IL-6 remained significantly associated with stroke risk (HR 2.0; 1.3-3.2). HRs for IL-8 and IL-10 were 1.1 (0.7-1.6) and 1.2 (0.8-1.8), respectively. IL-6 remained significantly associated with stroke risk when C-reactive protein was added to the model (HR 1.7; 1.1-2.7). Associations did not differ by race. Conclusions: In this population-based sample of US black and white adults, IL-6, but not IL-8 or IL-10, was associated with stroke risk factors and risk of incident stroke. Further study is needed on the clinical utility of IL-6 measurement in stroke risk assessment.

OBJECTIVE Given temporal changes in diabetes prevalence and stroke incidence, this study investigated age, race, and sex differences in the diabetes–stroke association in a contemporary prospective cohort, the REasons for Geographic and Racial Differences in Stroke (REGARDS) Study. RESEARCH DESIGN AND METHODS We included 23,002 non-Hispanic black and white U.S. adults aged ≥45 years without prevalent stroke at baseline (2003–2007). Diabetes was defined as fasting glucose ≥126 mg/dL, random glucose ≥200 mg/dL, or use of glucose-lowering medication. Incident stroke events were expert adjudicated and available through September 2017. RESULTS The prevalence of diabetes was 19.1% at baseline. During follow-up, 1,018 stroke events occurred. Among adults aged &lt;65 years, comparing those with diabetes to those without diabetes, the risk of stroke was increased for white women (hazard ratio [HR] 3.72 [95% CI 2.10–6.57]), black women (HR 1.88 [95% CI 1.22–2.90]), and white men (HR 2.01 [95% CI 1.27–3.27]) but not black men (HR 1.27 [95% CI 0.77–2.10]) after multivariable adjustment. Among those aged ≥65 years, diabetes increased the risk of stroke for white women and black men, but not black women (HR 1.05 [95% CI 0.74–1.48]) or white men (HR 0.86 [95% CI 0.62–1.21]). CONCLUSIONS In this contemporary cohort, the diabetes–stroke association varied by age, race, and sex together, with a more pronounced effect observed among adults aged &lt;65 years. With the recent increase in the burden of diabetes complications at younger ages in the U.S., additional efforts are needed earlier in life for stroke prevention among adults with diabetes.

Background The association between resting heart rate and ischemic stroke remains unclear. Aim To examine the association between resting heart rate and ischemic stroke. Methods A total of 24 730 participants (mean age: 64 ± 9·3 years; 59% women; 41% blacks) from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study who were free of stroke at the time of enrollment (2003–2007) were included in this analysis. Resting heart rate was determined from baseline electrocardiogram data. Heart rate was examined as a continuous variable per 10 bpm increase and also as a categorical variable using tertiles (<61 bpm, 61 to 70 bpm, and >70 bpm). First-time ischemic stroke events were identified during follow-up and adjudicated by physician review. Results Over a median follow-up of 7·6 years, a total of 646 ischemic strokes occurred. In a Cox regression model adjusted for socio-demographics, cardiovascular risk factors, and potential confounders, each 10 bpm increase in heart rate was associated with a 10% increase in the risk of ischemic stroke (hazard ratio = 1·10, 95% confidence interval = 1·02, 1·18). In the categorical model, an increased risk of ischemic stroke was observed for heart rates in the middle (hazard ratio = 1·29, 95% confidence interval = 1·06, 1·57) and upper (hazard ratio = 1·37, 95% confidence interval = 1·12, 1·67) tertiles compared with the lower tertile. The results were consistent when the analysis was stratified by age, gender, race, exercise habits, hypertension, and coronary heart disease. Conclusion In REGARDS, high resting heart rates were associated with an increased risk of ischemic stroke compared with low heart rates. Further research is needed to examine whether interventions aimed to reduce heart rate decrease stroke risk.

Background and Purpose— We compared the associations of self-reported atrial fibrillation (AF) and ECG-detected AF with incident stroke in the Risk of Stroke in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. Methods— In this analysis, 27 109 participants aged 45 years or older without previous stroke were included. Stroke cases were identified and adjudicated during an average of 4.4 years of follow-up. Cox proportional hazards analysis was used to calculate hazard ratios (HR) of self-reported AF, ECG-detected AF, and AF detected by either method with incident stroke. We also examined the predictive ability of the Framingham Stroke Risk Score (FSRS) when the component AF was defined by different methods. Results— After adjustment for components of the FSRS, self-reported AF, ECG-detected AF, and AF by either method were predictive of incident stroke (HR, 1.41; 95% CI, 1.05–1.88; HR, 1.90; 95% CI, 1.10–3.27; HR, 1.53; 95% CI, 1.16–2.01, respectively). When self-report, ECG, or either method, separately, were considered as the method of AF ascertainment in the FSRS, the HR per 1% increase in the FSRS were identical across AF ascertainment methods (HR, 1.04; 95% CI, 1.03–1.04; HR, 1.04; 95% CI, 1.04–1.05; HR, 1.04; 95% CI, 1.03–1.04; respectively). Conclusions— Self-reported AF is a strong predictor of stroke that can be used interchangeably or in combination with ECG-detected AF in stroke risk prediction models.