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The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) is a national prospective study of adults comprising 14 established US prospective cohort studies. Starting as early as 1971, investigators in the C4R cohort studies have collected data on clinical and subclinical diseases and their risk factors, including behavior, cognition, biomarkers, and social determinants of health. C4R links this pre-coronavirus disease 2019 (COVID-19) phenotyping to information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and acute and postacute COVID-related illness. C4R is largely population-based, has an age range of 18-108 years, and reflects the racial, ethnic, socioeconomic, and geographic diversity of the United States. C4R ascertains SARS-CoV-2 infection and COVID-19 illness using standardized questionnaires, ascertainment of COVID-related hospitalizations and deaths, and a SARS-CoV-2 serosurvey conducted via dried blood spots. Master protocols leverage existing robust retention rates for telephone and in-person examinations and high-quality event surveillance. Extensive prepandemic data minimize referral, survival, and recall bias. Data are harmonized with research-quality phenotyping unmatched by clinical and survey-based studies; these data will be pooled and shared widely to expedite collaboration and scientific findings. This resource will allow evaluation of risk and resilience factors for COVID-19 severity and outcomes, including postacute sequelae, and assessment of the social and behavioral impact of the pandemic on long-term health trajectories.

Falls can have life-altering consequences for older adults, including extended recovery periods and compromised independence. Higher household income may mitigate the risk of falls by providing financial resources for mobility tools, remediation of environmental hazards, and needed supports, or it may buffer the impact of an initial fall on subsequent risk through improved assistance and care. Household income has not had a consistently observed association with falls in older adults; however, a segmented association may exist such that associations are attenuated above a certain income threshold. In this study, we utilized segmented negative binomial regression analysis to examine the association between household income and recurrent falls among 2,302 participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study recruited between 2003 and 2007. Income-fall association segments separated by changes in slope were considered. Model results indicated a 2-segment association between household income and recurrent falls in the past year. In the range below the breakpoint, household income was negatively associated with the rate of recurrent falls across all age groups examined; in a higher income range (from $20,000–$49,999 to ≥$150,000), the association was attenuated (weaker negative trend) or reversed (positive trend). These findings point to potential benefits of ensuring that incomes for lower-income adults exceed the threshold needed to confer a reduced risk of recurrent falls. [ABSTRACT FROM AUTHOR]