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IntroductionDepressive symptoms are associated with higher risk of dementia, but how they impact cognition in diverse populations is unclear.MethodsAsian, Black, Latino, or White participants (n = 2227) in the Kaiser Healthy Aging and Diverse Life Experiences (age 65+) and the Study of Healthy Aging in African Americans (age 50+) underwent up to three waves of cognitive assessments over 4 years. Multilevel models stratified by race/ethnicity were used to examine whether depressive symptoms were associated with cognition or cognitive decline and whether associations differed by race/ethnicity.ResultsHigher depressive symptoms were associated with lower baseline verbal episodic memory scores (-0.06, 95% CI: -0.12, -0.01; -0.15, 95% CI: -0.25, -0.04), and faster decline annually in semantic memory (-0.04, 95% CI: -0.07, -0.01; -0.10, 95% CI: -0.15, -0.05) for Black and Latino participants. Depressive symptoms were associated with lower baseline but not decline in executive function.DiscussionDepressive symptoms were associated with worse cognitive outcomes, with some evidence of heterogeneity across racial/ethnic groups.HighlightsWe examined whether baseline depressive symptoms were differentially associated with domain-specific cognition or cognitive decline by race/ethnicity. Depressive symptoms were associated with worse cognitive scores for all racial/ethnic groups across different domains examined. Higher depressive symptoms were associated with faster cognitive decline for semantic memory for Black and Latino participants. The results suggest a particularly harmful association between depressive symptoms and cognition in certain racial/ethnic groups.

Background: Few epidemiologic studies have examined the association of ambient heat with spontaneous abortion, a common and devastating pregnancy outcome. Methods: We conducted a case-crossover study nested within Pregnancy Study Online, a preconception cohort study (2013-2022). We included all participants reporting spontaneous abortion (N = 1,524). We defined the case window as the 7 days preceding the event and used time-stratified referent selection to select control windows matched on calendar month and day of week. Within each 7-day case and control window, we measured the mean, maximum, and minimum of daily maximum outdoor air temperatures. We fit splines to examine nonlinear relationships across the entire year and conditional logistic regression to estimate odds ratios (ORs) and 95% confidence interval (CI) of spontaneous abortion with increases in temperature during the warm season (May-September) and decreases during the cool season (November-March). Results: We found evidence of a U-shaped association between outdoor air temperature and spontaneous abortion risk based on yearround data. When restricting to warm season events (n = 657), the OR for a 10-percentile increase in the mean of lag 0-6 daily maximum temperatures was 1.1 (95% CI: 0.96, 1.2) and, for the maximum, 1.1 (95% CI: 0.99, 1.2). The OR associated with any extreme heat days (>95th county-specific percentile) in the preceding week was 1.2 (95% CI: 0.95, 1.5). Among cool season events (n = 615), there was no appreciable association between lower temperatures and spontaneous abortion risk. Conclusion: Our study provides evidence of an association between high outdoor temperatures and the incidence of spontaneous abortion. [ABSTRACT FROM AUTHOR]

Background: Extreme ambient heat is unambiguously associated with a higher risk of illness and death. The Optum Labs Data Warehouse (OLDW), a database of medical claims from US-based patients with commercial or Medicare Advantage health insurance, has been used to quantify heat-related health impacts. Whether results for the insured subpopulation are generalizable to the broader population has, to our knowledge, not been documented. We sought to address this question, for the US population in California from 2012 to 2019. Methods: We examined changes in daily rates of emergency department encounters and in-patient hospitalization encounters for allcauses, heat-related outcomes, renal disease, mental/behavioral disorders, cardiovascular disease, and respiratory disease. OLDW was the source of health data for insured individuals in California, and health data for the broader population were gathered from the California Department of Health Care Access and Information. We defined extreme heat exposure as any day in a group of 2 or more days with maximum temperatures exceeding the county-specific 97.5th percentile and used a space-time-stratified case-crossover design to assess and compare the impacts of heat on health. Results: Average incidence rates of medical encounters differed by dataset. However, rate ratios for emergency department encounters were similar across datasets for all causes [ratio of incidence rate ratios (rIRR) = 0.989; 95% confidence interval (CI) = 0.969, 1.009], heat-related causes (rIRR = 1.080; 95% CI = 0.999, 1.168), renal disease (rIRR = 0.963; 95% CI = 0.718, 1.292), and mental health disorders (rIRR = 1.098; 95% CI = 1.004, 1.201). Rate ratios for inpatient encounters were also similar. Conclusions: This work presents evidence that OLDW can continue to be a resource for estimating the health impacts of extreme heat. [ABSTRACT FROM AUTHOR]

This article introduces bayesian spatial smoothing models for disease mapping—a specific application of small area estimation where the full universe of data is known—to a wider audience of public health professionals using firearm suicide as a motivating example. Besag, York, and Mollié (BYM) Poisson spatial and space–time smoothing models were fitted to firearm suicide counts for the years 2014-2018. County raw death rates in 2018 ranged from 0 to 24.81 deaths per 10 000 people. However, the highest mortality rate was highly unstable, based on only 2 deaths in a population of approximately 800, and 80.5% of contiguous US counties experienced fewer than 10 firearm suicide deaths and were thus suppressed. Spatially smoothed county firearm suicide mortality estimates ranged from 0.06 to 4.05 deaths per 10 000 people and could be reported for all counties. The space–time smoothing model produced similar estimates with narrower credible intervals as it allowed counties to gain precision from adjacent neighbors and their own counts in adjacent years. bayesian spatial smoothing methods are a useful tool for evaluating spatial health disparities in small geographies where small numbers can result in highly variable rate estimates, and new estimation techniques in R software have made fitting these models more accessible to researchers. [ABSTRACT FROM AUTHOR]