This study examined changes in COVID-19-related death certification practices (ie, reporting COVID-19 in Part I of the death certificate) and the quality of cause-of-death (COD) reporting (acceptable and rejected causal sequences) in the United States from 2020 to 2023. In 2020, 2021, 2022, and 2023, 385 293, 463 273, 246 196, and 76 024 death certificates, respectively, mentioned COVID-19. COVID-19 was selected as the underlying COD in 91%, 90%, 76%, and 65% of these certificates, corresponding to reporting COVID-19 in Part I in 93%, 91%, 78%, and 67% of certificates, respectively. Acceptable causal sequences were reported in 51%, 51%, 45%, and 41% of certificates, whereas rejected causal sequences were reported in 21%, 22%, 23%, and 23%, respectively. Using 2020 as the reference year, the adjusted odds ratios (95% confidence intervals) for 2023 were 0.18 (0.18, 0.18) for reporting COVID-19 in Part I, 0.77 (0.76, 0.78) for reporting acceptable causal sequences, and 1.14 (1.12, 1.16) for reporting rejected causal sequences. In conclusion, COVID-19-related death certification practices changed substantially over the study period, paralleling the decline in the virulence of SARS-CoV-2. The quality of COVID-19-related COD reporting declined modestly from 2020 to 2023.
The COVID-19 pandemic led to stay-at-home orders, resulting in sudden changes in movement patterns and social restrictions that impacted mental health. This study examined changes in individual behaviors during the pandemic using detailed smartphone-based activity data and determined whether behaviors and green space exposure buffered against mental health concerns. A total of 224 twins from the Washington State Twin Registry provided smartphone location data and completed a baseline survey and up to seven waves of follow-up surveys on mental health outcomes (ie, anxiety, depression, and stress). Objective measures of activities, locations, and time spent in green space were collected using Google Location History. Green space exposures were assessed using all location data. Associations between activities, locations, green space, and each mental health outcome were assessed using linear mixed models. There were changes in activities and locations from baseline to wave 1; by wave 7, the direction and magnitude of changes varied across measures, with some remaining above and others below their baseline levels. Mental health outcomes were associated with a subset of locations, activities, and green space measures although associations were not observed consistently across all exposure-outcome combinations examined.
BACKGROUND:Despite the association between lower perceived neighborhood social cohesion (PNSC) and higher risk of hypertension, there is limited research on this relationship. This population-based study examined the association between PNSC and incident hypertension among a nationally representative population of older adults across racial and/or ethnic groups and sex. METHODS:Data were obtained from 2,998 US older adult participants (mean age=62.5 years, SD=8.12) from the Health and Retirement Study. Normotensive participants at baseline (2006 or 2008) were assessed for hypertension incidence at 4- and 8-year follow-up visits. PNSC was classified into tertiles. Weighted Cox proportional hazards regression was used to estimate the association between PNSC and incident hypertension. Interaction terms (PNSC and racial and/or ethnic group, PNSC and sex) were included in models adjusted for appropriate covariates (sex, racial and/or ethnic group, education, body mass index, alcohol use, cigarette smoking status), and models were stratified by racial and/or ethnic group and sex. RESULTS:Overall, 40.5% of the study population developed hypertension. Low perceived neighborhood social cohesion (versus high) was associated with increased hypertension risk (HR=1.22, 95% CI: 1.08-1.37). This association was the strongest among Black adults (HR=2.28, 95% CI: 1.23-4.25) and Black females (HR=3.40, 95% CI: 1.34-8.62), though the interaction terms were not statistically significant (p-values for interaction >0.05). CONCLUSIONS:Individuals perceiving low neighborhood social cohesion may have increased hypertension risk, particularly among Black females. Future studies could test potential underlying mechanisms that explain this association and implement community-based interventions promoting cohesive communities as a potential strategy for reducing disparities in hypertension risk.
Depression analyses in UK Biobank may be affected by selection bias due to incomplete participation in the optional Mental Health Questionnaire. We used one-sample Mendelian randomization (MR) to assess the causal effects of BMI, educational attainment (EA), and CRP on depression, applying inverse probability of selection weighting (IPW) and the instrumental variable for selection (IVsel) to adjust for selection into the MHQ sample. Sensitivity analyses included the use of psychiatrist consultation as a proxy outcome. MR analyses indicated that higher BMI increased the risk of depression (OR=1.09 [1.05, 1.13]), EA was protective (OR=0.81 [0.75, 0.88]), and there was limited evidence that CRP affected depression (OR=1.01 [0.92, 1.10]). Estimates obtained using IPW and IVsel were broadly consistent, although with IVsel estimates closer to the null, within BMI (ORIPW = 1.10 [1.05, 1.15], ORIVsel = 1.04 [1.02, 1.06]), EA (ORIPW = 0.81 [0.74, 0.88], ORIVsel = 0.93 [0.90, 0.96]) and CRP (ORIPW = 1.03 [0.92, 1.15], ORIVsel = 1.00 [0.97, 1.04]). Proxy outcome analyses suggested similar patterns of selection bias, supporting their utility for assessing potential bias in MHQ-restricted analyses. Bias-adjustment methods and sensitivity analyses provide a framework to assess the impact of selection bias in large cohort studies with optional components.
A recent news article suggested observational studies confuse correlation and causation. Such confusion often arises from unclear research aims rather than inherent limitations of study design. We use this recent public critique of epidemiologic research to illustrate how ambiguity in study questions contributes to misinterpretation of findings. We argue that epidemiologic studies generally fall into three distinct categories (descriptive, predictive, and causal) and that each has different goals, assumptions, analytic approaches, and criteria for evaluation. Descriptive studies characterize the distribution of health outcomes, predictive studies aim to identify who will experience those outcomes, and causal studies seek to estimate the effects of interventions or exposures under well-defined counterfactual contrasts. Failure to clearly state which of these aims is being pursued makes it difficult to evaluate methods, assess validity, and interpret results. It also creates opportunities for critiques that may mischaracterize study intent or overstate limitations. We emphasize that observational studies can contribute to causal inference when aligned with explicit causal questions and supported by appropriate assumptions and design choices. We conclude that explicitly stating study aims and estimands would improve scientific communication, facilitate more appropriate critique, and strengthen the contribution of epidemiologic evidence to public health decision-making.
Health insurance claims-based analyses inform many large epidemiologic studies but may have unmeasured confounding. Electronic health records (EHRs) have more detailed health information, but data may be missing on some individuals. Informed by recent work comparing approaches to address missing data, we selected and applied generalized raking (GR) and multiple imputation (MI) to illustrate how to efficiently combine data from these two sources. We considered a previously conducted claims-based study comparing 90-day arterial thromboembolism (ATE) risk among patients hospitalized with COVID-19 versus influenza, for which body mass index (BMI), a potential confounder, was unavailable. Using linked claims-EHR data, we adjusted for the same covariates as the original study and used GR and MI to additionally control for EHR-ascertained BMI, available for a subset. We included 912 hospitalized Kaiser Permanente Washington patients, 449 with COVID-19 and 463 with influenza (31.0% and 38.5% had EHR-ascertained BMI in the prior 90 days, respectively). Adjusted hazard ratios of ATE were similar with and without adjustment for BMI, in GR and MI analyses. In the setting of a claims-EHR-based study with missing data on a potential confounder, we describe our selection of the missing data approach, and its implementation, to demonstrate a broadly applicable process.
Should original research articles routinely contain prominent policy claims or broad calls to action? Growing emphasis on research impact might be welcome yet have unintended consequences (eg, incentivising overextrapolation, and undermining the perceived objectivity of scientists). We examined 45 807 abstracts from ten leading Epidemiology and Public Health journals (1990-2024). Using a large language model with human validation, we classified policy claims and mapped trends. Claims markedly increased from 17.6% to 35.8%, with wide variation across countries and journals (>60% vs < 4%). Keywords linked to higher claim rates differed by topic and time: some corresponded to topics with clear causal evidence of harm as well as topics with notable advocacy. Claims were most common in qualitative or cross-sectional studies, and less common in cohort, quasi-experimental, or experimental studies. We argue that these patterns reflect a research culture increasingly oriented toward claiming policy relevance-and incentives that encourage attaching claims to single studies. Our findings raise questions about how scientists and journals balance evidence, advocacy, and scientific credibility. Ensuring that policy claims and calls to action remain commensurate with evidence will be central to building trust as policy impact continues to be incentivised.
Prior studies suggest fluoride exposure in drinking water above 1500 μg/L is associated with lower child cognition, but evidence is limited at lower exposure levels and in U.S. populations. We evaluated whether prenatal exposure to fluoride in regulated public drinking water was associated with cognition in a pooled U.S. cohort. We analyzed observational data from the Environmental influences on Child Health Outcomes (ECHO) Cohort, including 2514 children born 2006-2019 across 17 sites in 23 states. Individual prenatal time-weighted average public water fluoride concentrations were estimated by linking census tract-level concentrations to residential addresses across pregnancy. Fluid and crystallized cognition were assessed using NIH Toolbox scores. Generalized estimating equation models estimated adjusted mean differences using restricted cubic spline and linear change-point models. Individual prenatal time-weighted average water fluoride concentrations ranged from < 1.0-1940.0 μg/L (mean = 396.9 μg/L). Cubic spline models showed significant inverse associations for fluid cognition above 1107.0 μg/L. Linear change-point models identified 675 μg/L as the best-fitting change-point for fluid cognition; above this value, fluid scores were 0.67 points lower (95% CI, -0.92, -0.42) per 100 μg/L higher fluoride. These findings indicate that prenatal fluoride exposure in regulated public water is nonlinearly associated with lower fluid cognition scores in U.S. children at concentrations below current WHO and U.S. EPA thresholds.
The potential for trace amounts of selenium supplements to prevent prostate cancer and other neoplasms was studied in SELECT, a randomized trial conducted in North America for 7-12 years. 34 887 eligible men age 55 or older were assigned to take either 200 μg selenium from L-selenomethionine, 400 IU vitamin E, both selenium and vitamin E, or placebo. The trial was stopped for futility and concerns about adverse effects. Subsequently, we used incidence and mortality data from Medicare through 2019 and the National Death Index through 2024 to investigate the extent to which selenium administration was associated with increased long-term risk of cancer and neurodegenerative disease, during 341 697 person-years of follow-up. Lung cancer showed little increased risk. Associations were near null for leukemia, non-Hodgkin lymphoma and Parkinson's disease, and there was a lower risk for melanoma and colorectal cancer. In contrast, we found a higher risk for Hodgkin lymphoma, multiple myeloma and amyotrophic lateral sclerosis, which has been associated with selenium overexposure in some nonexperimental studies. Though these latter associations were based on few cases, they were consistent with findings from a range of other studies, possibly indicating long-term adverse effects of this organic selenium compound. (Registration no. NCT00006392).
Target trial emulation prompts investigators to frame their analysis question in terms of a hypothetical clinical trial. Although this does not solve the problem of confounding, the framework can protect against other sources of bias. A natural question is this: what kinds of trials can be emulated? In a late-phase trial (Phase 3 or 4), the goal is to obtain a well-defined causal estimate that closely approximates the impact of a proposed intervention. In an early-phase trial (Phase 2 or earlier), the estimate is a means to an end rather than an end in itself. An early-phase trial provides proof-of-concept evidence on the impact of an intervention in the exposure in terms of efficacy and safety, but the estimand may not correspond to the intervention to be implemented in practice. In a natural experiment where causal inferences rely on a plausibly random (or quasi-random) comparison, the estimand may not be directly translatable to applied practice. In this case, the analysis may be conceptualized as an early-phase target trial. This provides less specific evidence than a late-stage target trial, but in many cases, a more valid but less applicable comparison is preferable to a more applicable comparison that is more susceptible to bias.
Major disease diagnoses can create shocks that ripple through households. Population-level evidence in the U.S. on how spouses and partners fare after a partner's major diagnosis remains sparse and is often limited to caregivers or employment-related outcomes. Using the U.S. Health and Retirement Study (1992-2020), we evaluated population-level health spillovers to spouses after a partner's first diagnosis of cancer, stroke, or heart disease, using a stacked event-study design with matching (n = 29 166). Outcomes included subjective health, doctor-diagnosed conditions, limitations in basic and instrumental activities of daily living (ADL/IADL), and health behaviors and preventive care use. We evaluated heterogeneity by sociodemographic and economic conditions. Partners of newly diagnosed patients experienced modest but persistent deterioration: more frequent reports of poor health ( $\boldsymbol{\beta}$=0.02, 95% confidence interval [CI]: 0.00, 0.03), more doctor-diagnosed conditions ( $\boldsymbol{\beta}$=0.05, 95% CI: 0.01, 0.08) and higher depressive symptoms ( $\boldsymbol{\beta}$=0.08, 95% CI: 0.01, 0.16). Effects were larger for women partnered to men with a diagnosis and for spouses with lower education or fewer assets. Health and preventive-care behaviors were largely unchanged. Findings motivate early, spouse-focused support and underscore the need for targeted interventions for spouses at highest risk.
Point-in-time measures of neighborhood poverty exposure suggest a link with physiological aging, but it remains unclear how histories and the timing of residential disadvantage connect to weathering. In this issue, D'Alessio et al. (Am J of Epidemiol. 2026) use 25 years of prospective residential data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to link neighborhood poverty across adolescence, early adulthood, and early midlife to three epigenetic clocks. They identify selective dose-response relationships. Each additional life stage in a high-poverty tract is associated with accelerated biological aging, and exposure is distributed with sharp racial inequality. The per-stage effect is modest in individual terms but consequential at population scale. Its unequal allocation makes residential segregation a plausible epigenetic mechanism of population-level racial disparities in aging. We argue that-despite its considerable strengths-the study leaves two questions unresolved. First, does health-selective residential mobility drive part of the association? Second, does the prominence of mid-life exposure reflect a true sensitive period, or the fact that mid-life poverty is measured in close proximity to biospecimen collection? We outline potential follow-up work that could address these questions via structured life course methods and better-powered cohorts with tract-linked data, including Add Health.
Abstract Heatwaves pose a growing global health threat, yet the mental health impacts of different heatwave types under climate change remain unclear. We quantified current and future mental disorder mortality attributable to daytime, nighttime, and compound heatwaves in Shanghai, China. Baseline associations between heatwaves and daily mental disorder deaths (2016-2019) were estimated using a time-stratified case-crossover design. Heatwaves were defined using a Health-based Excess Heat Factor, and attributable fractions in the 2010s were projected to the 2030s, 2060s, and 2090s under 3 Shared Socioeconomic Pathways and 3 population adaptation scenarios. All heatwave types were associated with increased mental disorder mortality, with the strongest effects observed for daytime heatwaves, followed by compound and nighttime heatwaves. Without adaptation, excess deaths attributable to daytime heatwaves were projected to increase by 8.3%-10.7% in the 2060s and 13.6%-15.3% in the 2090s, while the exposure–response function projected smaller increases in attributable deaths for compound and nighttime heatwaves. Schizophrenia exhibited the greatest vulnerability, particularly to daytime heatwaves, with attributable deaths rising by 27.9%-34.9% by the 2090s. Suicide and dementia were most affected by compound and nighttime heatwaves, respectively. Partial and full adaptation substantially reduced future burdens, especially for nighttime heatwaves. Enhanced population adaptation could mitigate the mortality burden of mental disorders from heatwaves.
Abstract We sought to characterize racial identity response transitions among multiparous birthing people who ever identified as American Indian or Alaska Native (AIAN) and assess whether this fluidity alters year-specific statewide AIAN maternal health and birth outcome prevalence estimates. To do so, we used Michigan birth certificates (2014-2022) to identify individuals with $\boldsymbol{\ge}$2 live births who self-reported as AIAN for $\boldsymbol{\ge}\mathbf{1}$ birth (n = 4823). We categorized individuals as having changed or not changed their reported racial identity across births and recalculated AIAN-specific outcome estimates accordingly. Only 23.9% (1155) consistently identified as AIAN across all births, while 76.1% (3668) used different racial identity responses. Among those whose racial identity response changed, transitioning from identifying as AIAN to no longer identifying as AIAN was the most common pattern. Accounting for these transitions had minimal effect on prevalence estimates for preterm birth and gestational diabetes, while there were some prevalence changes for the prevalence of smoking before/during pregnancy. AIAN birthing people in Michigan often shift racial identity responses over time. Although this fluidity had little impact on surveillance of perinatal outcomes, reliance on point-in-time race data may inadequately reflect AIAN identity in public health data.
Extreme Risk Protection Orders (ERPO), known as Gun Violence Restraining Orders in California, are designed to reduce gun violence by allowing courts to temporarily remove firearms from individuals who pose a risk to themselves or others. Population-level evidence on their impacts remains limited. We used the Longitudinal Study of Handgun Ownership and Transfer, which links California voter, mortality, and firearm transaction records, to evaluate the impact of California's ERPO law on homicide risk among cohabitants of handgun owners who are not themselves handgun owners. Using a difference-in-differences design, we compared changes in 1-year homicide risks before (2012-2015) and after (2016-2021) policy implementation among adults living with handgun owners versus those in households without known handgun owners. We estimated a 1-year difference of -0.42 (95% CI, -1.86, 1.02) homicide deaths per 100 000 persons among cohabitants of handgun owners who are not themselves handgun owners, with null findings robust across sensitivity analyses. Though our findings did not suggest substantial population-level protective effects, ERPOs may still reduce risk of homicide among cohabitants of ERPO respondents in ways not detectable at the population level. At the population level, their effectiveness may depend on more frequent or better-targeted use.
