American journal of epidemiology最新文献

Prior research shows that while recent immigrants arriving in the United States exhibit better health than their US-born counterparts, this advantage diminishes with immigrants' tenure of US residence, a phenomenon known as the "healthy immigrant effect." Scholars continue to debate the mechanisms-including immigrant cultural practices, selective migration, and characteristics of the destination country-that produce immigrants' unique health profiles. We focus on the case of immigrants from the Dominican Republic residing in the mainland US or Puerto Rico to provide empirical clarity on this debate. Using 5-year samples of the 2010-2014 and 2015-2019 waves of the American Community Survey and the Puerto Rico Community Survey, we show that upon arrival in the United States, Dominican immigrants are less likely to report a disability than the native-born populations in both locations. Among recent Dominican immigrants, those in the mainland United States report lower levels of disability than those in Puerto Rico, suggesting a more robust pattern of health selection among the former group. However, prolonged US residence is associated with a larger negative health change for immigrants in the mainland than in Puerto Rico. These results highlight the importance of initial health selection and the influence of the destination environment on immigrants' health. This article is part of a Special Collection on Methods in Social Epidemiology.

We estimated sex-specific population effects of hypothetical interventions to limit sugar sweetened-beverages (SSBs) and 100% fruit juice throughout childhood on central adiposity, insulin resistance, and glycemic outcomes in adolescence in Project Viva prebirth cohort. Among 481 females and 491 males, mothers reported beverage intake from 3 to 10 years from a food frequency questionnaire. The primary outcome was the homeostatic model assessment for insulin resistance (HOMA-IR), and secondary outcomes were waist circumference, truncal fat mass, fasting glucose, and glycated hemoglobin in late adolescence. We applied inverse probability weighting of longitudinal marginal structural models to account for baseline and time-varying confounding, and censoring. We estimated that limiting SSBs to 1 serving weekly across childhood would reduce HOMA-IR by 0.28 units (95% confidence interval [CI], -0.61 to 0.02), waist circumference by 1.91 cm (95% CI, -3.79 to -0.05), truncal fat mass by 0.64 kg (95% CI, -1.33 to 0.05), and fasting glucose by 1.02 mg/dL (95% CI, -2.40 to 0.35) in males compared to no intervention. In females, effect estimates were near 0 and less precise than males. Effect estimates for 100% fruit juice were small, with imprecise CI in both sexes. Overall, limiting SSBs in childhood may have small effects on insulin resistance, central adiposity, and glycemia in males in this population of low consumers. Trial registration: NCT02820402; https://clinicaltrials.gov/study/NCT02820402.

In social science and epidemiological research, individual risk factors for mortality are often examined in isolation, while approaches that consider multiple risk factors simultaneously remain less common. Using the Health and Retirement Study in the United States, the Survey of Health, Ageing and Retirement in Europe, and the English Longitudinal Study of Ageing in the UK, we explore the predictability of death with machine learning and explainable AI algorithms, which integrate explanation and prediction simultaneously. Specifically, we extract information from all datasets in 7 health-related domains, including demographic, socioeconomic, psychology, social connections, childhood adversity, adulthood adversity, and health behaviors. Our self-devised algorithm reveals consistent domain-level patterns across datasets, with demography and socioeconomic factors being the most significant. However, at the individual risk-factor level, notable differences emerge, emphasizing the context-specific nature of certain predictors. This article is part of a Special Collection on Cross-National Gerontology.

Racial misclassification on death certificates leads to inaccurate mortality data for American Indian and Alaska Native (AI/AN) populations. We describe methods for correcting for racial misclassification among non-Hispanic AI/AN (NH-AI/AN) populations using data from the year 2020. We linked National Death Index records with the Indian Health Service (IHS) patient registration database to identify AI/AN decedents. Matches were then linked to the National Vital Statistics System mortality data to identify AI/AN individuals who had been misclassified as another race on their death certificates. Analyses were limited to NH-AI/AN and purchased/referred care delivery areas or urban areas. We compared death rates and counts pre- and postlinkage and calculated sensitivity and classification ratios by region, sex, age, cause of death, and urban area. Racial misclassification on death certificates among NH-AI/AN varied by geographic region. Some of the highest racial misclassification occurred in the Southern Plains and Pacific Coast. Death rates for NH-AI/AN people and differences between NH-AI/AN and non-Hispanic White people were larger using the linked data. Improving AI/AN mortality data using linkages between vital statistics data and IHS strengthens data quality and can help address health disparities through public health planning efforts.

Particulate air pollution is associated with autism spectrum disorder (ASD), with disadvantaged neighborhoods potentially increasing vulnerability due to stress or other social determinants of health. Understanding the impact of air pollution interventions on ASD incidence across neighborhood disadvantage levels can guide policies to protect vulnerable populations. We examined 2 sets of hypothetical particulate matter (PM)2.5 interventions: percentage reduction and regulatory standards as thresholds, to assess their potential effects on ASD cumulative incidence. Using G-computation under a counterfactual framework, we estimated changes in the cumulative incidence of ASD by age 5 under hypothetical interventions compared to observed exposures. Our study involved a birth cohort of 318 298 children born between 2001-2014 in Southern California, with 4548 diagnosed with ASD by age 5. Pregnancy average PM2.5 and neighborhood disadvantage were assigned to residential addresses. Adjusted Cox regression models were applied to estimate ASD cumulative incidence. Reducing pregnancy average PM2.5 by 30% or below 9 μg/m3 would have prevented 10.6 (95% CI, 3.6-19.2) and 12.5 (2.7-23.6) ASD cases per 10 000 children, respectively. The decreases in ASD cumulative incidence under hypothetical interventions were similar across neighborhood disadvantage levels. These findings suggest that reducing ambient PM2.5 levels to meet or surpass current standards could help prevent ASD.

Death anniversaries may trigger stress responses that negatively affect health in bereaved individuals. Little is known about such reactions after adult sibling loss. This study examined whether mortality risk increases around the anniversary of a sibling's death. Using Swedish national register data (1990-2016), we conducted a time-stratified case-crossover study including 12 789 adults who experienced sibling loss and later died. Conditional logistic regression estimated associations between mortality and death anniversaries (including pre-anniversary and post-anniversary periods), adjusting for time-invariant confounders. Analyses were stratified by the bereaved's sex and age, the sibling's sex, sibling order, and whether ≥1 parent was alive at the bereaved's death. Among women, mortality risk was lower on the anniversary date (OR, 0.44; 95% CI, 0.21-0.93), and in the period from 1 day before and up to the anniversary date for women who lost a younger or same-age sibling (OR, 0.45; 95% CI, 0.20-1.00). In contrast, men bereaved before age 50 years had a heightened risk in the period ranging from 12 days before and up to the anniversary (OR, 1.40; 95 % CI, 1.05-1.86). Overall, sibling-death anniversaries were not associated with elevated mortality, though observed sex- and age-specific patterns merits further investigation.

We examined mortality rates of adolescents and young adults before and after exiting foster care. We used administrative records to identify individuals born in California 1985 to 2005 who had a foster care episode on or after their 16th birthday. We linked these records to vital death records through 2022 to identify deaths occurring ages 16-23 years. We defined three care statuses: pretransition, transition, and posttransition. Pretransition was age 16 years to the last day of care. Transition was the first 120 days after exiting care. Posttransition was all days after transition. We calculated gender-standardized mortality rates (SMRs) and used a Cox proportional hazards model adjusted for demographics to estimate hazard ratios of total mortality. In total, 1743 deaths occurred among 144 128 individuals. Standardized mortality rates for pretransition, transition, and posttransition per 100 000 person-years were 116, 259, and 209, respectively. Time-varying hazards models detected that these high and disparate rates were driven by higher risk during transition and posttransition for those leaving care before age 20 years. Moreover, the transition period featured particularly heightened risk for those leaving care before age 18 years. Results suggest targeted support during the transition period could help safeguard this population from harm.

Exposure-proximal antibody levels, or scalar correlates of protection (COPs), are increasingly used to assess infection risk following vaccination or prior infection. A version of the test-negative design (TND), adapted from vaccine effectiveness studies, has been proposed to estimate this relationship, but its validity for continuous immune measures under realistic epidemic conditions remains unclear. We used individual-based transmission models incorporating waning and boosting immunity and simulated two scenarios: one with homogenous baseline risk and another with a high-risk group. Infection risk was modeled as a function of COP, both linearly and nonlinearly. TND samples were drawn from single or multiple days and analyzed using logistic regression and generalized additive models (GAMs). Model validity, defined as the ability to recover the true COP-infection incidence rate ratio relationship, was evaluated using mean absolute error. Transformed logistic regression recovered the true relationship when the correct functional form was known, including in the presence of confounding. When the parametric model was misspecified, GAMs outperformed logistic regression, particularly with large sample sizes and broad COP coverage. Because the true functional form is often unknown, flexible semiparametric approaches may be preferred in well-powered TND studies with antibody measurements.

Although retrospective chart-review studies are typically performed using waiver of written informed consent, many institutional review boards (IRB) do not approve such waiver for chart-review studies using prospective enrollment, which could introduce selection bias in participant characteristics and outcomes, thereby impairing representativeness and validity. We aim to determine this bias in a chart-review prostate cancer (PCa) study using prospective enrollment. Using an IRB-approved chart-review protocol with waiver of written informed consent for prospective enrollment, we identified 2,202 patients scheduled for initial prostate biopsy from 2007-2021 at Durham Veterans Affairs Healthcare System. These patients were simultaneously approached for enrollment into a separate minimal-risk prospective observational study protocol requiring blood collection and written consent. 1,238 subjects provided written consent to the blood collection protocol; 964 did not. Patients who provided written consent differed in several key characteristics, including younger age, but had a similar racial distribution. Importantly, participants providing written consent had a significantly lower risk of PCa (multivariable OR=0.41,95%CI=0.31-0.54,p<0.001). As such, patients who provided written consent had younger age, similar race and lower PCa risk and therefore might not accurately represent the full eligible population. To minimize selection bias, waiver of written consent should be allowed for chart-review studies using prospective enrollment.

Dramatic changes in the US abortion policy landscape have led to growing interest in studying the health and social impacts of abortion bans. Many studies of population-level impacts necessarily rely on panel designs using aggregate state-level data to strengthen causal inference, yet such analyses risk pitfalls if they apply generic evaluation frameworks that overlook the complexity of the US abortion context and relevant outcomes. This commentary provides practical guidance for researchers engaged in panel studies of abortion policy, as well as for peer reviewers who may be less familiar with the methodological and substantive considerations in this area. Drawing from recent work, we highlight abortion-specific challenges that require attention, including time-varying confounding and violation of parallel trends, COVID-era disruptions, data suppression, spillover effects, and subgroup heterogeneity. We further recommend assessing sensitivity to including Texas, given its earlier implementation of abortion restrictions and potential outsized influence on results. Ultimately, we emphasize that rigorous evaluation of abortion policies requires thoughtful study design, context-specific considerations, and collaboration between methodologists and subject-matter experts.