American journal of epidemiology最新文献

In environmental epidemiology, we use systematic reviews to evaluate the evidence of exposure-outcome relationships with an eye towards regulation. Conflicting results across studies thwart consensus on toxicity. In humans, only observational data is available from studies of environmental exposures, complicating the construction of dose-response relationships across the full range of exposure levels. Individual studies often lack the complete range of exposure levels because environmental exposure levels are tied to study settings. Pooling data across populations seems a natural solution, but strong population-dependent confounding may bias dose-response curves. Using the oft-debated association of polychlorinated bi-phenyls and birthweight as a case study, we describe simulations used to investigate the relative impacts of exposure range-dependent power limitations and confounding on our ability to correctly identify an assumed linear dose-response curve across a representative exposure range. While varying levels of confounding minimally biased estimates in our pooled and meta-analyses, we report very low confidence to ascertain a set underlying dose-response relationship in low-exposure cohorts with a narrow exposure distribution, but high ability in high-exposure cohorts with wide exposure distributions. Our simulations suggest that pooling and meta-analysis should be prioritized despite possible differences in confounding structures, particularly when exposure distributions in individual cohorts are limited. This article is part of a Special Collection on Environmental Epidemiology.

Type 1 diabetes (T1D) is known to have adverse long-term health and social outcomes, but the modifying factors are largely unknown. We investigate to what extent T1D outcomes are modified by area-, household-, and individual-level social and economic characteristics in Finland. National registers from 1987 to 2020 were used to identify all 3048 children with T1D diagnosed at ages 7-17 years and matched controls (n = 78 883). Using causal forests, we estimated the average association between T1D and adult health, social, and economic outcomes at ages 28-30 years, and the modifying roles of more than 30 covariates. Individuals with T1D were more likely to be deceased (2.3% vs 0.9% in the control group), to use antidepressants (17% vs 13%), and to be unpartnered (36% vs 32%), and had more months of unemployment (1.18 vs 1.02) and lower annual income (25 697 euros vs 27 453 euros), but not significantly lower educational attainment (10.8% vs 10.3% with only basic education). Type 1 diabetes had a heterogenous association with all outcomes except mortality and income, but no specific population subgroup was vulnerable across all outcomes. However, women with T1D had particularly high rates of antidepressant use, and individuals from low socioeconomic families were more likely to be unpartnered.

We examined the association between ambient air pollution exposure and risk of spontaneous abortion (SAB) using Georgia state-wide fetal death records from 2005-2014. Each SAB case was matched to four non-SAB pregnancies by maternal residential county and conception month. Daily concentrations of ten pollutants were estimated and linked to maternal residential census tracts. Cox regression was used to estimate hazard ratios (HR) across four prenatal exposure windows (first month, weekly, cumulative weekly average over the first trimester, cumulative weekly average over the second trimester). Our dataset contained 47,649 SABs with a median gestational age of nine weeks. Carbon monoxide (CO) showed the strongest association, with an HR of 1.12 (1.05, 1.20) per 0.43 ppm increase in average first month exposure, and 1.06 (1.02, 1.10) per 0.42 ppm increase in average weekly exposure. Nitrogen dioxide (NO2) also exhibited elevated HRs. Other pollutants like nitrate compounds (NO3), nitrogen oxides (NOx), and organic carbon (OC) showed positive associations, while ozone (O3), PM2.5, PM10, elemental carbon (EC), and ammonium ions (NH4) were null. Early pregnancy exposure to traffic-related pollutants may increase SAB risk, highlighting potential benefits of air pollution regulation.

This study assesses the prevalence and trends of depression in the European population aged 50 years and older between 2004 and 2022 in different geographical regions. We retrieved and pooled data from waves 1, 2, and 4-9 of the Survey of Health, Aging and Retirement in Europe (SHARE) conducted between 2004 and 2022. The 12-item EURO-D scale was used as a continuous marker of depression risk. Twenty-eight countries were classified into five geographical regions (Eastern, Western, Southern and Northern Europe, and Israel) as defined by the United Nations. A total of 375 693 observations corresponding to 146 888 participants (weighted mean age 65.8 years [SE = 0.2], 54.2% women) were included in the study. In 2022, the overall prevalence of self-reported depression in Europe was 28.3% (95% CI, 27.0-29.7). From 2004 to 2022, the prevalence of self-reported depression in Europe decreased significantly by 5.9% (95% CI, -8.1 to -3.8). In particular, in Southern Europe, the prevalence of depression decreased significantly by 8.8% (95% CI, -12.3 to -5.2). No significant differences were observed in the other geographic regions. These findings highlight the need to develop depression screening and prevention strategies focused on countries with higher prevalence and identified associated factors. This article is part of a Special Collection on Cross-National Gerontology.

Reported increases in US Maternal Mortality rates and entrenched Black/White rate inequity are alarming. We analyze rigorously collected US Pregnancy Mortality Surveillance System data to describe Pregnancy-related Mortality Ratios (PRMR) levels and trends, 2000-2019. Conceptually guided by the weathering framework, we hypothesize that the increasing maternal age distribution of births contributed to PRMR increases. Stratifying on maternal age, race, educational level, and cause of death, we find U.S. PRMR inequities persist, yet actual PRMR increases do not apply uniformly. White women with no more than a high school education averaged a 3.61% increase per year, with little growth observed among the more educated. Less-educated Black women exhibited a small proportional increase. PRMRs for Black women at higher educational levels declined by over 1% per year, still remaining in excess of White rates. Simulations suggest that the shift in the maternal age distribution contributed a small amount to White PRMR increases. It would have increased PRMRs overall for Black mothers by just over 1% per year, suggesting that if the maternal age distribution had not shifted upward the overall Black/White PRMR inequity would have decreased.

Complete-case analysis (CCA) is often criticized on the belief that CCA is only valid if data are missing completely at random (MCAR). Influential papers therefore recommend abandoning CCA in favor of methods that make a weaker missing-at-random (MAR) assumption. We argue for a different view: that CCA with principled covariate adjustment usefully complements MAR-based methods, such as multiple imputation. When estimating treatment effects, appropriate covariate control can, for some causal structures, eliminate bias in CCA. This can be true even when data are missing-not-at-random (MNAR) and when MAR-based methods are biased. We describe principles for choosing adjustment covariates for CCA, and we characterize the causal structures for which covariate adjustment does, or does not, eliminate bias. Even when CCA is biased, principled covariate adjustment often reduces the bias of CCA, and this method will sometimes be less biased than multiple imputation and other MAR-based methods. Therefore, when multiple imputation is used under the MAR assumption, adjusted CCA remains an important sensitivity analysis. When conducted with the same attention to covariate control that epidemiologists already afford to confounding, adjusted CCA belongs in the suite of reasonable methods for missing data. There is thus good justification for resurrecting CCA as a principled method.

We examined the association between residential segregation and late-stage colorectal cancer (CRC) in the United States. The restricted-use United States Cancer Statistics database was used to identify all CRC cases diagnosed during 2009-2017. Late-stage CRC was determined according to the presence of distant involvement of the tumor at diagnosis. Residential segregation was measured at the county level by the Index of Concentration at the Extremes based on income, race/ethnicity and its combination, using the 2013-2017 American Community Survey data. Multilevel logistic regression models accounting for clustering at counties were fit. Analyses were stratified by race/ethnicity, sex and age. Overall, patients residing in counties with a high concentration of least advantaged residents had increased odds of late-stage CRC compared to their counterparts residing in counties with a high concentration of most advantaged people. These findings were observed on all measures of residential segregation, with clear gradients for economic and racialized economic segregation. In stratified analyses, stronger associations were observed among racial/ethnic minoritized people and younger age groups; results did not differ by sex. These findings underscore the role of institutionalized racism as a contributor to health inequities, such that laws and policies driving residential segregation may impact timely preventive care.