American journal of epidemiology最新文献

Knowledge of patterns in COVID-19 deaths by area-level income over time and the mediating role of vaccination in inequality patterns remains limited. We used data from a population-based retrospective cohort of 11 248 572 adults in Ontario, Canada. Cause-specific hazard models were used examine the relationship between income (2016 Census at the dissemination area level) and COVID-19 deaths between March 1, 2020 and January 30, 2022, stratified by wave. We used regression-based causal mediation analyses to examine the mediating role of vaccination in the relationship between income and COVID-19 deaths during waves 4 and 5. After accounting for demographics, baseline health, and other social determinants of health, inequalities in COVID-19 deaths by income persisted over time (HR [95% CI] comparing lowest income vs highest income quintiles were 1.37 [0.98-1.92] for wave 1, 1.21 [0.99-1.48] for wave 2, 1.55 [1.22-1.96] for wave 3, and 1.57 [1.15-2.15] for waves 4 and 5). By the start of wave 4, 7 534 259 (67.7%) of those alive were vaccinated, with lower odds of vaccination in the lowest income vs highest income quintiles (0.71 [0.70-0.71]). This inequality in vaccination accounted for 56.9% [22.5%-91.3%] of inequalities in COVID-19 deaths between individuals in the lowest income vs highest income quintiles. Efforts are needed to address vaccination gaps and residual heightened risks associated with lower income to improve health equity in COVID-19 outcomes.

Understanding the absolute risk of developing a second primary cancer is important to guide patient surveillance and education. We aimed to examine the cumulative incidence and factors associated with development of a second primary cancer (melanoma versus other) after diagnosis of a first primary melanoma (invasive or in situ). We analyzed a population-based study cohort of 154 695 people diagnosed with a first primary melanoma in New South Wales, Australia, between 1982 and 2019. The cohort was followed for future cancer incidence and vital status for a median of 7.0 years. We used Fine-Gray models to account for death as a competing risk. After a first primary melanoma, 23.7% developed a second primary cancer, including 12.7% who developed a second primary invasive or in situ melanoma (mean 5-year risk: 7.6%). The next most common second primary cancer types were prostate, breast and colon cancers, with mean 5-year risks after the initial melanoma diagnosis of 2.8% (male-specific incidence), 0.7% (2.8% female-specific incidence), and 0.6%, respectively. The most common second primary cancer among people with a first primary melanoma was another melanoma (invasive or in situ), requiring long-term careful surveillance of their skin even if the probability of recurrence from the first melanoma is low.

Our aim is to investigate the association between visit-to-visit variability of nine risk factors and incident cardiovascular disease (CVD) in a large multi-ethnic population cohort study. We used the Multi-Ethnic Study of Atherosclerosis cohort. We included individuals with no previous history of CVD, with at least three repeated measurements on each risk factor including total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), non-HDL-C, triglyceride, Chol/HDL-C ratio, diastolic blood pressure, systolic blood pressure (SBP), and body mass index (BMI). Visit-to-visit variability was estimated via the variability independent of the mean. A Cox proportional hazards model was used to estimate the association between visit-to-visit variability and the hazard of developing CVD. There was a statistically significant association between visit-to-visit variability in SBP, BMI, HDL-C, and the rate of incident CVD. This rate was higher in individuals with high visit-to-visit variability for SBP (HR, 1.28; 95% CI, 1.01-1.63; P = .04), BMI (HR, 1.58; 95% CI, 1.25-2.00; P < .001), and HDL-C (HR, 1.3; 95% CI, 1.03-1.65; P = .025), compared to those with low visit-to-visit variability. Our findings suggest that visit-to-visit variability in some CVD risk factors could be independently associated with incident CVD and may be useful to clinicians in risk stratification.

Time-to-event outcomes are widely used in clinical and epidemiological research. For instance, studies of medical product safety often involve comparative analyses of rare time-to-event outcomes. The effects of misclassified outcomes and error in survival times for time-to-event data have not been widely investigated. In this Monte Carlo simulation study, we compared the relative bias of absolute and relative measures of effect under varying degrees of outcome misclassification, outcome incidences, direction of error in survival times, and the time point of inference. Relative measures of effect were susceptible to considerable downward bias, which was larger when the outcome incidence and specificity were lower, error in survival times led to earlier times, time point of inference was earlier, and the estimation excluded samples for which an estimate could not be obtained. For absolute measures of effect, the pattern of bias was much simpler, greater downward bias was primarily a function of the degree of sensitivity. The results suggest when the outcome incidence is rare, specificity and sensitivity are high, absolute measures of effect may be preferable to relative measures of effect.

Both ambient air pollution exposure and biological aging are associated with incident liver diseases, but previous studies mainly focused on single-factor associations. This study aimed to assess the combined effects of air pollutant exposure and biological aging on liver diseases incidence and investigate the potential mediating role of biological aging. We analyzed 418 576 UK Biobank participants. Annual mean concentrations of PM2.5, PM10, PM2.5-10, NO2, and NO in 2010 were used to generate a weighted air pollution score. Biological ages were assessed using the Klemera-Doubal method biological age (KDM-BA) and phenotypic age (PhenoAge). Cox regression models and quantile g-computation were used to evaluate associations and joint effects. Mediation analyses explored the role of biological aging. Over a median follow-up of 13.57 years, 7991 (1.91%) participants developed liver diseases. Exposure to all pollutants and biological aging were associated with higher liver diseases risk. And NO2 contributed 42.31% to the mixture effect. Participants with higher levels of air pollutant exposure and biologically older status had a higher risk. Furthermore, the mediated proportion of accelerated biological aging was 1.9% to 7.7% for air pollution-associated liver diseases. Ambient air pollution exposure may increase liver diseases risk, with biological aging potentially involved in the mechanisms.

Purpose: To describe a novel data ecosystem and quantify improvements in documented COVID-19 vaccine uptake using closed claims data and state vaccine registries.

Methods: De-identified data from administrative claims were linked via tokenization to state vaccine registry data from two states with mandatory vaccine reporting. COVID-19 vaccine uptake in claims alone versus supplemented with registry data was measured separately for original wildtype, BA.4/5-adapted bivalent, and XBB.1.5-adapted formulations.

Results: There were over 9 million individuals included in each formulation distribution study period. Using claims data alone versus with additional state registry data, 20% versus 57% of individuals had documented receipt of a primary wildtype series (≥2 doses of two-dose or ≥ 1 of single-dose series), 7% versus 10% BA.4/5-adapted bivalent, and 6% versus 8% XBB.1.5-adapted COVID-19 vaccine. Most claims-based vaccination events were captured with drug, rather than procedure, codes. Individuals with Medicaid-based insurance were less likely to be captured as vaccinated in claims data.

Conclusions: The addition of state vaccine registries increased COVID-19 vaccine capture by 182% for primary wildtype series, 41% BA.4/5-adapted bivalent, and 38% XBB.1.5-adapted formulations. Tokenization of claims data to vaccine registries presents an opportunity for more accurate estimates of vaccine uptake and effectiveness than claims data alone.

Adolescents' menstrual cycle characteristics can be "vital signs" of health and impact quality of life. While endocrine-disrupting pesticides are commonly used in agriculture, limited research exists on how exposure might affect the adolescent menstrual cycle. We examined the association between prenatal residential proximity to 11 agricultural pesticides and menstrual cycle characteristics at 16 years of age among 273 Latina adolescents from the Center for the Health Assessment of Mothers and Children of Salinas study. We estimated prenatal pesticide exposure by linking maternal residential addresses to California's pesticide use reporting database. Menstrual characteristics, including cycle length irregularities, painful menstruation, and heavy bleeding, were evaluated through a questionnaire. We used generalized linear models to evaluate exposure-outcome associations 1 pesticide at a time. To adjust for co-exposure to pesticides, we used Bayesian hierarchical models to include all pesticide exposures in 1 model. In our single-exposure model, we observed increased odds of heavy bleeding (OR, 1.29; 95% CI, 1.01-1.64) for each doubling in prenatal methomyl exposure. This association persisted in our joint exposure model (OR, 1.09; CrI, 0.99-1.19). Our results suggest prenatal exposure to endocrine-disrupting pesticides may impact certain adolescent menstrual cycle characteristics.

In a recent paper, Ghoroubi et al. (Am J Epidemiol 2025 Jan 8;194(1):302-310) used the indirect attributable fraction (AF) method to provide estimates of fractions of all-cause mortality attributable to work-related factors. This commentary discusses the limitations and potential of this paper and provides insights and guidance to make optimal use of indirect AF estimation in occupational epidemiology. The crucial steps are the choice of the datasets and input data related to the prevalence of exposure and relative risk (RR), requiring comparability of time period, population characteristics, and the definition and measurement of exposure. Published systematic literature reviews with meta-analyses are essential or, if not available, conducting meta-analyses to provide estimates of RR. Finally, it is important to verify the assumptions for the chosen AF formula including evidence of causality, consideration of confounding and (in)dependence between exposures when several exposures are studied at the same time. We conclude by suggesting that the paper by Ghoroubi et al. may have provided a proof of concept for 1 work-related factor only, but considerable additional research will be required to represent work-related factors overall.

Evidence demonstrating the beneficial effects of improved air quality on lipid health is scarce. This study addresses this gap by examining whether reducing PM2.5 exposure can decrease the risk of dyslipidemia. We conducted a longitudinal quasi-experimental study using the Taiwan MJ and Hong Kong MJ cohorts from 2000 to 2018. A total of 8808 adults with consistently high PM2.5 exposure (≥ 25 μg/m3) were paired with 4612 adults whose PM2.5 exposure decreased from high to low levels (< 25 μg/m3) using propensity score matching. Cox regression models with time-dependent covariates were used to analyze the associations between PM2.5 reduction and the risk of dyslipidemia, as well as individual lipid abnormalities. We found that participants with reducing PM2.5 exposure had a significantly lower risk of dyslipidemia compared to their counterparts (hazard ratio [HR], 0.75; 95% confidence interval [CI], 0.68-0.84). Nonlinear concentration-response relationships were observed. Similar associations were found for elevated TC (HR, 0.61; 95% CI, 0.51-0.74) and LDL-C (HR, 0.69; 95% CI, 0.57-0.84), and decreased HDL-C (HR, 0.59; 95% CI, 0.47-0.75). Reducing PM2.5 exposure significantly lowers the risk of dyslipidemia and improves lipid profiles, providing direct evidence of the health benefits associated with air quality improvement.

Mendelian randomization can reveal the etiological association between body mass index (BMI) and lung cancer. However, the associations between the trajectories of BMI and the risk of lung cancer remain inconclusive. We employed growth mixture modeling to identify trajectories of pre-diagnostic BMI in 163 545 individuals (117 445 women from the Nurses' Health Study and 46 100 men from the Health Professionals Follow-Up Study). We assessed the associations between BMI trajectories and lung cancer risk, as well as the effects within subgroups. Four trajectories were identified: normal-moderate increasing (class 1), overweight-marked increasing (class 2), overweight-obese turning (class 3), and obese-persistent (class 4). We observed a decreased risk of lung cancer in class 2 (adjusted hazard ratio [aHR], 0.53; 95% CI, 0.38-0.75; P = 2.32 ×10-4) and class 3 (aHR, 0.67; 95% CI, 0.48-0.94; P = .022). In stratification analysis, we observed that the effects of class 4 on lung cancer risk vary among histological subtypes. Additionally, within the class 1 population, the top quintile of BMI also demonstrated different effects among histological subtypes. Increasing lifetime BMI was associated with a decreased risk of lung cancer, with this association varying by histological subtypes, indicating histology-specific mechanisms in lung carcinogenesis.