American journal of epidemiology最新文献

CP*Trends is a widely used SEER website used to explore temporal effects of period and cohort on cancer incidence and mortality. It provides a graphical display of smoothed rates, and a C-P Score that helps to assess the magnitude of the effect of cohort and period. This update provides results for African Americans and Whites. The C-P Score has an intrinsic bias favoring cohort because there are many more cohorts than periods. An adjusted C-P Score removes some of this advantage. Bootstrap confidence intervals are given, which allow one to see the effects of different sample sizes on the model results. Finally, users may control window size used in the smoothing algorithm, which helps to avoid over smoothing or masking of trends. The method is illustrated using data on cervical cancer incidence trends for African Americans and Whites, 1975-2018. Rates are higher for African Americans, and both races have contributions for cohort. However, the period effect is only strongly evident in Whites. Visual inspection of White trends suggests possible differences for those older and younger than age 50. These methods are applied in an interactive website displaying incidence and mortality trends for over 20 cancer sites in the US.

In this study, we assessed the overall impact of the Covid-19 pandemic in the United States between 2020 and 2023 through estimates of excess all-cause mortality. Monthly mortality rates over a 19-year period, stratified by age, sex and state of residence were used to forecast expected mortality for the pandemic years. A combination of models - two timeseries, a spatial random effects and a generalized additive -- was used to better capture uncertainty. Results indicate that US national excess mortality decreased in 2023 to 157 thousand (95% prediction interval: 35K-282K) from 502K (436K-567K), 574K(484K-666K) and 377K (264K-484K) during the years 2020-2022, respectively. Unlike in previous years, deaths with Covid-19 as the underlying-cause-of-death possibly accounted for all excess deaths during 2023. While for the older age groups (75+ years) the year 2020, before vaccines were available, had the highest excess mortality rate, the two younger age groups had the highest excess mortality in 2021. In each age group, women were estimated to have consistently lower excess mortality than men. West Virginia had the highest age-standardized excess mortality among all states in 2021 and 2022. Our findings demonstrate the value of a multi-model approach in capturing heterogeneity in excess mortality.

Background: Gaps in life expectancy between Americans with and without a college degree have widened markedly over the past three decades. One explanation points to increasing educational attainment changing the type of people with and without a degree. If pre-existing health in the two education groups changes as the fraction with a degree changes, health selection might explain the widening mortality gap.

Methods: We examine this explanation using (a) education and mortality in each birth cohort of men and women from 1940 to 1988, and (b) the natural experiment caused by the Vietnam War, which increased the fractions of men with a degree in affected birth cohorts. For each cohort, we examine the relationship between the mortality gap and the fraction with a degree.

Results: We find no relationship between the fraction of a birth cohort with a degree and the corresponding mortality gap. For men, the large increase in college going spurred by Vietnam has no perceptible counterpart in the mortality gap.

Conclusion: The evidence from the natural experiment induced by the Vietnam War does not support a health-selection explanation for the widening mortality gap.

Antibiotic resistance is an urgent public health threat. Actions to reduce this threat include requiring prescriptions for antibiotic use, antibiotic stewardship programs, educational programs targeting patients and healthcare providers, and limiting antibiotic use in agriculture, aquaculture, and animal husbandry. Wastewater surveillance might complement clinical surveillance by tracking time/space variation essential for detecting outbreaks and evaluating efficacy of evidence-based interventions; identifying high-risk populations for targeted monitoring; providing early warning of the emergence and spread of antibiotic resistant bacteria and identifying novel antibiotic resistant threats. Wastewater surveillance was an effective early warning system for SARS-CoV-2 spread and detection of the emergence of new viral strains. In this data-driven commentary we explore whether monitoring wastewater for antibiotic resistant genes and/or bacteria resistant to antibiotics might provide useful information for public health action. Using carbapenem resistance as an example, we highlight technical challenges associated with using wastewater to quantify temporal/spatial trends in antibiotic resistant bacteria (ARBs) and antibiotic resistant genes (ARGs) and compare with clinical information. While ARGs and ARBs are detectable in wastewater enabling early detection of novel ARGs, quantitation of ARBs and ARGs with current methods is too variable to reliably track space/time variation.

While inverse probability of treatment weighting (IPTW) is a commonly used approach for treatment comparisons in observational data, the resulting estimates may be subject to bias and excessively large variance under lack of overlap. By smoothly down-weighting units with extreme propensity scores, i.e., those that are close (or equal) to zero or one, overlap weighting (OW) can help mitigate the bias and variance issues associated with IPTW. Although theoretical and simulation results have supported the use of OW with continuous and binary outcomes, its performance with survival outcomes remains to be further investigated, especially when the target estimand is defined based on the restricted mean survival time (RMST). We combine propensity score weighting and inverse probability of censoring weighting to estimate the restricted mean counterfactual survival times, and provide computationally-efficient variance estimators when the propensity scores are estimated by logistic regression and the censoring process is estimated by Cox regression. We conduct simulations to compare the performance of weighting methods in terms of bias, variance, and 95% interval coverage, under various degrees of overlap. Under moderate and weak overlap, we demonstrate the advantage of OW over IPTW, trimming and truncation, with respect to bias, variance, and coverage when estimating RMST.

Since 2010, the U.S. has experienced adverse trends in cardiovascular disease (CVD) mortality, which dramatically slowed long-standing life expectancy improvements. The extent to which the national trend in CVD mortality masks heterogeneity in trends across states and counties is poorly understood. We provide a detailed accounting of post-2010 trends in CVD mortality by U.S. state and county to understand how features of place relate to trends. We compare trends during 2010-2019 to that of 2000-2009. We observe flattening declines in CVD mortality in nearly every state at both midlife (ages 40-64) and old age (ages 65-84) across the two decades. Many states exhibited increases in midlife CVD mortality in 2010-2019. Old age CVD mortality was still declining in most states post-2010, although much slower compared to the previous decade. States in the Southeast recorded some of the fastest post-2010 declines in CVD mortality at old age. County-level median household income was associated with level of CVD mortality, but all income deciles, even the wealthiest counties, experienced stagnating CVD mortality declines. Findings highlight the ubiquitous nature of CVD stagnation, pointing to the need to identify risk factor affecting trends across regions and socioeconomic strata across the United States.

Longitudinal cohort studies, which follow a group of individuals over time, provide the opportunity to examine causal effects of complex exposures on long-term health outcomes. Utilizing data from multiple cohorts has the potential to add further benefit by improving precision of estimates through data pooling and by allowing examination of effect heterogeneity through replication of analyses across cohorts. However, the interpretation of findings can be complicated by biases that may be compounded when pooling data, or, contribute to discrepant findings when analyses are replicated. The "target trial" is a powerful tool for guiding causal inference in single-cohort studies. Here we extend this conceptual framework to address the specific challenges that can arise in the multi-cohort setting. By representing a clear definition of the target estimand, the target trial provides a central point of reference against which biases arising in each cohort and from data pooling can be systematically assessed. Consequently, analyses can be designed to reduce these biases and the resulting findings appropriately interpreted in light of potential remaining biases. We use a case study to demonstrate the framework and its potential to strengthen causal inference in multi-cohort studies through improved analysis design and clarity in the interpretation of findings. Special Collection: N/A.

Most prior studies of cardiovascular (CVD) events have focused on incident events. We analyzed differences by race/ethnicity in incident and recurrent CVD events in the Multi-Ethnic Study of Atherosclerosis from baseline in 2000-2002 through 2019 using joint and multivariable adjusted Cox proportional hazards modeling. Among 6,814 men and women aged 45-85 years without known CVD at enrollment, during median follow up of 17.7 years, 1206 incident and 695 recurrent CVD events were observed; 891 individuals with a non-fatal incident event were at risk for recurrent events. Rates of combined incident and recurrent CVD events among Black, White, Chinese, and Hispanic participants were 16.8, 18.6, 13.3, and 19.3 per 1000 person-years, respectively. First recurrent CVD event rates in Black, White, Chinese, and Hispanic participants were 87.7, 68.7, 78.1, and 80.7 per 1000 person-years, respectively. Revascularization rates were lower in Black versus White participants (3.8 vs 6.4 per 1000 person-years, p<0.0001). Adjusted hazard for CVD mortality was higher for Black vs. White participants (hazard ratio 1.85; 95% CI: 1.03, 3.29). In this multi-ethnic cohort, Black participants had a lower or similar rate of incident and recurrent CVD events, lower rate of revascularization, and higher rate of fatal CVD compared to White participants.