American journal of epidemiology最新文献

Between the early 2000s and 2020s, the suicide prevalence among non-Hispanic (NH) Black youth increased more than three-fold, making suicide the second leading cause of death in this population. Structural factors such as poverty and place-based racial inequities may contribute to the high prevalence of suicide among this population. We examine whether changes in racialized economic segregation correspond with changes in Black youth suicide prevalence, over time. As our exposure, we utilize longitudinal measures of the Index of the Concentration of the Extremes (ICE) race/income. As the outcome, we use counts of suicides among NH Black youth aged 5-19, across three epochs (2000-2004, 2006-2010, 2014-2018) in 703 counties in the US. We use county-level fixed effects Poisson models that include population offsets and adjust for time trends, percent poverty, unmarried households, educational attainment, and public assistance. A standard deviation increase in ICE race/income, or less concentrated Black poverty, coincides with a 7% decline in NH Black youth suicide over time (Incidence Rate Ratio [IRR]: 0.93, 95% CI: 0.87 - 0.99). Despite overall increases in Black youth suicide in the US, reductions in concentrated Black poverty may attenuate this trend.

We recommend three well-established yet underused statistical methods in social epidemiology: Multiple Informant Models (MIMs), Fractional Regression Model (FRM), and Restricted Mean Survival Time (RMST). MIMs improve how we identify critical windows of exposure over time. FRM addresses the inadequacies of ordinary least squares and logistic regression when dealing with fractional outcomes that are naturally proportions or rates, thereby accommodating data at the boundaries of the unit interval without requiring transformations. RMST offers a robust alternative to the hazard ratio in the presence of non-proportional hazards, providing an interpretable summary of treatment effects over time that is not dependent on the proportional hazards assumption. We illustrate the utility of each method using simulated case examples. These methodologies enrich the analytical toolbox of social epidemiologists, offering refined approaches to unraveling the complexities of social determinants of health inequities.

Background: GLOB-cAGE is a newly established unprecedented consortium that brings together cohorts of older persons with cardiovascular disease worldwide. GLOB-cAGE aims to harmonize non-identifiable data from longitudinal cohorts examining cardiovascular health and cardiovascular disease diagnosis and management in older individuals to perform meta-regression analyses using combined repositories of standardized subject-level data points.

Methods and design: Studies registered into GLOB-cAGE are population-based longitudinal cohort studies or clinical trials, either ongoing or completed, that involve assessing cardiovascular health as a central objective. Cross-sectional studies that significantly contribute to cardiovascular research in older individuals may also be included. The GLOB-cAGE will consist of individuals already diagnosed with cardiovascular disease and primary prevention of individuals at different risks of cardiovascular disease. The studies should have a minimum sample size of 100 participants, and the participants are either adults aged above 65 years or above 40 years with longitudinal follow-up over the next few decades. Enrollment in GLOB-cAGE may involve collaboration on non-identifiable or anonymized raw or processed data for joint analyses. Sites unable to provide raw or processed data due to institutional or other reasons may participate in alternative ways, including performing separate analyses in-house. At the time of writing, there are at least ten participating teams from nine countries and 27 studies enrolled in GLOB-cAGE.

Conclusions: The GLOB-cAGE consortium is an international effort to bring together cardiovascular disease research in older persons, focusing on providing greater representation from diverse countries battling population aging. It addresses the evidence gaps from the insufficient enrolment of older individuals in randomized controlled trials and permits investigators to conduct high-quality epidemiological studies.

Survey response times (RTs) have hitherto untapped potential to allow researchers to gain more detailed insights into the cognitive performance of participants in online panel studies. We examined if RTs recorded from a brief online survey could serve as a digital biomarker for processing speed. Data from 9,893 adults enrolled in the nationally representative Understanding America Study were used in the analyses. Hypotheses included that people's average survey RTs would have a large correlation with an established processing speed test, small to moderate correlations with other cognitive tests, and associations with functional impairment. We also hypothesized that survey RTs would have sensitivity to various participant characteristics comparable to the established processing speed test's sensitivity (e.g., similar standardized means by gender). Overall, results support the validity and reliability of people's average RTs to survey items as a digital biomarker for processing speed. The correlation between survey RTs (reverse scored) and the formal processing speed test was 0.61 (p<0.001), and small to moderate associations with most other cognitive and functional status measures were observed. Sensitivity of survey RTs to various participant characteristics was nearly identical to the formal processing speed test. Survey RTs may be useful as proxies for processing speed.

Utilization of maternal and child interventions is typically tracked in low- and middle-income countries (LMICs) using coverage estimates from population representative surveys. These estimates cannot be directly applied to assess resource gaps in intervention delivery for which data on the population eligible is required. Moreover, coverage improvements may not necessarily reflect an expansion in utilization because of a decline in the population eligible. We develop a method to estimate the populations eligible for interventions across the continuum of care. The method uses data from the World Population Prospects and the Demographic Health Survey, data sources which are available for most LMICs. Additionally, we develop a method to estimate the eligible population covered by each intervention. Using the illustration of India, we estimate populations eligible for, and covered by interventions during preconception, pregnancy, delivery, lactation, and childhood. We find that between 2015 and 2020, the eligible population declined for all beneficiary groups. Additionally, coverage expansion was not entirely driven by an increase in the population accessing an intervention, but rather also by a decline in the eligible population. Our illustration highlights the importance of including population estimates alongside coverage for interventions, particularly in LMIC contexts due to changing fertility dynamics.

Research question: Previous evidence suggests a positive association between temperature and homicide, but the association was less clear in Brazil where homicide is one of the leading causes of death. This study aimed to quantify the association between ambient daily temperature and homicides in Brazil with potential lag effects and to quantify the temperature attributed fractions of homicides in Brazil.

Methods: A space-time-stratified case-crossover design with distributed lag models was used to evaluate the temperature-homicide association from 1·1·2010 to 31·12·2019 in Brazil. The odds ratios (OR), attributable fractions and their confidence intervals (CI) were calculated.

Results: Overall every 5°C increase in daily mean temperature was associated with a 10·6% (OR=1·106, 95% CI: 1·085-1·127) increase in homicidal deaths at lag 0-8 days. The temperature-homicide association is stronger for females and elderly, homicides by fights, sharp objects or firearm, and in North region. During the study period, 1·8% (95% CI: 1·1%-2·7%) of homicides could be attributed to temperature above immediate-region-specific median temperature corresponding to 10,921 additional deaths (95% CI: 6,350-15,372).

Conclusion: Our nationwide study suggests that the homicides in Brazil may increase with temperature and recommends targeted preventions for certain risk groups to high temperature, considering future climate change circumstances.

The Systolic Blood Pressure Intervention Trial (SPRINT) estimated the effect of intensive SBP treatment (target <120 mmHg) compared to standard (<140 mmHg) on the risk of cardiovascular events in adults aged 50+ years. Clinical trial participants may differ from an intervention's target population. We generalized the SPRINT results to U.S. adults who would qualify for treatment under SPRINT eligibility criteria. We applied these eligibility criteria to participants of the National Health and Nutrition Examination Survey (NHANES) in 2011 - 2018 to describe the target population. We estimated Cox proportional hazards models and Kaplan-Meier risk curves, weighted with the inverse odds of sampling, to estimate hazards ratios (HR) and 5-year risk differences for the effect of intensive treatment on cardiovascular and adverse events in the target population. The HR for CVD events was 0.76 (0.53, 1.08) comparing intensive to standard treatment, which is consistent with the estimates from the original SPRINT trial. The 5-year risk difference for a cardiovascular event was -2.2% (-5.3%, 1.6%). The HR for serious adverse events was 0.97 (0.83, 1.13). Despite differences between the SPRINT and target populations, we estimated a similar benefit of intensive treatment and similar rates of SAEs, in the target population.

Identifying the determinants of pregnancy loss is a critical public health concern. However, pregnancy loss is often not noticed, and even when it is, it is inconsistently recorded. Thus, past studies have been limited to medically-identified losses or small, highly selected cohorts, which can lead to biased or non-generalizable results. We show mathematically and through simulations a novel approach that overcomes this measurement challenge to infer effects about pregnancy loss by utilizing more available data: the number of conceptions that led to live births-i.e., live-birth-identified conceptions (LBICs). We simulated ten years of conceptions, pregnancies, losses, and births under several confounding patterns, and two NO2-pregnancy loss relationships (no effect, mid-gestation effect). We fitted distributed lag models (DLMs) adjusted for season, year, and temperature, and assessed model performance through bias and coverage. Our simulations showed that our models, across all scenarios, identified the two NO2-pregnancy loss relationships with appropriate coverage (>90% of confidence intervals captured the true effect) and low bias (never exceeded ±2%). In an applied example using NO2-a traffic emissions tracer-and live birth data from a large tertiary-care hospital in Massachusetts, USA, we found that higher prenatal NO2 was associated with more pregnancy losses. Our proposed approach based on LBICs provides an alternative way to study causes of pregnancy loss.