American journal of epidemiology最新文献

In 2023, Mexico revised its Dietary Guidelines to promote healthy and sustainable diets, yet their impact on type 2 diabetes (T2D) prevention remains unexamined. We emulated a target trial within the Mexican Teachers Cohort to estimate the 9-year risk of T2D under an intervention adopting the Guidelines at baseline, compared to usual diet, using the parametric g-formula. Eligible participants were women aged ≥40 years without prior T2D, cardiovascular disease, or cancer between 2008-2010. Due to low baseline adherence to several components, we adapted thresholds so that ≥10% of the population met each recommendation. Incident T2D was identified through self-report or administrative data linkage. Among 11,307 eligible participants (mean age 46.0 years), 636 developed T2D over 9-years. The estimated 9-year risk of T2D was 8.44% (95% CI: 7.84, 9.12) under the usual diet and 7.11% (95% CI: 5.05, 9.82) under the intervention (risk ratio 0.84; 95% CI: 0.61, 1.15; risk difference -1.33 percentage points; 95% CI: -3.21, 1.29). These findings suggest a modest potential benefit if all participants had adhered to the adapted Guidelines at baseline compared with no intervention. The 95% confidence intervals indicate data are compatible with a small benefit, no effect, or a slight increase in risk.

The impact of oral nicotine product (ONP) use on e-cigarette use persistence among adolescents and young adults (AYA) remains unclear. We applied augmented inverse probability weighting (AIPW) with machine learning (ML) to reduce bias from model misspecification and improve precision of treatment effect estimates. We combined data from two Southern California cohorts of adolescents (n=269; M[SD]=16.3[0.6] years) and young adults (n=449; age M[SD]=23.4[0.4] years) who reported past 6-month e-cigarette use at baseline in 2022 and were re-assessed an average of 8 months later in 2023. We examined the effects of baseline ONP use on e-cigarette use persistence using AIPW with ML, with analyses stratified by age group. Among 718 AYAs (53.1% female; 58.4% Hispanic), 14.2% reported ONP use at baseline and 64.3% reported persistent e-cigarette use at follow-up. Adolescents who used ONPs had a lower risk of e-cigarette persistence compared with non-users (risk difference=-0.18, 95% CI=-0.31, -0.04; risk ratio=0.70, 95% CI=0.52, 0.95), whereas no association was observed among young adults. Findings suggest ONPs may serve as a partial substitute for e-cigarettes among adolescents, but not young adults. Regulatory policies should balance potential harm-reduction benefits with age-specific risks and address dual e-cigarette and ONP among young adults.

American Indian and Alaska Native (AI/AN) persons in the United States experience high rates of chlamydia and gonorrhea (CT/GC) infections, but the burden among persons receiving healthcare through the Indian Health Service (IHS) has not been recently described. Currently, there is no surveillance system to regularly monitor IHS-specific CT/GC burden and trends. We used two capture recapture (CRC) methods with two IHS medical records sources: diagnostic codes and laboratory test results linked by person within 30 days among persons ≥15 years during 2016 to 2021. CRC estimates were used with the IHS population to create prevalence rates and compared to the medical records counts. CRC estimated 88,680 (1,189 per 100,000) and 83,884 CT infections (1,125), and 36,713 (492) and 35,930 GC infections (482). Diagnostic codes were 60% and 63% of CT and 54% and 56% of GC CRC estimates and combined with laboratory test results were 88% and 94% of CT and 86% and 88% of GC CRC estimates. These findings underscore the value of IHS medical records for estimating CT/GC infections and measuring undercounting of single source CT/GC medical records. These sources can be leveraged to provide surveillance estimates for monitoring trends; strategic outreach; and supporting testing, treatment, and staffing needs.

Prenatal e-cigarette and cannabis use are increasing. Though concerning, the risks associated with these emerging exposures are unclear due to methodological limitations of available research. To address these gaps, the Mountain Mama & Baby Study prospectively enrolled a cohort of pregnant women in their first trimester during their initial telehealth visit with a nurse navigator at West Virginia University Medicine obstetric clinics. The study's goals were to: (1) demonstrate the feasibility of our recruitment methodology and the representativeness of the sample; (2) establish first and third trimester exposure rates and describe the epidemiology of prenatal e-cigarette and cannabis use; and (3) explore associations between prenatal e-cigarette and cannabis exposure and adverse maternal-infant outcomes. This report describes the rationale, study design, protocol, and the feasibility and generalizability of recruitment. We enrolled 417 of 920 eligible participants (45.3%; 95% confidence interval: 42.1% to 48.6%), exceeding our 20% benchmark. Enrolled participants and those non-enrolled were similar across most sociodemographic characteristics (e.g., age, race/ethnicity, marital status, rurality, area deprivation). The Mountain Mama & Baby Study will provide clinicians, pregnant women, and public health practitioners with critical information on the potential harms of prenatal e-cigarette and cannabis use, guiding the design of interventions and recommendations.

Directed acyclic graphs (DAGs) are now standard tools for selecting covariates and identifying estimands in causal inference. Yet in most applications, DAGs are treated as static and study-specific and then discarded rather than maintained as cumulative infrastructure. This Opinion piece argues that DAGs can serve a much broader role: as epistemic infrastructure that supports cumulative science. By treating DAGs as living, shared representations of causal systems-annotated with levels of evidence, revised over time, and tested empirically-we enable a mode of scientific practice that is transparent, collaborative, and intervention-oriented. Examples from spaceflight risk management and cerebral palsy research demonstrate how DAGs are already being used this way. I call on the field of epidemiology to adopt this approach more broadly: to share, refine, and re-use DAGs not just as tools of analysis, but as frameworks for designing better questions and building a more cumulative science.

During epidemics, emergency department (ED) syndromic surveillance of patient arrivals provides timely but non-virus-specific assessment of epidemic intensity. Surveillance of severe infection outcomes (intensive care admission or death) is less timely because outcomes can take weeks to occur. Time series models can be used to estimate the frequency of severe infection outcomes due to viruses. We developed and evaluated daily time series modelling applied to linked ED, infection and outcomes data from Australia to better predict population and health system burden during acute respiratory virus epidemics. In retrospective daily surveillance emulation, generalised additive models nowcasted (short-term forecast) the frequency of ED arrivals attributable to each of influenza and COVID-19 that will have a severe infection outcome within 28 days. Daily nowcasts spanned days -29 to -4 from each date for which surveillance was emulated. To validate the method, nowcasts were compared with subsequently observed severe infection outcome frequencies for December 2021 through February 2023. During this period, the mean daily day -4 nowcast error was 2.7 (34.2%), compared with 3.5 (43.8%) if outcomes known at day -1 were used. With increasing real-world data availability, this method could improve rapid, automated epidemic assessment for timely public health action.

When using real-world data to construct an external comparator arm for a single-arm trial, there may be differences in how and when patients are assessed for disease between trial and real-world settings. Such differences can generate outcome measurement error when comparing time to event endpoints and lead to biased findings. Recent methods have been developed to mitigate measurement error bias in real-world endpoints; however, they rely on the existence of a validation sample, ie, data on a set of patients where both the "true" trial-like and "mis-measured" real-world measures are collected. We demonstrate how novel statistical methods can be leveraged as quantitative bias analyses (QBA) to contextualize real-world evidence findings when outcome measurement error is of concern, but validation samples are infeasible to collect. QBA allows researchers to set plausible ranges for the amount of error when not directly measurable. We highlight how to conduct QBA with two recent methods, Cumulative Incidence Curve Correction and Survival Regression Calibration, and illustrate how to generate plausible parameter values through simulation. We provide an illustrative QBA example in a cohort of real-world patients with Newly Diagnosed Multiple Myeloma and provide practical guidance to apply QBA for outcome measurement error and interpret results.

Observational studies evaluating the effectiveness of cancer screening are often biased due to nonalignment at time zero, which can be avoided by target trial emulation (TTE). We aimed to illustrate this by evaluating site-specific effectiveness of screening colonoscopy regarding colorectal cancer (CRC) incidence. Based on a German health care database, we assessed the effect of screening colonoscopy vs no screening colonoscopy in preventing CRC in the distal and the proximal colon over 12 years of follow-up in 55-69-year-old persons. We compared four different study designs: cohort and case-control study, each with/without alignment at time zero. In both analyses with time zero-alignment, screening colonoscopy showed a rather similar effectiveness in reducing the incidence of distal and proximal CRC (cohort analysis: 32% (95% CI, 27%-37%) vs 28% (20%-35%); case-control analysis: 27% vs 33%). Both analyses without alignment suggested a difference by site: Incidence reduction regarding distal and proximal CRC, respectively, was 65% (61%-68%) vs 37% (31%-43%) in the cohort analysis and 77% (67%-84%) vs 46% (25%-61%) in the case-control analysis. Violations of basic design principles can substantially bias the results of observational studies. In our example, it falsely suggested a much stronger preventive effect of colonoscopy in the distal vs the proximal colon. Our study illustrates that TTE avoids such design-induced biases.

Population aging in the Asia-Pacific will not proceed along the paths already followed by more developed countries, but differences in later-life health across the Asia-Pacific region are poorly understood. Using data from five harmonized longitudinal surveys in the region, we examine gender and cross-national differences in life expectancy (LE) and health expectancies (HEs) at age 50 in Australia, Japan, South Korea, China, and Indonesia. We adopt a microsimulation-based multistate life table model to provide estimates of HEs across four dimensions of health, including life expectancy free of poor self-related health, ADL disability, functional limitations, and chronic diseases. We find that older adults in the Asia-Pacific are experiencing substantially different regimes of health in later life, with cross-national differences arising across measures of health, over age, and between men and women. Older adults China and Indonesia experience more lifetime with physical limitations compared to those in Australia or Japan. Older adults in China spend a markedly higher proportion of remaining LE with chronic diseases compared to other countries. Our results provide much-needed evidence examining current conditions across the region, deepening understanding of how Asia-Pacific populations are currently fairing in terms of later-life health and functioning.