Schools used a wide range of infection prevention strategies during 2022 when there were surges of the Omicron variant of SARS-CoV-2, respiratory syncytial virus, and influenza. We examined data from the Spring semester of the 2021/2022 school year in the United States to describe use of school-based infection prevention strategies, factors associated with implementation, and associations with COVID-19 and respiratory disease related outcomes.

Educational Engagement Modules (EEMs) are teaching materials for educators and students that facilitate a deeper understanding of key epidemiological methods and concepts. Each EEM poses a series of questions using a recently published paper in Annals to further understanding of a specific study design and to encourage critical thinking and careful evaluation. This EEM focuses on the use of propensity score matching and multinomial models in a study exploring the association between experiencing a stroke and psychological distress and references the following article: Dagli C, Patel PG, Gonzalez K, Nair M, Al-Antary N, Lin C, Adjei Boakye E. Psychological distress among stroke survivors in the US: An analysis of the National Health Interview Survey. Ann Epidemiol. 2025 Jun 30;109:8-13. doi: 10.1016/j.annepidem.2025.06.019. Epub ahead of print. PMID: 40602697.

Combining evidence-based interventions (EBIs) is a discrete process from adapting EBIs, and specific guidance for how to combine EBIs could be helpful amidst the proliferation of frameworks that combine and stage EBIs and calls for services to be combined or bundled. To address this gap, we developed and applied the COllaborative Method for Building INterventions from Existing Evidence-Based Interventions (COMBINE-EBIs) approach, a five-step process for combining EBIs.

Residential segregation is linked to racial and socioeconomic inequity in outcomes for numerous infections including SARS-CoV-2, influenza, STIs, and tuberculosis. Despite the importance of segregation as a driver of infection inequity, there are few mathematical models to inform our understanding of these dynamics.

We aimed to identify actionable, effective sustainment strategies for a community-based suicide prevention program implemented in Tribal contexts through a participatory process of system dynamics modeling.

The need for greater evidence on equity-denied population groups continues to be recognized across public health, epidemiology, and community medicine. Despite a number of existing frameworks and tools on the inclusion of equity considerations in evidence syntheses, there was a gap in tools providing step-wise guidance and facilitating the documentation of the process.

The vaginal microbiome is dynamic, typically shifting during pregnancy toward enrichment of Lactobacillus. However, proliferation of Lactobacillus may be absent among women with preterm births (PTBs). We sought to identify robust vaginal microbiota signatures along with host factors that predicted PTB across diverse U.S. cohorts.

Artificial Intelligence (AI) holds immense promise for public health, yet its potential is undermined by alignment failures where systems act contrary to human values, often exacerbating health disparities. This paper challenges the narrow view that algorithmic bias is solely a data problem, arguing instead that misalignment arises at every stage of the AI development lifecycle. We introduce a comprehensive seven-stage framework, spanning problem definition, team assembly, study design, data acquisition, model training, validation, and post-deployment implementation, viewed through an epidemiological lens. This approach systematically integrates core principles such as population representativeness, rigorous study design, bias characterization, and causal reasoning to identify and mitigate alignment risks. For each stage, we define specific alignment failures, from flawed problem formulation to post-market performance degradation, and propose actionable, evidence-based solutions. By embedding epidemiological rigor throughout the entire AI lifecycle, this framework provides a structured, proactive pathway for researchers, developers, and policymakers to create trustworthy, safe, and fair AI systems. This systemic approach is critical to harnessing AI's transformative benefits for population health while preventing the perpetuation of inequity and harm.

The chronic kidney disease (CKD) patients were at high risk for severe clinical complications during the COVID-19 pandemic. Our objectives were to evaluate comorbidity prevalence; predict mortality risks for CKD patients during the pandemic; assess how various health factors interact to influence mortality; and provide insights for targeted prevention strategies.

To quantify long-term trends and multi-year cycles in dengue in Mexico (1985-2025) and examine recent serotype-severity patterns.

To extend the existing quantile g-computation framework for studying environmental exposure mixtures to estimate local effects of ambient air pollution mixtures on birth weight. This framework has traditionally been applied to estimate global mixture effects without accounting for spatial heterogeneity.

Integrase Strand Transfer Inhibitors (INSTIs) are effective and well-tolerated in HIV treatment, but their cardiovascular impact remains uncertain. This study evaluated the association between INSTI-based antiretroviral therapy (ART) and cardiovascular disease (CVD) risk in people with HIV (PWH).

Attrition of participants over time poses a challenge in longitudinal research. This study aimed to explore how partner participation influenced maternal retention.

The estimation of smoking-attributable mortality (SAM) is subject to the acceptance of different assumptions that may influence the estimates. We aimed to assess lung cancer mortality attributable to smoking by using both a prevalence-independent method (PIM) and a prevalence-dependent method (PDM) with different lags between exposure (smoking prevalence) and outcome (lung cancer mortality).

Clinical prediction models (CPM) are essential tools for diagnosis and prognosis in clinical epidemiology. Machine learning (ML) and deep learning (DL) approaches provide flexible methods that can complement regression-based methods for CPM when complex predictors such as clinical biomarkers are of interest. However, concerns have been raised on the ability of ML and DL to address desired properties of CPMs such as parsimony, generalizability, and interpretability.

This study examines national trends in mortality from cardiovascular disease (CVD) and select subtypes among U.S. young adults aged 18-34 years from 2000 to 2023.

Electronic health records (EHR) offer a unique opportunity to systematically collect sexual orientation and gender identity (SOGI) data. This study examined the prevalence and determinants of SOGI reporting in an EHR-based cohort of transgender and gender diverse (TGD) individuals.

Primary liver cancer (PLC) remains a major public health concern, particularly in China where the incidence is high. Existing prediction models often focus on high-risk populations and depend heavily on laboratory data, which limits their utility in general population screening.

In many public health and clinical research studies that use regression models for analyses, race is often considered a confounder and "controlled" for in the regression model with simple indicators for race and non-Hispanic White as the reference group, without much introspection from the data analyst. From a health equity perspective, multiple issues exist with this approach. We examine and compare several methods for coding race in linear and logistic regression models. We compare several coding methods using a sample of 8097 participants (≥18 years old) from the 2020 New York City Community Health Survey. To illustrate the importance of coding methods for race, we conducted regression analyses to compare the results from six coding approaches: dummy, simple effect, difference (forward and backward), deviation, and analyst-defined coding. Body mass index measured continuously and diabetes status measured dichotomously were the outcome variables in the linear and logistic regression models. Results showed that selecting a coding method has implications for identifying racial health inequities. The reference group selection is critical to measuring racial inequities in health outcomes. This study emphasizes the need to consider the impact of coding techniques on research study design, particularly when racial health inequities are the research focus.

Acknowledging the importance of subjective financial measures that objective indicators may not be able to fully capture, this study investigates whether and how perceived economic hardship influences self-rated health among women. Specifically, it examines the cumulative effects of perceived economic hardship while exploring variations across different age groups.

Educational Engagement Modules (EEMs) are teaching materials for educators and students that facilitate a deeper understanding of key epidemiological methods and concepts. Each EEM poses a series of questions using a recently published paper in Annals to further understanding of a specific study design and to encourage critical thinking and careful evaluation. This EEM focuses on cancer screening and references the following article: Huang CS, Hsiao BY, Wu MH, Chiang CJ, Hsieh PC, Chen MJ, Cheng WF, Lee WC. Association between screening history and prognosis of cervical carcinoma in situ and invasive cervical cancer: A population-based cohort study. Ann Epidemiol. 2025 Jun;106:75-81. doi: 10.1016/j.annepidem.2025.04.015. Epub 2025 May 3. PMID: 40324609 [1].