Current epidemiology reports最新文献

Purpose of review: Cardiovascular disease (CVD) is the major complication and leading cause of death among individuals with type 2 diabetes (T2D). Women and younger adults with T2D face a disproportionately higher risk of CVD, compared with their male or older counterparts. This review summarizes clinical and epidemiological evidence on the mechanisms underlying these disparities and highlights directions for future research and clinical practice.

Recent findings: Women with T2D lose the cardioprotection typically seen in the general population and face disproportionately higher cardiovascular risk. This excess risk is influenced by adverse metabolic profiles preceding T2D onset; female-specific factors such as polycystic ovary syndrome and gestational complications; and non-biological contributors, including delayed diagnosis and less optimal healthcare utilization and delivery in women compared with men. Young adults with early-onset T2D also experience a heightened cardiovascular burden, driven by a more aggressive disease course, prolonged exposure to metabolic abnormalities, and distinctive psychosocial stressors that compound their risk. Despite these disparities, both female and young adult patients with T2D remain understudied, hindering the development of precision prevention and management strategies.

Summary: Future mechanistic and interventional research that integrates sex and age as key biological factors will be critical for advancing precision approaches and reducing disparities in diabetes care and outcomes.

Purpose of review: Studies suggest ambient air pollution increases risk of individual-level adverse COVID-19 outcomes. Our review seeks to understand how air pollution influences adverse COVID-19 outcomes, by identifying how researchers accounted for cardiovascular morbidity, a predominant COVID-19 risk factor that is strongly linked to air pollution exposure.

Recent findings: Our review primarily consisted of retrospective cohorts from the US and Europe, that examined both historical and short-term air pollution. Studies typically found that air pollution was associated with greater risk of individual-level adverse COVID-19 outcomes and adjusted for cardiovascular morbidities as confounders. Few hypothesized cardiovascular morbidity as a mediator or effect modifier in this relationship.

Summary: Improved understanding of cardiovascular morbidity's potential role as an effect modifier or mediator can help better explain the link between air pollution and COVID-19, in addition to identifying and assisting populations that may be at greater risk for adverse pandemic outcomes.

Recent findings: Research on the drivers of health across the life course would ideally be based in diverse longitudinal cohorts that repeatedly collect detailed assessments of risk factors over the full life span. However, few extant data sources in the US possess these ideal features. A "longitudinal synthetic cohort"-a dataset created by stacking or linking multiple individual cohorts spanning different but overlapping periods of the life course-can overcome some of these challenges, leveraging the strengths of each component study. This type of synthetic cohort is especially useful for aging research; it enables description of the long-term natural history of disease and novel investigations of earlier-life factors and mechanisms shaping health outcomes that typically manifest in older age, such as Alzheimer's disease and related dementias (ADRD).

Purpose of review: We review current understanding of synthetic cohorts for life course research. We first discuss chief advantages of longitudinal synthetic cohorts, focusing on their utility for aging/ADRD research to concretize the discussion. We then summarize the conditions needed for valid inference in a synthetic cohort, depending on research goals. We end by highlighting key challenges to creating longitudinal synthetic cohorts and conducting life course research within them.

Summary: The idea of combining multiple data sources to investigate research questions that are not feasible to answer using a single cohort is gaining popularity in epidemiology. The use of longitudinal synthetic cohorts in applied research-and especially in ADRD research-has been limited, however, likely due to methodologic complexity. In particular, little guidance and few examples exist for the creation of a longitudinal synthetic cohort for causal research goals. While building synthetic cohorts requires much thought and care, it offers tremendous opportunity to address novel and critical scientific questions that could not be examined in a single study.

Purpose of review: The goal of this narrative review is to introduce and apply Hswen's AI Checklist (AI-Y) for Population Ethics, a structured ethical framework created to evaluate the development and deployment of artificial intelligence (AI) technologies in public health. The review addresses key questions: How can AI be ethically assessed across global healthcare contexts and what principles are needed to ensure contextually appropriate AI use in population health.

Recent findings: Recent research highlights a significant disconnect between AI development and ethical implementation, especially in low-resource settings. Studies reveal issues such as homogeneity in the training data, and limited accessibility. Through six global case studies-spanning dementia care in Sweden, environmental forecasting in Europe, suicide prevention in Native American communities, schizophrenia care in India and the U.S., and cervical cancer and tuberculosis diagnosis in Low- and Middle-Income Countries-researchers demonstrate AI's promise in enhancing preparedness diagnosis, screening, and care delivery while also underscoring ethical gaps in accountability, and governance.

Summary: Our examination using the AI-Y Checklist found that ethical blind spots are widespread in the development and deployment of AI tools for population health-particularly in areas of model generalizability, accountability, and transparency of AI decision-making. Although AI demonstrates strong potential to enhance disease detection, resource allocation, and preventive care across diverse global settings, most systems evaluated in our six case studies did not meet key ethical criteria such as access, and localized validation and development. The major takeaway is that technical excellence alone is insufficient; ethical alignment is critical to the responsible implementation of AI in public health. The AI-Y Checklist provides a scalable framework to identify risks, guide ethical decision-making, and foster global accountability. For future research, this framework enables standardized evaluation of AI systems, encourages community co-design practices, and supports the creation of policy and governance structures that ensure AI technologies advance health ethics.

Purpose of review: A growing body of research has now identified the criminal legal system as a major social determinant of population health and health disparities in the United States. The current review provides a description of the U.S. criminal legal landscape, briefly summarizes recent research findings, and identifies new and needed directions for research.

Recent findings: Building on prior research first identifying direct contact with the prison system as a social determinant of health, recent research provides evidence of elevated risks for physical and mental morbidity and mortality among those with direct or indirect contact with the criminal legal system. This research has been expanded to include community supervision and contact with police as important drivers of health.While this evidence base has grown, our understanding of the role of the criminal legal system in population health has remained limited for several reasons: (1) prison and jail incarceration remain the primary forms of contact examined despite the existence of other relevant forms of carceral contact and control; (2) the longitudinal health consequences of contact with the criminal legal system have largely gone undocumented; (3) the majority of the research is descriptive and does not employ causal modeling approaches; and (4) relatedly, the mechanisms that link criminal legal system contact with health are not adequately measured.

Summary: The criminal legal system has emerged as a significant social determinant of health. While advances have been made in documenting the direct and indirect consequences of contact for population health and health disparities, more work is needed to better ascertain how and why this contact matters.

Purpose of review: Despite the growth of research on social policies and health in recent years, few studies have systematically summarized the methodological approaches used in this growing literature. This review characterizes the range of and trends in analytic methods used in studies of the health effects of US social policies published in leading health journals during January 2014-July 2024.

Recent findings: Among the 117 studies reviewed, confounder-control approaches were the most commonly used method to assess health effects of social policies. Quasi-experimental methods were also frequently used, especially difference-in-differences designs. Heterogeneous subgroup effects were consistently assessed.

Summary: Although there was frequent use of quasi-experimental designs that meet standards for rigorous evidence used to inform policymaking, many opportunities for improvement remain. We suggest improvements to data infrastructure, highlight less frequently studied policies as fruitful future research opportunities, and encourage researchers to implement quasi-experimental approaches best suited to identify causal estimates.

Purpose of review: Southeast Asian (SEA) American populations-including Filipino, Vietnamese, Cambodian, Laotian, Burmese, and Hmong communities-experience disproportionate burdens of dyslipidemia and cardiovascular disease in the United States (U.S.) population. Despite these disparities, SEA Americans remain underrepresented in cardiovascular research and epidemiology. This review synthesizes U.S.-based epidemiologic, clinical, and community-level evidence on dyslipidemia and stroke across SEA subgroups, emphasizing how aggregated data obscure subgroup-specific disparities.

Recent findings: Disaggregated data reveal that Filipino and Vietnamese adults-among the most studied SEA subgroups-exhibit high rates of elevated low-density lipoprotein cholesterol and hypertriglyceridemia, aligning with increased ischemic stroke prevalence, higher age-standardized cerebrovascular mortality, and greater years of potential life lost. Hemorrhagic stroke mortality is also comparatively high in these groups. In contrast, data for Cambodian, Laotian, Hmong, and Burmese Americans remain sparse, limiting risk characterization. Emerging evidence highlights that cardiometabolic disorders in SEA populations reflect heterogeneous and multifactorial influences, including genetic variability in statin metabolism, cultural health beliefs, psychosocial stressors, and systemic barriers to preventive care.

Summary: Cardiovascular risk in SEA American populations is shaped by the interplay of biological and social determinants of health. Aggregating diverse Asian subgroups into a single racial category masks heterogeneity, widens knowledge gaps, and perpetuates inequities in care. Advancing cardiovascular health equity requires intentional inclusion of underrepresented SEA subgroups in clinical research, systematic data disaggregation, and culturally responsive approaches to lipid management and stroke prevention.

Purpose of review: Despite cervical cancer (CC) being a cancer that can be eliminated, CC disparities persist such that minoritized populations shoulder a disproportionate mortality burden. This may reflect upstream, fundamental drivers of health that impede equitable access to prevention, screening, early detection, and treatment among some groups. This systematic review summarizes evidence on the relationships between structural racism and CC care across the continuum.

Recent findings: Following PRISMA guidelines, we conducted a comprehensive search for peer-reviewed, English-language studies relevant to our research question that were published from 2012-2022 using PubMed, CINAHL, Web of Science, and Embase. Of 8,924 articles identified, 4,383 duplicates were removed, and 4,541 underwent screening, with 206 articles meeting eligibility criteria for inclusion in our data synthesis. Among reviewed studies, 60.2% (n = 124) compared CC outcomes by race and ethnicity, often as proxies for upstream racism. Key findings included evidence of lower CC screening rates among Asian American and Pacific Islander women and higher rates among Black and Hispanic/Latinx women. Barriers to healthcare access and socioeconomic status (SES) factors contributed to delayed follow-up, later-stage CC diagnoses, and poorer outcomes, particularly for Black and Hispanic/Latinx women and those residing in low-SES neighborhoods.

Summary: This review underscores associations between race, ethnicity, SES, and outcomes across the CC continuum. Most studies examined racial and ethnic disparities in the outcomes of interest rather than directly evaluating measures of structural racism. Future research should refine measures of structural racism to deepen our understanding of its impact on CC across the care continuum.

Supplementary information: The online version contains supplementary material available at 10.1007/s40471-025-00360-y.

Purpose of review: This scoping review synthesizes current evidence on glucagon-like peptide-1 receptor agonist (GLP-1RA) use after ST-elevation myocardial infarction (STEMI), highlighting their potential as adjunctive therapy.

Recent findings: Ten studies investigated exenatide and liraglutide in adults with STEMI, evaluating imaging, clinical, mechanistic, and safety outcomes. GLP-1RA use was safe and well tolerated. Exenatide demonstrated improvements in infarct size, myocardial salvage, and cardiac function, although two trials in broader STEMI populations reported no post-infarction improvements. Across three trials, liraglutide was associated with improved myocardial salvage, infarct size, left ventricular ejection fraction, stroke volume and no-reflow, supported by favorable biomarker changes, but without significant reductions in major cardiovascular events.

Summary: While most existing evidence is based on studies with limited generalizability, GLP-1RA use shows promise in improving post-STEMI outcomes. The consistent benefits reported support the need for larger, multicenter trials to clarify GLP-1RA role in cardioprotection and long-term outcomes.

Purpose of review: This review summarizes recent developments in causal decomposition analysis (CDA), a modeling framework for reducing disparities. Rather than describing the current or past drivers of a disparity, CDA estimates the effect of an intervention to change the distribution of a variable or set of variables that are distributed differently or have different effects between groups. Furthermore, CDA clarifies how, through covariate adjustment, ethics and justice are implicit in any definition of disparity and may be incorporated into an intervention.

Recent findings: CDA has been applied to disparities in health, sociology, education, and computer science. The CDA framework consists of four steps: formulating a meaningful estimand, articulating identification assumptions to link an appropriate dataset with the estimand, choosing an appropriate estimator, and conducting statistical inference. Estimators have been developed for various types of data and to address particular statistical challenges. However, some estimators adjust for all available covariates in all parts of the model, without discussing ethical implications. Meanwhile, the literature has covered some but not all potential violations of standard CDA modeling assumptions.

Summary: CDA builds on previous methods for studying disparities by articulating causal estimands that transparently reflect implicit value judgements about health disparities. This review outlines the broad framework of CDA methodology, selected implementations, practical considerations, and current limitations and alternatives.