The Yale School of Public Health (YSPH) was founded in 1915 by Charles-Edward Amory Winslow and is one of the oldest public health masters programs in the United States. It is consistently rated among the best schools of public health in the country, receiving recent rankings of 3rd for its doctoral program in epidemiology. YSPH has a unique hybrid existence with the Yale School of Medicine, as it is both a department (established in 1915) within the School of Medicine as well as an independent, CEPH-certified school of public health (established in 1946). According to the school's website, the community benefits greatly from the Yale School of Public Health's dual roles of providing a world–class education as an accredited, fully functioning school, and by conducting cutting–edge, interdisciplinary research through its collaborative departmental partnerships at the School of Medicine and across the Yale campus.
Climate change and its severe health impacts raise serious concerns about climate justice. To measure a population's vulnerability to climate change, researchers often apply indicator-based composite indices. In this work, we present a modeling framework for constructing a climate health vulnerability index (CHVI) and examine how methodological choices influence the identification of vulnerable communities. Using 44 indicators in New York State and two structural designs-inductive (principal component analysis) and deductive (indicator aggregation)-we conducted multiple sensitivity analyses to evaluate the robustness of CHVI outcomes. The deductive design was less sensitive to model inputs and specifications than the inductive design. Among the construction steps, principal component selection and indicator normalization were the most influential factors for the inductive and deductive designs, respectively. Quantification of climate-change-related vulnerability through transparent and reproducible index development can inform policy planning and resource allocation for the most disadvantaged populations.
With the increasing accessibility of individual-level data from genome wide association studies, it is now common for researchers to have individual-level data of some traits in one specific population. For some traits, we can only access public released summary-level data due to privacy and safety concerns. The current methods to estimate genetic correlation can only be applied when the input data type of the two traits of interest is either both individual-level or both summary-level. When researchers have access to individual-level data for one trait and summary-level data for the other, they have to transform the individual-level data to summary-level data first and then apply summary data-based methods to estimate the genetic correlation. This procedure is computationally and statistically inefficient and introduces information loss. We introduce GENJI (Genetic correlation EstimatioN Jointly using Individual-level and summary data), a method that can estimate within-population or transethnic genetic correlation based on individual-level data for one trait and summary-level data for another trait. Through extensive simulations and analyses of real data on within-population and transethnic genetic correlation estimation, we show that GENJI produces more reliable and efficient estimation than summary data-based methods. Besides, when individual-level data are available for both traits, GENJI can achieve comparable performance than individual-level data-based methods. Downstream applications of genetic correlation can benefit from more accurate estimates. In particular, we show that more accurate genetic correlation estimation facilitates the predictability of cross-population polygenic risk scores.
The Human Exposome Project (HEP) aims to chart lifelong environmental exposures and their biological consequences, furnishing the environmental counterpart to the genomic revolution. Yet the fine‑grained, multimodal data streams that fuel exposomics—biospecimens, geolocation traces, wearable‑sensor feeds, and socio‑environmental metadata—raise privacy, justice, and governance questions that may exceed the reach of conventional bioethics. Building on lessons from genomics, biobanking, digital health, and environmental‑justice research, we identify five foundational ethical domains for exposome science: (1) privacy and data sovereignty, (2) informed consent and sustained participant engagement, (3) environmental justice, (4) governance and oversight, and (5) actionability and the responsible return of results,as well as (6)the adherence to research program goals. Similar to the “values in design” construct widely used in the socio-technical field and the “ethics by design” in the artificial intelligence (AI) field, we translate these domains into operational pillars for ethics‑by‑design research practice: dynamic or tiered consent architectures; participatory governance mechanisms such as community advisory boards; embedded ethics research programs; algorithmic‑fairness protocols for artificial‑intelligence analytics; and dedicated review bodies equipped to evaluate longitudinal, sensor‑based, multi‑omics studies. Concrete recommendations include federated data stewardship to minimize re‑identification risk, Evidence‑to‑Decision frameworks that couple exposomic evidence with societal values, and transparent pathways for communicating context‑dependent findings to individuals, communities, and policymakers. Ethical preparedness and action are a prerequisite for the scientific impact and social license of exposome research. Institutionalizing the proposed roadmap—via an international Exposome Ethics Consortium, expanded training for Institutional Review Boards, harmonized regulatory guidance, and sustained community co‑governance—will help protect privacy, promote equity, and foster public trust. Embedding systematic ethical reflection as core infrastructure will enable the Human Exposome Project to realize its promise of precision public health without replicating patterns of opaque surveillance, marginalization, or data commodification. The Human Exposome Project (HEP) represents an ambitious endeavor to characterize lifelong environmental exposures in relation to health. Yet, this vision brings profound ethical challenges: from managing massive, sensitive datasets to ensuring justice for disproportionately exposed communities. This article synthesizes foundational work on exposome ethics, outlines core ethical challenges, and proposes a proactive ethical governance model that ensures scientific integrity and social legitimacy.
BACKGROUND:Population-based data on COVID-19's impact are essential for informing public health policies, particularly in low- and middle-income countries. Here, we investigated the history of COVID-19 diagnoses across the Brazilian population, considering factors such as sex, age, skin colour/ethnicity, comorbidity, schooling, and socioeconomic level. METHODS:In this nationwide study, EPICOVID 2.0, we surveyed 133 cities in all Brazilian states between March and June 2024. We randomly selected 250 households per city and one individual from each household. Crude and adjusted prevalence ratios were calculated to examine the association between covariates and self-reports of prior COVID-19 diagnoses and hospitalizations, and odds ratios to assess the factors associated with the higher number of COVID-19 episodes. RESULTS:Our analysis of 33 250 individuals revealed that 28.6% [95% confidence interval (CI), 27.3-29.9] of the study population had a previous COVID-19 diagnosis, with most of those reported presenting one episode (77.7%), followed by two (17.3%) and three episodes (5.0%). We observed a positive association between higher educational attainment and wealth quintiles and self-reported COVID-19 diagnoses. We also found that women presented a higher COVID-19 prevalence. About 4.5% (95% CI, 3.8-5.2) of participants who reported previous COVID-19 were hospitalized, with an average length of stay of 10.4 days. We did not find a difference in the prevalence of hospitalizations between socioeconomic strata. We observed that those who experienced two or more COVID-19 episodes had three times higher odds of being hospitalized. CONCLUSION:These findings highlight significant disparities in the COVID-19 impact across socioeconomic and demographic groups in Brazil, underscoring the need for targeted public health interventions to address vulnerabilities and reduce the COVID-19 burden in Brazil.
Untargeted metabolomics provides a direct window into biochemical activities but faces critical challenges in determining metabolite origins and interpreting unannotated metabolic features. Here, we present TidyMass2, an enhanced computational framework for Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted metabolomics that addresses these limitations. TidyMass2 introduces three major innovations compared to its predecessor, TidyMass: (1) a comprehensive metabolite origin inference capability that traces metabolites to human, microbial, dietary, pharmaceutical, and environmental sources through integration of 11 metabolite databases containing 532,488 metabolites with source information; (2) a metabolic feature-based functional module analysis approach that bypasses the annotation bottleneck by leveraging metabolic network topology to extract biological insights from unannotated metabolic features; and (3) a graphical interface that makes advanced metabolomics analyses accessible to researchers without programming expertise. Applied to longitudinal urine metabolomics data from human pregnancy, TidyMass2 identified diverse metabolites originating from human, microbiome, and environment, and uncovered 27 dysregulated metabolic modules. It increased the proportion of biologically interpretable metabolic features from 5.8
