286 papers
Epidemiology is the study of how diseases spread through populations and what factors influence their patterns. Rather than focusing on individual patients, this field examines broader trends to identify outbreaks, track transmission, and guide public health decisions. By analyzing data on infection rates and risk factors, researchers work to prevent future health crises and protect communities worldwide.
On Gist.Science, we process every new preprint in this category directly from medRxiv to make these critical findings instantly accessible. For each study, we provide both a plain-language explanation for general readers and a detailed technical summary for specialists. This dual approach ensures that vital insights into disease dynamics are understood clearly and quickly by everyone who needs them.
Explore the latest research below to see how scientists are currently mapping disease trends and developing strategies to safeguard global health.
This study presents a large-scale proteogenomic atlas of 7,289 plasma proteins in 3,965 Chinese adults, identifying thousands of protein-phenotype associations and causal links that reveal ancestry-specific genetic insights and biological networks, particularly in lipid metabolism and cardiovascular disease, which are often missed in European-centric research.
This paper presents a practical toolkit and conceptual guide developed by the Insight Net Modeling Guidance for Public Health Working Group, which uses early COVID-19 data from Michigan to demonstrate how to generate transparent, evidence-backed scenario projections with quantified uncertainty across three critical phases of outbreak response to better support public health decision-making.
An analysis of the 2021 Global Youth Tobacco Survey reveals that while 61.6% of Zambian adolescents are exposed to anti-tobacco messages primarily through mass media and schools, this exposure is significantly influenced by social and household factors, with male adolescents and those in smoking-affected environments showing distinct patterns of reach.
This systematic review and consensus study evaluates the current landscape and methodological practices of 605 individual participant data (IPD) meta-analyses of randomised trials, identifying persistent shortcomings in transparency and data utilization while providing expert recommendations to enhance future research quality and clinical decision-making.
This 2024 cross-sectional study of 550 nursing home residents in Shiraz, Iran, found that 72% experienced at least one accident, primarily slips in bathrooms resulting in contusions, with mobility limitation identified as the sole significant risk factor.
This cross-sectional study protocol outlines a plan to evaluate the association between lead accumulation in tooth enamel, cognitive performance, and dietary patterns among 384 school-age children in Guayaquil, Ecuador, using stratified sampling, dental biopsies, the WISC-V intelligence test, and a validated food frequency questionnaire.
This systematic review synthesizes evidence from 40 studies demonstrating that data-driven population segmentation analysis, particularly using K-means clustering and latent profile analysis, effectively identifies distinct accelerometer-derived physical behavior profiles in adults and reveals their preliminary associations with various health outcomes.
This study utilizes untargeted mass spectrometry in the Generation Scotland cohort to identify specific blood-based proteomic biomarkers associated with cognitive function, APOE ε4 status, and reduced risk of incident dementia, highlighting selenoprotein P, afamin, and complement proteins as promising candidates for future replication and early detection efforts.
This study utilizing long-term data from the CAP randomized trial demonstrates that prostate cancer overdiagnosis increases substantially with age due to competing mortality risks, suggesting that opportunistic screening policies should be re-evaluated for older men.
This study demonstrates that while individual parameters in epidemiological models may not be uniquely identifiable, the basic reproduction number often is, and that adding minimal complementary data can render otherwise non-identifiable models globally identifiable, thereby shifting the focus of public health surveillance toward ensuring the identifiability of decision-relevant quantities rather than complete model identifiability.