384 papers
Public and global health examines how we protect communities from disease, manage outbreaks, and improve well-being across the world. This vital field connects diverse research on everything from infectious disease control and vaccination strategies to the social factors that shape our collective health. Because these studies often impact policy and daily life, understanding them quickly is more important than ever.
At Gist.Science, we track every new preprint published in this category on medRxiv to ensure you never miss a breakthrough. We process each submission to provide both plain-language explanations for general readers and detailed technical summaries for experts, making complex findings accessible to everyone.
Below are the latest papers in public and global health, freshly summarized from the most recent medRxiv releases.
This longitudinal cohort study of over 20,000 older adults in New York City found that higher residential bird biodiversity is significantly associated with lower frailty severity, particularly among individuals aged 65 to 74, suggesting that urban biodiversity may serve as a modifiable environmental factor supporting healthy aging.
This study presents an open, multi-language machine learning benchmark using Behavioral Risk Factor Surveillance System (BRFSS) data that demonstrates stable, well-calibrated prediction of future Subjective Cognitive Decline (SCD) across temporal splits while identifying key predictors like decision-making difficulty through convergent interpretability methods.
This pooled analysis of Demographic and Health Survey data from five Sub-Saharan African countries reveals that while complete continuity of maternal healthcare is associated with lower neonatal mortality in unadjusted models, this protective effect disappears after controlling for sociodemographic factors, indicating that the observed benefit is driven by selection bias rather than a direct causal impact of the care continuum itself.
This study in southern Malawi utilized fine-scale spatial modeling to demonstrate that *Schistosoma haematobium* infection intensity is significantly associated with anaemia severity, revealing distinct spatial risk patterns and high-risk clusters that support the need for targeted interventions.
Using data from the UK Millennium Cohort Study, this research demonstrates that adolescent health, particularly mental health conditions and their co-occurrence with physical issues like obesity, significantly increases the risk of young people being Not in Education, Employment, or Training (NEET) at age 23, with effects being more pronounced for females and those who are economically inactive.
This study demonstrates that a hybrid neural-network model integrating high-resolution temperature data with respiratory infection surveillance significantly improves the accuracy of daily all-cause mortality reconstruction in Germany compared to models relying solely on weather or infection data, while also revealing that temperature-only models may overestimate the mortality risk attributed to cold exposure by failing to account for overlapping winter infection effects.
This study analyzes the 2022 Ghana Demographic and Health Survey to reveal that breast and cervical cancer screening uptake is significantly lower in northern regions and strongly associated with wealth and education, demonstrating that combining spatial clustering analysis with machine learning can identify high-risk local hotspots beyond traditional regional reporting.
This study establishes the first structured framework of core competencies for doctoral-level expertise in infectious disease dynamics by utilizing an e-Delphi process to validate a consensus set of cross-cutting, applied modeling, and theory skills, thereby providing a standardized guide for curriculum development and trainee evaluation across the field.
This mixed-methods study in Lagos, Nigeria, reveals that adolescent substance use is driven by both social exposure and perceived community availability, with the risk significantly amplified when these factors interact, thereby supporting multilevel prevention strategies that address environmental access alongside social influences.
This study utilizes an SEIDR model and advanced optimization algorithms to analyze the dynamics of the 2026 Bundibugyo Ebola outbreak in the DRC, identifying a basic reproduction number of 1.83 and demonstrating that a time-varying optimal control strategy could shorten the epidemic by seven months while significantly reducing cross-border spillover risks to neighboring African nations, France, and Canada.