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 paper introduces and models "hitchhiker bias," a phenomenon where co-infection with a symptomatic pathogen inflates hospitalization rates for milder viruses in symptom-based surveillance, and proposes a framework to correct these distortions to accurately assess true pathogen severity and dynamics.
This paper's simulation demonstrates that within the framework of large-scale propensity score adjustment, including instruments with a treatment correlation below 0.5 and an equipoise preference score above 0.5 introduces only minor bias, supporting the strategy of adjusting for many covariates rather than attempting to identify a limited set of confounders.
This study develops a novel mechanistic modeling framework to demonstrate that Brazil's COVID-19 mortality gradient was primarily driven by pre-existing social inequalities and unequal intervention uptake, but could be reversed through equitable vaccination strategies and uniform non-pharmaceutical intervention adoption.
By applying hierarchical clustering to longitudinal health checkup data from Japanese men, this study identified six distinct pre-onset pathways to metabolic syndrome characterized by varying sequences of abdominal obesity, hypertension, dyslipidemia, and hyperglycemia, thereby highlighting the heterogeneous nature of MetS development and supporting the need for targeted early intervention strategies.
This study demonstrates that while ECG-derived risk scores independently predict 10-year major adverse cardiac and cerebrovascular events in UK Biobank participants, their addition to traditional QRISK3 clinical factors yields only a marginal improvement in overall risk prediction, with the most notable benefit being enhanced risk stratification for women.
In a large UK Biobank study, glycaemia exhibits non-linear, inverted J-shaped associations with brain structure across the entire glucose spectrum, with higher glucose levels linked to reduced brain volumes and steeper declines observed in females compared to males.
This cross-sectional study of adults in rural Western Kenya found that higher dietary diversity is significantly associated with lower depressive symptoms, particularly among individuals from lower-wealth households and those with more severe depression.
This cross-sectional study of 613 adults in Cape Town reveals a high prevalence of suicidal thoughts and behaviors strongly linked to mental disorders, community and family violence, and perceived stress, while highlighting that these psychosocial and structural stressors remain significant risk factors even after accounting for psychiatric diagnoses.
Based on the 2021 WHO STEPS survey, this study reveals that while healthcare-delivered lifestyle counseling in Iran is positively associated with improved health behaviors, its delivery remains inconsistent across sociodemographic and regional groups, highlighting the need for more equitable and targeted preventive strategies to combat non-communicable diseases.
This study demonstrates that in global COVID-19 incidence forecasting under structural change, simple baseline models like drift and seasonal naive often outperform complex statistical methods, with optimal model performance being strongly dependent on the forecast horizon.