Evaluating the association of social determinants of health with chronic diseases at the population level requires access to individual-level factors associated with disease, which are rarely available for large populations. This study used data concerning disease status and various biological, social and other variables from Allegheny County, Pennsylvania, collected from January 2015 to December 2016, to build a semisynthetic population. The results of the study suggest that creating a geographically explicit synthetic population from real and synthetic data is feasible and that synthetic populations are useful for modeling disease in large populations and for estimating the outcome of interventions.
Recent Abstracts
Catalyzing Support for CRVS Improvement – Examples from the Data for Health Initiative
A Guide to Designing Contextualized Civil Registration and Vital Statistics E-Learning Courses
Guide to Developing Standard Operating Procedures (SOPs) for Civil Registration Using a Case-Based…
Building Safe and Healthy Communities
Uncovering the Hidden Risks of PM 2.5 Exposure Among School-Aged Children in Jakarta
Foundations & Futures: Reimagining Public Health in the Artificial Intelligence Era
Strengthening Health Systems to Address Air Pollution in Ethiopia
Policy Brief: Childhood Blood Lead Surveillance in Indonesia – Findings and Policy Recommendations
Impact of Blue Lanes on Road Safety: Crashes, Speed and Motorcyclists’ Perceptions in…
Impacto da Faixa Azul na Segurança Viária: Sinistros, velocidade e percepções de motociclistas…