Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk in Allegheny County, Pennsylvania, an area heavily affected by the opioid crisis.
Recent Abstracts
Testimonials: Personal stories that have the power to save lives on the road
Knowledge, Attitudes, and Practice Study on Lead Poisoning and Pollution in Indonesia
Improving Civil Registration and Vital Statistics Systems in French-Speaking Countries: Opportunities and Challenges
Strengthening the Civil Registration and Vital Statistics (CRVS) System in Colombia
Estimation of the direct and indirect costs attributable to alcohol consumption in Brazil
Guidance for Collection and Processing of Cause-of-Death Data in the Civil Registration and…
Vital Strategies: Reimagine Public Health
Public perceptions of emissions testing in Jakarta, Indonesia
Cost-Benefit Analysis for Air Pollution Control Strategies in Jakarta
Key Messages on Alcohol Harms and Policy Solutions