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
Creating Safe Care: Supporting Pregnant and Parenting People Who Use Drugs
How the “Are We Drinking Ourselves Sick?” Communication Campaign Built Support for Policy…
Tobacco Control Case Study—Philippines
Psychosocial determinants of adherence to public health and social measures (PHSMs) in 18…
Beyond safety: the 2022 WHO abortion guidelines and the future of abortion safety…
Capabilities Statement
Measuring misclassification of Covid-19 as garbage codes: Results of investigating 1,365 deaths and…
A Comprehensive Approach to Improving Emergency Obstetric and Newborn Care in Kigoma, Tanzania
Improving Maternal and Reproductive Health in Kigoma, Tanzania: A 13-Year Initiative
Covitel – Inquérito Telefônico de Fatores de Risco para Doenças Crônicas não Transmissíveis…