Predictors of emergency medical transport refusal following opioid overdose in Washington, DC.

Background and aims: Patient initiated transport refusal during Emergency Medical Service (EMS) opioid overdose encounters has become an endemic problem. This study aimed to quantify circumstantial and environmental factors which predict refusal of further care.

Design: In this cross-sectional analysis, a case definition for opioid overdose was applied retrospectively to EMS encounters. Selected cases had sociodemographic and situational/incident variables extracted using patient information and free text searches of case narratives. 50 unique binary variables were used to build a logistic model.

Setting: Prehospital EMS overdose encounters in Washington, DC, USA, from July 2017 to July 2023.

Participants: Of EMS encounters in the study timeframe, 14 587 cases were selected as opioid overdoses.

Measurements: Predicted probability for covariates was the outcome variable. Model performance was assessed using Stratified K-Fold Cross-Validation and scored with positive predictive value, sensitivity and F1. Prediction accuracy and McFadden's pseudo-R squared are also determined.

Findings: The model achieved a predictive accuracy of 78% with a high positive predictive value (0.83) and moderate sensitivity (0.68). Bystander type influenced the refusal outcome, with decreased refusal probability associated with family (nondescript) (-28%) and parents (-16%), while presence of a girlfriend increased it (+28%). Negative situational factors like noted physical trauma (-62%), poor weather (-14%) and lack of housing (-14%) decreased refusal probability. Characteristics of the emergency response team, like a prior crew member encounter (+20%) or crew experience <1 year (-36%), had a variable association with transport.

Conclusions: Refusal of emergency transport for opioid overdose cases in Washington, DC, USA, has expanded by 43.8% since 2017. Several social, environmental and systematic factors can predict this refusal. Logistic regression models can be used to quantify broad categories of behavior in surveillance medical research.