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 analy...
| Publicado en: | Addiction Vol. 120; no. 2; pp. 296 - 306 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183757988&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183757988 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09652140 AIO jtl: Addiction issn: 09652140 maglogo: Y pubinfo: dt: Feb2025 vid: 120 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 183757988 180196488 183757988 183757988 10.1111/add.16686 183757988 ppf: 296 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Predictors of emergency medical transport refusal following opioid overdose in Washington, DC. aug: au: Turley, Ben Zamore, Kenan Holman, Robert P. affil: DC Department of Health, Washington DC,, USA sug: subj: Opiate Overdose District of Columbia Emergency Service Transportation of Patients Statistics and Numerical Data Treatment Refusal Psychosocial Factors Socioeconomic Factors Human District of Columbia Cross Sectional Studies Sociodemographic Factors Descriptive Statistics Funding Source Social Class Seasons ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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