Identification of Non-Fatal Opioid Overdose Cases Using 9-1-1 Computer Assisted Dispatch and Prehospital Patient Clinical Record Variables.

Background: The current epidemic of opioid overdoses in the United States necessitates a robust public health and clinical response. We described patterns of non-fatal opioid overdoses (NFOODs) in a small western region using data from the 9-1-1 Computer Assisted Dispatch (CAD) record and electronic Patient Clinical Records (ePCR) completed by EMS responders. We determined whether CAD and ePCR variables could identify NFOOD cases in 9-1-1 data for intervention and surveillance efforts. Methods: We conducted a retrospective analysis of 1 year of 9-1-1 emergency medical CAD and ePCR (including naloxone administration) data from the sole EMS provider in the response area. Cases were identified based on clinician review of the ePCR, and categorized as definitive NFOOD, probable NFOOD, or non-OOD. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) of the most prevalent CAD and ePCR variables were calculated. We used a machine learning technique-Random-Forests (RF) modeling-to optimize our ability to accurately predict NFOOD cases within census blocks. Results: Of 37,960 9-1-1 calls, clinical review identified 158 NFOOD cases (0.4%), of which 123 (77.8%) were definitive and 35 (22.2%) were probable cases. Overall, 106 (67.1%) received naloxone from the EMS responder at the scene. As a predictor of NFOOD, naloxone administration by paramedics had 67.1% sensitivity, 99.6% specificity, 44% PPV, and 99.9% NPV. Using CAD variables alone achieved a sensitivity of 36.7% and specificity of 99.7%. Combining ePCR variables with CAD variables increased the diagnostic accuracy with the best RF model yielding 75.9% sensitivity, 99.9% specificity, 71.4% PPV, and 99.9% NPV. Conclusion: CAD problem type variables and naloxone administration, used alone or in combination, had sub-optimal predictive accuracy. However, a Random Forests modeling approach improved accuracy of identification, which could foster improved surveillance and intervention efforts. We identified the set of NFOODs that EMS encountered in a year and may be useful for future surveillance efforts.

Factors Associated With Calling 911 for an Overdose: An Ethnographic Decision Tree Modeling Approach.

Objectives. To identify factors that influence when people who use drugs (PWUDs) call 911 for an overdose. Methods. We conducted 45 qualitative interviews and 180 surveys with PWUDs who had recently witnessed overdoses in Southern California from 2017 to 2019. We used conditional inference tree and random forest models to generate and validate a model to predict whether 911 would be called. Results. Our model had good in- (83%) and out-of-sample (84%) predictive accuracy. Three aspects of the social and policy environment influenced calling 911 for an overdose: the effectiveness of response strategies employed, the behavior of other bystanders, and whether the responder believes it is their responsibility to call. Conclusions. Even in the presence of policies that provide some protections, PWUDs are faced with difficult decisions about calling 911 and must weigh their own safety against that of an overdose victim. Potential interventions include strengthening training and safety planning for PWUDs, bolstering protections for PWUDs when they call 911, and separating law enforcement response from emergency medical response to overdoses.

Post-overdose interventions triggered by calling 911: Centering the perspectives of people who use drugs (PWUDs).

BACKGROUND: Opioid overdose deaths have increased exponentially in the United States. Bystander response to opioid overdose ideally involves administering naloxone, providing rescue breathing, and calling 911 to summon emergency medical assistance. Recently in the US, public health and public safety agencies have begun seeking to use 911 calls as a method to identify and deliver post-overdose interventions to opioid overdose patients. Little is known about the opinions of PWUDs about the barriers, benefits, or potential harms of post-overdose interventions linked to the 911 system. We sought to understand the perspectives of PWUDs about a method for using 911 data to identify opioid overdose cases and trigger a post-overdose intervention. METHODS AND FINDINGS: We conducted three focus groups with 11 PWUDs in 2018. Results are organized into 4 categories: willingness to call 911 (benefits and risks of calling), thoughts about a technique to identify opioid overdoses in 911 data (benefits and concerns), thoughts about the proposed post-overdose intervention (benefits and concerns), and recommendations for developing an ideal post-overdose intervention. For most participants, calling 911 was synonymous with "calling the police" and law enforcement-related fears were pervasive, limiting willingness to engage with the 911 system. The technique to identify opioid overdoses and the proposed post-overdose intervention were identified as potentially lifesaving, but the benefits were balanced by concerns related to law enforcement involvement, intervention timing, and risks to privacy/reputation. Nearly universally, participants wished for a way to summon emergency medical assistance without triggering a law enforcement response. CONCLUSIONS: The fact that the 911 system in the US inextricably links emergency medical assistance with law enforcement response inherently problematizes calling 911 for PWUDs, and has implications for surveillance and intervention. It is imperative to center the perspectives of PWUDs when designing and implementing interventions that rely on the 911 system for activation.

A mixed methods study of the social support networks of female sex workers and their primary noncommercial male partners in Tijuana, Mexico.

Female sex workers (FSWs) are at risk for multiple health harms, including HIV. This article describes a mixed methods study of the social support networks of 19 FSWs and their primary male sex partners in Tijuana, Mexico. We collected quantitative and qualitative social network data, including quantitative network measures, qualitative narratives, and network visualizations. Methodologically, we illustrate how a convergent mixed methods approach to studying personal social support networks of female sex workers can yield a more holistic understanding of network composition and role. From a health-related perspective, we show how migration/deportation and stigma shape social networks and might be leveraged to support HIV prevention interventions. We believe others can benefit from a mixed methods approach to studying social networks.