Michigan system for opioid overdose surveillance

Using RE-AIM Framework to Evaluate Recovery Opioid Overdose Team Plus: A Peer-Led Post-overdose Quick Response Team

Peer recovery coaches utilize their lived experiences to support overdose survivors, a role gaining prominence across communities. A convergent mixed methods design, informed by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework, was used to evaluate the Recovery Opioid Overdose Team Plus (ROOT +), through an iterative evaluation using web-based surveys and qualitative interviews. Reach: Over 27 months, ROOT + responded to 83% of suspected overdose referrals (n = 607) and engaged with 41% of survivors (n = 217) and 7% of survivors' family/friends (n = 38). Effectiveness: Among those initially engaged with ROOT +, 36% of survivors remained engaged, entered treatment, or were in recovery at 90 days post-overdose (n = 77). Adoption: First responders completed 77% of ROOT + referrals (n = 468). Implementation: Barriers included lack of awareness of ROOT + , working phones, and access to treatment from community partner interviews (n = 15). Maintenance: Adaptations to ROOT + were made to facilitate implementation. Peer-led teams are promising models to engage with overdose survivors.

Rapid Identification of Suspected Drug Overdose Deaths by Death Investigators, New Jersey, 2020

OBJECTIVE

While the number of overdoses in the United States continues to increase, lags in data availability have undermined efforts to monitor, respond to, and prevent drug overdose deaths. We examined the performance of a single-item mandatory radio button implemented into a statewide medical examiner database to identify suspected drug overdose deaths in near-real time.

MATERIALS AND METHODS

The New Jersey Office of the Chief State Medical Examiner operates a statewide mandated case management data system to document deaths that fall under the jurisdiction of a medical examiner office. In 2018, the New Jersey Office of the Chief State Medical Examiner implemented a radio button into the case management data system that requires investigators to report whether a death is a suspected drug overdose death. We examined the performance of this tool by comparing confirmed drug overdose deaths in New Jersey during 2020 with suspected drug overdose deaths identified by investigators using the radio button. To measure performance, we calculated sensitivity, specificity, positive predictive value, negative predictive value, and false-positive and false-negative error rates.

RESULTS

During 2020, New Jersey medical examiners investigated 26 527 deaths: 2952 were confirmed by the state medical examiner as a drug overdose death and 3050 were identified by investigators using the radio button as a suspected drug overdose death. Sensitivity was calculated as 96.1% (2837/2952), specificity as 99.1% (23 362/23 575), positive predictive value as 93.0% (2837/3050), negative predictive value as 99.5% (23 362/23 477), false-positive error rate as 7.0% (213/3050), and false-negative error rate as 3.9% (115/2952).

PRACTICE IMPLICATIONS

Implementation of a radio button into death investigation databases provides a simple and accurate method for identifying and tracking drug overdose deaths in near-real time.

PROVIDENT: Development and Validation of a Machine Learning Model to Predict Neighborhood-level Overdose Risk in Rhode Island

BACKGROUND

Drug overdose persists as a leading cause of death in the United States, but resources to address it remain limited. As a result, health authorities must consider where to allocate scarce resources within their jurisdictions. Machine learning offers a strategy to identify areas with increased future overdose risk to proactively allocate overdose prevention resources. This modeling study is embedded in a randomized trial to measure the effect of proactive resource allocation on statewide overdose rates in Rhode Island (RI).

METHODS

We used statewide data from RI from 2016 to 2020 to develop an ensemble machine learning model predicting neighborhood-level fatal overdose risk. Our ensemble model integrated gradient boosting machine and super learner base models in a moving window framework to make predictions in 6-month intervals. Our performance target, developed a priori with the RI Department of Health, was to identify the 20% of RI neighborhoods containing at least 40% of statewide overdose deaths, including at least one neighborhood per municipality. The model was validated after trial launch.

RESULTS

Our model selected priority neighborhoods capturing 40.2% of statewide overdose deaths during the test periods and 44.1% of statewide overdose deaths during validation periods. Our ensemble outperformed the base models during the test periods and performed comparably to the best-performing base model during the validation periods.

CONCLUSIONS

We demonstrated the capacity for machine learning models to predict neighborhood-level fatal overdose risk to a degree of accuracy suitable for practitioners. Jurisdictions may consider predictive modeling as a tool to guide allocation of scarce resources.

Database quality assessment in research in paramedicine: a scoping review

BACKGROUND

Research in paramedicine faces challenges in developing research capacity, including access to high-quality data. A variety of unique factors in the paramedic work environment influence data quality. In other fields of healthcare, data quality assessment (DQA) frameworks provide common methods of quality assessment as well as standards of transparent reporting. No similar DQA frameworks exist for paramedicine, and practices related to DQA are sporadically reported. This scoping review aims to describe the range, extent, and nature of DQA practices within research in paramedicine.

METHODS

This review followed a registered and published protocol. In consultation with a professional librarian, a search strategy was developed and applied to MEDLINE (National Library of Medicine), EMBASE (Elsevier), Scopus (Elsevier), and CINAHL (EBSCO) to identify studies published from 2011 through 2021 that assess paramedic data quality as a stated goal. Studies that reported quantitative results of DQA using data that relate primarily to the paramedic practice environment were included. Protocols, commentaries, and similar study types were excluded. Title/abstract screening was conducted by two reviewers; full-text screening was conducted by two, with a third participating to resolve disagreements. Data were extracted using a piloted data-charting form.

RESULTS

Searching yielded 10,105 unique articles. After title and abstract screening, 199 remained for full-text review; 97 were included in the analysis. Included studies varied widely in many characteristics. Majorities were conducted in the United States (51%), assessed data containing between 100 and 9,999 records (61%), or assessed one of three topic areas: data, trauma, or out-of-hospital cardiac arrest (61%). All data-quality domains assessed could be grouped under 5 summary domains: completeness, linkage, accuracy, reliability, and representativeness.

CONCLUSIONS

There are few common standards in terms of variables, domains, methods, or quality thresholds for DQA in paramedic research. Terminology used to describe quality domains varied among included studies and frequently overlapped. The included studies showed no evidence of assessing some domains and emerging topics seen in other areas of healthcare. Research in paramedicine would benefit from a standardized framework for DQA that allows for local variation while establishing common methods, terminology, and reporting standards.

Tying Overdose Data to Action: North Carolina's Opioid and Substance Use Action Plan Data Dashboard

From 2000 to 2020, more than 28 000 North Carolina (NC) residents died of drug overdose. In response, NC Department of Health and Human Services worked with community partners to develop an Opioid and Substance Use Action Plan (OSUAP), now in its third iteration. The NC OSUAP data dashboard brings together data on 15 public health indicators and 16 local actions across 8 strategies. We share innovations in design, data structures, user tasks, and visual elements over 5 years of dashboard development and maintenance, with a special focus and supplemental material covering the technical details and techniques that dashboard design and implementation teams may benefit from.

Pharmacists and opioid use disorder care during COVID-19: Call for action

Opioid use disorder (OUD) is a chronic relapsing condition characterized by problematic opioid use causing significant impairment in daily life. Medication for opioid use disorder using buprenorphine, methadone, and naltrexone with behavioral therapy reduces illicit opioid use and risk of overdose death. Despite evidence and decades of experience, barriers limit access to treatment and care for individuals with OUD. Barriers include a lack of treatment centers particularly in rural areas, regulations on buprenorphine prescribing, and stigma from the community and health care professionals. While many barriers are longstanding, the coronavirus disease 2019 (COVID‐19) pandemic‐forced isolation and associated stress has exacerbated challenges for individuals with mental health conditions such as OUD. Pharmacists are well‐positioned to bridge existing gaps in OUD care, particularly during the COVID‐19 pandemic. Roles for pharmacists include OUD risk identification and screening, referral of patients to treatment and support programs, ensuring medication access, expanding naloxone access, and advocacy initiatives. This review article identifies barriers to care for patients with OUD during the COVID‐19 pandemic and explores opportunities and resources for pharmacists to improve OUD care during the pandemic and beyond.

Commentary: Reducing youth firearm violence and the associated health disparities requires enhanced surveillance and modern behavioral intervention strategies - a commentary on Bottiani et al. (2021)

Youth firearm injury is a worsening public health crisis, and the risks are not distributed evenly. Bottiani et al. skillfully explicated those health disparities, described sociological factors underlying them, and explored avenues for prevention. We supplement their analysis by detailing problems and solutions related to a critical barrier to firearm violence prevention - the nonexistence both of reliable 'gold standard' nonfatal firearm injury surveillance data, and systems for near real-time surveillance of firearm injuries at granular spatial scales that would enable to optimization of rapid response protocols and neighborhood-based prevention programs. We conclude with a discussion of modern, scalable, behavioral intervention approaches that could be leveraged to fill the largely absent evidence base resulting from the documented underfunding of youth firearm violence prevention research.