Faculty Bibliography

Opioid analgesics and benzodiazepines remain substantial contributors to unintentional drug overdose deaths in the United States. To promote judicious prescribing and improve care for patients with substance use disorders, the New York City Department of Health and Mental Hygiene piloted the Prescriber Notification Program, an educational initiative to deliver targeted public health messaging to providers who had prescribed opioid analgesics and/or benzodiazepines to patients who died from overdose in New York City. This article reports on provider responses to receipt of patient death notifications and program feasibility. Findings demonstrate that a majority of prescribers were not aware of patient deaths prior to receiving notification letters. Public health authorities considering prescriber notification systems should address barriers to implementation and sustainability-in particular, consistent and routine access to and linkage of overdose mortality and prescription monitoring data-as part of planning such programs.

The ways in which prescription drug monitoring programs (PDMPs) have been integrated into clinical practice remain understudied, and research into PDMP implementation in states where PDMP use by providers is mandated remains scant. This qualitative study describes how use of a state-mandated PDMP influenced clinical practice and opioid analgesic prescribing. We conducted face-to-face, in-depth interviews with 53 New York State-licensed primary care physicians who reported that they currently prescribed opioid analgesic medication, including those providers who reported consistent use of the PDMP (n = 38) in this sample. We used a thematic analytic approach to identify patterns of PDMP implementation into practice following enactment of the New York State legislative usage mandate. Among physicians who consistently used the PDMP, we found two distinct groups: (1) physicians who reported no change in their clinical practice and (2) physicians who acknowledged changes to both clinical practice and administrative management. In the latter group, most physicians felt the PDMP had benefited their patient relationships by fostering dialogue around patient substance use; however, some used the PDMP to dismiss patients from care. Findings suggest that increased education for providers relating to judicious prescribing, opioid use disorder, and best practice for PDMP utilization are needed.

BACKGROUND AND AIMS/OBJECTIVE:In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxone distribution efforts). We aimed to develop PROVIDENT (Preventing Overdose using Information and Data from the Environment), a machine learning-based forecasting tool to predict future overdose deaths at the census block group (i.e. neighbourhood) level. DESIGN/METHODS:Randomized, population-based, community intervention trial. SETTING/METHODS:Rhode Island, USA. PARTICIPANTS/METHODS:All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes. INTERVENTION/METHODS:Each of the state's 39 municipalities will be randomized to the intervention (PROVIDENT) or comparator condition. An interactive, web-based tool will be developed to visualize the PROVIDENT model predictions. Municipalities assigned to the treatment arm will receive neighbourhood risk predictions from the PROVIDENT model, and state agencies and community-based organizations will direct resources to neighbourhoods identified as high risk. Municipalities assigned to the control arm will continue to receive surveillance information and overdose prevention resources, but they will not receive neighbourhood risk predictions. MEASUREMENTS/METHODS:The primary outcome is the municipal-level rate of fatal and non-fatal drug overdoses. Fatal overdoses will be defined as unintentional drug-related death; non-fatal overdoses will be defined as an emergency department visit for a suspected overdose reported through the state's syndromic surveillance system. Intervention efficacy will be assessed using Poisson or negative binomial regression to estimate incidence rate ratios comparing fatal and non-fatal overdose rates in treatment vs. control municipalities. COMMENTS/CONCLUSIONS:The findings will inform the utility of predictive modelling as a tool to improve public health decision-making and inform resource allocation to communities that should be prioritized for prevention, treatment, recovery and overdose rescue services.

BACKGROUND:The ways in which prescription drug monitoring programs (PDMPs) have been integrated into primary care practice remain understudied, and research into physician utilization of PDMPs in states where PDMP use is mandated remains scant. OBJECTIVES:To characterize primary care physician perspectives on and utilization of a mandatory PDMP in New York City. METHODS:We conducted face-to-face, in-depth interviews with primary care physicians who reported that they currently prescribed opioid analgesic medication. We used a thematic analytic approach to characterize physician perspectives on the PDMP mandate and physician integration of mandatory PDMP use into primary care practice. RESULTS:Primary care providers demonstrated a continuum of PDMP utilization, ranging from consistent use to the specifications of the mandate to inconsistent use to no use. Providers reported a range of perspectives on the purpose and function of the PDMP mandate, as well as a lack of clarity about the mandate and its enforcement. CONCLUSION:Findings suggest a need for increased clinical and public health education about the use of PDMPs as clinical tools to identify and treat patients with potential substance use disorders in primary care.

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016-2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.

PURPOSE/UNASSIGNED:As part of COVID-19 control policy, the Centers for Disease Control and Prevention has advised local jurisdictions to permit the formation of homeless encampments to prevent community disease spread. This new federal public health guidance is in conflict with existing police policies in many jurisdictions to raze or evict homeless encampments upon discovery. However, no empirical research on homeless encampment policy actions exists. METHODS/UNASSIGNED:This study utilized interrupted time series to estimate the impact of the 2017 closure of "the Hole"-a longstanding encampment of homeless people who use drugs in the Bronx, New York City-on crime complaints. Daily crime complaints originating from public spaces within 1 mile of the encampment were captured during the 30-day periods before and after closure. RESULTS/UNASSIGNED:Closure was associated with no short-term changesin complaints [IRR=1.01; 95% CI (0.81-1.27)], with daily complaints remaining at baseline levels during the post-closure period [IRR 0.99; 95% CI (0.98-1.00)]. DISCUSSION/UNASSIGNED:Findings preliminarily suggest that the presence of a homeless encampment may not have been associated with increased levels of crime in the neighborhood where it was located. Future research is necessary to understand the health and social impacts of homeless encampments and inform municipal policymakers.