Faculty Bibliography

This pilot study explores the utilization of the Overdose Detection Mapping Application Program (ODMAP) as a tool for enhancing collaboration between the public health and public safety sectors to address the overdose epidemic in the United States. Through qualitative interviews with ODMAP users, key themes emerged, including the role of data sharing in facilitating collaboration, challenges posed by divergent data privacy standards, and the need for clearer guidance on cross-sector data sharing. Findings highlight ODMAP's potential to integrate data for targeted interventions at individual and population levels. Future research directions include overcoming data sharing barriers, strategically utilizing data across sectors, and rigorously evaluating the impact of cross-sector partnerships on overdose morbidity and mortality. Overall, this study underscores the importance of ODMAP in fostering coordinated responses to the overdose crisis and provides valuable insights for improving overdose surveillance and intervention efforts.

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 2016-2020 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.

Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision supports for public health practitioners. To facilitate practitioner use of machine learning as decision support for area-level intervention, this study developed and applied four practice-based predictive model evaluation criteria (implementation capacity, preventive potential, health equity, and jurisdictional practicalities). We used a case study of overdose prevention in Rhode Island to illustrate how these criteria could inform public health practice and health equity promotion. We used Rhode Island overdose mortality records from January 2016 to June 2020 (N=1,408) and neighborhood-level Census data. We learned two disparate machine learning models, Gaussian process and random forest, to illustrate the comparative utility of our criteria to guide interventions. Our models predicted 7.5-36.4% of overdose deaths during the test period, illustrating the preventive potential of overdose interventions assuming 5-20% statewide implementation capacities for neighborhood-level resource deployment. We described the health equity implications of predictive modeling to guide interventions along urbanicity, racial/ethnic composition, and poverty. In sum, our study discussed considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice.

Substance use disorders (SUD) are associated with increased risk of worse COVID-19 outcomes. Likewise, racial/ethnic minority patients experience greater risk of severe COVID-19 disease compared to white patients. Providers should understand the role of race and ethnicity as an effect modifier on COVID-19 severity among individuals with SUD. This retrospective cohort study assessed patient race/ethnicity as an effect modifier of the risk of severe COVID-19 disease among patients with histories of SUD and overdose. We used merged electronic health record data from 116,471 adult patients with a COVID-19 encounter between March 2020 and February 2021 across five healthcare systems in New York City. Exposures were patient histories of SUD and overdose. Outcomes were risk of COVID-19 hospitalization and subsequent COVID-19-related ventilation, acute kidney failure, sepsis, and mortality. Risk factors included patient age, sex, and race/ethnicity, as well as medical comorbidities associated with COVID-19 severity. We tested for interaction between SUD and patient race/ethnicity on COVID-19 outcomes. Findings showed that Non-Hispanic Black, Hispanic/Latino, and Asian/Pacific Islander patients experienced a higher prevalence of all adverse COVID-19 outcomes compared to non-Hispanic white patients. Past-year alcohol (OR 1.24 [1.01-1.53]) and opioid use disorders (OR 1.91 [1.46-2.49]), as well as overdose history (OR 4.45 [3.62-5.46]), were predictive of COVID-19 mortality, as well as other adverse COVID-19 outcomes. Among patients with SUD, significant differences in outcome risk were detected between patients of different race/ethnicity groups. Findings indicate that providers should consider multiple dimensions of vulnerability to adequately manage COVID-19 disease among populations with SUDs.

The association between recent release from incarceration and dramatically increased risk of fatal overdose is well-established at the individual level. Fatal overdose and. arrest/release are spatially clustered, suggesting that this association may persist at the neighborhood level. We analyzed multicomponent data from Rhode Island, 2016-2020, and observed a modest association at the census tract level between rates of release per 1000 population and fatal overdose per 100,000 person-years, adjusting for spatial autocorrelation in both the exposure and outcome. Our results suggest that for each additional person released to a given census tract per 1000 population, there is a corresponding increase in the rate of fatal overdose by 2 per 100,000 person years. This association is more pronounced in suburban tracts, where each additional release awaiting trial is associated with an increase in the rate of fatal overdose of 4 per 100,000 person-years and 6 per 100,000 person-years for each additional release following sentence expiration. This association is not modified by the presence or absence of a licensed medication for opioid use disorder (MOUD) treatment provider in the same or surrounding tracts. Our results suggest that neighborhood-level release rates are moderately informative as to tract-level rates of fatal overdose and underscore the importance of expanding pre-release MOUD access in correctional settings. Future research should explore risk and resource environments particularly in suburban and rural areas and their impacts on overdose risk among individuals returning to the community.

Pre-existing mental disorders are linked to COVID-19-related outcomes. However, the findings are inconsistent and a thorough analysis of a broader spectrum of outcomes such as COVID-19 infection severity, morbidity, and mortality is required. We investigated whether the presence of psychiatric diagnoses and/or the use of antidepressants influenced the severity of the outcome of COVID-19. This retrospective cohort study evaluated electronic health records from the INSIGHT Clinical Research Network in 116,498 individuals who were diagnosed with COVID-19 between March 1, 2020, and February 23, 2021. We examined hospitalization, intubation/mechanical ventilation, acute kidney failure, severe sepsis, and death as COVID-19-related outcomes. After using propensity score matching to control for demographics and medical comorbidities, we used contingency tables to assess whether patients with (1) a history of psychiatric disorders were at higher risk of more severe COVID-19-related outcomes and (2) if use of antidepressants decreased the risk of more severe COVID-19 infection. Pre-existing psychiatric disorders were associated with an increased risk for hospitalization, and subsequent outcomes such as acute kidney failure and severe sepsis, including an increased risk of death in patients with schizophrenia spectrum disorders or bipolar disorders. The use of antidepressants was associated with significantly reduced risk of sepsis (p = 0.033), death (p = 0.026). Psychiatric disorder diagnosis prior to a COVID-19-related healthcare encounter increased the risk of more severe COVID-19-related outcomes as well as subsequent health complications. However, there are indications that the use of antidepressants might decrease this risk. This may have significant implications for the treatment and prognosis of patients with COVID-19.

NYC RxStat, the United States' first public health and public safety partnership aiming to reduce overdose deaths, began in 2012 and established a national model for cross-sector partnerships. The partnership aimed to integrate data-driven policing with actionable public health interventions and surveillance to develop and implement cross-sector overdose responses. With federal support, jurisdictions nationally have implemented public health and public safety partnerships modeled on RxStat. To inform partnership replication efforts, we conducted a stakeholder evaluation of RxStat. We conducted in-depth, semi-structured interviews with 25 current and former RxStat stakeholders. Interviews probed stakeholder perceptions of RxStat's successes, challenges, and opportunities for growth. Interview data were iteratively coded and thematically analyzed. Stakeholders reported certainty about the need for cross-sector collaboration and described cross-disciplinary tensions, challenges to collaboration and implementation, and opportunities for partnership optimization and growth. Findings informed 12 strategies to improve RxStat and partnerships in its model, organized into three opportunity areas: (1) ensure stakeholder and agency accountability; (2) build secure and mutually beneficial data systems; and (3) structure partnerships to facilitate equitable collaboration. Cross-sector partnerships offer a promising strategy to integrate the public health and safety sectors, but disciplinary tensions in approach may hamper implementation. Findings can inform efforts to implement and scale cross-sector partnerships.

BACKGROUND AND AIMS/OBJECTIVE:Individuals with opioid use disorder (OUD) suffer disproportionately from COVID-19. To inform clinical management of OUD patients, research is needed to identify characteristics associated with COVID-19 progression and death among this population. We aimed to investigate the role of OUD and specific comorbidities on COVID-19 progression among hospitalized OUD patients. DESIGN/METHODS:Retrospective cohort study of merged electronic health records (EHR) from five large private health systems SETTING: New York City, New York, USA, 2011-2021 PARTICIPANTS: Adults with a COVID-19 encounter and OUD or opioid overdose diagnosis between March 2020-February 2021 MEASUREMENTS: Primary exposure included diagnosis of OUD/opioid overdose. Risk factors included age, sex, race/ethnicity and common medical, substance use and psychiatric co-morbidities known to be associated with COVID-19 severity. Outcomes included COVID-19 hospitalization and subsequent intubation, acute kidney failure, severe sepsis and death. FINDINGS/RESULTS:Of 110,917 COVID-19+ adults, 1.17% were ever diagnosed with OUD/opioid overdose. OUD patients had higher risk of COVID-19 hospitalization (adjusted risk ratio [aRR]: 1.40 [95% confidence interval (CI) 1.33, 1.47]), intubation (adjusted aood ratio [aOR]: 2.05 [95% CI 1.74, 2.42]), kidney failure (aRR: 1.51 [95% CI 1.34, 1.70)), sepsis (aRR: 2.30 [95% CI 1.88, 2.81]), and death (aRR: 2.10 [95% CI 1.84, 2.40)]. Among hospitalized OUD patients, risks for COVID-19 outcomes included being male; older; of a race/ethnicity other than white, Black or Hispanic; and having co-morbid chronic kidney disease, diabetes, obesity or cancer. Protective factors included having asthma, hepatitis-C, and chronic pain. CONCLUSIONS:Opioid use disorder patients appears to have a substantial risk for COVID-19-associated morbidity and mortality, with particular comorbidities and treatments potentially moderating this risk.

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.

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.