Preventing Overdose Using Information and Data From the Environment

Study Overview
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Brief Summary
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The objectives of this project are to leverage surveillance data to predict future overdose outbreaks, and to evaluate the impact of a randomized, statewide, community-level intervention trial to target overdose prevention programs to neighborhoods at highest risk of future overdose deaths. This study develops and tests an opioid overdose forecasting tool, which will allow other states to identify and deploy interventions to communities at highest risk of opioid-related death. The findings from this study have the potential to significantly improve the allocation of resources to curb the opioid overdose epidemic in the United States.

Detailed Description
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Overdose deaths have skyrocketed in the United States since 1999. The epidemic has prompted widespread federal and state actions, yet the number of people who die of an overdose continues to increase. In light of the accelerating and rapidly evolving overdose epidemic, new strategies are needed to identify communities most at risk, and to utilize resources more effectively to curb overdose deaths. To address these public health priorities, we will develop a forecasting tool to predict overdose deaths before they occur, and then conduct a randomized, statewide, community-level intervention to evaluate the impact of resource targeting based on these predictions. The study will take place in Rhode Island, a state with the 10th highest rate of overdose fatality in 2016. The study has two phases. First, we will develop a predictive analytics model that forecasts future overdose mortality at the neighborhood-level, using publicly available information and data from a multicomponent overdose surveillance system. This tool, called PROVIDENT (Preventing Overdose using Information and Data from the Environment) will be used to predict the likelihood of future overdose deaths in every neighborhood across Rhode Island. As all data to be analyzed as part of this study is collected through ongoing public health surveillance activities and the use of protected health information involves no more than a minimal risk to the privacy of individuals, the institutional review board (IRB) of record approved a waiver of research participants' authorization for use/disclosure of information about them for research purposes, in accordance with 45 Code of Federal Regulations (CFR) § 164.512(i)(2)(iv). Next, we will conduct a randomized policy experiment to evaluate whether targeting overdose prevention interventions to neighborhoods at highest risk reduces overdose morbidity and mortality. The state's department of health will receive PROVIDENT model predictions for half of the 39 cities/towns in Rhode Island. Within these cities/towns, the health department will work with stakeholders to target overdose prevention interventions to neighborhoods with the highest predicted probability of future overdose deaths. Interventions include efforts to: (1) prevent high-risk prescribing (through academic detailing and other educational efforts); (2) expand access to opioid agonist therapy, including buprenorphine and methadone; (3) increase naloxone distribution (through community and pharmacy-based efforts); and (4) expand street-based peer recovery coaching and referrals. Control cities/towns will continue to receive these same interventions, but will not receive information about the neighborhoods at the highest predicted risk of overdose. Fatal and non-fatal opioid overdose rates in the control cities/towns will be compared to those that received the PROVIDENT model predictions. To achieve these aims, we will leverage a unique partnership between an academic institution and a state's health department, which allows for unprecedented access to and sharing of population-based overdose surveillance data. Our results will improve public health decision-making and inform resource allocation to communities that should be prioritized for evidence-based prevention, treatment, recovery, and overdose rescue services. If found to be effective, the PROVIDENT forecasting model will be disseminated to other states, which could adapt the tool to guide resource allocation and maximize public health impact. In sum, this project is highly responsive to a top research priority of the National Institute on Drug Abuse, and directly addresses one of the nation's most challenging public health crises.

Official Title
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Reducing Drug-Related Mortality Using Predictive Analytics: A Randomized, Statewide, Community Intervention Trial

Conditions
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Opioid Overdose, Drug Overdose

Intervention / Treatment
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* Behavioral: PROVIDENT


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: - Cities and towns in Rhode Island Exclusion Criteria: - There are no exclusion criteria
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Health Services Research
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: We will conduct a randomized policy experiment to evaluate whether targeting overdose prevention interventions to neighborhoods at highest risk reduces overdose morbidity and mortality. The state's department of health will receive PROVIDENT model predictions for half of the 39 cities/towns in Rhode Island.
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Intervention<br>Within these cities/towns, the health department will work with stakeholders to prioritize overdose prevention interventions to neighborhoods with the highest probability of future overdose deaths, as predicted by the PROVIDENT model. | Behavioral: PROVIDENT<br>* Each of the state's 39 municipalities will be randomised 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 neighborhood risk predictions from the PROVIDENT model, and state agencies and community-based organizations will direct resources to neighborhoods 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 neighborhood risk predictions from this study.<br>|
| No Intervention: Control<br>Cities/towns assigned to the control arm will continue to work with the health department and distribute these interventions at existing resource levels, but without receiving information on predicted probability of overdose risk for specific neighborhoods. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in accidental fatal and non-fatal drug overdoses | The primary outcome is the municipal-level rate of fatal and non-fatal drug overdoses. Fatal overdoses will be defined as drug-related deaths deemed accidental by a state medical examiner. The location of these overdose events will be aggregated to the city/town level. Non-fatal overdoses will be defined as an Emergency Department (ED) visit for a suspected overdose reported through the state's 48-Hour Overdose Reporting System. Since patient outcomes are recorded, patients who did not survive or who were dead upon arrival will be excluded to avoid double-counting. | Baseline up to 2.5 years following intervention, with assessment of primary outcome at 2.5 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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fentanyl, naloxone, harm reduction, machine learning, predictive analytics, community intervention, drug overdose, opioids