Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population‐based, community intervention trial.
Background and Aims: 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 PROVI...
| Publicado en: | Addiction Vol. 117; no. 4; pp. 1152 - 1163 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | pictorial protocol tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155656574&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155656574 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09652140 AIO jtl: Addiction issn: 09652140 maglogo: Y pubinfo: dt: Apr2022 vid: 117 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 155656574 153837613 155656574 155656574 10.1111/add.15731 155656574 ppf: 1152 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population‐based, community intervention trial. aug: au: Marshall, Brandon D. L. Alexander‐Scott, Nicole Yedinak, Jesse L. Hallowell, Benjamin D. Goedel, William C. Allen, Bennett Schell, Robert C. Li, Yu Krieger, Maxwell S. Pratty, Claire Ahern, Jennifer Neill, Daniel B. Cerdá, Magdalena affil: Department of Epidemiology, Brown University School of Public Health, Providence RI,, USA sug: subj: Overdose Prevention and Control Machine Learning Forecasting Randomized Controlled Trials Opiate Overdose Mortality Prediction Models ab: Background and Aims: 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 Randomized, population‐based, community intervention trial. Setting: Rhode Island, USA. Participants: All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes. Intervention: 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 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 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. pubtype: Academic Journal doctype: pictorial protocol tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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