{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Marshall BDL"],"funding":["NIDA NIH HHS","NIA NIH HHS","National Institute on Drug Abuse","National Institute of General Medical Sciences","NIGMS NIH HHS"],"pagination":["1152-1162"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8904285"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["117(4)"],"pubmed_abstract":["<h4>Background and aims</h4>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.<h4>Design</h4>Randomized, population-based, community intervention trial.<h4>Setting</h4>Rhode Island, USA.<h4>Participants</h4>All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes.<h4>Intervention</h4>Each of the st"],"journal":["Addiction (Abingdon, England)"],"pubmed_title":["Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial."],"pmcid":["PMC8904285"],"funding_grant_id":["R01 DA046620","P20‐GM125507","P20 GM125507","R01‐DA046620","T32 AG000246"],"pubmed_authors":["Krieger MS","Hallowell BD","Goedel WC","Cerda M","Allen B","Schell RC","Neill DB","Yedinak JL","Li Y","Marshall BDL","Alexander-Scott N","Ahern J","Pratty C"],"additional_accession":[]},"is_claimable":false,"name":"Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial.","description":"<h4>Background and aims</h4>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.<h4>Design</h4>Randomized, population-based, community intervention trial.<h4>Setting</h4>Rhode Island, USA.<h4>Participants</h4>All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes.<h4>Intervention</h4>Each of the st","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Apr","modification":"2026-05-09T13:57:20.065Z","creation":"2024-11-05T19:19:04.621Z"},"accession":"S-EPMC8904285","cross_references":{"pubmed":["34729851"],"doi":["10.1111/add.15731"]}}