<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Yedinak JL</submitter><funding>NIGMS NIH HHS</funding><pagination>103395</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8568646</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>96</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Multiple areas in the United States of America (USA) are experiencing high rates of overdose and outbreaks of bloodborne infections, including HIV and hepatitis C virus (HCV), due to non-sterile injection drug use. We aimed to identify neighbourhoods at increased vulnerability for overdose and infectious disease outbreaks in Rhode Island, USA. The primary aim was to pilot machine learning methods to identify which neighbourhood-level factors were important for creating "vulnerability assessment scores" across the state. The secondary aim was to engage stakeholders to pilot an interactive mapping tool and visualize the results.&lt;h4>Methods&lt;/h4>From September 2018 to November 2019, we conducted a neighbourhood-level vulnerability assessment and stakeholder engagement proces</pubmed_abstract><journal>The International journal on drug policy</journal><pubmed_title>Machine learning takes a village: Assessing neighbourhood-level vulnerability for an overdose and infectious disease outbreak.</pubmed_title><pmcid>PMC8568646</pmcid><funding_grant_id>P20 GM125507</funding_grant_id><pubmed_authors>Krieger MS</pubmed_authors><pubmed_authors>Bertrand T</pubmed_authors><pubmed_authors>Marak T</pubmed_authors><pubmed_authors>Nelson E</pubmed_authors><pubmed_authors>Civitarese AM</pubmed_authors><pubmed_authors>Lee H</pubmed_authors><pubmed_authors>Howe K</pubmed_authors><pubmed_authors>Ndoye CD</pubmed_authors><pubmed_authors>Samuels EA</pubmed_authors><pubmed_authors>Yedinak JL</pubmed_authors><pubmed_authors>Li Y</pubmed_authors><pubmed_authors>Marshall BDL</pubmed_authors><pubmed_authors>Chan PA</pubmed_authors></additional><is_claimable>false</is_claimable><name>Machine learning takes a village: Assessing neighbourhood-level vulnerability for an overdose and infectious disease outbreak.</name><description>&lt;h4>Background&lt;/h4>Multiple areas in the United States of America (USA) are experiencing high rates of overdose and outbreaks of bloodborne infections, including HIV and hepatitis C virus (HCV), due to non-sterile injection drug use. We aimed to identify neighbourhoods at increased vulnerability for overdose and infectious disease outbreaks in Rhode Island, USA. The primary aim was to pilot machine learning methods to identify which neighbourhood-level factors were important for creating "vulnerability assessment scores" across the state. The secondary aim was to engage stakeholders to pilot an interactive mapping tool and visualize the results.&lt;h4>Methods&lt;/h4>From September 2018 to November 2019, we conducted a neighbourhood-level vulnerability assessment and stakeholder engagement proces</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Oct</publication><modification>2025-04-04T10:27:42.228Z</modification><creation>2025-02-19T01:21:35.737Z</creation></dates><accession>S-EPMC8568646</accession><cross_references><pubmed>34344539</pubmed><doi>10.1016/j.drugpo.2021.103395</doi></cross_references></HashMap>