Evidence map›Paper›PMID 31714940›Full record

ArticlePLoS medicine2019

Suspected heroin-related overdoses incidents in Cincinnati, Ohio: A spatiotemporal analysis.

Zehang Richard Li, Evaline Xie, Forrest W Crawford, Joshua L Warren, Kathryn McConnell, J Tyler Copple, Tyler Johnson, Gregg S Gonsalves

Abstract read
In one paragraph

Article in PLoS medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zehang Richard LiDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America.ORCID 0000-0001-9551-9638
Evaline XieYale College, New Haven, Connecticut, United States of America.
Forrest W CrawfordDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America.ORCID 0000-0002-0046-0547
Joshua L WarrenDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America.
Kathryn McConnellYale School of Forestry & Environmental Studies, New Haven, Connecticut, United States of America.ORCID 0000-0003-4395-5483
J Tyler CoppleDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.
Tyler JohnsonDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.ORCID 0000-0002-0542-8300
Gregg S GonsalvesDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.ORCID 0000-0002-5789-9841

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR37DA015612 · NIDA · STANFORD UNIVERSITY · PI DOUGLAS K OWENS · 2019 to 2026
$6.3M
Novel approaches to the design and evaluation of combination HIV preventionR01MH105203 · NIMH · YALE UNIVERSITY · PI PALTIEL, A. DAVID · 2014 to 2018
$3.3M
Novel, Adaptive Approaches to Predicting and Responding to Outbreaks of Overdose, HIV and HCV Among People Who Use DrugsDP2DA049282 · NIDA · YALE UNIVERSITY · PI GONSALVES, GREGG STEVEN · 2019 to 2019
$2.5M
Network-based epidemiology for hidden and hard-to-reach populationsDP2HD091799 · NICHD · YALE UNIVERSITY · PI CRAWFORD, FORREST WRENN · 2016 to 2016
$2.5M
NCATS NIH HHS UL1 TR001863NICHD NIH HHS DP2 HD091799NIDA NIH HHS DP2 DA049282NIDA NIH HHS R37 DA015612NIMH NIH HHS R01 MH105203
6 · The paper itself

Abstract

backgroundOpioid misuse and deaths are increasing in the United States. In 2017, Ohio had the second highest overdose rates in the US, with the city of Cincinnati experiencing a 50% rise in opioid overdoses since 2015. Understanding the temporal and geographic variation in overdose emergencies may help guide public policy responses to the opioid epidemic. METHODS AND

findingsWe used a publicly available data set of suspected heroin-related emergency calls (n = 6,246) to map overdose incidents to 280 census block groups in Cincinnati between August 1, 2015, and January 30, 2019. We used a Bayesian space-time Poisson regression model to examine the relationship between demographic and environmental characteristics and the number of calls within block groups. Higher numbers of heroin-related incidents were found to be associated with features of the built environment, including the proportion of parks (relative risk [RR] = 2.233; 95% credible interval [CI]: [1.075-4.643]), commercial (RR = 13.200; 95% CI: [4.584-38.169]), manufacturing (RR = 4.775; 95% CI: [1.958-11.683]), and downtown development zones (RR = 11.362; 95% CI: [3.796-34.015]). The number of suspected heroin-related emergency calls was also positively associated with the proportion of male population, the population aged 35-49 years, and distance to pharmacies and was negatively associated with the proportion aged 18-24 years, the proportion of the population with a bachelor's degree or higher, median household income, the number of fast food restaurants, distance to hospitals, and distance to opioid treatment programs. Significant spatial and temporal heterogeneity in the risks of incidents remained after adjusting for covariates. Limitations of this study include lack of information about the nature of incidents after dispatch, which may differ from the initial classification of being related to heroin, and lack of information on local policy changes and interventions.

conclusionsWe identified areas with high numbers of reported heroin-related incidents and features of the built environment and demographic characteristics that are associated with these events in the city of Cincinnati. Publicly available information about opiate overdoses, combined with data on spatiotemporal risk factors, may help municipalities plan, implement, and target harm-reduction measures. In the US, more work is necessary to improve data availability in other cities and states and the compatibility of data from different sources in order to adequately measure and monitor the risk of overdose and inform health policies.

Indexed as

Bayes TheoremDatabases, FactualDrug OverdoseEmergency Medical ServicesEmergency Service, HospitalFemaleHeroinHeroin DependenceHumansMaleOhioRisk FactorsSpatio-Temporal AnalysisSubstance-Related DisordersUnited StatesHeroin

Identifiers

PMID31714940
PMCPMC6850525

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.