Evidence map›Paper›PMID 26800987›Full record

ArticleJournal of urban health : bulletin of the New York Academy of Medicine2016

Neighborhood-Level and Spatial Characteristics Associated with Lay Naloxone Reversal Events and Opioid Overdose Deaths.

Christopher Rowe, Glenn-Milo Santos, Eric Vittinghoff, Eliza Wheeler, Peter Davidson, Philip O Coffin

Open access · bronzeAbstract read
In one paragraph

Article in Journal of urban health : bulletin of the New York Academy of Medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
7.6field-weighted citation impact, top 3% of its field
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

42 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.

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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

6 authors at 4 institutions in 1 country.

Christopher RoweSan Francisco Department of Public Health, 25 Van Ness Avenue, Ste. 500, San Francisco, CA, 94102, USA. chris.rowe@sfdph.org.
Glenn-Milo SantosSan Francisco Department of Public Health, 25 Van Ness Avenue, Ste. 500, San Francisco, CA, 94102, USA.
Eric VittinghoffUniversity of California, San Francisco, 500 Parnassus Avenue, San Francisco, CA, 94143, USA.
Eliza WheelerDrug Overdose Prevention and Education Project, Harm Reduction Coalition, 1440 Broadway, Suite 902, Oakland, CA, 94612, USA.
Peter DavidsonUniversity of California, San Diego, 9500 Gilman Drive, La Jolla, California, CA, 92093, USA.
Philip O CoffinSan Francisco Department of Public Health, 25 Van Ness Avenue, Ste. 500, San Francisco, CA, 94102, USA.
San Francisco Department of Public Health · USUniversity of California, San Francisco · USHarm Reduction Services · USUniversity of California, San Diego · US

Funding

A Mixed-Method Study of Injection Drug Use Settings Among FSWs in TijuanaK01DA032443 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DAVIDSON, PETER JOHN · 2012 to 2015
$723k
An Assessment of Opioid Overdoses in San Francisco from 2010 through 2012R03DA038084 · NIDA · PUBLIC HEALTH FOUNDATION ENTERPRISES · PI COFFIN, PHILLIP O · 2014 to 2015
$109k
NIDA NIH HHS K01 DA032443NIDA NIH HHS R03 DA038084
6 · The paper itself

Abstract

There were over 23,000 opioid overdose deaths in the USA in 2013, and opioid-related mortality is increasing. Increased access to naloxone, particularly through community-based lay naloxone distribution, is a widely supported strategy to reduce opioid overdose mortality; however, little is known about the ecological and spatial patterns of the distribution and utilization of lay naloxone. This study aims to investigate the neighborhood-level correlates and spatial relationships of lay naloxone distribution and utilization and opioid overdose deaths. We determined the locations of lay naloxone distribution sites and the number of unintentional opioid overdose deaths and reported reversal events in San Francisco census tracts (n = 195) from 2010 to 2012. We used Wilcoxon rank-sum tests to compare census tract characteristics across tracts adjacent and not adjacent to distribution sites and multivariable negative binomial regression models to assess the association between census tract characteristics, including distance to the nearest site, and counts of opioid overdose deaths and naloxone reversal events. Three hundred forty-two opioid overdose deaths and 316 overdose reversals with valid location data were included in our analysis. Census tracts including or adjacent to a distribution site had higher income inequality, lower percentage black or African American residents, more drug arrests, higher population density, more overdose deaths, and more reversal events (all p < 0.05). In multivariable analysis, greater distance to the nearest distribution site (up to a distance of 4000 m) was associated with a lower count of Naloxone reversals [incidence rate ratio (IRR) = 0.51 per 500 m increase, 95% CI 0.39-0.67, p < 0.001] but was not significantly associated with opioid overdose deaths. These findings affirm that locating lay naloxone distribution sites in areas with high levels of substance use and overdose risk facilitates reversals of opioid overdoses in those immediate areas but suggests that alternative delivery methods may be necessary to reach individuals in other areas with less concentrated risk.

Indexed as

Analgesics, OpioidDrug OverdoseHealth Services AccessibilityHumansNaloxoneNarcotic AntagonistsOpioid-Related DisordersResidence CharacteristicsSan FranciscoSocioeconomic FactorsSpatial AnalysisUrban HealthAnalgesics, OpioidNaloxoneNarcotic AntagonistsNaloxoneOpioidsOverdoseSpatial analysis

Identifiers

PMID26800987
PMCPMC4794468
OpenAlexW2262155408

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.