Evidence map›Paper›PMID 27470224›Full record

ArticleAddiction (Abingdon, England)2017

Prescription opioid poisoning across urban and rural areas: identifying vulnerable groups and geographic areas.

Magdalena Cerdá, Andrew Gaidus, Katherine M Keyes, William Ponicki, Silvia Martins, Sandro Galea, Paul Gruenewald

Open access · greenAbstract read
In one paragraph

Article in Addiction (Abingdon, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
12.1field-weighted citation impact, top 1% 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

51 citing papers in PubMed, 104 citations in OpenAlex.

  1. Review
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  7. The Impact of COVID-19 on Opioid-Related Overdose Deaths in Texas.International journal of environmental research and public health · 2022
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  17. Assessing opioid overdose risk: a review of clinical prediction models utilizing patient-level data.Translational research : the journal of laboratory and clinical medicine · 2021
    Review
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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

7 authors at 4 institutions in 1 country.

Magdalena CerdáDepartment of Emergency Medicine, University of California, Davis, Sacramento, CA, USA.
Andrew GaidusPrevention Research Center, Pacific Institute for Research and Evaluation, Oakland, CA, USA.
Katherine M KeyesDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
William PonickiPrevention Research Center, Pacific Institute for Research and Evaluation, Oakland, CA, USA.
Silvia MartinsDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
Sandro GaleaSchool of Public Health, Boston University, Boston, MA, USA.
Paul GruenewaldPrevention Research Center, Pacific Institute for Research and Evaluation, Oakland, CA, USA.
Pacific Institute For Research and Evaluation · USColumbia University · USBoston University · USUniversity of California, Davis · US

Funding

Race, alcohol consumption and vehicle crashes: an epidemiologic paradoxK01AA021511 · NIAAA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI KEYES, KATHERINE M. · 2013 to 2017
$904k
NIAAA NIH HHS K01 AA021511
6 · The paper itself

Abstract

aimsTo determine (1) whether prescription opioid poisoning (PO) hospital discharges spread across space over time, (2) the locations of 'hot-spots' of PO-related hospital discharges, (3) how features of the local environment contribute to the growth in PO-related hospital discharges and (4) where each environmental feature makes the strongest contribution.

designHierarchical Bayesian Poisson space-time analysis to relate annual discharges from community hospitals to postal code characteristics over 10 years.

settingCalifornia, USA.

participantsResidents of 18 517 postal codes in California, 2001-11. MEASUREMENTS: Annual postal code-level counts of hospital discharges due to PO poisoning were related to postal code pharmacy density, measures of medical need for POs (i.e. rates of cancer and arthritis-related hospital discharges), economic stressors (i.e. median household income, percentage of families in poverty and the unemployment rate) and concentration of manual labor industries.

findingsPO-related hospital discharges spread from rural and suburban/exurban 'hot-spots' to urban areas. They increased more in postal codes with greater pharmacy density [rate ratio (RR) = 1.03; 95% credible interval (CI) = 1.01, 1.05], more arthritis-related hospital discharges (RR = 1.08; 95% CI = 1.06, 1.11), lower income (RR = 0.85; 95% CI = 0.83, 0.87) and more manual labor industries (RR = 1.15; 95% CI = 1.10, 1.19 for construction; RR = 1.12; 95% CI = 1.04, 1.20 for manufacturing industries). Changes in pharmacy density primarily affected PO-related discharges in urban areas, while changes in income and manual labor industries especially affected PO-related discharges in suburban/exurban and rural areas.

conclusionsHospital discharge rates for prescription opioid (PO) poisoning spread from rural and suburban/exurban hot-spots to urban areas, suggesting spatial contagion. The distribution of age-related and work-place-related sources of medical need for POs in rural areas and, to a lesser extent, the availability of POs through pharmacies in urban areas, partly explain the growth of PO poisoning across California, USA.

Indexed as

AdolescentAdultAge FactorsAnalgesics, OpioidBayes TheoremCaliforniaChildChild, PreschoolFemaleHumansInfantMaleMiddle AgedPovertyRural PopulationSocioeconomic FactorsAnalgesics, OpioidBayesian space-time modelsdrug overdosegeographyhospital dischargesprescription opioidsrural and urban

Identifiers

PMID27470224
PMCPMC5148642
OpenAlexW2496997673

What OpenQuestion holds

Textmetadata
LicenceTDM
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.