Evidence map›Paper›PMID 33319025›Full record

ArticleSSM - population health2020

The economic underpinnings of the drug epidemic.

Nathan Seltzer

Abstract read
In one paragraph

Article in SSM - population health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Opioid Misuse: A Review of the Main Issues, Challenges, and Strategies.International journal of environmental research and public health · 2022
    Review
  6. Article
  7. Review
  8. Article
  9. Article
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

1 author.

Nathan SeltzerDepartment of Demography, Berkeley Population Center, University of California, Berkeley, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

U.S. labor markets have experienced transformative change over the past half century. Spurred on by global economic change, robotization, and the decline of labor unions, state labor markets have shifted away from an occupational regime dominated by the production of goods to one characterized by the provision of services. Prior studies have proposed that the deterioration of employment opportunities may be associated with the rise of substance use disorders and drug overdose deaths, yet no clear link between changes in labor market dynamics in the U.S. manufacturing sector and drug overdose deaths has been established. Using restricted-use vital registration records between 1999 and 2017 that comprise over 700,000 drug deaths, I test two questions: First, what is the association between manufacturing decline and drug and opioid overdose mortality rates? Second, how much of the increase in these drug-related outcomes can be predicted by manufacturing decline? The findings provide strong evidence that the restructuring of the U.S. labor market has played an important upstream role in the current drug crisis. Up to 92,000 overdose deaths for men and up to 44,000 overdose deaths for women are predicted by the decline of state-level manufacturing over this nearly two-decade period. These results persist in models that adjust for other social, economic, and policy trends changing at the same time. Critically, the findings signal the value of policy interventions that aim to reduce persistent economic precarity experienced by individuals and communities, especially the economic strain placed upon the middle class.

Indexed as

DeindustrializationDrug overdose deathsEconomic conditionsMortalityOpioid crisisSocial determinants

Identifiers

PMID33319025
PMCPMC7725949

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.