Evidence map›Paper›PMID 29220668›Full record

ArticleDrug and alcohol dependence2018

Nonmedical prescription opioids and pathways of drug involvement in the US: Generational differences.

Melanie Wall, Keely Cheslack-Postava, Mei-Chen Hu, Tianshu Feng, Pamela Griesler, Denise B Kandel

Abstract read
In one paragraph

Article in Drug and alcohol dependence, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
–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

27 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

6 authors.

Melanie WallDepartment of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA; Department of Psychiatry, Columbia University, New York, NY 10032, USA; New York State Psychiatric Institute, New York, NY 10032, USA; Research Foundation for Mental Hygiene, New York, NY 10032, USA.
Keely Cheslack-PostavaDepartment of Psychiatry, Columbia University, New York, NY 10032, USA.
Mei-Chen HuDepartment of Psychiatry, Columbia University, New York, NY 10032, USA.
Tianshu FengResearch Foundation for Mental Hygiene, New York, NY 10032, USA.
Pamela GrieslerDepartment of Psychiatry, Columbia University, New York, NY 10032, USA; New York State Psychiatric Institute, New York, NY 10032, USA.
Denise B KandelDepartment of Psychiatry, Columbia University, New York, NY 10032, USA; New York State Psychiatric Institute, New York, NY 10032, USA; Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA. Electronic address: dbk2@cumc.columbia.edu.

Funding

PRESCRIPTION DRUG USE IN THE US POPULATION: GATEWAY EFFECTS AND FAMILY PATTERNSR01DA036748 · NIDA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI KANDEL, DENISE B · 2015 to 2018
$1.7M
NIDA NIH HHS R01 DA036748
6 · The paper itself

Abstract

backgroundThis study sought to specify (1) the position of nonmedical prescription opioids (NMPO) in drug initiation sequences among Millennials (1979-96), Generation X (1964-79), and Baby Boomers (1949-64) and (2) gender and racial/ethnic differences in sequences among Millennials.

methodsData are from the 2013-2014 National Surveys on Drug Use and Health (n = 73,026). We identified statistically significant drug initiation sequences involving alcohol/cigarettes, marijuana, NMPO, cocaine, and heroin using a novel method distinguishing significant sequences from patterns expected only due to correlations induced by common liability among drugs.

resultsAlcohol/cigarettes followed by marijuana was the most common sequence. NMPO or cocaine use after marijuana, and heroin use after NMPO or cocaine, differed by generation. Among successively younger generations, NMPO after marijuana and heroin after NMPO increased. Millennials were more likely to initiate NMPO than cocaine after marijuana; Generation X and Baby Boomers were less likely (odds ratios = 1.4;0.3;0.2). Millennials were more likely than Generation X and Baby Boomers to use heroin after NMPO (hazards ratios = 7.1;3.4;2.5). In each generation, heroin users were far more likely to start heroin after both NMPO and cocaine than either alone. Sequences were similar by gender. Fewer paths were significant among African-Americans.

conclusionsNMPOs play a more prominent role in drug initiation sequences among Millennials than prior generations. Among Millennials, NMPO use is more likely than cocaine to follow marijuana use. In all generations, transition to heroin from NMPO significantly occurs only when both NMPO and cocaine have been used. Delineation of drug sequences suggests optimal points in development for prevention and treatment efforts.

Indexed as

Social ChangeSurveys and QuestionnairesAdolescentAdultAge FactorsAnalgesics, OpioidCohort EffectFemaleHumansMaleMiddle AgedOpioid-Related DisordersPrescription Drug MisuseUnited StatesYoung AdultAnalgesics, OpioidCocaineDrug initiation sequencesGenerationsHeroinNonmedical prescription opioids

Identifiers

PMID29220668
PMCPMC5870126

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.