Evidence map›Paper›PMID 31707270›Full record

ArticleDrug and alcohol dependence2019

Age trends in alcohol use behavior patterns among U.S. adults ages 18-65.

Bethany C Bray, John J Dziak, Stephanie T Lanza

Abstract read
In one paragraph

Article in Drug and alcohol dependence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. DNA Methylation in Alcohol Use Disorder.International journal of molecular sciences · 2023
    Review
  10. Article
  11. Stress and Affect as Daily Risk Factors for Substance Use Patterns: an Application of Latent Class Analysis for Daily Diary Data.Prevention science : the official journal of the Society for Prevention Research · 2022
    Article
  12. Review
  13. 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

3 authors.

Bethany C BrayThe Methodology Center, The Pennsylvania State University, 404 Health and Human Development Bldg, University Park, PA, 16802, United States; Center for Dissemination and Implementation Science, University of Illinois at Chicago, 818 S Wolcott Ave, Chicago, IL 60612, United States. Electronic address: bcbray@uic.edu.
John J DziakThe Methodology Center, The Pennsylvania State University, 404 Health and Human Development Bldg, University Park, PA, 16802, United States. Electronic address: jjd264@psu.edu.
Stephanie T LanzaThe Methodology Center, The Pennsylvania State University, 404 Health and Human Development Bldg, University Park, PA, 16802, United States; Edna Bennett Piece Prevention Research Center, The Pennsylvania State University, 302 Biobehavioral Health Bldg, University Park, PA 16802, United States; Department of Biobehavioral Health, The Pennsylvania State University, 219 Biobehavioral Health Bldg, University Park, PA 16802, United States. Electronic address: SLanza@psu.edu.

Funding

Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LI, RUNZE · 2015 to 2019
$13.9M
Age-Varying Effects in the Epidemiology of Drug AbuseR01DA039854 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LANZA, STEPHANIE T · 2015 to 2017
$676k
NIDA NIH HHS P50 DA039838NIDA NIH HHS R01 DA039854
6 · The paper itself

Abstract

introductionAlthough much of the work on risky alcohol use behaviors, such as heavy drinking, focuses on adolescence and young adulthood, these behaviors are associated with negative health consequences across all ages. Existing studies on age trends have focused on a single alcohol use behavior across many ages, using methods such as time-varying effect modeling, or a single age period with many behaviors, using methods such as latent class analysis. This study integrates aspects of both modeling approaches to examine age trends in alcohol use behavior patterns across ages 18-65.

methodsData from the National Epidemiologic Survey on Alcohol and Related Conditions-III were used to identify past-year alcohol use behavior patterns among a nationally representative sample of U.S. adults (n = 30,997; 51.1% women; 63.5% White Non-Hispanic) and flexibly estimate nonlinear trends in the prevalences of those patterns across ages 18-65.

resultsFive patterns were identified: Non-Drinkers, Frequent Light Drinkers, Infrequent Heavy Episodic Drinkers, Frequent Heavy Episodic Drinkers, and Extreme Drinkers. Pattern prevalences were allowed to vary flexibly across the entire age range. Prevalences of the Infrequent Heavy Episodic and Extreme Drinkers peaked around ages 22-24, but peaked for Frequent Heavy Episodic Drinkers around age 49. Non-Drinkers were most prevalent across all ages except during the early 20 s when Extreme Drinkers were more prevalent. Around ages 24-30, the Non-, Frequent Light, and Extreme Drinkers were approximately equally prevalent.

conclusionsThe approach used here holds promise for understanding characteristics associated with behavior patterns at different ages and long-term age trends in complex behaviors.

Indexed as

AgingAdolescentAdultAgedAge FactorsAlcohol DrinkingAlcoholic IntoxicationCohort StudiesCross-Sectional StudiesFemaleHealth BehaviorHumansMaleMiddle AgedPrevalenceUnited StatesAge trendsAlcohol use patternsLatent class analysisTime-varying effect modeling

Identifiers

PMID31707270
PMCPMC6957076

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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.