Evidence map›Paper›PMID 38107548›Full record

ArticleSystem dynamics review

Grounding alcohol simulation models in empirical and theoretical alcohol research: a model for a Northern Plains population in the United States.

Arielle R Deutsch, Edward Chau, Nikki Motabar, Mohammad S Jalali

Open access · greenAbstract read
In one paragraph

Article in System dynamics review. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 25% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. 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

4 authors at 4 institutions in 1 country.

Arielle R DeutschAvera Research Institute, Avera Health, Sioux Falls, SD, USA.
Edward ChauSt. Louis University, St Louis, MO, USA.
Nikki MotabarUniversity of California Santa Barbara, Santa Barbara, CA, USA.
Mohammad S JalaliUniversity of California Santa Barbara, Santa Barbara, CA, USA.
Harvard University · USSaint Louis University · USUniversity of California, Santa Barbara · USUniversity of South Dakota · US

Funding

Community Based System Dynamics Models of Alcohol and Substance Exposed Pregnancy in Northern Plains American Indian WomenR01DA050696 · NIDA · AVERA MCKENNAN · PI DEUTSCH, ARIELLE R. · 2020 to 2023
$1.5M
NIDA NIH HHS R01 DA050696
6 · The paper itself

Abstract

The growing number of systems science simulation models for alcohol use (AU) are often disconnected from AU models within empirical and theoretical alcohol research. As AU prevention/intervention efforts are typically grounded in alcohol research, this disconnect may reduce policy testing results, impact, and implementation. We developed a simulation model guided by AU research (accounting for the multiple AU stages defined by AU behavior and risk for harm and diverse transitions between stages). Simulated projections were compared to historical data to evaluate model accuracy and potential policy leverage points for prevention and intervention at risky drinking (RD) and alcohol use disorder (AUD) stages. Results indicated prevention provided the greatest RD and AUD reduction; however, focusing exclusively on AUD prevention may not be effective for long-term change, given the continued increase in RD. This study makes a case for the strength and importance of aligning subject-based research with systems science simulation models.

Indexed as

alcohol usepolicy testingprevention

Identifiers

PMID38107548
PMCPMC10723070
OpenAlexW4380624211

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.