Evidence map›Paper›PMID 35483641›Full record

ArticleAnnals of epidemiology2022

Heterogeneous treatment effects in social policy studies: An assessment of contemporary articles in the health and social sciences.

Dakota W Cintron, Nancy E Adler, Laura M Gottlieb, Erin Hagan, May Lynn Tan, David Vlahov, Madellena Maria Glymour, Ellicott C Matthay

Abstract read
In one paragraph

Article in Annals of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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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

8 authors.

Dakota W CintronCenter for Health and Community, School of Medicine, University of California, San Francisco, CA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA.
Nancy E AdlerCenter for Health and Community, School of Medicine, University of California, San Francisco, CA.
Laura M GottliebCenter for Health and Community, School of Medicine, University of California, San Francisco, CA.
Erin HaganCenter for Health and Community, School of Medicine, University of California, San Francisco, CA.
May Lynn TanCenter for Health and Community, School of Medicine, University of California, San Francisco, CA.
David VlahovYale School of Nursing, Yale University, New Haven, CT.
Madellena Maria GlymourCenter for Health and Community, School of Medicine, University of California, San Francisco, CA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA.
Ellicott C MatthayDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA; Department of Population Health, New York University Grossman School of Medicine, New York, NY; Center for Opioid Epidemiology and Policy, Division of Epidemiology, Department of Population Health, New York University Grossman School of Medicine, 180 Madison Ave, New York, NY 10016.. Electronic address: ellicott.matthay@nyulangone.org.

Funding

Joint impacts of local alcohol and cannabis policies and outlet densities on violenceK99AA028256 · NIAAA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MATTHAY, ELLICOTT COLSON · 2020 to 2021
$342k
NIAAA NIH HHS K99 AA028256
6 · The paper itself

Abstract

purposeSocial policies are important determinants of population health but may have varying effects on subgroups of people. Evaluating heterogeneous treatment effects (HTEs) of social policies is critical to determine how social policies will affect health inequities. Methods for evaluating HTEs are not standardized. Little is known about how often and by what methods HTEs are assessed in social policy and health research.

methodsA sample of 55 articles from 2019 on the health effects of social policies were evaluated for frequency of reporting HTEs; for what subgroupings HTEs were reported; frequency of a priori specification of intent to assess HTEs; and methods used for assessing HTEs.

resultsA total of 24 (44%) studies described some form of HTE assessment, including by age, gender, education, race/ethnicity, and/or geography. Among studies assessing HTEs, 63% specified HTE assessment a priori, and most (71%) used descriptive methods such as stratification; 21% used statistical tests (e.g., interaction terms in a regression); and no studies used data-driven algorithms.

conclusionsAlthough understanding HTEs could enhance policy and practice-based efforts to reduce inequities, it is not routine research practice. Increased evaluation of HTEs across relevant subgroups is needed.

Indexed as

Health EquityHealth PolicyHumansPublic PolicySocial Determinants of HealthSocial SciencesEffect modificationHealth equityHeterogeneous treatment effectsSocial policySubgroup analysis

Identifiers

PMID35483641
PMCPMC9701103

What OpenQuestion holds

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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.