Evidence map›Paper›PMID 36873266›Full record

ArticleSSM - population health2023

A quantitative assessment of the frequency and magnitude of heterogeneous treatment effects in studies of the health effects of social policies.

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

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in SSM - population health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

8 citing papers in PubMed, 7 citations in OpenAlex.

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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

7 authors at 3 institutions in 1 country.

Dakota W CintronCenter for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143, USA.
Laura M GottliebCenter for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143, USA.
Erin HaganCenter for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143, USA.
May Lynn TanCenter for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143, USA.
David VlahovYale School of Nursing at Yale University, 400 West Campus Drive, Room 32306, Orange, CT, 06477, USA.
M Maria GlymourCenter for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143, USA.
Ellicott C MatthayCenter for Opioid Epidemiology and Policy, Division of Epidemiology, Department of Population Health, New York University School of Medicine, 180 Madison Ave, New York, NY, 10016, USA.
University of California, San Francisco · USNew York University · USYale University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Substantial heterogeneity in effects of social policies on health across subgroups may be common, but has not been systematically characterized. Using a sample of 55 contemporary studies on health effects of social policies, we recorded how often heterogeneous treatment effects (HTEs) were assessed, for what subgroups (e.g., male, female), and the subgroup-specific effect estimates expressed as Standardized Mean Differences (SMDs). For each study, outcome, and dimension (e.g., gender), we fit a random-effects meta-analysis. We characterized the magnitude of heterogeneity in policy effects using the standard deviation of the subgroup-specific effect estimates (τ). Among the 44% of studies reporting subgroup-specific estimates, policy effects were generally small (<0.1 SMDs) with mixed impacts on health (67% beneficial) and disparities (50% implied narrowing of disparities). Across study-outcome-dimensions, 54% indicated any heterogeneity in effects, and 20% had τ > 0.1 SMDs. For 26% of study-outcome-dimensions, the magnitude of τ indicated that effects of opposite signs were plausible across subgroups. Heterogeneity was more common in policy effects not specified

Indexed as

Effect modifiersHealth equityPopulation heterogeneitySocial policy

Identifiers

PMID36873266
PMCPMC9975308
OpenAlexW4319166168

What Socratic 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 Socratic graph.