Evidence map›Paper›PMID 28384086›Full record

ReviewAnnual review of public health2017

Evaluating the Health Impact of Large-Scale Public Policy Changes: Classical and Novel Approaches.

Sanjay Basu, Ankita Meghani, Arjumand Siddiqi

Registry-linked trialAbstract readReview
In one paragraph

Review in Annual review of public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06528535 (Evaluation of a Health Promotion Intervention on Food Safety in Women During Pregnancy), which is not on this map. Cited by 73 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 3 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.

NCT06528535 unknown statusnot on this mapstarted 2023, after this paper: background citation

Evaluation of a Health Promotion Intervention on Food Safety in Women During Pregnancy

TypeobservationalSponsorCentre Hospitalier de CayenneRan2023 to 2025Enrolled200ConditionsFood Safety, Pregnancy Related, Health PromotionArmsHealth promotion intervention
3 · Its place in the literature

Who cites it

73 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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13 more citing papers are in PubMed but not listed here.

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.

Sanjay BasuCenters for Health Policy, Primary Care and Outcomes Research; Center on Poverty and Inequality; and Institute for Economic Policy Research, Stanford University, Stanford, California 94305; email: basus@stanford.edu.
Ankita MeghaniDepartment of Medicine, Stanford University, Stanford, California 94305; email: ameghani@stanford.edu.
Arjumand SiddiqiDepartment of Epidemiology and Department of Social and Behavioral Health Sciences, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario M5T 3M7, Canada; email: aa.siddiqi@utoronto.ca.

Funding

Trial of strategies to communicate genetic information to different ethnic and racial subpopulationsU54MD010724 · NIMHD · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD · 2016 to 2021
$16.3M
Cohort filtering models to identify social program effects on health disparitiesDP2MD010478 · NIMHD · STANFORD UNIVERSITY · PI BASU, SANJAY · 2015 to 2015
$2.4M
Studying social factors in sodium consumption to reduce hypertension disparitiesK08HL121056 · NHLBI · STANFORD UNIVERSITY · PI BASU, SANJAY · 2014 to 2017
$599k
NHLBI NIH HHS K08 HL121056NIMHD NIH HHS DP2 MD010478NIMHD NIH HHS U54 MD010724
6 · The paper itself

Abstract

Large-scale public policy changes are often recommended to improve public health. Despite varying widely-from tobacco taxes to poverty-relief programs-such policies present a common dilemma to public health researchers: how to evaluate their health effects when randomized controlled trials are not possible. Here, we review the state of knowledge and experience of public health researchers who rigorously evaluate the health consequences of large-scale public policy changes. We organize our discussion by detailing approaches to address three common challenges of conducting policy evaluations: distinguishing a policy effect from time trends in health outcomes or preexisting differences between policy-affected and -unaffected communities (using difference-in-differences approaches); constructing a comparison population when a policy affects a population for whom a well-matched comparator is not immediately available (using propensity score or synthetic control approaches); and addressing unobserved confounders by utilizing quasi-random variations in policy exposure (using regression discontinuity, instrumental variables, or near-far matching approaches).

Indexed as

Health PolicyPublic HealthPublic PolicyHumansdifference-in-differencesinstrumental variablesnear-far matchingpropensity scoreregression discontinuitysynthetic controls

Identifiers

PMID28384086
PMCPMC5815378

What OpenQuestion holds

Textmetadata
LicenceCC BY-SA
Read underepoch 390

Registered trials

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.