Evidence map›Paper›PMID 32996356›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2020

Public Health Interventions with Harms and Benefits: A Graphical Framework for Evaluating Tradeoffs.

Allison L Pitt, Jeremy D Goldhaber-Fiebert, Margaret L Brandeau

Open access · bronzeAbstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 22% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. A Novel Decision-Modeling Framework for Health Policy Analyses When Outcomes Are Influenced by Social and Disease Processes.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
    Article
  3. Article
  4. Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
  5. Review
  6. 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 at 2 institutions in 1 country.

Allison L PittDepartment of Management Science and Engineering, Stanford University, Stanford, CA.ORCID 0000-0002-9821-6031
Jeremy D Goldhaber-FiebertStanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA.
Margaret L BrandeauDepartment of Management Science and Engineering, Stanford University, Stanford, CA.
Stanford University · USStanford Health Care · US

Funding

Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR01DA015612 · NIDA · STANFORD UNIVERSITY · PI OWENS, DOUGLAS K · 2002 to 2017
$9.4M
Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR37DA015612 · NIDA · STANFORD UNIVERSITY · PI DOUGLAS K OWENS · 2019 to 2026
$6.3M
NIDA NIH HHS R01 DA015612NIDA NIH HHS R37 DA015612
6 · The paper itself

Abstract

backgroundEvaluations of public health interventions typically report benefits and harms aggregated over the population. However, benefits and harms are not always evenly distributed. Examining disaggregated outcomes enables decision makers to consider health benefits and harms accruing to both intended intervention recipients and others in the population.

methodsWe provide a graphical framework for categorizing and comparing public health interventions that examines the distribution of benefit and harm between and within population subgroups for a single intervention and compares distributions of harm and benefit for multiple interventions. We demonstrate the framework through a case study of a hypothetical increase in the price of meat (5%, 10%, 25%, or 50%) that, via elasticity of demand, reduces consumption and consequently reduces body mass index. We examine how inequalities in benefits and harms (measured by quality-adjusted life-years) are distributed across a population of white and black males and females.

resultsA 50% meat price increase would yield the greatest net benefit to the population. However, because of reduced consumption among low-weight individuals, black males would bear disproportionate harm relative to the benefit they receive. With increasing meat price, the distribution of harm relative to benefit becomes less "internal" to those receiving benefit and more "distributed" to those not receiving commensurate benefit. When we segment the population by sex only, this result does not hold.

conclusionsDisaggregating harms and benefits to understand their differential impact on subgroups can strongly affect which decision alternative is deemed optimal, as can the approach to segmenting the population. Our framework provides a useful tool for illuminating key tradeoffs relevant to harm-averse decision makers and those concerned with both equity and efficiency.

Indexed as

Decision Support TechniquesHumansPublic Health AdministrationRisk Evaluation and Mitigationdistributional effectsharm aversionhealth technology assessmentmulticriteria decision analysis

Identifiers

PMID32996356
PMCPMC8056742
OpenAlexW3089339427

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.