Evidence map›Paper›PMID 36925181›Full record

ReviewThe Lancet. Global health2023

When should global health actors prioritise more uncertain interventions?

Leah Pierson, Stéphane Verguet

Abstract readReview
In one paragraph

Review in The Lancet. Global health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. 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

2 authors.

Leah PiersonHarvard/MIT MD-PhD Program, Harvard Medical School, Harvard University, Boston, MA, USA; Department of Global Health and Population, Harvard T H Chan School of Public Health, Harvard University, Boston, MA, USA. Electronic address: leah_pierson@hms.harvard.edu.
Stéphane VerguetDepartment of Global Health and Population, Harvard T H Chan School of Public Health, Harvard University, Boston, MA, USA.

Funding

Medical Scientist Training ProgramT32GM007753 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI WALENSKY, LOREN DAVID · 1985 to 2021
$50.0M
Medical Scientist Training ProgramT32GM144273 · NIGMS · HARVARD MEDICAL SCHOOL · PI David Shumway Jones, Jacqueline A. Lees · 2022 to 2026
$14.7M
NIGMS NIH HHS T32 GM007753NIGMS NIH HHS T32 GM144273
6 · The paper itself

Abstract

Global health actors use economic evaluations, including cost-effectiveness analyses, to estimate the effect of different interventions they might fund. However, producing reliable cost-effectiveness estimates is difficult, meaning organisations must often choose between funding interventions for which reliable predictions of efficacy exist and those for which they do not. In practice, many organisations appear to be risk-averse, favouring more certain interventions simply because they are more certain. We argue that this practice is not justifiable. Prioritising projects backed by greater evidence might often produce greater health benefits. However, a general tendency to prefer more certain interventions will cause global health actors to overlook opportunities to help less well-studied populations, support promising but complex interventions, address the upstream causes of illness, and conduct the most important impact evaluations. We argue that global health actors should instead adopt nuanced attitudes towards uncertainty and be willing to fund highly uncertain interventions in some cases. We further describe the considerations they should take into account in rendering these judgements.

Indexed as

Cost-Effectiveness AnalysisGlobal HealthCost-Benefit AnalysisHumansUncertainty

Identifiers

PMID36925181
PMCPMC10060118

What OpenQuestion holds

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LicenceCC BY
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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.