Evidence map›Paper›PMID 36090595›Full record

ArticleJournal of benefit-cost analysis2022

Valuing COVID-19 Morbidity Risk Reductions.

Lisa A Robinson, Michael R Eber, James K Hammitt

Abstract read
In one paragraph

Article in Journal of benefit-cost analysis, 2022. 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. Lisa Robinson: Benefit-Cost Analyses for Evidence-Based Decisions.Risk analysis : an official publication of the Society for Risk Analysis · 2026
    Article
  2. Article
  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

3 authors.

Lisa A RobinsonHarvard T.H. Chan School of Public Health.ORCID 0000-0003-1972-4204
Michael R EberHarvard T.H. Chan School of Public Health and Harvard Graduate School of Arts and Sciences.
James K HammittHarvard T.H. Chan School of Public Health and Toulouse School of Economics, Université de Toulouse Capitole.

Funding

HEALTH POLICY TRAINING PROGRAMT32HS000055 · AHRQ · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI CUTLER, DAVID M · 1994 to 2024
$7.1M
AHRQ HHS T32 HS000055
6 · The paper itself

Abstract

Many economic analyses, including those that address the COVID-19 pandemic, focus on the value of averting deaths and do not include the value of averting nonfatal illnesses. Yet incorporating the value of averting nonfatal cases may change conclusions about the desirability of the policy. While per case values may be small, the number of nonfatal cases is often large, far outstripping the number of fatal cases. The value of averting nonfatal cases is also increasingly important in evaluating COVID-19 policy options as vaccine- and infection-related immunity and treatments reduce the case-fatality rate. Unfortunately, little valuation research is available that explicitly addresses COVID-19 morbidity. We describe and implement an approach for approximating the value of averting nonfatal illnesses or injuries and apply it to COVID-19 in the United States. We estimate gains from averting COVID-19 morbidity of about 0.01 quality-adjusted life year (QALY) per mild case averted, 0.02 QALY per severe case, and 3.15 QALYs per critical case. These gains translate into monetary values of about $5,300 per mild case, $11,000 per severe case, and $1.8 million per critical case. While these estimates are imprecise, they suggest the magnitude of the effects.

Indexed as

COVID-19quality-adjusted life years (QALYs)value per statistical life (VSL)valuing morbidity risk reductionswillingness to pay (WTP)

Identifiers

PMID36090595
PMCPMC9455599

What OpenQuestion holds

Textmetadata
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.