Evidence map›Paper›PMID 34235643›Full record

ArticleApplied health economics and health policy2021

Avoiding Trouble Ahead: Lessons Learned and Suggestions for Economic Evaluations of COVID-19 Vaccines.

Chris Painter, Wanrudee Isaranuwatchai, Juthamas Prawjaeng, Hwee Lin Wee, Brandon Wen Bing Chua, Vinh Anh Huynh, Jing Lou, Fang Ting Goh, Nantasit Luangasanatip, Wirichada Pan-Ngum and 3 more

Open access · hybridAbstract read
In one paragraph

Article in Applied health economics and health policy, 2021. 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
0.5field-weighted citation impact, top 40% 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

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. COVID-19-related health utility values and changes in COVID-19 patients and the general population: a scoping review.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  2. Assessing the cost-effectiveness of COVID-19 vaccines in a low incidence and low mortality setting: the case of Thailand at start of the pandemic.The European journal of health economics : HEPAC : health economics in prevention and care · 2023
    Article
  3. Article
  4. Review
  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

13 authors at 3 institutions in 2 countries.

Chris PainterHealth Intervention and Technology Assessment Program, Nonthaburi, Thailand. chris.painter@me.com.ORCID 0000-0001-7970-039X
Wanrudee IsaranuwatchaiHealth Intervention and Technology Assessment Program, Nonthaburi, Thailand.
Juthamas PrawjaengHealth Intervention and Technology Assessment Program, Nonthaburi, Thailand.
Hwee Lin WeeSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Brandon Wen Bing ChuaSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Vinh Anh HuynhSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Jing LouSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Fang Ting GohSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Nantasit LuangasanatipMahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand.
Wirichada Pan-NgumMahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand.
Wang YiSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Hannah ClaphamSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Yot TeerawattananonHealth Intervention and Technology Assessment Program, Nonthaburi, Thailand.
National University of Singapore · SGHealth Intervention and Technology Assessment Program · THMahidol Oxford Tropical Medicine Research Unit · TH

Funding

Bill and Melinda Gates Foundation OPP1202541Thailand Research Fund RTA5980011
6 · The paper itself

Abstract

With vaccines for coronavirus disease 2019 (COVID-19) being introduced in countries across the world, policy makers are facing many practical considerations about how best to implement a vaccination programme. The supply of vaccines is insufficient for the global population, so decisions must be made as to which groups are prioritised for any vaccination and when. Furthermore, the aims of vaccination programmes will differ between countries, with some prioritising economic benefits that could stem from the relaxation of non-pharmaceutical interventions and others seeking simply to reduce the number of COVID-19 cases or deaths. This paper aims to share the experiences and lessons learned from conducting economic evaluations in Singapore and Thailand on hypothetical COVID-19 vaccines to provide a basis for other countries to develop their own contextualised economic evaluations, with particular focus on the key uncertainties, technical challenges, and characteristics that modellers should consider in partnership with key stakeholders. Which vaccines, vaccination strategies, and policy responses are most economically beneficial remains uncertain. It is therefore important for all governments to conduct their own analyses to inform local policy responses to COVID-19, including the implementation of COVID-19 vaccines in both the short and the long run. It is essential that such studies are designed, and ideally conducted, before vaccines are introduced so that policy decisions and implementation procedures are not delayed.

Indexed as

Cost-Benefit AnalysisCOVID-19COVID-19 VaccinesHealth PolicyHumansImmunization ProgramsSARS-CoV-2SingaporeThailandVaccinationCOVID-19 Vaccines

Identifiers

PMID34235643
PMCPMC8263163
OpenAlexW3177900283

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.