Evidence map›Paper›PMID 39149127›Full record

ArticleHealth policy OPEN2024

Was priority setting considered in COVID-19 response planning? A global comparative analysis.

Claudia-Marcela Vélez, Lydia Kapiriri, Susan Goold, Marion Danis, Iestyn Williams, Bernardo Aguilera, Beverley M Essue, Elysee Nouvet

Abstract read
In one paragraph

Article in Health policy OPEN, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Do Moral Views Change during a Crisis? An Experiment on Health Care Priority Setting.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
    Article
  2. Article
  3. 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

8 authors.

Claudia-Marcela VélezDepartment of Health, Aging & Society, McMaster University, Hamilton, Ontario, Canada, 1280 Main Street West, Kenneth Taylor Hall Room 226, Postal code L8S 4M4 and Faculty of Medicine, University of Antioquia, Cra 51d #62-29, Medellín, Antioquia, Colombia.
Lydia KapiririDepartment of Health, Aging & Society, McMaster University, 1280 Main Street West, Kenneth Taylor Hall Room 226, Postal code L8S 4M4, Hamilton, Ontario, Canada.
Susan GooldCenter for Bioethics and Social Sciences in Medicine, University of Michigan, Ann Arbor, MI, USA.
Marion DanisDepartment of Bioethics, National Institutes of Health, Bethesda, MD, USA.
Iestyn WilliamsHealth Services Management Centre, University of Birmingham, 40 Edgbaston Park Rd, Postal code B15 2RT, Birmingham, UK.
Bernardo AguileraFaculty of Medicine and Science at the Universidad San Sebastian, Santiago de Chile, Chile; Providencia, Región Metropolitana.
Beverley M EssueCentre for Global Health Research, St. Michael's Hospital, 30 Bond St, Postal code M5B 1W8, Toronto, Ontario, Canada.
Elysee NouvetSchool of Health Studies, Western University, 1151 Richmond Street, Postal code N6A 3K7, London, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The COVID-19 pandemic forced governments across the world to consider how to prioritize resource allocation. Most countries produced pandemic preparedness plans that guide and coordinate healthcare, including how to allocate scarce resources such as ventilators, human resources, and therapeutics. The objective of this study was to compare and contrast the extent to which established parameters for effective priority setting (PS) were incorporated into COVID-19 pandemic response planning in several countries around the world. Methods: We used the Kapriri and Martin framework for effective priority setting and performed a quantitative descriptive analysis to explore whether and how countries' type of health system, political, and economic contexts impacted the inclusion of those parameters in their COVID-19 pandemic plans. We analyzed 86 country plans across six regions of the World Health Organization. Results: The countries sampled represent 40% of nations in AFRO, 54.5% of EMRO, 45% of EURO, 46% of PAHO, 64% of SEARO, and 41% of WPRO. They also represent 39% of all HICs in the world, 39% of Upper-Middle, 54% of Lower-Middle, and 48% of LICs. No pattern in attention to parameters of PS emerged by WHO region or country income levels. The parameters: evidence of political will, stakeholder participation, and use of scientific evidence/ adoption of WHO recommendations were each found in over 80% of plans. We identified a description of a specific PS process in 7% of the plans; explicit criteria for PS in 36.5%; inclusion of publicity strategies in 65%; mention of mechanisms for appealing decisions or implementing procedures to improve internal accountability and reduce corruption in 20%; explicit reference to public values in 15%; and a description of means for enhancing compliance with the decisions in 5%. Conclusion: The findings provide a basis for policymakers to reflect on their prioritization plans and identify areas that need to be strengthened. Overall, there is little consideration for explicit prioritization processes and tools and restricted attention to equity considerations; this may be a starting point for policymakers interested in improving future preparedness and response planning. Although the study focused on the COVID-19 pandemic, priority setting remains one of the policymakers' most prominent challenges. Policymakers should consider integrating systematic priority setting in their routine decision-making processes.

Indexed as

COVID-19EvaluationGlobal comparative analysisPandemic plansPriority setting

Identifiers

PMID39149127
PMCPMC11325004

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.