Evidence map›Paper›PMID 42776948›Full record

ArticlePloS one2026

Determinants and typologies of health system resilience: A data-driven framework for prioritising investment.

Richmond Balinia Adda, Conrad Pwayirane

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Richmond Balinia AddaDepartment of Biometry, School of Mathematical Sciences, University of Technology and Applied Sciences, Navrongo, Ghana.ORCID https://orcid.org/0000-0002-5209-4219
Conrad PwayiraneDepartment of Biometry, School of Mathematical Sciences, University of Technology and Applied Sciences, Navrongo, Ghana.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Health system resilience is essential for effective pandemic response and health security, yet existing assessments often lack actionable guidance for policymakers. This study develops a data-driven framework to identify key resilience contributors and country capability profiles using the complete 2021 Global Health Security Index (GHSI) dataset for 195 countries. Through a multi-method analytical approach, we derive a composite resilience measure that captures 70.6% of shared variance across six GHSI domains. After accounting for inter-domain correlations, Detection, Health Capacity, and Prevention show the strongest independent associations with overall resilience (ridge coefficients of 0.242, 0.228, and 0.219, respectively), with Detection contributing most. Cluster analysis reveals two distinct country typologies: a high-performing, balanced-capability group (33% of countries) spanning both high-income and several upper-middle-income countries, and a larger group (67%) with lower and more structurally uneven capabilities, particularly in Prevention and Health Capacity. These profiles suggest different strategic pathways for system strengthening-from sustaining advanced capacities in high-performing countries to targeted investments in detection and core health functions in settings with more fragmented capabilities. Our findings provide an essential framework for comparing resilience across countries and prioritising investments where they matter most, offering practical guidance for national and global health security planning.

Indexed as

Health PrioritiesInvestmentsCluster AnalysisGlobal HealthHumansPublic Health Infrastructure

Identifiers

PMID42776948
PMCPMC13600498

What OpenQuestion holds

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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.