Evidence map›Paper›PMID 38831424›Full record

ArticlePopulation health metrics2024

Country-specific determinants for COVID-19 case fatality rate and response strategies from a global perspective: an interpretable machine learning framework.

Cui Zhou, Åsa M Wheelock, Chutian Zhang, Jian Ma, Zhichao Li, Wannian Liang, Jing Gao, Lei Xu

Abstract read
In one paragraph

Article in Population health metrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

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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.

Cui ZhouVanke School of Public Health, Tsinghua University, Beijing, China.
Åsa M WheelockRespiratory Medicine Unit, Department of Medicine & Centre for Molecular Medicine, Karolinska Institutet, Karolinska Institutet, Slona, 171 65, Stockholm, Sweden.
Chutian ZhangVanke School of Public Health, Tsinghua University, Beijing, China.
Jian MaVanke School of Public Health, Tsinghua University, Beijing, China.
Zhichao LiKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Wannian LiangVanke School of Public Health, Tsinghua University, Beijing, China. liangwn@tsinghua.edu.cn.
Jing GaoVanke School of Public Health, Tsinghua University, Beijing, China. jing.gao@ki.se.
Lei XuVanke School of Public Health, Tsinghua University, Beijing, China. xu_lei@tsinghua.edu.cn.

Funding

Karolinska Institutet Research Foundation Grants 022-02329National Key Research and Development Program of China 2021ZD0114103
6 · The paper itself

Abstract

backgroundThere are significant geographic inequities in COVID-19 case fatality rates (CFRs), and comprehensive understanding its country-level determinants in a global perspective is necessary. This study aims to quantify the country-specific risk of COVID-19 CFR and propose tailored response strategies, including vaccination strategies, in 156 countries.

methodsCross-temporal and cross-country variations in COVID-19 CFR was identified using extreme gradient boosting (XGBoost) including 35 factors from seven dimensions in 156 countries from 28 January, 2020 to 31 January, 2022. SHapley Additive exPlanations (SHAP) was used to further clarify the clustering of countries by the key factors driving CFR and the effect of concurrent risk factors for each country. Increases in vaccination rates was simulated to illustrate the reduction of CFR in different classes of countries.

findingsOverall COVID-19 CFRs varied across countries from 28 Jan 2020 to 31 Jan 31 2022, ranging from 68 to 6373 per 100,000 population. During the COVID-19 pandemic, the determinants of CFRs first changed from health conditions to universal health coverage, and then to a multifactorial mixed effect dominated by vaccination. In the Omicron period, countries were divided into five classes according to risk determinants. Low vaccination-driven class (70 countries) mainly distributed in sub-Saharan Africa and Latin America, and include the majority of low-income countries (95.7%) with many concurrent risk factors. Aging-driven class (26 countries) mainly distributed in high-income European countries. High disease burden-driven class (32 countries) mainly distributed in Asia and North America. Low GDP-driven class (14 countries) are scattered across continents. Simulating a 5% increase in vaccination rate resulted in CFR reductions of 31.2% and 15.0% for the low vaccination-driven class and the high disease burden-driven class, respectively, with greater CFR reductions for countries with high overall risk (SHAP value > 0.1), but only 3.1% for the ageing-driven class.

conclusionsEvidence from this study suggests that geographic inequities in COVID-19 CFR is jointly determined by key and concurrent risks, and achieving a decreasing COVID-19 CFR requires more than increasing vaccination coverage, but rather targeted intervention strategies based on country-specific risks.

Indexed as

COVID-19Global HealthMachine LearningSARS-CoV-2COVID-19 VaccinesHumansPandemicsRisk FactorsVaccinationCOVID-19 VaccinesCase fatality rateCOVID-19Global healthPandemicsSHAPStrategyVaccinationXGBoost

Identifiers

PMID38831424
PMCPMC11149258

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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.