Evidence map›Paper›PMID 42771579›Full record

ArticlePLoS medicine2026

Global, regional, and national impact of epidemic disasters on health workforce equality between 1990 and 2019: An ecological and modeling study.

Manman Chen, Wanzhou Wang, Feng Sha, Zichen Ye, Yuankai Zhao, Chao Yang, Ze Liang, Yu Jiang, Xijie Wang, Jinling Tang

Abstract read
In one paragraph

Article in PLoS medicine, 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
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0citing papers in PubMed
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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

10 authors.

Manman ChenSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0009-0000-2645-0673
Wanzhou WangNational Institute of Health Data Science, Peking University, Beijing, China.
Feng ShaDepartment of Computational Biology and Medical Big Data, Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, Guangdong, China.ORCID https://orcid.org/0000-0003-2676-508X
Zichen YeSchool of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Yuankai ZhaoSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0009-0002-9102-857X
Chao YangCenter for Digital Health and Artificial Intelligence, Peking University First Hospital, Beijing, China.
Ze LiangSchool of Economics and Management, Harbin Institute of Technology, Harbin, China.ORCID https://orcid.org/0000-0001-5875-5009
Yu JiangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0002-2443-911X
Xijie WangSchool of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.ORCID https://orcid.org/0000-0002-2035-4311
Jinling TangDepartment of Computational Biology and Medical Big Data, Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGlobal distribution of human resources for health (HRH) is highly unequal and less resourced regions may particularly be vulnerable to disastrous emergencies. We examined HRH losses attributable to epidemics of infectious diseases, both globally and geographically. METHODS AND

findingsFirst, using 30-year ecological data from the 1990 to 2019 records at international Emergency Events Database and HRH density statistics from the Global Burden of Disease study, we extracted estimates of annual country-specific epidemic events and cadre-specific HRH density data across 194 countries and territories. The association between the number of epidemics and HRH density (number of HRH per 10,000 population) was established using the Generalized Estimating Equation model, adjusted for geographic and socioeconomic status. Second, based on the country- and year-specific number of epidemics, HRH density, and epidemic-HRH density associations, we further estimated the annual average absolute number of HRH losses and the average relative number of HRH losses per 10,000 HRHs that can be attributed to epidemic events, globally, by region, and by country. From 1990 to 2019, 1,185 epidemic events were recorded in 194 countries and regions. Globally, an increase in 1 epidemic event in the preceding year was associated with an average decrease of 57.5 × 10-3 (95% confidence interval (CI) [18.5, 96.4]; p = 0.004) in HRH density in the following year. Assuming the observed epidemic-HRH associations were causal, we estimated that globally an average loss of 17,549 (95% CI [5,661, 29,437]) HRHs, or 2.57 (95% CI [0.83, 4.31]) per 10,000 HRHs were attributable to epidemics each year. During the entire observation period, South Asia and sub-Saharan Africa had the highest average attributable fractions, at 20.52 and 18.03 per 10,000, respectively. Low- (15.89 per 10,000) and lower-middle-income (12.76 per 10,000) countries showed higher average attributable fractions than upper-middle- (0.96 per 10,000) and high-income countries (0.12 per 10,000). The top 10 countries with the highest average attributable fractions were observed mostly in sub-Saharan Africa, such as Niger (57.06 per 10,000), Somalia (44.19 per 10,000), and Ethiopia (37.12 per 10,000). The results may be biased due to uncontrolled confounders and reverse causation, and potential underestimation in countries with weaker disaster-surveillance systems.

conclusionsEpidemics of infectious diseases are associated with HRH losses globally, particularly in low- and middle-income countries, further aggravating the global inequity in the health workforce.

Indexed as

DisastersEpidemicsGlobal HealthHealth WorkforceHumans

Identifiers

PMID42771579
PMCPMC13596819

What Socratic holds

Textmetadata
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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 Socratic graph.