---
title: "Global and national effects of epidemics on health workforce inequality, 1990–2019"
id: "plos-medicine-1-global-regional-and-national-impact-of-epidemic-disasters-on-health-workforce"
canonical_url: "https://medichelpline.com/clinical-feed/plos-medicine-1-global-regional-and-national-impact-of-epidemic-disasters-on-health-workforce"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "PLOS Medicine"
source_url: "https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134"
published_at: "2026-09-22T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Global and national effects of epidemics on health workforce inequality, 1990–2019
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-medicine-1-global-regional-and-national-impact-of-epidemic-disasters-on-health-workforce
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** PLOS Medicine
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134)
- **Published At:** 2026-09-22T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This ecological and modeling study combined 30 years (1990–2019) of epidemic-event records from EM-DAT with cadre-specific health workforce density estimates from the Global Burden of Disease to assess losses in **human resources for health (HRH)** across 194 countries and territories. - The authors used a Generalized Estimating Equation (**GEE**) model adjusted for geographic and socioeconomic covariates (income level, socio-demographic index, urbanization, population density) to link the number of country-level epidemic events with the following year’s HRH density (per 10,000 population). - Between 1990 and 2019, 1,185 epidemic events were recorded across the studied countries and territories. - An increase of one epidemic event in the preceding year was associated with an average decrease of 57.5 × 10−3 HRHs per 10,000 population (95% CI 18.5 to 96.4; p = 0.004) in the following year. - Assuming the association was causal, the study estimated an average global annual loss of 17,549 HRHs (95% CI 5,661 to 29,437), equivalent to 2.57 (95% CI 0.83 to 4.31) lost per 10,000 HRHs attributable to epidemics each year. - Regional and income-group disparities were notable: South Asia and sub-Saharan Africa showed the highest attributable fractions (20.52 and 18.03 per 10,000 respectively), and low- and lower-middle-income countries had larger average attributable fractions (15.89 and 12.76 per 10,000) than upper-middle- and high-income countries (0.96 and 0.12 per 10,000). - The countries with the highest average attributable fractions were largely in sub-Saharan Africa; examples reported include Niger (57.06 per 10,000), Somalia (44.19 per 10,000), and Ethiopia (37.12 per 10,000). - The authors highlight potential biases, including uncontrolled confounding, reverse causation, and underestimation in countries with weaker disaster-surveillance systems, and note that country-level data cannot fully distinguish mechanisms such as migration or endemic disease impacts. - The study concludes that **epidemics** are associated with HRH losses globally and disproportionately affect low- and middle-income countries, thereby exacerbating pre-existing global inequities in the health workforce.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosmedicine%2Farticle%3Fid%3D10.1371%2Fjournal.pmed.1005134) * * About * Browse * Publish * [](https://journals.plos.org/plosmedicine/ "PLOS Medicine") * Search [advanced search](https://journals.plos.org/plosmedicine/search) * 0 [Save](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005134#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005134#savedHeader) * 0 [Citation](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005134#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005134#citedHeader) * 56 [View](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005134#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005134#viewedHeader) * 0 [Share](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005134#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005134#discussedHeader) Open Access Peer-reviewed Research Article # Global, regional, and national impact of epidemic disasters on health workforce equality between 1990 and 2019: An ecological and modeling study * Manman Chen , Contributed equally to this work with: Manman Chen, Wanzhou Wang Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Writing – original draft Affiliation School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0000-2645-0673 ](https://orcid.org/0009-0000-2645-0673 "ORCID Registry") ⨯ * Wanzhou Wang , Contributed equally to this work with: Manman Chen, Wanzhou Wang Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft Affiliations National Institute of Health Data Science, Peking University, Beijing, China, Center for Digital Health and Artificial Intelligence, Peking University First Hospital, Beijing, China ⨯ * Feng Sha, Roles Conceptualization, Data curation, Resources Affiliations Department of Computational Biology and Medical Big Data, Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, Guangdong, China, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0003-2676-508X ](https://orcid.org/0000-0003-2676-508X "ORCID Registry") ⨯ * Zichen Ye, Roles Resources Affiliation School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China ⨯ * Yuankai Zhao, Roles Resources Affiliation School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0002-9102-857X ](https://orcid.org/0009-0002-9102-857X "ORCID Registry") ⨯ * Chao Yang, Roles Resources Affiliations Center for Digital Health and Artificial Intelligence, Peking University First Hospital, Beijing, China, Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China ⨯ * Ze Liang, Roles Resources Affiliation School of Economics and Management, Harbin Institute of Technology, Harbin, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0001-5875-5009 ](https://orcid.org/0000-0001-5875-5009 "ORCID Registry") ⨯ * Yu Jiang , Roles Supervision, Writing – review & editing * E-mail: jiangyu@pumc.edu.cn (YJ); xijie_wang@126.com (XW); tangjinling@suat-sz.edu.cn (JT) Affiliations School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-2443-911X ](https://orcid.org/0000-0002-2443-911X "ORCID Registry") ⨯ * Xijie Wang , Roles Supervision, Writing – review & editing * E-mail: jiangyu@pumc.edu.cn (YJ); xijie_wang@126.com (XW); tangjinling@suat-sz.edu.cn (JT) Affiliation School of Disaster and Emergency Medicine, Tianjin University, Tianjin, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-2035-4311 ](https://orcid.org/0000-0002-2035-4311 "ORCID Registry") ⨯ * Jinling Tang Roles Supervision, Writing – review & editing * E-mail: jiangyu@pumc.edu.cn (YJ); xijie_wang@126.com (XW); tangjinling@suat-sz.edu.cn (JT) Affiliations Department of Computational Biology and Medical Big Data, Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, Guangdong, China, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China ⨯ # 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 ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: September 22, 2026 * * [Article](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134) * [Authors](https://journals.plos.org/plosmedicine/article/authors?id=10.1371/journal.pmed.1005134) * [Metrics](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005134) * [Comments](https://journals.plos.org/plosmedicine/article/comments?id=10.1371/journal.pmed.1005134) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pmed.1005134) * [Abstract](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#abstract0) * [Author summary](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#abstract1) * [Introduction](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#sec004) * [Methods](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#sec005) * [Results](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#sec011) * [Supporting information](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#sec017) * [References](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#references) * [Reader Comments](https://journals.plos.org/plosmedicine/article/comments?id=10.1371/journal.pmed.1005134) * [Figures](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134) ## Abstract ### Background Global 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 findings First, 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. ### Conclusions Epidemics 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. ## Author summary ### Why was this study done? * Health workers are unevenly distributed around the world, and countries with the fewest health workers may be least able to absorb the shock of an infectious disease outbreak. * Short-term links between epidemics and health workforce strain have been reported before, but no study had quantified how many health workers are lost to epidemics at the global, regional, and national levels, or whether these losses are unequal among countries. * Understanding this burden could help direct support toward the health workforce protection and epidemic preparedness, particularly in countries that can least afford further losses. ### What did the researchers do and find? * The researchers combined 30 years (1990–2019) of data on epidemic disasters from the emergency event records with health worker density data for 16 types of health worker across 194 countries and territories. * Using statistical models that adjusted for a country’s income level, socio-demographic development, urbanization, and population density, this study found that an outbreak of epidemics in a country was followed by a fall in health worker density in the subsequent year. * Assuming these associations were causal, this study estimated that epidemics were associated with an average loss of around 17,500 health workers globally each year, and low- and lower-middle-income countries experienced disproportionately greater losses. ### What do these findings mean? * Epidemics may be an underappreciated and unequally distributed cause of health workforce losses, compounding pre-existing shortages in countries that already have too few health workers. * Plans to prepare for future epidemics could usefully include specific measures to protect and retain health workers, especially nurses, midwives, and physicians, especially in lower-resource settings. * Relying on country-level rather than individual-level of data, the study cannot fully rule out other explanations, such as reverse causation, migration, or endemic diseases, and may underestimate losses in countries with weak disaster-reporting systems. ## Figures ![Fig 3](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.g003) ![Fig 4](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.g004) ![Fig 5](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.g005) ![Fig 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.g001) ![Fig 2](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.g002) ![Table 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.t001) ![Fig 3](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.g003) ![Fig 4](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.g004) ![Fig 5](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.g005) ![Fig 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.g001) ![Fig 2](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.g002) ![Table 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1005134.t001) **Citation:** Chen M, Wang W, Sha F, Ye Z, Zhao Y, Yang C, et al. (2026) Global, regional, and national impact of epidemic disasters on health workforce equality between 1990 and 2019: An ecological and modeling study. PLoS Med 23(9): e1005134. https://doi.org/10.1371/journal.pmed.1005134 **Academic Editor:** Margaret E. Kruk, Washington University In St Louis: Washington University in St Louis, UNITED STATES OF AMERICA **Received:** May 17, 2026; **Accepted:** August 17, 2026; **Published:** September 22, 2026 **Copyright:** © 2026 Chen et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. **Data Availability:** The EM-DAT dataset used in this study is publicly available at after online registration. The HRH dataset used in this study is publicly available at . The urban population proportion, population density, and GDP per capita are publicly available from the World Bank ( ). The SDI data are publicly available at . All data used in this study are publicly available online at . The codes used in this study are also available online at . **Funding:** This study was supported by National Natural Science Foundation of China (825B2103 to M.C.), National Natural Science Foundation of China (82204067 to X.W.), and Beijing Nova Program (20250484916 to C.Y.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Natural Science Foundation: . Beijing Nova Program: . **Competing interests:** The authors have declared that no competing interests exist. **Abbreviations:** AF, attributable fraction; AP, attributable person; CHWs, Community Health Workers; CI, confidence interval; COVID-19, coronavirus disease 2019; DALYs, disability-adjusted life-years; EM-DAT, Emergency Events Database; GATHER, Guidelines for Accurate and Transparent Health Estimates Reporting; GBD, Global Burden of Disease; GDP, gross domestic product; GEE, generalized estimating equation; HRH, human resources for health; ISCO, International Standard Classification of Occupations; MERS, Middle East Respiratory Syndrome; NHWA, National Health Workforce Accounts; QIC, quasi-likelihood information criterion; RECORD, Reporting of Studies Conducted using Observational Routinely-Collected Data; SDI, Socio-demographic Index; WHO, World Health Organization ## Introduction Human resources for health (HRH), namely all people engaged in work that aims chiefly to improve health, is a fundamental pillar of health systems and essential for achieving universal health coverage [[1](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref001),[2](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref002)]. Optimizing the management of the HRH is necessary for the progressive realization of universal health coverage [[3](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref003),[4](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref004)]. One of the specific targets of the Sustainable Development Goals 3 (SDG target 3.C) is to substantially increase health financing and the recruitment, development, training, and retention of the health workforce in developing countries, especially in less developed countries and small island developing states [[5](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref005)]. However, there are still profound inequalities in the global distribution of the health workforce [[6](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref006),[7](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref007)]. United Nations’ statistics from 2014 to 2020 reveal that, despite a steady global increase in medical doctor density, the disparity among regions ranges from an estimated 40 physicians per 10,000 people in Europe to only 2 in sub-Saharan Africa [[8](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref008)], and the disparities between high-income and low-income countries also reached 6.5 fold [[9](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref009)]. Projections indicate that by 2030, the shortage of the global HRH will decrease by 33%, while that in African and eastern Mediterranean regions will decline by only 7% and 15% [[9](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref009)]. The stability and capacity of HRH fundamentally determine epidemic preparedness and health system resilience [[10](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref010),[11](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref011)]. As the frontline defense against global health threats, such as Zika viruses, Middle East Respiratory Syndrome (MERS), and pandemic influenza, healthcare workers face direct mortality risks during epidemic outbreaks [[12](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005134#pmed.1005134.ref012)]. For instance, during the first year of the coronavi
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