{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T18:03:07Z","timestamp":1790100187645,"version":"4.0.1"},"reference-count":47,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["825B2103"],"award-info":[{"award-number":["825B2103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82204067"],"award-info":[{"award-number":["82204067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005090","name":"Beijing Nova Program","doi-asserted-by":"publisher","award":["20250484916"],"award-info":[{"award-number":["20250484916"]}],"id":[{"id":"10.13039\/501100005090","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosmedicine.org"],"crossmark-restriction":false},"short-container-title":["PLoS Med"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods and findings<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                    <jats:p>\n                      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\u2009\u00d7\u200910\n                      <jats:sup>\u22123<\/jats:sup>\n                      (95% confidence interval (CI) [18.5, 96.4];\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.004) 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pmed.1005134","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T17:21:11Z","timestamp":1790097671000},"page":"e1005134","update-policy":"https:\/\/doi.org\/10.1371\/journal.pmed.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Global, regional, and national impact of epidemic disasters on health workforce equality between 1990 and 2019: An ecological and modeling study"],"prefix":"10.1371","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2645-0673","authenticated-orcid":true,"given":"Manman","family":"Chen","sequence":"first","affiliation":[{"name":"School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanzhou","family":"Wang","sequence":"additional","affiliation":[{"name":"National Institute of Health Data Science, Peking University","place":["Beijing, China"]},{"name":"Peking University First Hospital","place":["Beijing, China"],"department":["Center for Digital Health and Artificial Intelligence"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2676-508X","authenticated-orcid":true,"given":"Feng","family":"Sha","sequence":"additional","affiliation":[{"name":"Shenzhen University of Advanced Technology","place":["Shenzhen, Guangdong, China"],"department":["Department of Computational Biology and Medical Big Data, Faculty of Computer Science and Artificial Intelligence"]},{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences","place":["Shenzhen, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zichen","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9102-857X","authenticated-orcid":true,"given":"Yuankai","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Yang","sequence":"additional","affiliation":[{"name":"Peking University First Hospital","place":["Beijing, China"],"department":["Center for Digital Health and Artificial Intelligence"]},{"name":"Peking University First Hospital, Peking University Institute of Nephrology","place":["Beijing, China"],"department":["Renal Division, Department of Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5875-5009","authenticated-orcid":true,"given":"Ze","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Harbin Institute of Technology","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2443-911X","authenticated-orcid":true,"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College","place":["Beijing, China"]},{"name":"School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2035-4311","authenticated-orcid":true,"given":"Xijie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Disaster and Emergency Medicine, Tianjin University","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinling","family":"Tang","sequence":"additional","affiliation":[{"name":"Shenzhen University of Advanced Technology","place":["Shenzhen, Guangdong, China"],"department":["Department of Computational Biology and Medical Big Data, Faculty of Computer Science and Artificial Intelligence"]},{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences","place":["Shenzhen, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"pmed.1005134.ref001","unstructured":"World Health Organization. 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