TY - JOUR
T1 - Measuring the availability of human resources for health and its relationship to universal health coverage for 204 countries and territories from 1990 to 2019
T2 - a systematic analysis for the Global Burden of Disease Study 2019
AU - GBD 2019 Human Resources for Health Collaborators
AU - Haakenstad, Annie
AU - Irvine, Caleb Mackay Salpeter
AU - Knight, Megan
AU - Bintz, Corinne
AU - Aravkin, Aleksandr Y.
AU - Zheng, Peng
AU - Gupta, Vin
AU - Abrigo, Michael R.M.
AU - Abushouk, Abdelrahman I.
AU - Adebayo, Oladimeji M.
AU - Agarwal, Gina
AU - Alahdab, Fares
AU - Al-Aly, Ziyad
AU - Alam, Khurshid
AU - Alanzi, Turki M.
AU - Alcalde-Rabanal, Jacqueline Elizabeth
AU - Alipour, Vahid
AU - Alvis-Guzman, Nelson
AU - Amit, Arianna Maever L.
AU - Andrei, Catalina Liliana
AU - Andrei, Tudorel
AU - Antonio, Carl Abelardo T.
AU - Arabloo, Jalal
AU - Aremu, Olatunde
AU - Ayanore, Martin Amogre
AU - Banach, Maciej
AU - Bärnighausen, Till Winfried
AU - Barthelemy, Celine M.
AU - Bayati, Mohsen
AU - Benzian, Habib
AU - Berman, Adam E.
AU - Bienhoff, Kelly
AU - Bijani, Ali
AU - Bikbov, Boris
AU - Biondi, Antonio
AU - Boloor, Archith
AU - Busse, Reinhard
AU - Butt, Zahid A.
AU - Cámera, Luis Alberto
AU - Campos-Nonato, Ismael R.
AU - Cárdenas, Rosario
AU - Carvalho, Felix
AU - Chansa, Collins
AU - Chattu, Soosanna Kumary
AU - Chattu, Vijay Kumar
AU - Chu, Dinh Toi
AU - Dai, Xiaochen
AU - Dandona, Lalit
AU - Dandona, Rakhi
AU - Dangel, William James
AU - Daryani, Ahmad
AU - De Neve, Jan Walter
AU - Dhimal, Meghnath
AU - Dipeolu, Isaac Oluwafemi
AU - Djalalinia, Shirin
AU - Do, Hoa Thi
AU - Doshi, Chirag P.
AU - Doshmangir, Leila
AU - Ehsani-Chimeh, Elham
AU - El Tantawi, Maha
AU - Fernandes, Eduarda
AU - Fischer, Florian
AU - Foigt, Nataliya A.
AU - Fomenkov, Artem Alekseevich
AU - Foroutan, Masoud
AU - Fukumoto, Takeshi
AU - Fullman, Nancy
AU - Gad, Mohamed M.
AU - Ghadiri, Keyghobad
AU - Ghafourifard, Mansour
AU - Ghashghaee, Ahmad
AU - Glucksman, Thomas
AU - Goudarzi, Houman
AU - Gupta, Rajat Das
AU - Hamadeh, Randah R.
AU - Hamidi, Samer
AU - Haro, Josep Maria
AU - Hasanpoor, Edris
AU - Hay, Simon I.
AU - Hegazy, Mohamed I.
AU - Heibati, Behzad
AU - Henry, Nathaniel J.
AU - Hole, Michael K.
AU - Hossain, Naznin
AU - Househ, Mowafa
AU - Ilesanmi, Olayinka Stephen
AU - Imani-Nasab, Mohammad Hasan
AU - Irvani, Seyed Sina Naghibi
AU - Islam, Sheikh Mohammed Shariful
AU - Jahani, Mohammad Ali
AU - Joshi, Ankur
AU - Kalhor, Rohollah
AU - Kayode, Gbenga A.
AU - Khalid, Nauman
AU - Khatab, Khaled
AU - Kisa, Adnan
AU - Kochhar, Sonali
AU - Krishan, Kewal
AU - Kuate Defo, Barthelemy
AU - Lal, Dharmesh Kumar
AU - Lami, Faris Hasan
AU - Larsson, Anders O.
AU - Leasher, Janet L.
AU - LeGrand, Kate E.
AU - Lim, Lee Ling
AU - Mahotra, Narayan B.
AU - Majeed, Azeem
AU - Maleki, Afshin
AU - Manjunatha, Narayana
AU - Massenburg, Benjamin Ballard
AU - Mestrovic, Tomislav
AU - Mini, G. K.
AU - Mirica, Andreea
AU - Mirrakhimov, Erkin M.
AU - Mohammad, Yousef
AU - Mohammed, Shafiu
AU - Mokdad, Ali H.
AU - Morrison, Shane Douglas
AU - Naghavi, Mohsen
AU - Ndwandwe, Duduzile Edith
AU - Negoi, Ionut
AU - Negoi, Ruxandra Irina
AU - Ngunjiri, Josephine W.
AU - Nguyen, Cuong Tat
AU - Nigatu, Yeshambel T.
AU - Onwujekwe, Obinna E.
AU - Ortega-Altamirano, Doris V.
AU - Otstavnov, Nikita
AU - Otstavnov, Stanislav S.
AU - Owolabi, Mayowa O.
AU - Pakhare, Abhijit P.
AU - Pepito, Veincent Christian Filipino
AU - Perico, Norberto
AU - Pham, Hai Quang
AU - Pigott, David M.
AU - Pokhrel, Khem Narayan
AU - Rabiee, Mohammad
AU - Rabiee, Navid
AU - Rahimi-Movaghar, Vafa
AU - Rawaf, David Laith
AU - Rawaf, Salman
AU - Rawal, Lal
AU - Remuzzi, Giuseppe
AU - Renzaho, Andre M.N.
AU - Resnikoff, Serge
AU - Rezaei, Nima
AU - Rezapour, Aziz
AU - Rickard, Jennifer
AU - Roever, Leonardo
AU - Sahu, Maitreyi
AU - Samy, Abdallah M.
AU - Sanabria, Juan
AU - Santric-Milicevic, Milena M.
AU - Saraswathy, Sivan Yegnanarayana Iyer
AU - Seedat, Soraya
AU - Senthilkumaran, Subramanian
AU - Serván-Mori, Edson
AU - Shaikh, Masood Ali
AU - Sheikh, Aziz
AU - Silva, Diego Augusto Santos
AU - Stein, Caroline
AU - Stein, Dan J.
AU - Titova, Mariya Vladimirovna
AU - Topp, Stephanie M.
AU - Tovani-Palone, Marcos Roberto
AU - Ullah, Saif
AU - Unnikrishnan, Bhaskaran
AU - Vacante, Marco
AU - Valdez, Pascual R.
AU - Vasankari, Tommi Juhani
AU - Venketasubramanian, Narayanaswamy
AU - Vlassov, Vasily
AU - Vos, Theo
AU - Yearwood, Jamal Akeem
AU - Yonemoto, Naohiro
AU - Younis, Mustafa Z.
AU - Yu, Chuanhua
AU - Zadey, Siddhesh
AU - Zaman, Sojib Bin
AU - Zerfu, Taddese Alemu
AU - Zhang, Zhi Jiang
AU - Ziapour, Arash
AU - Zodpey, Sanjay
AU - Lim, Stephen S.
AU - Murray, Christopher J.L.
AU - Lozano, Rafael
N1 - Publisher Copyright:
© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
PY - 2022/6/4
Y1 - 2022/6/4
N2 - Background: Human resources for health (HRH) include a range of occupations that aim to promote or improve human health. The UN Sustainable Development Goals (SDGs) and the WHO Health Workforce 2030 strategy have drawn attention to the importance of HRH for achieving policy priorities such as universal health coverage (UHC). Although previous research has found substantial global disparities in HRH, the absence of comparable cross-national estimates of existing workforces has hindered efforts to quantify workforce requirements to meet health system goals. We aimed to use comparable and standardised data sources to estimate HRH densities globally, and to examine the relationship between a subset of HRH cadres and UHC effective coverage performance. Methods: Through the International Labour Organization and Global Health Data Exchange databases, we identified 1404 country-years of data from labour force surveys and 69 country-years of census data, with detailed microdata on health-related employment. From the WHO National Health Workforce Accounts, we identified 2950 country-years of data. We mapped data from all occupational coding systems to the International Standard Classification of Occupations 1988 (ISCO-88), allowing for standardised estimation of densities for 16 categories of health workers across the full time series. Using data from 1990 to 2019 for 196 of 204 countries and territories, covering seven Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) super-regions and 21 regions, we applied spatiotemporal Gaussian process regression (ST-GPR) to model HRH densities from 1990 to 2019 for all countries and territories. We used stochastic frontier meta-regression to model the relationship between the UHC effective coverage index and densities for the four categories of health workers enumerated in SDG indicator 3.c.1 pertaining to HRH: physicians, nurses and midwives, dentistry personnel, and pharmaceutical personnel. We identified minimum workforce density thresholds required to meet a specified target of 80 out of 100 on the UHC effective coverage index, and quantified national shortages with respect to those minimum thresholds. Findings: We estimated that, in 2019, the world had 104·0 million (95% uncertainty interval 83·5–128·0) health workers, including 12·8 million (9·7–16·6) physicians, 29·8 million (23·3–37·7) nurses and midwives, 4·6 million (3·6–6·0) dentistry personnel, and 5·2 million (4·0–6·7) pharmaceutical personnel. We calculated a global physician density of 16·7 (12·6–21·6) per 10 000 population, and a nurse and midwife density of 38·6 (30·1–48·8) per 10 000 population. We found the GBD super-regions of sub-Saharan Africa, south Asia, and north Africa and the Middle East had the lowest HRH densities. To reach 80 out of 100 on the UHC effective coverage index, we estimated that, per 10 000 population, at least 20·7 physicians, 70·6 nurses and midwives, 8·2 dentistry personnel, and 9·4 pharmaceutical personnel would be needed. In total, the 2019 national health workforces fell short of these minimum thresholds by 6·4 million physicians, 30·6 million nurses and midwives, 3·3 million dentistry personnel, and 2·9 million pharmaceutical personnel. Interpretation: Considerable expansion of the world's health workforce is needed to achieve high levels of UHC effective coverage. The largest shortages are in low-income settings, highlighting the need for increased financing and coordination to train, employ, and retain human resources in the health sector. Actual HRH shortages might be larger than estimated because minimum thresholds for each cadre of health workers are benchmarked on health systems that most efficiently translate human resources into UHC attainment. Funding: Bill & Melinda Gates Foundation.
AB - Background: Human resources for health (HRH) include a range of occupations that aim to promote or improve human health. The UN Sustainable Development Goals (SDGs) and the WHO Health Workforce 2030 strategy have drawn attention to the importance of HRH for achieving policy priorities such as universal health coverage (UHC). Although previous research has found substantial global disparities in HRH, the absence of comparable cross-national estimates of existing workforces has hindered efforts to quantify workforce requirements to meet health system goals. We aimed to use comparable and standardised data sources to estimate HRH densities globally, and to examine the relationship between a subset of HRH cadres and UHC effective coverage performance. Methods: Through the International Labour Organization and Global Health Data Exchange databases, we identified 1404 country-years of data from labour force surveys and 69 country-years of census data, with detailed microdata on health-related employment. From the WHO National Health Workforce Accounts, we identified 2950 country-years of data. We mapped data from all occupational coding systems to the International Standard Classification of Occupations 1988 (ISCO-88), allowing for standardised estimation of densities for 16 categories of health workers across the full time series. Using data from 1990 to 2019 for 196 of 204 countries and territories, covering seven Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) super-regions and 21 regions, we applied spatiotemporal Gaussian process regression (ST-GPR) to model HRH densities from 1990 to 2019 for all countries and territories. We used stochastic frontier meta-regression to model the relationship between the UHC effective coverage index and densities for the four categories of health workers enumerated in SDG indicator 3.c.1 pertaining to HRH: physicians, nurses and midwives, dentistry personnel, and pharmaceutical personnel. We identified minimum workforce density thresholds required to meet a specified target of 80 out of 100 on the UHC effective coverage index, and quantified national shortages with respect to those minimum thresholds. Findings: We estimated that, in 2019, the world had 104·0 million (95% uncertainty interval 83·5–128·0) health workers, including 12·8 million (9·7–16·6) physicians, 29·8 million (23·3–37·7) nurses and midwives, 4·6 million (3·6–6·0) dentistry personnel, and 5·2 million (4·0–6·7) pharmaceutical personnel. We calculated a global physician density of 16·7 (12·6–21·6) per 10 000 population, and a nurse and midwife density of 38·6 (30·1–48·8) per 10 000 population. We found the GBD super-regions of sub-Saharan Africa, south Asia, and north Africa and the Middle East had the lowest HRH densities. To reach 80 out of 100 on the UHC effective coverage index, we estimated that, per 10 000 population, at least 20·7 physicians, 70·6 nurses and midwives, 8·2 dentistry personnel, and 9·4 pharmaceutical personnel would be needed. In total, the 2019 national health workforces fell short of these minimum thresholds by 6·4 million physicians, 30·6 million nurses and midwives, 3·3 million dentistry personnel, and 2·9 million pharmaceutical personnel. Interpretation: Considerable expansion of the world's health workforce is needed to achieve high levels of UHC effective coverage. The largest shortages are in low-income settings, highlighting the need for increased financing and coordination to train, employ, and retain human resources in the health sector. Actual HRH shortages might be larger than estimated because minimum thresholds for each cadre of health workers are benchmarked on health systems that most efficiently translate human resources into UHC attainment. Funding: Bill & Melinda Gates Foundation.
UR - http://www.scopus.com/inward/record.url?scp=85131397944&partnerID=8YFLogxK
U2 - 10.1016/S0140-6736(22)00532-3
DO - 10.1016/S0140-6736(22)00532-3
M3 - Article
C2 - 35617980
AN - SCOPUS:85131397944
SN - 0140-6736
VL - 399
SP - 2129
EP - 2154
JO - The Lancet
JF - The Lancet
IS - 10341
ER -