TY - JOUR KW - General Medicine AU - Haakenstad A AU - Irvine CMS AU - Knight M AU - Bintz C AU - Aravkin AY AU - Zheng P AU - Gupta V AU - Abrigo MRM AU - Abushouk AI AU - Adebayo OM AU - Agarwal G AU - Alahdab F AU - Al-Aly Z AU - Alam K AU - Alanzi TM AU - Alcalde-Rabanal JE AU - Alipour V AU - Alvis-Guzman N AU - Amit AML AU - Andrei CL AU - Andrei T AU - Antonio CAT AU - Arabloo J AU - Aremu O AU - Ayanore MA AU - Banach M AU - Bärnighausen TW AU - Barthelemy CM AU - Bayati M AU - Benzian H AU - Berman AE AU - Bienhoff K AU - Bijani A AU - Bikbov B AU - Biondi A AU - Boloor A AU - Busse R AU - Butt ZA AU - Cámera LA AU - Campos-Nonato IR AU - Cárdenas R AU - Carvalho F AU - Chansa C AU - Chattu SK AU - Chattu VK AU - Chu D AU - Dai X AU - Dandona L AU - Dandona R AU - Dangel WJ AU - Daryani A AU - De Neve J AU - Dhimal M AU - Dipeolu IO AU - Djalalinia S AU - Do HT AU - Doshi CP AU - Doshmangir L AU - Ehsani-Chimeh E AU - El Tantawi M AU - Fernandes E AU - Fischer F AU - Foigt NA AU - Fomenkov AA AU - Foroutan M AU - Fukumoto T AU - Fullman N AU - Gad MM AU - Ghadiri K AU - Ghafourifard M AU - Ghashghaee A AU - Glucksman T AU - Goudarzi H AU - Gupta RD AU - Hamadeh RR AU - Hamidi S AU - Haro JM AU - Hasanpoor E AU - Hay SI AU - Hegazy MI AU - Heibati B AU - Henry NJ AU - Hole MK AU - Hossain N AU - Househ M AU - Ilesanmi OS AU - Imani-Nasab M AU - Irvani SSN AU - Islam SMS AU - Jahani MA AU - Joshi A AU - Kalhor R AU - Kayode GA AU - Khalid N AU - Khatab K AU - Kisa A AU - Kochhar S AU - Krishan K AU - Kuate Defo B AU - Lal DK AU - Lami FH AU - Larsson AO AU - Leasher JL AU - LeGrand KE AU - Lim L AU - Mahotra NB AU - Majeed A AU - Maleki A AU - Manjunatha N AU - Massenburg BB AU - Mestrovic T AU - Mini G AU - Mirica A AU - Mirrakhimov EM AU - Mohammad Y AU - Mohammed S AU - Mokdad AH AU - Morrison SD AU - Naghavi M AU - Ndwandwe DE AU - Negoi I AU - Negoi RI AU - Ngunjiri JW AU - Nguyen CT AU - Nigatu YT AU - Onwujekwe OE AU - Ortega-Altamirano DV AU - Otstavnov N AU - Otstavnov SS AU - Owolabi MO AU - Pakhare AP AU - Pepito VCF AU - Perico N AU - Pham HQ AU - Pigott DM AU - Pokhrel KN AU - Rabiee M AU - Rabiee N AU - Rahimi-Movaghar V AU - Rawaf DL AU - Rawaf S AU - Rawal L AU - Remuzzi G AU - Renzaho AMN AU - Resnikoff S AU - Rezaei N AU - Rezapour A AU - Rickard J AU - Roever L AU - Sahu M AU - Samy AM AU - Sanabria J AU - Santric-Milicevic MM AU - Saraswathy SYI AU - Seedat S AU - Senthilkumaran S AU - Serván-Mori E AU - Shaikh MA AU - Sheikh A AU - Silva DAS AU - Stein C AU - Stein DJ AU - Titova MV AU - Topp SM AU - Tovani-Palone MR AU - Ullah S AU - Unnikrishnan B AU - Vacante M AU - Valdez PR AU - Vasankari TJ AU - Venketasubramanian N AU - Vlassov V AU - Vos T AU - Yearwood JA AU - Yonemoto N AU - Younis MZ AU - Yu C AU - Zadey S AU - Zaman SB AU - Zerfu TA AU - Zhang Z AU - Ziapour A AU - Zodpey S AU - Lim SS AU - Murray CJL AU - Lozano R 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.

BT - The Lancet DO - 10.1016/s0140-6736(22)00532-3 LA - eng 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.

PB - Elsevier BV PY - 2022 T2 - The Lancet TI - Measuring the availability of human resources for health and its relationship to universal health coverage for 204 countries and territories from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019 UR - https://www.thelancet.com/action/showPdf?pii=S0140-6736%2822%2900532-3 SN - 0140-6736 ER -