Abstract
Background: Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods: Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings: Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation: The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC. Funding: Bill & Melinda Gates Foundation.
Original language | English |
---|---|
Pages (from-to) | 1250-1284 |
Number of pages | 35 |
Journal | The Lancet |
Volume | 396 |
Issue number | 10258 |
DOIs | |
Publication status | Published - 17 Oct 2020 |
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In: The Lancet, Vol. 396, No. 10258, 17.10.2020, p. 1250-1284.
Research output: Contribution to journal › Review article › peer-review
TY - JOUR
T1 - Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019
T2 - a systematic analysis for the Global Burden of Disease Study 2019
AU - GBD 2019 Universal Health Coverage Collaborators
AU - Lozano, Rafael
AU - Fullman, Nancy
AU - Mumford, John Everett
AU - Knight, Megan
AU - Barthelemy, Celine M.
AU - Abbafati, Cristiana
AU - Abbastabar, Hedayat
AU - Abd-Allah, Foad
AU - Abdollahi, Mohammad
AU - Abedi, Aidin
AU - Abolhassani, Hassan
AU - Abosetugn, Akine Eshete
AU - Abreu, Lucas Guimarães
AU - Abrigo, Michael R.M.
AU - Abu Haimed, Abdulaziz Khalid
AU - Abushouk, Abdelrahman I.
AU - Adabi, Maryam
AU - Adebayo, Oladimeji M.
AU - Adekanmbi, Victor
AU - Adelson, Jaimie
AU - Adetokunboh, Olatunji O.
AU - Adham, Davoud
AU - Advani, Shailesh M.
AU - Afshin, Ashkan
AU - Agarwal, Gina
AU - Agasthi, Pradyumna
AU - Aghamir, Seyed Mohammad Kazem
AU - Agrawal, Anurag
AU - Ahmad, Tauseef
AU - Akinyemi, Rufus Olusola
AU - Alahdab, Fares
AU - Al-Aly, Ziyad
AU - Alam, Khurshid
AU - Albertson, Samuel B.
AU - Alemu, Yihun Mulugeta
AU - Alhassan, Robert Kaba
AU - Ali, Muhammad
AU - Ali, Saqib
AU - Alipour, Vahid
AU - Aljunid, Syed Mohamed
AU - Alla, François
AU - Almadi, Majid Abdulrahman Hamad
AU - Almasi, Ali
AU - Almasi-Hashiani, Amir
AU - Almasri, Nihad A.
AU - Al-Mekhlafi, Hesham M.
AU - Almulhim, Abdulaziz M.
AU - Alonso, Jordi
AU - Al-Raddadi, Rajaa M.
AU - Altirkawi, Khalid A.
AU - Alvis-Guzman, Nelson
AU - Alvis-Zakzuk, Nelson J.
AU - Amini, Saeed
AU - Amini-Rarani, Mostafa
AU - Amiri, Fatemeh
AU - Amit, Arianna Maever L.
AU - Amugsi, Dickson A.
AU - Ancuceanu, Robert
AU - Anderlini, Deanna
AU - Andrei, Catalina Liliana
AU - Androudi, Sofia
AU - Ansari, Fereshteh
AU - Ansari-Moghaddam, Alireza
AU - Antonio, Carl Abelardo T.
AU - Antony, Catherine M.
AU - Antriyandarti, Ernoiz
AU - Anvari, Davood
AU - Anwer, Razique
AU - Arabloo, Jalal
AU - Arab-Zozani, Morteza
AU - Aravkin, Aleksandr Y.
AU - Aremu, Olatunde
AU - Ärnlöv, Johan
AU - Asaad, Malke
AU - Asadi-Aliabadi, Mehran
AU - Asadi-Pooya, Ali A.
AU - Athari, Seyyed Shamsadin
AU - Atout, Maha Moh d.Wahbi
AU - Ausloos, Marcel
AU - Avila-Burgos, Leticia
AU - Ayala Quintanilla, Beatriz Paulina
AU - Ayano, Getinet
AU - Ayanore, Martin Amogre
AU - Aynalem, Yared Asmare
AU - Aynalem, Getie Lake
AU - Ayza, Muluken Altaye
AU - Azari, Samad
AU - Azzopardi, Peter S.
AU - B, Darshan B.
AU - Babaee, Ebrahim
AU - Badiye, Ashish D.
AU - Bahrami, Mohammad Amin
AU - Baig, Atif Amin
AU - Bakhshaei, Mohammad Hossein
AU - Bakhtiari, Ahad
AU - Bakkannavar, Shankar M.
AU - Balachandran, Arun
AU - Banach, Maciej
AU - Banerjee, Srikanta K.
AU - Banik, Palash Chandra
AU - Bante, Agegnehu Bante
AU - Bante, Simachew Animen
AU - Barker-Collo, Suzanne Lyn
AU - Bärnighausen, Till Winfried
AU - Barrero, Lope H.
AU - Bassat, Quique
AU - Basu, Sanjay
AU - Baune, Bernhard T.
AU - Bayati, Mohsen
AU - Baye, Bayisa Abdissa
AU - Bedi, Neeraj
AU - Beghi, Ettore
AU - Behzadifar, Masoud
AU - Bekuma, Tariku Tesfaye Tesfaye
AU - Bell, Michelle L.
AU - Bensenor, Isabela M.
AU - Berman, Adam E.
AU - Bernabe, Eduardo
AU - Bernstein, Robert S.
AU - Bhagavathula, Akshaya Srikanth
AU - Bhandari, Dinesh
AU - Bhardwaj, Pankaj
AU - Bhat, Anusha Ganapati
AU - Bhattacharyya, Krittika
AU - Bhattarai, Suraj
AU - Bhutta, Zulfiqar A.
AU - Bijani, Ali
AU - Bikbov, Boris
AU - Bilano, Ver
AU - Biondi, Antonio
AU - Birihane, Binyam Minuye
AU - Bockarie, Moses John
AU - Bohlouli, Somayeh
AU - Bojia, Hunduma Amensisa
AU - Bolla, Srinivasa Rao Rao
AU - Boloor, Archith
AU - Brady, Oliver J.
AU - Braithwaite, Dejana
AU - Briggs, Andrew M.
AU - Briko, Nikolay Ivanovich
AU - Burugina Nagaraja, Sharath
AU - Busse, Reinhard
AU - Butt, Zahid A.
AU - Caetano dos Santos, Florentino Luciano
AU - Cahuana-Hurtado, Lucero
AU - Cámera, Luis Alberto
AU - Cárdenas, Rosario
AU - Carreras, Giulia
AU - Carrero, Juan J.
AU - Carvalho, Felix
AU - Castaldelli-Maia, Joao Mauricio
AU - Castañeda-Orjuela, Carlos A.
AU - Castelpietra, Giulio
AU - Castro, Franz
AU - Catalá-López, Ferrán
AU - Cederroth, Christopher R.
AU - Cerin, Ester
AU - Chandan, Joht Singh
AU - Chang, Angela Y.
AU - Charan, Jaykaran
AU - Chattu, Vijay Kumar
AU - Chaturvedi, Sarika
AU - Chin, Ken Lee
AU - Cho, Daniel Youngwhan
AU - Choi, Jee Young Jasmine
AU - Christensen, Hanne
AU - Chu, Dinh Toi
AU - Chung, Michael T.
AU - Ciobanu, Liliana G.
AU - Cirillo, Massimo
AU - Compton, Kelly
AU - Cortesi, Paolo Angelo
AU - Costa, Vera Marisa
AU - Cousin, Ewerton
AU - Dahlawi, Saad M.A.
AU - Damiani, Giovanni
AU - Dandona, Lalit
AU - Dandona, Rakhi
AU - Darega Gela, Jiregna
AU - Darwesh, Aso Mohammad
AU - Daryani, Ahmad
AU - Dash, Aditya Prasad
AU - Davey, Gail
AU - Dávila-Cervantes, Claudio Alberto
AU - Davletov, Kairat
AU - De Neve, Jan Walter
AU - Denova-Gutiérrez, Edgar
AU - Deribe, Kebede
AU - Dervenis, Nikolaos
AU - Desai, Rupak
AU - Dharmaratne, Samath Dhamminda
AU - Dhungana, Govinda Prasad
AU - Dianatinasab, Mostafa
AU - Dias da Silva, Diana
AU - Diaz, Daniel
AU - Dippenaar, Ilse N.
AU - Do, Hoa Thi
AU - Dorostkar, Fariba
AU - Doshmangir, Leila
AU - Duncan, Bruce B.
AU - Duraes, Andre Rodrigues
AU - Eagan, Arielle Wilder
AU - Edvardsson, David
AU - El Sayed, Iman
AU - El Tantawi, Maha
AU - Elgendy, Islam Y.
AU - Elyazar, Iqbal RF
AU - Eskandari, Khalil
AU - Eskandarieh, Sharareh
AU - Esmaeilnejad, Saman
AU - Esteghamati, Alireza
AU - Ezekannagha, Oluchi
AU - Farag, Tamer
AU - Farahmand, Mohammad
AU - Faraon, Emerito Jose A.
AU - Farinha, Carla Sofia e.Sá
AU - Farioli, Andrea
AU - Faris, Pawan Sirwan
AU - Faro, Andre
AU - Fazlzadeh, Mehdi
AU - Feigin, Valery L.
AU - Fernandes, Eduarda
AU - Ferrara, Pietro
AU - Feyissa, Garumma Tolu
AU - Filip, Irina
AU - Fischer, Florian
AU - Fisher, James L.
AU - Flor, Luisa Sorio
AU - Foigt, Nataliya A.
AU - Folayan, Morenike Oluwatoyin
AU - Fomenkov, Artem Alekseevich
AU - Foroutan, Masoud
AU - Francis, Joel Msafiri
AU - Fu, Weijia
AU - Fukumoto, Takeshi
AU - Furtado, João M.
AU - Gad, Mohamed M.
AU - Gaidhane, Abhay Motiramji
AU - Gakidou, Emmanuela
AU - Galles, Natalie C.
AU - Gallus, Silvano
AU - Gardner, William M.
AU - Geberemariyam, Biniyam Sahiledengle
AU - Gebrehiwot, Abiyu Mekonnen
AU - Gebremeskel, Leake G.
AU - Gebremeskel, Gebreamlak Gebremedhn
AU - Gesesew, Hailay Abrha
AU - Ghadiri, Keyghobad
AU - Islam, Sheikh Mohammed Shariful
AU - Kinfu, Yohannes
N1 - Funding Information: Lucas Guimar\u00E3es Abreu acknowledges support from Coordena\u00E7\u00E3o de Aperfei\u00E7oamento de Pessoal de N\u00EDvel Superior - Brasil (Capes) - Finance Code 001, Conselho Nacional de Desenvolvimento Cient\u00EDfico e Tecnol\u00F3gico (CNPq) and Funda\u00E7\u00E3o de Amparo \u00E0 Pesquisa do Estado de Minas Gerais (FAPEMIG). Olatunji O Adetokunboh acknowledges South African Department of Science & Innovation, and National Research Foundation. Anurag Agrawal acknowledges support from the Wellcome Trust DBT India Alliance Senior Fellowship IA/CPHS/14/1/501489. Rufus Olusola Akinyemi acknowledges Grant U01HG010273 from the National Institutes of Health (NIH) as part of the H3Africa Consortium. Rufus Olusola Akinyemi is further supported by the FLAIR fellowship funded by the UK Royal Society and the African Academy of Sciences. Syed Mohamed Aljunid acknowledges the Department of Health Policy and Management, Faculty of Public Health, Kuwait University and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia for the approval and support to participate in this research project. Marcel Ausloos, Claudiu Herteliu, and Adrian Pana acknowledge partial support by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Till Winfried B\u00E4rnighausen acknowledges support from the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. Juan J Carrero was supported by the Swedish Research Council (2019-01059). Felix Carvalho acknowledges UID/MULTI/04378/2019 and UID/QUI/50006/2019 support with funding from FCT/MCTES through national funds. Vera Marisa Costa acknowledges support from grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Funda\u00E7\u00E3o para a Ci\u00EAncia e a Tecnologia (FCT), IP, under the Norma Transit\u0100 3 ria DL57/2016/CP1334/CT0006. Jan-Walter De Neve acknowledges support from the Alexander von Humboldt Foundation. Kebede Deribe acknowledges support by Wellcome Trust grant number 201900/Z/16/Z as part of his International Intermediate Fellowship. Claudiu Herteliu acknowledges partial support by a grant co-funded by European Fund for Regional Development through Operational Program for Competitiveness, Project ID P_40_382. Praveen Hoogar acknowledges the Centre for Bio Cultural Studies (CBiCS), Manipal Academy of Higher Education(MAHE), Manipal and Centre for Holistic Development and Research (CHDR), Kalghatgi. Bing-Fang Hwang acknowledges support from China Medical University (CMU108-MF-95), Taichung, Taiwan. Mihajlo Jakovljevic acknowledges the Serbian part of this GBD contribution was co-funded through the Grant OI175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. Aruna M Kamath acknowledges funding from the National Institutes of Health T32 grant (T32GM086270). Srinivasa Vittal Katikireddi acknowledges funding from the Medical Research Council (MC_UU_12017/13 & MC_UU_12017/15), Scottish Government Chief Scientist Office (SPHSU13 & SPHSU15) and an NRS Senior Clinical Fellowship (SCAF/15/02). Yun Jin Kim acknowledges support from the Research Management Centre, Xiamen University Malaysia (XMUMRF/2018-C2/ITCM/0001). Kewal Krishan acknowledges support from the DST PURSE grant and UGC Center of Advanced Study (CAS II) awarded to the Department of Anthropology, Panjab University, Chandigarh, India. Manasi Kumar acknowledges support from K43 TW010716 Fogarty International Center/NIMH. Ben Lacey acknowledges support from the NIHR Oxford Biomedical Research Centre and the BHF Centre of Research Excellence, Oxford. Iv\u00E1n Landires is a member of the Sistema Nacional de Investigaci\u0100 3 n (SNI), which is supported by the Secretar\u00EDa Nacional de Ciencia Tecnolog\u00EDa e Innovaci\u00F3n (SENACYT), Panam\u00E1. Jeffrey V Lazarus acknowledges support by a Spanish Ministry of Science, Innovation and Universities Miguel Servet grant (Instituto de Salud Carlos III/ESF, European Union [CP18/00074]). Peter T N Memiah acknowledges CODESRIA; HISTP. Subas Neupane acknowledges partial support from the Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital. Shuhei Nomura acknowledges support from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (18K10082). Alberto Ortiz acknowledges support by ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009 Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686 CIFRA2-CM. These funding sources had no role in the writing of the manuscript or the decision to submit it for publication. George C Patton acknowledges support from a National Health & Medical Research Council Fellowship. Marina Pinheiro acknowledges support from FCT for funding through program DL 57/2016 - Norma transit\u0100 3 ria. Alberto Raggi, David Sattin, and Silvia Schiavolin acknowledge support by a grant from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C Besta, Linea 4 - Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche). Daniel Cury Ribeiro acknowledges support from the Sir Charles Hercus Health Research Fellowship - Health Research Council of New Zealand (18/111). Perminder S Sachdev acknowledges funding from the NHMRC Australia. Abdallah M Samy acknowledges support from a fellowship from the Egyptian Fulbright Mission Program. Milena M Santric-Milicevic acknowledges support from the Ministry of Education, Science and Technological Development of the Republic of Serbia (Contract No. 175087). Rodrigo Sarmiento-Su\u00E1rez acknowledges institutional support from University of Applied and Environmental Sciences in Bogota, Colombia, and Carlos III Institute of Health in Madrid, Spain. Maria In\u00EAs Schmidt acknowledges grants from the Foundation for the Support of Research of the State of Rio Grande do Sul (IATS and PrInt) and the Brazilian Ministry of Health. Sheikh Mohammed Shariful Islam acknowledges a fellowship from the National Heart Foundation of Australia and Deakin University. Aziz Sheikh acknowledges support from Health Data Research UK. Kenji Shibuya acknowledges Japan Ministry of Education, Culture, Sports, Science and Technology. Joan B Soriano acknowledges support by Centro de Investigaci\u00F3n en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. Rafael Tabar\u00E9s-Seisdedos acknowledges partial support from grant PI17/00719 from ISCIII-FEDER. Santosh Kumar Tadakamadla acknowledges support from the National Health and Medical Research Council Early Career Fellowship, Australia. Marcello Tonelli acknowledges the David Freeze Chair in Health Services Research at the University of Calgary, AB, Canada. Funding Information: Lucas Guimar\u00E3es Abreu acknowledges support from Coordena\u00E7\u00E3o de Aperfei\u00E7oamento de Pessoal de N\u00EDvel Superior - Brasil (Capes) - Finance Code 001, Conselho Nacional de Desenvolvimento Cient\u00EDfico e Tecnol\u00F3gico (CNPq) and Funda\u00E7\u00E3o de Amparo \u00E0 Pesquisa do Estado de Minas Gerais (FAPEMIG). Olatunji O Adetokunboh acknowledges South African Department of Science & Innovation, and National Research Foundation. Anurag Agrawal acknowledges support from the Wellcome Trust DBT India Alliance Senior Fellowship IA/CPHS/14/1/501489. Rufus Olusola Akinyemi acknowledges Grant U01HG010273 from the National Institutes of Health (NIH) as part of the H3Africa Consortium. Rufus Olusola Akinyemi is further supported by the FLAIR fellowship funded by the UK Royal Society and the African Academy of Sciences. Syed Mohamed Aljunid acknowledges the Department of Health Policy and Management, Faculty of Public Health, Kuwait University and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia for the approval and support to participate in this research project. Marcel Ausloos, Claudiu Herteliu, and Adrian Pana acknowledge partial support by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Till Winfried B\u00E4rnighausen acknowledges support from the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. Juan J Carrero was supported by the Swedish Research Council (2019-01059). Felix Carvalho acknowledges UID/MULTI/04378/2019 and UID/QUI/50006/2019 support with funding from FCT/MCTES through national funds. Vera Marisa Costa acknowledges support from grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Funda\u00E7\u00E3o para a Ci\u00EAncia e a Tecnologia (FCT), IP, under the Norma Transit\u01003ria DL57/2016/CP1334/CT0006. Jan-Walter De Neve acknowledges support from the Alexander von Humboldt Foundation. Kebede Deribe acknowledges support by Wellcome Trust grant number 201900/Z/16/Z as part of his International Intermediate Fellowship. Claudiu Herteliu acknowledges partial support by a grant co-funded by European Fund for Regional Development through Operational Program for Competitiveness, Project ID P_40_382. Praveen Hoogar acknowledges the Centre for Bio Cultural Studies (CBiCS), Manipal Academy of Higher Education(MAHE), Manipal and Centre for Holistic Development and Research (CHDR), Kalghatgi. Bing-Fang Hwang acknowledges support from China Medical University (CMU108-MF-95), Taichung, Taiwan. Mihajlo Jakovljevic acknowledges the Serbian part of this GBD contribution was co-funded through the Grant OI175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. Aruna M Kamath acknowledges funding from the National Institutes of Health T32 grant (T32GM086270). Srinivasa Vittal Katikireddi acknowledges funding from the Medical Research Council (MC_UU_12017/13 & MC_UU_12017/15), Scottish Government Chief Scientist Office (SPHSU13 & SPHSU15) and an NRS Senior Clinical Fellowship (SCAF/15/02). Yun Jin Kim acknowledges support from the Research Management Centre, Xiamen University Malaysia (XMUMRF/2018-C2/ITCM/0001). Kewal Krishan acknowledges support from the DST PURSE grant and UGC Center of Advanced Study (CAS II) awarded to the Department of Anthropology, Panjab University, Chandigarh, India. Manasi Kumar acknowledges support from K43 TW010716 Fogarty International Center/NIMH. Ben Lacey acknowledges support from the NIHR Oxford Biomedical Research Centre and the BHF Centre of Research Excellence, Oxford. Iv\u00E1n Landires is a member of the Sistema Nacional de Investigaci\u01003n (SNI), which is supported by the Secretar\u00EDa Nacional de Ciencia Tecnolog\u00EDa e Innovaci\u00F3n (SENACYT), Panam\u00E1. Jeffrey V Lazarus acknowledges support by a Spanish Ministry of Science, Innovation and Universities Miguel Servet grant (Instituto de Salud Carlos III/ESF, European Union [CP18/00074]). Peter T N Memiah acknowledges CODESRIA; HISTP. Subas Neupane acknowledges partial support from the Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital. Shuhei Nomura acknowledges support from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (18K10082). Alberto Ortiz acknowledges support by ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009 Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686 CIFRA2-CM. These funding sources had no role in the writing of the manuscript or the decision to submit it for publication. George C Patton acknowledges support from a National Health & Medical Research Council Fellowship. Marina Pinheiro acknowledges support from FCT for funding through program DL 57/2016 - Norma transit\u01003ria. Alberto Raggi, David Sattin, and Silvia Schiavolin acknowledge support by a grant from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C Besta, Linea 4 - Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche). Daniel Cury Ribeiro acknowledges support from the Sir Charles Hercus Health Research Fellowship - Health Research Council of New Zealand (18/111). Perminder S Sachdev acknowledges funding from the NHMRC Australia. Abdallah M Samy acknowledges support from a fellowship from the Egyptian Fulbright Mission Program. Milena M Santric-Milicevic acknowledges support from the Ministry of Education, Science and Technological Development of the Republic of Serbia (Contract No. 175087). Rodrigo Sarmiento-Su\u00E1rez acknowledges institutional support from University of Applied and Environmental Sciences in Bogota, Colombia, and Carlos III Institute of Health in Madrid, Spain. Maria In\u00EAs Schmidt acknowledges grants from the Foundation for the Support of Research of the State of Rio Grande do Sul (IATS and PrInt) and the Brazilian Ministry of Health. Sheikh Mohammed Shariful Islam acknowledges a fellowship from the National Heart Foundation of Australia and Deakin University. Aziz Sheikh acknowledges support from Health Data Research UK. Kenji Shibuya acknowledges Japan Ministry of Education, Culture, Sports, Science and Technology. Joan B Soriano acknowledges support by Centro de Investigaci\u00F3n en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. Rafael Tabar\u00E9s-Seisdedos acknowledges partial support from grant PI17/00719 from ISCIII-FEDER. Santosh Kumar Tadakamadla acknowledges support from the National Health and Medical Research Council Early Career Fellowship, Australia. Marcello Tonelli acknowledges the David Freeze Chair in Health Services Research at the University of Calgary, AB, Canada. Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations. Publisher Copyright: © 2020 Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
PY - 2020/10/17
Y1 - 2020/10/17
N2 - Background: Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods: Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings: Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation: The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC. Funding: Bill & Melinda Gates Foundation.
AB - Background: Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods: Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings: Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation: The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC. Funding: Bill & Melinda Gates Foundation.
UR - http://www.scopus.com/inward/record.url?scp=85090485900&partnerID=8YFLogxK
U2 - 10.1016/S0140-6736(20)30750-9
DO - 10.1016/S0140-6736(20)30750-9
M3 - Review article
C2 - 32861314
AN - SCOPUS:85090485900
SN - 0140-6736
VL - 396
SP - 1250
EP - 1284
JO - The Lancet
JF - The Lancet
IS - 10258
ER -