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Record W2946640224 · doi:10.1016/s2214-109x(19)30154-8

How concentrated are academic publications of countries' progression towards universal health coverage?

2019· article· en· W2946640224 on OpenAlexaboutno aff
Adrian Gheorghe, Kalipso Chalkidou, Anthony J. Culyer

Bibliographic record

VenueThe Lancet Global Health · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
FundersMedical Research CouncilDepartment for International Development
KeywordsScopusGlobal healthEconLitWonderDeveloping countryMedicineMEDLINEPolitical scienceHealth careLibrary scienceEconomic growthComputer sciencePsychologyLaw

Abstract

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All UN member states aim to achieve universal health coverage (UHC) by 2030 as part of the Sustainable Development Goals.1UNTransforming our world: the 2030 agenda for sustainable development.https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdfDate: 2015Date accessed: March 25, 2019Google Scholar Countries' progression towards UHC can be monitored using the WHO and World Bank standardised framework,2WHOWorld BankTracking universal health coverage: 2017 global monitoring report. World Health Organization, Geneva2017Google Scholar whereas the World Bank Universal Health Coverage Study Series (UNICO) documents in depth the progress towards UHC of more than 40 countries. As more knowledge of countries' pathways towards UHC accumulates and given the call that “all nations need to be producers as well as consumers of research”,3WHOThe World Health Report 2013: research for Universal Health Coverage. World Health Organization, Geneva2013Google Scholar one might wonder whether the available knowledge base on the UHC journey relies on a wide range of country experiences or only on a handful of distinctive and potentially unrepresentative ones. We searched six electronic databases (Scopus, MEDLINE, Embase, Global Health, Econlit, and Web of Science) on March 5, 2019, for “universal health coverage” as keyword, title, or abstract—with no other restrictions—to quantify the extent to which individual countries are referenced in direct association with UHC. Of 7889 total records, 5204 duplicates were removed and the remaining 2685 unique records were imported into R statistical software. Their titles and abstracts were searched for the occurrence of root country names (eg, “Leban” for “Lebanon” or “Lebanese”) based on the list of 183 countries with a reported service coverage index in the WHO and World Bank monitoring report;2WHOWorld BankTracking universal health coverage: 2017 global monitoring report. World Health Organization, Geneva2017Google Scholar the distinct records mentioning each country were counted. Multiple root words per country were used when appropriate (eg, “United Kingdom”, “Britain”, and “British” for the UK; “Turkey” and “Turkish” for Turkey vs “Turkm” for Turkmenistan); exclusions were made when appropriate (eg, occurrences of “United States dollars” were not counted towards the US). Further technical details are described in the appendix. 1641 (61%) of 2685 records mention at least one country root word. Firstly, it appears that several countries are invoked more often than others (figure, appendix). India, Thailand, China, South Africa, and Indonesia lead the chart with at least 75 distinct records each; Japan, Brazil, Ghana, Nigeria, Bangladesh, Kenya, and Mexico are mentioned between 55 and 75 times each. At the other end of the spectrum, 24 countries are never explicitly mentioned and another 27 have only one mention each. 101 countries appear in less than five records each. Secondly, the countries cited the most tend to be low-income and middle-income countries (LMIC), whereas those with few or no mentions tend to be high-income (HIC). Nonetheless, there are cases of well documented HICs (eg, Japan, France, USA, Canada, and UK) and many more undocumented LMICs. Thirdly, the distribution of UNICO country case-studies mirrors reasonably closely the distribution of the literature, with most case-studies concentrated among the well studied countries. However, the UNICO series also includes several case-studies of countries that are under-represented in the literature: Jamaica (1 record), Gabon (1 record), Armenia (1 record), Kyrgyzstan (1 record), Croatia (2 records), Azerbaijan (2 records), and the Dominican Republic (3 records). This analysis has limitations: it focuses on a single (albeit, arguably, the most relevant) search term, it assumes all country mentions are comparable (eg, not all countries mentioned in a given abstract might be the focus of the document), and it is restricted to titles and abstracts. As a result, Kyrgyzstan, for example, whose health financing reforms have been relatively well documented over time, appears only once. The emerging picture is nevertheless one of a UHC research focus to date on a small number of countries (particularly Brazil, Russia, India, China, and South Africa), potentially leaving the experiences of many others largely unknown. Cross-country variations in the national governments' political commitment towards the UHC agenda, domestic capacity to conduct health systems research, and (research) funding priorities of development partners, among others, might well explain some of this disparity. For example, the signature reforms of nationwide health insurance roll out in Ghana4Alhassan RK Nketiah-Amponsah E Arhinful DK A review of the national health insurance scheme in Ghana: what are the sustainability threats and prospects?.PLoS One. 2016; 11: e0165151Crossref PubMed Scopus (107) Google Scholar since 2004 (the first comprehensive insurance scheme in sub-Saharan Africa) and health technology assessment (HTA) in Thailand5Mohara A Youngkong S Velasco R et al.Using health technology assessment for informing coverage decisions in Thailand.J Comp Eff Res. 2012; 1: 137-146Crossref PubMed Scopus (60) Google Scholar (the first large-scale HTA programme in Asia) have attracted researchers' attention for more than a decade. The governments of China and India have also long engaged in various reforms towards UHC.6Reddy SK Health care reforms in India.J Am Med Assoc. 2018; 319: 2477-2478Crossref Scopus (17) Google Scholar, 7Hsiao W Li M Zhang S Universal Health Coverage: the case of China. UNRISD, Geneva2014Google Scholar South Africa's domestic political drive for UHC was reinforced with the publication of the White Paper on National Health Insurance in June, 2018, and is supported by strong domestic capacity for health systems research.8Philips JF Sheff M Boyer CB The astronomy of Africa's health systems literature during the MDG era: where are the systems clusters?.Glob Health Sci Pract. 2015; 3: 482-502Crossref PubMed Scopus (14) Google Scholar Exploratory correlation and regression analyses broadly do not suggest an association between how frequently a country is mentioned in the literature and measures of service coverage (service coverage index calculated as the geometric mean of 16 tracer indicators across four areas: reproductive, maternal, new-born, and child health; infectious diseases; noncommunicable diseases; and service capacity and access,2WHOWorld BankTracking universal health coverage: 2017 global monitoring report. World Health Organization, Geneva2017Google Scholar financial protection (percentage of households spending more than 10% of income on health care),2WHOWorld BankTracking universal health coverage: 2017 global monitoring report. World Health Organization, Geneva2017Google Scholar and external health expenditure per capita (purchasing power parity international dollars), in 2015;9WHOGlobal health expenditure database.http://apps.who.int/nha/database/Select/Indicators/enDate: 2019Date accessed: March 7, 2019Google Scholar however, more detailed analyses are needed to establish whether some form of publication bias is apparent. Validating and, if confirmed, understanding and addressing the sources of this cross-country variability in research attention might be useful for advancing the UHC agenda. Countries seeking to achieve UHC as rapidly and cost-effectively as possible could learn from the successes of many others, particularly how they handled the various hurdles, at least some of which are unlikely to be unique. The over-representation of few countries in research for UHC is bound to introduce biases in the lessons to be had from countries' practical experience, whether positive or negative. This bias could potentially undermine the fundamental UHC promise that every country must find its own way by limiting the options to what has been tried (and documented) already in a limited number of countries. The time seems ripe for a concerted effort to widen the base of countries and experiences. For more on UNICO see http://www.worldbank.org/en/topic/health/publication/universal-health-coverage-study-series For more on UNICO see http://www.worldbank.org/en/topic/health/publication/universal-health-coverage-study-series AG and KC acknowledge joint Medical Research Council (MRC) Centre for Global Infectious Disease Analysis funding from the UK MRC and Department for International Development. AC declares no competing interests. Download .pdf (.61 MB) Help with pdf files Supplementary appendix

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.530
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.332
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2019
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