SummaryBackgroundThe ambitious development agenda of the Sustainable Development Goals (SDGs) requires substantial investments across several sectors, including for SDG 3 (healthy lives and wellbeing). No estimates of the additional resources needed to strengthen comprehensive health service delivery towards the attainment of SDG 3 and universal health coverage in low-income and middle-income countries have been published.MethodsWe developed a framework for health systems strengthening, within which population-level and individual-level health service coverage is gradually scaled up over time. We developed projections for 67 low-income and middle-income countries from 2016 to 2030, representing 95% of the total population in low-income and middle-income countries. We considered four service delivery platforms, and modelled two scenarios with differing levels of ambition: a progress scenario, in which countries' advancement towards global targets is constrained by their health system's assumed absorptive capacity, and an ambitious scenario, in which most countries attain the global targets. We estimated the associated costs and health effects, including reduced prevalence of illness, lives saved, and increases in life expectancy. We projected available funding by country and year, taking into account economic growth and anticipated allocation towards the health sector, to allow for an analysis of affordability and financial sustainability.FindingsWe estimate that an additional $274 billion spending on health is needed per year by 2030 to make progress towards the SDG 3 targets (progress scenario), whereas US$371 billion would be needed to reach health system targets in the ambitious scenario—the equivalent of an additional $41 (range 15–102) or $58 (22–167) per person, respectively, by the final years of scale-up. In the ambitious scenario, total health-care spending would increase to a population-weighted mean of $271 per person (range 74–984) across country contexts, and the share of gross domestic product spent on health would increase to a mean of 7·5% (2·1–20·5). Around 75% of costs are for health systems, with health workforce and infrastructure (including medical equipment) as the main cost drivers. Despite projected increases in health spending, a financing gap of $20–54 billion per year is projected. Should funds be made available and used as planned, the ambitious scenario would save 97 million lives and significantly increase life expectancy by 3·1–8·4 years, depending on the country profile.InterpretationAll countries will need to strengthen investments in health systems to expand service provision in order to reach SDG 3 health targets, but even the poorest can reach some level of universality. In view of anticipated resource constraints, each country will need to prioritise equitably, plan strategically, and cost realistically its own path towards SDG 3 and universal health coverage.FundingWHO.

BackgroundEstimating health care costs, either in the context of understanding resource utilization in the implementation of a health plan, or in the context of economic evaluation, has become a common activity of health planners, health technology assessment agencies and academic groups. However, data sources for costs outside of direct service delivery are often scarce. WHO-CHOICE produces global price databases and guidance on quantity assumptions to support country level costing exercises. This paper presents updates to the WHO-CHOICE methodology and price databases for programme costs.MethodsWe collated publicly available databases for 14 non-traded cost variables, as well as a set of traded items used within health systems (traded goods are those which can be purchased from anywhere in the world, whereas non-traded goods are those which must be produced locally, such as human resources). Within each of the variables, missing data was present for some proportion of the WHO member states. For each variables statistical or econometric models were used to model prices for each of the 194 WHO member states in 2010 International Dollars. Literature reviews were used to update quantity assumptions associated with each variable to contribute to the support costs of disease control programmes.ResultsA full database of prices for disease control programme support costs is available for country-specific costing purposes. Human resources are the largest driver of disease control programme support costs, followed by supervision costs.ConclusionsDespite major advances in the availability of data since the previous version of this work, there are still some limitations in data availability to respond to the needs of those wishing to develop cost and cost-effectiveness estimates. Greater attention to programme support costs in cost data collection activities would contribute to an understanding of how these costs contribute to quality of health service delivery and should be encouraged.

Background Primary health care (PHC) is a driving force for advancing towards universal health coverage (UHC). PHC-oriented health systems bring enormous benefits but require substantial financial investments. Here, we aim to present measures for PHC investments and project the associated resource needs. Methods This modelling study analysed data from 67 low-income and middle-income countries (LMICs). Recognising the variation in PHC services among countries, we propose three measures for PHC, with different scope for included interventions and system strengthening. Measure 1 is centred on public health interventions and outpatient care; measure 2 adds general inpatient care; and measure 3 further adds cross-sectoral activities. Cost components included in each measure were based on the Declaration of Astana, informed by work delineating PHC within health accounts, and finalised through an expert and country validation meeting. We extracted the subset of PHC costs for each measure from WHO's Sustainable Development Goal (SDG) price tag for the 67 LMICs, and projected the associated health impact. Estimates of financial resource need, health workforce, and outpatient visits are presented as PHC investment guide posts for LMICs. Findings An estimated additional US$200-328 billion per year is required for the various measures of PHC from 2020 to 2030. For measure 1, an additional $32 is needed per capita across the countries. Needs are greatest in lowincome countries where PHC spending per capita needs to increase from $25 to $65. Overall health workforces would need to increase from 5•6 workers per 1000 population to 6•7 per 1000 population, delivering an average of 5•9 outpatient visits per capita per year. Increasing coverage of PHC interventions would avert an estimated 60•1 million deaths and increase average life expectancy by 3•7 years. By 2030, these incremental PHC costs would be about 3•3% of projected gross domestic product (GDP; median 1•7%, range 0•1-20•2). In a business-as-usual financing scenario, 25 of 67 countries will have funding gaps in 2030. If funding for PHC was increased by 1-2% of GDP across all countries, as few as 16 countries would see a funding gap by 2030. Interpretation The resources required to strengthen PHC vary across countries, depending on demographic trends, disease burden, and health system capacity. The proposed PHC investment guide posts advance discussions around the budgetary implications of strengthening PHC, including relevant system investment needs and achievable health outcomes. Preliminary findings suggest that low-income and lower-middle-income countries would need to at least double current spending on PHC to strengthen their systems and universally provide essential PHC services. Investing in PHC will bring substantial health benefits and build human capital. At country level, PHC interventions need to be explicitly identified, and plans should be made for how to most appropriately reorient the health system towards PHC as a key lever towards achieving UHC and the ...

BackgroundHuman resources are consistently cited as a leading contributor to health care costs; however the availability of internationally comparable data on health worker earnings for all countries is a challenge for estimating the costs of health care services. This paper describes an econometric model using cross sectional earnings data from the International Labour Organization (ILO) that the World Health Organizations (WHO)-Choosing Interventions that are Cost-effective programme (CHOICE) has used to prepare estimates of health worker earnings (in 2010 USD) for all WHO member states.MethodsThe ILO data contained 324 observations of earnings data across 4 skill levels for 193 countries. Using this data, along with the assumption that data were missing not at random, we used a Heckman two stage selection model to estimate earning data for each of the 4 skill levels for all WHO member states.ResultsIt was possible to develop a prediction model for health worker earnings for all countries for which GDP data was available. Health worker earnings vary both within country due to skill level, as well as across countries. As a multiple of GDP per capita, earnings show a negative correlation with GDP—that is lower income countries pay their health workers relatively more than higher income countries.ConclusionsLimited data on health worker earnings is a limiting factor in estimating the costs of global health programmes. It is hoped that these estimates will support robust health care intervention costings and projections of resources needs over the Sustainable Development Goal period.Electronic supplementary materialThe online version of this article (10.1186/s12962-018-0093-z) contains supplementary material, which is available to authorized users.

Long waiting times have been a persistent policy issue in the United Kingdom that the COVID‐19 pandemic has exacerbated. This study analyses the causal effect of hospital spending on waiting times in England using a first‐differences panel approach and an instrumental variable strategy to deal with residual concerns for endogeneity. We use data from 2014 to 2019 on waiting times from general practitioner referral to treatment (RTT) measured at the level of local purchasers (known as Clinical Commissioning Groups). We find that increases in hospital spending by local purchasers of 1% reduce median RTT waiting time for patients whose pathway ends with a hospital admission (admitted pathway) by 0.6 days but the effect is not statistically significant at 5% level (only at the 10% level). We also find that higher hospital spending does not affect the RTT waiting time for patients whose pathway ends with a specialist consultation (non‐admitted pathway). Nor does higher spending have a statistically significant effect on the volume of elective activity for either pathway. Our findings suggest that higher spending is no guarantee of higher volumes and lower waiting times, and that additional mechanisms need to be put in place to ensure that increased spending benefits elective patients.