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Background: Comparable estimates of health spending are crucial for the assessment of health systems and to optimally deploy health resources. The methods used to track health spending continue to evolve, but little is known about the distribution of spending across diseases. We developed improved estimates of health spending by source, including development assistance for health, and, for the first time, estimated HIV/AIDS spending on prevention and treatment and by source of funding, for 188 countries. Methods: We collected published data on domestic health spending, from 1995 to 2015, from a diverse set of international agencies. We tracked development assistance for health from 1990 to 2017. We also extracted 5385 datapoints about HIV/AIDS spending, between 2000 and 2015, from online databases, country reports, and proposals submitted to multilateral organisations. We used spatiotemporal Gaussian process regression to generate complete and comparable estimates for health and HIV/AIDS spending. We report most estimates in 2017 purchasing-power parity-adjusted dollars and adjust all estimates for the effect of inflation. Findings: Between 1995 and 2015, global health spending per capita grew at an annualised rate of 3·1% (95% uncertainty interval [UI] 3·1 to 3·2), with growth being largest in upper-middle-income countries (5·4% per capita [UI 5·3–5·5]) and lower-middle-income countries (4·2% per capita [4·2–4·3]). In 2015, $9·7 trillion (9·7 trillion to 9·8 trillion) was spent on health worldwide. High-income countries spent $6·5 trillion (6·4 trillion to 6·5 trillion) or 66·3% (66·0 to 66·5) of the total in 2015, whereas low-income countries spent $70·3 billion (69·3 billion to 71·3 billion) or 0·7% (0·7 to 0·7). Between 1990 and 2017, development assistance for health increased by 394·7% ($29·9 billion), with an estimated $37·4 billion of development assistance being disbursed for health in 2017, of which $9·1 billion (24·2%) targeted HIV/AIDS. Between 2000 and 2015, $562·6 billion (531·1 billion to 621·9 billion) was spent on HIV/AIDS worldwide. Governments financed 57·6% (52·0 to 60·8) of that total. Global HIV/AIDS spending peaked at 49·7 billion (46·2–54·7) in 2013, decreasing to $48·9 billion (45·2 billion to 54·2 billion) in 2015. That year, low-income and lower-middle-income countries represented 74·6% of all HIV/AIDS disability-adjusted life-years, but just 36·6% (34·4 to 38·7) of total HIV/AIDS spending. In 2015, $9·3 billion (8·5 billion to 10·4 billion) or 19·0% (17·6 to 20·6) of HIV/AIDS financing was spent on prevention, and $27·3 billion (24·5 billion to 31·1 billion) or 55·8% (53·3 to 57·9) was dedicated to care and treatment. Interpretation: From 1995 to 2015, total health spending increased worldwide, with the fastest per capita growth in middle-income countries. While these national disparities are relatively well known, low-income countries spent less per person on health and HIV/AIDS than did high-income and middle-income countries. Furthermore, declines in development assistance for health continue, including for HIV/AIDS. Additional cuts to development assistance could hasten this decline, and risk slowing progress towards global and national goals. Funding: The Bill & Melinda Gates Foundation.
Each health financing component we tracked required unique input data and, consequently, estimation focused on different time periods. We tracked health spending by source from 1995 to 2015, development assistance for health from 1990 to 2017, and HIV/AIDS spending from 2000 through to the end of 2015. Most spending estimates reported in this paper are reported using 2017 purchasing-power parity-adjusted dollars to adjust for inflation and to reflect the country-specific purchasing power of the resources. Development assistance for health estimates that are stratified by source, channel, or health focus area are tracked using 2017 US$ to reflect the quantity of development assistance for health provided by donors, using an internationally recognisable currency (ie, US$). Development assistance for health estimates stratified by recipient country are converted into 2017 purchasing-power parity-adjusted dollars based on the country to which the resources were provided. We extracted data about transfers from government domestic revenue (allocated to health purposes), social insurance contributions, compulsory prepayment, voluntary prepayment, other domestic revenue from households, corporations, and non-profit institutions serving the household, and gross domestic product (GDP), each measured in local currency, from the WHO Global Health Expenditure Database.3 We divided each health spending variable by GDP, also reported by WHO. To estimate domestic government spending on health, we added the value of transfers from government domestic revenue (allocated to health purposes), social insurance contributions, and compulsory prepayment. To estimate domestic prepaid private health spending, we added the values of voluntary prepayment, other domestic revenues from corporations, and other domestic revenues from non-profit institutions serving the household. Out-of-pocket spending is comprised of payments by households. Our tracking of domestic health spending focuses on current health spending and excludes major investment, such as building hospitals and research and development. We multiplied all health financing fractions by the GDP per capita series, measured in 2017 purchasing-power parity-adjusted dollars, to estimate spending per person in that currency.16 Many of the extracted data are not tied to an underlying data source and are estimated. Although more information is available in recent iterations,6, 17 the documentation of these tracking, estimation, and imputation methods remains, in some cases, poorly defined and inconsistent, or simply unreported. Furthermore, for a given country, these data vary substantially across time. To estimate health spending across time, country, and spending category, we used a spatiotemporal Gaussian process regression model.18 This model was developed for the Global Burden of Disease (GBD) Study to identify patterns across time and geographies.18 A further description of spatiotemporal Gaussian process regression model can be found in the appendix, along with out-of-sample statistics. To prevent data with unclear methods or proper data source identification from influencing our spatiotemporal Gaussian process regression model estimation, we developed a data weighting procedure. Each datapoint was assessed and assigned a weight between one and five on the basis of the point-specific metadata provided in the Global Health Expenditure Database. We based weights upon metadata completeness, documented source information, and documented methods for estimation. Our guidelines for assessing the metadata are included in the appendix. Development assistance for health includes the financial and in-kind resources provided by development agencies to low-income and middle-income countries, with the primary objective of maintaining or improving health. We estimated development assistance for health using project records, annual reports, budgets, and financial statements from international organisations. We relied on commitment and budget data to generate estimates for the most recent years when disbursement data were not available. Our estimates of development assistance for health tracked disbursements from the originating source through the disbursing agency, called the channel, to the recipient country and targeted health focus area or programme area. We used disbursement and income data to remove resources that were passed between development agencies before being disbursed to prevent double counting. We also accounted for the administrative expenses incurred by estimating in-kind expenses. We disaggregated development assistance for health disbursements into nine health focus areas: HIV/AIDS, tuberculosis, malaria, maternal health, newborn and child health, other infectious diseases, non-communicable diseases, sector-wide approaches and health system strengthening, and other. The other category captured all projects that did not align with any of the other health focus areas. We further disaggregated these health focus areas by programme area, which are spending categories that represent programmatic aims or implementation approaches within the broader health focus areas. For example, we disaggregated development assistance for HIV/AIDS into treatment, diagnosis, care and support, counselling and testing, orphan and vulnerable children, prevention of mother-to-child transmission, and HIV/AIDS system support. Additionally, we tracked development assistance for pandemic preparedness as a programme area within sector-wide approaches and health system strengthening, and treatment and diagnosis as separate programme areas under tuberculosis. We used keywords from project titles, descriptions, and budgets to determine the targeted health focus and programme areas for projects. We report development assistance for health estimates in 2017 US$, but converted disbursements from 2017 US$ to 2017 purchasing-power parity-adjusted dollars to add them to domestic spending estimates. We did this by first deflating disbursements to current US$, exchanging disbursements to the current national currency units of the recipient country, deflating to constant 2017 local currency, and then exchanging to 2017 purchasing-power parity-adjusted dollars. Detailed explanations of the methods used to track development assistance for health, including how disbursements for cross-cutting areas are allocated, are included in the appendix. We estimated HIV/AIDS spending measures by financing source (government spending, out-of-pocket, and prepaid private spending) and three HIV/AIDS spending categories (prevention, care and treatment, and all other spending). We extracted HIV/AIDS spending data from five data sources. First, we used the spending data in the AIDSinfo database.19 This UNAIDS-curated database collates countries’ annual reports on progress towards global HIV/AIDS goals, which capture HIV/AIDS spending by governments and the private sector. Second, we used the public and private spending data reported by countries in proposals and concept notes submitted to the Global Fund to Fight AIDS, Tuberculosis and Malaria. We included only current and past spending data reported in these submissions. Third, we extracted data from all National Health Accounts that capture HIV/AIDS spending, including sub-accounts and data produced under the updated System of Health Accounts (2011) approach. Fourth, we extracted data from all National AIDS Spending Assessments, including spending on prevention and care and treatment.4, 20 Finally, we downloaded data for the Asia–Pacific region from the AIDS data hub. We converted all reported spending measures to 2017 purchasing power parity. We aimed to adhere to the definition and boundaries of health spending as defined by the System of Health Accounts 2011 framework. This approach required us to harmonise the extracted data to correct for known definitional differences between data sources and observed biases within the data. The National AIDS Spending Assessment’s definition of HIV/AIDS spending included spending on non-health related categories such as spending on orphan and vulnerable children, enabling environment, and social protection. To correct for this, we extracted data from these three non-health-related spending categories and subtracted their values from all National AIDS Spending Assessment-reported spending by financing source. This correction probably accounted for most definitional biases between National AIDS Spending Assessments and National Health Accounts, but the granularity with which the data were reported limited further efforts to harmonise these two data sources. Similarly, we removed orphan and vulnerable children disbursements from our development assistance for health data. Not all data sources reported spending as granularly as we required. For example, some data sources only reported total domestic spending (sum of government, out-of-pocket, and prepaid private) or reported only private spending (sum of out-of-pocket and prepaid private). Although these spending measures did not match our measures of interest, they still provided valuable information. To use all available data, we estimated a total of five HIV/AIDS financing by source models (domestic, private, government, out-of-pocket, and prepaid private). To ensure internal consistency across all models, we developed a sophisticated aggregating procedure that included information about the number of underlying datapoints each series had, and how the estimated series related to each other. More information is provided in the appendix. We used a spatiotemporal Gaussian process regression model to model each HIV/AIDS financing source and spending category model. For all HIV/AIDS spending variables, the model consisted of a mixed-effect model with random effects on GBD super-region, region, and country, as well as covariates ranging from antiretroviral therapy coverage to the natural log of lag distributed GDP per capita, natural log of HIV prevalence, natural log of HIV incidence, natural log of HIV mortality rate, and, the natural log of antiretroviral therapy prices. We determined the exact specifications of each model through out-of-sample prediction tests (appendix). We sourced all covariate estimates from the GBD Study 2016.21 To detect and reduce the influence of outlier datapoints, we used our previous model to measure the Cook’s distance for each datapoint and excluded the datapoint if Cook’s distance, D, was greater than 4/n where n is the number of extracted datapoints. We reported health spending for each country, income group, and geographical region. We used 2017 World Bank income groups and GBD Study 2016 regions to categorise all years of data.21, 22 We aggregated rates by calculating total spending for the income group or region relative to the total income, number of prevalent cases, or health spending for the group or region. These measures reflect the income group or region as a whole, rather than reflecting the average of the nations that make up the group or region. We also grouped countries into three HIV/AIDS prevalence categories: low prevalence (5% prevalence). For these HIV/AIDS disease severity groups we extracted data from the GBD Study 2016.23 Categories were informed by cutoffs developed by UNAIDS.24 Finally, to compare health spending to health burden, we extracted country-specific disability-adjusted life-year estimates from the GBD Study 2016.23 We did this analysis using R (version 3.4.0), Stata (version 13), and Python (version 3.6). The funder of this study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all the data in the study, and JLD and CJLM had final responsibility for the decision to submit for publication.
