Trends in future health financing and coverage: future health spending and universal health coverage in 188 countries, 2016–40

Background: Achieving universal health coverage (UHC) requires health financing systems that provide prepaid pooled resources for key health services without placing undue financial stress on households. Understanding current and future trajectories of health financing is vital for progress towards UHC. We used historical health financing data for 188 countries from 1995 to 2015 to estimate future scenarios of health spending and pooled health spending through to 2040. Methods: We extracted historical data on gross domestic product (GDP) and health spending for 188 countries from 1995 to 2015, and projected annual GDP, development assistance for health, and government, out-of-pocket, and prepaid private health spending from 2015 through to 2040 as a reference scenario. These estimates were generated using an ensemble of models that varied key demographic and socioeconomic determinants. We generated better and worse alternative future scenarios based on the global distribution of historic health spending growth rates. Last, we used stochastic frontier analysis to investigate the association between pooled health resources and UHC index, a measure of a country's UHC service coverage. Finally, we estimated future UHC performance and the number of people covered under the three future scenarios. Findings: In the reference scenario, global health spending was projected to increase from US$10 trillion (95$20 trillion (18 trillion to 22 trillion) in 2040. Per capita health spending was projected to increase fastest in upper-middle-income countries, at 4·2% (3·4–5·1) per year, followed by lower-middle-income countries (4·0%, 3·6–4·5) and low-income countries (2·2%, 1·7–2·8). Despite global growth, per capita health spending was projected to range from only $40 (24–65) to$413 (263–668) in 2040 in low-income countries, and from $140 (90–200) to$1699 (711–3423) in lower-middle-income countries. Globally, the share of health spending covered by pooled resources would range widely, from 19·8% (10·3–38·6) in Nigeria to 97·9% (96·4–98·5) in Seychelles. Historical performance on the UHC index was significantly associated with pooled resources per capita. Across the alternative scenarios, we estimate UHC reaching between 5·1 billion (4·9 billion to 5·3 billion) and 5·6 billion (5·3 billion to 5·8 billion) lives in 2030. Interpretation: We chart future scenarios for health spending and its relationship with UHC. Ensuring that all countries have sustainable pooled health resources is crucial to the achievement of UHC. Funding: The Bill & Melinda Gates Foundation

Measuring Healthcare Value in OECD Countries

Thesis (Master's)--University of Washington, 2020Background Identifying the most efficient healthcare delivery systems and the characteristics that are associated with them may inform the decisions that governments make regarding the structure and regulation of healthcare. This study sought to estimate the value of healthcare delivery systems for 36 countries of the Organization for Economic Cooperation and Development (OECD) and determine which system features are associated with higher value. Methods Disease condition-specific death and incidence data were paired with total health spending per person from each OECD country from 1995 to 2017. A frontier analysis model was used to evaluate condition-specific mortality-incidence ratios for 141 major diseases, adjusting for per capita spending and covariates including smoking rates, age, educational attainment, and obesity to account for differences in the underlying health risks of each country. Inefficiency estimates for each country, year, and disease condition were extracted from the model and combined to create a single estimate of healthcare delivery system value for each country from 1995 to 2017. Associations between estimated healthcare value and 11 healthcare system characteristics were assessed using linear regression. Results The countries with the highest estimated healthcare delivery system value in 2017 were Italy, Estonia, Australia, Spain, and Slovenia. These countries had the lowest mortality-incidence ratios relative to per capita spending and baseline population health. Lithuania, Hungary, Chile, Poland, and Mexico attained the lowest levels of healthcare delivery system value in 2017. Higher insurance coverage rates, more consultations with physicians, and higher physician, nurse and midwife density are associated with higher value systems

Estimating health care delivery system value for each US

ObjectiveTo estimate health care systems’ value in treating major illnesses for each US state and identify system characteristics associated with value.Data sourcesAnnual condition-specific death and incidence estimates for each US state from the Global Burden Disease 2019 Study and annual health care spending per person for each state from the National Health Expenditure Accounts.Study designUsing non-linear meta-stochastic frontier analysis, mortality incidence ratios for 136 major treatable illnesses were regressed separately on per capita health care spending and key covariates such as age, obesity, smoking, and educational attainment. State- and year-specific inefficiency estimates were extracted for each health condition and combined to create a single estimate of health care delivery system value for each US state for each year, 1991–2014. The association between changes in health care value and changes in 23 key health care system characteristics and state policies was measured.Data collection/extraction methodsNot applicable.Principal findingsUS state with relatively high spending per person or relatively poor health-outcomes were shown to have low health care delivery system value. New Jersey, Maryland, Florida, Arizona, and New York attained the highest value scores in 2014 (81 [95% uncertainty interval 72-88], 80 [72-87], 80 [71-86], 77 [69-84], and 77 [66-85], respectively), after controlling for health care spending, age, obesity, smoking, physical activity, race, and educational attainment. Greater market concentration of hospitals and of insurers were associated with worse health care value (p-value ranging from <0.01 to 0.02). Higher hospital geographic density and use were also associated with worse health care value (p-value ranging from 0.03 to 0.05). Enrollment in Medicare Advantage HMOs was associated with better value, as was more generous Medicaid income eligibility (p-value 0.04 and 0.01).ConclusionsSubstantial variation in the value of health care exists across states. Key health system characteristics such as market concentration and provider density were associated with value.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/172832/1/hesr13676_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/172832/2/hesr13676.pd

Spending on health and HIV/AIDS: domestic health spending and development assistance in 188 countries, 1995\u20132015

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\ub71% (95% uncertainty interval [UI] 3\ub71 to 3\ub72), with growth being largest in upper-middle-income countries (5\ub74% per capita [UI 5\ub73\u20135\ub75]) and lower-middle-income countries (4\ub72% per capita [4\ub72\u20134\ub73]). In 2015, 9\ub77 trillion (9\ub77 trillion to 9\ub78 trillion) was spent on health worldwide. High-income countries spent 6\ub75 trillion (6\ub74 trillion to 6\ub75 trillion) or 66\ub73% (66\ub70 to 66\ub75) of the total in 2015, whereas low-income countries spent 70\ub73 billion (69\ub73 billion to 71\ub73 billion) or 0\ub77% (0\ub77 to 0\ub77). Between 1990 and 2017, development assistance for health increased by 394\ub77% (29\ub79 billion), with an estimated 37\ub74 billion of development assistance being disbursed for health in 2017, of which 9\ub71 billion (24\ub72%) targeted HIV/AIDS. Between 2000 and 2015, 562\ub76 billion (531\ub71 billion to 621\ub79 billion) was spent on HIV/AIDS worldwide. Governments financed 57\ub76% (52\ub70 to 60\ub78) of that total. Global HIV/AIDS spending peaked at 49\ub77 billion (46\ub72\u201354\ub77) in 2013, decreasing to 48\ub79 billion (45\ub72 billion to 54\ub72 billion) in 2015. That year, low-income and lower-middle-income countries represented 74\ub76% of all HIV/AIDS disability-adjusted life-years, but just 36\ub76% (34\ub74 to 38\ub77) of total HIV/AIDS spending. In 2015, 9\ub73 billion (8\ub75 billion to 10\ub74 billion) or 19\ub70% (17\ub76 to 20\ub76) of HIV/AIDS financing was spent on prevention, and 27\ub73 billion (24\ub75 billion to 31\ub71 billion) or 55\ub78% (53\ub73 to 57\ub79) 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