U.S. dementia care spending by state: 2010-2019.

INTRODUCTION: Dementia is the fourth largest cause of death for individuals 70 years of age and older in the United States, and it is tremendously costly. Existing estimates of the indirect costs of dementia are dated and do not report on differences across the United States. METHODS: We used data from multiple surveys to create cost estimates and projections for informal dementia caregiving at the U.S. state level from 2010 through 2050. RESULTS: In 2019, the annual replacement cost of informal caregiving was $42,422 per prevalent case, and the forgone wage cost was $10,677 per prevalent case. In 2019, it would have cost $230 billion to hire home health aides to provide all this care. If past trends persist, this cost is expected to grow to $404 billion per year in 2050. DISCUSSION: The cost of informal care varied substantially by state and is expected to grow through at least 2050. HIGHLIGHTS: In the United States in 2019, foregone wages due to informal dementia care was $58 billion. Replacing informal dementia care with health aides would have cost $230 billion. These costs vary dramatically by state, even when assessed per prevalent case. These costs are expected to nearly double by 2050.

Catastrophic health expenditure during the COVID-19 pandemic in five countries: a time-series analysis.

BACKGROUND: The COVID-19 pandemic disrupted health systems in 2020, but it is unclear how financial hardship due to out-of-pocket (OOP) health-care costs was affected. We analysed catastrophic health expenditure (CHE) in 2020 in five countries with available household expenditure data: Belarus, Mexico, Peru, Russia, and Viet Nam. In Mexico and Peru, we also conducted an analysis of drivers of change in CHE in 2020 using publicly available data. METHODS: In this time-series analysis, we defined CHE as when OOP health-care spending exceeds 10% of consumption expenditure. Data for 2004-20 were obtained from individual and household level survey microdata (available for Mexico and Peru only), and tabulated data from the National Statistical Committee of Belarus and the World Bank Health Equity and Financial Protection Indicator database (for Viet Nam and Russia). We compared 2020 CHE with the CHE predicted from historical trends using an ensemble model. This method was also used to assess drivers of CHE: insurance coverage, OOP expenditure, and consumption expenditure. Interrupted time-series analysis was used to investigate the role of stay-at-home orders in March, 2020 in changes in health-care use and sector (ie, private vs public). FINDINGS: In Mexico, CHE increased to 5·6% (95% uncertainty interval [UI] 5·1-6·2) in 2020, higher than predicted (3·2%, 2·5-4·0). In Belarus, CHE was 13·5% (11·8-15·2) in 2020, also higher than predicted (9·7%, 7·7-11·3). CHE was not different than predicted by past trends in Russia, Peru, and Viet Nam. Between March and April, 2020, health-care visits dropped by 4·6 (2·6-6·5) percentage points in Mexico and by 48·3 (40·6-56·0) percentage points in Peru, and the private share of health-care visits increased by 7·3 (4·3-10·3) percentage points in Mexico and by 20·7 (17·3-24·0) percentage points in Peru. INTERPRETATION: In three of the five countries studied, health systems either did not protect people from the financial risks of health care or did not maintain health-care access in 2020, an indication of health systems failing to maintain basic functions. If the 2020 response to the COVID-19 pandemic accelerated shifts to private health-care use, policies to cover costs in that sector or motivate patients to return to the public sector are needed to maintain financial risk protection. FUNDING: The Bill & Melinda Gates Foundation.

Production losses from morbidity and mortality by disease, age and sex in Norway.

AIM: The inclusion of production losses in health care priority setting is extensively debated. However, few studies allow for a comparison of these losses across relevant clinical and demographic categories. Our objective was to provide comprehensive estimates of Norwegian production losses from morbidity and mortality by age, sex and disease category. METHODS: National registries, tax records, labour force surveys, household and population statistics and data from the Global Burden of Disease were combined to estimate production losses for 12 disease categories, 38 age and sex groups and four causes of production loss. The production losses were estimated via lost wages in accordance with a human capital approach for 2019. RESULTS: The main causes of production losses in 2019 were mental and substance use disorders, totalling NOK121.6bn (32.7% of total production losses). This was followed by musculoskeletal disorders, neurological disorders, injuries, and neoplasms, which accounted for 25.2%, 7.4%, 7.4% and 6.5% of total production losses, respectively. Production losses due to sick leave, disability insurance and work assessment allowance were higher for females than for males, whereas production losses due to premature mortality were higher for males. The latter was related to neoplasms, cardiovascular disease and injuries. Across age categories, non-fatal conditions with a high prevalence among working populations caused the largest production losses. CONCLUSIONS: The inclusion of production losses in health care priority debates in Norway could result in an emphasis on chronic diseases that occur among younger populations at the expense of fatal diseases among older age groups.

A small area model to assess temporal trends and sub-national disparities in healthcare quality.

Monitoring subnational healthcare quality is important for identifying and addressing geographic inequities. Yet, health facility surveys are rarely powered to support the generation of estimates at more local levels. With this study, we propose an analytical approach for estimating both temporal and subnational patterns of healthcare quality indicators from health facility survey data. This method uses random effects to account for differences between survey instruments; space-time processes to leverage correlations in space and time; and covariates to incorporate auxiliary information. We applied this method for three countries in which at least four health facility surveys had been conducted since 1999 - Kenya, Senegal, and Tanzania - and estimated measures of sick-child care quality per WHO Service Availability and Readiness Assessment (SARA) guidelines at programmatic subnational level, between 1999 and 2020. Model performance metrics indicated good out-of-sample predictive validity, illustrating the potential utility of geospatial statistical models for health facility data. This method offers a way to jointly estimate indicators of healthcare quality over space and time, which could then provide insights to decision-makers and health service program managers.

Disease-specific health spending by age, sex, and type of care in Norway: a national health registry study.

BACKGROUND: Norway is a high-income nation with universal tax-financed health care and among the highest per person health spending in the world. This study estimates Norwegian health expenditures by health condition, age, and sex, and compares it with disability-adjusted life-years (DALYs). METHODS: Government budgets, reimbursement databases, patient registries, and prescription databases were combined to estimate spending for 144 health conditions, 38 age and sex groups, and eight types of care (GPs; physiotherapists & chiropractors; specialized outpatient; day patient; inpatient; prescription drugs; home-based care; and nursing homes) totaling 174,157,766 encounters. Diagnoses were in accordance with the Global Burden of Disease study (GBD). The spending estimates were adjusted, by redistributing excess spending associated with each comorbidity. Disease-specific DALYs were gathered from GBD 2019. RESULTS: The top five aggregate causes of Norwegian health spending in 2019 were mental and substance use disorders (20.7%), neurological disorders (15.4%), cardiovascular diseases (10.1%), diabetes, kidney, and urinary diseases (9.0%), and neoplasms (7.2%). Spending increased sharply with age. Among 144 health conditions, dementias had the highest health spending, with 10.2% of total spending, and 78% of this spending was incurred at nursing homes. The second largest was falls estimated at 4.6% of total spending. Spending in those aged 15-49 was dominated by mental and substance use disorders, with 46.0% of total spending. Accounting for longevity, spending per female was greater than spending per male, particularly for musculoskeletal disorders, dementias, and falls. Spending correlated well with DALYs (Correlation r = 0.77, 95% CI 0.67-0.87), and the correlation of spending with non-fatal disease burden (r = 0.83, 0.76-0.90) was more pronounced than with mortality (r = 0.58, 0.43-0.72). CONCLUSIONS: Health spending was high for long-term disabilities in older age groups. Research and development into more effective interventions for the disabling high-cost diseases is urgently needed.

Estimating health spending associated with chronic multimorbidity in 2018: An observational study among adults in the United States.

BACKGROUND: The rise in health spending in the United States and the prevalence of multimorbidity-having more than one chronic condition-are interlinked but not well understood. Multimorbidity is believed to have an impact on an individual's health spending, but how having one specific additional condition impacts spending is not well established. Moreover, most studies estimating spending for single diseases rarely adjust for multimorbidity. Having more accurate estimates of spending associated with each disease and different combinations could aid policymakers in designing prevention policies to more effectively reduce national health spending. This study explores the relationship between multimorbidity and spending from two distinct perspectives: (1) quantifying spending on different disease combinations; and (2) assessing how spending on a single diseases changes when we consider the contribution of multimorbidity (i.e., additional/reduced spending that could be attributed in the presence of other chronic conditions). METHODS AND FINDINGS: We used data on private claims from Truven Health MarketScan Research Database, with 16,288,894 unique enrollees ages 18 to 64 from the US, and their annual inpatient and outpatient diagnoses and spending from 2018. We selected conditions that have an average duration of greater than one year among all Global Burden of Disease causes. We used penalized linear regression with stochastic gradient descent approach to assess relationship between spending and multimorbidity, including all possible disease combinations with two or three different conditions (dyads and triads) and for each condition after multimorbidity adjustment. We decomposed the change in multimorbidity-adjusted spending by the type of combination (single, dyads, and triads) and multimorbidity disease category. We defined 63 chronic conditions and observed that 56.2% of the study population had at least two chronic conditions. Approximately 60.1% of disease combinations had super-additive spending (e.g., spending for the combination was significantly greater than the sum of the individual diseases), 15.7% had additive spending, and 23.6% had sub-additive spending (e.g., spending for the combination was significantly less than the sum of the individual diseases). Relatively frequent disease combinations (higher observed prevalence) with high estimated spending were combinations that included endocrine, metabolic, blood, and immune disorders (EMBI disorders), chronic kidney disease, anemias, and blood cancers. When looking at multimorbidity-adjusted spending for single diseases, the following had the highest spending per treated patient and were among those with high observed prevalence: chronic kidney disease ($14,376 [12,291,16,670]), cirrhosis ($6,465 [6,090,6,930]), ischemic heart disease (IHD)-related heart conditions ($6,029 [5,529,6,529]), and inflammatory bowel disease ($4,697 [4,594,4,813]). Relative to unadjusted single-disease spending estimates, 50 conditions had higher spending after adjusting for multimorbidity, 7 had less than 5% difference, and 6 had lower spending after adjustment. CONCLUSIONS: We consistently found chronic kidney disease and IHD to be associated with high spending per treated case, high observed prevalence, and contributing the most to spending when in combination with other chronic conditions. In the midst of a surging health spending globally, and especially in the US, pinpointing high-prevalence, high-spending conditions and disease combinations, as especially conditions that are associated with larger super-additive spending, could help policymakers, insurers, and providers prioritize and design interventions to improve treatment effectiveness and reduce spending.

State-Level Cardiovascular Mortality Rates Among Hispanic, Non-Hispanic Black, and Non-Hispanic White Populations, 1990 to 2019.

Importance: Cardiovascular disease (CVD) is the leading cause of death in the US, with considerable variation by both state and race and ethnicity group. Consistent, comparable measures of mortality by specific CVD cause at the state level and by race and ethnicity have not previously been available and are necessary for supporting policy decisions aimed at reducing health inequities. Objective: To quantify and describe levels and trends of mortality due to overall CVD and its component causes for 3 mutually exclusive race and ethnicity groups and by state. Design, Setting, and Participants: This cross-sectional study used Census data, population surveys, and US vital registration records to estimate cause-specific cardiovascular mortality by state and by the following race and ethnicity groups, defined by the US Office of Management and Budget: Hispanic of any race, non-Hispanic Black (hereafter, Black), and non-Hispanic White (hereafter, White). Data were analyzed from January 2020 to September 2022. Exposures: State of residence at time of death; Hispanic ethnicity and Black or White race. Main Outcomes and Measures: CVD death counts and mortality rates. Results: An estimated 25 397 029 persons died of cardiovascular diseases from 1990 to 2019. The mean (SD) age of individuals was 78.20 (14.01); 13 087 290 individuals (51.53%) were female and 12 309 739 (48.47%) were male; 2 921 650 (11.50%) were Black, 1 159 498 (4.57%) were Hispanic, and 21 315 880 (83.93%) were White. Age-standardized CVD mortality per 100 000 persons in 2019 was 194.4 (95% uncertainty interval [UI], 172.7 to 207.4), 107.7 (95% UI, 92.9 to 121.4), and 153.8 (95% UI, 133.8 to 163.8) among Black, Hispanic, and White populations, respectively. The median (IQR) percentage change across states was smaller for 2010 to 2019 compared with 1990 to 2000 for both White female and White male populations (-6.8 [-10.1 to -4.3] vs -10.2 [-12.9 to -5.9] and -4.6 [-8.6 to -2.5] vs -16.5 [-19.3 to -15.4]). For the Black and Hispanic groups, the percentage change (IQR) was larger for the female populations for the latter time period (-15.1 [-18.9 to -11.7] vs -12.6 [-19.6 to -7.8] and -23.5 [-29.2 to -18.5] vs -8.2 [-17.8 to 5.96]). The converse was observed among male individuals in both groups, with smaller percentage change (IQR) values in 2010 to 2019 compared with 1990 to 2000 (-13.1 [-18.7 to -8.6] vs -18.6 [-25.5 to -14.7] among the Black male population and -20.4 [-25.6 to -15.6] vs -21.5 [-31.1 to -5.7] among the Hispanic male population). There was substantial variability at the state level for death due to total CVD and component causes in 2019 and changes in CVD mortality from 1990 through 2019. Conclusions and Relevance: The findings of this study indicate that CVD mortality varied widely by state and race and ethnicity group. Changes over the time period were not consistent for all groups and varied by cardiovascular subcause. These results highlight ongoing health disparities in cardiovascular mortality.

Health Care Spending on Respiratory Diseases in the United States, 1996-2016.

Rationale: Respiratory conditions account for a large proportion of health care spending in the United States. A full characterization of spending across multiple conditions and over time has not been performed. Objectives: To estimate health care spending in the United States for 11 respiratory conditions from 1996 to 2016, providing detailed trends and an evaluation of factors associated with spending growth. Methods: We extracted data from the Institute of Health Metrics and Evaluation's Disease Expenditure Project Database, producing annual estimates in spending for 38 age and sex groups, 7 types of care, and 3 payer types. We performed a decomposition analysis to estimate the change in spending associated with changes in each of five factors (population growth, population aging, disease prevalence, service usage, and service price and intensity). Measurements and Main Results: Total spending across all respiratory conditions in 2016 was $170.8 billion (95% confidence interval [CI], $164.2-179.2 billion), increasing by $71.7 billion (95% CI, $63.2-80.8 billion) from 1996. The respiratory conditions with the highest spending in 2016 were asthma and chronic obstructive pulmonary disease, contributing $35.5 billion (95% CI, $32.4-38.2 billion) and $34.3 billion (95% CI, $31.5-37.3 billion), respectively. Increasing service price and intensity were associated with 81.4% (95% CI, 70.3-93.0%) growth from 1996 to 2016. Conclusions: U.S. spending on respiratory conditions is high, particularly for chronic conditions like asthma and chronic obstructive pulmonary disease. Our findings suggest that service price and intensity, particularly for pharmaceuticals, should be a key focus of attention for policymakers seeking to reduce health care spending growth.

The health-adjusted dependency ratio as a new global measure of the burden of ageing: a population-based study.

BACKGROUND: The old-age dependency ratio (OADR), which is the ratio of older people (aged ≥65 years) to working age people (aged 20-64 years), is the most common way to assess and compare the burden of population ageing in different countries. However, the relationship between chronological age and dependency varies widely across countries. We therefore present the health-adjusted dependency ratio (HADR), a new measure of ageing burden based on the ageing-related health of the adult population. METHODS: In this population-based study we used health data for diseases and injuries for 2017 from the Global Burden of Disease project and population data for 2017 from the UN's Population Division to identify the number of adults (aged >20 years) in each country who have the same or higher ageing-related disease burden as the global average 65-year-old. We then calculated the HADR as the ratio of adults who were less healthy than the average 65-year-old (dependent population) to those in better health (supporting population) and compared the HADR with the OADR for 188 countries. We also used cross-sectional, bivariate regression analysis to investigate whether the HADR is a more powerful predictor of changes in per capita health-care expenditure than the OADR as a measure of predictive validity. FINDINGS: Many demographically younger populations have an earlier onset of ageing-related disease, and many demographically older populations have a later onset. For instance, Pakistan has an OADR of 0·09 and an HADR of 0·19, and France has an OADR of 0·35 and an HADR of 0·13. Relative to the OADR, the HADR suggests that Asia, western Europe, and North America have a lower ageing burden, whereas central Asia, southern Asia, and Africa have a greater burden. While Japan and countries in western Europe have the highest OADR, Russia, Papua New Guinea, and countries in southeast Europe have the highest HADR. Relative to the OADR, the HADR suggests that there is much less variation in the burden of ageing across countries than has previously been assumed. HADR was also more closely associated with growth in health spending than the OADR. A 0·1 increase in the HADR was associated with a 2·9 percentage points larger growth rate in per capita spending (p=0·0001), and a 0·1-point increase in the OADR was associated with a 1·8 percentage point larger growth rate. INTERPRETATION: The OADR probably overestimates the burden of population ageing in many demographically older countries and underestimates the ageing burden in many demographically younger countries, which implies that the challenges associated with ageing are more universal than previously thought, and that the world cannot easily be divided in a young and an old groups of nations. FUNDING: None.

Varied Health Spending Growth Across US States Was Associated With Incomes, Price Levels, And Medicaid Expansion, 2000-19.

Little is known about health care spending variation across the US for recent years. To estimate health spending by state and payer, we combined data from the government's State Health Expenditure Accounts, which have estimates through 2014, with other primary data on spending. In 2019 state-specific per person spending ranged from $7,250 to $14,500. After adjustment for inflation, annualized per person spending growth for each state ranged from 1.0 percent in Washington, D.C., to 4.2 percent in South Dakota between 2013 and 2019. The factors that explained the most variation across states were incomes (25.3 percent) and consumer prices (21.7 percent). Medicaid expansion was associated with increases in total spending per person, although the median of spending in expansion states showed slower growth in out-of-pocket spending than the median in nonexpansion states. Contemporary estimates of state health spending are valuable for tracking divergent expenditure trajectories in the US and assessing the associated factors.

Health Care Spending Effectiveness: Estimates Suggest That Spending Improved US Health From 1996 To 2016.

Health care spending effectiveness is the ratio of an increase in spending per case of illness or injury to an increase in disability-adjusted life-years (DALYs) averted per case. We report US spending-effectiveness ratios, using comprehensive estimates of health care spending from the Disease Expenditure Project and DALYs from the Global Burden of Disease Study 2017. We decomposed changes over time to estimate spending per case and DALYs averted per case, controlling for changes in population size, age-sex structure, and incidence or prevalence of cases. Across all causes of health care spending and disease burden, median spending was US$114,339 per DALY averted between 1996 and 2016. Twelve of thirty-four causes with the highest spending or highest burden had median spending that was less than $100,000 per DALY averted. Using decomposition results, we calculated an outcome-adjusted health care price index by assigning a dollar value to DALYs averted per case. When we used $100,000 as the dollar value per DALY averted, prices increased by 4 percent more than the broader economy; when we used $150,000 per DALY averted, relative prices fell by 13 percent, meaning that much of the growth in health care spending over time has purchased health improvements.

Life Expectancy for White, Black, and Hispanic Race/Ethnicity in U.S. States: Trends and Disparities, 1990 to 2019.

BACKGROUND: Life expectancy (LE) differences within and between states by race/ethnicity have not been examined. OBJECTIVE: To estimate LE for selected race/ethnicity groups in states from 1990 to 2019. DESIGN: Cross-sectional time-series analysis. SETTING: United States. PARTICIPANTS: Deidentified death records and Census data were used to construct regression models with smoothed time series of mortality from 1990 to 2019. MEASUREMENTS: LE at birth, by sex and year, for subgroups of people reporting Hispanic, non-Hispanic Black, or non-Hispanic White race/ethnicity. RESULTS: Disparities in LE across states were 8.0 years for females and 12.2 years for males in 1990 and 7.9 years for females and 7.8 years for males in 2019. When race/ethnicity groups were accounted for, disparities across states were 20.7 years for females and 24.5 years for males in 1990, decreasing to 18.5 years for females and 23.7 years for males in 2019. Disparities across states increased within each race/ethnicity group between 1990 and 2019, with the largest increase for non-Hispanic White males and the smallest for Hispanic females. The disparity between race/ethnicity groups within states decreased for most of the 23 states with estimates for all 3 groups but increased for females in 7 states and males in 5 states. LIMITATION: Because of small sample size, LE was not estimated for 37 of 153 state-race/ethnicity groups. CONCLUSION: Disparity in LE across states was greater when race/ethnicity groups were considered. Disparities across all state-race/ethnicity groups in general have decreased over the past 3 decades. Within each race/ethnicity group, disparities across states have increased. Although racial/ethnic disparities decreased in most of the 23 states for which LE was estimated for all 3 groups, they increased for females in 7 states and males in 5 states. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute.

Spending on emergency care due to nontraumatic dental conditions in the United States, 1996 through 2016.

BACKGROUND: Emergency department (ED) use for oral health care is a growing problem in the United States. The objective of the study was to describe spending on ED visits due to nontraumatic dental conditions (NTDCs) in the United States and to quantify changes in spending and its drivers. METHODS: Spending estimates for ED visits due to NTDCs according to type of payer were analyzed for the period from 1996 through 2016 and estimates about the drivers of change were analyzed for the period from 1996 through 2013. NTDCs included caries, periodontitis, edentulism, and other oral disorders. Estimates were calculated according to age, sex, and type of payer (that is, public, private, and out of pocket), adjusted for inflation, and expressed in 2016 US dollars. The estimate of expenses was decomposed into 5 drivers for the period from 1996 through 2013 (that is, population, aging, prevalence of oral disorders, service use, and service price and intensity). RESULTS: The total change in spending from 1996 through 2016 amounted to $540 million, an increase of 216%. The drivers of changes in spending from 1996 through 2013 were price and intensity ($360 million), service use ($220 million), and population size ($68 million). CONCLUSIONS: Spending on ED visits due to NTDCs more than tripled during the study period, with price and intensity representing the main drivers. This increase was primarily in adults and paid via the public sector. PRACTICAL IMPLICATIONS: Possible solutions include strengthening the oral health care safety net, especially for the most vulnerable populations.

Development assistance for human resources for health, 1990-2020.

BACKGROUND: Investing in the health workforce is key to achieving the health-related Sustainable Development Goals. However, achieving these Goals requires addressing a projected global shortage of 18 million health workers (mostly in low- and middle-income countries). Within that context, in 2016, the World Health Assembly adopted the WHO Global Strategy on Human Resources for Health: Workforce 2030. In the Strategy, the role of official development assistance to support the health workforce is an area of interest. The objective of this study is to examine progress on implementing the Global Strategy by updating previous analyses that estimated and examined official development assistance targeted towards human resources for health. METHODS: We leveraged data from IHME's Development Assistance for Health database, COVID development assistance database and the OECD's Creditor Reporting System online database. We utilized an updated keyword list to identify the relevant human resources for health-related activities from the project databases. When possible, we also estimated the fraction of human resources for health projects that considered and/or focused on gender as a key factor. We described trends, examined changes in the availability of human resources for health-related development assistance since the adoption of the Global Strategy and compared disease burden and availability of donor resources. RESULTS: Since 2016, development assistance for human resources for health has increased with a slight dip in 2019. In 2020, fueled by the onset of the COVID-19 pandemic, it reached an all-time high of $4.1 billion, more than double its value in 2016 and a 116.5% increase over 2019. The highest share (42.4%) of support for human resources for health-related activities has been directed towards training. Since the adoption of the Global Strategy, donor resources for health workforce-related activities have on average increased by 13.3% compared to 16.0% from 2000 through 2015. For 47 countries identified by the WHO as having severe workforce shortages, the availability of donor resources remains modest. CONCLUSIONS: Since 2016, donor support for health workforce-related activities has increased. However, there are lingering concerns related to the short-term nature of activities that donor funding supports and its viability for creating sustainable health systems.

Global and regional spending on dementia care from 2000-2019 and expected future health spending scenarios from 2020-2050: An economic modelling exercise.

Background: The global burden of dementia is increasing. As diagnosis and treatment rates increase and populations grow and age, additional diagnosed cases will present a challenge to healthcare systems globally. Even modelled estimates of the current and future healthcare spending attributable to dementia are valuable for decision makers and advocates to prepare for growing demand. Methods: We modelled healthcare spending attributable to dementia from 2000 to 2019 and expected estimated future spending from 2020 to 2050 under multiple scenarios. Data were from the Global Burden of Diseases 2019 study and from two systematic literature reviews. We used meta-regression to estimate the fraction of dementia spending that is attributable to dementia for those receiving nursing home-based care and for those receiving community-based care. We used spatiotemporal Gaussian process regression to account for data missingness and model diagnosis and treatment rates, nursing home-based care and community-based care rates, and unit costs for the many countries without their own underlying estimates. Projections of future spending estimate a baseline scenario from 2020 to 2050 based on ongoing growth. Alternative scenarios assessed faster growth rates for dementia diagnosis and treatment rates, nursing home-based care, and healthcare costs. All spending is reported in 2019 United States dollars or 2019 purchasing-power parity-adjusted dollars. Findings: Based on observed and modelled inputs, we estimated that global spending on dementia increased by 4.5% (95% uncertainty interval: 3.4-5.4%) annually from 2000 to 2019, reaching $263 billion (95% uncertainty interval [UI] $199- $333) attributable to dementia in 2019. We estimated total healthcare spending on patients with dementia was $594 billion (95% UI $457-$843). Under the baseline scenario, we estimated that attributable dementia spending will reach $1.6 trillion (95% UI $0.9-$2.6) by 2050. We project it will represent 11% (95% UI 6-18%) of all expected health spending, although it could be as high as 17% (95% UI 10-26%) under alternative scenarios. Interpretation: Health systems will experience increases in the burden of dementia in the near future. These modelled direct cost estimates, built from a relatively small set of data and linear time trends, highlight the magnitude of health system resources expected to be used to provide care and ensure sufficient and adequate resources for aging populations and their caretakers. More data are needed to corroborate these important trends.

Estimating health care delivery system value for each US state and testing key associations.

OBJECTIVE: To estimate health care systems' value in treating major illnesses for each US state and identify system characteristics associated with value. DATA SOURCES: Annual 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 DESIGN: Using 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 METHODS: Not applicable. PRINCIPAL FINDINGS: US 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). CONCLUSIONS: Substantial 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.

Estimating total spending by source of funding on routine and supplementary immunisation activities in low-income and middle-income countries, 2000-17: a financial modelling study.

BACKGROUND: Childhood immunisation is one of the most cost-effective health interventions. However, despite its known value, global access to vaccines remains far from complete. Although supply-side constraints lead to inadequate vaccine coverage in many health systems, there is no comprehensive analysis of the funding for immunisation. We aimed to fill this gap by generating estimates of funding for immunisation disaggregated by the source of funding and the type of activities in order to highlight the funding landscape for immunisation and inform policy making. METHODS: For this financial modelling study, we estimated annual spending on immunisations for 135 low-income and middle-income countries (as determined by the World Bank) from 2000 to 2017, with a focus on government, donor, and out-of-pocket spending, and disaggregated spending for vaccines and delivery costs, and routine schedules and supplementary campaigns. To generate these estimates, we extracted data from National Health Accounts, the WHO-UNICEF Joint Reporting Forms, comprehensive multi-year plans, databases from Gavi, the Vaccine Alliance, and the Institute for Health Metrics and Evaluation's 2019 development assistance for health database. We estimated total spending on immunisation by aggregating the government, donor, prepaid private, and household spending estimates. FINDINGS: Between 2000 and 2017, funding for immunisation totalled US$112·4 billion (95% uncertainty interval 108·5-118·5). Aggregated across all low-income and middle-income countries, government spending consistently remained the largest source of funding, providing between 60·0% (57·7-61·9) and 79·3% (73·8-81·4) of total immunisation spending each year (corresponding to between $2·5 billion [2·3-2·8] and $6·4 billion [6·0-7·0] each year). Across income groups, immunisation spending per surviving infant was similar in low-income and lower-middle-income countries and territories, with average spending of $40 (38-42) in low-income countries and $42 (39-46) in lower-middle-income countries, in 2017. In low-income countries and territories, development assistance made up the largest share of total immunisation spending (69·4% [64·6-72·0]; $630·2 million) in 2017. Across the 135 countries, we observed higher vaccine coverage and increased government spending on immunisation over time, although in some countries, predominantly in Latin America and the Caribbean and in sub-Saharan Africa, vaccine coverage decreased over time, while spending increased. INTERPRETATION: These estimates highlight the progress over the past two decades in increasing spending on immunisation. However, many challenges still remain and will require dedication and commitment to ensure that the progress made in the previous decade is sustained and advanced in the next decade for the Immunization Agenda 2030. FUNDING: Bill & Melinda Gates Foundation.

Healthcare spending in U.S. emergency departments by health condition, 2006-2016.

BACKGROUND: Healthcare spending in the emergency department (ED) setting has received intense focus from policymakers in the United States (U.S.). Relatively few studies have systematically evaluated ED spending over time or disaggregated ED spending by policy-relevant groups, including health condition, age, sex, and payer to inform these discussions. This study's objective is to estimate ED spending trends in the U.S. from 2006 to 2016, by age, sex, payer, and across 154 health conditions and assess ED spending per visit over time. METHODS AND FINDINGS: This observational study utilized the National Emergency Department Sample, a nationally representative sample of hospital-based ED visits in the U.S. to measure healthcare spending for ED care. All spending estimates were adjusted for inflation and presented in 2016 U.S. Dollars. Overall ED spending was $79.2 billion (CI, $79.2 billion-$79.2 billion) in 2006 and grew to $136.6 billion (CI, $136.6 billion-$136.6 billion) in 2016, representing a population-adjusted annualized rate of change of 4.4% (CI, 4.4%-4.5%) as compared to total healthcare spending (1.4% [CI, 1.4%-1.4%]) during that same ten-year period. The percentage of U.S. health spending attributable to the ED has increased from 3.9% (CI, 3.9%-3.9%) in 2006 to 5.0% (CI, 5.0%-5.0%) in 2016. Nearly equal parts of ED spending in 2016 was paid by private payers (49.3% [CI, 49.3%-49.3%]) and public payers (46.9% [CI, 46.9%-46.9%]), with the remainder attributable to out-of-pocket spending (3.9% [CI, 3.9%-3.9%]). In terms of key groups, the majority of ED spending was allocated among females (versus males) and treat-and-release patients (versus those hospitalized); those between age 20-44 accounted for a plurality of ED spending. Road injuries, falls, and urinary diseases witnessed the highest levels of ED spending, accounting for 14.1% (CI, 13.1%-15.1%) of total ED spending in 2016. ED spending per visit also increased over time from $660.0 (CI, $655.1-$665.2) in 2006 to $943.2 (CI, $934.3-$951.6) in 2016, or at an annualized rate of 3.4% (CI, 3.3%-3.4%). CONCLUSIONS: Though ED spending accounts for a relatively small portion of total health system spending in the U.S., ED spending is sizable and growing. Understanding which diseases are driving this spending is helpful for informing value-based reforms that can impact overall health care costs.

Democracies Linked To Greater Universal Health Coverage Compared With Autocracies, Even In An Economic Recession.

Despite widespread recognition that universal health coverage is a political choice, the roles that a country's political system plays in ensuring essential health services and minimizing financial risk remain poorly understood. Identifying the political determinants of universal health coverage is important for continued progress, and understanding the roles of political systems is particularly valuable in a global economic recession, which tests the continued commitment of nations to protecting their health of its citizens and to shielding them from financial risk. We measured the associations that democracy has with universal health coverage and government health spending in 170 countries during the period 1990-2019. We assessed how economic recessions affect those associations (using synthetic control methods) and the mechanisms connecting democracy with government health spending and universal health coverage (using machine learning methods). Our results show that democracy is positively associated with universal health coverage and government health spending and that this association is greatest for low-income countries. Free and fair elections were the mechanism primarily responsible for those positive associations. Democracies are more likely than autocracies to maintain universal health coverage, even amid economic recessions, when access to affordable, effective health services matters most.