Societal Costs of Dementia: 204 Countries, 2000-2019.

Background: Dementia prevalence is expected to increase as populations grow and age. Therefore, additional resources will be needed to meet the global demand for care for Alzheimer's disease and related dementias (ADRD). Objective: Estimate global and country-level health care spending attributable to ADRD and the cost of informal care for people living with ADRD. Methods: We gathered data from three systematic literature reviews and the Global Burden of Disease 2019 study. We used spatiotemporal Gaussian process regression to impute estimates for the many countries without underlying data. We projected future costs to 2050 based on past trends in costs, diagnosis rates, and institutionalization rate. Results: We estimated that in 2019, the direct health care spending attributable to ADRD across 204 countries reached $260.6 billion (95% uncertainty interval [UI] 131.6-420.4) and the cost of informal ADRD care was $354.1 billion (95% UI 190.0-544.1). On average, informal care represents 57% (95% UI 38-75%) of the total cost of care. We estimated that direct health care spending attributable to ADRD will reach $1.6 trillion (95% UI 0.6-3.3) in 2050, or 9.4% (95% UI 3.9-19.6%) of projected health spending worldwide. We estimated the cost of informal care will reach $0.9 trillion (95% UI 0.3-1.7) in 2050. Conclusions: These cost estimates underscore the magnitude of resources needed to ensure sufficient resources for people living with ADRD and highlight the role that informal care plays in provision of their care. Incorporating informal care cost estimates is critical to capture the social cost of ADRD.

Examining geographical inequalities for malaria outcomes and spending on malaria in 40 malaria-endemic countries, 2010-2020.

BACKGROUND: While substantial gains have been made in the fight against malaria over the past 20 years, malaria morbidity and mortality are marked by inequality. The equitable elimination of malaria within countries will be determined in part by greater spending on malaria interventions, and how those investments are allocated. This study aims to identify potential drivers of malaria outcome inequality and to demonstrate how spending through different mechanisms might lead to greater health equity. METHODS: Using the Gini index, subnational estimates of malaria incidence and mortality rates from 2010 to 2020 were used to quantify the degree of inequality in malaria burden within countries with incidence rates above 5000 cases per 100,000 people in 2020. Estimates of Gini indices represent within-country distributions of disease burden, with high values corresponding to inequitable distributions of malaria burden within a country. Time series analyses were used to quantify associations of malaria inequality with malaria spending, controlling for country socioeconomic and population characteristics. RESULTS: Between 2010 and 2020, varying levels of inequality in malaria burden within malaria-endemic countries was found. In 2020, values of the Gini index ranged from 0.06 to 0.73 for incidence, 0.07 to 0.73 for mortality, and 0.00 to 0.36 for case fatality. Greater total malaria spending, spending on health systems strengthening for malaria, healthcare access and quality, and national malaria incidence were associated with reductions in malaria outcomes inequality within countries. In addition, government expenditure on malaria, aggregated government and donor spending on treatment, and maternal educational attainment were also associated with changes in malaria outcome inequality among countries with the greatest malaria burden. CONCLUSIONS: The findings from this study suggest that prioritizing health systems strengthening in malaria spending and malaria spending in general especially from governments will help to reduce inequality of the malaria burden within countries. Given heterogeneity in outcomes in countries currently fighting to control malaria, and the challenges in increasing both domestic and international funding allocated to control and eliminate malaria, the efficient targeting of limited resources is critical to attain global malaria eradication goals.

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.

Tracking development assistance for health from India to low- and middle-income countries, 2009-2020.

BACKGROUND: Development assistance for health (DAH) is an important source of financing for health for many low-income and some middle-income countries. Most DAH has predominantly been contributed by high-income countries. However, in the context of economic progress and changing global priorities, DAH contributions from countries of the Global South such as India have gained importance. In this paper, we estimate DAH contributed by India between 2009 and 2020. METHODS: We leveraged data from budgetary documents, databases, and financial reports of the Ministry of External Affairs and multilateral organizations to estimate DAH contributions. The proportions of development assistance that go towards health in major recipient countries were estimated and reported by recipient country and year. RESULTS: Between 2009 and 2020, DAH contributed by India to bilateral and multilateral partners totaled $206.0 million. South Asian countries including Bangladesh, Bhutan, Nepal, Sri Lanka, and Myanmar received the most DAH from India. DAH contributed relative to DAH received ranged from 1.42% in 2009 to 5.26% in 2018, the latest year with country-level data. Health focus areas prioritized by India included technical training and innovation, health care infrastructure support, and supply of medications and medical equipment. CONCLUSION: India is an important development partner to many countries-particularly to those in the South Asian region. India's DAH allocation strategy prioritizes contributions toward neighboring countries in the South Asia region in several health focus areas. Detailed project-level data are needed to estimate DAH contributions from India with greater precision and accuracy.

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.

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.

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.

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.