US Health Care Spending by Payer and Health Condition, 1996-2016.

Importance: US health care spending has continued to increase and now accounts for 18% of the US economy, although little is known about how spending on each health condition varies by payer, and how these amounts have changed over time. Objective: To estimate US spending on health care according to 3 types of payers (public insurance [including Medicare, Medicaid, and other government programs], private insurance, or out-of-pocket payments) and by health condition, age group, sex, and type of care for 1996 through 2016. Design and Setting: Government budgets, insurance claims, facility records, household surveys, and official US records from 1996 through 2016 were collected to estimate spending for 154 health conditions. Spending growth rates (standardized by population size and age group) were calculated for each type of payer and health condition. Exposures: Ambulatory care, inpatient care, nursing care facility stay, emergency department care, dental care, and purchase of prescribed pharmaceuticals in a retail setting. Main Outcomes and Measures: National spending estimates stratified by health condition, age group, sex, type of care, and type of payer and modeled for each year from 1996 through 2016. Results: Total health care spending increased from an estimated $1.4 trillion in 1996 (13.3% of gross domestic product [GDP]; $5259 per person) to an estimated $3.1 trillion in 2016 (17.9% of GDP; $9655 per person); 85.2% of that spending was included in this study. In 2016, an estimated 48.0% (95% CI, 48.0%-48.0%) of health care spending was paid by private insurance, 42.6% (95% CI, 42.5%-42.6%) by public insurance, and 9.4% (95% CI, 9.4%-9.4%) by out-of-pocket payments. In 2016, among the 154 conditions, low back and neck pain had the highest amount of health care spending with an estimated $134.5 billion (95% CI, $122.4-$146.9 billion) in spending, of which 57.2% (95% CI, 52.2%-61.2%) was paid by private insurance, 33.7% (95% CI, 30.0%-38.4%) by public insurance, and 9.2% (95% CI, 8.3%-10.4%) by out-of-pocket payments. Other musculoskeletal disorders accounted for the second highest amount of health care spending (estimated at $129.8 billion [95% CI, $116.3-$149.7 billion]) and most had private insurance (56.4% [95% CI, 52.6%-59.3%]). Diabetes accounted for the third highest amount of the health care spending (estimated at $111.2 billion [95% CI, $105.7-$115.9 billion]) and most had public insurance (49.8% [95% CI, 44.4%-56.0%]). Other conditions estimated to have substantial health care spending in 2016 were ischemic heart disease ($89.3 billion [95% CI, $81.1-$95.5 billion]), falls ($87.4 billion [95% CI, $75.0-$100.1 billion]), urinary diseases ($86.0 billion [95% CI, $76.3-$95.9 billion]), skin and subcutaneous diseases ($85.0 billion [95% CI, $80.5-$90.2 billion]), osteoarthritis ($80.0 billion [95% CI, $72.2-$86.1 billion]), dementias ($79.2 billion [95% CI, $67.6-$90.8 billion]), and hypertension ($79.0 billion [95% CI, $72.6-$86.8 billion]). The conditions with the highest spending varied by type of payer, age, sex, type of care, and year. After adjusting for changes in inflation, population size, and age groups, public insurance spending was estimated to have increased at an annualized rate of 2.9% (95% CI, 2.9%-2.9%); private insurance, 2.6% (95% CI, 2.6%-2.6%); and out-of-pocket payments, 1.1% (95% CI, 1.0%-1.1%). Conclusions and Relevance: Estimates of US spending on health care showed substantial increases from 1996 through 2016, with the highest increases in population-adjusted spending by public insurance. Although spending on low back and neck pain, other musculoskeletal disorders, and diabetes accounted for the highest amounts of spending, the payers and the rates of change in annual spending growth rates varied considerably.

Small area estimation of under-5 mortality in Bangladesh, Cameroon, Chad, Mozambique, Uganda, and Zambia using spatially misaligned data.

BACKGROUND: The under-5 mortality rate (U5MR) is an important metric of child health and survival. Country-level estimates of U5MR are readily available, but efforts to estimate U5MR subnationally have been limited, in part, due to spatial misalignment of available data sources (e.g., use of different administrative levels, or as a result of historical boundary changes). METHODS: We analyzed all available complete and summary birth history data in surveys and censuses in six countries (Bangladesh, Cameroon, Chad, Mozambique, Uganda, and Zambia) at the finest geographic level available in each data source. We then developed small area estimation models capable of incorporating spatially misaligned data. These small area estimation models were applied to the birth history data in order to estimate trends in U5MR from 1980 to 2015 at the second administrative level in Cameroon, Chad, Mozambique, Uganda, and Zambia and at the third administrative level in Bangladesh. RESULTS: We found substantial variation in U5MR in all six countries: there was more than a two-fold difference in U5MR between the area with the highest rate and the area with the lowest rate in every country. All areas in all countries experienced declines in U5MR between 1980 and 2015, but the degree varied both within and between countries. In Cameroon, Chad, Mozambique, and Zambia we found areas with U5MRs in 2015 that were higher than in other parts of the same country in 1980. Comparing subnational U5MR to country-level targets for the Millennium Development Goals (MDG), we find that 12.8% of areas in Bangladesh did not meet the country-level target, although the country as whole did. A minority of areas in Chad, Mozambique, Uganda, and Zambia met the country-level MDG targets while these countries as a whole did not. CONCLUSIONS: Subnational estimates of U5MR reveal significant within-country variation. These estimates could be used for identifying high-need areas and positive deviants, tracking trends in geographic inequalities, and evaluating progress towards international development targets such as the Sustainable Development Goals.

Challenges to implementing Gavi's health system strengthening support in Chad and Cameroon: results from a mixed-methods evaluation.

BACKGROUND: Since 2005, Gavi has provided health system strengthening (HSS) grants to address bottlenecks affecting immunization services. This study is the first to evaluate the Gavi HSS implementation process in either Cameroon or Chad, two countries with significant health system challenges and poor achievement on the child and maternal health Millennium Development Goals. METHODS: We triangulated quantitative and qualitative data including financial records, document review, field visit questionnaires, and key informant interviews (KII) with representatives from the Ministries of Health, Gavi, and other partners. We conducted a Root Cause Analysis of key implementation challenges, guided by the Consolidated Framework for Implementation Research. RESULTS: We conducted 124 field visits and 43 KIIs in Cameroon, and 57 field visits and 39 KIIs in Chad. Cameroon's and Chad's HSS programs were characterized by delayed disbursements, significant deviations from approved expenditures, and reprogramming of funds. Nearly a year after the programs were intended to be complete, many district and facility-level activities were only partially implemented and significant funds remained unabsorbed. Root causes of these challenges included unpredictable Gavi processes and disbursements, poor communication between the countries and Gavi, insufficient country planning without adequate technical assistance, lack of country staff and leadership, and weak country systems to manage finances and promote institutional memory. CONCLUSIONS: Though Chad and Cameroon both critically needed support to strengthen their weak health systems, serious challenges drastically limited implementation of their Gavi HSS programs. Implementation of future HSS programs in these and similar settings can be improved by transparent and reliable procedures and communication from Gavi, proposals that account for countries' programmatic capacity and the potential for delayed disbursements, implementation practices that foster learning and adaptation, and an early emphasis on developing managerial and other human resources.

Factors Associated With Increases in US Health Care Spending, 1996-2013.

Importance: Health care spending in the United States increased substantially from 1995 to 2015 and comprised 17.8% of the economy in 2015. Understanding the relationship between known factors and spending increases over time could inform policy efforts to contain future spending growth. Objective: To quantify changes in spending associated with 5 fundamental factors related to health care spending in the United States: population size, population age structure, disease prevalence or incidence, service utilization, and service price and intensity. Design and Setting: Data on the 5 factors from 1996 through 2013 were extracted for 155 health conditions, 36 age and sex groups, and 6 types of care from the Global Burden of Disease 2015 study and the Institute for Health Metrics and Evaluation's US Disease Expenditure 2013 project. Decomposition analysis was performed to estimate the association between changes in these factors and changes in health care spending and to estimate the variability across health conditions and types of care. Exposures: Change in population size, population aging, disease prevalence or incidence, service utilization, or service price and intensity. Main Outcomes and Measures: Change in health care spending from 1996 through 2013. Results: After adjustments for price inflation, annual health care spending on inpatient, ambulatory, retail pharmaceutical, nursing facility, emergency department, and dental care increased by $933.5 billion between 1996 and 2013, from $1.2 trillion to $2.1 trillion. Increases in US population size were associated with a 23.1% (uncertainty interval [UI], 23.1%-23.1%), or $269.5 (UI, $269.0-$270.0) billion, spending increase; aging of the population was associated with an 11.6% (UI, 11.4%-11.8%), or $135.7 (UI, $133.3-$137.7) billion, spending increase. Changes in disease prevalence or incidence were associated with spending reductions of 2.4% (UI, 0.9%-3.8%), or $28.2 (UI, $10.5-$44.4) billion, whereas changes in service utilization were not associated with a statistically significant change in spending. Changes in service price and intensity were associated with a 50.0% (UI, 45.0%-55.0%), or $583.5 (UI, $525.2-$641.4) billion, spending increase. The influence of these 5 factors varied by health condition and type of care. For example, the increase in annual diabetes spending between 1996 and 2013 was $64.4 (UI, $57.9-$70.6) billion; $44.4 (UI, $38.7-$49.6) billion of this increase was pharmaceutical spending. Conclusions and Relevance: Increases in US health care spending from 1996 through 2013 were largely related to increases in health care service price and intensity but were also positively associated with population growth and aging and negatively associated with disease prevalence or incidence. Understanding these factors and their variability across health conditions and types of care may inform policy efforts to contain health care spending.

US Spending on Personal Health Care and Public Health, 1996-2013.

Importance: US health care spending has continued to increase, and now accounts for more than 17% of the US economy. Despite the size and growth of this spending, little is known about how spending on each condition varies by age and across time. Objective: To systematically and comprehensively estimate US spending on personal health care and public health, according to condition, age and sex group, and type of care. Design and Setting: Government budgets, insurance claims, facility surveys, household surveys, and official US records from 1996 through 2013 were collected and combined. In total, 183 sources of data were used to estimate spending for 155 conditions (including cancer, which was disaggregated into 29 conditions). For each record, spending was extracted, along with the age and sex of the patient, and the type of care. Spending was adjusted to reflect the health condition treated, rather than the primary diagnosis. Exposures: Encounter with US health care system. Main Outcomes and Measures: National spending estimates stratified by condition, age and sex group, and type of care. Results: From 1996 through 2013, $30.1 trillion of personal health care spending was disaggregated by 155 conditions, age and sex group, and type of care. Among these 155 conditions, diabetes had the highest health care spending in 2013, with an estimated $101.4 billion (uncertainty interval [UI], $96.7 billion-$106.5 billion) in spending, including 57.6% (UI, 53.8%-62.1%) spent on pharmaceuticals and 23.5% (UI, 21.7%-25.7%) spent on ambulatory care. Ischemic heart disease accounted for the second-highest amount of health care spending in 2013, with estimated spending of $88.1 billion (UI, $82.7 billion-$92.9 billion), and low back and neck pain accounted for the third-highest amount, with estimated health care spending of $87.6 billion (UI, $67.5 billion-$94.1 billion). The conditions with the highest spending levels varied by age, sex, type of care, and year. Personal health care spending increased for 143 of the 155 conditions from 1996 through 2013. Spending on low back and neck pain and on diabetes increased the most over the 18 years, by an estimated $57.2 billion (UI, $47.4 billion-$64.4 billion) and $64.4 billion (UI, $57.8 billion-$70.7 billion), respectively. From 1996 through 2013, spending on emergency care and retail pharmaceuticals increased at the fastest rates (6.4% [UI, 6.4%-6.4%] and 5.6% [UI, 5.6%-5.6%] annual growth rate, respectively), which were higher than annual rates for spending on inpatient care (2.8% [UI, 2.8%-2.8%] and nursing facility care (2.5% [UI, 2.5%-2.5%]). Conclusions and Relevance: Modeled estimates of US spending on personal health care and public health showed substantial increases from 1996 through 2013; with spending on diabetes, ischemic heart disease, and low back and neck pain accounting for the highest amounts of spending by disease category. The rate of change in annual spending varied considerably among different conditions and types of care. This information may have implications for efforts to control US health care spending.

Resource Allocation Strategies to Increase the Efficiency and Sustainability of Gavi's Health System Strengthening Grants.

BACKGROUND: Despite the increase in Health System Strengthening (HSS) grants, there is no consensus among global health actors about how to maximize the efficiency and sustainability of HSS programs and their resulting gains. To formally analyze and compare the efficiency and sustainability of Gavi's HSS grants, we investigated the factors, events and root causes that increased the time and effort needed to implement HSS grants, decreased expected outcomes and threatened the continuity of activities and the sustainability of the results gained through these grants in Cameron and Chad. METHODS: We conducted 2 retrospective independent evaluations of Gavi's HSS support in Cameroon and Chad using a mixed methodology. We investigated the chain of events and situations that increased the effort and time required to implement the HSS programs, decreased the value of the funds spent and hindered the sustainability of the implemented activities and gains achieved. RESULTS: Root causes affecting the efficiency and sustainability of HSS grants were common to Cameroon and Chad. Weaknesses in health workforce and leadership/governance of the health system in both countries led to interrupting the HSS grants, reprogramming them, almost doubling their implementation period, shifting their focus during implementation toward procurements and service provision, leaving both countries without solid exit plans to maintain the results gained. CONCLUSIONS: To increase the efficiency and sustainability of Gavi's HSS grants, recipient countries need to consider health workforce and leadership/governance prior, or in parallel to strengthening other building blocks of their health systems.

Health Care Spending on Diabetes in the U.S., 1996-2013.

OBJECTIVE: Health care spending on diabetes in the U.S. has increased dramatically over the past several decades. This research describes health care spending on diabetes to quantify how that spending has changed from 1996 to 2013 and to determine what drivers are increasing spending. RESEARCH DESIGN AND METHODS: Spending estimates were extracted from the Institute for Health Metrics and Evaluation's Disease Expenditure 2013 database. Estimates were produced for each year from 1996 to 2013 for each of 38 age and sex groups and six types of care. Data on disease burden were extracted from the Global Burden of Disease 2016 study. We analyzed the drivers of spending by measuring the impact of population growth and aging and changes in diabetes prevalence, service utilization, and spending per encounter. RESULTS: Spending on diabetes in the U.S. increased from $37 billion (95% uncertainty interval $32-$42 billion) in 1996 to $101 billion ($97-$107 billion) in 2013. The greatest amount of health care spending on diabetes in 2013 occurred in prescribed retail pharmaceuticals (57.6% [53.8-62.1%] of spending growth) followed by ambulatory care (23.5% [21.7-25.7%]). Between 1996 and 2013, pharmaceutical spending increased by 327.0% (222.9-456.6%). This increase can be attributed to changes in demography, increased disease prevalence, increased service utilization, and, especially, increases in spending per encounter, which increased pharmaceutical spending by 144.0% (87.3-197.3%) between 1996 and 2013. CONCLUSIONS: Health care spending on diabetes in the U.S. has increased, and spending per encounter has been the biggest driver. This information can help policy makers who are attempting to control future spending on diabetes.

Spending on Children's Personal Health Care in the United States, 1996-2013.

Importance: Health care spending on children in the United States continues to rise, yet little is known about how this spending varies by condition, age and sex group, and type of care, nor how these patterns have changed over time. Objective: To provide health care spending estimates for children and adolescents 19 years and younger in the United States from 1996 through 2013, disaggregated by condition, age and sex group, and type of care. Evidence Review: Health care spending estimates were extracted from the Institute for Health Metrics and Evaluation Disease Expenditure 2013 project database. This project, based on 183 sources of data and 2.9 billion patient records, disaggregated health care spending in the United States by condition, age and sex group, and type of care. Annual estimates were produced for each year from 1996 through 2013. Estimates were adjusted for the presence of comorbidities and are reported using inflation-adjusted 2015 US dollars. Findings: From 1996 to 2013, health care spending on children increased from $149.6 (uncertainty interval [UI], 144.1-155.5) billion to $233.5 (UI, 226.9-239.8) billion. In 2013, the largest health condition leading to health care spending for children was well-newborn care in the inpatient setting. Attention-deficit/hyperactivity disorder and well-dental care (including dental check-ups and orthodontia) were the second and third largest conditions, respectively. Spending per child was greatest for infants younger than 1 year, at $11 741 (UI, 10 799-12 765) in 2013. Across time, health care spending per child increased from $1915 (UI, 1845-1991) in 1996 to $2777 (UI, 2698-2851) in 2013. The greatest areas of growth in spending in absolute terms were ambulatory care among all types of care and inpatient well-newborn care, attention-deficit/hyperactivity disorder, and asthma among all conditions. Conclusions and Relevance: These findings provide health policy makers and health care professionals with evidence to help guide future spending. Some conditions, such as attention-deficit/hyperactivity disorder and inpatient well-newborn care, had larger health care spending growth rates than other conditions.

Adjusting health spending for the presence of comorbidities: an application to United States national inpatient data.

BACKGROUND: One of the major challenges in estimating health care spending spent on each cause of illness is allocating spending for a health care event to a single cause of illness in the presence of comorbidities. Comorbidities, the secondary diagnoses, are common across many causes of illness and often correlate with worse health outcomes and more expensive health care. In this study, we propose a method for measuring the average spending for each cause of illness with and without comorbidities. METHODS: Our strategy for measuring cause of illness-specific spending and adjusting for the presence of comorbidities uses a regression-based framework to estimate excess spending due to comorbidities. We consider multiple causes simultaneously, allowing causes of illness to appear as either a primary diagnosis or a comorbidity. Our adjustment method distributes excess spending away from primary diagnoses (outflows), exaggerated due to the presence of comorbidities, and allocates that spending towards causes of illness that appear as comorbidities (inflows). We apply this framework for spending adjustment to the National Inpatient Survey data in the United States for years 1996-2012 to generate comorbidity-adjusted health care spending estimates for 154 causes of illness by age and sex. RESULTS: The primary diagnoses with the greatest number of comorbidities in the NIS dataset were acute renal failure, septicemia, and endocarditis. Hypertension, diabetes, and ischemic heart disease were the most common comorbidities across all age groups. After adjusting for comorbidities, chronic kidney diseases, atrial fibrillation and flutter, and chronic obstructive pulmonary disease increased by 74.1%, 40.9%, and 21.0%, respectively, while pancreatitis, lower respiratory infections, and septicemia decreased by 21.3%, 17.2%, and 16.0%. For many diseases, comorbidity adjustments had varying effects on spending for different age groups. CONCLUSIONS: Our methodology takes a unified approach to account for excess spending caused by the presence of comorbidities. Adjusting for comorbidities provides a substantially altered, more accurate estimate of the spending attributed to specific cause of illness. Making these adjustments supports improved resource tracking, accountability, and planning for future resource allocation.