Understanding what the US spends to treat mental health and substance use disorders (SUD) is important for understanding spending patterns and informing health policy. In this study, we determined that from 2000 through 2021, mental health and SUD nominal spending grew from $40.9 billion to $139.6 billion. Mental health and SUD accounted for 4.5 percent of all medical services spending in 2000 and 5.5 percent in 2021. Real per capita mental health and SUD spending grew at an average annual rate of 3.27 percent, which was faster than the growth rate for overall medical services (2.21 percent). Our decomposition analysis showed that mental health and SUD spending growth was driven primarily by increases in the number of people receiving treatment (representing 87.3 percent of the growth) and to a much lesser extent by increases in the cost per case (12.7 percent of the growth). However, because disease severity was unobserved, these patterns may partly reflect increased treatment of less severe cases rather than unchanged severity-adjusted treatment costs. During this period, the number of mental health and SUD cases treated grew by 253 percent. In contrast, for spending increases on all diseases, 66.3 percent of the total spending growth resulted from increases in cost per case, and 33.7 percent resulted from more people receiving treatment.

OBJECTIVES:We build on research of Cutler et al on the value of healthcare spending using a period life-expectancy framework. We use the framework to track health-adjusted life expectancy (HALE) and lifetime spending for all ages, show the value of improvements in healthcare, and demonstrate the contribution of expanding research to the full age range. METHODS:We used population-level results on mortality and years lived with disability from the 2019 Global Burden of Disease, Injuries, and Risk Factor Study and spending from the 2016 Disease Expenditure study. We used cause replacement methods to simulate effects of changes in healthcare. For 132 causes, we replaced cause-specific outcomes (or spending) per case from 1996 with those measures for 2016; effect is the difference between base year and simulated calculations. Spending is reported in 2016 US dollars ($). RESULTS:For all-cause aggregate calculated at birth, lifetime spending effect was $234 111 (95% uncertainty interval (UI): 221 395, 242 456) and HALE effect was 1.285 (95% UI: 1.161, 1.422) years per person. Value of improvements is the ratio of these 2 effects, $182 201 (95% UI: 181 494, 182 912) per HALE gained. Seventy-nine (60%) causes had an increase in mean HALE and lifetime spending. Value was $9315 (95% UI: 9204, 9427) for HIV/AIDS and $63 184 (95% UI: 62 352, 64 030) for ischemic heart disease. For drug use disorders, HALE effect was -0.331 (95%UI: -0.370, -0.296), which offset other gains. Increases in HALE often occur at older ages than lifetime spending. CONCLUSIONS:Comprehensive measures for all ages show value of healthcare by cause.

Who bears the consequences of administrative problems in healthcare? We use data on repeated interactions between a large sample of U.S. physicians and many different insurers to document the complexity of healthcare billing, and estimate its economic costs for doctors and consequences for patients. Observing the back-and-forth sequences of claim denials and resubmissions for past visits, we can estimate physicians’ costs of haggling with insurers to collect payments. Combining these costs with the revenue never collected, we estimate that physicians lose 18% of Medicaid revenue to billing problems, compared with 4.7% for Medicare and 2.4% for commercial insurers. Identifying off of physician movers and practices that span state boundaries, we find that physicians respond to billing problems by refusing to accept Medicaid patients in states with more severe billing hurdles. These hurdles are quantitatively just as important as payment rates for explaining variation in physicians’ willingness to treat Medicaid patients. We conclude that administrative frictions have first-order costs for doctors, patients, and equality of access to healthcare. We quantify the potential economic gains—in terms of reduced public spending or increased access to physicians—if these frictions could be reduced, and find them to be sizable.

The rising price of branded drugs has garnered considerable attention from the public and policy makers. This article investigates the complexities of pharmaceutical pricing, with an emphasis on the overlooked aspects of manufacturer rebates and out-of-pocket prices. Rebates granted by pharmaceutical manufacturers to insurers reduce the actual prices paid by insurers, causing the true prices of prescriptions to diverge from official statistics. We combined claims data on branded retail prescription drugs with estimates on rebates to provide new price index measures based on pharmacy prices, negotiated prices (after rebates), and out-of-pocket prices for the commercially insured population during the period 2007-20. We found that although retail pharmacy prices increased 9.1 percent annually, negotiated prices grew by a mere 4.3 percent, highlighting the importance of rebates in price measurement. Surprisingly, consumer out-of-pocket prices diverged from negotiated prices after 2016, growing 5.8 percent annually while negotiated prices remained flat. The concern over drug price inflation is more reflective of the rapid increase in consumer out-of-pocket expenses than the stagnated inflation of negotiated prices paid by insurers after 2016.

We examine whether Medicaid recipients receive the same health care services as those on Medicare. We track the services provided to the same individual as they age into Medicare from Medicaid at age 65, becoming dual enrolled. Cost sharing remains negligible across the insurance switch, implying that observed changes in service provision reflect supply-side factors. Service provision increases by about 20 percent upon switching to Medicare, across a range of categories and treatments including high-value care. We find that 60 to 90 percent of the increase in office visits is explained by physicians averse to accepting new Medicaid patients. Geographic variation in our estimates shows that the average increase in utilization is larger in those states with lower Medicaid acceptance rates and higher Medicare acceptance rates. By contrast, we find relatively small increases in care from existing Medicaid providers. This analysis indicates that Medicaid’s smaller provider network plays a large role in limiting service provision.

Economic geography data are typically reported using political units, such as counties, which often do not match economic units. Commuting zones (CZs) group counties into labor markets. However, CZs are not the most appropriate grouping for other economic activities. We introduce consumption zones (ConZs), groupings of counties appropriate for the analysis of consumption. We apply CZ methodology to payment card data, which report spending flows across US counties for fifteen industries. We find that different industries have different market sizes. Grocery stores have more than five times the number of ConZs as entertainment. We apply ConZs to measuring industry concentration.

Shortages and rationing are common in health care, yet we know little about the consequences.We examine an 18-month shortage of the pediatric Haemophilus Influenzae Type B (Hib) vaccine.Using insurance claims data and variation in shortage exposure across birth cohorts, we find that the shortage reduced uptake of high-value primary doses by 4 percentage points and low-value booster doses by 26 percentage points.This suggests providers largely complied with rationing recommendations.In the long-run, catch-up vaccination occurred but was incomplete: shortage-exposed cohorts were 4 percentage points less likely to have received their booster dose years later.We also find that the shortage and rationing caused provider switches, extra provider visits, and negative spillovers to other care.

We investigate alternative methods for constructing quality-adjusted medical price indexes both theoretically and empirically using medical claims data. The methodology and assumptions applied in the formation of the index have substantive effects on the magnitude of the quality-adjusted price changes. A method based on utility theory produces the most robust and accurate results, while alternative methods used in recent work overstate inflation. Based on Medicare claims data for three medical conditions, we find declining prices across each condition when properly adjusted for quality.

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.

More than two decades ago, a well-known study on heart attack treatments provided evidence suggesting that, when appropriately adjusted for quality, medical care prices were actually declining (Cutler, McClellan, Newhouse, and Remler (1998)). Our paper revisits this subject by leveraging estimates from more than 8000 cost-effectiveness studies across a broad range of conditions and treatments. We find large quality-adjusted price declines associated with treatment innovations. To incorporate these quality-adjusted indexes into an aggregate measure of inflation, we combine an unadjusted medical-care price index, quality-adjusted price indexes from treatment innovations, and proxies for the diffusion rate of new technologies. In contrast to official statistics that suggest medical care prices increased by 0.53 percent per year relative to economy-wide inflation from 2000 to 2017, we find that quality-adjusted medical care prices declined by 1.33 percent per year over the same period.

More than two decades ago, a well‐known study on heart attack treatments provided evidence suggesting that, when appropriately adjusted for quality, medical care prices were actually declining (Cutler, McClellan, Newhouse, and Remler (1998)). Our paper revisits this subject by leveraging estimates from more than 8000 cost‐effectiveness studies across a broad range of conditions and treatments. We find large quality‐adjusted price declines associated with treatment innovations. To incorporate these quality‐adjusted indexes into an aggregate measure of inflation, we combine an unadjusted medical‐care price index, quality‐adjusted price indexes from treatment innovations, and proxies for the diffusion rate of new technologies. In contrast to official statistics that suggest medical care prices increased by 0.53 percent per year relative to economy‐wide inflation from 2000 to 2017, we find that quality‐adjusted medical care prices declined by 1.33 percent per year over the same period.

The 2010 Patient Protection & Affordable Care Act (ACA) significantly expanded access to private and public health insurance for low-income individuals through income-based subsidies and income-based eligibility expansions, respectively. In this paper, we use the universe of hospitals from 2009 to 2015 to characterize how these expansions affected the financing of hospital visits, along with price, utilization, and potential spillovers in the quality of care. The insurance coverage expansions generated a shift in the composition of payers and a modest increase in the utilization of hospital outpatient services. While concerns have been raised that these shifts in utilization could cause negative spillovers to the already insured population (e.g., Medicare enrollees), we find no significant change in the quality of care experienced by those already insured. The primary result of both federally funded insurance expansions was to increase the profits generated and prices charged by the hospitals providing such services.

Pandemic-driven economic changes are measurable in days and weeks rather than months and years, underscoring a need for more timely economic data to inform the public. We summarize newly available estimates of spending using card transaction data that are available on the Bureau of Economic Analysis website and analyze correlations between card data and official series. We find that card data perform well in measuring large changes in the economy around the pandemic, although the correlations are much lower during a period of stability prior to the pandemic. This pattern is likely attributable to a shift in the signal-to-noise ratio over these periods.

Bureau of Economic Analysis (BEA), and Abe Dunn, Assistant Chief Economist at BEA, explain all that goes into capturing economic activity in one single number: the Gross Domestic Product (GDP).Data science can help the next generation of economic statistics to be even more relevant, timely, accurate, and detailed.

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. Question How does spending on different health conditions vary by payer (public insurance, private insurance, or out-of-pocket payments) and how has this spending changed over time? Findings From 1996 to 2016, total health care spending increased from an estimated $1.4 trillion to an estimated $3.1 trillion. In 2016, private insurance accounted for 48.0% (95% CI, 48.0%-48.0%) of health care spending, public insurance for 42.6% (95% CI, 42.5%-42.6%) of health care spending, and out-of-pocket payments for 9.4% (95% CI, 9.4%-9.4%) of health care spending. After adjusting for population size and aging, the annualized spending growth rate was 2.6% (95% CI, 2.6%-2.6%) for private insurance, 2.9% (95% CI, 2.9%-2.9%) for public insurance, and 1.1% (95% CI, 1.0%-1.1%) for out-of-pocket payments. Meaning Understanding how much each payer spent on each health condition and how these amounts have changed over time can inform health care policy. This study estimates health care spending for the most common health conditions in the United States, including low back pain and musculoskeletal disorders, diabetes, and ischemic heart disease, between 1996 and 2016.

We assess changes in multifactor productivity in delivering acute episodes of care (including services received after initial discharge from a hospital) for elderly Medicare beneficiaries over 2002-2014. For a majority of the eight episode types studied, productivity improved, exceeding an annualized growth rate of 1.0% in some cases. There is some evidence of negative productivity growth for heart failure episodes over this period. Our estimates reflect ─ and are meaningfully affected by the measurement of ─ trends in the quality of care, with patients experiencing improved outcomes for most episode types.

What does it cost healthcare providers to collect payment in the complex U.S. health insurance system?We study this question using rich data on repeated interactions between a large sample of physicians and many different payers, and investigate the consequences when these costs are high.Payment uncertainty is high and variable, with 19% of Medicaid visits not reimbursed after the first claim submission.In such cases, physicians either forgo substantial revenue or incur costs to collect payment.Using physician movers and practices that span state boundaries, we find that providers respond to these costs by refusing to accept Medicaid patients in states with more severe billing hurdles.This supply margin is even more responsive to these costs than to reimbursement rates.Using these supply estimates, we calculate that the costs of billing Medicaid consume one-quarter of the average revenue from a Medicaid visit.We estimate a model of the billing process, and find that the variable costs of billing each visit account for 21 percentage points of this total cost.Analyzing healthcare prices without accounting for billing costs and payment uncertainty may substantially misrepresent differences between private payers and Medicaid.

Geographic analysis of consumption is often constrained by geographic borders such as counties, but economic agents often cross borders to consume. Using unique card transaction data, we estimate across-county spending flows between firms and consumers for every county in the United States and for 15 industries to provide a new consumption-link across counties that has not been previously studied. To demonstrate the importance of this consumption link, we reexamine the 2007–2009 Great Recession following the work of Mian and Sufi (2013) and Mian, Rao, and Sufi (2014), who demonstrate that counties with the greatest decline in housing net worth also had the largest declines in consumption and employment. We show that the effect of the housing wealth decline crosses borders to affect consumption and employment in a pattern consistent with our spending flows, even for the non-tradable sector. We find that not accounting for cross-border effects tends to understate the impact of local housing wealth shocks on employment and spending by 26 and 17 percent, respectively; it also misallocates where those effects are occurring, by about 11 percent for both spending and employment.

This paper measures the costs and types of administrative inputs in health care. We use data on labor and nonlabor inputs by industry and categorize them as administrative or not. We find that nonlabor inputs are a critical part of administrative spending, over and above labor inputs. Trends in nonlabor administrative input spending have differed dramatically from that of labor input spending for hospitals over the last 20 years. Hospitals have substituted away from office workers and toward externally purchased inputs. The share of managers and technical workers in administration has grown. The technology of health care administration is changing.