Consequences of a shortage and rationing: Evidence from a pediatric vaccine.

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 the ir booster dose years later. We also find that the shortage and rationing caused provider switches, extra provider visits, and negative spillovers to other care.

A systematic examination of quality-adjusted price index alternatives for medical care using claims data.

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: 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.

Insurance expansions and hospital utilization: Relabeling and reabling?

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.

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.

A practical guide to episode groupers for cost-of-illness analysis in health services research.

Despite the prominence of episode groupers for analysis and reimbursement in US payer settings, peer-reviewed articles using episode groupers for cost-of-illness analysis that informs public health research and decision-making are uncommon. This article provides a brief practical guide to episode-based cost analysis and offers some examples of episode grouper products. It is intended for an audience of health services researchers and managers in public health settings who perform or commission cost-of-illness studies with the US healthcare claims fee-for-service data but lack familiarity with episode groupers.

Productivity growth of skilled nursing facilities in the treatment of post-acute-care-intensive conditions.

BACKGROUND: Health care is believed to be suffered from a "cost disease," in which a heavy reliance on labor limits opportunities for efficiencies stemming from technological improvement. Although recent evidence shows that U.S. hospitals have experienced a positive trend of productivity growth, skilled nursing facilities are relatively "low-tech" compared to hospitals, leading some to worry that productivity at skilled nursing facilities will lag behind the rest of the economy. OBJECTIVE: To assess productivity growth among skilled nursing facilities (SNFs) in the treatment of conditions which frequently involve substantial post-acute care after hospital discharge. METHODS: We constructed an analytic file with the records of Medicare beneficiaries that were discharged from acute-care hospitals to SNFs with stroke, hip fracture, or lower extremity joint replacement (LEJR) between 2006 and 2014. We populated each record for 90 days starting at the time of SNF admission, detailing for each day the treatment site and all associated costs. We used ordinary least square regression to estimate growth in SNF productivity, measured by the ratio of "high-quality SNF stays" to total treatment costs. The primary definition of a high-quality stay was a stay that ended with the return of the patient to the community within 90 days after SNF admission. We controlled for patient demographics and comorbidities in the regression analyses. RESULTS: Our sample included 1,076,066 patient stays at 14,394 SNFs with LEJR, 315,546 patient stays at 14,154 SNFs with stroke, and 739,608 patient stays at 14,588 SNFs with hip fracture. SNFs improved their productivity in the treatment of patients with LEJR, stroke, and hip fracture by 1.1%, 2.2%, and 2.0% per year, respectively. That pattern was robust to a number of alternative specifications. Regressions on year dummies showed that the productivity first decreased and then increased, with a lowest point in 2011. Over the study period, quality continued to rise, but dominated by higher costs at first. Costs then started to decrease, driving productivity to grow. CONCLUSION: There has been substantial productivity growth in recent years among SNFs in the U.S. in the treatment of post-acute-care-intensive conditions.

High Spending Growth Rates For Key Diseases In 2000-14 Were Driven By Technology And Demographic Factors.

We introduce a new source of detailed data on spending by medical condition to analyze US health care spending growth in the period 2000-14. We found that thirty conditions, which represented only 11.5 percent of all conditions studied, accounted for 42 percent of the real growth rate in per capita spending during this period, even though they accounted for only 13 percent of overall spending in 2000. Primary drivers of spending growth included the use of new technologies, a shift toward the provision of preventive-type services, and an aging and more obese population. The health benefits of many new technologies appeared to outweigh the associated expenditures on treatment, which indicates that these are cost-effective and provide a net value to society. However, while these technologies may be of value, new treatments are often more expensive than older ones.

The Complexity Of Billing And Paying For Physician Care.

The administrative costs of providing health insurance in the US are very high, but their determinants are poorly understood. We advance the nascent literature in this field by developing new measures of billing complexity for physician care across insurers and over time, and by estimating them using a large sample of detailed insurance "remittance data" for the period 2013-15. We found dramatic variation across different types of insurance. Fee-for-service Medicaid is the most challenging type of insurer to bill, with a claim denial rate that is 17.8 percentage points higher than that for fee-for-service Medicare. The denial rate for Medicaid managed care was 6 percentage points higher than that for fee-for-service Medicare, while the rate for private insurance appeared similar to that of Medicare Advantage. Based on conservative assumptions, we estimated that the health care sector deals with $11 billion in challenged revenue annually, but this number could be as high as $54 billion. These costs have significant implications for analyses of health insurance reforms.

Adjusting Health Expenditures for Inflation: A Review of Measures for Health Services Research in the United States.

OBJECTIVE: To provide guidance on selecting the most appropriate price index for adjusting health expenditures or costs for inflation. DATA SOURCES: Major price index series produced by federal statistical agencies. STUDY DESIGN: We compare the key characteristics of each index and develop suggestions on specific indexes to use in many common situations and general guidance in others. DATA COLLECTION/EXTRACTION METHODS: Price series and methodological documentation were downloaded from federal websites and supplemented with literature scans. PRINCIPAL FINDINGS: The gross domestic product implicit price deflator or the overall Personal Consumption Expenditures (PCE) index is preferable to the Consumer Price Index (CPI-U) to adjust for general inflation, in most cases. The Personal Health Care (PHC) index or the PCE health-by-function index is generally preferred to adjust total medical expenditures for inflation. The CPI medical care index is preferred for the adjustment of consumer out-of-pocket expenditures for inflation. A new, experimental disease-specific Medical Care Expenditure Index is now available to adjust payments for disease treatment episodes. CONCLUSIONS: There is no single gold standard for adjusting health expenditures for inflation. Our discussion of best practices can help researchers select the index best suited to their study.

Guidelines for Measuring Disease Episodes: An Analysis of the Effects on the Components of Expenditure Growth.

OBJECTIVE: To provide guidelines to researchers measuring health expenditures by disease and compare these methodologies' implied inflation estimates. DATA SOURCE: A convenience sample of commercially insured individuals over the 2003 to 2007 period from Truven Health. Population weights are applied, based on age, sex, and region, to make the sample of over 4 million enrollees representative of the entire commercially insured population. STUDY DESIGN: Different methods are used to allocate medical-care expenditures to distinct condition categories. We compare the estimates of disease-price inflation by method. PRINCIPAL FINDINGS: Across a variety of methods, the compound annual growth rate stays within the range 3.1 to 3.9 percentage points. Disease-specific inflation measures are more sensitive to the selected methodology. CONCLUSION: The selected allocation method impacts aggregate inflation rates, but considering the variety of methods applied, the differences appear small. Future research is necessary to better understand these differences in other population samples and to connect disease expenditures to measures of quality.

Health insurance and the demand for medical care: Instrumental variable estimates using health insurer claims data.

This paper takes a different approach to estimating demand for medical care that uses the negotiated prices between insurers and providers as an instrument. The instrument is viewed as a textbook "cost shifting" instrument that impacts plan offerings, but is unobserved by consumers. The paper finds a price elasticity of demand of around -0.20, matching the elasticity found in the RAND Health Insurance Experiment. The paper also studies within-market variation in demand for prescription drugs and other medical care services and obtains comparable price elasticity estimates.

Health Care Spending Slowdown From 2000 To 2010 Was Driven By Lower Growth In Cost Per Case, According To A New Data Source.

In 2015 the Bureau of Economic Analysis released an experimental set of measures referred to as the Health Care Satellite Account, which tracks national health care spending by medical condition. These statistics improve the understanding of the health care sector by blending medical claims data and survey data to present measures of national spending and cost of treatment by condition. This article introduces key aspects of the new account and uses it to study the health spending slowdown that occurred in the period 2000-10. Our analysis of the account reveals that the slowdown was driven by a reduction of growth in cost per case but that spending trends varied greatly across conditions and differentially affected the slowdown. More than half of the overall slowdown was accounted for by a slowdown in spending on circulatory conditions. However, there were more dramatic slowdowns in spending on categories such as endocrine system and musculoskeletal conditions than in spending on other categories, such as cancers.

Physician payments under health care reform.

This study examines the impact of major health insurance reform on payments made in the health care sector. We study the prices of services paid to physicians in the privately insured market during the Massachusetts health care reform. The reform increased the number of insured individuals as well as introduced an online marketplace where insurers compete. We estimate that, over the reform period, physician payments increased at least 11 percentage points relative to control areas. Payment increases began around the time legislation passed the House and Senate-the period in which their was a high probability of the bill eventually becoming law. This result is consistent with fixed-duration payment contracts being negotiated in anticipation of future demand and competition.

Implications of utilization shifts on medical-care price measurement.

The medical-care sector often experiences changes in medical protocols and technologies that cause shifts in treatments. However, the commonly used medical-care price indexes reported by the Bureau of Labor Statistics hold the mix of medical services fixed. In contrast, episode expenditure indexes, advocated by many health economists, track the full cost of disease treatment, even as treatments shift across service categories (e.g., inpatient to outpatient hospital). In our data, we find that these two conceptually different measures of price growth show similar aggregate rates of inflation over the 2003-2007 period. Although aggregate trends are similar, we observe differences when looking at specific disease categories.

Geographic variation in commercial medical-care expenditures: a framework for decomposing price and utilization.

This study introduces a new framework for measuring and analyzing medical-care expenditures. The framework focuses on expenditures at the disease level that are decomposed between price and utilization. We find that both price and utilization differences are important contributors to expenditure differences across commercial markets. Further examination shows that for some diseases utilization drives variation while for others price is more important. Finally, when disease-specific measures are aggregated across diseases, much of the important disease-specific variation is masked, leading to much smaller measures of aggregate variation.

Medical care price indexes for patients with employer-provided insurance: nationally representative estimates from MarketScan Data.

OBJECTIVE: Commonly observed shifts in the utilization of medical care services to treat diseases may pose problems for official price indexes at the Bureau of Labor Statistics (BLS) that do not account for service shifts. We examine how these shifts may lead to different price estimates than those observed in official price statistics at the BLS. DATA SOURCES: We use a convenience sample of enrollees with employer-provided insurance from the MarketScan database for the years 2003 to 2007. Population weights that consider the age, sex, and geographic distribution of enrollees are assigned to construct representative estimates. STUDY DESIGN: We compare two types of price indexes: (1) a Service Price Index (SPI) that is similar to the BLS index, which holds services fixed and measures the prices of the underlying treatments; (2) a Medical Care Expenditure Index (MCE) that measures the cost of treating diseases and allows for utilization shifts. PRINCIPAL FINDINGS: Over the entire period of study the CAGR of the SPI grows 0.7 percentage points faster than the preferred MCE index. CONCLUSIONS: Our findings suggest that the health component of inflation may be overstated by 0.7 percentage points per year, and real GDP growth may be understated by a similar amount. However, more work may be necessary to precisely replicate the indexes of the BLS to obtain a more accurate measure of these price differences.

The value of coverage in the medicare advantage insurance market.

This paper examines the impact of coverage on demand for health insurance in the Medicare Advantage (MA) insurance market. Estimating the effects of coverage on demand poses a challenge for researchers who must consider both the hundreds of benefits that affect out-of-pocket costs (OOPC) to consumers, but also the endogeneity of coverage. These problems are addressed in a discrete choice demand model by employing a unique measure of OOPC that considers a consumer's expected payments for a fixed bundle of health services and applying instrumental variable techniques to address potential endogeneity bias. The results of the demand model show that OOPC have a significant effect on consumer surplus and that not instrumenting for OOPC results in a significant underestimate of the value of coverage.