Politics and health care spending in the United States: A case study from the passage of the 2003 Medicare Modernization Act.

This paper analyzes the interplay between congressional politics, the actions of the executive branch, and hospitals' regulated Medicare payments. We focus on the 2003 Medicare Modernization Act (MMA) and analyze a provision in the law - Section 508 - that raised certain hospitals' regulated payments. We show, via our analysis of the Section 508 program, that Medicare payments are malleable and can be influenced by political dynamics. In the cross-section, hospitals represented by members of Congress who voted "yea" on the MMA were more likely to receive Section 508 payment increases. We interviewed the Secretary of Health and Human Services who oversaw the MMA, and he described how these payment increases were designed to win support for the law. The Section 508 payment increases raised hospitals' activity and spending. Members of Congress representing recipient hospitals received increased campaign contributions after the Section 508 payment increases were extended. Ultimately, our analysis highlights how Medicare payment increases can serve as an appealing tool for legislative leaders working to win votes for wider pieces of legislation.

Geographical Variation in Health Spending Across the US Among Privately Insured Individuals and Enrollees in Medicaid and Medicare.

Importance: Little is known about small-area variations in health care spending and utilization across the 3 major funders of health care in the US: Medicare, Medicaid, and private insurers. Objective: To measure regional health spending and utilization across Medicare, Medicaid, and the privately insured; to observe whether there are regions that are simultaneously low spending for all 3 payers; and to determine what factors are correlated with regional spending and utilization by payer. Design, Setting, and Participants: Observational cross-sectional analysis of the US health system in 2016 and 2017 for 241 of 306 hospital referral regions (HRRs) and 2 states. Participants include individuals with employer-sponsored coverage from Aetna, Humana, or UnitedHealth; individuals with Medicaid fee-for-service coverage in 2016 and 2017; and individuals with Medicare coverage. The analysis was carried out from January 2020 to May 2022. Main Outcomes and Measures: Spending per beneficiary and inpatient days per beneficiary by payer and overall. Results: The data include 25 381 167 individuals with employer-sponsored coverage, 69 891 299 with Medicaid coverage in 2016 and 2017, and 26 711 426 individuals with Medicare fee-for-service coverage. The percentage of enrollees who identified as female was 54.1% in the Medicaid program, 56.2% in the Medicare program, and 50.4% in private insurance. The mean (SD) age was 26.9 (21.8) years for Medicaid and 75.0 (7.9) years for Medicare enrollees; for private insurance enrollees, just age brackets were reported: 18 to 24 years (15.9%), 25 to 34 years (24.2%), 35 to 44 years (21.3%), 45 to 54 years (20.8%), and 55 to 64 years (17.8%). In 2017, the mean (SD) HRR-level spending per beneficiary was $4441 ($710) for private insurance, $10 281 ($1294) for Medicare, and $6127 ($1428) for Medicaid. Across HRRs, the correlation coefficients and 95% CIs were 0.020 (-0.106 to 0.146; P = .76) for private insurance and Medicare spending, 0.213 (0.090 to 0.330; P < .001) for private insurance and Medicaid, and 0.162 (0.037 to 0.282; P < .01) for Medicare and Medicaid. Just 3 HRRs (Boulder, Colorado; Bloomington, Illinois; and Olympia, Washington) were in the lowest spending quintile for all 3 insurance programs; 4 HRRs were in the highest (The Bronx, New York; Manhattan, New York; White Plains, New York; and Dallas, Texas). By contrast, the correlation coefficients and 95% CIs for utilization, measured in hospital days, were 0.465 (0.361 to 0.559; P < .001) for private insurance and Medicare, 0.527 (0.429 to 0.612; P < .001) for private insurance and Medicaid, and 0.278 (0.157 to 0.390; P < .001) for Medicare and Medicaid. Conclusions and Relevance: These findings suggest that payer-specific factors are correlated with health spending variation among Medicare beneficiaries, Medicaid beneficiaries, and the commercially insured and that payer-specific policies will be necessary to improve efficiency in the US health sector.

Hospital Lawsuits Over Unpaid Bills Increased By 37 Percent In Wisconsin From 2001 To 2018.

We analyzed Wisconsin court records from the period 2001-18 to document trends in hospital lawsuits to recover patients' unpaid medical bills. These lawsuits increased 37 percent during this period, from 1.12 per 1,000 residents in 2001 to 1.53 per 1,000 residents in 2018, with lawsuits being disproportionately directed at Black patients and patients living in poorer and less densely populated counties.

Physician agency, consumerism, and the consumption of lower-limb MRI scans.

We study where privately insured individuals receive planned MRI scans. Despite significant out-of-pocket costs for this undifferentiated service, privately insured patients often receive care in high-priced locations when lower priced options were available. The median patient in our data has 16 MRI providers within a 30-minute drive of her home. On average, patients bypass 6 lower-priced providers between their homes and their actual treatment locations. Referring physicians heavily influence where patients receive care. The share of the variance in the prices of patients' MRI scans that referrer fixed effects (52 percent) explain is dramatically greater than the share explained by patient cost-sharing (< 1 percent), patient characteristics (< 1 percent), or patients' home HRR fixed effects (2 percent). In order to access lower cost providers, patients must generally diverge from physicians' established referral patterns.

Out-Of-Network Billing And Negotiated Payments For Hospital-Based Physicians.

When physicians whom patients do not choose and cannot avoid can bill out of network for care delivered within in-network hospitals, it exposes patients to financial risk and undercuts the functioning of health care markets. Using data for 2015 from a large commercial insurer, we found that at in-network hospitals, 11.8 percent of anesthesiology care, 12.3 percent of care involving a pathologist, 5.6 percent of claims for radiologists, and 11.3 percent of cases involving an assistant surgeon were billed out of network. The ability to bill out of network allows these specialists to negotiate artificially high in-network rates. Out-of-network billing is more prevalent at hospitals in concentrated hospital and insurance markets and at for-profit hospitals. Our estimates show that if these specialists were not able to bill out of network, it would lower physician payments for privately insured patients by 13.4 percent and reduce health care spending for people with employer-sponsored insurance by 3.4 percent (approximately $40 billion annually).

Hospital Prices Grew Substantially Faster Than Physician Prices For Hospital-Based Care In 2007-14.

Evidence suggests that growth in providers' prices drives growth in health care spending on the privately insured. However, existing work has not systematically differentiated between the growth rate of hospital prices and that of physician prices. We analyzed growth in both types of prices for inpatient and hospital-based outpatient services using actual negotiated prices paid by insurers. We found that in the period 2007-14 hospital prices grew substantially faster than physician prices. For inpatient care, hospital prices grew 42 percent, while physician prices grew 18 percent. Similarly, for hospital-based outpatient care, hospital prices grew 25 percent, while physician prices grew 6 percent. A majority of the growth in payments for inpatient and hospital-based outpatient care was driven by growth in hospital prices, not physician prices. Our work suggests that efforts to reduce health care spending should be primarily focused on addressing growth in hospital rather than physician prices. Policy makers should consider a range of options to address hospital price growth, including antitrust enforcement, administered pricing, the use of reference pricing, and incentivizing referring physicians to make more cost-efficient referrals.

Variation In Health Spending Growth For The Privately Insured From 2007 To 2014.

We examined the growth in health spending on people with employer-sponsored private insurance in the period 2007-14. Our analysis relied on information from the Health Care Cost Institute data set, which includes insurance claims from Aetna, Humana, and UnitedHealthcare. In the study period private health spending per enrollee grew 16.9 percent, while growth in Medicare spending per fee-for-service beneficiary decreased 1.2 percent. There was substantial variation in private spending growth rates across hospital referral regions (HRRs): Spending in HRRs in the tenth percentile of private spending growth grew at 0.22 percent per year, while HRRs in the ninetieth percentile experienced 3.45 percent growth per year. The correlation between the growth in HRR-level private health spending and growth in fee-for-service Medicare spending in the study period was only 0.211. The low correlation across HRRs suggests that different factors may be driving the growth in spending on the two populations.

THE PRICE AIN'T RIGHT? HOSPITAL PRICES AND HEALTH SPENDING ON THE PRIVATELY INSURED.

We use insurance claims data covering 28% of individuals with employer-sponsored health insurance in the United States to study the variation in health spending on the privately insured, examine the structure of insurer-hospital contracts, and analyze the variation in hospital prices across the nation. Health spending per privately insured beneficiary differs by a factor of three across geographic areas and has a very low correlation with Medicare spending. For the privately insured, half of the spending variation is driven by price variation across regions, and half is driven by quantity variation. Prices vary substantially across regions, across hospitals within regions, and even within hospitals. For example, even for a nearly homogeneous service such as lower-limb magnetic resonance imaging, about a fifth of the total case-level price variation occurs within a hospital in the cross section. Hospital market structure is strongly associated with price levels and contract structure. Prices at monopoly hospitals are 12% higher than those in markets with four or more rivals. Monopoly hospitals also have contracts that load more risk on insurers (e.g., they have more cases with prices set as a share of their charges). In concentrated insurer markets the opposite occurs-hospitals have lower prices and bear more financial risk. Examining the 366 mergers and acquisitions that occurred between 2007 and 2011, we find that prices increased by over 6% when the merging hospitals were geographically close (e.g., 5 miles or less apart), but not when the hospitals were geographically distant (e.g., over 25 miles apart).

National Trends in Hospital Readmission Rates among Medicare Fee-for-Service Survivors of Mitral Valve Surgery, 1999-2010.

BACKGROUND: Older patients who undergo mitral valve surgery (MVS) have high 1-year survival rates, but little is known about the experience of survivors. Our objective was to determine trends in 1-year hospital readmission rates and length of stay (LOS) in these individuals. METHODS: We included 100% of Medicare Fee-for-Service patients ≥65 years of age who underwent MVS between 1999-2010 and survived to 1 year (N = 146,877). We used proportional hazards regression to analyze the post-MVS 1-year readmission rate in each year, mean hospital LOS (after index admission), and readmission rates by subgroups (age, sex, race). RESULTS: The 1-year survival rate among patients undergoing MVS was 81.3%. Among survivors, 49.1% experienced a hospital readmission within 1 year. The post-MVS 1-year readmission rate declined from 1999-2010 (49.5% to 46.9%, P<0.01), and mean hospital LOS decreased from 6.2 to 5.3 (P<0.01). Readmission rates were highest in oldest patients, but declined in all age subgroups (65-74: 47.4% to 44.4%; 75-84: 51.4% to 49.2%, ≥85: 56.4% to 50.0%, all P<0.01). There were declines in women and men (women: 51.7% to 50.8%, P<0.01; men: 46.9% to 43.0%, P<0.01), and in whites and patients of other race, but not in blacks (whites: 49.0% to 46.2%, P<0.01; other: 55.0% to 48.9%, P<0.01; blacks: 58.1% to 59.0%, P = 0.18). CONCLUSIONS: Among older adults surviving MVS to 1 year, slightly fewer than half experience a hospital readmission. There has been a modest decline in both the readmission rate and LOS over time, with worse outcomes in women and blacks.

Trends in hospitalizations among medicare survivors of aortic valve replacement in the United States from 1999 to 2010.

BACKGROUND: Mortality rates after aortic valve replacement have declined, but little is known about the risk of hospitalization among survivors and how that has changed with time. METHODS: Among Medicare patients who underwent aortic valve replacement from 1999 to 2010 and survived to 1 year, we assessed trends in 1-year hospitalization rates, mean cumulative length of stay (average number of hospitalization days per patient in the entire year), and adjusted annual Medicare payments per patient toward hospitalizations. We characterized hospitalizations by principal diagnosis and mean length of stay. RESULTS: Among 1-year survivors of aortic valve replacement, 43% of patients were hospitalized within that year, of whom 44.5% were hospitalized within 30 days (19.2% for overall cohort). Hospitalization rates were higher for older (50.3% for >85 years), female (45.1%), and black (48.9%) patients. One-year hospitalization rate decreased from 44.2% (95% confidence interval, 43.5 to 44.8) in 1999 to 40.9% (95% confidence interval, 40.3 to 41.4) in 2010. Mean cumulative length of stay decreased from 4.8 days to 4.0 days (p < 0.05 for trend); annual Medicare payments per patient were unchanged ($5,709 to $5,737; p = 0.32 for trend). The three most common principal diagnoses in hospitalizations were heart failure (12.7%), arrhythmia (7.9%), and postoperative complications (4.4%). Mean length of stay declined from 6.0 days to 5.3 days (p < 0.05 for trend). CONCLUSIONS: Among Medicare beneficiaries who survived 1 year after aortic valve replacement, 3 in 5 remained free of hospitalization; however, certain subgroups had higher rates of hospitalization. After the 30-day period, the hospitalization rate was similar to that of the general Medicare population. Hospitalization rates and cumulative days spent in hospital decreased with time.

Does Hospital Competition Save Lives? Evidence from the English NHS Patient Choice Reforms.

Recent substantive reforms to the English National Health Service expanded patient choice and encouraged hospitals to compete within a market with fixed prices. This study investigates whether these reforms led to improvements in hospital quality. We use a difference-in-difference-style estimator to test whether hospital quality (measured using mortality from acute myocardial infarction) improved more quickly in more competitive markets after these reforms came into force in 2006. We find that after the reforms were implemented, mortality fell (i.e. quality improved) for patients living in more competitive markets. Our results suggest that hospital competition can lead to improvements in hospital quality.