Research

Abstract: We use health care claims data from the Health Care Cost Institute to estimate the share of geographic variation in health care spending attributable to person-specific (demand) and place-specific (supply) factors. We exploit patient migration across 112 metropolitan areas between 2012 and 2016. Using an event study approach, we find that moving to an area with 10% higher (lower) spending leads to a 4.2% increase (decrease) in individual medical spending. Our estimate implies that 42% of variation in health care spending among the commercially insured is attributable to place-specific factors. We show that variation in both price and utilization jointly determine the place-specific impact on individual spending. All else equal, we find that moving to an area with 10% higher (lower) prices, on average leads to a 5% increase (decrease) in spending, while moving to an area with 10% higher (lower) utilization leads to a 3.6% increase (decrease).

Abstract

While out-of-network or potential “surprise” billing has garnered increasing attention, particularly in emergency department and inpatient settings, few national studies have examined out-of-network care overall or in other settings. We examined out-of-network spending and use among two large nationwide populations with employer-sponsored insurance. In a primary sample of 27,883,040 people in data for 2008–16 from the Truven MarketScan Commercial Claims and Encounters Database, we found that the unadjusted share of total spending that occurred out of network decreased from 7.0 percent in 2008–10 to 6.1 percent in 2014–16, an adjusted average decline of 0.10 percentage points per year. Using a secondary sample of 13,093,209 people in the Health Care Cost Institute database provided qualitatively similar results, including when provider charges (upper bound for balance billing) were used in place of observed out-of-network prices. In subgroup analyses of the primary sample, the share of out-of-network spending was stable or declined among all segments of care except hospitalist services, pathologist services, and laboratory tests across the study period. Out-of-network use demonstrated comparable patterns. Prices were higher out of network than in network. Policy makers should focus their efforts on protecting consumers from balance billing or potential surprise billing in clinical scenarios where patients often have little choice over their provider.

Abstract

Objective: To compare different methods of indexing health care service prices for the commercially insured population across geographic markets.

Data Sources: Health Care Cost Institute commercial claims data from 2012 to 2016.

Study Design: We compare price indices computed using methods with differing levels of computational intensity: weighted‐average versus regression‐based methods. We separately compute indices of the prices paid for set of common inpatient and set of common outpatient services in different markets across the United States using each type of method. We subsequently examined the variation of and correlations between the resulting index values.

Data Collection/Extraction Methods: We computed health care service price indices separately using samples of inpatient and outpatient facility claims from 2012 to 2016 across 112 Core‐Based Statistical Areas. Within each category of services, claims were limited to members under the age of 65 with employer‐sponsored insurance. Both samples were limited to a common set of services that made up nearly 80 percent of the service use in the full sample every year.

Principal Findings: We found that the methods studied produced highly correlated price indices (r > .94) with similar distributions across years for both inpatient and outpatient services.

Conclusions: Our findings suggest that weighted‐average methods, which are much less computationally intensive, will generate results similar to regression‐based methods.

Abstract

By providing affordable healthcare to many Americans for the first time, the Affordable Care Act increases demand for public mental health services. It is, however, unclear if states’ provision standards for supply of mental health services will be able to accommodate this demand increase. Both the demand and supply of public mental health services vary within states; it is necessary to measure both locally. In this paper, we estimate the prevalence of mental illness within 30 geographical regions in the state of Virginia, a representative example of how many states organize their public mental health systems and how mental illness prevalence can be measured locally. Our methodology extends the analysis in Stern (Health Serv. Outcomes Res. Methods 14:109–155, 2014) by covering an entire state and accounting for peoples’ insurance status. The latter allows us to compare estimates of demand for public mental health services among those 30 geographical regions. We find that over 66,000 uninsured and Medicaid-insured individuals in Virginia are not provided with public mental health services. The deficit varies locally, with several regions having no deficit and others having 5000 or more untreated people. We also estimate that a large portion of the unserved people with mental illness are uninsured but would be insured for mental health services through Medicaid if Virginia were to accept the Medicaid expansion associated with the Affordable Care Act. These results provide evidence that there is significant variation in the demand for and public health systems’ ability to supply mental health services within states. This implies states can better serve populations relying on mental health care by allocating scarce public mental health dollars to localities reflecting their need.