Border-Crossing Adjustment and Personal Health Care Spending by State

Article excerpt

This article presents the results of a pioneering effort by the Health Care Financing Administration (HCFA) to measure interstate border crossing for services used by Medicare and non-Medicare beneficiaries. A major focus is to provide estimates of per capita expenditures by State for individual services. Such estimates are not possible without adjustment for interstate border-crossing flows. This is HCFA's first attempt to furnish a unified per capita personal health care expenditures data base comprising all services and covering total population. The study also analyzes interstate differences in expenditure flows by computing rates of inflow and outflow of expenditures, and highlights Medicare/ non-Medicare flow differences.


The study incorporates the findings from a project initiated by HCFA's Office of the Actuary (OACT) to refine State estimates of health care expenditures. The project was undertaken in response to a request by 1993 President's Task Force on Health Care Reform for estimates of health care spending by States. As the first step, State estimates of personal health care spending were developed, using data based on provider locations. These estimates show expenditures on total personal health care services in each State, where "State" represents the location of the provider of service (Levis et al., 1995). Because State spending estimates based on location of providers differ from spending by persons residing in that State, estimates of per capita expenditures, however, could not be produced based on this data.

The data on per capita expenditure by State is an essential tool to identify differences among States in patterns and levels of spending. These estimates are useful for evaluating the effectiveness of individual State health reform initiatives, by providing information to address issues related to the impact of policy changes on spending patterns and growth in a State. The key toward accurately producing these data was to first create State expenditure estimates based on State of beneficiary residence. For this, the expenditure data based on provider location had to be converted to those based on location of beneficiary residence.

The difference between estimates based on provider location and those based on location of beneficiary residence is accounted for by flows of expenditures from one State to another as a result of the border crossing by State residents for services in another State. There are various reasons for such crossing of State borders, among which the need for specialized care probably tops the list (Mayer, 1983; Folland, 1983; Holahan and Zuckerman, 1993). Border crossing may also be circumstantial (Miller and Welch, 1992). If a beneficiary resides near a State border, simply going to the most convenient hospital may entail crossing a State border. Usually, significant amounts of border crossing occur when a hospital market area overlays State boundaries. Some States, such as Florida and Arizona, also experience large seasonal inflows of out-of-State patients. The creation of expenditure estimates based on beneficiary residence location, therefore, requires estimating these expenditure flows from beneficiary State to provider State.

To serve this process, the first step was to develop a data base defining interstate flows of expenditures for Medicare beneficiaries. The availability of Medicare data files containing expenditure data at the beneficiary level both by beneficiary residence-State and by provider location enabled the creation of complete interstate flow matrices for Medicare patients for a broad array of services. The results analyzing the border-crossing behavior by Medicare beneficiaries were reported in an earlier study (Basu, Lazenby, and Levit, 1995). Because similar data for the rest of the population were not available, adjustment factors developed for Medicare patients were used to serve as the building blocks for estimating border-crossing patterns and expenditures per capita of non-Medicare population. …