Suchergebnisse

This paper uses Bureau of Transportation data on 35 million US domestic flights between 1995 and 2001 to investigate the determinants of flight cancellations. The paper is novel in two regards, it focuses exclusively on flight cancellations, and it explores the service quality–flight revenue relationship. We find that carriers have some control over the occurrence of flight cancellations given that cancellations are significantly less likely on Thursdays, Fridays and Sundays and for the last flight of the day. There is some evidence linking cancellations with revenue.

BACKGROUND: The federal government uses multiple definitions for identifying rural communities based on various geographies and different elements of rurality. OBJECTIVES: The objectives of this study were to: (1) assess the degree to which rural definitions identify the same areas as rural; and (2) assess rural-urban disparities identified by each definition across socioeconomic, demographic, and health access and outcome measures. RESEARCH DESIGN: We determined the rural status of each census tract and calculated the rural-urban disparity resulting from each definition, as well as across the number of definitions in which tracts were designated as rural (rurality agreement). SUBJECTS: The population in 72,506 census tracts. MEASURES: We used 8 federal rural definitions. Population characteristics included percent with a bachelor's degree, income below 200% poverty, population density, percent with health insurance and whether various health care services were within 30 minutes driving time of the tract centroid. RESULTS: The rural population varied from slightly 75.5 million across definitions. The largest rural-urban disparities were found using Urban Influence Codes. Urbanized Area and Urbanized Cluster tended to generate smaller disparities. Population characteristics such as population density and percent White had notable discontinuities across levels of rurality, while others such as percent with a bachelor's degree and income below 200% poverty varied continuously. CONCLUSIONS: Rural-urban populations and disparities were sensitive to the specific definition and the relative strength of definitions varied across population characteristics. Researchers and policymakers should carefully consider the choice of outcome and region when deciding the most appropriate rural definition.

Critical Access Hospitals (CAHs) receive cost-based reimbursement from Medicare for inpatient care, including post–acute skilled care provided in swing beds (skilled swing days). Because the reimbursement formula treats swing bed and acute days equally, there is concern that CAH skilled swing days are "overreimbursed" as compared with skilled days provided in other settings. The reimbursement formula is complex; thus, empirical estimates are needed to identify the marginal cost per day to the hospital and the implied Medicare expenditure per day, accounting for fixed cost transfers between services. Using Medicare cost report data, we find that Medicare paid, on average, $581 for the routine portion of a CAH skilled swing day in 2009—more than the estimated marginal cost of $262, but less than the 2009 average per diem of $1,302. Estimates varied widely across the 1,300 CAHs; therefore, payment policy changes would likely have a broad range of effects.

Medicare skilled-nursing facility (SNF) patients exhibit differences in resource use and outcomes by whether the SNF is hospital-based or freestanding. Some of the differences may be attributable to patient selection rather than underlying institutional differences. This study adjusts for patient selection by stratifying Medicare SNF patients by their likelihood of hospital-based SNF referral. Three outcomes are analyzed to illustrate this approach: Medicare SNF length of stay, discharge to home within 30 days, and preventable hospital readmissions. The estimations use claims and patient-assessment data merged with facility and market characteristics. The results provide strong evidence that good candidates for faster recovery and discharge to the community are preferentially selected into hospital-based units. While the unstratified regression approach controls for much of the selection, stratified regressions provide further reductions in setting-specific differences. Remaining differences may be because of patterns of care or reflect residual bias from unobserved factors.

OBJECTIVE: To understand the role of county characteristics in the growing divide between rural and urban mortality from 1980 to 2010. DATA SOURCE: Age‐adjusted mortality rates for all U.S. counties from 1980 to 2010 were obtained from the CDC Compressed Mortality File and combined with county characteristics from the U.S. Census Bureau, the Area Health Resources File, and the Inter‐University Consortium for Political and Social research. STUDY DESIGN: We used Oaxaca–Blinder decomposition to assess the extent to which rural–urban mortality disparities are explained by observed county characteristics at each decade. PRINCIPAL FINDINGS: Decomposition shows that, at each decade, differences in rural/urban characteristics are sufficient to explain differences in mortality. Furthermore, starting in 1990, rural counties have significantly lower predicted mortality than urban counties when given identical county characteristics. We find changes in the effect of characteristics on mortality, not the characteristics themselves, drive the growing mortality divide. CONCLUSIONS: Differences in economic and demographic characteristics between rural and urban counties largely explain the differences in age‐adjusted mortality in any given year. Over time, the role these characteristics play in improving mortality has increased differentially for urban counties. As characteristics continue changing in importance as determinants of health, this divide may continue to widen.