Effect of dialysis center profit-status on patient survival: a comparison of risk-adjustment and instrumental variable approaches.

The percentage of for-profit dialysis centers increased from approximately 60 percent in 1991 to nearly 80 percent in 2001 (U.S. Renal Data System 2004). The effect of dialysis center “profit” status on provision of care and outcomes for patients with end-stage renal disease (ESRD) has been the topic of several research efforts. It has been reported that for-profit dialysis centers use fewer resources than their nonprofit counterparts (Held 1990, 1991; de Lissovoy et al. 1994; Farley 1996; Hirth et al. 2000; Ozgen and Ozcan 2002) and it has been suggested that patients treated at for-profit dialysis centers are at greater risk as a result (Levinsky 1999; Devereaux et al. 2002; Meyer and Kassirer 2002). Several researchers estimated the difference in survival rates between for-profit and nonprofit dialysis centers (McClellan et al. 1998; Garg et al. 1999; Irvin 2000; Port et al. 2000). Because of the inability to randomize patients between dialysis centers, these researchers used the profit status of the dialysis centers treating ESRD patients found in observational databases as the basis of their comparisons. A meta-analysis by Devereaux et al. 2002 of several of these studies showed a greater 1-year mortality risk for ESRD patients on hemodialysis treated at private for-profit centers relative to private nonprofit centers. The authors attribute this increased risk to the lower resource levels used in for-profit dialysis centers and surmised that 2,500 dialysis deaths could be avoided each year in the United States if only private nonprofit centers provided dialysis services. It is well known that exploiting “treatment” variation from observational databases for outcome estimation is fraught with inferential difficulties (Byar 1991; Jollis et al. 1993; Doll 1994; Hornberger and Wrone 1997). Inappropriate inferences will occur if patients are selected for treatment in a manner associated with expected outcomes—treatment selection bias, or treatment is simply correlated with unmeasured variables that also affect outcome—confounding bias. If we define the profit status of the dialysis center used by a patient as the “treatment,” treatment selection bias occurs if ESRD patients with less severe conditions are systematically directed to either for-profit or nonprofit centers. For example, if less severe patients are directed toward for-profit centers the estimated survival effect from using a for-profit center for dialysis relative to a nonprofit centers will have a positive bias relative to its true effect reflecting the higher initial health status of patients using for-profit centers. Confounding (or omitted variable) bias occurs if profit status is simply correlated to unmeasured variables that also affect survival. For example, the “ecological” characteristics of populations, such as intrapersonal factors, interpersonal processes and primary groups, institutional factors, community factors, and public policy (McLeroy et al. 1988), may vary across geographic areas with the predomination of either for-profit or nonprofit dialysis centers. For example, Subramanian et al. (2001) demonstrated significant variation in health status across states after controlling for differences in individual characteristics. Previous studies assessing the effect of dialysis profit status on patient outcomes dealt with selection and confounding problems by specifying a set of “risk” adjusters or measured confounders in outcome equations to control for patient differences directly. The measured confounders used varied with the information available in the study. Using data collected from two prospective studies of ESRD patients performed by the United States Renal Data System (USRDS) in the early 1990s, Garg et al. (1999) specified patient age at ESRD onset, race, gender, education status, employment status, whether the patient was a nursing home resident, census region of residence, residence in a census-defined metropolitan area, primary cause of ESRD, and 14 comorbid conditions. Irvin (2000) used the universe of Medicare patients in the USRDS database in 1996 that were treated at either freestanding for-profit or nonprofit dialysis centers and specified patient age, race, gender, primary cause of ESRD, region of the country (south, far west, east, other), the presence of certificate of need legislation in a state and the zip code-level variables per capita income, years schooling, and urban percentage. Port et al. (2000) used USRDS data from 1995 and 1996 and controlled for age, race, gender, primary cause of ESRD, geographic region, and the presence of 15 comorbid conditions. Pushkal and Powe (2001) argued that the risk adjusters specified by these authors were sufficient to eliminate selection and confounding problems. However, the inferences drawn from these results remain conditional on the validity of this argument. In this study, we focused on patients with at least 2 years of Medicare coverage before ESRD incidence (aged 67 years and older at incidence) and collected data from the USRDS on survival, clinical characteristics at dialysis initiation, residence zip code, and Medicare claims. We also obtained the universe of dialysis centers in each year from USRDS facility files that include center profit status and zip code. These data enabled us to use additional strategies to avoid selection and confounding problems. First, we measured an expanded set of potential confounders (previous patient health care utilization, distance from the patient to the nearest dialysis center, and state-level indicators of patient residence) unavailable in previous studies. In addition, we used an instrumental variable (IV) estimation approach used earlier by McClellan et al. (1994) in their estimation of the effects of invasive surgery on the survival of patients with acute myocardial infarction. For each patient in their study, McClellan et al. (1994) measured differential distance as the distance from the patient's residence at diagnosis to the nearest provider of a given classification (e.g., a high treatment provider) minus the distance from the patient's residence at diagnosis to the nearest provider not in that classification (e.g., a low treatment provider). For differential distance to be useful in IV estimation it must be related to treatment choice and not be related to patient outcomes directly or to unmeasured confounding variables. In this study, we calculated differential distance for each patient as the distance to the nearest for-profit center minus the distance to the nearest nonprofit center and then estimated the effect of the profit status of the patient's initial dialysis center on patient survival using both regression methods that risk-adjust for measured confounders and IV estimation and contrasted the findings in terms of the assumptions underlying each approach.