Predictors of nursing home residents' time to hospitalization.

The hospitalization of nursing home residents has recently emerged as an important area of interest for policy makers. These hospitalizations are known to be frequent (Intrator et al. 2007), costly (Grabowski, O'Malley, and Barhydt 2007), often preventable (Saliba et al. 2000), and potentially associated with negative health outcomes (Ouslander, Weinberg, and Phillips 2000). Reducing avoidable hospitalizations has been proposed as a performance measure in the planned Medicare nursing home value-based purchasing (NHVBP) demonstration. In conjunction with other quality dimensions, nursing homes with lower avoidable hospitalization rates will be rewarded with higher incentive-based payments. By law, the demonstration must be budget neutral. For example, Medicare demonstration bonus pool payments to nursing homes with lower hospitalization rates would be balanced against the savings to Medicare from reduced hospitalizations. If policy makers are going to use hospitalizations to reward “good” behavior and also generate savings, it is important that the resident and facility characteristics associated with these hospitalizations are known. In a recent review of the literature, Grabowski and colleagues (2008) identified 59 published studies between 1980 and 2006 examining predictors of acute hospitalization for nursing home residents. Based on these studies, resident-level factors associated with hospitalization included sociodemographic factors, health characteristics, and the presence of advance directives. The facility-level factors correlated with hospitalizations included physician and nurse staffing, presence of ancillary services, use of hospice, and ownership status of the facility. A potentially important source of bias and statistical inefficiency in the existing literature is the manner in which hospitalizations from nursing homes have been modeled. The standard approach has been to estimate the likelihood of any hospitalization over some fixed interval of time such as a quarter year (Carter 2003), half year (Intrator, Castle, and Mor 1999), or full year (Freiman and Murtaugh 1993). On policy, clinical, and statistical grounds, we assert that the more appropriate approach is to model the time to first hospitalization and more generally the time between subsequent hospitalizations. From a policy perspective, the incentives to hospitalize may vary based on the generosity of nursing home reimbursement (Intrator et al. 2007). If a qualifying hospital stay preceded entry into the nursing home, services are covered in part by Medicare for up to 100 days. After the Medicare benefit is exhausted, care is generally covered by Medicaid or privately out of pocket. Moreover, many private-pay nursing home residents eventually “spend down” their assets over the course of their stay and qualify for Medicaid. Given that Medicare payment rates and private out-of-pocket prices are considerably more generous relative to Medicaid payment rates (Troyer 2002), these shifts in payer status may introduce variation in the incentive to hospitalize over a fixed interval of time. From a clinical and statistical perspective, modeling time to (or rate of) hospitalization is preferred because the risk of hospitalization is not constant over time. For example, health status may deteriorate quite rapidly for disabled nursing home residents. Moreover, a time to event approach avoids the problem of having to designate a particular hospitalization—typically the initial hospitalization—as more important than others that occur during a fixed interval of time. The Medicare Payment Advisory Commission (MedPAC) heard testimony at its October 2008 public meeting related to the frequent cycling of individuals between the nursing home and hospital. The standard approach used in the literature—modeling any hospitalization over some fixed time interval—will not account for multiple hospitalizations over a given period. The only previous survival analyses of hospitalization of nursing home residents reported in the literature used Cox-proportional hazards (Murtaugh and Freiman 1995). This model facilitates an estimate of the relative risk of hospitalization, but it applies only to the first hospitalization and requires additional modeling to estimate the time until hospitalization or, equivalently, the probability that hospitalization occurs during a given time interval. We offer a novel approach to estimating the predictors of hospitalization using accelerated failure time (AFT) models for time to first hospitalization and time between subsequent hospitalizations, extending the AFT model to handle repeated measures data with time-varying covariates. Relative to previous research on hospitalizations for nursing home residents, these models have the advantage of modeling all hospitalizations, accounting for changes over time in the value of predictor variables including summary measures of the number of past hospitalizations, accounting for censoring, and generating interpretable outputs such as the probability that the next hospitalization occurs within a certain time.