Prior work has suggested that children's hospitals have longer LOS, higher readmission rates, and higher mortality rates compared with other types of hospitals. However, the addition of a travel variable to a traditional risk-adjustment model—whether geocoded travel times or nongeocoded travel distances—reduced these systematic differences (). Other severity adjustment tools, such as the inclusion of excess travel time or the MedisGroups physiologic score, improved our assessment of a patient's risk without further reducing the differences between types of hospitals. Augmenting traditional risk-adjusted models with a travel variable, then, may identify poorly performing hospitals without systematically favoring or penalizing hospitals based on their location and role in the community. Although the travel time variable may provide a more accurate assessment of the travel costs for families with ill children, the specific travel variable used in the augmented risk-adjustment model should be based on the benefits and costs of using each type of variable.
Why may travel time improve the severity adjustment of these models? We can draw insights from our analyses of which patients typically travel further to receive care. These children appear sicker by all available measurements: they are more likely to have high-severity conditions, such as bacterial meningitis or diabetes ketoacidosis; they are more likely to have one or more comorbid conditions; they are more likely to have had a prior admission; and they have higher MedisGroups scores (). However, even when we include this available information in our severity adjustment models, the use of any travel time variable improved the severity adjustment models, especially for conditions of lower usual illness severity (). Prior work in myocardial infarctions (McClellan, McNeil, and Newhouse 1994
; Piette and Moos 1996
) and asthma (Jones, Bentham, and Horwell 1999
) found an association between higher mortality rates and longer travel distances. Longer travel times, then, appear to further distinguish those children at higher risks of a poor outcome within specific diagnoses or among children with a comorbid condition. What is not clear is whether these patients are traveling because the parents believe that their children are sicker, or whether their local physicians are preferentially referring them to the larger, regionalized centers.
Similar to prior work (Lorch et al. 2004
), these data also suggest that the type of hospital where patients receive care is equally as important as whether patients travel at all: children traveling to children's hospitals appear to be the sickest patients with a given condition or set of comorbid conditions, as children's hospitals received the greatest improvement in assessment when a travel variable was included in our risk-adjustment models. The fact that rural hospitals also experience an improvement in assessment suggests that access to care may continue to be an issue for children residing far from medical care. This fact occurs even though rural hospitals compete for patients of higher income or higher severity with larger urban centers (Bronstein and Morrisey 1990
; Tai, Porell, and Adams 2004
; Liu, Bellamy, and McCormick 2007
Although prior work suggests that patients who bypass hospitals either do not have insurance or are sicker, especially rural patients (Hogan 1988
; Adams et al. 1991
), the addition of a “bypass” travel variable to our augmented model did not reduce the systematic difference between hospital types any further than the absolute travel time variable (). It is possible that at any given hospital, the actual number of patients traveling long excess distances is small, and most of these children were already traveling long distances. Also, factors that lead a patient to bypass the nearest hospital, such as the lack of health insurance, may be captured in the traditional risk-adjustment model.
The other interesting finding from this study is that hospital-specific measures of travel did not improve our results over either the travel time or travel distance models. This result emphasizes how different the organization of pediatric care is from adult care in the United States, particularly in states with large children's hospitals: many local community hospitals either do not care for children or have very small pediatric wards with little experience managing any sort of medical complexity in a child. Thus, hospitals that care for sick children may have wider markets than the typical adult hospital. For example, several large children's hospitals had 75 percent thresholds over 50 minutes (compared with 13.5 minutes for urban hospitals) and 90 percent thresholds of over 75 minutes. For Medicare patients, prior work on the markets of rural hospitals showed similar results (Adams et al. 1991
; Goody 1993
; Liu, Bellamy, and McCormick 2007
). Using the hospital-specific radii method for severity adjustment may not account for the regionalization of pediatric care that occurs in most parts of the United States.
One concern about augmenting a risk-adjustment model is “adjusting away” differences in quality between hospitals. For example, if hospitals poorly treat their asthmatic patients and as a result the children require intensive care or mechanical ventilation, the risk-adjustment model should not include these variables (Romano and Chan 2000
). Augmenting the typical risk-adjustment models with a travel variable would cause this bias only if (1) poor hospitals were the only hospitals accepting patients who travel or (2) hospitals were providing poor care only to those patients who travel. The majority of patients who travel for pediatric hospital care go to hospitals that should provide at least similar care to other hospitals in the community. These hospitals would include children's hospitals and other teaching hospitals. As for differential treatment of patients solely based on where they reside, it is unlikely that a poorly performing hospital would only prolong the LOS, increase the readmission rate, or increase the mortality rate of patients traveling greater distances. The fact that all three outcome measures used in this paper were influenced by prolonged travel suggests that this variable is somehow assessing previously unmeasured illness severity.
These results suggest several policy implications. First, these results could be used by state agencies in their assessment and publication of hospitals‘ performance or in pay-for-performance assessments. Secondly, these data can also help health planners identify areas of greater need for pediatric hospital care and resources. Similar work has been published for trauma networks (Branas et al. 2005
). Finally, the fact that children with HMO insurance were less likely to travel suggests that insurers may influence the place of initial hospitalization for children with common medical conditions.
Travel time did not eliminate the differences between hospitals for the mortality outcome, possibly because detailed clinical data were not available for each child. For each of the models, although, travel time added significant information even when the MedisGroups score was included in the risk-adjustment model. Thus, a travel variable may serve as an adequate proxy for situations where detailed clinical data are not available for risk adjustment. While Pennsylvania has a large variety of rural and urban hospitals, these results may not be generalizable to states with one central children's hospital or many smaller community hospitals that equally care for children. Finally, our results concern common pediatric conditions that make up the majority of pediatric admissions to any hospital. The care of more complex conditions that are typically regionalized, such as cancer and congenital heart disease, may not benefit from such a change to the risk-adjustment model.
Thus, using a travel time variable helps to improve severity adjustment in the assessment of pediatric hospital care, likely by adding otherwise unobserved information about these children. The performance of hospitals with large geographic markets, such as children's hospitals and rural hospitals, are most likely to be affected by the inclusion of travel time. When detailed clinical data are not available, policy makers should further investigate methods to improve the assessment of hospital performance.