This study found that the CSHCN information explained a higher proportion of the variance in annual expenditures than the subjectively rated health status, but less than HCC and the prior expenditures. These results are consistent with the findings reported in the literature (Newhouse et al. 1993
; Fowler and Anderson 1996
; Hwang et al. 2001
;), as shown in column 3 of . As in previous studies of risk adjustment, we found that prior use is the single powerful predictor of expenditures (van de Ven and Ellis 2000
Our analysis also indicated that neither the HCC model nor the model with prior spending effectively predicts for the group of CSHCN despite previous reports that they performed relatively well for all children (Newhouse et al. 1993
; Hwang et al. 2001
;). Using either of these two models would result in considerable underpayment for CSHCN. On the other hand, adding the CSHCN information to each of the two models would substantially increase capitation rates for CSHCN, reducing the adverse selection incentive against this group of children. Together these findings suggest CSHCN information could be useful risk adjusters for setting capitation rates.
As the first study using the survey-based CSHCN information as a risk adjuster, this paper takes advantage of comprehensive MEPS data on children's expenditures. In the literature, survey-based measures of health status and health needs have not been included in the studies of risk adjustment because most researchers have focused attention on models with detailed diagnostic and clinical data. As Zaslavsky and Buntin (2003)
argued, survey measures have a number of advantages compared with alternative adjusters. These measures are easy to collect; contain information, subjective or not, not always available in the medical record; are predictive of costs; and are less sensitive to care provision and data management than measures of utilization and diagnoses. For the purposes of our study, the information about CSHCN, which we use for risk adjustment, is easily obtained from parents based on the CSHCN Screener and could easily be verified by health care providers. In particular, “the policy research community has converged in its support for this mechanism [of identifying CSHCN through the CSHCN Screener]” (Davidoff 2004
), which has been adopted by national surveys, including the MEPS. Previous studies have reported that the CSHCN Screener is cost-effective in comparison with other instruments to identify CSHCN (Bethell et al. 2002a
; Blumberg et al. 2003
;). In particular, the mean household administration time for the CSHCN Screener was as short as 2 minutes and 6 seconds (Blumberg et al. 2003
). This may avoid cost concerns of using the CSHCN Screener measures as adjusters. Another advantage is that the CSHCN information may not be overstated by parents for insurance purposes because they may be reluctant to claim their healthy kids have special needs. This implies that the use of CSHCN information as an adjuster may not be particularly susceptible to gaming, though additional research about this question is clearly warranted. Although we found that the use of survey-based measures could improve risk-adjusted capitation rates, concerns about their acceptability as risk adjusters could delay their use. Thus, continued work by the research community to expand the evidence about their potential and to build the case for their reliability may be essential for the adoption of survey-based measures in risk-adjusted capitation rates.
One criticism of survey measures as adjusters is that they do not obtain the level of predictive power that is possible with clinical data. This is true either for the model with the dichotomous variable of CSHCN status (explaining 3.3 percent of the variance in annual expenditures) or for the models with one of the five types of special needs (with the explained proportion between 2 and 5 percent). Our analyses show, however, that all the CSHCN information explained 7.3 percent of the variance in annual expenditures, compared with 12.1 percent by the diagnosis-based HCC model. The performance of the CSHCN information for risk adjustment is impressive if one considers the ease with which it can be obtained from household survey, particularly in comparison with the cost of obtaining detailed clinical information required by the HCC and other diagnosis-based models. Our results also indicate that neither the dichotomous variable of CSHCN status nor any one variable of the five special needs has strong explanatory power. To improve the model performance, all the information about CSHCN should be included in the risk-adjustment model.
This study has several advantages over prior research on risk adjustment for children. In the first study of risk adjustment for children, for example, Newhouse et al. (1993)
used data that included outpatient expenditure only, and that were limited to children ages 14 and older. Another study by Fowler and Anderson used data on paid claims and gross eligible charges for the study population of children. Their analyses might underestimate total expenditures (Fowler and Anderson 1996
). In comparison, this study uses data from the MEPS, a nationally representative survey, covering all children of ages 0–18. In particular, the MEPS is unparalleled for the degree of detail in its data about health care expenditures as well as its ability to link data on health expenditures to the demographic, economic, health status, and other characteristics of survey respondents (Cohen et al. 1996
Although we found that the CSHCN information can be useful for risk adjustment, our risk-adjustment models still explained only a small proportion of the variance in expenditures. There are also limitations in our study based on the MEPS data. First, the MEPS excludes institutionalized children, and, although fewer than 100,000 in 1990 (Newacheck et al. 2000
), their exclusion could affect the analyses. Second, this study does not directly quantify the change in health insurance plans' incentive for risk selection based on the inclusion of CSHCN information in risk adjustment. Although we show the change in the magnitude of capitation rates, we do not provide a measure of the expected profitability of various selection strategies. One direction for future study is to simulate the incentives under different risk-adjustment models based on alternative enrollment strategies. Anther important direction for future work would be to develop a new risk-adjustment tool based on diagnostic information. Although the HCC model performed relatively well on pediatric populations, we found that only a small proportion of expenditure variation was explained by the HCC model. Finally, given the large amount of unexplained variation that remains after risk adjustment, another important topic in this area would be to study a blend of capitation with fee-for-services, as proposed by Newhouse (1986)