A Comparative Analysis Of Chronic And Nonchronic Insured Commercial Member Cost Trends

Disease management (DM) is increasingly encountered in health plans and employer groups as a health care intervention targeted to individuals with chronic diseases (“Chronics”). To justify the investment by payers in DM, it is important to demonstrate beneficial clinical and financial outcomes. In the absence of randomized control studies, financial results are often estimated in a pre/post-study in which the cost of Chronics in the absence of DM can be predicted by their pre-DM year cost (on a per member per month basis) adjusted for the Nonchronic population’s cost trend. The assumption made, not previously tested, is that absent DM, the Chronic and Nonchronic trends are identical.We calculated Chronic and Nonchronic trends between 1999 and 2002 and compared them under different assumptions regarding identification of chronic disease and medical services. Qualification for the Chronic group was defined as having coronary artery disease, heart failure, diabetes, asthma, or chronic obstructive lung disease. Our base case used an algorithm that identified a member as Chronic prospectively (that is, from the point of identification forward), with one or more of the chronic conditions. We used a data set of 1.5 million commercially insured members.When Chronic and Nonchronic members are identified and included in the population prospectively, the average three-year trend over the study period for chronic and nonchronic members adjusted for high cost outliers were 4.9% and 13.9%, respectively. Adjusting the population experience for differences in service mix had little effect on the divergence in trends. However, altering the Chronic selection algorithm to eliminate migration between groups (thus classifying a member as always Chronic if identified as Chronic at any point in the four years) caused the trends to converge (Chronics, 16.3%; Nonchronics 17.2%; total 16.0%). Using the original selection algorithm but risk-adjusting the populations annually also caused their trends to converge (Chronics, 12.5%; Nonchronics 11.9%). Finally, applying an annual “requalification” process (in which members who qualify as Chronic in one year but not the next are excluded in the year in which they fail to qualify), we see some, although not complete, convergence of trends.Estimating DM program financial outcomes based on the assumption that absent the program, the Chronic population would have had the same trend as the Nonchronic population can lead to erroneous conclusions. Identification of a Chronic member and the point at which that member is reclassified from one subpopulation to another can significantly affect the observed trends in both subpopulations, implying that great care must be taken over classification and interpretation of the resulting trends, and their use in DM savings calculations. Trends calculated using a prospective identification methodology introduce a bias into estimates of outcomes. We refer to this effect, which has not previously been described or discussed in the literature, as “migration bias.” It is critical to understand how trends in a reference population can vary according to selection criteria for disease in the chronic population, service mix, and changes in risk over time.