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The effect of adding trajectories on the model performance decreased as the comprehensiveness of the model increased. Such impact was most apparent in statistical indicators and the mean costs of the predicted groups, while ignorable or detrimental in trend ratio and proportions of true cases identified by models.
Therefore, when the baseline model did not include utilization measures, such as the demographics model, adding morbidity trajectories would help increase the model performance; on the contrary, when the baseline model included utilization measures, such as the prior cost model, the contribution from adding morbidity trajectories might become minimal due to the overlap of both variables. In addition, given that many healthcare expenditures are truly random and cannot be foreseen, an upper limit possibly exists on the risk adjustment model's ability to identify high-cost users; the most comprehensive cross-sectional model might have reached the limit so that adding additional information may not have any help.
Finally, the longitudinal information might have been lost during the process of being collapsed into six levels so that adding trajectories did not help the performance of the model.