Uncontrolled diabetes and health care utilisation: a bivariate Latent Markov model approach

Abstract: While uncontrolled diabetes (UD) or poor glycaemic control is a widespread condition with potentially life-threatening consequences, there is sparse evidence of its effects on health care utilisation. We jointly model the propensities to consume health care and poor glycaemic control by employing a bivariate Latent Markov model which allows for dynamic unobserved heterogeneity, movements between latent states and the endogeneity of UD. We estimate the effects of UD on primary and secondary health care consumption using a panel dataset of rich administrative records from Spain and measure UD using a biomarker. Differently from previous studies, we find that UD does not have a statistically significant effect on health care use. Furthermore, individuals appear to move across latent classes and increase their propensities to poor glycaemic control and health care use over time. Our results suggest that by ignoring time-varying unobserved heterogeneity and the endogeneity of UD, the effects of UD on health care utilisation might be overestimated and this could lead to biased findings. Our approach reveals heterogeneity in behaviour beyond standard groupings of frequent versus infrequent users of health care services. We argue that this dynamic latent Markov approach could be used more widely to model the determinants of health care use. 

This is a joint work with Joan Gil (Universitat de Barcelona, UB) and Paolo Li Donni (Universita' di Palermo, Italy).