Uncontrolled diabetes and health care utilisation: A bivariate latent Markov model approach

Abstract

Although 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 UD by employing an innovative bivariate latent Markov model that 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. We find that, conditional on time‐varying unobservables, UD does not have a statistically significant direct 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.

Disease management in diabetes care: When involving GPs improves patient compliance and health outcomes

“…Compensated diabetes in 2014 does not affect the probability of experiencing a new complication in the following year. On the contrary, compensated patients are more likely to be hospitalised, which is in keeping with recent findings in Spain [Gil, Li Donni, Zucchelli, 2018]. Most controls included at GP level do not affect the observed outcomes.…”

Estimation of disease progression for ischemic heart disease using latent Markov with covariates

“…LMMs can be used as an extension of latent class analysis for longitudinal data and have been suggested in many studies for public health and medical research. In particular, an LMM is employed to study the performance of nursing homes [12] and their rankings [13], self‐reported health statuses based on longitudinal data [14], the effect of health status on material hardship [15], the effect of population aging on future health services costs [16], the diagnostics for trachoma elimination [17], the effects of uncontrolled diabetes on health care consumption [18], and model smoking transitions [19].…”