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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. [ABSTRACT FROM AUTHOR]

Despite size and relevance of uncontrolled diabetes, robust evidence on its effects on health care utilisation is very limited, especially among European countries. We employed longitudinal administrative data from Spain (2004-2010) to explore the relationship between uncontrolled type 2 diabetes and health care utilisation. We used a biomarker (glycated haemoglobin, HbA1c) to detect the presence of uncontrolled diabetes and explore its effects on both primary and secondary health care. We estimated a range of panel count data models, including negative binomials with random effects, dynamic and hurdle specifications to account for unobserved heterogeneity, previous utilisation and selection. We found uncontrolled diabetes in between 27 and 30% of patients of both genders. Our estimates suggested that although women appeared to systematically consume more health care compared to men, their consumption levels did not seem to be influenced by uncontrolled diabetes. Conversely, among men uncontrolled diabetes increased the average number of GP visits per year by between 3 and 3.4%, specialist visits by 5.3-6.1%, depending on specifications, and also extended annual hospital length of stay by 15%. We also found some evidence of heterogeneity in utilisation based on the level of uncontrolled diabetes among male individuals. Overall, our results suggested the need for different diabetes management plans depending on gender and levels of glycaemic control. [ABSTRACT FROM AUTHOR]

Despite an increasing interest in the effect of health information on health-behaviours, evidence on the causal impact of a diagnosis on lifestyle factors is still mixed and does not often account for long-term effects. We explore the role of health information in individual health-related decisions by identifying the causal impact of a type-2 diabetes diagnosis on body mass index (BMI) and lifestyle behaviours. We employ a fuzzy regression discontinuity design (RDD) exploiting the exogenous cut-off value in the diagnosis of type-2 diabetes provided by a biomarker (glycated haemoglobin) drawn from unique administrative longitudinal data from Spain. We find that following a type-2 diabetes diagnosis individuals appear to reduce their weight in the short-term. Differently from previous studies, we also provide evidence of statistically significant long-term impacts of a type-2 diabetes diagnosis on BMI up to three years from the diagnosis. We do not find perceivable effects of a type-2 diabetes diagnosis on quitting smoking or drinking. Overall, health information appears to have a sustained causal impact on weight reduction, a key lifestyle and risk factor among individuals with type-2 diabetes. • We explore the causal impact of a diabetes diagnosis on BMI and lifestyle behaviours. • We use a regression discontinuity design and a biomarker as our running variable. • Individuals reduce their weight in the short-term following a diabetes diagnosis. • We identify long-term effects on weight up until three years since the diagnosis. • We do not find perceivable effects of a diagnosis on smoking or drinking. [ABSTRACT FROM AUTHOR]