Diabetes diagnosis based on glucose control levels and time until diagnosis : a regression discontinuity approach to assess the effect on direct healthcare costs

We estimate the difference in direct healthcare costs of individuals diagnosed with diabetes depending on their glucose level, considering different timespans and subgroups. Using data from administrative registers of 285,450 individuals in Catalonia from 2013 to 2017, we used a fuzzy regression discontinuity design to estimate the causal effect of being diagnosed with diabetes at a given timespan (based on an average glucose value equal to or above 6.5%, the treated group) vs. not (having an average glucose level below the threshold, the control group) on healthcare costs across different timespans (6, 9, 12, 15, 18, 21, and 24 months after the first laboratory test) and distances, in days, between the laboratory test and the doctor's diagnosis. When average glucose level was the only independent parameter and the time until diagnosis was 30 days or less, at the cut-off value (6.5%) healthcare costs were between €3,887 and €5,789 lower for the treated group compared to the control group. Smaller differences were reported as the delay in diagnosis increased, even when additionally controlling for sociodemographic characteristics and health status. Our results highlight the importance of prompt diagnosis and might open the debate about the usefulness of the 6.5% reference value in the blood glucose level as the main diagnostic tool in diabetes. Highlights • Values slightly above the blood glucose cut-off are related to lower costs. • Although always significant, differences in healthcare costs reduce as the delay in diagnosis increases. • The results hold regardless of the timespan and the considered covariates. • Our results point towards a diminished effect as the delay in diagnosis increases. • The findings highlight the role played by physicians in terms of on-time diagnoses