The Use of Antidepressants, Anxiolytics, and Hypnotics in People with Type 2 Diabetes and Patterns Associated with Use: The Hoorn Diabetes Care System Cohort.

Objective. With depression being present in approximately 20% of people with type 2 diabetes mellitus (T2DM), we expect equally frequent prescription of antidepressants, anxiolytics, and hypnotics. Nevertheless, prescription data in people with T2DM is missing and the effect of depression on glycaemic control is contradictory. The aim of this study was to assess the prevalence of antidepressants, anxiolytics, and/or hypnotics use in a large, managed, primary care system cohort of people with T2DM and to determine the sociodemographic characteristics, comorbidities, T2DM medication, and metabolic control associated with its use. Method. The prevalence of antidepressants, anxiolytics, and/or hypnotics use in the years 2007-2012 was assessed in the Hoorn Diabetes Care System Cohort from the Netherlands. Results. From the 7016 people with T2DM, 500 people (7.1%) used antidepressants only, 456 people (6.5%) used anxiolytics and/or hypnotics only, and 254 people (3.6%) used a combination. Conclusion. We conclude that in our managed, primary care system 17% of all people with T2DM used antidepressants, anxiolytics, and/or hypnotics. Users of antidepressants, anxiolytics, and/or hypnotics were more often female, non-Caucasian, lower educated, and more often treated with insulin.

Effectiveness of insulin therapy in people with Type 2 diabetes in the Hoorn Diabetes Care System.

AIMS: To identify HbA1c trajectories after the start of insulin treatment and to identify clinically applicable predictors of the response to insulin therapy. METHODS: The study population comprised 1203 people with Type 2 diabetes included in the Hoorn Diabetes Care System (n = 9849). Inclusion criteria were: age ≥ 40 years; initiation of insulin during follow-up after failure to reach HbA1c levels ≤ 53 mmol/mol (7%) with oral glucose-lowering agents; and a follow up ≥ 2 years after initiating insulin. Latent class growth modelling was used to identify trajectories of HbA1c . Subjects considered to be 'off target' had HbA1c levels ≥ 53 mmol/mol (7.0%) during one-third or more of the follow-up time, and those considered to be 'on target' had HbA1c levels ≥ 53 mmol/mol (7.0%) during less than one-third of the follow-up time. RESULTS: Four HbA1c trajectories were identified. Most people (88.7%) were classified as having a stable HbA1c trajectory of ~57 mmol/mol (7.4%). Only 24.4% of the people were on target in response to insulin; this was associated with lower HbA1c levels and a higher age at the start of insulin treatment. CONCLUSIONS: Using latent class growth modelling, four HbA1c trajectories were identified. A quarter of the people starting insulin were on target. Low HbA1c levels and advanced age at the start of insulin therapy were associated with better response to insulin therapy. Initiating insulin earlier improves the likelihood of achieving and sustaining glycaemic control.

Policy evaluation in diabetes prevention and treatment using a population-based macro simulation model: the MICADO model.

AIMS: To test a simulation model, the MICADO model, for estimating the long-term effects of interventions in people with and without diabetes. METHODS: The MICADO model includes micro- and macrovascular diseases in relation to their risk factors. The strengths of this model are its population scope and the possibility to assess parameter uncertainty using probabilistic sensitivity analyses. Outcomes include incidence and prevalence of complications, quality of life, costs and cost-effectiveness. We externally validated MICADO's estimates of micro- and macrovascular complications in a Dutch cohort with diabetes (n = 498,400) by comparing these estimates with national and international empirical data. RESULTS: For the annual number of people undergoing amputations, MICADO's estimate was 592 (95% interquantile range 291-842), which compared well with the registered number of people with diabetes-related amputations in the Netherlands (728). The incidence of end-stage renal disease estimated using the MICADO model was 247 people (95% interquartile range 120-363), which was also similar to the registered incidence in the Netherlands (277 people). MICADO performed well in the validation of macrovascular outcomes of population-based cohorts, while it had more difficulty in reflecting a highly selected trial population. CONCLUSIONS: Validation by comparison with independent empirical data showed that the MICADO model simulates the natural course of diabetes and its micro- and macrovascular complications well. As a population-based model, MICADO can be applied for projections as well as scenario analyses to evaluate the long-term (cost-)effectiveness of population-level interventions targeting diabetes and its complications in the Netherlands or similar countries.