Burden of Diabetes Mellitus Estimated with a Longitudinal Population-Based Study Using Administrative Databases

Abstract

ObjectiveTo assess the epidemiologic and economic burden of diabetes mellitus (DM) from a longitudinal population-based study.Research Design and MethodsLombardy Region includes 9.9 million individuals. Its DM population was identified through a data warehouse (DENALI), which matches with a probabilistic linkage demographic, clinical and economic data of different Healthcare Administrative databases. All individuals, who, during the year 2000 had an hospital discharge with a IDC-9 CM code 250.XX, and/or two consecutive prescriptions of drugs for diabetes (ATC code A10XXXX) within one year, and/or an exemption from co-payment healthcare costs specific for DM, were selected and followed up to 9 years. We calculated prevalence, mortality and healthcare costs (hospitalizations, drugs and outpatient examinations/visits) from the National Health Service’s perspective.ResultsWe identified 312,223 eligible subjects. The study population (51% male) had a mean age of 66 (from 0.03 to 105.12) years at the index date. Prevalence ranged from 0.4% among subjects aged ≤45 years to 10.1% among those >85 years old. Overall 43.4 deaths per 1,000 patients per year were estimated, significantly (p<0.001) higher in men than women. Overall, 3,315€/patient-year were spent on average: hospitalizations were the cost driver (54.2% of total cost). Drugs contributed to 31.5%, outpatient claims represented 14.3% of total costs. Thirty-five percent of hospital costs were attributable to cerebro−/cardiovascular reasons, 6% to other complications of DM, and 4% to DM as a main diagnosis. Cardiovascular drugs contributed to 33.5% of total drug costs, 21.8% was attributable to class A (16.7% to class A10) and 4.3% to class B (2.4% to class B01) drugs.ConclusionsMerging different administrative databases can provide with many data from large populations observed for long time periods. DENALI shows to be an efficient instrument to obtain accurate estimates of burden of diseases such as diabetes mellitus.

Medical Costs of Patients with Type 2 Diabetes in a Single Payer System: A Classification and Regression Tree Analysis

“…In this cost-of-illness study we analyzed the annual direct medical costs of a population of patients with T2DM comprising over 70% elderly, a large majority (72.9%) with a duration of illness > 5 years, and 65.6% with two or more chronic conditions. The mean annual medical cost per capita was €3086, consistent with the results of other studies conducted in Italy or in Europe [7,8,11,23,32]. However, the median annual medical cost (€1012) is a more accurate summary measure because the distribution of costs is skewed, with a few patients accounting for a large proportion of costs, in line with other studies [10,12].…”

“…However, the median annual medical cost (€1012) is a more accurate summary measure because the distribution of costs is skewed, with a few patients accounting for a large proportion of costs, in line with other studies [10,12]. We confirmed that hospitalization accounts for the large majority of costs (53.1%) followed by drugs (29.5%) and outpatient services (17.4%) [7,8,11,23].…”

“…A recent study by Marcellusi et al [22] reported that the total economic burden of patients with diabetes in Italy accounts for €20.3 billion/year, of which 54% is attributable to indirect costs and 46% to direct costs. It has been estimated that in Italy the mean annual healthcare cost for patients with diabetes is about €3000 [7,21,23,24]; that is more than twice that of patients without diabetes.…”

Cost-Utility Analysis of Bariatric Surgery in Italy: Results of Decision-Analytic Modelling

“…The costs of the end-stage organ damage health states were obtained from the Italian literature and international studies when no local cost was available [56,57,58,59,60]. Cost data are presented in 2015 in EUR.…”

Evaluating Digital Health Interventions

“…A SMART is a special case of the factorial experiment involving randomization at several stages, where each stage corresponds to one of the decisions that must be made about adapting the intervention, and some or all of the randomization may be contingent on response to treatment. 34, 40, 44 …”