Aims: Both survey and healthcare register data struggle as data sources to capture the phenomenon of alcohol problems. We study a large group of people for whom survey data and two types of register data are available, and examine the overlaps of similar or related measures in the different data sources to learn about potential weaknesses in each. We also examine how register-based data on the prevalence of alcohol problems change depending on which register data are used.
View Article and Find Full Text PDFAims: Alcohol use disorders (AUDs) are associated with high risk of comorbidities and excess use of social and healthcare services. We examined health service use (HSU) frequencies of patients with AUD in comparison to those with type 2 diabetes mellitus (T2DM).
Design: A random sample of individuals with AUD ( = 396) were identified based on ICD-10 codes and HSU patterns, morbidity and mortality were compared with age- and gender-matched T2DM controls ( = 792) using logistic regression analysis.
Objectives: To examine the direct effects of risk factors associated with the 5-year costs of care in persons with alcohol use disorder (AUD) and to examine whether remission decreases the costs of care.
Methods: Based on Electronic Health Record data collected in the North Karelia region in Finland from 2012 to 2016, we built a non-causal augmented naïve Bayesian (ANB) network model to examine the directional relationship between 16 risk factors and the costs of care for a random cohort of 363 AUD patients. Jouffe's proprietary likelihood matching algorithm and van der Weele's disjunctive confounder criteria (DCC) were used to calculate the direct effects of the variables, and sensitivity analysis with tornado diagrams and analysis maximizing/minimizing the total cost of care were conducted.
Objective: Alcohol use disorders (AUDs) are associated with high social and health care costs. We compare the direct social and health care costs of patients with AUDs, according to four service use profiles: (a) AUD treatment, (b) mental health (MH) treatment, (c) AUD + MH treatment, (d) no treatment. A separate analysis of the costliest 10% is included.
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