Healthc Inform Res.  2020 Apr;26(2):129-145. 10.4258/hir.2020.26.2.129.

Factors Associated with 5-Year Costs of Care among a Cohort of Alcohol Use Disorder Patients: A Bayesian Network Model

• Rautiainen E
• Ryynänen O
• Laatikainen T
• Kekolahti

• KMID: 2501338
• DOI: http://doi.org/10.4258/hir.2020.26.2.129

Abstract

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.

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.

The highest direct effect on the total cost of care was observed for a number of chronic conditions, indicating on average more than a €26,000 increase in the 5-year mean cost for individuals with multiple ICD-10 diagnoses compared to individuals with less than two chronic conditions. Remission had a decreasing effect on the total cost accumulation during the 5-year follow-up period; the percentage of the lowest cost quartile (42.9% vs. 23.9%) increased among remitters, and that of the highest cost quartile (10.71% vs. 26.27%) decreased compared with current drinkers.

The ANB model with application of DCC identified that remission has a favorable causal effect on the total cost accumulation. A high number of chronic conditions was the main contributor to excess cost of care, indicating that comorbidity is an essential mediator of cost accumulation in AUD patients.

Keyword

Bayes Theorem; Causality; Alcohol-Related Disorders; Health Care Costs; Costs and Cost Analysis

Figure

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  Figure 1 Diagram of sampling and data extraction process. ICD-10: the 10th revision of the International Statistical Classification of Diseases and Related Health Problems, EHR: Electronic Health Record.

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  Figure 2 The augmented naive Bayes model of factors associated with the outcome variable total costs (totalcost_2012–2016). Node sizes express each variable’s direct effect on the target node. Node colors indicate node force, with green being the highest and red lowest, and yellow in between. Lines between nodes indicate the relationship between them (Kullback-Leibler divergence).

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  Figure 3 Panels showing the outcome variable “total cost_2012–2016” in relation to drinking “status2012”. Cost quartiles include: low costs, ≤€4,486.54; medium cost, €4,486.55–€15,746.10; high cost, €15,746.11–€46,864.36; and very high cost, €46,864.37–€1,180,863.75 and drinking status in 2012 was defined as continuous drinking versus remitted. In Panel 1, both variables are unfixed. Panel 2 shows the distribution of costs in the outcome variable “totalcost_2012–2016” when the variable “status2012” is fixed for the value drinking=100% and all other variables (not shown) are fixed to their original distribution. In Panel 3, the variable “status2012” is fixed for the value remitted=100%, demonstrating the causal change in costs (totalcost_2012–2016) after achieving remission.

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  Figure 4 Tornado diagrams showing variables with the strongest impact on the outcome variable. Bars pointing to the right represent a positive impact, and bars to the left a negative impact. (A) Panel 1 shows the effect on “low cost” value of the outcome variable, (B) Panel 2 on “medium cost”, (C) Panel 3 on “high cost”, and (D) Panel 4 on “very high cost”.

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