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01.12.2015 | Research article | Ausgabe 1/2015 Open Access

BMC Public Health 1/2015

Modelling the consequences of a reduction in alcohol consumption among patients with alcohol dependence based on real-life observational data

Zeitschrift:
BMC Public Health > Ausgabe 1/2015
Autoren:
Nora Rahhali, Aurélie Millier, Benjamin Briquet, Philippe Laramée, Samuel Aballéa, Mondher Toumi, Clément François, Jürgen Rehm, Jean-Bernard Daeppen
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12889-015-2606-4) contains supplementary material, which is available to authorized users.

Competing interests

NR, PL and CF are employees of Lundbeck SA, and AM, BB, SA are employees of Creativ-Ceutical, who were contracted by Lundbeck to support the study. JR received grants from WHO, GWT-TUD and Lundbeck, and received personal fees and participated as a board member (Nalmefene) for Lundbeck, all outside the current submitted work. J-BD received honoraria from Lundbeck for conferences and advisory boards and has no other conflicts of interest. MT received honoraria from Lundbeck.

Authors’ contributions

JR contributed to the methodology of establishing risk relations between different dimensions of alcohol consumption and disease outcomes, and to the final manuscript. J-BD contributed to the study design, collection of patient data, and to the final manuscript. CF, PL, NR, SA, AM and MT contributed to the design of the microsimulation model. SA and AM developed the microsimulation model. BB and AM conducted the statistical analyses from the microsimulation model. BB, AM and NR contributed to the draft manuscript. All authors approved the final article.

Abstract

Background

Most available pharmacotherapies for alcohol-dependent patients target abstinence; however, reduced alcohol consumption may be a more realistic goal. Using randomized clinical trial (RCT) data, a previous microsimulation model evaluated the clinical relevance of reduced consumption in terms of avoided alcohol-attributable events. Using real-life observational data, the current analysis aimed to adapt the model and confirm previous findings about the clinical relevance of reduced alcohol consumption.

Methods

Based on the prospective observational CONTROL study, evaluating daily alcohol consumption among alcohol-dependent patients, the model predicted the probability of drinking any alcohol during a given day. Predicted daily alcohol consumption was simulated in a hypothetical sample of 200,000 patients observed over a year. Individual total alcohol consumption (TAC) and number of heavy drinking days (HDD) were derived. Using published risk equations, probabilities of alcohol-attributable adverse health events (e.g., hospitalizations or death) corresponding to simulated consumptions were computed, and aggregated for categories of patients defined by HDDs and TAC (expressed per 100,000 patient-years). Sensitivity analyses tested model robustness.

Results

Shifting from >220 HDDs per year to 120–140 HDDs and shifting from 36,000-39,000 g TAC per year (120–130 g/day) to 15,000–18,000 g TAC per year (50–60 g/day) impacted substantially on the incidence of events (14,588 and 6148 events avoided per 100,000 patient-years, respectively). Results were robust to sensitivity analyses.

Conclusions

This study corroborates the previous microsimulation modeling approach and, using real-life data, confirms RCT-based findings that reduced alcohol consumption is a relevant objective for consideration in alcohol dependence management to improve public health.
Zusatzmaterial
Additional file 1: Table S1a. Deterministic sensitivity analysis (risk parameters) - Confidence intervals of number of events per 100,000 patient-years by HDD category. Table S1b. Deterministic sensitivity analysis (risk parameters) - Confidence intervals of number of events per 100,000 patient-years by TAC category. (ZIP 30 kb)
12889_2015_2606_MOESM1_ESM.zip
Additional file 2: Table S2a. Probabilisitic sensitivity analysis (alcohol consumption simulation coefficients) - Confidence intervals of number of events per 100,000 patient-years by HDD category. Table S2b. Probabilisitic sensitivity analysis (alcohol consumption simulation coefficients) - Confidence intervals of number of events per 100,000 patient-years by TAC category. (ZIP 30 kb)
12889_2015_2606_MOESM2_ESM.zip
Literatur
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