# Using patient-level decision modeling to improve use of treatments for alcohol use disorder

> **NIH NIH R01** · CLEVELAND CLINIC LERNER COM-CWRU · 2024 · $635,563

## Abstract

Project Summary
Alcohol use disorder (AUD), previously alcohol abuse and dependence, is a significant public health problem.
In the US, excessive alcohol use is responsible for 93,000 deaths and 2.7 million years of potential life lost, at
a cost of $250 billion annually. In 2012-13, the 12-month and life-time prevalence of AUD in US adults was
14% and 29%, respectively, increasing by more than 40% from a decade earlier. Since the Covid-19
pandemic, alcohol consumption has increased even more. Excessive alcohol consumption affects various
bodily organs, leading to many health consequences. Fortunately, AUD is more treatable than commonly
believed. Cognitive behavioral therapy, brief interventions, and motivational enhancement therapy are the most
widely studied behavioral interventions and are similarly efficacious, while Alcoholics Anonymous (AA) is the
most sought out mutual support group. Pharmacotherapy can be combined with behavioral interventions to
increase success. However, treatments for AUD are underutilized. The misconception among patients and
physicians that treatment is ineffective is an important obstacle to use. Another is the common view that
treatment success requires total abstinence. In reality, recovery from AUD can include some heavy drinking,
and AUD treatment can be effective. Expressing treatment effectiveness into long-term health benefits could
help convince physicians to offer treatment and encourage patients to use it. However, randomized controlled
trials (RCTs) alone cannot provide this information because they do not measure long-term health outcomes,
nor do they compare all treatments head-to-head, making it difficult to choose among them. Simulation
modeling offers a comprehensive approach to comparing treatments, but none of existing AUD models were
designed to assess all treatments. We designed a decision aid (DA) describing treatment options, but it does
not yet include estimates of long-term benefits due to lack of such data at the time of development. Project
objective: To develop, validate, and apply a computer simulation model to inform policy makers, physicians,
and patients of the lifetime benefit of treatments for AUD. Aim 1: Develop and validate a microsimulation model
of the natural history of alcohol-associated complications accounting for the change in drinking behaviors and
AUD status over time. Aim 2: Assess the comparative effectiveness of AUD treatments. Aim 3: Incorporate
patient and provider feedback to assess the clarity, content, and acceptability of augmenting an existing DA
with comparative effectiveness data. Impact: Our model will offer a better understanding of the expected
benefit of AUD treatments and provide an innovative approach to decision modeling and treatment selection.
By incorporating long-term effectiveness data into an existing DA, our study will provide physicians and
patients with valuable evidence and a tool to support selection of optimal treatments, thereby helping to ...

## Key facts

- **NIH application ID:** 10929990
- **Project number:** 5R01AA030970-02
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** Phuc Le
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $635,563
- **Award type:** 5
- **Project period:** 2023-09-15 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10929990

## Citation

> US National Institutes of Health, RePORTER application 10929990, Using patient-level decision modeling to improve use of treatments for alcohol use disorder (5R01AA030970-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10929990. Licensed CC0.

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