# Leveraging large-scale national data to understand, reduce, and prevent benzodiazepine-related harms among older adults

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $348,477

## Abstract

Benzodiazepine (BZD) use in the U.S. is common and increases with age. In a recent analysis, 8.7% of adults
aged 65-80 years were prescribed BZDs during one year, even though a robust set of studies have established
their association with a variety of adverse outcomes in older adults, including increased risk of falls and
fractures, motor vehicle accidents, impaired cognition, and pharmaceutical overdose. Patients and their
providers are then reluctant to change use once started, which may account for why older adults experience
the highest rates of long-term BZD use. Relatively little is known about the patient, provider, and community
characteristics associated with starting and continuing BZD prescribing to older adults, yet this is critical to
develop effective selective and indicated prevention strategies. In Aim 1, we will describe the patient,
provider, and community characteristics associated with BZD initiation and continuation using a
national 20% sample of Medicare beneficiaries (n=3.6 million) linked to provider data from the American
Medical Association (AMA) Physician Masterfile and community characteristics from the Area Health
Resources File (AHRF). We will extend our analysis with OptumInsight data (n=6.7 million) to gain additional
insights among commercially insured adults aged 50-64 given increased substance use among the Baby
Boom cohort. Those patients currently prescribed BZDs and most at risk for BZD misuse (e.g., overlapping
BZD prescriptions from multiple providers) and BZD-related overdose should receive indicated prevention
strategies to address this potentially harmful use. In Aim 2, among those prescribed BZD, we will
determine specific risk factors associated with BZD misuse and BZD-related overdose; these data will
be used to develop a misuse clinical prediction tool. Using BZD users 50+ years old identified in Medicare
and Optum, we will determine characteristics of patients and their prescribed BZD (e.g., high potency) most
associated with misuse and overdose. We will then use machine learning to create a simple clinical prediction
tool that providers can use to identify older adults at risk for misuse in their practices. Finally, in Aim 3 we will
conduct semi-structured interviews with providers and patients to package and script the use of the
clinical prediction tool for providers seeking to engage high-risk BZD use patients. This aim is critical to
improve the impact of our findings since psychological dependence on BZD can make reducing use a difficult
topic for physicians and patients to address. We will conduct interviews with providers and older adult primary
care patients (n=15 each) to obtain feedback to package and script the use of the clinical prediction tool, which
we will make publicly available by website. The impact of our work will be to: 1) provide a detailed, national
portrait of the factors that contribute to BZD use and misuse; 2) determine the older adults most at risk for
serious adverse e...

## Key facts

- **NIH application ID:** 10113574
- **Project number:** 5R01DA045705-04
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** DONOVAN T MAUST
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $348,477
- **Award type:** 5
- **Project period:** 2018-05-15 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10113574

## Citation

> US National Institutes of Health, RePORTER application 10113574, Leveraging large-scale national data to understand, reduce, and prevent benzodiazepine-related harms among older adults (5R01DA045705-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10113574. Licensed CC0.

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