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The American Journal of Managed Care December 2019
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Benzodiazepine and Unhealthy Alcohol Use Among Adult Outpatients
Matthew E. Hirschtritt, MD, MPH; Vanessa A. Palzes, MPH; Andrea H. Kline-Simon, MS; Kurt Kroenke, MD; Cynthia I. Campbell, PhD, MPH; and Stacy A. Sterling, DrPH, MSW
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Benzodiazepine and Unhealthy Alcohol Use Among Adult Outpatients

Matthew E. Hirschtritt, MD, MPH; Vanessa A. Palzes, MPH; Andrea H. Kline-Simon, MS; Kurt Kroenke, MD; Cynthia I. Campbell, PhD, MPH; and Stacy A. Sterling, DrPH, MSW
Among outpatients who were screened for alcohol use, those with unhealthy alcohol use, women, and those who were older, white, and of lower socioeconomic status were more likely to use benzodiazepines.
Outcome: Benzodiazepine Use

We included outpatient dispensations for benzodiazepines filled at KPNC pharmacies in the 12 months centered around a patient’s first AVS screening visit in a primary care clinic. We extracted data for oral formulations of all benzodiazepine-containing medications (ie, alprazolam, chlordiazepoxide, clobazam, clonazepam, clorazepate, diazepam, estazolam, flurazepam, lorazepam, midazolam, oxazepam, temazepam, and triazolam) dispensed during the study period. For each patient, we assessed the following prescription attributes: (1) whether a benzodiazepine had been dispensed; (2) the duration of the benzodiazepine prescriptions (calculated by summing the total number of days supplied in the study period20); and (3) the mean daily benzodiazepine dose, expressed in mean lorazepam-equivalent daily dose (LEDD), which is an average over the 12-month study period (eAppendix Methods and eAppendix Table 1).

Covariates

Patient sex, age, and race/ethnicity (Asian, black/African American, Hispanic, white, and other/unknown) were extracted from the EHR at the time of the first AVS screening. We estimated household income from the United States Census 2010 median household income data by geocoding patients’ residential addresses to Census blocks.21,22 Using International Classification of Diseases, Ninth Edition and International Classification of Diseases, Tenth Edition, Clinical Modification codes, we assessed for the presence of the following encounter-associated codes in the study period: anxiety disorders, insomnia, musculoskeletal pain, alcohol use disorders (including alcohol-related psychoses, dependence, and abuse; excluding conditions in remission), and epilepsy (eAppendix Table 2). In addition, we estimated patients’ medical comorbidity burden using the Charlson Comorbidity Index score, which estimates 1-year mortality risk based on a weighted score of 17 conditions.23-25

To determine whether a patient engaged in unhealthy alcohol use, we used EHR-documented alcohol screening data collected as part of the AVS protocol during the study period. During the primary care visit “rooming” process, medical assistants asked all patients to estimate within the past 90 days (1) the mean number of days per week they consumed alcohol and (2) how many alcoholic drinks they consumed “on a typical drinking day.” Using these variables, the EHR calculates the mean number of alcoholic drinks consumed weekly (ie, mean number of drinking days per week multiplied by the number of drinks consumed on a typical drinking day).26 Based on guidelines established by the National Institute on Alcohol Abuse and Alcoholism,6 we defined drinking severity as “low-risk alcohol use” (0-14 drinks per week for men ≤65 years or 0-7 drinks per week for all women and for men >65 years) or “unhealthy alcohol use” (>14 drinks per week for men ≤65 years or >7 drinks per week for all women and for men >65 years).

Analytic Approach

We applied χ2 and Wilcoxon rank-sum tests to examine differences in categorical and continuous patient demographic and clinical characteristic variables (including presence of unhealthy alcohol use) by whether the patient filled a prescription for a benzodiazepine in the 12 months centered around their first AVS screening visit. We fit multivariable logistic regression models to estimate the adjusted odds ratios (AORs) and corresponding 95% CIs of being dispensed a benzodiazepine by patient characteristics. Among patients who were dispensed a benzodiazepine during the study period, we fit log-linear and negative binomial regression models (accounting for positive skew and overdispersion of the data) to estimate the adjusted rate ratios (ARRs) of mean LEDD and duration of their prescriptions, respectively, by patient characteristics. All analyses were performed using SAS version 9.4 (SAS Institute Inc; Cary, North Carolina). Significance was assessed at 2-sided P <.05.

RESULTS

Cohort Characteristics

Approximately 87% of eligible KPNC members during the defined study period completed 1 or more AVS screenings, and 70% of the benzodiazepine prescriptions were ordered by a primary care provider (ie, internal, family, preventive, or adolescent medicine or pediatrics). Among KPNC members included in these analyses, approximately 97% had a continuous prescription benefit for KPNC pharmacies during the 12-month study period. The analytic cohort consisted of 2,089,525 patients (median [interquartile range] age = 49.0 [29.0] years; 53.7% female; 50.0% white; 4.0% unhealthy alcohol users) after excluding patients without continuous KPNC coverage in the 12 months centered around the first AVS screening (n = 803,991) and patients with missing Census-derived estimated household income data (n = 1390) (Table 1).


 
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