Yuanyuan Liang1,2,3, Martin W Goros1, Barbara J Turner4,3,5. 1. *Department of Epidemiology and Biostatistics. 2. Center for Research to Advance Community Health (ReACH), University of Texas Health Science Center at San Antonio (UTHSCSA), San Antonio, Texas. 3. School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas. 4. Center for Research to Advance Community Health (ReACH), University of Texas Health Science Center at San Antonio (UTHSCSA), San Antonio, Texas turner@uthscsa.edu. 5. Department of Medicine, UTHSCSA. San Antonio, Texas, USA.
Abstract
OBJECTIVE: To examine risk factors for drug overdose by sex reflecting differing patterns of opioid and other drug use. DESIGN: National privately insured cohort. SUBJECTS: 206,869 subjects filling ≥2 opioid prescriptions from January 2009 through July 2012. METHODS: Sex-specific prediction models for future drug overdose developed and validated using variables measured within 6 months after starting opioids: demographics, substance use, comorbidities, opioid dose, and psychoactive drugs. Logistic regression and split-sample validation were used. RESULTS: Area under the receiver operating curves (AUCs) for both sex-specific risk models (0.80) were higher (P < 0.001) than for daily opioid dose alone. Risk factors for drug overdose were similar by sex but effects differed. For both sexes, substance use was the strongest predictor but the adjusted odds ratio (AOR) [95% CI] was 5.95 [4.33, 8.06] for women vs. 4.69 [3.24, 6.68] for men. AORs for daily opioid dose rose monotonically in men to 2.42 [1.76, 3.28] for high vs. low dose but were non-monotonic in women with 1.79 [1.35, 2.35] for high dose. AOR for 1-60 days of antidepressants vs. none was significant only in men (1.98 [1.32, 2.9]). AOR for benzodiazepine use was higher in men than women (2.75 vs 2.35, respectively). Zolpidem use was significant only in women. AUCs for sex-specific models were lower for the opposite sex and significantly lower for the men's model in the women's derivation dataset. CONCLUSIONS: These models reveal similar risk factors by sex for drug overdose in opioid users but significant differences in effects that, if validated in other cohorts, may inform differing risk management strategies.
OBJECTIVE: To examine risk factors for drug overdose by sex reflecting differing patterns of opioid and other drug use. DESIGN: National privately insured cohort. SUBJECTS: 206,869 subjects filling ≥2 opioid prescriptions from January 2009 through July 2012. METHODS: Sex-specific prediction models for future drug overdose developed and validated using variables measured within 6 months after starting opioids: demographics, substance use, comorbidities, opioid dose, and psychoactive drugs. Logistic regression and split-sample validation were used. RESULTS: Area under the receiver operating curves (AUCs) for both sex-specific risk models (0.80) were higher (P < 0.001) than for daily opioid dose alone. Risk factors for drug overdose were similar by sex but effects differed. For both sexes, substance use was the strongest predictor but the adjusted odds ratio (AOR) [95% CI] was 5.95 [4.33, 8.06] for women vs. 4.69 [3.24, 6.68] for men. AORs for daily opioid dose rose monotonically in men to 2.42 [1.76, 3.28] for high vs. low dose but were non-monotonic in women with 1.79 [1.35, 2.35] for high dose. AOR for 1-60 days of antidepressants vs. none was significant only in men (1.98 [1.32, 2.9]). AOR for benzodiazepine use was higher in men than women (2.75 vs 2.35, respectively). Zolpidem use was significant only in women. AUCs for sex-specific models were lower for the opposite sex and significantly lower for the men's model in the women's derivation dataset. CONCLUSIONS: These models reveal similar risk factors by sex for drug overdose in opioid users but significant differences in effects that, if validated in other cohorts, may inform differing risk management strategies.
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