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The American Journal of Managed Care April 2019
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Deaths Among Opioid Users: Impact of Potential Inappropriate Prescribing Practices
Jayani Jayawardhana, PhD; Amanda J. Abraham, PhD; and Matthew Perri, PhD
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Deaths Among Opioid Users: Impact of Potential Inappropriate Prescribing Practices

Jayani Jayawardhana, PhD; Amanda J. Abraham, PhD; and Matthew Perri, PhD
Inappropriate prescribing practices of opioids are a major risk factor for mortality among opioid users in the Georgia Medicaid population, although risk is lower in managed Medicaid.
METHODS

Data

Individual pharmacy claims data from the Georgia Medicaid pharmacy claims database from 2009 to 2014 were used in the study. If any individual in the study sample during the 2009 to 2014 period died in 2015, that information was included in the data sample. Following extant research, individuals with cancer diagnoses were identified using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes (including 140-172.9, 174-215.9, 217-229.10, 235-239.9, and 338.3) and were excluded from the study sample.2,19,20 The study sample was restricted to individuals who received an opioid prescription (short-acting or long-acting extended-release [LA/ER] opioids) and were aged 18 to 64 years during the study period.

The final study sample included 3,562,227 patient-prescription observations representing 401,488 individuals over the study period. It should be noted that about 8.6% of the sample did not include race/ethnicity information for the study period, although other demographic characteristics, such as age, gender, and insurance type, were included. Therefore, a missing-race category was created to include the individuals with missing race/ethnicity information in the analysis.9

Measures

The key dependent variable of interest was individual deaths among opioid users in the study sample, which was coded as a dichotomous measure (1/0). The key independent variable of interest was whether an individual experienced any incidences of potential inappropriate prescribing practices, which was also coded as a dichotomous measure (1/0). The indicators of potential inappropriate prescribing practices by providers were adapted following previous expert panels and clinical guidelines, and they were measured using the following 5 indicators: (1) overlapping opioid prescriptions, defined as opioid prescriptions that overlap by 7 days or more; (2) overlapping opioid and benzodiazepine prescriptions, defined as opioid and benzodiazepine prescriptions that overlap by 7 days or more; (3) overlapping opioid and buprenorphine-naloxone (BUP-NX; Suboxone) prescriptions, defined as opioid and BUP-NX prescriptions that overlap by 1 day or more; (4) LA/ER opioid prescriptions for acute pain; and (5) high daily doses of opioid prescriptions, defined as receiving more than 100 MME.21-25 The other confounding variables in the analysis included sociodemographic variables of age (continuous), gender (female, male [reference]), race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic other, missing race, non-Hispanic white [reference]), and type of insurance (managed care, FFS [reference]). Because the severity of an individual’s health conditions (comorbidities) could have an impact on their death, we controlled for the severity of their comorbidities using Charlson Comorbidity Index (CCI) scores.26 CCI scores were calculated using ICD-9-CM diagnosis codes in the data. Additional confounding variables, such as number of prescriptions received (opioids, LA/ER opioids, benzodiazepines, and BUP-NX) and whether an individual experienced a diagnosis of acute pain, chronic pain, or OUD, were included in the analysis.

Statistical Analysis

Descriptive statistics of the sample were calculated for all study variables by whether an individual experienced any incidences of potentially inappropriate prescribing practices during the study period. Significance tests were conducted using the t test to examine statistical significance for continuous variables and the χ2 test for categorical variables. To show the variation in number of deaths among opioid users in the sample, the number of deaths was graphed by demographic characteristics age, gender, race/ethnicity, and type of insurance over time. A multivariate logistic regression model was used to examine the association between incidences of potentially inappropriate prescribing practices and deaths among the Medicaid population receiving opioid prescriptions. All statistical analyses were conducted in Stata version 14.2 (StataCorp LP; College Station, Texas).


 
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