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. 2019 Apr 1:197:141-148.
doi: 10.1016/j.drugalcdep.2018.11.034. Epub 2019 Feb 13.

Medicaid trends in prescription opioid and non-opioid use by HIV status

Chelsea Canan  1 , G Caleb Alexander  2 , Richard Moore  3 , Irene Murimi  4 , Geetanjali Chander  3 , Bryan Lau  4
Affiliations

Medicaid trends in prescription opioid and non-opioid use by HIV status

Chelsea Canan et al. Drug Alcohol Depend. .

Abstract

Background: Pain is more common among people living with HIV (PLWH) than their counterparts; however, it is unclear whether analgesic use differs by HIV status.

Methods: We analyzed Medicaid pharmacy claims from adults in 14 US states from 2001 to 2009 to identify opioid and non-opioid analgesic prescriptions and compared prescribing trends by HIV status. We accounted for clinical and demographic differences by using inverse probability weights and by restricting the sample to a subgroup with a common comorbidity, diabetes, chosen for its high prevalence and association with lifestyle and chronic pain. We estimated the incidence of chronic opioid therapy (COT) (≥90 consecutive days with an opioid prescription) among opioid-naïve individuals.

Results: Rates of opioid and non-opioid use increased approximately two-fold from 2001 to 2009. PLWH received approximately twice as many prescriptions as those without HIV. In an unadjusted Cox regression, PLWH were three times more likely to receive COT compared to those without HIV (hazard ratio (HR) = 3.06, 95% CI 2.76-3.39). When restricting to patients with diabetes and adjusting for age, sex, state, comorbidity score, depression, bipolar disorder, and schizophrenia, the HR decreased to 1.26 (95% CI 0.97-1.63).

Conclusions: Higher opioid use among PLWH was largely a function of patients' demographic characteristics and health status. The high incidence of COT among PLWH underscores the importance of practice guidelines that minimize adverse events associated with opioid use.

Keywords: Chronic opioid therapy; HIV; Medicaid; Opioids; Prescription analgesics; Trends.

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Conflict of interest statement

Conflicts of Interest

Dr. Alexander is Chair of FDA’s Peripheral and Central Nervous System Advisory Committee; serves as a paid advisor to IQVIA; serves on the advisory board of MesaRx Innovations; holds equity in Monument Analytics, a health care consultancy whose clients include the life sciences industry as well as plaintiffs in opioid litigation; and is a member of OptumRx’s National P&T Committee. This arrangement has been reviewed and approved by Johns Hopkins University in accordance with its conflict of interest policies. Dr. Moore was a consultant for Medscape. No other authors have conflicts of interest relevant to the contents of this manuscript.

Figures

Figure 1.
Figure 1.
Total number of opioid analgesic prescriptions dispensed from 2001–2009 per 100 people, by HIV status. Left panel: full analytic sample; right panel: restricted to patients with diabetes. Dashed lines depict unadjusted trends; solid lines depict trends weighted by inverse probability of HIV weights and by the inverse probability of censoring.
Figure 2.
Figure 2.
Total number of non-opioid analgesic prescriptions dispensed from 2001–2009 per 100 people, by HIV status. Left panel: full analytic sample; right panel: restricted to patients with diabetes. Dashed lines depict unadjusted trends; solid lines depict trends weighted by inverse probability of HIV weights and by the inverse probability of censoring.
Figure 3.
Figure 3.
Proportion of days covered by an opioid analgesic prescription among individuals with at least one opioid analgesic prescription in the given year, weighted by inverse probability of HIV and by the inverse probability of censoring (left panel: all individuals; right panel: subset with diabetes). The shaded box depicts the interquartile range, the whiskers indicate the minimum and maximum values, the horizontal line shows the median, and the open circle (HIV group) or cross (non-HIV group) shows the mean. The table below the x-axis displays the proportion of individuals in each year with at least one opioid prescription.
Figure 4.
Figure 4.
Proportion of days covered by a non-opioid analgesic prescription among individuals with at least one non-opioid analgesic prescription in the given year, weighted by inverse probability of HIV and by the inverse probability of censoring (left panel: all individuals; right panel: subset with diabetes). The shaded box depicts the interquartile range, the whiskers indicate the minimum and maximum values, the horizontal line shows the median, and the open circle (HIV group) or cross (non-HIV group) shows the mean. The table below the x-axis displays the proportion of individuals in each year with at least one non-opioid prescription.
Figure 5.
Figure 5.
Cumulative incidence of chronic opioid therapy (COT) among a sample of opioidnaïve individuals (left panel) and subset with diabetes (right panel) followed from January 1, 2002 until the first day of COT (defined as the 90th day of consecutive opioid use), Medicaid dis-enrollment, or December 31, 2009, weighted by inverse probability of censoring.
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