Prescription opioid abuse is a serious and growing problem in the United States. At any time, 15% of VA primary care patients are prescribed opioid medication, thus exposing a large population to the potentially deadly or disabling harms of opioid misuse. Identifying patients at a high risk for prescription opioid abuse can help providers improve patient outcomes and continue to provide adequate pain management.
The primary objective of this project was to identify risk factors for patients having problems with prescription opioids (e.g. prescription opioid abuse). These risk factors can then be incorporated into screening tools or extracted from the VA electronic medical record (EMR) to identify patients at high risk for problematic opioid use. A computerized decision support system, such as the existing ATHENA-Opioid Therapy, could be used to warn primary care clinicians that a patient is high-risk and guide them through safe prescribing practices.
Patients at the VA Palo Alto who received a prescription for an opioid within the last 12 months were eligible to participate. Almost 200 patients chose to participate out of the more than 1,200 patients who were invited by their primary care physicians (a response rate of 17%). Subjects were interviewed for an hour and a half, completed 15 assessments, and received a $40 gift card. Subjects were coded as using their medication appropriately, over using it, or under using it. A large number of potential risk factors (47) were considered using the Receiver Operating Characteristic (ROC) methodology.
We first attempted to predict appropriate users (n=109) as compared to non-appropriate users (under (n=37) and over users (n=13)). The most powerful predictor of using medications appropriately was being age 60 or over. Among younger patients, those reporting a high level of fear of activity or re-injury were more likely to use appropriately, particularly if they reported relatively low-levels of pain-related depression and anxiety.
The second analysis compared under users to all other users. The most powerful predictor of under-using medications was expressing a dislike of all medications. Among those who disliked medications, those who admitted having trouble taking their medication as prescribed were more likely to under-use. When the adherence measure was removed, we found that self-reported non-white ethnicity was the next best predictor of under-use among those who dislike medications.
The third analysis compared over users to all other users. Previously developed assessments of opioid abuse were found to best predict over-use. The most powerful predictor of over-using medications was a high score on the Addiction Behavior Checklist (ABC). When this variable was removed, the most powerful predictor was a high score on the Screener and Opioid Assessment for Patients with Pain (SOAPP). When both these variables were removed, extremely high levels of pain related negative affect was the most powerful predictor of over use.
It is worth noting that none of the measures of substance use problems was a powerful predictor of either over or under use, although the ABC and SOAPP incorporate this information into their calculations.
This research project can substantially impact how the VA prescribes opioids by identifying simple steps providers can take to identify patients at risk of over or under using their medications. Risk factors that have been preliminarily identified are simple and non-judgmental about any previous behaviors, and do not require time-consuming assessments by providers.
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