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. 2013 Jul 17;8(7):e69241.
doi: 10.1371/journal.pone.0069241. Print 2013.

Estimating the prevalence of opioid diversion by "doctor shoppers" in the United States

Douglas C McDonald  1 , Kenneth E Carlson
Affiliations

Estimating the prevalence of opioid diversion by "doctor shoppers" in the United States

Douglas C McDonald et al. PLoS One. .

Abstract

Background: Abuse of prescription opioid analgesics is a serious threat to public health, resulting in rising numbers of overdose deaths and admissions to emergency departments and treatment facilities. Absent adequate patient information systems, "doctor shopping" patients can obtain multiple opioid prescriptions for nonmedical use from different unknowing physicians. Our study estimates the prevalence of doctor shopping in the US and the amounts and types of opioids involved.

Methods and findings: The sample included records for 146.1 million opioid prescriptions dispensed during 2008 by 76% of US retail pharmacies. Prescriptions were linked to unique patients and weighted to estimate all prescriptions and patients in the nation. Finite mixture models were used to estimate different latent patient populations having different patterns of using prescribers. On average, patients in the extreme outlying population (0.7% of purchasers), presumed to be doctor shoppers, obtained 32 opioid prescriptions from 10 different prescribers. They bought 1.9% of all opioid prescriptions, constituting 4% of weighed amounts dispensed.

Conclusions: Our data did not provide information to make a clinical diagnosis of individuals. Very few of these patients can be classified with certainty as diverting drugs for nonmedical purposes. However, even patients with legitimate medical need for opioids who use large numbers of prescribers may signal dangerously uncoordinated care. To close the information gap that makes doctor shopping and uncoordinated care possible, states have created prescription drug monitoring programs to collect records of scheduled drugs dispensed, but the majority of physicians do not access this information. To facilitate use by busy practitioners, most monitoring programs should improve access and response time, scan prescription data to flag suspicious purchasing patterns and alert physicians and pharmacists. Physicians could also prevent doctor shopping by adopting procedures to screen new patients for their risk of abuse and to monitor patients' adherence to prescribed treatments.

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Conflict of interest statement

Competing Interests: Prescription LRx Data, 2008 was obtained by Abt Associates under license from IMS Health Incorporated; all rights reserved. Both authors are employed by Abt Associates Inc. There are no further patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.

Figures

Figure 1
Figure 1. Observed and Predicted Numbers of Different Prescribers, Assuming One and Three Populations of Opioid Purchasers.
Panel A illustrates that the number of different prescribers predicted using a Poisson model with only a single population shows that the assumption of a single patient population underestimates the number of different prescribers actually observed. Prediction based on the assumption that the population is a mixture of three different populations having distinct distributions of number of different prescribers fits the observed data very closely (Panel B). Source: Computed from LRx Data, 2008 obtained from IMS Health, Incorporated.
Figure 2
Figure 2. Observed Distributions of Number of Opioid Prescribers for Three Populations of Patients, by Payment Method.
Figure shows the distribution of actual rates of using different prescribers for patients predicted to be in each of the latent populations, showing patients who purchased all prescriptions with insurance separately from those who paid cash for at least one prescription. The distributions of observed rates conformed quite closely to the predicted distributions for the different populations, showing random variation around the mean rate in each. The vertical axis is scaled so that the area of each graph equals 1. Source: Computed from LRx Data, 2008 obtained from IMS Health, Incorporated.
Figure 3
Figure 3. Probability of Membership and Average Number of Different Prescribers in 3 Latent Populations, by Age.
The predicted probability of being in each of the three latent patient populations varied according to patients' age (Panel A), with the probability of being in the doctor shopping population (3- extreme) highest among patients in their thirties. Patients in this extreme population (presumed doctor shoppers) obtained prescriptions from many more different prescribers than patients in other populations (Panel B). The average number of different prescribers used by patients predicted to be in the doctor shopper population (3) peaked in the 30–45 age range. Source: Computed from LRx Data, 2008 obtained from IMS Health, Incorporated.
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References

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