Alan G. White

OBJECTIVE: To (a) describe the demographics of opioid abusers; (b) compare the prevalence rates of selected comorbidities and the medical and drug utilization patterns of opioid abusers with patien...

Prescription opioid abuse and addiction are serious problems with growing societal and medical costs, resulting in billions of dollars of excess costs to private and governmental health insurers annually. Although difficult to accurately assess, prescription opioid abuse also leads to increased insurance costs in the form of property and liability claims, and costs to state and local governments for judicial, emergency, and social services. This manuscript's objective is to provide payers with strategies to control these costs, while supporting safe use of prescription opioid medications for patients with chronic pain. A Tufts Health Care Institute Program on Opioid Risk Management meeting was convened in June 2010 with private and public payer representatives, public health and law enforcement officials, pain specialists, and other stakeholders to present research and develop recommendations on solutions that payers might implement to combat this problem. While protecting access to prescription opioids for patients with pain, private and public payers can implement strategies to mitigate financial risks associated with opioid abuse, using internal strategies such as formulary controls, claims data surveillance, and claims matching; and external policies and procedures that support and educate physicians on reducing opioid risks among patients with chronic pain. Reimbursement policies, incentives, and health technology systems that encourage physicians to use universal precautions, to consult prescription monitoring program (PMP) data, and to implement Screening, Brief Intervention, and Referral to Treatment protocols have a high potential to reduce insurer risks while addressing a serious public health problem.

To assess the feasibility of using medical and prescription drug claims data to develop models that identify patients at risk for prescription opioid abuse or misuse. Deidentified prescription drug and medical claims for approximately 632,000 privately insured patients in Maine from 2005 to 2006 were used. Patients receiving prescription opioids were divided into 2 mutually exclusive groups, namely, prescription opioid abusers and nonabusers. Potential risk factors for prescription opioid abuse were incorporated into logistic models to identify their effects on the probability that a prescription opioid user was diagnosed as having prescription opioid abuse. Different models were based on data available to prescription monitoring programs and managed care organizations. Best-fitting models were identified based on statistical significance (P <or=.05), parsimony, clinical relevance, and area under the receiver operating characteristic curve. The drug claims models found that the following factors (measured over a 3-month period) were associated with risk for prescription opioid abuse: age 18 to 34 years, male sex, 4 or more opioid prescriptions, opioid prescriptions from 2 or more pharmacies, early prescription opioid refills, escalating morphine sulfate dosages, and opioid prescriptions from 2 or more physicians. The model integrating drug and medical claims found that the following factors (measured over a 12-month period) were associated with risk for prescription opioid abuse or misuse: age 18 to 24 years, male sex, 12 or more opioid prescriptions, opioid prescriptions from 3 or more pharmacies, early prescription opioid refills, escalating morphine dosages, psychiatric outpatient visits, hospital visits, and diagnoses of nonopioid substance abuse, depression, posttraumatic stress disorder, and hepatitis. Using drug and medical claims data, it is feasible to develop models that could assist prescription-monitoring programs, payers, and healthcare providers in evaluating patient characteristics associated with elevated risk for prescription opioid abuse.

Objective: To assess the health care utilization and cost of illness for osteoarthritis (OA) patients taking pain medications. Specifically, the goals were to estimate the direct health care and indirect costs of OA. Methods: A claims database of privately insured patients was used to identify OA patients. Prescription drug pain treatments included tramadol, cyclooxygenase-II inhibitors, and nonsteroidal anti-inflammatory drugs. Mean annual per patient costs were calculated from an employer’s perspective. Results: OA patients were prescribed multiple drugs simultaneously and/or sequentially to manage pain. OA patients had a number of prevalent comorbid conditions. Average annual direct medical, drug, and indirect work loss costs were $8601, $2941, and $4603, respectively. Conclusions: There was a substantial payer burden associated with OA resulting from the drug, medical, and disability costs and OA-related comorbidities and high concomitant medication utilization.

We sought to compare the medical services utilization and average annual direct (ie, medical and medication) and indirect (ie, work-loss) costs for employees with painful conditions to a random sample (“average employee”). An employer administrative claims database (approximately 600,000 insured lives) was used to identify patients with ICD-9 codes for painful conditions. Direct costs were total employer medical and medication costs and indirect costs were associated with medically related absenteeism days and disability claims. Among employees with painful conditions, total costs were 1.5 to 3.5 times as high (P < 0.01) as those of the average employee (ie, $7088 to $16,874 compared with $4,849) depending on the painful condition. Painful conditions are costly to employers, and better management/treatment of such conditions may help reduce the associated employer economic burden.

OBJECTIVE: To (a) describe the demographics of opioid abusers; (b) compare the prevalence rates of selected comorbidities and the medical and drug utilization patterns of opioid abusers with patients from a control group, for the period from 1998 to 2002; and (c) calculate the mean annual per-patient total health care costs (e.g., inpatient, outpatient, emergency room, drug, other) from the perspective of a private payer. METHODS: An administrative database of medical and pharmacy claims from 1998 to 2002 of 16 self-insured employer health plans with approximately 2 million lives was used to identify opioid abusers patients with claims associated with ICD-9-CM (International Classification of Diseases, 9th Revision, Clinical Modification) codes for opioid abuse (304.0, 304.7, 305.5, and 965.0 [excluding 965.01]). A control group of nonabusers was selected using a matched sample (by age, gender, employment status, and census region) in a 3:1 ratio. Per-patient annual health care costs (mean total medical a...