Bradley C. Martin

Trends in use of opioids for non-cancer pain conditions 2000-2005 in commercial and Medicaid insurance plans: The TROUP Study

&NA; Opioids are widely prescribed for non‐cancer pain conditions (NCPC), but there have been no large observational studies in actual clinical practice assessing patterns of opioid use over extended periods of time. The TROUP (Trends and Risks of Opioid Use for Pain) study reports on trends in opioid therapy for NCPC in two disparate populations, one national and commercially insured population (HealthCore plan data) and one state‐based and publicly‐insured (Arkansas Medicaid) population over a six year period (2000–2005). We track enrollees with the four most common NCPC conditions: arthritis/joint pain, back pain, neck pain, headaches, as well as HIV/AIDS. Rates of NCPC diagnosis and opioid use increased linearly during this period in both groups, with the Medicaid group starting at higher rates and the HealthCore group increasing more rapidly. The proportion of enrollees receiving NCPC diagnoses increased (HealthCore 33%, Medicaid 9%), as did the proportion of enrollees with NCPC diagnoses who received opioids (HealthCore 58%, Medicaid 29%). Cumulative yearly opioid dose (in mg. morphine equivalents) received by NCPC patients treated with opioids increased (HealthCore 38%, Medicaid 37%) due to increases in number of days supplied rather than dose per day supplied. Use of short‐acting Drug Enforcement Administration Schedule II opioids increased most rapidly, both in proportion of NCPC patients treated (HealthCore 54%, Medicaid 38%) and in cumulative yearly dose (HealthCore 95%, Medicaid 191%). These trends have occurred without any significant change in the underlying population prevalence of NCPC or new evidence of the efficacy of long‐term opioid therapy and thus likely represent a broad‐based shift in opioid treatment philosophy.

Good and poor adherence: optimal cut-point for adherence measures using administrative claims data

ABSTRACT Objective: To identify the adherence value cut-off point that optimally stratifies good versus poor compliers using administratively derived adherence measures, the medication possession ratio (MPR) and the proportion of days covered (PDC) using hospitalization episode as the primary outcome among Medicaid eligible persons diagnosed with schizophrenia, diabetes, hypertension, congestive heart failure (CHF), or hyperlipidemia. Research design and methods: This was a retrospective analysis of Arkansas Medicaid administrative claims data. Patients ≥18 years old had to have at least one ICD-9-CM code for the study diseases during the recruitment period July 2000 through April 2004 and be continuously eligible for 6 months prior and 24 months after their first prescription for the target condition. Adherence rates to disease-specific drug therapy were assessed during 1 year using MPR and PDC. Main outcome measure and analysis scheme: The primary outcome measure was any-cause and disease-related hospitalization. Univariate logistic regression models were used to predict hospitalizations. The optimum adherence value was based on the adherence value that corresponded to the upper most left point of the ROC curve corresponding to the maximum specificity and sensitivity. Results: The optimal cut-off adherence value for the MPR and PDC in predicting any-cause hospitalization varied between 0.63 and 0.89 across the five cohorts. In predicting disease-specific hospitalization across the five cohorts, the optimal cut-off adherence values ranged from 0.58 to 0.85. Conclusions: This study provided an initial empirical basis for selecting 0.80 as a reasonable cut-off point that stratifies adherent and non-adherent patients based on predicting subsequent hospitalization across several highly prevalent chronic diseases. This cut-off point has been widely used in previous research and our findings suggest that it may be valid in these conditions; it is based on a single outcome measure, and additional research using these methods to identify adherence thresholds using other outcome metrics such as laboratory or physiologic measures, which may be more strongly related to adherence, is warranted.

Smartphone medication adherence apps: Potential benefits to patients and providers

OBJECTIVES To provide an overview of medication adherence, discuss the potential for smartphone medication adherence applications (adherence apps) to improve medication nonadherence, evaluate features of adherence apps across operating systems (OSs), and identify future opportunities and barriers facing adherence apps. PRACTICE DESCRIPTION Medication nonadherence is a common, complex, and costly problem that contributes to poor treatment outcomes and consumes health care resources. Nonadherence is difficult to measure precisely, and interventions to mitigate it have been largely unsuccessful. PRACTICE INNOVATION Using smartphone adherence apps represents a novel approach to improving adherence. This readily available technology offers many features that can be designed to help patients and health care providers improve medication-taking behavior. MAIN OUTCOME MEASURES Currently available apps were identified from the three main smartphone OSs (Apple, Android, and Blackberry). In addition, desirable features for adherence apps were identified and ranked by perceived importance to user desirability using a three-point rating system: 1, modest; 2, moderate; or 3, high. The 10 highest-rated apps were installed and subjected to user testing to assess app attributes using a standard medication regimen. RESULTS 160 adherence apps were identified and ranked. These apps were most prevalent for the Android OS. Adherence apps with advanced functionality were more prevalent on the Apple iPhone OS. Among all apps, MyMedSchedule, MyMeds, and RxmindMe rated the highest because of their basic medication reminder features coupled with their enhanced levels of functionality. CONCLUSION Despite being untested, medication apps represent a possible strategy that pharmacists can recommend to nonadherent patients and incorporate into their practice.

Risks for opioid abuse and dependence among recipients of chronic opioid therapy: Results from the TROUP Study

OBJECTIVE To estimate the prevalence of and risk factors for opioid abuse/dependence in long-term users of opioids for chronic pain, including risk factors for opioid abuse/dependence that can potentially be modified to decrease the likelihood of opioid abuse/dependence, and non-modifiable risk factors for opioid abuse/dependence that may be useful for risk stratification when considering prescribing opioids. METHODS We used claims data from two disparate populations, one national, commercially insured population (HealthCore) and one state-based, publicly insured (Arkansas Medicaid). Among users of chronic opioid therapy, we regressed claims-based diagnoses of opioid abuse/dependence on patient characteristics, including physical health, mental health and substance abuse diagnoses, sociodemographic factors, and pharmacological risk factors. RESULTS Among users of chronic opioid therapy, 3% of both the HealthCore and Arkansas Medicaid samples had a claims-based opioid abuse/dependence diagnosis. There was a strong inverse relationship between age and a diagnosis of opioid abuse/dependence. Mental health and substance use disorders were associated with an increased risk of opioid abuse/dependence. Effects of substance use disorders were especially strong, although mental health disorders were more common. Concerning opioid exposure; lower days supply, lower average doses, and use of Schedule III-IV opioids only, were all associated with lower likelihood of a diagnosis of opioid abuse/dependence. CONCLUSION Opioid abuse and dependence are diagnosed in a small minority of patients receiving chronic opioid therapy, but this may under-estimate actual misuse. Characteristics of the patients and of the opioid therapy itself are associated with the risk of abuse and dependence.

Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report--Part II.

OBJECTIVES The goal of comparative effectiveness analysis is to examine the relationship between two variables, treatment, or exposure and effectiveness or outcome. Unlike data obtained through randomized controlled trials, researchers face greater challenges with causal inference with observational studies. Recognizing these challenges, a task force was formed to develop a guidance document on methodological approaches to addresses these biases. METHODS The task force was commissioned and a Chair was selected by the International Society for Pharmacoeconomics and Outcomes Research Board of Directors in October 2007. This report, the second of three reported in this issue of the Journal, discusses the inherent biases when using secondary data sources for comparative effectiveness analysis and provides methodological recommendations to help mitigate these biases. RESULTS The task force report provides recommendations and tools for researchers to mitigate threats to validity from bias and confounding in measurement of exposure and outcome. Recommendations on design of study included: the need for data analysis plan with causal diagrams; detailed attention to classification bias in definition of exposure and clinical outcome; careful and appropriate use of restriction; extreme care to identify and control for confounding factors, including time-dependent confounding. CONCLUSIONS Design of nonrandomized studies of comparative effectiveness face several daunting issues, including measurement of exposure and outcome challenged by misclassification and confounding. Use of causal diagrams and restriction are two techniques that can improve the theoretical basis for analyzing treatment effects in study populations of more homogeneity, with reduced loss of generalizability.

Good Research Practices for Comparative Effectiveness Research: Analytic Methods to Improve Causal Inference from Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—Part III

OBJECTIVES Most contemporary epidemiologic studies require complex analytical methods to adjust for bias and confounding. New methods are constantly being developed, and older more established methods are yet appropriate. Careful application of statistical analysis techniques can improve causal inference of comparative treatment effects from nonrandomized studies using secondary databases. A Task Force was formed to offer a review of the more recent developments in statistical control of confounding. METHODS The Task Force was commissioned and a chair was selected by the ISPOR Board of Directors in October 2007. This Report, the third in this issue of the journal, addressed methods to improve causal inference of treatment effects for nonrandomized studies. RESULTS The Task Force Report recommends general analytic techniques and specific best practices where consensus is reached including: use of stratification analysis before multivariable modeling, multivariable regression including model performance and diagnostic testing, propensity scoring, instrumental variable, and structural modeling techniques including marginal structural models, where appropriate for secondary data. Sensitivity analyses and discussion of extent of residual confounding are discussed. CONCLUSIONS Valid findings of causal therapeutic benefits can be produced from nonrandomized studies using an array of state-of-the-art analytic techniques. Improving the quality and uniformity of these studies will improve the value to patients, physicians, and policymakers worldwide.

Risks for possible and probable opioid misuse among recipients of chronic opioid therapy in commercial and medicaid insurance plans: The TROUP Study.

&NA; The use of chronic opioid therapy (COT) for chronic non‐cancer pain (CNCP) has increased dramatically in the past two decades. There has also been a marked increase in the abuse of prescribed opioids and in accidental opioid overdose. Misuse of prescribed opioids may link these trends, but has thus far only been studied in small clinical samples. We therefore sought to validate an administrative indicator of opioid misuse among large samples of recipients of COT and determine the demographic, clinical, and pharmacological risks associated with possible and probable opioid misuse. A total of 21,685 enrollees in commercial insurance plans and 10,159 in Arkansas Medicaid who had at least 90 days of continuous opioid use 2000–2005 were studied for one year. Criteria were developed for possible and probable opioid misuse using administrative claims data concerning excess days supplied of short‐acting and long‐acting opioids, opioid prescribers and opioid pharmacies. We estimated possible misuse at 24% of COT recipients in the commercially insured sample and 20% in the Medicaid sample and probable misuse at 6% in commercially insured and at 3% in Medicaid. Among non‐modifiable factors, younger age, back pain, multiple pain complaints and substance abuse disorders identify patients at high risk for misuse. Among modifiable factors, treatment with high daily dose opioids (especially >120 mg MED per day) and short‐acting Schedule II opioids appears to increase the risk of misuse. The consistency of the findings across diverse patient populations and the varying levels of misuse suggest that these results will generalize broadly, but await confirmation in other studies.

Emergency department visits among recipients of chronic opioid therapy

BACKGROUND There has been an increase in overdose deaths and emergency department visits (EDVs) involving use of prescription opioids, but the association between opioid prescribing and adverse outcomes is unclear. METHODS Data were obtained from administrative claim records from Arkansas Medicaid and HealthCore commercially insured enrollees, 18 years and older, who used prescription opioids for at least 90 continuous days within a 6-month period between 2000 and 2005 and had no cancer diagnoses. Regression analysis was used to examine risk factors for EDVs and alcohol- or drug-related encounters (ADEs) in the 12 months following 90 days or more of prescribed opioids. RESULTS Headache, back pain, and preexisting substance use disorders were significantly associated with EDVs and ADEs. Mental health disorders were associated with EDVs in HealthCore enrollees and with ADEs in both samples. Opioid dose per day was not consistently associated with EDVs but doubled the risk of ADEs at morphine-equivalent doses over 120 mg/d. Use of short-acting Drug Enforcement Agency Schedule II opioids was associated with EDVs compared with use of non-Schedule II opioids alone (relative risk range, 1.09-1.74). Use of Schedule II long-acting opioids was strongly associated with ADEs (relative risk range, 1.64-4.00). CONCLUSIONS Use of Schedule II opioids, headache, back pain, and substance use disorders are associated with EDVs and ADEs among adults prescribed opioids for 90 days or more. It may be possible to increase the safety of chronic opioid therapy by minimizing the prescription of Schedule II opioids in these higher-risk recipients.

Trends in use of opioids for chronic noncancer pain among individuals with mental health and substance use disorders: the TROUP study.

ObjectivesUse of prescription opioids for chronic pain is increasing, as is abuse of these medications, though the nature of the link between these trends is unclear. These increases may be most marked in patients with mental health (MH) and substance use disorders (SUDs). We analyzed trends between 2000 and 2005 in opioid prescribing among individuals with noncancer pain conditions (NCPC), with and without MH and SUDs. MethodsSecondary data analysis of longitudinal administrative data from 2 dissimilar populations: a national, commercially insured population and Arkansas Medicaid enrollees. We examined these opioid outcomes: (1) rates of any prescription opioid use in the past year, (2) rates of chronic use of prescription opioids (greater than 90 d in the past year), (3) mean days supply of opioids, (4) mean daily opioid dose in morphine equivalents, and (5) percentage of total opioid dose that was Schedule II opioids. ResultsIn 2000, among individuals with NCPC, chronic opioid use was more common among those with a MH or SUD than among those without in commercially insured (8% vs. 3%, P<0.001) and Arkansas Medicaid (20% vs. 13%, P<0.001) populations. Between 2000 and 2005, in commercially insured, rates of chronic opioid use increased by 34.9% among individuals with an MH or SUD and 27.8% among individuals without these disorders. In Arkansas Medicaid chronic, opioid use increased by 55.4% among individuals with an MH or SUD and 39.8% among those without. DiscussionChronic use of prescription opioids for NCPC is much higher and growing faster in patients with MH and SUDs than in those without these diagnoses. Clinicians should monitor the use of prescription opioids in these vulnerable groups to determine whether opioids are substituting for or interfering with appropriate MH and substance abuse treatment.

An empirical basis for standardizing adherence measures derived from administrative claims data among diabetic patients.

Objective:To compare the predictive validity of 8 different adherence measures by studying the variability explained between each measure and 2 outcome measures: hospitalization episodes and total nonpharmacy cost among Medicaid eligible persons diagnosed with diabetes. Research Design:This study was a retrospective analysis of the Arkansas Medicaid administrative claims data from January 2000 to December 2006. Subjects:Diabetic (ICD-9-CM = 250.0x–250.9x, where x = 0 or 2) patients were identified in the recruitment period July 2000 through April 2004. Patients had to be ≥18 years old and have at least 2 prescription fills in the index period for an oral antidiabetic drug. Measures:Adherence rates to oral antidiabetic therapy were contrasted using the following 8 measures; including the medication possession ratio (MPR), proportion of days covered (PDC), refill compliance rate (RCR), compliance ratio (CR), medication possession ratio, modified (MPRm), continuous measure of medication gaps (CMG), and continuous multiple interval measure of oversupply (CMOS and continuous, single interval measure of medication acquisition (CSA). Multivariate and univariate linear and logistic regression models were used to prospectively predict nonpharmacy costs and hospitalizations in the follow-up year. Results:A total of 4943 diabetic patients were studied. In predicting any cause hospitalization, univariate models with PDC and CMG had the highest predictive validity (C-statistic: 0.544). Multivariate models with MPR, PDC, CMG or continuous multiple interval measure of oversupply (CMOS) as adherence measures had the highest C-statistics of 0.701 in predicting diabetes specific hospitalizations. None of the adherence measures were significantly associated with nonpharmacy cost. Conclusions:MPR and PDC had the highest predictive validity for hospitalization episodes. These 2 measures should be considered first when selecting among adherence measures when using administrative prescription claims data.