Teresa J. Hudson

risk factors for clinically recognized opioid abuse and dependence among veterans using opioids for chronic non-cancer pain

Abstract A central question in prescribing opioids for chronic non‐cancer pain (CNCP) is how to best balance the risk of opioid abuse and dependence with the benefits of pain relief. To achieve this balance, clinicians need an understanding of the risk factors for opioid abuse, an issue that is only partially understood. We conducted a secondary data analysis of regional VA longitudinal administrative data (years 2000–2005) for chronic users of opioids for CNCP (n = 15,160) to investigate risk factors for the development of clinically recognized (i.e., diagnosed) opioid abuse or dependence among these individuals. We analyzed four broad groups of possible risk factors: (i) non‐opioid substance abuse disorders, (ii) painful physical health disorders, (iii) mental health disorders, and (iv) socio‐demographic factors. In adjusted models, a diagnosis of non‐opioid substance abuse was the strongest predictor of opioid abuse/dependence (OR = 2.34, p < 0.001). Mental health disorders were moderately strong predictors (OR = 1.46, p = 0.005) of opioid abuse/dependence. However, the prevalence of mental health disorders was much higher than the prevalence of non‐opioid substance abuse disorders (45.3% vs. 7.6%) among users of opioids for CNCP, suggesting that mental health disorders account for more of the population attributable risk for opioid abuse than does non‐opioid substance abuse. Males, younger adults, and individuals with greater days supply of prescription opioids dispensed in 2002 were more likely to develop opioid abuse/dependence. Clinicians need to carefully screen for substance abuse and mental health disorders in candidates for opioid therapy and facilitate appropriate treatment of these disorders.

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.

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.

Practice Based Versus Telemedicine Based Collaborative Care for Depression in Rural Federally Qualified Health Centers: A Pragmatic Randomized Comparative Effectiveness Trial

OBJECTIVE Practice-based collaborative care is a complex evidence-based practice that is difficult to implement in smaller primary care practices that lack on-site mental health staff. Telemedicine-based collaborative care virtually co-locates and integrates mental health providers into primary care settings. The objective of this multisite randomized pragmatic comparative effectiveness trial was to compare the outcomes of patients assigned to practice-based and telemedicine-based collaborative care. METHOD From 2007 to 2009, patients at federally qualified health centers serving medically underserved populations were screened for depression, and 364 patients who screened positive were enrolled and followed for 18 months. Those assigned to practice-based collaborative care received evidence-based care from an on-site primary care provider and a nurse care manager. Those assigned to telemedicine-based collaborative care received evidence-based care from an on-site primary care provider and an off-site team: a nurse care manager and a pharmacist by telephone, and a psychologist and a psychiatrist via videoconferencing. The primary clinical outcome measures were treatment response, remission, and change in depression severity. RESULTS Significant group main effects were observed for both response (odds ratio=7.74, 95% CI=3.94-15.20) and remission (odds ratio=12.69, 95% CI=4.81-33.46), and a significant overall group-by-time interaction effect was observed for depression severity on the Hopkins Symptom Checklist, with greater reductions in severity over time for patients in the telemedicine-based group. Improvements in outcomes appeared to be attributable to higher fidelity to the collaborative care evidence base in the telemedicine-based group. CONCLUSIONS Contracting with an off-site telemedicine-based collaborative care team can yield better outcomes than implementing practice-based collaborative care with locally available staff.

Greater Prevalence and Incidence of Dementia in Older Veterans with Posttraumatic Stress Disorder

To explore the association between posttraumatic stress disorder (PTSD) and dementia in older veterans.

Does PTSD impair Cognition beyond the effect of Trauma

This systematic review analyzed data from studies examining memory and cognitive function in subjects with posttraumatic stress disorder (PTSD), compared with subjects exposed to trauma (but without PTSD). Based on analysis of 21 articles published in English from 1968 to 2009, the conclusion is that individuals with PTSD, particularly veterans, show signs of cognitive impairment when tested with neuropsychological instruments, more so than individuals exposed to trauma who do not have PTSD.

Telemedicine-Based Collaborative Care for Posttraumatic Stress Disorder: A Randomized Clinical Trial

IMPORTANCE Posttraumatic stress disorder (PTSD) is prevalent, persistent, and disabling. Although psychotherapy and pharmacotherapy have proven efficacious in randomized clinical trials, geographic barriers impede rural veterans from engaging in these evidence-based treatments. OBJECTIVE To test a telemedicine-based collaborative care model designed to improve engagement in evidence-based treatment of PTSD. DESIGN, SETTING, AND PARTICIPANTS The Telemedicine Outreach for PTSD (TOP) study used a pragmatic randomized effectiveness trial design with intention-to-treat analyses. Outpatients were recruited from 11 Department of Veterans Affairs (VA) community-based outpatient clinics serving predominantly rural veterans. Inclusion required meeting diagnostic criteria for current PTSD according to the Clinician-Administered PTSD Scale. Exclusion criteria included receiving PTSD treatment at a VA medical center or a current diagnosis of schizophrenia, bipolar disorder, or substance dependence. Two hundred sixty-five veterans were enrolled from November 23, 2009, through September 28, 2011, randomized to usual care (UC) or the TOP intervention, and followed up for 12 months. INTERVENTIONS Off-site PTSD care teams located at VA medical centers supported on-site community-based outpatient clinic providers. Off-site PTSD care teams included telephone nurse care managers, telephone pharmacists, telepsychologists, and telepsychiatrists. Nurses conducted care management activities. Pharmacists reviewed medication histories. Psychologists delivered cognitive processing therapy via interactive video. Psychiatrists supervised the team and conducted interactive video psychiatric consultations. MAIN OUTCOMES AND MEASURES The primary outcome was PTSD severity as measured by the Posttraumatic Diagnostic Scale. Process-of-care outcomes included medication prescribing and regimen adherence and initiation of and adherence to cognitive processing therapy. RESULTS During the 12-month follow-up period, 73 of the 133 patients randomized to TOP (54.9%) received cognitive processing therapy compared with 16 of 132 randomized to UC (12.1%) (odds ratio, 18.08 [95% CI, 7.96-41.06]; P < .001). Patients in the TOP arm had significantly larger decreases in Posttraumatic Diagnostic Scale scores (from 35.0 to 29.1) compared with those in the UC arm (from 33.5 to 32.1) at 6 months (β = -3.81; P = .002). Patients in the TOP arm also had significantly larger decreases in Posttraumatic Diagnostic Scale scores (from 35.0 to 30.1) compared with those in the UC arm (from 33.5 to 31.7) at 12 months (β = -2.49; P=.04). There were no significant group differences in the number of PTSD medications prescribed and adherence to medication regimens were not significant. Attendance at 8 or more sessions of cognitive processing therapy significantly predicted improvement in Posttraumatic Diagnostic Scale scores (β = -3.86 [95% CI, -7.19 to -0.54]; P = .02) and fully mediated the intervention effect at 12 months. CONCLUSIONS AND RELEVANCE Telemedicine-based collaborative care can successfully engage rural veterans in evidence-based psychotherapy to improve PTSD outcomes. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00821678.

Prospective Validation of Eight Different Adherence Measures for Use with Administrative Claims Data among Patients with Schizophrenia

OBJECTIVE The aim of this study was to compare the predictive validity of eight different adherence measures by studying the variability explained between each measure and hospitalization episodes among Medicaid-eligible persons diagnosed with schizophrenia on antipsychotic monotherapy. METHODS This study was a retrospective analysis of the Arkansas Medicaid administrative claims data. Continuously eligible adult schizophrenia (ICD-9-CM = 295.**) patients on antipsychotic monotherapy were identified in the recruitment period from July 2000 through April 2004. Adherence rates to antipsychotic therapy in year 1 were calculated using eight different measures identified from the literature. Univariate and multivariable logistic regression models were used to prospectively predict all-cause and mental health-related hospitalizations in the follow-up year. RESULTS Adherence rates were computed for 3395 schizophrenic patients with a mean age of 42.9 years, of which 52.5% (n = 1782) were females, and 52.8% (n = 1793) were white. The proportion of days covered (PDC) and continuous measure of medication gaps measures of adherence had equal C-statistics of 0.571 in predicting both all-cause and mental health-related hospitalizations. The medication possession ratio (MPR) continuous multiple interval measure of oversupply were the second best measures with equal C-statistics of 0.568 and 0.567 for any-cause and mental health-related hospitalizations. The multivariate adjusted models had higher C-statistics but provided the same rank order results. CONCLUSIONS MPR and PDC were among the best predictors of any-cause and mental health-related hospitalization, and are recommended as the preferred adherence measures when a single measure is sought for use with administrative claims data for patients not on polypharmacy.

Annual prevalence of diagnosed schizophrenia in the USA: a claims data analysis approach

BACKGROUND Schizophrenia is a debilitating chronic mental illness. However, the annual prevalence of schizophrenia is not well understood because of under-representation of schizophrenia patients in epidemiological surveys. This study used multiple administrative claims databases to estimate the annual prevalence of diagnosed schizophrenia in the USA. METHOD The annual prevalence of diagnosed schizophrenia in the USA was estimated for different health insurance coverage groups. The prevalence for privately insured individuals was calculated from an administrative claims database of approximately 3 million privately insured beneficiaries covering the period 1999-2003. The prevalence for Medicaid enrollees was calculated from California Medicaid claims covering the period 2000-2002. The prevalence for Medicare and Medicaid/Medicare dual eligibles was estimated using a combination of both databases. Published statistics were used to estimate the prevalence of schizophrenia in the uninsured and veteran populations and to weight the prevalence rates obtained to the population of the USA. RESULTS The 12-month prevalence of diagnosed schizophrenia in the USA in 2002 was estimated at 5.1 per 1000 lives. The Medicaid population was identified with the highest prevalence rate among the populations studied. Sensitivity analyses taking into consideration the Veterans Affairs population only changed the estimate slightly to 5.3 per 1000 lives. CONCLUSION Analyses of administrative claims data contribute to the understanding of the prevalence of diagnosed schizophrenia.

Patterns of opioid use for chronic noncancer pain in the Veterans Health Administration from 2009 to 2011.

&NA; Among Veterans Health Administration patients with chronic noncancer pain, chronic opioid therapy occurs frequently, but the median daily dose is usually modest. &NA; Although opioids are frequently prescribed for chronic noncancer pain (CNCP) among Veterans Health Administration (VHA) patients, little has been reported on national opioid prescribing patterns in the VHA. Our objective was to better characterize the dosing and duration of opioid therapy for CNCP in the VHA. We analyzed national VHA administrative and pharmacy data for fiscal years 2009 to 2011. For individuals with CNCP diagnoses and any opioid use in the fiscal year, we calculated the distribution of individual mean daily opioid dose, individual total days covered with opioids in a year, and individual total opioid dose in a year. We also investigated the factors associated with being in the top 5% of individuals for total opioid dose in a year, which we term receipt of high‐volume opioids. About half of the patients with CNCP received opioids in a given fiscal year. The median daily dose was 21 mg morphine equivalents. Approximately 4.5% had a mean daily dose higher than 120 mg morphine equivalents. The median days covered in a year was 115 to 120 days in these years for those receiving opioids. Fifty‐seven percent had at least 90 days covered with opioids per year. Major depression and posttraumatic stress disorder were positively associated with receiving high‐volume opioids, but nonopioid substance use disorders were not. Among VHA patients with CNCP, chronic opioid therapy occurs frequently, but for most patients, the average daily dose is modest. Doses and duration of therapy were unchanged from 2009 to 2011.