Development and feasibility of the misuse, abuse, and diversion drug event reporting system (MADDERS®)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134867/1/ajad12459.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134867/2/ajad12459_am.pd

Toward a multiscale modeling framework for understanding serotonergic function

Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin

Semantic contributions to word naming with artificial lexicons

Inconsistencies in spelling-sound correspondence in English offer unique opportunities to examine the cognitive workings of the reading system. Prior studies have exploited these inconsistencies to demonstrate that meaning plays a significant role in resolving pronunciations from the written form of words. Strain et al. (1995, 2002) have shown an interaction between word regularity and imageability whereby highly imageable exception words are read aloud as quickly as words with regular pronunciations. However, these studies have suffered several criticisms, including failure to control for potentially confounding correlations between word regularity and age-of-acquisition. The current study introduces a technique, the trained pseudoword lexicon, which provides a means of controlling age of acquisition and other factors by experimental design, rather than statistical means. Experiment 1 presents evidence that this paradigm does indeed produce results consistent with classic regularity effect findings. Experiment 2 replicates findings of semantic contributions to single-word naming when a variety of factors are controlled by using the trained pseudoword lexicon. Implications of these findings are discussed for the interactive triangle and dual-route models of reading, the results being consistent with the predictions of a triangle model. Potential future applications of the trained pseudoword lexicon are also addressed.

Assessment of cognitive impairment, treatment adherence, and healthcare resource utilization among treated schizophrenia patients

Background: Cognitive impairment associated with schizophrenia (CIAS) is common and can impact health outcomes (1). Information on the use of cognitive testing in clinical practice is lacking, and the relationship between cognitive impairment and healthcare resource utilization (HCRU) is poorly understood. This study assessed the measurement of CIAS and the associa-tion between CIAS, treatment adherence, and HCRU. Methods: Claims data from the Henry Ford Health System, a vertically integrated healthcare system serving metropolitan Detroit, Michigan, were used to identify patients aged 18 to 64 years with schizophrenia and use of antipsychotics from January 01, 2009, to June 30, 2014. Patients had ≥12 months of continuous enrollment preindex and ≥6 months postindex. The index date was the date of frst diagnosis of schizophrenia. Patients with cognitive decline independent of CIAS or schizoaf-fective disorder were excluded. Electronic medical records (EMR) were used to confrm diagnosis of schizophrenia and to assess measures of CIAS. Due to the lack of standardized measures of CIAS, 2 independent expert reviewers assessed mental status exam (MSE) records for evidence of CIAS and classifed patients as “clearly impaired” or “not impaired” during the assessment period. Administrative claims were used to assess treatment adherence during the study period and HCRU in the 12 months following index among patients with ≥12 months of follow-up. Descriptive statistics were performed as the study was exploratory in nature. Results: Following application of the inclusion/exclusion criteria and EMR assessment, 65 subjects were confrmed schizophrenia patients. The sample was 59% male with average age at index date of 43 years; 22% of patients were white, 62% black, and the remaining were unclassifed. Only 8% of patients had evidence of a standardized cognitive assessment. However, all patients had ≥1 MSE records; 60% (N = 39) of patients were classifed with CIAS through MSE review. The average medication possession ratio (MPR) for antipsychotic medica-tions was 0.4 ± 0.3 for cognitively impaired patients (N = 29) and 0.6 ± 0.4 for nonimpaired patients (N = 25). CIAS patients had 0.5 ± 0.4 outpatient visits, 0.08 ± 0.0 hospitalizations, and 0.15 ± 0.08 emergency department visits per patient per month (PPPM), whereas nonimpaired patients had 0.7 ± 0.7, 0 ± 0.0, and 0.08 ± 0.0 visits, respectively. Conclusion: Although cognitive impairment is prevalent in schizophrenia patients, the results suggest that CIAS is not formally assessed in clinical practice. Additional research is needed to understand the relationship between CIAS, HCRU, and outcomes

Development and preliminary validation of the focused analgesia selection test to identify accurate pain reporters

Clinical trials of analgesics have been plagued with poor assay sensitivity due, in part, to variability in subjects’ pain reporting. Herein, we develop and evaluate the focused analgesia selection test (FAST), a method to measure patients’ pain reporting skills. Subjects with osteoarthritis of the hip, knee, and/or ankle with pain intensity of ≥3/10 on a 0–10 numerical rating scale were enrolled. Subjects underwent the FAST procedure, which consists of recording subjects’ pain reports in response to repeated administration of thermal noxious stimuli of various intensities applied on the arm with the Medoc® Thermal Sensory Analyzer II. Subjects also rated non-noxious stimuli consisting of visual contrast rating. After performing an exercise task, subjects also rated clinical pain and were asked to report whether their pain had increased, decreased, or stayed the same. Overall, 88 subjects were enrolled, and 83 were included in the analyses. FAST’s outcomes including the R2, intraclass correlation coefficient (ICC), and coefficient of variation (CoV) indicated that subjects’ pain reporting skills were widely distributed. Higher FAST ICC significantly predicted greater changes in clinical pain following exercise (p=0.017), whereas the visual contrast test did not predict postexercise pain. FAST is the first method that measures subjects’ pain reporting skills. Using FAST to enrich clinical trials with “good” pain reporters (with high FAST ICC) could increase assay sensitivity. Further evaluation of FAST is ongoing

Can valid cases of schizophrenia be identified in administrative claims data?

Background: Large data sources, such as administrative claims, can be used to better understand the natural history, treatment and outcomes of schizophrenia provided that valid cases can be identified. International Classification of Diseases (ICD) codes or a combination of ICD codes and prescription claims have been used to identify schizophrenia patients, but validation studies of these methods for schizophrenia are limited. Objectives: To determine if valid cases of patients with schizophrenia can be identified using administrative claims data. Methods: Claims data from the Henry Ford Health System, an integrated healthcare system serving metropolitan Detroit, Michigan, were used to identify patients aged 18-64 years with schizophrenia from 01/ 01/2009 to 06/30/2014. Potential cases had ≥2 ICD-9 codes (295.x) for schizophrenia disorder in any position, ≥2 claims for an antipsychotic medication, ≥12 months of continuous enrollment pre-index, and ≥6 months of continuous enrollment post-index. Index date was defined as the first 295.x ICD-9 code. Patients with organic cognitive decline or schizoaffective disorder independent of schizophrenia were excluded. Trained medical records abstractors performed a structured review of all relevant fields including inpatient and outpatient records of the electronic medical record (EMR) (e.g. diagnosis fields; free text) to verify the schizophrenia diagnosis ±12 months from the index date. Results: Of the 145 patients who met inclusion/exclusion criteria, EMR review was completed on a random sample of 111 patients. Of these, 65 had an EMR-confirmed diagnosis of schizophrenia for a positive predictive value (PPV) of 59% (95% confidence interval: 52-64%). Unconfirmed patients had diagnoses of bipolar disorder (N = 25; 54%), major depressive disorder (N = 28; 61%), and/or schizoaffective disorder (N = 3; 7%). These diagnoses may be comorbid with a schizophrenia diagnosis, but no schizophrenia diagnosis was recorded. Conclusions: Identifying valid cases of schizophrenia in administrative claims data is challenging. There are few published studies of validated claims-based algorithms that identify cases of treated schizophrenia. This study, requiring ≥2 ICD-9 codes and ≥2 prescription claims, did not yield a high PPV for schizophrenia. Reasons may include diagnostic challenges in differentiating psychiatric conditions or comorbid diagnoses where only 1 diagnosis is recorded. Future studies of validated algorithms to identify schizophrenia patients are warranted

Quantifying the importance of inhaler attributes corresponding to items in the patient satisfaction and preference questionnaire in patients using Combivent Respimat

Abstract Background Physicians consider ease of use, satisfaction, and preferences when prescribing an inhaler device. These factors may impact appropriate usage and compliance. Methods The objectives were to quantify the relative importance of inhaler attributes in patients currently using Combivent Respimat by eliciting preferences for performance and convenience attributes assessed by items in the Patient Satisfaction and Preference Questionnaire (PASAPQ). Using a pharmacy database, 19,964 adults in the United States who filled ≥2 Combivent Respimat prescriptions were identified. Of those, 8150 patients were randomly selected to receive invitation letters. The online cross-sectional survey included the PASAPQ and best-worst scaling (BWS) questions. The PASAPQ measures satisfaction with medication attributes across two domains: performance and convenience. BWS questions asked participants to select the most and least important device attributes. A descriptive statistics analysis of the PASAPQ and a random-parameters logit model of BWS responses were conducted. Results The survey was completed by 503 participants. Most were female (57.3%), white (88.5%), and 51–70 years old (67.6%). Approximately 47% reported a chronic obstructive pulmonary disease diagnosis, 21.9% asthma, 8.2% other lung disease, and 23.1% more than one lung disease. PASAPQ scores indicated that the majority were satisfied or very satisfied; up to 20% reported being dissatisfied with Combivent Respimat. The three most important inhaler attributes were Feeling that your medicine gets into your lungs, Inhaler works reliably, and Inhaler makes inhaling your medicine easy. The most important attributes corresponded to six of seven items in the PASAPQ performance domain. Conclusions Most participants reported satisfaction with Combivent Respimat. Performance attributes were more important than convenience attributes

Response scale selection in adult pain measures: results from a literature review

Abstract Background The purpose of this literature review was to examine the existing patient-reported outcome measurement literature to understand the empirical evidence supporting response scale selection in pain measurement for the adult population. Methods The search strategy involved a comprehensive, structured, literature review with multiple search objectives and search terms. Results The searched yielded 6918 abstracts which were reviewed against study criteria for eligibility across the adult pain objective. The review included 42 review articles, consensus guidelines, expert opinion pieces, and primary research articles providing insights into optimal response scale selection for pain assessment in the adult population. Based on the extensive and varied literature on pain assessments, the adult pain studies typically use simple response scales with single-item measures of pain—a numeric rating scale, visual analog scale, or verbal rating scale. Across 42 review articles, consensus guidelines, expert opinion pieces, and primary research articles, the NRS response scale was most often recommended in these guidance documents. When reviewing the empirical basis for these recommendations, we found that the NRS had slightly superior measurement properties (e.g., reliability, validity, responsiveness) across a wide variety of contexts of use as compared to other response scales. Conclusions Both empirical studies and review articles provide evidence that the 11-point NRS is likely the optimal response scale to evaluate pain among adult patients without cognitive impairment