Application of a Graphical Depiction of Longitudinal Study Designs to Managed Care Pharmacy Research

With big data comes big responsibility: Strategies for utilizing aggregated, standardized, de-identified electronic health record data for research

Electronic health records (EHRs), though they are maintained and utilized for clinical and billing purposes, may provide a wealth of information for research. Currently, sources are available that offer insight into the health histories of well over a quarter of a billion people. Their use, however, is fraught with hazards, including introduction or reinforcement of biases, clarity of disease definitions, protection of patient privacy, definitions of covariates or confounders, accuracy of medication usage compared with prescriptions, the need to introduce other data sources such as vaccination or death records and the ensuing potential for inaccuracy, duplicative records, and understanding and interpreting the outcomes of data queries. On the other hand, the possibility of study of rare disorders or the ability to link apparently disparate events are extremely valuable. Strategies for avoiding the worst pitfalls and hewing to conservative interpretations are essential. This article summarizes many of the approaches that have been used to avoid the most common pitfalls and extract the maximum information from aggregated, standardized, and de-identified EHR data. This article describes 26 topics broken into three major areas: (1) 14 topics related to design issues for observational study using EHR data, (2) 7 topics related to analysis issues when analyzing EHR data, and (3) 5 topics related to reporting studies using EHR data.

A prediction model for nonpersistence or nonadherence to direct oral anticoagulants in hospitalized patients with atrial fibrillation

BACKGROUND: Medication adherence and persistence is fundamental for drug effectiveness, which is also true for the prevention of strokes in patients with atrial fibrillation (AF). Adherence to direct oral anticoagulants (DOACs) as first-line agents is often high in the early posthospital period. However, adherence often sharply declines (or eventually leads to nonpersistence) in the post-discharge ambulatory period, rendering stroke prevention ineffective. If patients at high risk of nonpersistence or nonadherence could be identified early, they could be offered early intervention measures to improve adherence and/or persistence. OBJECTIVE: To develop and internally validate a predictive model for medication nonadherence and nonpersistence to DOAC treatment in patients with AF after discharge using health insurance claims data. METHODS: We selected health insurance claims data between 2011 and 2016 from 8,055 patients with AF who were newly treated with rivaroxaban or apixaban after a hospital admission for stroke, transient ischemic attack, or AF. In the post-discharge ambulatory period, medication adherence was derived as the proportion of days covered, calculated from drug dispensation data. A maximum permissible 90-day gap between the end of a prescription and the next dispensation was used to estimate persistence. Candidate predictors were either derived from the index hospital admission or summarized from the previous year (eg, comorbidities or medication adherence to long-term treatments, such as ß-blockers, renin-angiotensin system inhibitors, statins, and thyroid hormones). A regularized logistic regression model was fitted using the least absolute shrinkage and selection operator in a split-sample approach (66.7% training data; 33.3% test data) to predict a composite of medication nonadherence/nonpersistence. Discrimination performance was assessed using the area under the receiver operating characteristic curve, the maximum sensitivity/specificity, and the scaled Brier score. A calibration curve fitted by linear regression was used to evaluate model calibration. RESULTS: The average age of the study participants was 79.7 years, 62% were female, and 3,515 patients (44%) were adherent and persistent (median follow-up of 185 days). Medication adherence to previous long-term treatments showed strong predictive properties. The developed model discriminated well (concordance statistic: 0.755), was well calibrated, and showed a scaled Brier score of 0.202 for identification of patients at risk. CONCLUSIONS: The model successfully predicted medication non-adherence/nonpersistence to DOAC treatment after discharge. Such a model could help ensure that targeted interventions are already in place at the time of hospital discharge, potentially preventing strokes and reducing costs. DISCLOSURES: Mr Wirbka is funded by the German Innovation Funds according to § 92a (2) Volume V of the Social Insurance Code (§ 92a Abs. 2, SGBV-Fünftes Buch Sozialgesetzbuch), grant number: 01VSF18019. Dr Haefeli received financial support from Daiichi-Sankyo, app development (https://www.easydoac.de/), and Bayer. He also received personal speaker fees from Bristol Myers-Squibb and Daiichi-Sankyo Online Seminar. Dr Meid is funded by the Physician-Scientist Programme of the Medical Faculty of Heidelberg University.

A Framework for Visualizing Study Designs and Data Observability in Electronic Health Record Data

Background

There is growing interest in using evidence generated from clinical practice data to support regulatory, coverage and other healthcare decision-making. A graphical framework for depicting longitudinal study designs to mitigate this barrier was introduced and has found wide acceptance. We sought to enhance the framework to contain information that helps readers assess the appropriateness of the source data in which the study design was applied.

Methods

For the enhanced graphical framework, we added a simple visualization of data type and observability to capture differences between electronic health record (EHR) and other registry data that may have limited data continuity and insurance claims data that have enrollment files.

Results

We illustrate the revised graphical framework with 2 example studies conducted using different data sources, including administrative claims only, EHR only, linked claims and EHR, as well as specialty community based EHRs with and without external linkages.

Conclusion

The enhanced visualization framework is important because evaluation of study validity needs to consider the triad of study question, design, and data together. Any given data source or study design may be appropriate for some questions but not others.

Real-world evidence enhances decision making

DISCLOSURES: No funding supported the writing of this commentary. The author has nothing to disclose.

Assessment of basal insulin adherence using 2 methodologies among Texas Medicaid enrollees with type 2 diabetes

BACKGROUND: Basal insulin is often recommended as the initial therapy for patients with type 2 diabetes who require insulin treatment. Adequate adherence is critical to diabetes management, yet suboptimal insulin adherence has been reported. Second-generation long-acting (SGLA) insulin has higher dosing flexibility and lower hypoglycemia risk and may improve adherence. However, little is known regarding adherence to SGLA insulin and how adherence to SGLA insulin compares with intermediate-acting neutral protamine Hagedorn (NPH) and first-generation long-acting (FGLA) insulin. Measurement of insulin adherence is challenging because of the inaccuracies of recorded days supply of insulin, and traditional medication possession ratio (MPR) may be negatively affected. Adjusted MPR (aMPR) has been developed in an effort to address this issue. OBJECTIVE: To examine the unadjusted and adjusted associations between basal insulin type and adherence to basal insulin using MPR and aMPR. METHODS: This retrospective database study used Texas Medicaid prescription claims from January 1, 2014, through June 30, 2017. The index date was the date of the first basal insulin prescription without the same prescription 6 months before (pre-index), and all patients were followed for 12 months (post-index). Patients aged 18-63 years with ≥ 1 pre-index prescription of an oral hypoglycemia agent (OHA) or a glucagon-like peptide-1 receptor agonist (GLP-1 RA), without any post-index prescription of premixed insulin or a basal insulin different from index insulin, and with continuous enrollment throughout the pre- and post-index periods, were included. The dependent variable was basal insulin adherence over 12 months, measured using MPR and aMPR. Unadjusted and adjusted adherence comparisons were conducted by basal (background) insulin type (NPH, FGLA, and SGLA). Covariates included age, gender, baseline use of basal insulins and comorbid medications, total number of medications, OHA adherence, post-index number of OHAs, and use of bolus insulins and GLP-1 RAs. Analysis of variance, chi-square tests, and multiple logistic regression analyses were performed. RESULTS: Of the 5,034 patients included, NPH, FGLA, and SGLA insulin users accounted for 3.7%, 89.8%, and 6.5%, respectively. The overall mean (SD) age was 50.9 (9.9) years, and 65.9% were female. In the unadjusted bivariate analyses, SGLA insulin users had significantly higher adherence, using either MPR (SGLA 0.68 [0.25] vs. FGLA 0.59 [0.27] vs. NPH 0.55 [0.27]; P < 0.0001) or aMPR (0.83 [0.23] vs. 0.78 [0.26] vs. 0.73 [0.28]; P = 0.0001). After controlling for covariates, insulin type was not significantly associated with the likelihood of being adherent (MPR or aMPR ≥ 0.8) using either measure. CONCLUSIONS: Adherence to SGLA insulin was not different from adherence to other basal insulins after controlling for patient characteristics. Yet, MPR and aMPR have limitations and warrant further confirmation of the study findings. Before new adherence measures for insulin therapy are developed, MPR and aMPR should be used with caution. DISCLOSURES: No specific funding was received for this manuscript. The authors report no potential conflicts of interest. Part of the data from this study was presented as posters at the American Pharmacists Association 2020 Annual Meeting & Exposition, March 20-23, 2020, in National Harbor, MD, and at the International Society for Pharmacoeconomics and Outcomes Research 2020 Conference, May 16-20, 2020, in Orlando, FL.