Importance: Prescription opioid misuse is a public health problem that leads to overdose. Although existing interventions focus on limiting prescribing to patients at high risk, individuals may still access prescription opioids dispensed to family members.
Objective: To determine whether opioid prescriptions to family members were associated with overdose for individuals who themselves did not have an opioid prescription.
Design, Setting, and Participants: We conducted a 1:4 matched case-control study using health care utilization data from 2004 through 2015 from a large US commercial insurance company. Eligible individuals were required to have at least 12 months of continuous enrollment and 1 or more family members in the database. Individuals who experienced overdose were identified by their first opioid overdose after the baseline period and matched to control participants by time in the database, calendar time, age, sex, and number of individuals in the family unit. Both groups were restricted to individuals with no prior opioid dispensing of their own. Data analysis was conducted from January 2018 to August 2018.
Exposures: Any prior opioid dispensing to a family member, total morphine milligram equivalents dispensed to family members, and the type of opioid product dispensed.
Main Outcomes and Measures: Individual odds of opioid overdose resulting in an emergency department visit or hospitalization were the primary end point. The primary analysis evaluated the odds of overdose among individuals whose family members had been dispensed an opioid. Sensitivity analyses examined the odds stratified by age and timing relative to the dispensing of opioids to family members.
Results: A total of 2303 individuals who experienced opioid overdose and 9212 matched control individuals were identified. The mean (SD) age was 23.2 (18.1) years; 1158 affected individuals and 4632 control individuals (50.3%) were female. The mean (SD) time in the database before an overdose case was 3.2 (3.3) years. Prior opioid dispensing to family members was associated with individual overdose (odds ratio [OR], 2.89 [95% CI, 2.59-3.23]). There was a significant dose-response association between increasing amounts of opioids dispensed to family members and odds of overdose (>0-<50 morphine milligram equivalents per day: OR, 2.71 [95% CI, 2.42-3.03]; 50-<90 morphine milligram equivalents per day: OR, 7.80 [95% CI, 3.63-16.78]; ≥90 morphine milligram equivalents per day: OR, 15.08 [95% CI, 8.66-26.27]).
Conclusions and Relevance: In this analysis, opioid prescriptions to family members were associated with overdose among individuals who do not receive opioid prescriptions. Interventions may focus on expanding access to opioid antagonists, locking prescription opioids in the home, and providing greater patient education to limit fatal overdose among family members.
We sought to develop a semiautomated screening approach using electronic healthcare data to identify drug-drug interactions (DDIs) that result in clinical outcomes. Using a case-crossover design with 30-day hazard and referent windows, we evaluated codispensed drugs (potential precipitants) in 7,801 patients who experienced rhabdomyolysis while on cytochrome P450 (CYP)3A4-metabolized statins and in 15,147 who experienced bleeding while on dabigatran. Estimates of direct associations between precipitant drugs and outcomes were used to adjust for bias and precipitants' direct effects. The P values were adjusted for multiple testing using the false discovery rate (FDR). From among 460 drugs codispensed with statins, 1 drug (clarithromycin) generated an alert (adjusted odds ratio (OR) 5.83, FDR < 0.05). From among 485 drugs codispensed with dabigatran, 2 drugs (naproxen and enoxaparin, ORs 2.50 and 2.75; FDR < 0.05) generated an alert. All three signals reflected known pharmacologic interactions, confirming the potential of case-crossover-based approaches for DDI screening in electronic healthcare data.
BACKGROUND: The obstetric comorbidity index (OB-CMI) summarizes the burden of maternal comorbidities into a single number and holds promise as a maternal risk-assessment tool.
OBJECTIVE: The aim of this study is to assess the clinical performance of this comorbidity-based screening tool to accurately identify women on labor and delivery at risk of severe maternal morbidity (SMM) on labor and delivery (L&D) in real time.
STUDY DESIGN: All patients with pregnancies at or beyond 23 weeks' gestation presenting to L&D at a single tertiary-care center from February through July 2018 were included in the study. The patient's primary L&D nurse assessed patient comorbidities and calculated the patient's OB-CMI. The score was recalculated at each 12-hour shift change. A multidisciplinary panel of clinicians determined whether patients experienced SMM based on the American College of Obstetrics and Gynecology and Society for Maternal-Fetal Medicine consensus definition, blinded to the patient's OB-CMI score. We analyzed the association between the OB-CMI score and the occurrence of SMM.
RESULTS: The study included 2,828 women, of whom 1.73% developed SMM (n=49). The OB-CMI ranged from 0 to 15 for women in the study cohort with a median OB-CMI of 1 (interquartile range (IQR) 0-3). The median OB-CMI score for women experiencing the SMM was 5 (IQR, 3-7) compared to a median of 1 (IQR, 0-3) for those without SMM (p<0.01). The frequency of SMM increased from 0.41% for those with a score of 0 to 18.75% for those with a score greater than or equal to 9. For every one-point increase in the score patients experienced a 1.55 increase in odds of SMM (95% confidence interval (CI), 1.42-1.70). The c-statistic for the OB-CMI score was 0.83 (95% CI 0.76-0.89) indicating strong discrimination.
CONCLUSIONS: The OB-CMI can prospectively identify women at risk of severe maternal morbidity in a clinical setting. A particular strength of the OB-CMI is its ability to integrate multiple compounding comorbidities and highlight the cumulative risk associated with the patients' conditions. Routine clinical use of the OB-CMI has the potential to identify at-risk women warranting increased surveillance and targeted care to prevent adverse maternal outcomes.
BACKGROUND: Self-controlled designs, both case-crossover and self-controlled case series, are well suited for evaluating outcomes of drug-drug interactions in electronic healthcare data. Their comparative performance in this context, however, is unknown.
METHODS: We simulated cohorts of patients exposed to two drugs: a chronic drug (object) and a short-term drug (precipitant) with an associated interaction of 2.0 on the odds ratio scale. We analyzed cohorts using case-crossover and self-controlled case series designs evaluating exposure to the precipitant drug within person-time exposed to the object drug. Scenarios evaluated violations of key design assumptions: (1) time-varying, within-person confounding; (2) time trend in precipitant drug exposure prevalence; (3) non-transient precipitant exposure; and (4) event-dependent object drug discontinuation.
RESULTS: Case-crossover analysis produced biased estimates when 30% of patients persisted on the precipitant drug (estimated OR 2.85) and when the use of the precipitant drug was increasing in simulated cohorts (estimated OR 2.56). Self-controlled case series produced biased estimates when patients discontinued the object drug following the occurrence of an outcome (estimated incidence ratio (IR) of 2.09 [50% of patients stopping therapy] and 2.22 [90%]. Both designs yielded similarly biased estimates in the presence of time-varying, within-person confounding.
CONCLUSION: In settings with independent or rare outcomes and no substantial event-dependent censoring (<50%), self-controlled case series may be preferable to case-crossover design for evaluating outcomes of drug-drug interactions. With heavy event-dependent drug discontinuation, a case-crossover design may be preferable provided there are no time-related trends in drug exposure.
Purpose: Little is known about how disease risk score (DRS) development should proceed under different pharmacoepidemiologic follow-up strategies. In an analysis of dabigatran vs. warfarin and risk of major bleeding, we compared the results of DRS adjustment when models were developed under "intention-to-treat" (ITT) and "as-treated" (AT) approaches.
Methods: We assessed DRS model discrimination, calibration, and ability to induce prognostic balance via the "dry run analysis". AT treatment effects stratified on each DRS were compared with each other and with a propensity score (PS) stratified reference estimate. Bootstrap resampling of the historical cohort at 10 percent-90 percent sample size was performed to assess the impact of sample size on DRS estimation.
Results: Historically-derived DRS models fit under AT showed greater decrements in discrimination and calibration than those fit under ITT when applied to the concurrent study population. Prognostic balance was approximately equal across DRS models (-6 percent to -7 percent "pseudo-bias" on the hazard ratio scale). Hazard ratios were between 0.76 and 0.78 with all methods of DRS adjustment, while the PS stratified hazard ratio was 0.83. In resampling, AT DRS models showed more overfitting and worse prognostic balance, and led to hazard ratios further from the reference estimate than did ITT DRSs, across sample sizes.
Conclusions: In a study of anticoagulant safety, DRSs developed under an AT principle showed signs of overfitting and reduced confounding control. More research is needed to determine if development of DRSs under ITT is a viable solution to overfitting in other settings.
PURPOSE: Bootstrapping can account for uncertainty in propensity score (PS) estimation and matching processes in 1:1 PS-matched cohort studies. While theory suggests that the classical bootstrap can fail to produce proper coverage, practical impact of this theoretical limitation in settings typical to pharmacoepidemiology is not well studied.
METHODS: In a plasmode-based simulation study, we compared performance of the standard parametric approach, which ignores uncertainty in PS estimation and matching, with two bootstrapping methods. The first method only accounted for uncertainty introduced during the matching process (the observation resampling approach). The second method accounted for uncertainty introduced during both PS estimation and matching processes (the PS reestimation approach). Variance was estimated based on percentile and empirical standard errors, and treatment effect estimation was based on median and mean of the estimated treatment effects across 1000 bootstrap resamples. Two treatment prevalence scenarios (5% and 29%) across two treatment effect scenarios (hazard ratio of 1.0 and 2.0) were evaluated in 500 simulated cohorts of 10 000 patients each.
RESULTS: We observed that 95% confidence intervals from the bootstrapping approaches but not the standard approach, resulted in inaccurate coverage rates (98%-100% for the observation resampling approach, 99%-100% for the PS reestimation approach, and 95%-96% for standard approach). Treatment effect estimation based on bootstrapping approaches resulted in lower bias than the standard approach (less than 1.4% vs 4.1%) at 5% treatment prevalence; however, the performance was equivalent at 29% treatment prevalence.
CONCLUSION: Use of bootstrapping led to variance overestimation and inconsistent coverage, while coverage remained more consistent with parametric estimation.
INTRODUCTION: While medical chart review remains the gold standard to validate health conditions or events identified in administrative claims and electronic health record databases, it is time consuming, expensive and can involve subjective decisions.
AIM: The aim of this study was to describe the landscape of technology-enhanced approaches that could be used to facilitate medical chart review within and across distributed data networks.
METHOD: We conducted a semi-structured survey regarding processes for medical chart review with organizations that either routinely do medical chart review or use technologies that could facilitate chart review.
RESULTS: Fifteen out of 17 interviewed organizations used optical character recognition (OCR) or natural language processing (NLP) in their chart review process. None used handwriting recognition software. While these organizations found OCR and NLP to be useful for expediting extraction of useful information from medical charts, they also mentioned several challenges. Quality of medical scans can be variable, interfering with the accuracy of OCR. Additionally, linguistic complexity in medical notes and heterogeneity in reporting templates used by different healthcare systems can reduce the transportability of NLP-based algorithms to diverse healthcare settings.
CONCLUSION: New technologies including OCR and NLP are currently in use by various organizations involved in medical chart review. While technology-enhanced approaches could scale up capacity to validate key variables and make information about important clinical variables from medical records more generally available for research purposes, they often require considerable customization when employed in a distributed data environment with multiple, diverse healthcare settings.
PURPOSE: As more biosimilars become available in the United States, postapproval noninterventional studies describing biosimilar switching and comparing effectiveness and/or safety between switchers and nonswitchers will play a key role in generating real-world evidence to inform clinical practices and policy decisions. Ensuring sound methodology is critical for making valid inferences from these studies.
METHODS: The Biologics and Biosimilars Collective Intelligence Consortium (BBCIC) convened a workgroup consisting of academic researchers, industry scientists, and practicing clinicians to establish best practice recommendations for the conduct of noninterventional studies of biosimilar and reference biologic switching. The workgroup members participated in eight teleconferences between August 2017 and February 2018 to discuss specific topics and build consensus.
RESULTS: This report provides workgroup recommendations covering five main considerations relating to noninterventional studies describing reference biologic to biosimilar switching and comparing reference biologic to biosimilars for safety and effectiveness in the presence of switching at treatment initiation and during follow-up: (a) selecting appropriate data sources from a range of available options including insurance claims, electronic health records, and registries; (b) study designs; (c) outcomes of interest including health care utilization and clinical endpoints; (d) analytic approaches including propensity scores, disease risk scores, and instrumental variables; and (e) special considerations including avoiding designs that ignore history of biologic use, avoiding immortal time bias, exposure misclassification, and accounting for postindex switching.
CONCLUSION: Recommendations provided in this report provide a framework that may be helpful in designing and critically evaluating postapproval noninterventional studies involving reference biologic to biosimilar switching.
INTRODUCTION: Patients taking non-vitamin K antagonist oral anticoagulants (NOACs) such as dabigatran, rivaroxaban, and apixaban have reported experiencing angioedema in randomized trials and routine care.
OBJECTIVE: The aim of this study was to quantify the association between NOACs and angioedema relative to warfarin among routinely treated patients with atrial fibrillation in a cohort study. We also compared warfarin users with non-users in a case-crossover study.
METHODS: We utilized a cohort design that drew eligible patients from the Truven Health MarketScan Commercial database, the Optum Clinformatics Data Mart, and Medicare. Within each database, we compared the 6-month relative rate of angioedema among new users of NOACs (dabigatran, rivaroxaban, apixaban) and new users of warfarin. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) after adjusting for confounders using propensity score stratification, and meta-analyzed the database-specific HRs using a random-effects model. We also estimated an odds ratio (OR) for the association between warfarin and angioedema using a case-crossover design, a self-controlled design that controls for time-invariant confounders.
RESULTS: In the cohort study, we observed 249 incident angioedema events among 267,681 NOAC initiators and 281,143 warfarin initiators across all databases. The meta-analyzed HR for angioedema comparing any NOAC versus warfarin was 0.98 (95% CI 0.76-1.27). In the case-crossover design, the OR for the association between warfarin and angioedema was 0.91 (95% CI 0.68-1.21) based on 431 cases.
CONCLUSIONS: Our estimates were inconsistent with substantial short-term relative increases in the rate of angioedema associated with oral anticoagulant therapy.
Importance: There is a need for better understanding of the comparative safety of systemic medications used in the treatment of psoriasis.
Objective: To compare the risk of serious infection associated with biologic and nonbiologic systemic medications in patients with psoriasis.
Design, Setting, and Participants: An observational cohort study was conducted using medical and outpatient pharmacy claims from 2 large US health insurance claims databases from January 1, 2003, through September 30, 2015. We included patients with a diagnosis of psoriasis who were new users of systemic medications for psoriasis.
Exposures: Prescription claims for acitretin, adalimumab, apremilast, etanercept, infliximab, methotrexate, or ustekinumab.
Main Outcomes and Measures: The primary outcome was serious infection, defined by inpatient discharge diagnosis International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. Cox proportional hazards regression was used to compare rates of serious infection for each exposure (acitretin, adalimumab, apremilast, etanercept, infliximab, and ustekinumab) with the referent group (methotrexate). We used pairwise 1:1 propensity score (PS) matching to adjust for potential confounders, which were assessed during a 180-day baseline period prior to study drug initiation. Results from the 2 databases were pooled via fixed-effects analysis.
Results: The databases included 31 595 patients in the Optum Clinformatics Data Mart and 76 112 patients in Truven MarketScan who were new users of acitretin, adalimumab, apremilast, etanercept, infliximab, methotrexate, and ustekinumab. Users of acitretin, apremilast, infliximab, and methotrexate were older and had higher baseline comorbidity scores than subcutaneous biologic users (adalimumab, etanercept, and ustekinumab). The pooled PS-matched analysis yielded a decreased rate of overall serious infection in users of apremilast (hazard ratio [HR], 0.50; 95% CI, 0.26-0.94), etanercept (HR, 0.75; 95% CI, 0.61-0.93), and ustekinumab (HR, 0.65; 95% CI, 0.47-0.89) compared with methotrexate. We did not find a different rate of overall serious infection among users of acitretin, adalimumab, and infliximab compared with methotrexate. Subanalysis by type of serious infection showed a significantly increased risk of cellulitis among users of acitretin compared with methotrexate (PS-adjusted HR, 1.76; 95% CI, 1.11-2.80).
Conclusions and Relevance: Among patients with psoriasis treated with systemic medications in 2 large US claims databases, new users of apremilast, etanercept, and ustekinumab had a decreased rate of serious infection compared with methotrexate.
Research that makes secondary use of administrative and clinical healthcare databases is increasingly influential for regulatory, reimbursement, and other healthcare decision-making. Consequently, there are numerous guidance documents on reporting for studies that use 'real-world' data captured in administrative claims and electronic health record (EHR) databases. These guidance documents are intended to improve transparency, reproducibility, and the ability to evaluate validity and relevance of design and analysis decisions. However, existing guidance does not differentiate between structured and unstructured information contained in EHRs, registries, or other healthcare data sources. While unstructured text is convenient and readily interpretable in clinical practice, it can be difficult to use for investigation of causal questions, e.g., comparative effectiveness and safety, until data have been cleaned and algorithms applied to extract relevant information to structured fields for analysis. The goal of this paper is to increase transparency for healthcare decision makers and causal inference researchers by providing general recommendations for reporting on steps taken to make unstructured text-based data usable for comparative effectiveness and safety research. These recommendations are designed to be used as an adjunct for existing reporting guidance. They are intended to provide sufficient context and supporting information for causal inference studies involving use of natural language processing- or machine learning-derived data fields, so that researchers, reviewers, and decision makers can be confident in their ability to evaluate the validity and relevance of derived measures for exposures, inclusion/exclusion criteria, covariates, and outcomes for the causal question of interest.
We sought to refine understanding about associations identified in prior studies between angiotensin-II receptor blockers, metformin, selective serotonin reuptake inhibitors, fibric-acid derivatives, or calcium channel blockers and progression to glaucoma filtration surgery for open-angle glaucoma (OAG). We used new-initiator, active-comparator cohort designs to investigate these drugs in two data sources. We adjusted for confounders using stabilized inverse-probability-of-treatment weights and evaluated results using "intention-to-treat" and "as-treated" follow-up approaches. In both data sources, Kaplan-Meier curves showed trends for more rapid progression to glaucoma filtration surgery in patients taking calcium channel blockers compared with thiazides with as-treated (MarketScan P = 0.15; Medicare P = 0.03) and intention-to-treat follow-up (MarketScan P < 0.01; Medicare P = 0.10). There was suggestion of delayed progression for selective serotonin reuptake inhibitor compared with tricyclic antidepressants in Medicare, which was not observed in MarketScan. Our study provided support for a relationship between calcium channel blockers and OAG progression but not for other investigated drugs.
BACKGROUND: The case-crossover design may be useful for evaluating the clinical impact of drug-drug interactions in electronic healthcare data; however, experience with the design in this context is limited.
METHODS: Using US healthcare claims data (1994-2013), we evaluated two examples of interacting drugs with prior evidence of harm: (1) cytochrome P450 (CYP)3A4-metabolized statins + clarithromycin or erythromycin and rhabdomyolysis; and (2) clopidogrel + fluoxetine or fluvoxamine and ischemic events. We conducted case-crossover analyses with (1) a three-parameter model with a product term and a six-parameter saturated model that distinguished initiation order of the two drugs; and (2) with or without active comparators.
RESULTS: In the statin example, the three-parameter model produced estimates consistent with prior evidence with the active comparator (product term odds ratio [OR] = 2.05, 95% confidence interval [CI] = 1.00, 4.23) and without (OR = 1.99, 95% CI = 1.04, 3.81). In the clopidogrel example, this model produced results opposite of expectation (OR = 0.78, 95% = 0.68, 0.89), but closer to what was observed in prior studies when active comparator was used (OR = 1.03, 95% CI = 0.90, 1.19). The saturated model revealed heterogeneity of estimates across strata and considerable confounding; strata with concordant clopidogrel exposure likely produced the least biased estimates.
CONCLUSION: The three-parameter model assumes a common drug-drug interaction effect, whereas the saturated model is useful for identifying potential effect heterogeneity or differential confounding across strata. Restriction to certain strata or use of an active comparator may be necessary in the presence of within-person confounding.
BACKGROUND: To the extent that outcomes are mediated through negative perceptions of generics (the nocebo effect), observational studies comparing brand-name and generic drugs are susceptible to bias favoring the brand-name drugs. We used authorized generic (AG) products, which are identical in composition and appearance to brand-name products but are marketed as generics, as a control group to address this bias in an evaluation aiming to compare the effectiveness of generic versus brand medications.
METHODS AND FINDINGS: For commercial health insurance enrollees from the US, administrative claims data were derived from 2 databases: (1) Optum Clinformatics Data Mart (years: 2004-2013) and (2) Truven MarketScan (years: 2003-2015). For a total of 8 drug products, the following groups were compared using a cohort study design: (1) patients switching from brand-name products to AGs versus generics, and patients initiating treatment with AGs versus generics, where AG use proxied brand-name use, addressing negative perception bias, and (2) patients initiating generic versus brand-name products (bias-prone direct comparison) and patients initiating AG versus brand-name products (negative control). Using Cox proportional hazards regression after 1:1 propensity-score matching, we compared a composite cardiovascular endpoint (for amlodipine, amlodipine-benazepril, and quinapril), non-vertebral fracture (for alendronate and calcitonin), psychiatric hospitalization rate (for sertraline and escitalopram), and insulin initiation (for glipizide) between the groups. Inverse variance meta-analytic methods were used to pool adjusted hazard ratios (HRs) for each comparison between the 2 databases. Across 8 products, 2,264,774 matched pairs of patients were included in the comparisons of AGs versus generics. A majority (12 out of 16) of the clinical endpoint estimates showed similar outcomes between AGs and generics. Among the other 4 estimates that did have significantly different outcomes, 3 suggested improved outcomes with generics and 1 favored AGs (patients switching from amlodipine brand-name: HR [95% CI] 0.92 [0.88-0.97]). The comparison between generic and brand-name initiators involved 1,313,161 matched pairs, and no differences in outcomes were noted for alendronate, calcitonin, glipizide, or quinapril. We observed a lower risk of the composite cardiovascular endpoint with generics versus brand-name products for amlodipine and amlodipine-benazepril (HR [95% CI]: 0.91 [0.84-0.99] and 0.84 [0.76-0.94], respectively). For escitalopram and sertraline, we observed higher rates of psychiatric hospitalizations with generics (HR [95% CI]: 1.05 [1.01-1.10] and 1.07 [1.01-1.14], respectively). The negative control comparisons also indicated potentially higher rates of similar magnitude with AG compared to brand-name initiation for escitalopram and sertraline (HR [95% CI]: 1.06 [0.98-1.13] and 1.11 [1.05-1.18], respectively), suggesting that the differences observed between brand and generic users in these outcomes are likely explained by either residual confounding or generic perception bias. Limitations of this study include potential residual confounding due to the unavailability of certain clinical parameters in administrative claims data and the inability to evaluate surrogate outcomes, such as immediate changes in blood pressure, upon switching from brand products to generics.
CONCLUSIONS: In this study, we observed that use of generics was associated with comparable clinical outcomes to use of brand-name products. These results could help in promoting educational interventions aimed at increasing patient and provider confidence in the ability of generic medicines to manage chronic diseases.
During 2017, opioids were associated with 47,600 deaths in the United States, approximately one third of which involved a prescription opioid (1). Amid concerns that overprescribing to patients with acute pain remains an essential factor underlying misuse, abuse, diversion, and unintentional overdose, several states have restricted opioid analgesic prescribing (2,3). To characterize patterns of opioid analgesic use for acute pain in primary care settings before the widespread implementation of limits on opioid prescribing (2,3), patients filling an opioid analgesic prescription for acute pain were identified from a 2014 database of commercial claims. Using a logistic generalized additive model, the probability of obtaining a refill was estimated as a function of the initial number of days supplied. Among 176,607 patients with a primary care visit associated with an acute pain complaint, 7.6% filled an opioid analgesic prescription. Among patients who received an initial 7-day supply, the probability of obtaining an opioid analgesic prescription refill for nine of 10 conditions was <25%. These results suggest that a ≤7-day opioid analgesic prescription might be sufficient for most, but not all, patients seen in primary care settings with acute pain who appear to need opioid analgesics. However, treatment strategies should account for patient and condition characteristics, which might alternatively reduce or extend the anticipated duration of benefit from opioid analgesic therapy.
PURPOSE: The U.S. Food and Drug Administration's Sentinel Initiative "modular programs" have been shown to replicate findings from conventional protocol-driven, custom-programmed studies. One such parallel assessment-dabigatran and warfarin and selected outcomes-produced concordant findings for three of four study outcomes. The effect estimates and confidence intervals for the fourth-acute myocardial infarction-had more variability as compared with other outcomes. This paper evaluates the potential sources of that variability that led to unexpected divergence in findings.
METHODS: We systematically compared the two studies and evaluated programming differences and their potential impact using a different dataset that allowed more granular data access for investigation. We reviewed the output at each of five main processing steps common in both study programs: cohort identification, propensity score estimation, propensity score matching, patient follow-up, and risk estimation.
RESULTS: Our findings point to several design features that warrant greater investigator attention when performing observational database studies: (a) treatment of recorded events (eg, diagnoses, procedures, and dispensings) co-occurring on the index date of study drug dispensing in cohort eligibility criteria and propensity score estimation and (b) construction of treatment episodes for study drugs of interest that have more complex dispensing patterns.
CONCLUSIONS: More precise and unambiguous operational definitions of all study parameters will increase transparency and reproducibility in observational database studies.
INTRODUCTION: Lawyer-submitted reports may have unintended consequences on safety signal detection in spontaneous adverse event reporting systems.
OBJECTIVE: Our objective was to assess the impact of lawyer-submitted reports primarily for one adverse event (AE) on the ability to detect a signal of disproportional reporting for another AE for the same drug in the US FDA Adverse Event Reporting System (FAERS).
METHODS: FAERS reports from January 2004 to September 2015 were used to estimate yearly cumulative proportional reporting ratios (PRRs) for three known drug-AE pairs-isotretinoin-birth defects, atorvastatin-rhabdomyolysis, and rosuvastatin-rhabdomyolysis-with and without lawyer-submitted reports. Isotretinoin and atorvastatin have been the subject of high-profile tort litigation regarding other AEs. A lower bound of the 95% confidence interval (CI) of one or more based on three or more reports defined a signal.
RESULTS: Cumulative PRRs met signaling criteria in all analyses. For isotretinoin, lawyer-submitted reports increased PRRs for birth defects before 2008, with the largest increase in 2006 (2.9 [95% CI 2.4-3.5] to 3.3 [95% CI 2.8-3.9]); lawyer-submitted reports decreased PRRs for birth defects after 2011, with the largest decrease in 2013 (2.2 [95% CI 2.0-2.5] to 1.9 [95% CI 1.7-2.1]). For atorvastatin, lawyer-submitted reports reduced PRRs for rhabdomyolysis after 2013, with the largest decrease in 2015 (18.0 [95% CI 17.1-19.1] to 15.4 [95% CI 14.5-16.2]). Lawyer-submitted reports had little impact on PRRs for rosuvastatin and rhabdomyolysis.
CONCLUSIONS: Inclusion of lawyer-submitted reports in FAERS did not meaningfully distort known safety signals for two drugs subject to high-profile tort litigation for other AEs.
Methodologic research evaluating confounding due to socioeconomic status (SES) in observational studies of medications is limited. We identified 7,109 patients who initiated brand or generic atorvastatin from Medicare claims (2011-2013) linked to electronic medical records and census data. We created a propensity score (PS) containing only claims-based covariates and augmented it with additional claims-based proxies for SES, ZIP code, and block group level SES. Cox models with PS fine-stratification and weighting were used to compare rates of a cardiovascular end point and emergency department visits. Adjustment with only claims-based variables substantially improved balance on all SES variables compared with the unadjusted. Although inclusion of SES in PS models further improved balance on SES variables compared with models with claims-based covariates only, it did not materially change point estimates for either outcome. Inclusion of claims-based proxies may mitigate confounding by SES when aggregate-level SES information is unavailable.
OBJECTIVES: Preference weights derived from general population samples are often used for therapeutic decision making. In contrast, patients with cardiovascular disease may have different preferences concerning the benefits and risks of anticoagulant therapy. Using a discrete choice experiment, we compared preferences for anticoagulant treatment outcomes between the general population and patients with cardiovascular disease.
METHODS: A sample of the general US population and a sample of patients with cardiovascular disease were selected from online panels. We used a discrete choice experiment questionnaire to elicit preferences in both populations concerning treatment benefits and risks. Seven attributes described hypothetical treatments: non-fatal stroke, non-fatal myocardial infarction, cardiovascular death, minor bleeding, major bleeding, fatal bleeding, and the need for monitoring. We measured preference weights and maximum acceptable risks in both populations.
RESULTS: A total of 352 individuals from the general population and 341 patients completed the questionnaire. After propensity score matching, 284 from each group were included in the analysis. On average, the general population members valued a 1% increased risk of fatal bleeding as being the same as a 4.2% increase in a non-fatal myocardial infarction, a 2.8% increase in cardiovascular death, or a 14.1% increase in minor bleeding. Patients, in contrast, perceived a 1% increased risk of fatal bleeding as being the same as a 2.0% increase in a non-fatal myocardial infarction, a 3.2% increase in cardiovascular death, and a 16.7% increase in minor bleeding.
CONCLUSIONS: The general population and patients with cardiovascular disease had slightly different preferences for treatment outcomes. The differences can potentially influence estimated benefits and risks and patient-centered treatment decisions.
Importance: Prices for newer analogue insulin products have increased. Lower-cost human insulin may be effective for many patients with type 2 diabetes.
Objective: To evaluate the association between implementation of a health plan-based intervention of switching patients from analogue to human insulin and glycemic control.
Design, Setting, and Participants: A retrospective cohort study using population-level interrupted times series analysis of members participating in a Medicare Advantage and prescription drug plan operating in 4 US states. Participants were prescribed insulin between January 1, 2014, and December 31, 2016 (median follow-up, 729 days). The intervention began in February 2015 and was expanded to the entire health plan system by June 2015.
Exposures: Implementation of a health plan program to switch patients from analogue to human insulin.
Main Outcomes and Measures: The primary outcome was the change in mean hemoglobin A1c (HbA1c) levels estimated over three 12-month periods: preintervention (baseline) in 2014, intervention in 2015, and postintervention in 2016. Secondary outcomes included rates of serious hypoglycemia or hyperglycemia using ICD-9-CM and ICD-10-CM diagnostic codes.
Results: Over 3 years, 14 635 members (mean [SD] age: 72.5 [9.8] years; 51% women; 93% with type 2 diabetes) filled 221 866 insulin prescriptions. The mean HbA1c was 8.46% (95% CI, 8.40%-8.52%) at baseline and decreased at a rate of -0.02% (95% CI, -0.03% to -0.01%; P <.001) per month before the intervention. There was an association between the start of the intervention and an overall HbA1c level increase of 0.14% (95% CI, 0.05%-0.23%; P = .003) and slope change of 0.02% (95% CI, 0.01%-0.03%; P < .001). After the completion of the intervention, there were no significant differences in changes in the level (0.08% [95% CI, -0.01% to 0.17%]) or slope (<0.001% [95% CI, -0.008% to 0.010%]) of mean HbA1c compared with the intervention period (P = .09 and P = 0.81, respectively). For serious hypoglycemic events, there was no significant association between the start of the intervention and a level (2.66/1000 person-years [95% CI, -3.82 to 9.13]; P = .41) or slope change (-0.66/1000 person-years [95% CI, -1.59 to 0.27]; P = .16). The level (1.64/1000 person-years [95% CI, -4.83 to 8.11]; P = .61) and slope (-0.23/1000 person-years [95% CI, -1.17 to 0.70]; P = .61) changes in the postintervention period were not significantly different compared with the intervention period. The baseline rate of serious hyperglycemia was 22.33 per 1000 person-years (95% CI, 12.70-31.97). For the rate of serious hyperglycemic events, there was no significant association between the start of the intervention and a level (4.23/1000 person-years [95% CI, -8.62 to 17.08]; P = .51) or slope (-0.51/1000 person-years [95% CI, -2.37 to 1.34]; P = .58) change.
Conclusions and Relevance: Among Medicare beneficiaries with type 2 diabetes, implementation of a health plan program that involved switching patients from analogue to human insulin was associated with a small increase in population-level HbA1c.
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