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Discussion guide. Questions used during focus groups. (DOCX 16 kb)

In clinical safety and effectiveness research using secondary health databases, patient medical histories are typically assessed using fixed look-back approaches. Conventional applications of these approaches exclude patients who are not continuously enrolled in the database for the entire look-back period (e.g. one year), and data occurring outside this period is ignored. An alternate approach has been suggested which assesses all of the available data history, though concerns exist that results may be biased by systematic variation in the amount of available database across important study groups. We used applied analyses as well as plasmode simulation methods to explore the application of short (1-year) and long (3-year) fixed look-backs and all-available data approaches in analyses of Medicare fee-for-service (FFS) claims data. We assessed the bias and efficiency of effect estimates when we used the different look-backs to 1) assess cohort eligibility and to 2) identify and adjust for confounders. In the applied analysis, we evaluated the effect of statin initiation (vs. non-use) on incidence of 1) cancer within six months (a negative control outcome we expected a priori to be null) and 2) all-cause mortality within two years. In the plasmode simulation, exposures (conceptually: statin initiation vs. non-initiation) and outcomes (conceptually: inpatient hospitalization) were simulated as a function of self-reported interview data obtained from the Medicare Current Beneficiary Survey (MCBS, which represented the true underlying confounder of exposure-outcome associations. We evaluated estimates after applying different look-back approaches in the linked claims data. Compared to short fixed look-back approaches, all-available approaches selected cohorts with superior classification and produced less biased estimates. Compared to long fixed look-back approaches, all-available approaches selected more inclusive cohorts and produced more precise estimates. Though these studies were conducted in a fairly narrow (applied) setting, our findings provide real-world evidence that using all-available look-backs to classify patient histories is superior to fixed look-back approaches. Our findings provide context to investigators seeking to understand the mechanisms through which the different look-backs may produce different estimates.

ObjectiveTo evaluate trends in urodynamic procedures in the U.S. males from 2000–2012 and determine if a 2010 decline in reimbursement was associated with decreased utilization.Subjects and methodsWe analyzed 2000–2012 administrative healthcare claims from Truven Health’s Marketscan Database and evaluated males ≥18 years of age. We identified cystometrograms and any concurrent procedures using procedure billing codes. Covariates included age, year of cystometrogram, region and associated diagnosis codes. We estimated standardized cystometrogram utilization rates per 10,000 person-years (PY). We used age, region, and calendar year adjusted Poisson regression models to estimate the independent effect of calendar year and region.ResultsDuring 127,558,186 PY of observation, we identified 153,168 cystometrograms for an overall utilization rate of 12.0 per 10,000 PY (95% CI 11.9–12.1). Cystometrogram utilization increased with age, peaking at age 85 with a rate of 77.7 per 10,000 PY (95% CI 74.7–80.7). Adjusted cystometrogram utilization rate ratios show that compared to a referent of 2000–2004, utilization was significantly higher in each year 2005 to 2011 among all patients and in 2012 among patients ≥ 65. Standardized utilization rates peaked in 2008 at 12.4 per 10,000 PY (95% CI 12.2–12.6), remained elevated until 2010, then decreased slightly in 2011 and substantially in 2012 to 8.5 per 10,000 PY (95% CI 8.4–8.7).ConclusionsUtilization of urodynamic procedures increased until 2010 and decreased thereafter. Utilization was greatest among men older than 65.

Objective Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations’ variability on patient characteristics. Materials and Methods Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams’ cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics. Results On average, the teams’ interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts’ size varied from one-third of the master cohort size to 10 times the cohort size (2159–63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3–16.2%). The teams’ cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5. Conclusions Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.

Alpha-1 blockers, often used to treat benign prostate hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storms release. We conducted a prevalent-user active-comparator cohort study to assess association between alpha-1 blocker use and risks of three COVID-19 outcomes: diagnosis, hospitalization, and hospitalization requiring intensive services. Our study included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH therapy during the period between November 2019 and January 2020, found in electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We found no differential risk for any of COVID-19 outcome, pointing to the need for further research on potential COVID-19 therapies.

Introduction: Angiotensin converting enzyme inhibitors (ACEs) and angiotensin receptor blockers (ARBs) could influence infection risk of coronavirus disease (COVID-19). Observational studies to date lack pre-specification, transparency, rigorous ascertainment adjustment and international generalizability, with contradictory results., Methods: Using electronic health records from Spain (SIDIAP) and the United States (Columbia University Irving Medical Center and Department of Veterans Affairs), we conducted a systematic cohort study with prevalent ACE, ARB, calcium channel blocker (CCB) and thiazide diuretic (THZ) use to determine relative risk of COVID-19 diagnosis and related hospitalization outcomes. The study addressed confounding through large-scale propensity score adjustment and negative control experiments., Results: Following over 1.1 million antihypertensive users identified between November 2019 and January 2020, we observed no significant difference in relative COVID-19 diagnosis risk comparing ACE/ARB vs CCB/THZ monotherapy (hazard ratio: 0.98; 95% CI 0.84 - 1.14), nor any difference for mono/combination use (1.01; 0.90 - 1.15). ACE alone and ARB alone similarly showed no relative risk difference when compared to CCB/THZ monotherapy or mono/combination use. Directly comparing ACE vs. ARB demonstrated a moderately lower risk with ACE, non-significant for monotherapy (0.85; 0.69 - 1.05) and marginally significant for mono/combination users (0.88; 0.79 - 0.99). We observed, however, no significant difference between drug- classes for COVID-19 hospitalization or pneumonia risk across all comparisons., Conclusion: There is no clinically significant increased risk of COVID-19 diagnosis or hospitalization with ACE or ARB use. Users should not discontinue or change their treatment to avoid COVID-19.