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ImportanceDelivery of adjuvant chemotherapy can differ substantially between trial and real-world populations. Adherence metrics like relative dose intensity (RDI) cannot capture the timing of modifications and mask differences in the total amount of chemotherapy received.ObjectiveTo compare oxaliplatin delivery between MOSAIC trial participants and patients treated in the US Oncology Network with stage III colon cancer using a longitudinal cumulative dose (LCD).Design, Setting, and ParticipantsThis cohort study used secondary data from the MOSAIC trial, an international randomized clinical trial (concluded in 2004), and electronic health records from US Oncology (2009-2018), a network of community oncology practices in the US. It included participants in MOSAIC with stage III colon cancer who were randomized to receive treatment with oxaliplatin and fluorouracil/leucovorin (n = 663) and US Oncology patients with stage III colon cancer who were treated with a modified FOLFOX-6 regimen (n = 2523).ExposuresOxaliplatin and fluorouracil/leucovorin.Outcomes and MeasuresWe evaluated RDI and LCD over time and at the end of treatment in the MOSAIC and US Oncology populations. We used bootstrapping to estimate 95% confidence bands for LCD differences between the populations.ResultsThe 663 MOSAIC participants (296 women [44.7%]) and 2523 US Oncology patients (1245 women [49.4%]) were generally similar with respect to demographic characteristics. Median RDI was lower in US Oncology (80% in MOSAIC vs 70% in US Oncology). The LCD also suggested differences in the total amount of oxaliplatin received between populations; the final median LCD in US Oncology was 10.2% lower than in MOSAIC, equivalent to receiving 1.2 fewer treatment cycles less of oxaliplatin. This difference only began 133 days into treatment and persisted after accounting for covariates, likely in terms of more frequent oxaliplatin treatment discontinuation in US Oncology patients than their MOSAIC counterparts.Conclusions and RelevanceThe study results suggest that real-world patients in community practice in the US treated with modified FOLFOX 6 received less oxaliplatin than their historical counterparts in the MOSAIC trial, with differences manifesting late in the treatment course. The LCD allowed us to identify the amount and extent of these differences, the timing of which was unclear when using RDI alone.Trial RegistrationClinicalTrials.gov identifier: NCT00275210

Implausibly high algorithm-identified cancer incidence within a new user study after medication initiation may result from increased healthcare utilization (HU) around initiation ("catch-up care") that increases diagnostic opportunity. Understanding the relationships between HU prior to and around initiation and subsequent cancer rates and timing is important to avoiding protopathic bias.We identified a cohort of 417 458 Medicare beneficiaries (2007-2014) aged ≥66 initiating an antihypertensive (AHT) after ≥180 days of non-use. Initiators were stratified into groups of 0, 1, 2-3, and ≥4 outpatient visits (OV) 60-360 days before initiation. We calculated algorithm-identified colorectal cancer (aiCRC) rates stratified by OVs and time since AHT initiation: (0-90, 91-180, 181-365, 366-730, and 731+ days). We summarized HU -360/+60 days around AHT initiation by aiCRC timing: (0-29, 30-89, 90-179, and ≥180 days).AiCRC incidence (311 per 100 000 overall) peaked in the first 0-90 days, was inversely associated with HU before initiation, and stabilized ≥180 days after AHT initiation. Catch-up care was greatest among persons with aiCRCs identified30 days in follow-up. Catch-up care magnitude decreased as time to the aiCRC date increased, with aiCRCs identified ≥180 days after AHT initiation exhibiting similar HU compared with the full cohort.Lower HU before-and increased HU around AHT initiation-seem to drive excess short-term aiCRC incidence. Person-time and case accrual should only begin when incidence stabilizes. When comparison groups within a study differ by HU, outcome-detection bias may exist. Similar observations may exist in other settings when typical HU is delayed (e.g., cancer screening during SARS-CoV-2).

We aim to assess the reporting of key patient-level demographic and clinical characteristics among COVID-19 related randomized controlled trials (RCTs).We queried English-language articles from PubMed, Web of Science, clinicaltrials.gov, and the CDC library of gray literature databases using keywords of "coronavirus," "covid," "clinical trial" and "randomized controlled trial" from January 2020 to June 2021. From the search, we conducted an initial review to rule-out duplicate entries, identify those that met inclusion criteria (i.e., had results), and exclude those that did not meet the definition of an RCT. Lastly, we abstracted the demographic and clinical characteristics reported on within each RCT.From the initial 43 627 manuscripts, our final eligible manuscripts consisted of 149 RCTs described in 137 articles. Most of the RCTs (113/149) studied potential treatments, while fewer studied vaccines (29), prophylaxis strategies (5), and interventions to prevent transmission among those infected (2). Study populations ranged from 10 to 38 206 participants (median = 100, IQR: 60-300). All 149 RCTs reported on age, 147 on sex, 50 on race, and 110 on the prevalence of at least one comorbidity. No RCTs reported on income, urban versus rural residence, or other indicators of socioeconomic status (SES).Limited reporting on race and other markers of SES make it difficult to draw conclusions about specific external target populations without making strong assumptions that treatment effects are homogenous. These findings highlight the need for more robust reporting on the clinical and demographic profiles of patients enrolled in COVID-19 related RCTs.

To describe the creation of prevalent new user (PNU) cohorts and compare the relative bias and computational efficiency of several alternative analytic and matching approaches in PNU studies.In a simulated cohort, we estimated the effect of a treatment of interest vs a comparator among those who switched to the treatment of interest using the originally proposed time-conditional propensity score (TCPS) matching, standardized morbidity ratio weighting (SMRW), disease risk scores (DRS), and several alternative propensity score matching approaches. For each analytic method, we compared the average RR (across 2000 replicates) to the known risk ratio (RR) of 1.00.SMRW and DRS yielded unbiased results (RR = 0.998 and 0.997, respectively). TCPS matching with replacement was also unbiased (RR = 0.999). TCPS matching without replacement was unbiased when matches were identified starting with patients with the shortest treatment history as initially proposed (RR = 0.999), but it resulted in very slight bias (RR = 0.983) when starting with patients with the longest treatment history. Similarly, creating a match pool without replacement starting with patients with the shortest treatment history yielded an unbiased estimate (RR = 0.997), but matching with the longest treatment history first resulted in substantial bias (RR = 0.903). The most biased strategy was matching after selecting one random comparator observation per individual that continued on the comparator (RR = 0.802).Multiple analytic methods can estimate treatment effects without bias in a PNU cohort. Still, researchers should be wary of introducing bias when selecting controls for complex matching strategies beyond the initially proposed TCPS.

Confounding by indication poses a significant threat to the validity of non-experimental studies assessing effectiveness and safety of medical interventions. While no different from other forms of confounding in theory, confounding by indication often requires specific methods to address the bias it creates in addition to common epidemiological adjustment or restriction methods. Clinical indication influencing treatment prescription is patient-specific and complex, making it challenging to measure within non-experimental research. Restriction of the study population to patients with the indication for treatment would effectively mitigate confounding by indication and bring about comparability between exposure and comparator populations with respect to probability of the exposure. Active comparators are often an effective practical solution to restrict the study population in this manner when indication cannot be measured accurately. This article discusses various forms of confounding by indication, the utility of active comparators for non-experimental studies of treatment effects, and the active comparator, new user (ACNU) study design to implicitly condition on indication. Considerations for selecting active comparators and conducting an ACNU study design are discussed to enable increased adoption of these methods, improve quality of non-experimental studies, and ultimately strengthen our evidence base for intended and unintended treatment effects in relevant target populations.

Background: Adherence and persistence studies face several methodologic difficulties, including short-term mortality. We compared approaches to quantify adherence and persistence to first line (1L) oral targeted therapy (TT) in patients diagnosed with metastatic renal cell carcinoma (mRCC). Methods: Patients with mRCC ages 66 years or more who initiated TTs within 4 months of diagnosis were identified in the Surveillance, Epidemiology, and End Results Medicare-linked database (2007–2015). Adherence [proportion of days covered (PDC) >80%] was calculated using (i) PDC with a fixed 6-month denominator including then excluding patients who died within the 6 months and (ii) PDC with a denominator measuring time on treatment. Risk of nonpersistence was obtained by censoring death or treating death as a competing risk using cumulative incidence functions. Results: Among 485 patients with mRCC initiating a 1L oral TT (sunitinib, 64%; pazopanib, 25%; other, 11%), 40% died within 6 months. Adherence was higher after restricting to patients who survived (60%) compared with including those patients and assigning zero days covered after death (47%). Risk of nonpersistence was higher when censoring patients at death, 0.91 [95% confidence interval (CI), 0.88–0.94], compared with treating death as a competing risk, 0.75 (95% CI, 0.71–0.79). Conclusions: Different approaches to handling death resulted in different adherence and persistence estimates in the metastatic setting. Future studies should explicitly report the proportion of patient deaths over time and explore appropriate methods to account for death as competing risk. Impact: Use of several approaches can provide a more comprehensive picture of medication-taking behavior in the metastatic setting where death is a major competing risk.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Background: In response to concerns about opioid addiction following surgery, many states have implemented laws capping the days supplied for initial postoperative prescriptions. However, few studies have examined changes in the risk of prolonged opioid use associated with the initial amount prescribed. Objective: The objective of this study was to estimate the risk of prolonged opioid use associated with the length of initial opioid prescribed and the potential impact of prescribing limits. Research design: Using Medicare insurance claims (2007-2017), we identified opioid-naive adults undergoing surgery. Using G-computation methods with logistic regression models, we estimated the risk of prolonged opioid use (≥1 opioid prescription dispensed in 3 consecutive 30-d windows following surgery) associated with the varying initial number of days supplied. We then estimate the potential reduction in cases of prolonged opioid use associated with varying prescribing limits. Results: We identified 1,060,596 opioid-naive surgical patients. Among the 70.0% who received an opioid for postoperative pain, 1.9% had prolonged opioid use. The risk of prolonged use increased from 0.7% (1 d supply) to 4.4% (15+ d). We estimated that a prescribing limit of 4 days would be associated with a risk reduction of 4.84 (3.59, 6.09)/1000 patients and would be associated with 2255 cases of prolonged use potentially avoided. The commonly used day supply limit of 7 would be associated with a smaller reduction in risk [absolute risk difference=2.04 (-0.17, 4.25)/1000]. Conclusions: The risk of prolonged opioid use following surgery increased monotonically with increasing prescription duration. Common prescribing maximums based on days supplied may impact many patients but are associated with relatively low numbers of reduced cases of prolonged use. Any prescribing limits need to be weighed against the need for adequate pain management.

Long-term sequelae of severe acute respiratory coronavirus-2 (SARS-CoV-2) infection may include an increased incidence of diabetes. Our objective was to describe the temporal relationship between new diagnoses of diabetes mellitus and SARS-CoV-2 infection in a nationally representative database. There appears to be a sharp increase in diabetes diagnoses in the 30 days surrounding SARS-CoV-2 infection, followed by a decrease in new diagnoses in the post-acute period, up to 360 days after infection. These results underscore the need for further investigation, as understanding the timing of new diabetes onset after COVID-19 has implications regarding potential etiology and screening and treatment strategies.

BACKGROUND: While preoperative gabapentinoids are commonly used in surgical multimodal analgesia protocols, little is known regarding the effects this therapy has on prolonged postsurgical opioid use. In this observational study, we used data from a large integrated health care system to estimate the association between preoperative day-of-surgery gabapentinoids and the risk of prolonged postsurgical opioid use. METHODS: We identified adults age ≥65 years undergoing major therapeutic surgical procedures from a large integrated health care system from 2016 to 2019. Exposure to preoperative gabapentinoids on the day of surgery was measured using inpatient medication administration records, and the outcome of prolonged opioid use was measured using outpatient medication orders. We used stabilized inverse probability of treatment-weighted log-binomial regression to estimate risk ratios and 95% confidence intervals (CIs) of prolonged opioid use, comparing patients who received preoperative gabapentinoids to those who did not and adjusting for relevant clinical factors. The main analysis was conducted in the overall surgical population, and a secondary analysis was conducted among procedures where at least 30% of all patients received a preoperative gabapentinoid. RESULTS: Overall, 13,958 surgical patients met inclusion criteria, of whom 21.0% received preoperative gabapentinoids. The observed 90-day risk of prolonged opioid use following surgery was 0.91% (95% CI, 0.77–1.08). Preoperative gabapentinoid administration was not associated with a reduced risk of prolonged opioid use in the main analysis conducted in a broad surgical population (adjusted risk ratio [adjRR], 1.19 [95% CI, 0.67–2.12]) or in the secondary analysis conducted in patients undergoing colorectal resection, hip arthroplasty, knee arthroplasty, or hysterectomy (adjRR, 1.01 [95% CI, 0.30–3.33]). CONCLUSIONS: In a large integrated health system, we did not find evidence that preoperative gabapentinoids were associated with reduced risk of prolonged opioid use in patients undergoing a broad range of surgeries.

Objective: To evaluate the association between COVID-19 infection and severity of infection on longer-term glycemic control and weight in persons with type 2 diabetes mellitus (T2D) in the U.S. Research Design and Methods: We conducted a retrospective cohort study using longitudinal electronic health record data of patients with COVID-19 infection from the National COVID Cohort Collaborative (N3C). Patients were ≥18 years old with an ICD-10 diagnosis of T2D and at least 1 HbA1c and weight measurement prior to and after an index date of their first COVID-19 diagnosis or negative SARS-CoV-2 test. We used propensity scores to identify a matched cohort balanced on demographic characteristics, comorbidities, and medications used to treat diabetes. The primary outcome was the post-index average hemoglobin A1c (HbA1c) and post-index average weight over a 1-year time period beginning 90 days after the index date among patients who did and did not have COVID-19 infection. Secondary outcomes were post-index average HbA1c and weight in patients who required hospitalization or mechanical ventilation. Results: There was no significant difference in the post-index average HbA1c or weight in patients who had COVID-19 infection compared to controls. Mechanical ventilation was associated with a decrease in average HbA1c after COVID-19 infection. Conclusions: In a multicenter cohort of patients in the U.S. with pre-existing T2D, there was no significant change in longer-term average HbA1c or weight among patients who had COVID-19 infection. For patients with COVID-19 infection, mechanical ventilation was associated with a decrease in HbA1c after infection.

Background Inverse probability of treatment weighting (IPTW) may be biased by influential observations, which can occur from misclassification of strong exposure predictors. Methods We evaluated bias and precision of IPTW estimators in the presence of a misclassified confounder and assessed the effect of propensity score (PS) trimming. We generated 1000 plasmode cohorts of size N = 10 000, sampled with replacement from 6063 NHANES respondents (1999-2014) age 40 to 79 with labs and no statin use. We simulated statin exposure as a function of demographics and CVD risk factors; and outcomes as a function of 10-year CVD risk score and statin exposure (rate ratio [RR] = 0.5). For 5% of the people in selected populations (eg, all patients, exposed, those with outcomes), we randomly misclassified a confounder that strongly predicted exposure. We fit PS models and estimated RRs using IPTW and 1:1 PS matching, with and without asymmetric trimming. Results IPTW bias was substantial when misclassification was differential by outcome (RR range: 0.38-0.63) and otherwise minimal (RR range: 0.51-0.53). However, trimming reduced bias for IPTW, nearly eliminating it at 5% trimming (RR range: 0.49-0.52). In one scenario, when the confounder was misclassified for 5% of those with outcomes (0.3% of cohort), untrimmed IPTW was more biased and less precise (RR = 0.37 [SE(logRR) = 0.21]) than matching (RR = 0.50 [SE(logRR) = 0.13]). After 1% trimming, IPTW estimates were unbiased and more precise (RR = 0.49 [SE(logRR) = 0.12]) than matching (RR = 0.51 [SE(logRR) = 0.14]). Conclusions Differential misclassification of a strong predictor of exposure resulted in biased and imprecise IPTW estimates. Asymmetric trimming reduced bias, with more precise estimates than matching.

Background: At least two-thirds of people with hemodialysis-dependent kidney failure have comorbid diabetes which associates with higher mortality. Some GLP-1 receptor agonists (RA) do not require renal dosing and could uniquely benefit dialysis patients, individuals with tremendous cardiovascular disease burden. Methods: Using data from a national surveillance system for kidney failure in the U.S. that includes Medicare Part D prescription drug claims, we: 1) calculated the quarterly prevalence of antihyperglycemic use among diabetic hemodialysis patients from 2012 to 2017 and 2) characterized antihyperglycemic users in October 2017. Results: Among the 129,865 hemodialysis patients with comorbid diabetes, insulin was the most prescribed agent (33%) , followed by sulfonylureas (SU, 7%) and DPP4 inhibitors (DPP4i, 5%) in October 2017. SU use fell, and DPP4i use rose from 2012 to 2017. Less than 5% of patients received combination therapy (insulin + a 2nd agent; Figure 1) . Disregarding kidney disease as an atherosclerotic cardiovascular disease (ASCVD) risk factor, 64% of patients met American Diabetes Association ASCVD criteria and 98% were at high risk for ASCVD in 2017. GLP-1 RA were prescribed to Conclusions: GLP-1 RA use by hemodialysis patients is limited. There is compelling need to establish the safety and efficacy of GLP-1 RA in this high-risk population. Disclosure K. Klein: None. V. Pate: None. M. M. Assimon: None. T. Stürmer: Research Support; Novo Nordisk. Stock/Shareholder; Novartis AG, Novo Nordisk, Roche Pharmaceuticals. Other Relationship; AbbVie Inc., Boehringer Ingelheim International GmbH, GlaxoSmithKline plc., Takeda Pharmaceutical Company Limited, UCB, Inc. J. B. Buse: Consultant; Alkahest, Anji, AstraZeneca, Boehringer Ingelheim International GmbH, Cirius Therapeutics, Inc., Eli Lilly and Company, Fortress biotech, GentiBio, Glycadia, Glyscend, Janssen Pharmaceuticals, Inc., Mellitus Health, Moderna, Inc., Pendulum Therapeutics, Praetego, LLC, Stability Health, Valo, Zealand Pharma A/S. Research Support; AstraZeneca, Dexcom, Inc., Eli Lilly and Company, Novo Nordisk, vTv Therapeutics. Stock/Shareholder; Glyscend, Mellitus Health, Pendulum Therapeutics, PhaseBio Pharmaceuticals, Inc., Praetego, LLC, Stability Health. Other Relationship; Adocia, AstraZeneca, Eli Lilly and Company, Intarcia Therapeutics, Inc., MannKind Corporation, Novo Nordisk, Sanofi, Senseonics, vTv Therapeutics. J. Flythe: Advisory Panel; Fresenius Medical Care. Consultant; AstraZeneca. Research Support; Fresenius Medical Care.

OBJECTIVE To evaluate the association of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severity of infection with longer-term glycemic control and weight in people with type 2 diabetes (T2D) in the U.S. RESEARCH DESIGN AND METHODS We conducted a retrospective cohort study using longitudinal electronic health record data of patients with SARS-CoV-2 infection from the National COVID Cohort Collaborative (N3C). Patients were ≥18 years old with an ICD-10 diagnosis of T2D and at least one HbA1c and weight measurement prior to and after an index date of their first coronavirus disease 2019 (COVID-19) diagnosis or negative SARS-CoV-2 test. We used propensity scores to identify a matched cohort balanced on demographic characteristics, comorbidities, and medications used to treat diabetes. The primary outcome was the postindex average HbA1c and postindex average weight over a 1 year time period beginning 90 days after the index date among patients who did and did not have SARS-CoV-2 infection. Secondary outcomes were postindex average HbA1c and weight in patients who required hospitalization or mechanical ventilation. RESULTS There was no significant difference in the postindex average HbA1c or weight in patients who had SARS-CoV-2 infection compared with control subjects. Mechanical ventilation was associated with a decrease in average HbA1c after COVID-19. CONCLUSIONS In a multicenter cohort of patients in the U.S. with preexisting T2D, there was no significant change in longer-term average HbA1c or weight among patients who had COVID-19. Mechanical ventilation was associated with a decrease in HbA1c after COVID-19.

Confounding can cause substantial bias in nonexperimental studies that aim to estimate causal effects. Propensity score methods allow researchers to reduce bias from measured confounding by summarizing the distributions of many measured confounders in a single score based on the probability of receiving treatment. This score can then be used to mitigate imbalances in the distributions of these measured confounders between those who received the treatment of interest and those in the comparator population, resulting in less biased treatment effect estimates. This methodology was formalized by Rosenbaum and Rubin in 1983 and, since then, has been used increasingly often across a wide variety of scientific disciplines. In this review article, we provide an overview of propensity scores in the context of real-world evidence generation with a focus on their use in the setting of single treatment decisions, that is, choosing between two therapeutic options. We describe five aspects of propensity score analysis: alignment with the potential outcomes framework, implications for study design, estimation procedures, implementation options, and reporting. We add context to these concepts by highlighting how the types of comparator used, the implementation method, and balance assessment techniques have changed over time. Finally, we discuss evolving applications of propensity scores.

PURPOSE: Medications with anticholinergic and sedative properties are widely used among older adults despite strong evidence of harm. The drug burden index (DBI), a pharmacological screening tool, measures these properties across drug classes, and higher DBI drug exposure (DBI > 1) has been associated with certain physical function-related adverse events. Our aim was to quantify mean daily DBI drug exposure among older adults in the United States (US). METHODS: We screened medications for DBI properties and operationalized the DBI for US Medicare claims. We then conducted a retrospective cohort study of a 20% random, nationwide sample of 4 137 384 fee-for-service Medicare beneficiaries aged 66+ years (134 757 039 person-months) from January 2013 to December 2016. We measured the monthly distribution based on mean daily DBI, categorized as (a) >0 vs 0 (any use) and (b) 0, 0 < DBI ≤ 1, 1 < DBI ≤ 2, and DBI > 2, and examined temporal trends. We described patient-level factors (eg, demographics, healthcare use) associated with high (>2) vs low (0 < DBI≤1) DBI drug exposure. RESULTS: The distribution of the mean daily DBI, aggregated at the month-level, was: 58.1% DBI = 0, 29.0% 0 < DBI≤1, 9.3% 1 < DBI≤2, and 3.7% DBI > 2. Predictors of high monthly DBI drug exposure (DBI > 2) included certain indicators of increased healthcare use (eg, high number of drug claims), white race, younger age, frailty, and a psychosis diagnosis code. CONCLUSIONS: The predictors of high DBI drug exposure can inform discussions between patients and providers about medication appropriateness and potential deprescribing. Future Medicare-based studies should assess the association between the DBI and adverse events.

Background Ondansetron is commonly used to treat nausea and vomiting in pregnancy despite inconclusive evidence of its safety. Previous studies have reported no increase in risk of miscarriage but relied on methods that failed to account for gestational weeks at risk and non-user comparators, which may increase the potential for unmeasured confounding. Our objective was to estimate the risk of miscarriage among women prescribed ondansetron vs alternative antiemetics during the first 20 weeks of pregnancy. Methods A pregnancy cohort was created using electronic health record data from a health care system in North Carolina. Women were classified as exposed to either ondansetron or comparator antiemetics (metoclopramide or promethazine) based on the first antiemetic prescription received in the first 20 weeks of gestation. Cumulative incidence of miscarriage at 20 weeks was estimated in each antiemetic group. Hazard ratios (HR) were estimated with 95% confidence intervals and measured confounding was controlled using inverse probability of treatment weights. Sensitivity analyses assessed the potential impact of exposure misclassification, latency period, and selection bias. Results We identified 2620 eligible pregnancies with antiemetic orders; 65% had a first ondansetron order and 35% had a first comparator antiemetic order. In total, 95 women had a miscarriage. After adjustment, there was no difference in risk of miscarriage (HR 1.21, 95% CI 0.77, 1.90). Results from the per-protocol and other sensitivity analyses were similar to the main analysis. Conclusions We did not observe an increase in the risk of miscarriage for pregnancies exposed to ondansetron vs comparator antiemetics.

Purpose Estimates of cancer therapy effects can differ in clinical trials and clinical practice, partly due to underrepresentation of certain patient subgroups in trials. We utilize a hybrid approach, combining clinical trial and real-world data, to estimate the comparative effectiveness of two adjuvant chemotherapy regimens for colon cancer. Methods We identified patients aged 66 and older enrolled in the Multicenter International Study of Oxaliplatin/5FU-LV in the Adjuvant Treatment of Colon Cancer. Similar patients were identified in the Surveillance, Epidemiology, and End Results (SEER)-Medicare database, initiating adjuvant chemotherapy with either 5-fluorouracil (5FU) alone or in combination with oxaliplatin (FOLFOX). We used logistic regression to estimate the likelihood of trial enrollment as a function of age, sex, and substage. Using inverse odds of sampling weights (IOSW), we compared 5-year mortality in patients randomized to FOLFOX vs 5FU using weighted Cox proportional hazards regression, the Nelson-Aalen estimator for cumulative hazards, and bootstrapping for 95% confidence intervals (CIs). Results There were 690 trial participants and 3834 SEER-Medicare patients. The SEER-Medicare population was older and had a higher proportion of stage IIIB and IIIC patients than the trial. After controlling for differences between populations, the IOSW 5-year HR was 1.21 (0.89, 1.65), slightly farther from the null than the trial estimate (HR = 1.14, 95%CI: 0.87, 1.49). Conclusions This study supports mounting evidence of little to no incremental reduction in 5-year mortality for FOLFOX vs 5FU in older adults with stage II-III colon cancer, emphasizing the importance of combining clinical trial and real-world data to support such conclusions.

The glycopeptide antibiotic vancomycin is a mainstay in the treatment of Gram-positive infection. While its association with acute kidney injury (AKI) has waxed and waned, recent data suggest nephrotoxicity, even as mono-therapy. Our study aimed to evaluate the 2-week risk of AKI after at least 3 days of intravenous vancomycin mono-therapy initiated within 5 days of hospitalization compared to other intravenous antibiotics used for similar indications. We used a new user-active comparator study design and identified patients with a first hospitalization during which they received vancomycin or comparator, from commercial claims based in the United States. We estimated incidence rates, hazard ratios using adjusted cox-regression models, and standardized mortality/morbidity ratio weighted cox-regression models. In the 32,997 patients vancomycin was used in 17% of patients and 129 cases of AKI were observed. Overall incidence of AKI was 9.3 (95% CI 0.78–1.22) per 100 person-years. The adjusted hazard ratio for vancomycin versus all other comparators was 0.74 (95% CI 0.45–1.21). Separate models for respective comparators resulted in hazard ratios below the null, except for vancomycin vs. cefazolin. Intravenous vancomycin mono-therapy does not increase the risk of AKI compared to other intravenous antibiotics used for similar indication in this cohort of hospitalized patients.

Much has been written about real-world evidence (RWE), a concept that offers an understanding of the effects of healthcare interventions using routine clinical data. The reflection of diverse real-world practices is a double-edged sword that makes RWE attractive but also opens doors to several biases that need to be minimised both in the design and analytical phases of non-experimental studies. Additionally, it is critical to ensure that researchers who conduct these studies possess adequate methodological expertise and ability to accurately implement these methods. Critical design elements to be considered should include a clearly defined research question using a causal inference framework, choice of a fit-for-purpose data source, inclusion of new users of a treatment with comparators that are as similar as possible to that group, accurately classifying person-time and deciding censoring approaches. Having taken measures to minimise bias 'by design', the next step is to implement appropriate analytical techniques (for example propensity scores) to minimise the remnant potential biases. A clear protocol should be provided at the beginning of the study and a report of the results after, including caveats to consider. We also point the readers to readings on some novel analytical methods as well as newer areas of application of RWE. While there is no one-size-fits-all solution to evaluating RWE studies, we have focused our discussion on key methods and issues commonly encountered in comparative observational cohort studies with the hope that readers are better equipped to evaluate non-experimental studies that they encounter in the future. Graphical abstract.

PURPOSE Outpatient cardiac rehabilitation (CR) participation after myocardial infarction (MI) reduces all-cause mortality; however, less is known about effects of CR on post-MI hospitalization. The study objective was to investigate effects of CR on hospitalization following acute MI among older adults. METHODS Medicare beneficiaries aged 65 to 88 yr hospitalized in 2008 with acute MI, who survived at least 60 d post-discharge, had a revascularization procedure during index hospitalization, and did not have an MI in previous year were eligible for this study. CR initiation was assessed in the 60 d post-discharge. Competing risk survival analysis was used to estimate the proportion of discharged beneficiaries hospitalized between the end of 60-d exposure window and December 31, 2009, treating death as a competing event. RESULTS The mean ± SD age of 32 851 Medicare beneficiaries meeting study criteria was 75 ± 6.0 yr, approximately half were male (52%), and the majority were white (88%). In this study, 21% of beneficiaries initiated CR within the exposure window. At 1 yr post-discharge, CR initiators had a lower risk of recurrent MI (4.2% [95% CI, 3.5-5.1]), cardiovascular (15.7% [95% CI, 14.3-17.2]), and all-cause (30.4% [95% CI, 28.8-32.1]) hospitalization than noninitiators (5.2% [95% CI, 5.0-5.5]; 18.0% [95% CI, 17.6-18.4]; and 33.2% [95% CI, 32.5-33.8], respectively). There was no difference in fracture risk (negative control outcome). CONCLUSIONS This study provides evidence that CR can reduce the 1-yr risk of cardiovascular and all-cause hospital admissions in Medicare aged MI survivors.

Over the past century, the field of epidemiology has evolved and adapted to changing public health needs. Challenges include newly emerging public health concerns across broad and diverse content areas, new methods, and vast data sources. We recognize the need to engage and educate the next generation of epidemiologists and prepare them to tackle these issues of the 21st century. In this commentary, we suggest a skeleton framework upon which departments of epidemiology should build their curriculum. We propose domains that include applied epidemiology, biological and social determinants of health, communication, creativity and ability to collaborate and lead, statistical methods, and study design. We believe all students should gain skills across these domains to tackle the challenges posed to us. The aim is to train smart thinkers, not technicians, to embrace challenges and move the expanding field of epidemiology forward.

Objective To evaluate the risk of venous thromboembolism (VTE, i.e. deep vein thrombosis or pulmonary embolism, or both) following new use of NSAIDs in a long-term cohort of U.S. women. Methods We investigated initiation of coxibs and traditional NSAIDs (excluding aspirin) and incident VTE in 39 876 women enrolled in the Women’s Health Study from 1993–95 and followed with yearly questionnaires until 2012. We defined initiation as the first reported use of NSAIDs for ≥4 days per month. Incident VTE was confirmed by an end point committee. We estimated hazard ratios (HRs) and risk differences (RDs, expressed as percentages) comparing NSAID initiation with non-initiation and acetaminophen initiation (active comparator) via standardization using a propensity score that incorporated age, BMI, calendar time, and relevant medical, behavioural, and socioeconomic variables updated over time. Results The HR (95% CI) for risk of VTE in the as treated analyses comparing initiation with non-initiation, was 1.5 (1.2, 1.8) for any NSAID, 1.3 (1.1, 1.7) for traditional NSAIDs, and 2.0 (1.3, 3.1) for coxibs, with 2-year RDs 0.11, 0.08 and 0.32, respectively. When comparing the risk of VTE after initiation of any NSAID with that after acetaminophen initiation, the HRs were 0.9 (0.6, 1.5), 0.9 (0.5, 1.5) and 1.4 (0.6, 3.4), with 2-year RDs 0.03, –0.01, and 0.13, respectively. Conclusion New use of NSAIDs was associated with increased VTE risk compared with non-use, but the association was null or diminished when compared with acetaminophen initiation. Elevated VTE risks associated with NSAID use in observational studies may in part reflect different baseline risks among individuals who need analgesics and may overstate the risk patients incur compared with pharmacologic alternatives.

The use of hydroxychloroquine or chloroquine (HCQ/CQ) as monotherapy or combined with azithromycin for the treatment of coronavirus disease 2019 (COVID-19) may increase the risk of serious cardiovascular adverse events (SCAEs).Our objective was to describe and evaluate the risk of SCAEs with HCQ/CQ as monotherapy or combined with azithromycin compared with that for therapeutic alternatives.We performed a disproportionality analysis and descriptive case series using the US FDA Adverse Event Reporting System.Compared with remdesivir, HCQ/CQ was associated with increased reporting of SCAEs (reporting odds ratio [ROR] 2.1; 95% confidence interval [CI] 1.8-2.5), torsade de pointes (TdP)/QTc prolongation (ROR 35.4; 95% CI 19.4-64.5), and ventricular arrhythmia (ROR 2.5; 95% CI 1.6-3.9); similar results were found in comparison with other therapeutic alternatives. Compared with lopinavir/ritonavir, HCQ/CQ was associated with increased reporting of ventricular arrhythmia (ROR 10.5; 95% CI 3.3-33.4); RORs were larger when HCQ/CQ was used in combination with azithromycin. In 2020, 312 of the 575 reports of SCAEs listed concomitant use of HCQ/CQ and azithromycin, including QTc prolongation (61.4%), ventricular arrhythmia (12.0%), atrial fibrillation (8.2%), TdP (4.9%), and cardiac arrest (4.4%); 88 (15.3%) cases resulted in hospitalization and 79 (13.7%) resulted in death. In total, 122 fatal QTc prolongation-related cardiovascular reports were associated with 1.4 times higher odds of reported death than those induced by SCAEs; 87 patients received more than one QTc-prolonging agent.Patients treated with HCQ/CQ monotherapy or HCQ/CQ + azithromycin may be at increased risk of SCAEs, TdP/QTc prolongation, and ventricular arrhythmia. Cardiovascular risks need to be considered when evaluating the benefit/harm balance of treatment with HCQ/CQ, especially with the concurrent use of QTc-prolonging agents and cytochrome P450 3A4 inhibitors when treating COVID-19.

Purpose: Pharmacoepidemiology studies often use insurance claims and/or electronic health records (EHR) to capture information about medication exposure. The choice between these data sources has important implications. Methods: We linked EHR from a large academic health system (2015-2017) to Medicare insurance claims for patients undergoing surgery. Drug utilization was characterized based on medication order dates in the EHR, and prescription fill dates in Medicare claims. We compared opioid use documented in EHR orders to prescription claims in four time periods: 1) Baseline (182 days before surgery); 2) Perioperative period; 3) Discharge date; 4) Follow-up (90 days after surgery). Results: We identified 11 128 patients undergoing surgery. During baseline, 34.4% (EHR) versus 44.1% (claims) had evidence of opioid use, and 56.9% of all baseline use was reflected only in one data source. During the perioperative period, 78.8% (EHR) versus 47.6% (claims) had evidence of use. On the day of discharge, 59.6% (EHR) versus 45.5% (claims) had evidence of use, and 51.8% of all discharge use was reflected only in one data source. During follow-up, 4.3% (EHR) versus 10.4% (claims) were identified with prolonged opioid use following surgery with 81.4% of all prolonged use reflected only in one data source. Conclusions: When characterizing opioid exposure, we found substantial discrepancies between EHR medication orders and prescription claims data. In all time periods assessed, most patients' use was reflected only in the EHR, or only in the claims, not both. The potential for misclassification of drug utilization must be evaluated carefully, and choice of data source may have large impacts on key study design elements.

Background While vaccination is the most important way to combat the SARS-CoV-2 pandemic, there may still be a need for early outpatient treatment that is safe, inexpensive, and currently widely available in parts of the world that do not have access to the vaccine. There are in-silico, in-vitro, and in-tissue data suggesting that metformin inhibits the viral life cycle, as well as observational data suggesting that metformin use before infection with SARS-CoV2 is associated with less severe COVID-19. Previous observational analyses from single-center cohorts have been limited by size. Methods Conducted a retrospective cohort analysis in adults with type 2 diabetes (T2DM) for associations between metformin use and COVID-19 outcomes with an active comparator design of prevalent users of therapeutically equivalent diabetes monotherapy: metformin versus dipeptidyl-peptidase-4-inhibitors (DPP4i) and sulfonylureas (SU). This took place in the National COVID Cohort Collaborative (N3C) longitudinal U.S. cohort of adults with +SARS-CoV-2 result between January 1 2020 to June 1 2021. Findings included hospitalization or ventilation or mortality from COVID-19. Back pain was assessed as a negative control outcome. Results 6,626 adults with T2DM and +SARS-CoV-2 from 36 sites. Mean age was 60.7 +/- 12.0 years; 48.7% male; 56.7% White, 21.9% Black, 3.5% Asian, and 16.7% Latinx. Mean BMI was 34.1 +/- 7.8kg/m2. Overall 14.5% of the sample was hospitalized; 1.5% received mechanical ventilation; and 1.8% died. In adjusted outcomes, compared to DPP4i, metformin had non-significant associations with reduced need for ventilation (RR 0.68, 0.32–1.44), and mortality (RR 0.82, 0.41–1.64). Compared to SU, metformin was associated with a lower risk of ventilation (RR 0.5, 95% CI 0.28–0.98, p = 0.044) and mortality (RR 0.56, 95%CI 0.33–0.97, p = 0.037). There was no difference in unadjusted or adjusted results of the negative control. Conclusions There were clinically significant associations between metformin use and less severe COVID-19 compared to SU, but not compared to DPP4i. New-user studies and randomized trials are needed to assess early outpatient treatment and post-exposure prophylaxis with therapeutics that are safe in adults, children, pregnancy and available worldwide.

Key Points Question What is the incidence of sulfonylurea or insulin treatment deintensification among older adults after an episode of severe hypoglycemia? Findings In this cohort study of 76 278 older adults with diabetes who had a hypoglycemia-associated emergency department visit or hospitalization while receiving sulfonylurea and/or insulin therapy, deintensification of the hypoglycemic treatment regimen occurred in fewer than 50% of episodes within 100 days after the event. Meaning This study’s findings suggest that a gap exists between evidence-based and current practices regarding deintensification of hypoglycemia-inducing medications among older adults with diabetes who experienced severe hypoglycemia requiring an emergency department visit or hospitalization., Importance Practice guidelines recommend deintensification of hypoglycemic agents among older adults with diabetes who are at high risk of hypoglycemia, yet real-world treatment deintensification practices are not well characterized. Objective To examine the incidence of sulfonylurea and insulin deintensification after a hypoglycemia-associated emergency department (ED) visit or hospitalization among older adults with diabetes and to identify factors associated with deintensification of treatment. Design, Setting, and Participants This retrospective cohort study included a random sample of 20% of nationwide fee-for-service US Medicare beneficiaries aged 65 years and older with concurrent Medicare parts A, B, and D coverage between January 1, 2007, and December 31, 2017. Individuals with diabetes who had at least 1 hypoglycemia-associated ED visit or hospitalization were included. Data were analyzed from August 1, 2020, to August 1, 2021. Exposures Baseline medication for the treatment of diabetes (sulfonylurea, insulin, or both). Main Outcomes and Measures Incidence of treatment deintensification (yes or no) in the 100 days after a severe hypoglycemic episode requiring an ED visit or hospitalization, with treatment deintensification defined as (1) a decrease in sulfonylurea dose, (2) a change from long-acting to short-acting sulfonylurea (glipizide), (3) discontinuation of sulfonylurea, or (4) discontinuation of insulin based on pharmacy dispensing claims. Results Among 76 278 distinct Medicare beneficiaries who had a hypoglycemia-associated ED visit or hospitalization, the mean (SD) age was 76.6 (7.6) years. Of 106 293 total hypoglycemic episodes requiring hospital attention, 69 084 (65.0%) occurred among women, 26 056 (24.5%) among Black individuals; 4761 (4.5%) among Hispanic individuals; 69 704 (65.6%) among White individuals; and 5772 (5.4%) among individuals of other races and ethnicities (comprising Asian, North American Native, unknown race or ethnicity, and unspecified race or ethnicity). A total of 32 074 episodes (30.2%) occurred among those receiving sulfonylurea only, 60 350 (56.8%) occurred among those receiving insulin only, and 13 869 (13.0%) occurred among those receiving both sulfonylurea and insulin. Treatment deintensification rates were highest among individuals receiving both sulfonylurea and insulin therapies at the time of their hypoglycemic episode (6677 episodes [48.1%]), followed by individuals receiving sulfonylurea only (14 192 episodes [44.2%]) and insulin only (14 495 episodes [24.0%]). Treatment deintensification rates increased between 2007 and 2017 (sulfonylurea only: from 41.4% to 49.7%; P, This cohort study uses Medicare claims data to assess the incidence of sulfonylurea and/or insulin treatment deintensification after an episode of severe hypoglycemia requiring an emergency department visit or hospitalization among older adults with diabetes.

Clinical trials often have insufficient power to estimate drug effects in older adult populations. Medicare claims data are a valuable resource for studying healthcare delivery for older adults. The Faurot frailty index (FFI) is a validated algorithm that uses demographic, enrollment, and ICD-9-CM-based claims information to predict ADL dependency as a proxy for frailty. The original FFI consists of 20 constructs, including those related to geriatric syndromes, of which 16 are based on ICD-9-CM diagnosis codes (e.g., arthritis, dementia). In October 2015, the US healthcare system transitioned to ICD-10-CM. In this study, we updated and validated the FFI for the ICD-10-CM era . We used General Equivalence Mapping (GEM) to translate the ICD-9-CM codes to ICD-10-CM. For each construct, we manually reviewed the ICD-10-CM codes after GEM, assessed the suitability, and plotted the monthly prevalence before and after the transition (2013-2017). Code lists for all but 1 construct required editing after translation using GEM (adding and/or removing codes). We observed increasing monthly prevalence for several constructs, although trend lines were consistent across the ICD-9-CM and ICD-10-CM eras: bladder dysfunction (1.8-2.5%), weakness (2.7-3.9%), and psychiatric illnesses (6.9-9.1%). Rehabilitation services did not translate well using diagnosis codes, however, adding CPT codes improved capture. The updated FFI was strongly associated with frailty-related geriatric outcomes (1-year mortality, hospitalization, SNF admission). Studies of drug effects in older adults should use validated indices, such as the FFI, to reduce bias. Thorough evaluation of claims-based algorithms is essential to support healthcare services research using these measures.