Incidence, Prevalence, and Racial and Ethnic Distribution of Inflammatory Bowel Disease in the United States.

BACKGROUND & AIMS: We sought to estimate the incidence, prevalence, and racial-ethnic distribution of physician-diagnosed inflammatory bowel disease (IBD) in the United States. METHODS: The study used 4 administrative claims data sets: a 20% random sample of national fee-for-service Medicare data (2007 to 2017); Medicaid data from Florida, New York, Pennsylvania, Ohio, and California (1999 to 2012); and commercial health insurance data from Anthem beneficiaries (2006 to 2018) and Optum's deidentified Clinformatics Data Mart (2000 to 2017). We used validated combinations of medical diagnoses, diagnostic procedures, and prescription medications to identify incident and prevalent diagnoses. We computed pooled age-, sex-, and race/ethnicity-specific insurance-weighted estimates and pooled estimates standardized to 2018 United States Census estimates with 95% confidence intervals (CIs). RESULTS: The age- and sex-standardized incidence of IBD per 100,000 person-years was 10.9 (95% CI, 10.6-11.2). The incidence of IBD peaked in the third decade of life, decreased to a relatively stable level across the fourth to eighth decades, and declined further. The age-, sex- and insurance-standardized prevalence of IBD was 721 per 100,000 population (95% CI, 717-726). Extrapolated to the 2020 United States Census, an estimated 2.39 million Americans are diagnosed with IBD. The prevalence of IBD per 100,000 population was 812 (95% CI, 802-823) in White, 504 (95% CI, 482-526) in Black, 403 (95% CI, 373-433) in Asian, and 458 (95% CI, 440-476) in Hispanic Americans. CONCLUSIONS: IBD is diagnosed in >0.7% of Americans. The incidence peaks in early adulthood and then plateaus at a lower rate. The disease is less commonly diagnosed in Black, Asian, and Hispanic Americans.

Validation of an international classification of disease, tenth revision, clinical modification (ICD-10-CM) algorithm in identifying severe hypoglycaemia events for real-world studies.

AIM: The transition to the ICD-10-CM coding system has reduced the utility of hypoglycaemia algorithms based on ICD-9-CM diagnosis codes in real-world studies of antidiabetic drugs. We mapped a validated ICD-9-CM hypoglycaemia algorithm to ICD-10-CM codes to create an ICD-10-CM hypoglycaemia algorithm and assessed its performance in identifying severe hypoglycaemia. MATERIALS AND METHODS: We assembled a cohort of Medicare patients with DM and linked electronic health record (EHR) data to the University of North Carolina Health System and identified candidate severe hypoglycaemia events from their Medicare claims using the ICD-10-CM hypoglycaemia algorithm. We confirmed severe hypoglycaemia by EHR review and computed a positive predictive value (PPV) of the algorithm to assess its performance. We refined the algorithm by removing poor performing codes (PPV ≤0.5) and computed a Cohen's κ statistic to evaluate the agreement of the EHR reviews. RESULTS: The algorithm identified 642 candidate severe hypoglycaemia events, and we confirmed 455 as true severe hypoglycaemia events, PPV of 0.709 (95% confidence interval: 0.672, 0.744). When we refined the algorithm, the PPV increased to 0.893 (0.862, 0.918) and missed <2.42% (<11) true severe hypoglycaemia events. Agreement between reviewers was high, κ = 0.93 (0.89, 0.97). CONCLUSIONS: We translated an ICD-9-CM hypoglycaemia algorithm to an ICD-10-CM version and found its performance was modest. The performance of the algorithm improved by removing poor performing codes at the trade-off of missing very few severe hypoglycaemia events. The algorithm has the potential to be used to identify severe hypoglycaemia in real-world studies of antidiabetic drugs.

Healthcare system engagement and algorithm-identified cancer incidence following initiation of a new medication.

PURPOSE: 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. METHODS: 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). RESULTS: 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 identified <30 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. CONCLUSION: 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).

Nondifferential Treatment Misclassification Biases Toward the Null? Not a Safe Bet for Active Comparator Studies.

Active comparator studies are increasingly common, particularly in pharmacoepidemiology. In such studies, the parameter of interest is a contrast (difference or ratio) in the outcome risks between the treatment of interest and the selected active comparator. While it may appear treatment is dichotomous, treatment is actually polytomous as there are at least 3 levels: no treatment, the treatment of interest, and the active comparator. Because misclassification may occur between any of these groups, independent nondifferential treatment misclassification may not be toward the null (as expected with a dichotomous treatment). In this work, we describe bias from independent nondifferential treatment misclassification in active comparator studies with a focus on misclassification that occurs between each active treatment and no treatment. We derive equations for bias in the estimated outcome risks, risk difference, and risk ratio, and we provide bias correction equations that produce unbiased estimates, in expectation. Using data obtained from US insurance claims data, we present a hypothetical comparative safety study of antibiotic treatment to illustrate factors that influence bias and provide an example probabilistic bias analysis using our derived bias correction equations.

Impacts of Initial Prescription Length and Prescribing Limits on Risk of Prolonged Postsurgical Opioid Use.

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.

Considerations for observational study design: Comparing the evidence of opioid use between electronic health records and insurance claims.

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.

Trends in the use of disease-modifying therapies among reproductive-aged women with multiple sclerosis in the United States from 2010 to 2019.

PURPOSE: Multiple sclerosis (MS) is a chronic disease of the central nervous system that disproportionately affects women, with typical onset during reproductive age. Several disease-modifying therapies (DMTs) are FDA-approved to slow disease progression, but are not indicated for use during pregnancy. Our objective was to describe trends over time (2010-2019) in monthly point prevalence of DMT use among reproductive-age women, overall and by generic name. METHODS: This study used administrative claims data from the US during 2009-2019 to identify women age 15-44 with MS and continuous insurance coverage for ≥12 months. DMTs were identified using prescription fills and procedural claims for alemtuzumab, daclizumab, dimethyl fumarate, fingolimod, glatiramer acetate, interferon beta, mitoxantrone, natalizumab, ocrelizumab, and teriflunomide. Monthly prevalent use was defined as ≥1 days' supply of a DMT in the month. Age- and region-standardized monthly prevalence was estimated nonparametrically. RESULTS: Among 42 281 reproductive-aged women over 818 179 person-months, DMT use increased from a minimum monthly prevalence of 49.3% (February, 2011) to a maximum of 58.7% (April, 2019). In 2010, prevalence of injectable DMTs was 43.1% compared to 2.5% for oral DMTs; by 2014, however, oral DMTs (26.5%) surpassed injectable DMTs (23.7%) as the most common route of administration. In the most recent data available (December, 2019), the most common DMTs were dimethyl fumarate, glatiramer acetate, and fingolimod. CONCLUSIONS: DMT use among reproductive-aged women has rapidly evolved during the past decade. Collaborative treatment decision making between women with MS and clinicians may help optimize MS care and improve DMT uptake during reproductive years.

Postsurgical Opioid Prescriptions and Risk of Long-term Use: An Observational Cohort Study Across the United States.

OBJECTIVE: The aim of this study was to evaluate differences in risk of long-term opioid therapy after surgery among an opioid-naive population using varying cutoffs based on days supplied (DS), total morphine milligram equivalents (MME) dispensed, and quantity of pills (QTY) dispensed. BACKGROUND: In response to the US opioid crisis, opioid prescription (Rx) limits have been implemented on a state-by-state basis beginning in 2016. However, there is limited evidence informing appropriate prescribing limits, and the effect of these policies on long-term opioid therapy. METHODS: Using the MarketScan claims databases, we identified all opioid-naive patients undergoing outpatient surgery between July 1, 2006 and June 30, 2015. We identified the initial postsurgical opioid prescribed, examining the DS, total MME, and QTY dispensed. We used Poisson to estimate adjusted risk differences and risk ratios of long-term opioid use comparing those receiving larger versus smaller volume of opioids. RESULTS: We identified 5,148,485 opioid-naive surgical patients. Overall, 55.5% received an opioid for postoperative pain, with median days supply = 5 and median total MME = 240. The proportion of patients receiving prescriptions above 7 DS increased from 11% in 2006 to 19% in 2015. Among those receiving postoperative opioids, 8% had long-term opioid use, and risk of long-term use was 1.16 times [95% confidence interval (CI), 1.10-1.25] higher among those receiving >7 days compared with those receiving ≤7 days. Those receiving >400 total MME (15% of patients) were at 1.17 times (95% CI, 1.10-1.25) the risk of long-term use compared with those receiving ≤400 MME. CONCLUSIONS: Between 2005 and 2015, the amounts of opioids prescribed for postoperative pain increased dramatically, and receipt of larger volume of opioids was associated with increased risk of long-term opioid therapy.

Propensity score trimming mitigates bias due to covariate measurement error in inverse probability of treatment weighted analyses: A plasmode simulation.

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.

Ondansetron use in early pregnancy and the risk of miscarriage.

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.

Day-of-Surgery Gabapentinoids and Prolonged Opioid Use: A Retrospective Cohort Study of Medicare Patients Using Electronic Health Records.

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.

Methodological considerations when analysing and interpreting real-world data.

In the absence of relevant data from randomized trials, nonexperimental studies are needed to estimate treatment effects on clinically meaningful outcomes. State-of-the-art study design is imperative for minimizing the potential for bias when using large healthcare databases (e.g. claims data, electronic health records, and product/disease registries). Critical design elements include new-users (begin follow-up at treatment initiation) reflecting hypothetical interventions and clear timelines, active-comparators (comparing treatment alternatives for the same indication), and consideration of induction and latent periods. Propensity scores can be used to balance measured covariates between treatment regimens and thus control for measured confounding. Immortal-time bias can be avoided by defining initiation of therapy and follow-up consistently between treatment groups. The aim of this manuscript is to provide a non-technical overview of study design issues and solutions and to highlight the importance of study design to minimize bias in nonexperimental studies using real-world data.

Single-arm Trials With External Comparators and Confounder Misclassification: How Adjustment Can Fail.

BACKGROUND: "Single-arm trials" with external comparators that contrast outcomes in those on experimental therapy to real-world patients have been used to evaluate efficacy and safety of experimental drugs in rare and severe diseases. Regulatory agencies are considering expanding the role these studies can play; guidance thus far has explicitly considered outcome misclassification with little discussion of misclassification of confounding variables. OBJECTIVES: This work uses causal diagrams to illustrate how adjustment for a misclassified confounder can result in estimates farther from the truth than ignoring it completely. This theory is augmented with quantitative examples using plausible values for misclassification of smoking in real-world pharmaceutical claims data. A tool is also provided for calculating bias of adjusted estimates with specific input parameters. RESULTS: When confounder misclassification is similar in both data sources, adjustment generally brings estimates closer to the truth. When it is not, adjustment can generate estimates that are considerably farther from the truth than the crude. While all nonrandomized studies are subject to this potential bias, single-arm studies are particularly vulnerable due to perfect alignment of confounder measurement and treatment group. This is most problematic when the prevalence of the confounder does not differ between data sources and misclassification does, but can occur even with strong confounder-data source associations. DISCUSSION: Researchers should consider differential confounder misclassification when designing protocols for these types of studies. Subsample validation of confounders, followed by imputation or other bias correction methods, may be a key tool for combining trial and real-world data going forward.

Perioperative opioid prescriptions associated with stress incontinence and pelvic organ prolapse surgery.

BACKGROUND: There is an opioid epidemic in the United States with a contributing factor of opioids being prescribed for postoperative pain after surgery. OBJECTIVE: Among women who underwent stress urinary incontinence and pelvic organ prolapse surgeries, our primary objective was to determine the proportion of women who filled perioperative opioid prescriptions and to compare factors associated with these opioid prescriptions. We also sought to assess the risk of prolonged opioid use through 1 year after stress urinary incontinence and pelvic organ prolapse surgeries. STUDY DESIGN: Using a population-based cohort of commercially insured individuals in the 2005-2015 IBM MarketScan databases, we identified opioid-naive women ≥18 years who underwent stress urinary incontinence and/or pelvic organ prolapse procedures based on Current Procedural Terminology codes. We defined the perioperative period as the window beginning 30 days before surgery extending until 7 days after surgery. Any filled opioid prescription in this window was considered a perioperative prescription. For our primary outcome, we reported the proportion of opioid-naive women who filled a perioperative opioid prescription and reported the median quantity dispensed in the perioperative period. We also assessed demographic and perioperative factors associated with perioperative opioid prescription fills. Previous studies have defined prolonged use as the proportion of women who fill an opioid prescription between 90 and 180 days after surgery. We report this estimate as well as continuous opioid use, defined as the proportion of women with ongoing monthly opioid prescriptions filled through 1 year after stress urinary incontinence and/or pelvic organ prolapse surgery. RESULTS: Among the 217,460 opioid-naive women who underwent urogynecologic surgery, 61,025 (28.1%) had pelvic organ prolapse and stress urinary incontinence surgeries, 85,575 (39.4%) had stress urinary incontinence surgery without pelvic organ prolapse surgery, and 70,860 (32.6%) had pelvic organ prolapse surgery without stress urinary incontinence surgery. Overall, 167,354 (77.0%) filled a perioperative opioid prescription, and the median quantity was 30 pills (interquartile range, 20-30). In a multivariate regression model, younger age, pelvic organ prolapse surgery with or without stress urinary incontinence surgery, abdominal route, hysterectomy, and mesh use remained significantly associated with opioid prescriptions filled. Among those with a filled perioperative opioid prescription, the risk of prolonged use defined as an opioid prescription filled between 90 and 180 days was 7.5% (95% confidence interval, 7.3-7.6). However, the risk of prolonged use defined as continuous use with at least 1 monthly opioid prescription filled after surgery was significantly lower: 1.2% (1.13-1.24), 0.32% (0.29-0.35), 0.06% (0.05-0.08), and 0.04% (0.02-0.05) at 60, 90, 180, and 360 days after surgery, respectively. CONCLUSION: Among privately insured, opioid-naive women undergoing stress urinary incontinence and/or pelvic organ prolapse surgery, 77% of women filled an opioid prescription with a median of 30 opioid pills prescribed. For prolonged use, 7.5% (95% confidence interval, 7.3-7.6) filled an opioid prescription within 90 to 180 days after surgery, but the rates of continuously filled opioid prescriptions were significantly lower at 0.06% (95% confidence interval, 0.05-0.08) at 180 days and 0.04% (95% confidence interval, 0.02-0.05) at 1 year after surgery.

Linking electronic health data in pharmacoepidemiology: Appropriateness and feasibility.

PURPOSE: To provide guidance on data linkage appropriateness and feasibility to plan purposeful and sustainable new linkages that advance pharmacoepidemiology and healthcare research. Planning a new data linkage requires careful evaluation to weigh the resources required with the potential overall benefits. METHODS: In response to an International Society for Pharmacoepidemiology (ISPE) call for manuscripts, a working group comprised of members from academic, industry, and government determined priority content areas; appropriateness and feasibility of data linkage was selected. Within this topic, scientific and operational considerations were determined, reviewed, and formulated into key areas, and translated into 12 consensus recommendations. RESULTS: Guidance for feasibility assessment was categorized into five key areas: (1) research objectives and justification; (2) data quality and completeness; (3) the linkage process; (4) data ownership and governance; and (5) overall value added by linkage. Within these key areas, recommendations to consider prior to initiation were developed to evaluate suitability of the linkage to meet research objectives, assess source data completeness and population coverage, and ensure well-defined data governance standards and protections. When creating novel linked datasets, researchers must assess the feasibility of both scientific (data quality and linkage methods) and operational (access, data use and transfer, governance, and cost) aspects. CONCLUSIONS: The data linkage feasibility assessment considerations outlined can be used as a guide when designing sustainable linked data resources to generate actionable evidence in healthcare research. These recommendations were constructed for wide applicability and can be adapted depending on the geographic, structural, and data components of the linkage.

Medical Use of Long-term Extended-release Opioid Analgesics in Commercially Insured Adults in the United States.

OBJECTIVES: We examined the proportion of patients initiating extended-release (ER) opioids who become long-term users and describe how pain-related diagnoses before initiation of opioid therapy vary between drugs and over time. METHODS: Using MarketScan (2006-2015), a US national commercial insurance database, we examined pain-related diagnoses in the 182-day baseline period before initiation of ER opioid therapy to characterize indications for opioid initiation. We report the proportion who became long-term users, the median length of opioid therapy, and the proportion with cancer and other noncancer chronic pain, by active ingredient. RESULTS: Among 1,077,566 adults initiating ER opioids, 31% became long-term users, with a median length of use of 209 days. The most common ER opioids prescribed were oxycodone (26%) and fentanyl (23%), and the most common noncancer pain diagnoses were back pain (65%) and arthritis (48%). Among all long-term users, 16% had a diagnosis of cancer. We found notable variation by drug. Eighteen percent of patients initiating drugs approved by the Food and Drug Administration >10 years ago had evidence of cancer during baseline compared with only 8% of patients who received newer drugs. CONCLUSIONS: In a national sample of adults with private insurance, back pain was the most common diagnosis preceding initiation of opioid therapy. Opioids that have been approved within the last 10 years were more frequently associated with musculoskeletal pains and less frequently associated with cancer. Amid increasing concerns regarding long-term opioid therapy, our findings provide context regarding the conditions for which long-term opioid therapy is prescribed.

Comparison of Methods to Generalize Randomized Clinical Trial Results Without Individual-Level Data for the Target Population.

Our study explored the application of methods to generalize randomized controlled trial results to a target population without individual-level data. We compared 4 methods using aggregate data for the target population to generalize results from the international trial, Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER), to a target population of trial-eligible patients in the UK Clinical Practice Research Datalink (CPRD). The gold-standard method used individual data from both the trial and CPRD to predict probabilities of being sampled in the trial and to reweight trial participants to reflect CPRD patient characteristics. Methods 1 and 2 used weighting methods based on simulated individual data or the method of moments, respectively. Method 3 weighted the trial's subgroup-specific treatment effects to match the distribution of an effect modifier in CPRD. Method 4 calculated the expected absolute benefits in CPRD assuming homogeneous relative treatment effect. Methods based on aggregate data for the target population generally yielded results between the trial and gold-standard estimates. Methods 1 and 2 yielded estimates closest to the gold-standard estimates when continuous effect modifiers were represented as categorical variables. Although individual data or data on joint distributions remains the best approach to generalize trial results, these methods using aggregate data might be useful tools for timely assessment of randomized trial generalizability.

Renin-Angiotensin-Aldosterone System-based Antihypertensive Agents and the Risk of Colorectal Cancer Among Medicare Beneficiaries.

BACKGROUND: Biologic evidence suggests that angiotensin II may play a role in tumor progression or growth. We compared the short-term colorectal cancer (CRC) risk among initiators of angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB) versus guideline-recommended clinical alternatives (beta blockers, calcium channel blockers [CCB], and thiazides). METHODS: We conducted a new-user cohort study on U.S. Medicare beneficiaries aged over 65 years, who initiated antihypertensive monotherapy during 2007-2013 and were free of cancer diagnosis before drug initiation. Follow-up began 6 months postinitiation to allow time for the diagnostic delay. We estimated hazard ratios (HR) with 95% confidence intervals (CI) using propensity score weighted Cox regression, overall and stratified by time since drug initiation, and 5-year cumulative risk differences (RD) using Kaplan-Meier estimator. We assessed the potential for unmeasured confounding using supplemental data from Medicare Current Beneficiary Survey. RESULTS: For analyses without censoring for treatment changes, we observed 532 CRC events among 111,533 ACEI/ARB initiators. After a median follow-up of 2.2 years (interquartile range: 1.0-3.7), CRC risk was similar between ACEI/ARB and active comparators, with adjusted HRs of 1.0 (95% CI = 0.85, 1.1) for ACEI/ARB versus beta blockers, 1.2 (95% CI = 0.97, 1.4) for ACEI/ARB versus CCB and 1.0 (95% CI = 0.80, 1.3) for ACEI/ARB versus thiazide. Five-year RDs and as-treated analyses, which censored follow-up at medication changes, produced similar findings. CONCLUSIONS: Based on real-world antihypertensive utilization patterns in Medicare beneficiaries, our study suggests no association between ACEI/ARB initiation and the short-term CRC risk.

Comparison of Medicare Claims-based Proxy Measures of Poor Function and Associations With Treatment Receipt and Mortality in Older Colon Cancer Patients.

BACKGROUND: Multiple claims-based proxy measures of poor function have been developed to address confounding in observational studies of drug effects in older adults. We evaluated agreement between these measures and their associations with treatment receipt and mortality in a cohort of older colon cancer patients. METHODS: Medicare beneficiaries age 66+ diagnosed with stage II-III colon cancer were identified in the Surveillance, Epidemiology, and End Results-Medicare database (2004-2011). Poor function was operationalized by: (1) summing the total poor function indicators for each model; and (2) estimating predicted probabilities of poor function at diagnosis. Agreement was evaluated using Fleiss' κ and Spearman's correlation. Associations between proxy measures and: (1) laparoscopic versus open surgery; (2) chemotherapy versus none; (3) 5-fluorouracil (5FU)+oxaliplatin (FOLFOX) versus 5FU monotherapy; and (4) 1-year mortality were estimated using log-binomial regression, controlling for age, sex, stage, and comorbidity. Survival estimates were stratified by functional group, age, and comorbidity. RESULTS: Among 29,687 eligible colon cancer patients, 67% were 75+ years and 45% had stage III disease. Concordance across the poor function indicator counts was moderate (κ: 0.64) and correlation of predicted probability measures varied (ρ: 0.21-0.74). Worse function was associated with lower chemotherapy and FOLFOX receipt, and higher 1-year mortality. Within age and comorbidity strata, poor function remained associated with mortality. CONCLUSIONS: While agreement varied across the claims-based proxy measures, each demonstrated anticipated associations with treatment receipt and mortality independent of comorbidity. Claims-based comparative effectiveness studies in older populations should consider applying one of these models to improve confounding control.

Opioid tolerance and clinically recognized opioid poisoning among patients prescribed extended-release long-acting opioids.

BACKGROUND: In recognition of potential for increased overdose risk, drug labels for extended-release and long-acting (ER/LA) opioids emphasize the need for established opioid tolerance prior to initiating high dosages. OBJECTIVES: Describe the proportion of patients with opioid tolerance prior to initiation of 90 morphine milligram equivalents (MME) ER/LA opioids and examine subsequent risk of opioid poisoning. METHODS: We used Truven Health Analytics' MarketScan Databases (2006-2015) to identify patients initiating ER/LA opioids ≥90 MME. We examined prescription histories and describe the proportion of initiators with opioid tolerance (defined as ≥7 days of 60 MME in the prior 14 days). We adjusted for age, sex, year of initiation, and baseline comorbidities using inverse probability of treatment weighted Cox proportional hazards models. We estimated adjusted hazard ratios and 95% confidence intervals for the effect of opioid tolerance on the risk of clinically recognized opioid poisoning (based on diagnosis codes) in specific periods (0-7, 8-30, 31-90, and 91-365 days) following initiation. RESULTS: Among 372 038 initiators, 38% did not meet opioid tolerance criteria. The proportion of nontolerant initiators was highest among those initiating methadone (44%) and fentanyl (42%). Nontolerant patients were 37% more likely to be diagnosed with opioid poisoning (adjusted hazard ratios = 1.37 [1.07, 1.76]) in the week following ER/LA initiation. CONCLUSIONS: Over one-third of patients initiating ≥90 MME ER/LA opioids did not have evidence of opioid tolerance. The 7 days following high dose ER/LA initiation may represent a high-risk period for clinically diagnosed opioid poisoning in patients who do not have prior opioid tolerance.