A systematic assessment of the association between frequently prescribed medicines and the risk of common cancers: a series of nested case-control studies.

BACKGROUND: Studies systematically screening medications have successfully identified prescription medicines associated with cancer risk. However, adjustment for confounding factors in these studies has been limited. We therefore investigated the association between frequently prescribed medicines and the risk of common cancers adjusting for a range of confounders. METHODS: A series of nested case-control studies were undertaken using the Primary Care Clinical Informatics Unit Research (PCCIUR) database containing general practice (GP) records from Scotland. Cancer cases at 22 cancer sites, diagnosed between 1999 and 2011, were identified from GP records and matched with up to five controls (based on age, gender, GP practice and date of registration). Odds ratios (OR) and 95% confidence intervals (CI) comparing any versus no prescriptions for each of the most commonly prescribed medicines, identified from prescription records, were calculated using conditional logistic regression, adjusting for comorbidities. Additional analyses adjusted for smoking use. An association was considered a signal based upon the magnitude of its adjusted OR, p-value and evidence of an exposure-response relationship. Supplementary analyses were undertaken comparing 6 or more prescriptions versus less than 6 for each medicine. RESULTS: Overall, 62,109 cases and 276,580 controls were included in the analyses and a total of 5622 medication-cancer associations were studied across the 22 cancer sites. After adjusting for comorbidities 2060 medicine-cancer associations for any prescription had adjusted ORs greater than 1.25 (or less than 0.8), 214 had a corresponding p-value less than or equal to 0.01 and 118 had evidence of an exposure-dose relationship hence meeting the criteria for a signal. Seventy-seven signals were identified after additionally adjusting for smoking. Based upon an exposure of 6 or more prescriptions, there were 118 signals after adjusting for comorbidities and 82 after additionally adjusting for smoking. CONCLUSIONS: In this study a number of novel associations between medicine and cancer were identified which require further clinical and epidemiological investigation. The majority of medicines were not associated with an altered cancer risk and many identified signals reflected known associations between medicine and cancer.

Trends in Discharge Prescription of Digoxin After Norwood Operation: An Analysis of Data from the Pediatric Health Information System (PHIS) Database.

Quality improvement efforts have focused on reducing interstage mortality for infants with hypoplastic left heart syndrome (HLHS). In 1/2016, two publications reported that use of digoxin was associated with reduced interstage mortality. The degree to which these findings have affected real world practice has not been evaluated. The discharge medications of neonates with HLHS undergoing Norwood operation between 1/2007 and 12/2018 at Pediatric Health Information Systems Database hospitals were studied. Mixed effects models were calculated to evaluate the hypothesis that the likelihood of digoxin prescription increased after 1/2016, adjusting for measurable confounders with furosemide and aspirin prescription measured as falsification tests. Interhospital practice variation was measured using the median odds ratio. Over the study period, 6091 subjects from 45 hospitals were included. After adjusting for measurable covariates, discharge after 1/2016 was associated with increased odds of receiving digoxin (OR 3.9, p < 0.001). No association was seen between date of discharge and furosemide (p = 0.26) or aspirin (p = 0.12). Prior to 1/2016, the likelihood of receiving digoxin was decreasing (OR 0.9 per year, p < 0.001), while after 1/2016 the rate has increased (OR 1.4 per year, p < 0.001). However, there remains significant interhospital variation in the likelihood of receiving digoxin even after adjusting for known confounders (median odds ratio = 3.5, p < 0.0001). Following publication of studies describing an association between digoxin and improved interstage survival, the likelihood of receiving digoxin at discharge increased without similar changes for furosemide or aspirin. Despite concerted efforts to standardize interstage care, interhospital variation in pharmacotherapy in this vulnerable population persists.

Quantifying how small variations in design elements affect risk in an incident cohort study in claims.

BACKGROUND: Epidemiological study reporting is improving but is not transparent enough for easy evaluation or replication. One barrier is insufficient details about design elements in published studies. METHODS: Using a previously conducted drug safety evaluation in claims as a test case, we investigated the impact of small changes in five key design elements on risk estimation. These elements are index day of incident exposure's determination of look-back or follow-up periods, exposure duration algorithms, heparin exposure exclusion, propensity score model variables, and Cox proportional hazard model stratification. We covaried these elements using a fractional factorial design, resulting in 24 risk estimates for one outcome. We repeated eight of these combinations for two additional outcomes. We measured design effects on cohort sizes, follow-up time, and risk estimates. RESULTS: Small changes in specifications of index day and exposure algorithm affected the risk estimation process the most. They affected cohort size on average by 8 to 10%, follow-up time by up to 31%, and magnitude of log hazard ratios by up to 0.22. Other elements affected cohort before matching or risk estimate's precision but not its magnitude. Any change in design substantially altered the matched control-group subjects in 1:1 matching. CONCLUSIONS: Exposure-related design elements require attention from investigators initiating, evaluating, or wishing to replicate a study or from analysts standardizing definitions. The methods we developed, using factorial design and mapping design effect on causal estimation process, are applicable to planning of sensitivity analyses in similar studies.

Real-world data in Saudi Arabia: Current situation and challenges for regulatory decision-making.

PURPOSE: To present the process of establishing a pharmacoepidemiological database in Saudi Arabia, challenges and models used. METHODS: The database establishment has started in 2017 by piloting the conversion of electronic health records of one hospital to the Observational Health Data Sciences and Informatics (OHDSI), Observational Medical Outcomes Partnership's Common Data Model (OMOP). RESULTS: During the pilot phase we have faced several challenges such as limited contribution in providing data by local medical institution due to uncertainty about data governance, diversity of systems used by hospitals, inconsistent coding of medical information, and limited awareness about data structure from participating hospital. The pilot phase was completed in 2019 containing information about patient attributes, medical care, therapies, and other additional services for around 130 000 patients in Saudi Arabia. The majority of patients were below the age of 50 years (89%), and acute respiratory infections were the most frequent diagnosis. The data quality was acceptable and no major anomalies were detected during the conversion. CONCLUSIONS: We demonstrated a successful creation of a pilot database using OHDSI Common Data Model. Our experience with the pilot database could be extended to other institutions to create a national dataset that could be used to generate real-world evidence.

Quality of reporting of drug exposure in pharmacoepidemiological studies.

PURPOSE: Exposure definitions vary across pharmacoepidemiological studies. Therefore, transparent reporting of exposure definitions is important for interpretation of published study results. We aimed to assess the quality of reporting of exposure to identify where improvement may be needed. METHOD: We systematically reviewed observational pharmacoepidemiological studies that used routinely collected health data, published in 2017 in six pharmacoepidemiological journals. Reporting of exposure was scored using 11 items of the ISPE-ISPOR guideline on reporting of pharmacoepidemiological studies. RESULTS: Of the 91 studies included, all studies reported the type of exposure (100%), while most reported the exposure risk window (85%) and the exposure assessment window (98%). Operationalization of the exposure window was described infrequently: 16% (14/90) of the studies explicitly reported the presence or absence of an induction period if applicable, 11% (5/47), and 35% (17/49) reported how stockpiling and gaps between exposure episodes were handled, respectively, and 35% (17/49) explicitly mentioned the exposure extension. Switching/add-on was reported in 62% (50/81). How switching between drugs was dealt with and specific drug codes were reported in 52 (57%) and 24 (26%) studies, respectively. CONCLUSION: Publications of pharmacoepidemiological studies frequently reported the type of exposure, the exposure risk window, and the exposure assessment window. However, more details on exposure assessment are needed, especially when it concerns the operationalization of the exposure risk window (eg, the presence or absence of an induction period or exposure extension, handling of stockpiling and gaps, and specific codes), to allow for correct interpretation, reproducibility, and assessment of validity.

Tuberculosis drug safety and pharmacovigilance in health system of Kosova: A cross-sectional analysis.

BACKGROUND: Tuberculosis (TB) remains a significant worldwide social and life-threatening epidemiological problem. Because this disease requires multiple drug treatment and prolonged therapy for several months, followed by a high probability of adverse effects (AEs), we assessed AE monitoring for anti-TB drugs in the Health Care System of Kosova. METHODS: This survey was a cross-sectional analysis performed at the primary, secondary and tertiary health care levels in Kosova. We included 930 registered tuberculosis patients within three levels of this health system in our study. Furthermore, we interviewed 62 physicians and 71 nurses at TB health facilities. Data were collected from official TB registers and personal contact with patients for 12 months. RESULTS: The representative age group was 19 to 29 years (30.49%), followed by a group of patients aged >60 years (23.23%). Among 930 patients treated with TB drugs, the total incidence of adverse AEs was 29.03%. Female TB patients had a higher rate of AEs than did male patients (33.56% vs 28.84%, respectively). The highest incidence of registered AEs was recorded in the gastrointestinal system (270, 80.83%), followed by the central nervous system (CNS, 7.50%) and was lower in other organ systems. The reporting of anti-TB drug effects by medical staff (TB medical doctor and TB medical nurse) at different levels of TB medical settings occurred among 62.90% of medical doctors and 81.69% of nurses. Only 53.23% of medical doctors and 46.48% of nurses completed pharmacovigilance training. CONCLUSION: The pharmacovigilance approach in Health System of Kosova is not comprehensible and not systematic. The relatively low incidence of AEs among TB patients is due under reporting of these by medical staff. The knowledge, attitudes, and adherence of medical staff reveal low awareness for pharmacovigilance activities, and this concern should be addressed to reinforce this important issue for the safe treatment of TB patients.

Using nationally representative survey data for external adjustment of unmeasured confounders: An example using the NHANES data.

PURPOSE: To evaluate the use of data from population-based surveys such as the National Health and Nutrition Examination Survey (NHANES) for external adjustment for confounders imperfectly measured in health care databases in the United States. METHODS: Our example study used Medicaid Analytic eXtract (MAX) data to estimate the relative risk (RR) for prenatal serotonin-norepinephrine reuptake inhibitors (SNRIs) exposure and cardiac defects. Smoking and obesity are known confounders poorly captured in databases. NHANES collects information on lifestyle factors, depression, and prescription medications. External adjustment requires information on the prevalence of confounders and their association with SNRI use; which was obtained from the NHANES. It also requires estimates of their association with the outcome, which were based on the literature and allowed us to correct the RR using sensitivity analyses. RESULTS: In MAX, the RR for the association between prenatal SNRI exposure and cardiac defects was 1.51 unadjusted and 1.20 adjusted for measured confounders and restricted to women with depression. In NHANES, among women of childbearing age with depression, the prevalence of smoking was 60.2% (95% Confidence Interval 43.2, 74.3) for SNRI users and 44.1% (39.6, 48.8) for nonusers of antidepressants. The corresponding estimates for obesity were 59.2% (43.2, 74.3) and 40.5% (35.9, 45.0), respectively. If the associations between smoking and obesity with cardiac defects are independent from each other and from other measured confounders, additional adjustment for smoking and obesity would move the RR from 1.20 to around 1.10. CONCLUSION: National surveys like NHANES are readily available sources of information on potential confounders and they can be used to assess and improve the validity of RR estimates from observational studies missing data on known risk factors.

Empirical assessment of case-based methods for drug safety alert identification in the French National Healthcare System database (SNDS): Methodology of the ALCAPONE project.

OBJECTIVES: To introduce the methodology of the ALCAPONE project. BACKGROUND: The French National Healthcare System Database (SNDS), covering 99% of the French population, provides a potentially valuable opportunity for drug safety alert generation. ALCAPONE aimed to assess empirically in the SNDS case-based designs for alert generation related to four health outcomes of interest. METHODS: ALCAPONE used a reference set adapted from observational medical outcomes partnership (OMOP) and Exploring and Understanding Adverse Drug Reactions (EU-ADR) project, with four outcomes-acute liver injury (ALI), myocardial infarction (MI), acute kidney injury (AKI), and upper gastrointestinal bleeding (UGIB)-and positive and negative drug controls. ALCAPONE consisted of four main phases: (1) data preparation to fit the OMOP Common Data Model and select the drug controls; (2) detection of the selected controls via three case-based designs: case-population, case-control, and self-controlled case series, including design variants (varying risk window, adjustment strategy, etc.); (3) comparison of design variant performance (area under the ROC curve, mean square error, etc.); and (4) selection of the optimal design variants and their calibration for each outcome. RESULTS: Over 2009-2014, 5225 cases of ALI, 354 109 MI, 12 633 AKI, and 156 057 UGIB were identified using specific definitions. The number of detectable drugs ranged from 61 for MI to 25 for ALI. Design variants generated more than 50 000 points estimates. Results by outcome will be published in forthcoming papers. CONCLUSIONS: ALCAPONE has shown the interest of the empirical assessment of pharmacoepidemiological approaches for drug safety alert generation and may encourage other researchers to do the same in other databases.

Pharmacoepidemiology of testosterone: Impact of reimbursement policy on curbing off-label prescribing.

OBJECTIVES: To estimate the impact on testosterone prescribing over 3 years following the 2015 tightening of Pharmaceutical Benefits Scheme (PBS) criteria. DESIGN: Analysis of testosterone prescribing data from PBS and private (non-PBS) sources between 2012 and 2018 covering 2015 change in PBS prescribing criteria. MAIN OUTCOME MEASURES: New and total PBS testosterone prescriptions estimating usage by quarter analyzed by product type, patient age-group, indication and prescriber type. Total national testosterone prescriptions (private plus PBS) was verified from an independent data supplier (IQVIA). RESULTS: PBS usage peaked in 2014 declining by 30% in 2017-8 with PBS prescribing covering a fall from 97.6% by usage in 2014 to 74% in 2017-18 of all testosterone prescribing. The tighter 2015 PBS restrictions sustained the selective reduction in GP initiation of prescriptions for middle-aged men without pathological hypogonadism whereas specialist initiations and prescription for adult hypogonadism or pediatric/prepubertal indications were largely unaffected. CONCLUSIONS: The tightening of PBS criteria from 1 April 2015 to curb off-label prescribing remained effective and selective over 3 years yet total national testosterone prescribing continued with little change, reflecting a shift to private prescriptions. The continuation of off-label testosterone prescribing for unproven indications suggests that long-term androgen dependence is created in men without pathological hypogonadism who commence testosterone. This highlights the need to avoid prescribing testosterone to men without pathological hypogonadism in the absence of sound evidence of efficacy and safety, the latter including the little unrecognized risks of long-term androgen dependency when trying to quit.

Spillover effects of state medicaid antipsychotic prior authorization policies in US commercially insured youth.

PURPOSE: To evaluate spillover effects of Medicaid antipsychotic prior authorization (PA) policies among commercially insured youth. METHODS: Commercially insured youth residing in nine US states that implemented PA exclusively for antipsychotics in 2011 or 2012 were identified using a 10% random sample of enrollees in the IQVIA PharMetrics Plus database spanning 2007 to 2015. Youth were included if they were ≤18 years, met the age criteria of the PA at the time of dispensing, and had at least 1 month of prescription drug coverage from 2007 to 2015. The primary outcome of interest was the monthly prevalence of antipsychotics. We implemented segmented regression of interrupted time series analysis to estimate changes in the monthly prevalence of targeted medications, overall and stratified by age. Trends were compared in the 4-year period before and the 3-year period after implementation of PA policies. RESULTS: Antipsychotics prescribing significantly decreased 6.74/10 000 (95% CI, -9.04 to -4.44) enrollees per month immediately after PA implementation. However, PA was not associated with significant long-term trend changes (-0.06; 95% CI, -0.16 to 0.03). Antipsychotic prescribing in children <12 years-old significantly decreased 0.14/10 000 (95% CI, -0.21 to -0.07) enrollees per month after PA implementation, while prescribing in adolescents 12 to 18 years-old significantly increased 0.32/10 000 (95% CI, 0.16 to 0.47) enrollees per month. CONCLUSION: While Medicaid PA polices for antipsychotic oversight did not affect overall prescribing, there were spillover effects in U.S. commercially insured children <12 years-old. This suggests that state-level Medicaid policies intended to improve the quality of care and safe use of antipsychotics can have broad reach.

Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review.

BACKGROUND: The availability of clinical and therapeutic data drawn from medical records and administrative databases has entailed new opportunities for clinical and epidemiologic research. However, these databases present inherent limitations which may render them prone to new biases. We aimed to conduct a structured review of biases specific to observational clinical studies based on secondary databases, and to propose strategies for the mitigation of those biases. METHODS: Scoping review of the scientific literature published during the period 2000-2018 through an automated search of MEDLINE, EMBASE and Web of Science, supplemented with manually cross-checking of reference lists. We included opinion essays, methodological reviews, analyses or simulation studies, as well as letters to the editor or retractions, the principal objective of which was to highlight the existence of some type of bias in pharmacoepidemiologic studies using secondary databases. RESULTS: A total of 117 articles were included. An increasing trend in the number of publications concerning the potential limitations of secondary databases was observed over time and across medical research disciplines. Confounding was the most reported category of bias (63.2% of articles), followed by selection and measurement biases (47.0% and 46.2% respectively). Confounding by indication (32.5%), unmeasured/residual confounding (28.2%), outcome misclassification (28.2%) and "immortal time" bias (25.6%) were the subcategories most frequently mentioned. CONCLUSIONS: Suboptimal use of secondary databases in pharmacoepidemiologic studies has introduced biases in the studies, which may have led to erroneous conclusions. Methods to mitigate biases are available and must be considered in the design, analysis and interpretation phases of studies using these data sources.

Use of prescription drugs in the older adult population-a nationwide pharmacoepidemiological study.

PURPOSE: Multi-morbidity and polypharmacy are common among older people. It is essential to provide a better understanding of the complexity of prescription drug use among older adults to optimise rational pharmacotherapy. Population-based utilisation data in this age group is limited. Using the Danish nationwide health registries, we aimed to characterise drug use among Danish individuals ≥ 60 years. METHODS: This is a descriptive population-based study assessing drug prescription patterns in 2015 in the full Danish population aged ≥ 60 years. The use of specific therapeutic subgroups and chemical subgroups and its dependence on age were described using descriptive statistics. Profiles of drug combination patterns were evaluated using latent class analysis. RESULTS: We included 1,424,775 residents (median age 70 years, 53% women). Of all the older adults, 89% filled at least one prescription during 2015. The median number of drug groups used was five per person. The most used single drug groups were paracetamol and analogues (34%), statins (33%) and platelet aggregation inhibitors (24%). Eighteen drug profiles with different drug combination patterns were identified. One drug profile with expected use of zero drugs and 11 drug profiles expected to receive more than five different therapeutic subgroup drugs were identified. CONCLUSION: The use of drugs is extensive both at the population level and increasing with age at an individual level. Separating the population into different homogenous groups related to drug use resulted in 18 different drug profiles, of which 11 drug profiles received on average more than five different therapeutic subgroup drugs.

Validation studies of claims data in the Asia-Pacific region: A comprehensive review.

PURPOSE: To describe published validation studies of administrative health care claims data in the Asia-Pacific region. METHODS: A comprehensive literature search was conducted in PubMed for English language articles published through 31-Oct-2017 in humans from 10 Asian-Pacific countries or regions (Japan, Australia, New Zealand, China, Hong Kong, India, Singapore, South Korea, Taiwan, and Thailand) that validated claims-based diagnoses with a gold standard data source. Search terms included the: validation, validity, accuracy, sensitivity, agreement, specificity, positive predictive value, kappa, kappa coefficient, and Cohen's kappa. RESULTS: Forty-three studies across six countries were identified: Australia (21); Japan (6); South Korea (6); Taiwan (7); Singapore (2); and New Zealand (1). Gold standard diagnoses were obtained from: medical records (18); registry data (11); self-reported questionnaires (5); and other data sources (9). Validity measures used included sensitivity, specificity, positive and negative predictive values (12); sensitivity, specificity, and positive predictive value (4); sensitivity and specificity (4); sensitivity and positive predictive value (4); and combinations of other measures (19). Validated outcomes included medical conditions (28); disease-specific comorbidities (8); death, smoking, and other (ie, injury, hospital outcome measures) (5); medication/transfusion (2). Approximately 72% of the studies were published within the last 5 years. CONCLUSIONS: Validation studies of claims data published in the English language in the Asia-Pacific region are very limited. Given the increased reliance on administrative health care databases for pharmacoepidemiology and the need for ensuring the credibility of results from such data, additional support for the conduct of validation research of claims data in the Asia-Pacific region is needed.

Quantifying bias reduction with fixed-duration versus all-available covariate assessment periods.

PURPOSE: Implementing a cohort study in longitudinal healthcare databases requires looking back over some covariate assessment period (CAP) preceding cohort entry to measure confounders. We used simulations to compare fixed-duration versus all-available CAPs for confounder adjustment in the presence of differences in available baseline time between exposure groups. METHODS: We simulated cohorts of 10 000 patients with binary variables for a single confounder, exposure, and outcome. Baseline time was simulated based on the observed distribution in a claims-based comparison of statin users versus nonusers. We compared bias after measuring confounders using fixed-duration and all-available CAPs, both when exposure groups had similar and discrepant amounts of available baseline time. RESULTS: When the comparison groups had similar amounts of baseline time, an all-available CAP was less biased than a fixed-duration CAP. When baseline time differed between comparison groups, the preferable CAP approach depended on the direction of confounding and which exposure group had higher covariate sensitivity. These findings were consistent in direction across sensitivity analyses. CONCLUSION: In certain settings of differential available baseline time between exposure groups, the all-available CAP was more biased than the fixed-duration CAP. The relative directions and strengths of confounding and misclassification biases are an important consideration when choosing between a fixed-duration or all-available CAP, but they are often unknown. Therefore, we recommend comparing the amount of available baseline time between exposure groups. When there is a large discrepancy, despite appropriate design choices, we recommend a fixed-duration approach to avoid potential increases in bias because of differential data availability.

Concordance of hospitalizations between Clinical Practice Research Datalink and linked Hospital Episode Statistics among patients treated with oral antidiabetic therapies.

PURPOSE: The ability of the Clinical Practice Research Datalink (CPRD) to ascertain all-cause hospitalizations remains unknown. We determined the proportion of hospitalizations in CPRD that were also recorded in Hospital Episode Statistics (HES), and vice versa, among patients initiating oral antidiabetic (OAD) therapy. METHODS: We conducted a retrospective cohort study from October 2009 to September 2012 among OAD-treated patients registered with general practitioners who contribute to CPRD and consent to HES linkage. In CPRD, we identified initial hospitalizations for each calendar year by an Inpatient Referral, Consultation Type code, or Read code indicating an inpatient episode and determined if an admission date was recorded in HES within ±30 days. We then identified initial HES admission dates and determined if a hospitalization was documented in CPRD within ±30 days. Sensitivity analyses were conducted utilizing HES discharge, rather than admission, dates. RESULTS: Among 8574 OAD-treated HES-linked patients in CPRD, 6574 initial hospitalizations across the study period were identified in CPRD, and 5188 (78.9% [95% CI, 77.9%-79.9%]) were confirmed by a HES admission date within ±30 days (median difference, ±3 days [IQR, 1-7 days]). Among 8609 initial hospital admissions in HES, 4803 (55.7% [95% CI, 54.7%-56.8%]) hospitalizations were recorded in CPRD within ±30 days (median difference, ±4 days [IQR, 1-9 days]). Similar results were observed using HES discharge dates. CONCLUSION: A substantial minority of patient-level hospitalization data are nonconcordant between HES and CPRD. Pharmacoepidemiologic studies within CPRD that seek to identify hospitalizations should consider linkage with HES to ensure adequate ascertainment of inpatient events.

Agreement between self-reported and registry-based use of sleep medications and tranquilizers.

PURPOSE: The purpose of the present study was to assess the agreement between self-reported use of sleep medications and tranquilizers and dispensed hypnotics and anxiolytics. METHODS: Self-reported medication use was obtained from the population-based survey Health and Environment in Oslo (HELMILO) (2009-2010) (n = 13 019). Data on dispensed hypnotics and anxiolytics were obtained from the Norwegian Prescription Database (NorPD). As measures of validity, we calculated sensitivity and specificity using both self-reports and prescription records as the reference standard. Furthermore, we calculated Cohen's kappa. Current self-reported medication use was compared with prescription data in time windows of both 100 and 200 days preceding questionnaire completion. RESULTS: The highest sensitivity was observed for current sleep medication use in the 100-day time window (sensitivity = 0.76, 95% confidence interval [CI]: 0.74, 0.79) when using prescription records as the reference standard. Sensitivity was generally lower for tranquilizers compared with sleep medications. Cohen's kappa showed the highest agreement for the 200-day time window with substantial agreement for sleep medications (kappa = 0.64; 95% CI: 0.62, 0.67) and moderate agreement for tranquilizers (kappa = 0.45; 95% CI: 0.41, 0.48). CONCLUSIONS: The present study suggests moderate to substantial agreement between self-reported use of sleep medications and tranquilizers and dispensed drugs in a general adult population. The magnitude of agreement varied according to drug category and time window. Since self-reported and registry-based use of these drug classes does not match each other accurately, limitations of each data source should be considered when such medications are applied as the exposure or outcome in epidemiologic studies.

The Devil's in the details: Reports on reproducibility in pharmacoepidemiologic studies.

PURPOSE: The U.S. Food and Drug Administration's Sentinel Initiative "modular programs" have been shown to replicate findings from conventional protocol-driven, custom-programmed studies. One such parallel assessment-dabigatran and warfarin and selected outcomes-produced concordant findings for three of four study outcomes. The effect estimates and confidence intervals for the fourth-acute myocardial infarction-had more variability as compared with other outcomes. This paper evaluates the potential sources of that variability that led to unexpected divergence in findings. METHODS: We systematically compared the two studies and evaluated programming differences and their potential impact using a different dataset that allowed more granular data access for investigation. We reviewed the output at each of five main processing steps common in both study programs: cohort identification, propensity score estimation, propensity score matching, patient follow-up, and risk estimation. RESULTS: Our findings point to several design features that warrant greater investigator attention when performing observational database studies: (a) treatment of recorded events (eg, diagnoses, procedures, and dispensings) co-occurring on the index date of study drug dispensing in cohort eligibility criteria and propensity score estimation and (b) construction of treatment episodes for study drugs of interest that have more complex dispensing patterns. CONCLUSIONS: More precise and unambiguous operational definitions of all study parameters will increase transparency and reproducibility in observational database studies.

A comparison of opioid-involved fatalities captured in the National Poison Data System to data derived from US death certificate literal text.

PURPOSE: The purpose of the study is to describe and compare the number and characteristics of opioid-involved fatal cases captured in the National Poison Data System (NPDS) and in US death certificates. METHODS: NPDS, which collects data on all calls to US poison control centers, and Drug-Involved Mortality (DIM), which combines information from literal text of US death certificates and National Vital Statistics Systems, were queried for opioid-involved fatal cases from 2010 to 2015. Characteristics of the two case series were compared. RESULTS: DIM contained 154 016 opioid-involved overdose deaths, and NPDS contained 2524 fatal opioid exposures, a ratio of 61:1. The number of opioid deaths remained stable in NPDS but increased in DIM over the 6-year period. On average, deaths involving opioids with higher mean dosage strength (in morphine milligram equivalents) per unit among dispensed prescriptions were more likely to be captured in DIM relative to NPDS, as compared with those with a lower mean dosage strength per unit. The increase in fentanyl-related deaths seen in DIM since 2013 was not observed in NPDS. CONCLUSIONS: NPDS is a valuable drug safety surveillance resource due to its timeliness and drug specificity. However, it captures only a small fraction of opioid-involved fatal poisonings, and comparisons with data derived from death certificate literal text indicate that caution is warranted in making inferences about opioid-involved fatality trends over time or comparisons across opioids.

An empirical assessment of immeasurable time bias in the setting of nested case-control studies: Statins and all-cause mortality among patients with heart failure.

PURPOSE: Immeasurable time bias exaggerates drug benefits in pharmacoepidemiologic studies due to exposure misclassification that occurs due to the lack of inpatient drug data in many healthcare databases. METHODS: To estimate the magnitude of immeasurable time bias and assess potential approaches to minimize it, we conducted a nested case-control study of statin use and mortality among heart failure patients using the South Korean nationwide healthcare database, which contains both inpatient and outpatient medication data. Using both inpatient and outpatient medication data to define the gold standard exposure definition, we assessed 10 different analytical methods in which exposure was defined using outpatient medication data only. We compared different methodological approaches to reduce immeasurable time bias: restricting to nonhospitalized patients, adjusting for hospitalization, weighting by either measurable time (nonhospitalized time during 90-d period) or outpatient time, and computing the odds ratios (ORs) using 90-day cumulative probability of exposure produced by the Kaplan-Meier product-limit estimator for cases and controls. RESULTS: The three approaches that most closely approximated the gold standard (hazard ratio [HR] 1.20; 95% confidence interval [CI], 1.05-1.37) were weighting by either measurable (HR 1.09; 95% CI, 0.92-1.28) or outpatient time (HR 1.14; 95% CI, 0.96-1.34) in the unexposed or by estimating the 90-day exposure probability (HR 1.31; 95% CI, 1.11-1.51). CONCLUSION: The use of one of these three methods may be suggested as an approach to minimize immeasurable time bias in nested case-control studies.

Chronic obstructive pulmonary disease exacerbation episodes derived from electronic health record data validated using clinical trial data.

PURPOSE: To validate an algorithm for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) episodes derived in an electronic health record (EHR) database, against AECOPD episodes collected in a randomized clinical trial using an electronic case report form (eCRF). METHODS: We analyzed two data sources from the Salford Lung Study in COPD: trial eCRF and the Salford Integrated Record, a linked primary-secondary routine care EHR database of all patients in Salford. For trial participants, AECOPD episodes reported in eCRF were compared with algorithmically derived moderate/severe AECOPD episodes identified in EHR. Episode characteristics (frequency, duration), sensitivity, and positive predictive value (PPV) were calculated. A match between eCRF and EHR episodes was defined as at least 1-day overlap. RESULTS: In the primary effectiveness analysis population (n = 2269), 3791 EHR episodes (mean [SD] length: 15.1 [3.59] days; range: 14-54) and 4403 moderate/severe AECOPD eCRF episodes (mean length: 13.8 [16.20] days; range: 1-372) were identified. eCRF episodes exceeding 28 days were usually broken up into shorter episodes in the EHR. Sensitivity was 63.6% and PPV 71.1%, where concordance was defined as at least 1-day overlap. CONCLUSIONS: The EHR algorithm performance was acceptable, indicating that EHR-derived AECOPD episodes may provide an efficient, valid method of data collection. Comparing EHR-derived AECOPD episodes with those collected by eCRF resulted in slightly fewer episodes, and eCRF episodes of extreme lengths were poorly captured in EHR. Analysis of routinely collected EHR data may be reasonable when relative, rather than absolute, rates of AECOPD are relevant for stakeholders' decision making.