A plea to stop using the case-control design in retrospective database studies

Assessing the impact of social determinants of health on diabetes severity and management

Objective

Adverse Social Determinants of Health (SDoH) are considered major obstacles to effective management of type-2 diabetes. This study aims to quantify the impact of SDoH factors on diabetes management outcomes.

Materials and Methods

We quantified the joint impact of multiple SDoH by applying a self-control case series method-which accounts for confounding by using individuals as their own control-to electronic health record data from an academic health system in Maryland.

Results

We found a consistent increase in HbA1c levels associated with SDoH across alternative study designs. The estimated total contributions of SDoH ranged 0.014-0.065 across the alternative designs. Transportation issues demonstrated particularly significant contributions, with estimates of 0.077-0.144. When assuming SDoH's risk window to be ±45 days, for example, the total contribution was estimated to be 0.065 (95% CI [0.010, 0.120]) increase in HbA1c and the transportation issues' contribution 0.134 (95% CI [0.020, 0.249]).

Discussion and Conclusion

Our result suggests that reducing transportation barriers may be an effective SDoH intervention strategy for diabetes management; however, the clinical impact of such interventions warrants further investigation.

Addressing Cardiovascular Toxicity Risk of Electronic Nicotine Delivery Systems in the Twenty-First Century: "What Are the Tools Needed for the Job?" and "Do We Have Them?"

Cigarette smoking is positively and robustly associated with cardiovascular disease (CVD), including hypertension, atherosclerosis, cardiac arrhythmias, stroke, thromboembolism, myocardial infarctions, and heart failure. However, after more than a decade of ENDS presence in the U.S. marketplace, uncertainty persists regarding the long-term health consequences of ENDS use for CVD. New approach methods (NAMs) in the field of toxicology are being developed to enhance rapid prediction of human health hazards. Recent technical advances can now consider impact of biological factors such as sex and race/ethnicity, permitting application of NAMs findings to health equity and environmental justice issues. This has been the case for hazard assessments of drugs and environmental chemicals in areas such as cardiovascular, respiratory, and developmental toxicity. Despite these advances, a shortage of widely accepted methodologies to predict the impact of ENDS use on human health slows the application of regulatory oversight and the protection of public health. Minimizing the time between the emergence of risk (e.g., ENDS use) and the administration of well-founded regulatory policy requires thoughtful consideration of the currently available sources of data, their applicability to the prediction of health outcomes, and whether these available data streams are enough to support an actionable decision. This challenge forms the basis of this white paper on how best to reveal potential toxicities of ENDS use in the human cardiovascular system-a primary target of conventional tobacco smoking. We identify current approaches used to evaluate the impacts of tobacco on cardiovascular health, in particular emerging techniques that replace, reduce, and refine slower and more costly animal models with NAMs platforms that can be applied to tobacco regulatory science. The limitations of these emerging platforms are addressed, and systems biology approaches to close the knowledge gap between traditional models and NAMs are proposed. It is hoped that these suggestions and their adoption within the greater scientific community will result in fresh data streams that will support and enhance the scientific evaluation and subsequent decision-making of tobacco regulatory agencies worldwide.

Association between prolactin increasing antipsychotic use and the risk of breast cancer: a retrospective observational cohort study in a United States Medicaid population

Introduction

Results of retrospective studies examining the relationship between prolactin increasing antipsychotics and incident breast cancer have been inconsistent. This study assessed the association between use of high prolactin increasing antipsychotics (HPD) and the incidence of breast cancer using best practices in pharmacoepidemiology.

Methods

Using administrative claims data from the MarketScan Medicaid database, schizophrenia patients initiating antipsychotics were identified. Those initiating HPD were compared with new users of non/low prolactin increasing drugs (NPD). Two definitions of breast cancer, two at-risk periods, and two large-scale propensity score (PS) adjustment methods were used in separate analyses. PS models included all previously diagnosed conditions, medication use, demographics, and other available medical history. Negative control outcomes were used for empirical calibration.

Results

Five analysis variants passed all diagnostics for sufficient statistical power and balance across all covariates. Four of the five variants used an intent-to-treat (ITT) approach. Between 4,256 and 6,341 patients were included in each group for the ITT analyses, and patients contributed approximately four years of follow-up time on average. There was no statistically significant association between exposure to HPD and risk of incident breast cancer in any analysis, and hazard ratios remained close to 1.0, ranging from 0.96 (95% confidence interval 0.62 - 1.48) to 1.28 (0.40 - 4.07).

Discussion

Using multiple PS methods, outcome definitions and at-risk periods provided robust and consistent results which found no evidence of an association between use of HPD and risk of breast cancer.

The State of Use and Utility of Negative Controls in Pharmacoepidemiologic Studies

Uses of real-world data in drug safety and effectiveness studies are often challenged by various sources of bias. We undertook a systematic search of the published literature through September 2020 to evaluate the state of use and utility of negative controls to address bias in pharmacoepidemiologic studies. Two reviewers independently evaluated study eligibility and abstracted data. Our search identified 184 eligible studies for inclusion. Cohort studies (115, 63%) and administrative data (114, 62%) were, respectively, the most common study design and data type used. Most studies used negative control outcomes (91, 50%), and for most studies the target source of bias was unmeasured confounding (93, 51%). We identified 4 utility domains of negative controls: 1) bias detection (149, 81%), 2) bias correction (16, 9%), 3) P-value calibration (8, 4%), and 4) performance assessment of different methods used in drug safety studies (31, 17%). The most popular methodologies used were the 95% confidence interval and P-value calibration. In addition, we identified 2 reference sets with structured steps to check the causality assumption of the negative control. While negative controls are powerful tools in bias detection, we found many studies lacked checking the underlying assumptions. This article is part of a Special Collection on Pharmacoepidemiology.

Sedative-hypnotics and osteoporotic fractures: A systematic review of observational studies with over six million individuals

We reviewed and meta-analyzed 20 observational studies to examine the relationship between sedative-hypnotic use and osteoporotic fractures. We searched PubMed, Embase, APA PsycINFO, and Web of Science™ for studies that used cohort, case-control, case-crossover, and self-controlled case series designs. We further assessed the quality of each study and performed meta-analyses of association estimates, e.g., odds ratios (ORs). The analysis included 6,084,083 participants and found a slight association between the use of sedative-hypnotics and osteoporotic fractures, with differing strength of associations between different classes of drugs and greater sedative-hypnotics exposure. The pooled estimates ORs for case-control studies were 1.33 (95% CI 0.98-1.80) with benzodiazepines (BZD) and any fractures, 1.32 (95% CI 1.05-1.66) with BZDs and hip fractures, and case-crossover studies were 1.15 (95% CI 0.95-1.41) with BZDs and any fractures, 1.41 (95% CI 1.08-1.85) with Z-drugs and any fractures. The study suggests that more research is needed to aid medical professionals in balancing this potential risk of osteoporotic fractures associated with sedative-hypnotic use against other reported adverse events and anticipated therapy outcomes.

α1-Adrenergic Receptors: Insights into Potential Therapeutic Opportunities for COVID-19, Heart Failure, and Alzheimer's Disease

α1-Adrenergic receptors (ARs) are members of the G-Protein Coupled Receptor superfamily and with other related receptors (β and α2), they are involved in regulating the sympathetic nervous system through binding and activation by norepinephrine and epinephrine. Traditionally, α1-AR antagonists were first used as anti-hypertensives, as α1-AR activation increases vasoconstriction, but they are not a first-line use at present. The current usage of α1-AR antagonists increases urinary flow in benign prostatic hyperplasia. α1-AR agonists are used in septic shock, but the increased blood pressure response limits use for other conditions. However, with the advent of genetic-based animal models of the subtypes, drug design of highly selective ligands, scientists have discovered potentially newer uses for both agonists and antagonists of the α1-AR. In this review, we highlight newer treatment potential for α1A-AR agonists (heart failure, ischemia, and Alzheimer's disease) and non-selective α1-AR antagonists (COVID-19/SARS, Parkinson's disease, and posttraumatic stress disorder). While the studies reviewed here are still preclinical in cell lines and rodent disease models or have undergone initial clinical trials, potential therapeutics discussed here should not be used for non-approved conditions.

Thrombosis and thrombocytopenia after vaccination against and infection with SARS-CoV-2 in Catalonia, Spain

Population-based studies can provide important evidence on the safety of COVID-19 vaccines. Here we compare rates of thrombosis and thrombocytopenia following vaccination against SARS-CoV-2 with the background (expected) rates in the general population. In addition, we compare the rates of the same adverse events among persons infected with SARS-CoV-2 with background rates. Primary care and linked hospital data from Catalonia, Spain informed the study, with participants vaccinated with BNT162b2 or ChAdOx1 (27/12/2020-23/06/2021), COVID-19 cases (01/09/2020-23/06/2021) or present in the database as of 01/01/2017. We included 2,021,366 BNT162b2 (1,327,031 with 2 doses), 592,408 ChAdOx1, 174,556 COVID-19 cases, and 4,573,494 background participants. Standardised incidence ratios for venous thromboembolism were 1.18 (95% CI 1.06-1.32) and 0.92 (0.81-1.05) after first- and second dose BNT162b2, and 0.92 (0.71-1.18) after first dose ChAdOx1. The standardised incidence ratio for venous thromboembolism in COVID-19 was 10.19 (9.43-11.02). Standardised incidence ratios for arterial thromboembolism were 1.02 (0.95-1.09) and 1.04 (0.97-1.12) after first- and second dose BNT162b2, 1.06 (0.91-1.23) after first-dose ChAdOx1 and 4.13 (3.83-4.45) for COVID-19. Standardised incidence ratios for thrombocytopenia were 1.49 (1.43-1.54) and 1.40 (1.35-1.45) after first- and second dose BNT162b2, 1.28 (1.19-1.38) after first-dose ChAdOx1 and 4.59 (4.41- 4.77) for COVID-19. While rates of thrombosis with thrombocytopenia were generally similar to background rates, the standardised incidence ratio for pulmonary embolism with thrombocytopenia after first-dose BNT162b2 was 1.70 (1.11-2.61). These findings suggest that the safety profiles of BNT162b2 and ChAdOx1 are similar, with rates of adverse events seen after vaccination typically similar to background rates. Meanwhile, rates of adverse events are much increased for COVID-19 cases further underlining the importance of vaccination.

Electronic medical records and biobanking

Biosample preservation for future research is a fundamental component of translational medicine. At the same time, the value of stored biosamples is largely determined by the presence of related clinical data and other information. Electronic medical records are a unique source of a large amount of information received over a long period of time. In this regard, genetic and other types of data obtained from the biosample analysis can be associated with phenotypic and other types of information stored in electronic medical records, which pushes the boundaries in large-scale genetic research and improves healthcare. The aim of this review was to analyze the literature on the potential of combining electronic medical records and biobank databases in research and clinical practice.

International cohort study indicates no association between alpha-1 blockers and susceptibility to COVID-19 in benign prostatic hyperplasia patients

Purpose: Alpha-1 blockers, often used to treat benign prostatic hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storm release. The proposed treatment based on this hypothesis currently lacks support from reliable real-world evidence, however. We leverage an international network of large-scale healthcare databases to generate comprehensive evidence in a transparent and reproducible manner.

Methods: In this international cohort study, we deployed electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We assessed association between alpha-1 blocker use and risks of three COVID-19 outcomes—diagnosis, hospitalization, and hospitalization requiring intensive services—using a prevalent-user active-comparator design. We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We pooled database-specific estimates through random effects meta-analysis.

Results: Our study overall included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH medications. We observed no significant difference in their risks for any of the COVID-19 outcomes, with our meta-analytic HR estimates being 1.02 (95% CI: 0.92–1.13) for diagnosis, 1.00 (95% CI: 0.89–1.13) for hospitalization, and 1.15 (95% CI: 0.71–1.88) for hospitalization requiring intensive services.

Conclusion: We found no evidence of the hypothesized reduction in risks of the COVID-19 outcomes from the prevalent-use of alpha-1 blockers—further research is needed to identify effective therapies for this novel disease.

Prematurity, Delivery Method, and Infant Feeding Type Are Not Associated with Paediatric-onset Inflammatory Bowel Disease Risk: A Scottish Retrospective Birth Cohort Study

BACKGROUND AND AIMS

The incidence of paediatric-onset inflammatory bowel disease [PIBD] continues to rise globally. We aimed to determine whether mode of delivery, gestational age at birth, or type of infant feeding contribute to the development of PIBD in a nationwide cohort of Scottish children.

METHODS

All children born in Scotland between 1981 and 2017 were identified using linked health administrative data to determine mode of delivery, gestational age at birth, and type of infant feeding. PIBD cases were defined as onset of Crohn's disease [CD], ulcerative colitis [UC], or IBD-unclassified [IBDU] before age 16 years. Validation was performed within an entire Scottish health board [16% of total population] via individual case-note verification. Hazard ratios [HR] were calculated for each exposure using Cox proportional hazards models.

RESULTS

A study population of 2 013 851 children was identified including 1721 PIBD cases. Validation of 261 PIBD patients coded as CD and/or UC identified 242 [93%] as true positive. Children delivered vaginally did not have an altered risk of developing PIBD compared with those delivered by caesarean section, adjusted HR 0.95 [95% CI 0.84-1.08] [p = 0.46]. Compared with children born at term [≥37 weeks], children born prematurely did not have an altered risk of developing PIBD, i.e., at 24-31 weeks of gestation, HR 0.99 [95% CI 0.57-1.71] [p = 0.97] and at 32-36 weeks of gestation, HR 0.96 [95% CI 0.76-1.20] [p = 0.71]. Compared with children exclusively breastfed at age 6 weeks, children exclusively formula fed did not have an altered risk of developing PIBD: adjusted HR 0.97 [95% CI 0.81-1.15] [p = 0.69].

CONCLUSIONS

This population-based study demonstrates no association between mode of delivery, gestational age, or exclusive formula feeding at 6 weeks, and the development of PIBD.

Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies

INTRODUCTION

Therapeutic options for type 2 diabetes mellitus (T2DM) have expanded over the last decade with the emergence of cardioprotective novel agents, but without such data for older drugs, leaving a critical gap in our understanding of the relative effects of T2DM agents on cardiovascular risk.

METHODS AND ANALYSIS

The large-scale evidence generations across a network of databases for T2DM (LEGEND-T2DM) initiative is a series of systematic, large-scale, multinational, real-world comparative cardiovascular effectiveness and safety studies of all four major second-line anti-hyperglycaemic agents, including sodium-glucose co-transporter-2 inhibitor, glucagon-like peptide-1 receptor agonist, dipeptidyl peptidase-4 inhibitor and sulfonylureas. LEGEND-T2DM will leverage the Observational Health Data Sciences and Informatics (OHDSI) community that provides access to a global network of administrative claims and electronic health record data sources, representing 190 million patients in the USA and about 50 million internationally. LEGEND-T2DM will identify all adult, patients with T2DM who newly initiate a traditionally second-line T2DM agent. Using an active comparator, new-user cohort design, LEGEND-T2DM will execute all pairwise class-versus-class and drug-versus-drug comparisons in each data source, producing extensive study diagnostics that assess reliability and generalisability through cohort balance and equipoise to examine the relative risk of cardiovascular and safety outcomes. The primary cardiovascular outcomes include a composite of major adverse cardiovascular events and a series of safety outcomes. The study will pursue data-driven, large-scale propensity adjustment for measured confounding, a large set of negative control outcome experiments to address unmeasured and systematic bias.

ETHICS AND DISSEMINATION

The study ensures data safety through a federated analytic approach and follows research best practices, including prespecification and full disclosure of results. LEGEND-T2DM is dedicated to open science and transparency and will publicly share all analytic code from reproducible cohort definitions through turn-key software, enabling other research groups to leverage our methods, data and results to verify and extend our findings.

A data-driven pipeline to extract potential adverse drug reactions through prescription, procedures and medical diagnoses analysis: application to a cohort study of 2,010 patients taking hydroxychloroquine with an 11-year follow-up

Context

Real-life data consist of exhaustive data which are not subject to selection bias. These data enable to study drug-safety profiles but are underused because of their temporality, necessitating complex models (i.e., safety depends on the dose, timing, and duration of treatment). We aimed to create a data-driven pipeline strategy that manages the complex temporality of real-life data to highlight the safety profile of a given drug.

Methods

We proposed to apply the weighted cumulative exposure (WCE) statistical model to all health events occurring after a drug introduction (in this paper HCQ) and performed bootstrap to select relevant diagnoses, drugs and interventions which could reflect an adverse drug reactions (ADRs). We applied this data-driven pipeline on a French national medico-administrative database to extract the safety profile of hydroxychloroquine (HCQ) from a cohort of 2,010 patients.

Results

The proposed method selected eight drugs (metopimazine, anethole trithione, tropicamide, alendronic acid & colecalciferol, hydrocortisone, chlormadinone, valsartan and tixocortol), twelve procedures (six ophthalmic procedures, two dental procedures, two skin lesions procedures and osteodensitometry procedure) and two medical diagnoses (systemic lupus erythematous, unspecified and discoid lupus erythematous) to be significantly associated with HCQ exposure.

Conclusion

We provide a method extracting the broad spectrum of diagnoses, drugs and interventions associated to any given drug, potentially highlighting ADRs. Applied to hydroxychloroquine, this method extracted among others already known ADRs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01628-3.

• The challenge of drug-safety signal detection methods is to handle four types of difficulties:

○ The data source, the study of long-term adverse drug reactions or effects not suspected by healthcare professionals, requires the use of a real-life data source.

○ The consideration of a broad spectrum of potential adverse drug reactions (ADRs), and not only candidate ADRs.

○ The temporal impact (meaning that safety depends on the dose, date and duration of treatment).

○ The difference between true ADRs and disease natural course.

• We aimed to create a data-driven pipeline strategy, without any assumption of any ADRs, which take into account the complex temporality of real-life data to provide the safety profile of a given drug.

• Our pipeline used three sources of real-life data to establish a safety profile of a given drug: drug prescriptions, procedures and medical diagnoses.

• We successfully applied our data-driven pipeline strategy to hydroxychloroquine (HCQ). Our pipeline enabled us to find diagnoses, drugs and interventions related to HCQ and which could reflect an ADR due to HCQ or the disease itself.

• This data-driven pipeline strategy may be of interest to other experts involved in the pharmacovigilance discipline.

Current Approaches to Vaccine Safety Using Observational Data: A Rationale for the EUMAEUS (Evaluating Use of Methods for Adverse Events Under Surveillance-for Vaccines) Study Design

Post-marketing vaccine safety surveillance aims to detect adverse events following immunization in a population. Whether certain methods of surveillance are more precise and unbiased in generating safety signals is unclear. Here, we synthesized information from existing literature to provide an overview of the strengths, weaknesses, and clinical applications of epidemiologic and analytical methods used in vaccine monitoring, focusing on cohort, case-control and self-controlled designs. These designs are proposed to be evaluated in the EUMAEUS (Evaluating Use of Methods for Adverse Event Under Surveillance-for vaccines) study because of their widespread use and potential utility. Over the past decades, there have been an increasing number of epidemiological study designs used for vaccine safety surveillance. While traditional cohort and case-control study designs remain widely used, newer, novel designs such as the self-controlled case series and self-controlled risk intervals have been developed. Each study design comes with its strengths and limitations, and the most appropriate study design will depend on availability of resources, access to records, number and distribution of cases, and availability of population coverage data. Several assumptions have to be made while using the various study designs, and while the goal is to mitigate any biases, violations of these assumptions are often still present to varying degrees. In our review, we discussed some of the potential biases (i.e., selection bias, misclassification bias and confounding bias), and ways to mitigate them. While the types of epidemiological study designs are well established, a comprehensive comparison of the analytical aspects (including method evaluation and performance metrics) of these study designs are relatively less well studied. We summarized the literature, reporting on two simulation studies, which compared the detection time, empirical power, error rate and risk estimate bias across the above-mentioned study designs. While these simulation studies provided insights on the analytic performance of each of the study designs, its applicability to real-world data remains unclear. To bridge that gap, we provided the rationale of the EUMAEUS study, with a brief description of the study design; and how the use of real-world multi-database networks can provide insights into better methods evaluation and vaccine safety surveillance.

Introduction to Matching in Case-Control and Cohort Studies

Matching is a technique through which patients with and without an outcome of interest (in case-control studies) or patients with and without an exposure of interest (in cohort studies) are sampled from an underlying cohort to have the same or similar distributions of some characteristics. This technique is used to increase the statistical efficiency and cost efficiency of studies. In case-control studies, besides time in risk set sampling, controls are often matched for each case with respect to important confounding factors, such as age and sex, and covariates with a large number of values or levels, such as area of residence (e.g., post code) and clinics/hospitals. In the statistical analysis of matched case-control studies, fixed-effect models such as the Mantel-Haenszel odds ratio estimator and conditional logistic regression model are needed to stratify matched case-control sets and remove selection bias artificially introduced by sampling controls. In cohort studies, exact matching is used to increase study efficiency and remove or reduce confounding effects of matching factors. Propensity score matching is another matching method whereby patients with and without exposure are matched based on estimated propensity scores to receive exposure. If appropriately used, matching can improve study efficiency without introducing bias and could also present results that are more intuitive for clinicians.

Does changing healthcare use signal opportunities for earlier detection of cancer? A review of studies using information from electronic patient records

BACKGROUND

It has been proposed that changes in healthcare use before cancer diagnosis could signal opportunities for quicker detection, but systematic appreciation of such evidence is lacking. We reviewed studies examining pre-diagnostic changes in healthcare utilisation (e.g. rates of GP or hospital consultations, prescriptions or diagnostic tests) among patients subsequently diagnosed with cancer.

METHODS

We identified studies through Pubmed searches complemented by expert elicitation. We extracted information on the earliest time point when diagnosis could have been possible for at least some cancers, together with variation in the length of such 'diagnostic windows' by tumour and patient characteristics.

RESULTS

Across twenty-eight studies, changes in healthcare use were observable at least six months pre-diagnosis for many common cancers, and potentially even earlier for colorectal cancer, multiple myeloma and brain tumours. Early changes were also identified for brain and colon cancer sub-sites.

CONCLUSION

Changing healthcare utilisation patterns before diagnosis indicate that future improvements in diagnostic technologies or services could help to shorten diagnostic intervals for cancer. There is greatest potential for quicker diagnosis for certain cancer types and patient groups, which can inform priorities for the development of decision support tools.

Inclusion of unexposed subjects improves the precision and power of self-controlled case series method

The self-controlled case series is an important method in the studies of the safety of biopharmaceutical products. It uses the conditional Poisson model to make comparison within persons. In models without adjustment for age (or other time-varying covariates), cases who are never exposed to the product do not contribute any information to the estimation. We provide analytic proof and simulation results that the inclusion of unexposed cases in the conditional Poisson model with age adjustment reduces the asymptotic variance of the estimator of the exposure effect and increases power. We re-analysed a vaccine safety dataset to illustrate.

Antipsychotic Medication and Risk of Incident Seizure in People with Autism Spectrum Disorder: Analyses with Cohort and Within Individual Study Designs

There are many case reports of seizures apparently associated with the prescription of antipsychotics. This study aimed to examine whether there is an association between the prescription of antipsychotics and incident seizures in individuals with autism spectrum disorder using retrospective data based on patients’ chart review. A cohort study was conducted to compare the rate of incident seizure between 3923 users of antipsychotics with 10,086 users of other psychotropics. This was followed by a self-controlled case series (SCCS) analysis of 149 patients to eliminate the effect of time-invariant confounders. The results showed no evidence of increased risk of seizure after exposure to antipsychotic agents (Hazard Ratio 1.28, 95% CI 0.74–2.19) compared to other psychotropics.

The cognitive effect of anticholinergics for patients with overactive bladder

Overactive bladder (OAB) is often treated with medications that block the cholinergic receptors in the bladder (known as anticholinergics). The effect of this medication class on cognition and risk of dementia has been increasingly studied over the past 40 years after initial studies suggested that the anticholinergic medication class could affect memory. Short-term randomized clinical trials demonstrated that the administration of the anticholinergic oxybutynin leads to impaired memory and attention, and large, population-based studies showed associations between several different anticholinergic medications and dementia. However, trials involving anticholinergics other than oxybutynin have not shown such substantial effects on short-term cognitive function. This discordance in results between short-term cognitive safety of OAB anticholinergics and the long-term increased dementia risk could be explained by the high proportion of patients using oxybutynin in the OAB subgroups of the dementia studies, or a study duration that was too short in the prospective clinical trials on cognition with other OAB anticholinergics. Notably, all studies must be interpreted in the context of potential confounding factors, such as when prodromal urinary symptoms associated with the early stages of dementia lead to an increase in OAB medication use, rather than the use of OAB medication causing dementia. In patients with potential risk factors for cognitive impairment, the cautious use of selected OAB anticholinergic agents with favourable physicochemical and pharmacokinetic properties and clinical trial evidence of cognitive safety might be appropriate.

Random control selection for conducting high-throughput adverse drug events screening using large-scale longitudinal health data

Case-control design based high-throughput pharmacoinformatics study using large-scale longitudinal health data is able to detect new adverse drug event (ADEs) signals. Existing control selection approaches for case-control design included the dynamic/super control selection approach. The dynamic/super control selection approach requires all individuals to be evaluated at all ADE case index dates, as the individuals' eligibilities as control depend on ADE/enrollment history. Thus, using large-scale longitudinal health data, the dynamic/super control selection approach requires extraordinarily high computational time. We proposed a random control selection approach in which ADE case index dates were matched by randomly generated control index dates. The random control selection approach does not depend on ADE/enrollment history. It is able to significantly reduce computational time to prepare case-control data sets, as it requires all individuals to be evaluated only once. We compared the performance metrics of all control selection approaches using two large-scale longitudinal health data and a drug-ADE gold standard including 399 drug-ADE pairs. The F-scores for the random control selection approach were between 0.586 and 0.600 compared to between 0.545 and 0.562 for dynamic/super control selection approaches. The random control selection approach was ~ 1000 times faster than dynamic/super control selection approach on preparing case-control data sets. With large-scale longitudinal health data, a case-control design-based pharmacoinformatics study using random control selection is able to generate comparable ADE signals than the existing control selection approaches. The random control selection approach also significantly reduces computational time to prepare the case-control data sets.

Race, health, and socioeconomic disparities associated with malingering in psychiatric patients at an urban emergency department

OBJECTIVE

To examine clinical and sociodemographic differences between psychiatric patients suspected of malingering and non-malingering controls in an urban emergency department (ED) setting.

METHODS

We used retrospective chart review to compare 57 psychiatric patients suspected of malingering with 195 date-matched controls. We examined evidence used for malingering and documented secondary gains.

RESULTS

The prevalence of malingering was 5.6-7.1%, with documented evidence consistent with DSM-V guidelines. Malingering patients were more likely to be male (p < .001), > 45 years old (p = .002), Black (p = .049), unhoused (p < .001), and frequent users of ED (p < .001). Psychiatric diagnoses were found in ~75% of malingerers. Diagnosis of antisocial personality (OR = 8.03, p = .013) and substance use disorder (OR = 2.05, p = .018) predicted increased probability of malingering. Malingering encounters were more likely to result in discharges (p < .001). The most common secondary gains were unmet clinical needs.

CONCLUSIONS

Results support adaptational models of malingering. Malingering patients are more likely to have sociodemographic vulnerabilities. A disproportionate number of malingering patients have unmet needs for psychiatric treatment and resources. The study provides further evidence for inequities that may be related to social and structural determinants of health.

Quantifying bias in epidemiologic studies evaluating the association between acetaminophen use and cancer

Many observational studies explore the association between acetaminophen and cancer, but known limitations such as vulnerability to channeling, protopathic bias, and uncontrolled confounding hamper the interpretability of results. To help understand the potential magnitude of bias, we identify key design choices in these observational studies and specify 10 study design variants that represent different combinations of these design choices. We evaluate these variants by applying them to 37 negative controls - outcome presumed not to be caused by acetaminophen - as well as 4 cancer outcomes in the Clinical Practice Research Datalink (CPRD) database. The estimated odds and hazards ratios for the negative controls show substantial bias in the evaluated design variants, with far fewer of the 95% confidence intervals containing 1 than the nominal 95% expected for negative controls. The effect-size estimates for the cancer outcomes are comparable to those observed for the negative controls. A comparison of exposed and unexposed reveals many differences at baseline for which most studies do not correct. We observe that the design choices made in many of the published observational studies can lead to substantial bias. Thus, caution in the interpretation of published studies of acetaminophen and cancer is recommended.

Pharmacoepidemiology for nephrologists (part 1): concept, applications and considerations for study design

Randomized controlled trials on drug safety and effectiveness are the foundation of medical evidence, but they may have limited generalizability and be unpowered to detect rare and long-term kidney outcomes. Observational studies in routine care data can complement and expand trial evidence on the use, safety and effectiveness of medications and aid with clinical decisions in areas where evidence is lacking. Access to routinely collected large healthcare data has resulted in the proliferation of studies addressing the effect of medications in patients with kidney diseases and this review provides an introduction to the science of pharmacoepidemiology to critically appraise them. In this first review we discuss the concept and applications of pharmacoepidemiology, describing methods for drug-utilization research and discussing the strengths and caveats of the most commonly used study designs to evaluate comparative drug safety and effectiveness.

Independent Validation in a Large Privately Insured Population of the Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose

OBJECTIVE

To assess the generalizability of the overdose or serious opioid-induced respiratory depression risk index (VHA-RIOSORD), created by Zedler et al., using claims data from a large private insurer.

DESIGN

A retrospective nested case-control analysis of health care claims data.

SUBJECTS

Commercially insured individuals with a claim for an opioid prescription between October 1, 2014, and September 30, 2016 (N = 1,431,737).

METHODS

An overdose or serious opioid-induced respiratory depression (OSORD) occurred in 1,097 patients. Ten controls were selected per case (N = 10,970). Items and the assignment of point values to predictors were consistent with those determined by Zedler et al. Modeling of risk index scores produced predicted probabilities of OSORD; risk classes were defined by the predicted probability distribution.

RESULTS

All 15 items of the VHA-RIOSORD were used to determine a member's risk of OSORD. The average predicted probability of experiencing OSORD ranged from 3% in the lowest risk decile to 90% in the highest, with excellent agreement between predicted and observed incidence across risk classes. The model's C-statistic was 0.88.

CONCLUSIONS

Consistent with the findings of its developers, the VHA-RIOSORD performed well in identifying members of a large private insurance company who were medical users of prescription opioids at elevated risk of overdose or life-threatening respiratory depression, those most likely to benefit from preventive interventions.

Emulating a target trial in case-control designs: an application to statins and colorectal cancer

BACKGROUND

Previous case-control studies have reported a strong association between statin use and lower cancer risk. It is unclear whether this association reflects a benefit of statins or is the result of design decisions that cannot be mapped to a (hypothetical) target trial (that would answer the question of interest).

METHODS

We outlined the protocol of a target trial to estimate the effect of statins on colorectal cancer incidence among adults with low-density lipoprotein (LDL) cholesterol below 5 mmol/L. We then emulated the target trial using linked electronic health records of 752 469 eligible UK adults (CALIBER 1999-2016) under both a cohort design and a case-control sampling of the cohort. We used pooled logistic regression to estimate intention-to-treat and per-protocol effects of statins on colorectal cancer, with adjustment for baseline and time-varying risk factors via inverse-probability weighting. Finally, we compared our case-control effect estimates with those obtained using previous case-control procedures.

RESULTS

Over the 6-year follow-up, 3596 individuals developed colorectal cancer. Estimated intention-to-treat and per-protocol hazard ratios were 1.00 (95% confidence interval [CI]: 0.87, 1.16) and 0.90 (95% CI: 0.71, 1.12), respectively. As expected, adequate case-control sampling yielded the same estimates. By contrast, previous case-control analytical approaches yielded estimates that appeared strongly protective (odds ratio 0.57, 95% CI: 0.36, 0.91, for ≥5 vs. <5 years of statin use).

CONCLUSIONS

Our study demonstrates how to explicitly emulate a target trial using case-control data to reduce discrepancies between observational and randomized trial evidence. This approach may inform future case-control analyses for comparative effectiveness research.

Proton pump inhibitors and dementia risk: Evidence from a cohort study using linked routinely collected national health data in Wales, UK

OBJECTIVES

Proton pump inhibitors (PPIs) are commonly prescribed for prevention and treatment of gastrointestinal conditions or for gastroprotection from other drugs. Research suggests they are linked to increased dementia risk. We use linked national health data to examine the association between PPI use and the development of incident dementia.

METHODS AND FINDINGS

A population-based study using electronic health-data from the Secure Anonymised Information Linkage (SAIL) Databank, Wales (UK) from 1999 to 2015. Of data available on 3,765,744 individuals, a cohort who had ever been prescribed a PPI was developed (n = 183,968) for people aged 55 years and over and compared to non-PPI exposed individuals (131,110). Those with prior dementia, mild-cognitive-impairment or delirium codes were excluded. Confounding factors included comorbidities and/or drugs associated with them. Comorbidities might include head injury and some examples of medications include antidepressants, antiplatelets and anticoagulants. These commonly prescribed drugs were investigated as it was not feasible to explore all drugs in this study. The main outcome was a diagnosis of incident dementia. Cox proportional hazard regression modelling was used to calculate the Hazard ratio (HR) of developing dementia in PPI-exposed compared to unexposed individuals while controlling for potential confounders. The mean age of the PPI exposed individuals was 69.9 years and 39.8% male while the mean age of the unexposed individuals was 72.1 years and 41.1% male. The rate of PPI usage was 58.4% (183,968) and incident dementia rate was 11.8% (37,148/315,078). PPI use was associated with decreased dementia risk (HR: 0.67, 95% CI: 0.65 to 0.67, p<0.01).

CONCLUSIONS

This study, using large-scale, multi-centre health-data was unable to confirm an association between PPI use and increased dementia risk. Previously reported links may be associated with confounders of people using PPI's, such as increased risk of cardiovascular disease and/or depression and their associated medications which may be responsible for any increased risk of developing dementia.

Five analytic challenges in working with electronic health records data to support clinical trials with some solutions

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.

A plea to stop using the case-control design in retrospective database studies

The case‐control design is widely used in retrospective database studies, often leading to spectacular findings. However, results of these studies often cannot be replicated, and the advantage of this design over others is questionable. To demonstrate the shortcomings of applications of this design, we replicate two published case‐control studies. The first investigates isotretinoin and ulcerative colitis using a simple case‐control design. The second focuses on dipeptidyl peptidase‐4 inhibitors and acute pancreatitis, using a nested case‐control design. We include large sets of negative control exposures (where the true odds ratio is believed to be 1) in both studies. Both replication studies produce effect size estimates consistent with the original studies, but also generate estimates for the negative control exposures showing substantial residual bias. In contrast, applying a self‐controlled design to answer the same questions using the same data reveals far less bias. Although the case‐control design in general is not at fault, its application in retrospective database studies, where all exposure and covariate data for the entire cohort are available, is unnecessary, as other alternatives such as cohort and self‐controlled designs are available. Moreover, by focusing on cases and controls it opens the door to inappropriate comparisons between exposure groups, leading to confounding for which the design has few options to adjust for. We argue that this design should no longer be used in these types of data. At the very least, negative control exposures should be used to prove that the concerns raised here do not apply.