Core concepts in pharmacoepidemiology: Key biases arising in pharmacoepidemiologic studies

Network Analysis and Machine Learning for Signal Detection and Prioritization Using Electronic Healthcare Records and Administrative Databases: A Proof of Concept in Drug-Induced Acute Myocardial Infarction

BACKGROUND

Safety signals for potential drug-induced adverse events (AEs) typically emerge from multiple data sources, primarily spontaneous reporting systems, despite known limitations. Increasingly, real-world data from sources such as electronic health records (EHRs) and administrative databases are leveraged for signal detection. Although network analysis has shown promise in mapping relationships between clinical attributes for signal detection in spontaneous reporting system databases, its application in real-world data from EHRs and administrative databases remains limited.

OBJECTIVE

This study aimed to evaluate the performance of network analysis in detecting safety signals within Italian administrative databases, using drug-induced acute myocardial infarction (AMI) as a proof of concept.

METHODS

We employed a case-crossover design to explore the association between drug exposure and AMI using the Healthcare Administrative Database of Mantova, Italy, from 2014 to 2018. Patients with their first AMI hospitalization were identified after a 365-day washout period to exclude prior hospitalizations. We constructed a network to analyse the relationships between prescribed drugs and diagnoses, represented as nodes, with undirected edges illustrating their interactions. For each patient with AMI, we identified all diagnoses and drugs recorded or redeemed within 365 days of the first AMI episode and generated various drug-diagnosis, drug-drug, and diagnosis-diagnosis pairs. We calculated the frequency of these pairs, and three types of edge weights quantified the strength of connections. We identified outlier drug-AMI pairs using a predictive score (F) based on frequency (C) and full edge weights (WF), with validation for known AMI associations. We prioritized signals using the F score, C of AMI, and WF, analysed through k-means clustering to identify patterns in the data.

RESULTS

From 2014 to 2018, a total of 3918 patients had an AMI, with 4686 AMI diagnoses. Of those, 2866 had prescriptions in the previous year, totalling 498,591 prescriptions. A network analysis identified 2968 unique nodes, revealing 529,935 diagnosis-diagnosis connections, 235,380 drug-diagnosis connections, and 102,831 drug-drug connections. The median number of connections (C) was 404 (Q1-Q3: 194-671) for drug nodes and 380 (Q1-Q3: 216-664) for diagnosis nodes. The median WF was 11.8 (Q1-Q3: 9-14), and the median F score across pairs was 0.1 (Q1-Q3: 0.1-0.3). A total of 249 potential safety signals were detected, with 63.4% aligning with known AEs. Among the remaining signals, 80 were prioritized, and five emerged as the highest priority: terazosin, tamsulosin, allopurinol, esomeprazole, and omeprazole.

CONCLUSIONS

Overall, our novel method demonstrates that network analysis is a valuable tool for signal detection and prioritization in drug-induced AEs based on EHRs and administrative databases.

Antibiotic Use and Risk of Microscopic Colitis in Older Adults: A Nationwide Self-Controlled Case Series Study

BACKGROUND

Several drugs have been linked to the risk of microscopic colitis (MC), a condition characterised by watery, non-bloody diarrhoea. Antibiotics can induce similar symptoms, but their connection to MC remains unclear.

AIM

To investigate the antibiotic-related risks of MC in adults aged 65 years and older.

METHODS

This was a nationwide, self-controlled case series study including adults aged ≥ 65 years with a new prescription for antibiotics and biopsy-confirmed, incident MC (Sweden, 2007-17). We identified cases from the nationwide histopathology cohort ESPRESSO individually linked to several registers. Using conditional Poisson regression, we estimated incidence rate ratios (IRR) for four risk periods: on treatment, and 1-14, 15-91, and 92-365 days post-treatment, compared with the non-treatment periods in the same individual. We also conducted a negative control outcome analysis to assess whether the association was specific to MC or due to diagnostic workup. E-values were used to assess robustness to unmeasured confounding.

RESULTS

We identified 2393 persons with incident biopsy-confirmed MC (median age at diagnosis 74; 67% women). Compared with the non-treatment periods, the risk of MC in the age-adjusted analysis increased with antibiotic treatment (IRR: 1.44 [95% CI: 1.13-1.84], E-value: 2.24), 1-14 days (IRR: 1.12 [0.83-1.49]), 15-91 days (1.12 [0.97-1.31]) and 92-365 days post-treatment (1.19 [1.07-1.32]). The negative control outcome analysis showed similar risks of biopsy-confirmed normal mucosa after antibiotic use.

CONCLUSION

The observed association between antibiotic use and MC may result from detection bias rather than a causal relationship.

Use of psychotropic medications among glioma patients in Denmark, Norway, Sweden, and Wales

PURPOSE

Glioma patients often suffer from psychiatric and neurological conditions. However, little is known about the patterns of use of psychotropic drugs pre- and post-glioma diagnosis. Therefore, we assessed temporal patterns of psychotropic prescriptions among glioma patients, compared to an age and sex matched comparison cohort in four European countries.

METHODS

Incident gliomas were identified in Wales from the Secured Anonymized Information Linkage Databank (2005-2016) and population-based registries in Denmark (2001-2016), Norway (2006-2019), and Sweden (2008-2018). From each data source, a cancer-free comparison cohort was matched to the glioma cases by age and sex. We calculated rates of new psychotropic prescriptions and any psychotropic prescriptions during the 2 years prior to and post glioma diagnosis. Analyses were stratified by histological subtypes and subclasses of psychotropic medications.

RESULTS

We identified 16,007 glioma patients. The rate of new psychotropic drug use increased from 7 months before diagnosis, peaking around the month of glioma diagnosis (with peak rates ranging from 227 to 753 new psychotropic drugs per 1000 person-months). New use remained substantially higher among glioma patients than comparators throughout the 2-year follow-up period after glioma diagnosis, though rates of new use continued to decline throughout. New use was largely driven by antiepileptics, anxiolytics, hypnotics, and sedatives. Patterns were similar when analyses were stratified by histological subtype.

CONCLUSION

Psychotropic drug use among glioma patients was high, and elevations observed around the time of cancer diagnosis, largely driven by antiepileptics, anxiolytics, hypnotics, and sedatives, are likely associated with the consequences of the disease.

Immune checkpoint inhibitors and myocarditis in advanced non-small cell lung cancer: a nationwide cohort study

OBJECTIVE

Evidence suggests immune checkpoint inhibitor (ICI) can increase the risk of myocarditis. We investigated it in a large national cohort in China.

METHODS

Patients with stage IIIB-IV non-small cell lung cancer (NSCLC) using data from China's National Anti-Tumor Drug Surveillance System between January 2013 and December 2021. Exposure density sampling was applied to control for immortal time bias. Multivariate Cox regression with time-dependent exposures was used to examine the association between ICI therapy and the incidence of myocarditis while controlling for confounders.

RESULTS

55,219 patients were included. The median age was 61 years, and 62% were males. At one-year follow-up (median 335 days), there were 26 cases of myocarditis among ICI users and 28 cases among ICI non-users (a cumulative incidence of 4.8 and 0.6 per 1000 person-years respectively). The adjusted hazard ratio (HR) of myocarditis for ICI users was 7.41 (95% confidence interval [CI]: 3.29-16.67). For programmed cell death protein 1 inhibitor users the HR was 8.39 (95% CI: 3.56-19.77). No significant interactions were observed in subgroup analysis. The results remained unchanged in sensitivity analyses.

CONCLUSIONS

This study showed that ICI therapy considerably increased the risk of myocarditis, supporting the need for closer monitoring of patients receiving ICI therapies.

The effect of antidepressant treatment on viral suppression among people with HIV diagnosed with depression in an urban clinic

OBJECTIVE

To estimate the effect of antidepressant initiation on viral nonsuppression among people with HIV (PWH) with clinically recognized, untreated depression.

DESIGN

Retrospective, observational cohort study.

METHODS

We included clinical diagnoses of depression from January 2012 to June 2022 among PWH in the Johns Hopkins HIV Clinical Cohort without another serious psychiatric illness who had initiated antiretroviral therapy. We excluded diagnoses less than 90 days from a prior diagnosis, antidepressant prescription, or greater than one mental health visits. We estimated the association between initiating an antidepressant within 1 month of the index depression diagnosis and viral load nonsuppression (>200 copies/ml) on the first viral load 3-12 months subsequent. We adjusted for a comprehensive set of demographic and clinical confounders.

RESULTS

We included 2346 depression diagnoses among 946 patients; patients initiated an antidepressant following 16%. The risk of viral nonsuppression in the absence of antidepressant treatment was 15.6% [95% confidence interval (CI): 13.1-18.4]. Antidepressant initiation was not associated with viral nonsuppression (risk difference: 0.5%; 95% CI: -3.7 to 4.8) or secondary outcomes: improvement or resolution of depressive symptoms or adherence to scheduled clinic visits.

CONCLUSION

In this sample of patients with as-yet-untreated depression, in a setting with co-located, low-barrier psychiatric services, antidepressant treatment was not associated with improved viral suppression. Pharmacologic management of depression has documented benefits in other studies. However, there may be a subset of PWH with depression who have been previously unsuccessfully treated with antidepressants who are less likely to respond to approved pharmacologic options and who require different interventions to improve their viral suppression.

Evaluation of Drug-Drug Interactions in Pharmacoepidemiologic Research

Drug-drug interactions (DDIs) represent a significant concern for clinical care and public health, but the health consequences of many DDIs remain largely underexplored. This knowledge gap underscores the critical need for pharmacoepidemiologic research to evaluate real-world health outcomes of DDIs. In this review, we summarize the definitions commonly used in pharmacoepidemiologic DDI studies, discuss common sources of bias, and illustrate through examples how these biases can be mitigated.

Association of SGLT2 inhibitors with incident cancer

AIM

It remains unknown whether sodium-glucose cotransporter 2 inhibitors (SGLT2i) could be associated with incident cancer.

METHODS

We analyzed individuals having diabetes and newly prescribed SGLT2i or dipeptidyl peptidase 4 inhibitors (DPP4i) in a large-scale epidemiological database. The primary outcome was the incidence of cancer. A propensity score matching algorithm was employed to compare the subsequent development of cancer between the SGLT2i and DPP4i groups.

RESULTS

After 1:2 propensity score matching, 26,823 individuals (8,941 SGLT2i, 17,882 DPP4i) were analyzed. During the mean follow-up duration of 2.0 ± 1.6 years, 1,076 individuals developed cancer. SGLT2i administration was associated with a reduced risk of cancer (HR 0.80, 95 % CI 0.70-0.91). Particularly, SGLT2i administration was related to a lower risk of colorectal cancer (HR 0.71, 95 % CI 0.50-0.998). Our primary findings remained consistent across various sensitivity analyses, including overlap weighting analysis (HR 0.79, 95 % CI 0.66-0.94), inverse probability of treatment weighting 0.75 (95 % CI 0.65-0.86), and induction period settings 0.78 (95 % CI 0.65-0.93). The risk of developing cancer was comparable among individual SGLT2is (P-value of 0.1738).

CONCLUSION

Our investigation using nationwide real-world data demonstrated the potential advantage of SGLT2i over DPP4i in reducing the development of cancer in individuals with diabetes.

Core Concepts in Pharmacoepidemiology: Quantitative Bias Analysis

Pharmacoepidemiological studies provide important information on the safety and effectiveness of medications, but the validity of study findings can be threatened by residual bias. Ideally, biases would be minimized through appropriate study design and statistical analysis methods. However, residual biases can remain, for example, due to unmeasured confounders, measurement error, or selection into the study. A group of sensitivity analysis methods, termed quantitative bias analyses, are available to assess, quantitatively and transparently, the robustness of study results to these residual biases. These approaches include methods to quantify how the estimated effect would be altered under specified assumptions about the potential bias, and methods to calculate bounds on effect estimates. This article introduces quantitative bias analyses for unmeasured confounding, misclassification, and selection bias, with a focus on their relevance and application to pharmacoepidemiological studies.

Comparing the effectiveness and cost-effectiveness of sulfonylureas and newer diabetes drugs as second-line therapy for patients with type 2 diabetes

INTRODUCTION

We aimed to compare the effectiveness and cost-effectiveness profiles of glucagon-like peptide-1 receptor agonist (GLP-1-RA), sodium-glucose cotransporter 2 inhibitor (SGLT2i), and dipeptidyl peptidase-4 inhibitor (DPP-4i) compared with sulfonylureas and glinides (SU).

RESEARCH DESIGN AND METHODS

Population-based retrospective cohort study based on linked regional healthcare utilization databases. The cohort included all residents in Lombardy aged ≥40 years, treated with metformin in 2014, who started a second-line treatment between 2015 and 2018 with SU, GLP-1-RA, SGLT2i, or DPP-4i. For each cohort member who started SU, one patient who began other second-line treatments was randomly selected and matched for sex, age, Multisource Comorbidity Score, and previous duration of metformin treatment. Cohort members were followed up until December 31, 2022. The association between second-line treatment and clinical outcomes was assessed using Cox proportional hazards models. The incremental cost-effectiveness ratios (ICERs) were calculated and compared between newer diabetes drugs and SU.

RESULTS

Overall, 22 867 patients with diabetes were included in the cohort, among which 10 577, 8125, 2893 and 1272 started a second-line treatment with SU, DPP-4i, SGLT2i and GLP-1-RA, respectively. Among these, 1208 patients for each group were included in the matched cohort. As compared with SU, those treated with DPP-4i, SGLT2i and GLP-1-RA were associated to a risk reduction for hospitalization for major adverse cardiovascular events (MACE) of 22% (95% CI 3% to 37%), 29% (95% CI 12% to 44%) and 41% (95% CI 26% to 53%), respectively. The ICER values indicated an average gain of €96.2 and €75.7 each month free from MACE for patients on DPP-4i and SGLT2i, respectively.

CONCLUSIONS

Newer diabetes drugs are more effective and cost-effective second-line options for the treatment of type 2 diabetes than SUs.

Active surveillance pharmacovigilance for Clostridioides difficile infection and gastrointestinal bleeding: an analytic framework based on case-control studies

BACKGROUND

Active surveillance pharmacovigilance is an emerging approach to identify medications with unanticipated effects. We previously developed a framework called pharmacopeia-wide association studies (PharmWAS) that limits false positive medication associations through high-dimensional confounding adjustment and set enrichment. We aimed to assess the transportability and generalizability of the PharmWAS framework by using medical claims data to reproduce known medication associations with Clostridioides difficile infection (CDI) or gastrointestinal bleeding (GIB).

METHODS

We conducted case-control studies using Optum's de-identified Clinformatics Data Mart Database of individuals enrolled in large commercial and Medicare Advantage health plans in the United States. Individuals with CDI (from 2010 to 2015) or GIB (from 2010 to 2021) were matched to controls by age and sex. We identified all medications utilized prior to diagnosis and analysed the association of each with CDI or GIB using conditional logistic regression adjusted for risk factors for the outcome and a high-dimensional propensity score.

FINDINGS

For the CDI study, we identified 55,137 cases, 220,543 controls, and 290 medications to analyse. Antibiotics with Gram-negative spectrum, including ciprofloxacin (aOR 2.83), ceftriaxone (aOR 2.65), and levofloxacin (aOR 1.60), were strongly associated. For the GIB study, we identified 450,315 cases, 1,801,260 controls, and 354 medications to analyse. Antiplatelets, anticoagulants, and non-steroidal anti-inflammatory drugs, including ticagrelor (aOR 2.81), naproxen (aOR 1.87), and rivaroxaban (aOR 1.31), were strongly associated.

INTERPRETATION

These studies demonstrate the generalizability and transportability of the PharmWAS pharmacovigilance framework. With additional validation, PharmWAS could complement traditional passive surveillance systems to identify medications that unexpectedly provoke or prevent high-impact conditions.

FUNDING

U.S. National Institute of Diabetes and Digestive and Kidney Diseases.

Methodological guidance for the use of real-world data to measure exposure and utilization patterns of osteoporosis medications

Observational studies of osteoporosis medications can provide critical real-world evidence (RWE) that fills knowledge gaps left by clinical trials. However, careful consideration of study design is needed to yield reliable estimates of association. In particular, obtaining valid measurements of exposure to osteoporosis medications from real-world data (RWD) sources is complicated due to different medication classes, formulations, and routes of administration, each with different pharmacology. Extended half-lives of bisphosphonates and extended dosing of denosumab and zoledronic acid require particular attention. In addition, prescribing patterns and medication taking behavior often result in gaps in therapy, switching, and concomitant use of osteoporosis therapies. In this review, we present important considerations and provide specialized guidance for measuring osteoporosis drug exposures in RWD. First, we compare different sources of RWD used for osteoporosis drug studies and provide guidance on identifying osteoporosis medication use in these data sources. Next, we provide an overview of osteoporosis pharmacology and how it can influence decisions on exposure measurement within RWD. Finally, we present considerations for the measurement of osteoporosis medication exposure, adherence, switching, long-term exposures, and drug holidays using RWD. Ultimately, a thorough understanding of the differences in RWD sources and the pharmacology of osteoporosis medications is essential to obtain valid estimates of the relationship between osteoporosis medications and outcomes, such as fractures, but also to improve the critical appraisal of published studies.

Phosphodiesterase Type 5 Inhibitors in Men With Erectile Dysfunction and the Risk of Alzheimer Disease: A Cohort Study

BACKGROUND AND OBJECTIVES

Repurposing phosphodiesterase type 5 inhibitors (PDE5Is) as drugs for Alzheimer disease (AD) risk reduction has shown promise based on animal studies. However, evidence in humans remains inconclusive. Therefore, we conducted a cohort study to evaluate the association between PDE5I initiation compared with nonuse and the risk of developing AD in men with erectile dysfunction (ED).

METHODS

Using electronic health records from IQVIA Medical Research Data UK (formerly known as the THIN database), we identified men aged ≥40 years with a new diagnosis of ED between 2000 and 2017. Individuals with a previous diagnosis of dementia, cognitive impairment, confusion, or prescription for dementia symptoms were excluded. The occurrence of incident AD was identified using diagnostic read codes. To minimize immortal-time bias, PDE5I initiation was treated as a time-varying exposure variable. Potential confounders were adjusted using inverse probability of treatment weighting based on propensity scores. Cox proportional hazard models were used to estimate the adjusted hazard ratio (HR) with 95% CIs. A secondary analysis explored the association between AD and the cumulative number of PDE5I prescriptions. Sensitivity analyses included lag (delay) periods of 1 and 3 years after cohort entry to address the prodromal stage of AD.

RESULTS

The study included 269,725 men, with 1,119 newly diagnosed with AD during a median follow-up of 5.1 (interquartile range 2.9-8.9) years. The adjusted HR in PDE5I initiators compared with nonuse was 0.82 (95% CI 0.72-0.93). The associated risk of AD decreased in individuals issued >20 prescriptions: HR 0.56 (95% CI 0.43-0.73) for 21-50 prescriptions and HR 0.65 (95% CI 0.49-0.87) for >50 prescriptions. Sensitivity analysis with a 1-year lag period supported the primary findings (HR 0.82, 95% CI 0.72-0.94), but the results differed with the inclusion of a 3-year lag period (HR 0.93, 95% CI 0.80-1.08).

DISCUSSION

PDE5I initiation in men with ED was associated with a lower risk of AD, particularly in those most frequently issued prescriptions. The differences between primary and sensitivity analyses highlight the need to explore the optimal lag period. To enhance the generalizability of our findings, a randomized controlled trial including both sexes and exploring various PDE5I doses would be beneficial to confirm the association between PDE5I and AD.

New antipsychotic prescription and recurrent infections among adult sepsis survivors: A population-based cohort study

PURPOSE

Antipsychotic agents, which may increase the risk of infection through dopaminergic dysregulation, are prescribed to a fraction of patients following critical illness. We compared the rate of recurrent sepsis among patients who filled a prescription for antipsychotics with high- or low-D2 affinity.

METHODS

Population-based cohort with active comparator design. We included sepsis survivors older than 65 years with intensive care unit admission and new prescription of antipsychotics in Ontario 2008-2019. The primary outcome were recurrent sepsis episodes within 1 year of follow-up. Patients who filled a prescription within 30 days of hospital discharge for high-D2 affinity antipsychotics (e.g., haloperidol) were compared with patients who filled a prescription within 30 days of hospital discharge for low-D2 affinity antipsychotics (e.g., quetiapine). Multivariable zero-inflated Poisson regression models with robust standard errors adjusting for confounding at baseline were used to estimate incidence rate ratios (IRR) and 95% confidence intervals (CI).

RESULTS

Overall, 1879 patients filled a prescription for a high-D2, and 1446 patients filled a prescription for a low-D2 affinity antipsychotic. Patients who filled a prescription for a high-D2 affinity antipsychotic did not present a higher rate of recurrent sepsis during 1 year of follow-up, compared with patients who filled a prescription for a low-D2 affinity antipsychotic (IRR: 1.12; 95% CI: 0.94, 1.35).

CONCLUSIONS

We did not find conclusive evidence of a higher rate of recurrent sepsis associated with the prescription of high-D2 affinity antipsychotics (compared with low-D2 affinity antipsychotics) by 1 year of follow-up in adult sepsis survivors with intensive care unit admission.

Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review

Real world evidence is now accepted by authorities charged with assessing the benefits and harms of new therapies. Clinical trials based on real world evidence are much less expensive than randomized clinical trials that do not rely on "real world evidence" such as contained in electronic health records (EHR). Consequently, we can expect an increase in the number of reports of these types of trials, which we identify here as 'EHR-sourced trials.' 'In this selected literature review, we discuss the various designs and the ethical issues they raise. EHR-sourced trials have the potential to improve/increase common data elements and other aspects of the EHR and related systems. Caution is advised, however, in drawing causal inferences about the relationships among EHR variables. Nevertheless, we anticipate that EHR-CTs will play a central role in answering research and regulatory questions.

Pharmacoepidemiology: An Overview

The aims of this review are to provide a comprehensive overview of the definition and scope of pharmacoepidemiology, to summarize the study designs and methodologies used in the field, to discuss the future trends in the field and new methodologies to address bias and confounding, and finally to give some recommendations to clinicians interested in pharmacoepidemiologic research. Because drug efficacy and safety from randomized clinical trials do not reflect the real-world situation, pharmacoepidemiological studies on drug safety monitoring and drug effectiveness in large numbers of people are needed by healthcare professionals and regulatory institutions. We aim to highlight the importance of pharmacoepidemiologic research in informing evidence-based medicine and public health policy. The development of new designs and methodologies for the generation of valid evidence, as well as new initiatives to provide guidance and recommendations on how to incorporate real-world evidence into the drug development process, are reported on. In addition, we have touched on the implication of artificial intelligence in the management of real-world data. This overview aims to summarize all important aspects to consider when conducting or interpreting a pharmacoepidemiologic study.

Are early post-discharge physician contacts associated with 30-day psychiatric re-hospitalisation? A nationwide claims data based retrospective cohort study in Austria free of immortal time bias

OBJECTIVES

Cost containment and quality of care considerations have increased research interest in the potential preventability of early re-hospitalisations. Various registry-based retrospective cohort studies on psychiatric re-hospitalisation have focused on the role of early post-discharge service contacts, but either did not consider their time-dependent nature ('immortal time bias') or evaded the issue by analysing late re-hospitalisations. The present study takes care of the immortal time bias in studying early psychiatric re-hospitalisations.

METHODS

In a retrospective cohort study using nationwide electronic claims data in Austria, 10,689 adults discharged from acute psychiatric inpatient wards were followed up for 30 days. Cox regression analyses were performed with post-discharge psychiatric and general practitioner contacts as time-dependent covariates and time to first psychiatric re-hospitalisation as outcome.

RESULTS

Post-discharge ambulatory physician contacts were significantly associated with a decreased psychiatric re-hospitalisation rate (hazard ratio 0.77 [95% CI 0.69; 0.87], p < 0.0001), with similarly strong contributions to this association by general practitioners and psychiatrists.

CONCLUSIONS

Despite avoiding the immortal time bias and controlling for several confounders, we suggest to be cautious with a causal interpretation of the identified association, since potentially relevant confounders, such as disease severity, were unavailable in our claims data base.

Using administrative healthcare data to evaluate drug repurposing opportunities for cancer: the possibility of using beta-blockers to treat breast cancer

Introduction: Cancer registries and hospital electronic medical records are commonly used to investigate drug repurposing candidates for cancer. However, administrative data are often more accessible than data from cancer registries and medical records. Therefore, we evaluated if administrative data could be used to evaluate drug repurposing for cancer by conducting an example study on the association between beta-blocker use and breast cancer mortality. Methods: A retrospective cohort study of women aged ≥50 years with incident breast cancer was conducted using a linked dataset with statewide hospital admission data and nationwide medication claims data. Women receiving beta blockers and first-line anti-hypertensives prior to and at diagnosis were compared. Breast cancer molecular subtypes and metastasis status were inferred by algorithms from commonly prescribed breast cancer antineoplastics and hospitalization diagnosis codes, respectively. Subdistribution hazard ratios (sHR) and corresponding 95% confidence intervals (CIs) for breast cancer mortality were estimated using Fine and Gray's competing risk models adjusted for age, Charlson comorbidity index, congestive heart failure, myocardial infraction, molecular subtype, presence of metastasis at diagnosis, and breast cancer surgery. Results: 2,758 women were hospitalized for incident breast cancer. 604 received beta-blockers and 1,387 received first-line antihypertensives. In total, 154 breast cancer deaths were identified over a median follow-up time of 2.7 years. We found no significant association between use of any beta-blocker and breast-cancer mortality (sHR 0.86, 95%CI 0.58-1.28), or when stratified by beta-blocker type (non-selective, sHR 0.42, 95%CI 0.14-1.25; selective, sHR 0.95, 95%CI 0.63-1.43). Results were not significant when stratified by molecular subtypes (e.g., triple negative breast cancer (TNBC), any beta blocker, sHR 0.16, 95%CI 0.02-1.51). Discussion: It is possible to use administrative data to explore drug repurposing opportunities. Although non-significant, an indication of an association was found for the TNBC subtype, which aligns with previous studies using registry data. Future studies with larger sample size, longer follow-up are required to confirm the association, and linkage to clinical data sources are required to validate our methodologies.