Comparative renal outcomes of matched cohorts of patients with type 2 diabetes receiving SGLT2 inhibitors or GLP-1 receptor agonists under routine care.

AIMS/HYPOTHESIS: We compared the effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) on renal outcomes in individuals with type 2 diabetes, focusing on the changes in eGFR and albuminuria. METHODS: This was a multicentre retrospective observational study on new users of diabetes medications. Participant characteristics were assessed before and after propensity score matching. The primary endpoint, change in eGFR, was analysed using mixed-effects models. Secondary endpoints included categorical eGFR-based outcomes and changes in albuminuria. Subgroup and sensitivity analyses were performed to assess robustness of the findings. RESULTS: After matching, 5701 participants/group were included. Participants were predominantly male, aged 61 years, with a 10 year duration of diabetes, a baseline HbA1c of 64 mmol/mol (8.0%) and BMI of 33 kg/m2. Chronic kidney disease (CKD) was present in 23% of participants. During a median of 2.1 years, from a baseline of 87 ml/min per 1.73 m2, eGFR remained higher in the SGLT2i group compared with the GLP-1RA group throughout the observation period by 1.2 ml/min per 1.73 m2. No differences were detected in albuminuria change. The SGLT2i group exhibited lower rates of worsening CKD class and favourable changes in BP compared with the GLP-1RA group, despite lesser HbA1c decline. SGLT2i also reduced eGFR decline better than GLP-1RA in participants without baseline CKD. CONCLUSIONS/INTERPRETATION: In individuals with type 2 diabetes, treatment with SGLT2i was associated with better preservation of renal function compared with GLP-1RA, as evidenced by slower decline in eGFR. These findings reinforce SGLT2i as preferred agents for renal protection in this patient population.

Medication management in long-term care: using evidence generated from real-world data to effect policy change in the Australian setting.

Older individuals residing in long-term care facilities (LTCFs) are often living with multimorbidity and exposed to polypharmacy, and many experience medication-related problems. Because randomized controlled trials seldom include individuals in LTCFs, pharmacoepidemiological studies using real-world data are essential sources of new knowledge on the utilization, safety and effectiveness of pharmacotherapies and related health outcomes in this population. In this commentary, we discuss recent pharmacoepidemiological research undertaken to support the investigations and recommendations of a landmark public inquiry into the quality and safety of care provided in the approximately 3,000 Australian LTCFs which house over 240,000 residents annually and informed subsequent national medication-related policy reforms. Suitable sources of real-world data for pharmacoepidemiological studies in long-term care cohorts and methodological considerations are also discussed.

Interventional Pharmacoepidemiology: Origins, Current Status, and Future Possibilities.

Interventional pharmacoepidemiology applies quantitative analysis of patterns of medication use and outcomes to help design, guide and then evaluate programs to improve prescription drug use and outcomes. Surveillance of prescribing and drug-taking in large populations is increasingly practical because of the proliferation of detailed data on medication use decisions, often based on paid claims billing data. At the same time, increasingly granular clinical information is available on patient characteristics and outcomes. This can offer important opportunities to identify problematic use, focus interventions to address them, and measure their impact. Alexander et al (Am J Epidemiol. 0000;000(00):0000-0000) review the need for such research and provide methodological guidance for its performance. While randomized controlled trials of such interventions are ideal, real-world considerations often require other evaluation strategies, including stepped-wedge designs and interrupted time-series analysis. As drug therapy becomes more powerful and more costly, and the risks of poor medication choices as well as under-use of effective treatments become even better understood, the health care system will increasingly rely on such approaches to assess current patterns of prescribing and patient adherence, target programs to address problem areas, and measure the effectiveness of such interventions.

Building transparency and reproducibility into the practice of pharmacoepidemiology and outcomes research.

Real-world evidence (RWE) studies are increasingly used to inform policy and clinical decisions. However, there remain concerns about the credibility and reproducibility of RWE studies. While there is universal agreement on the critical importance of transparent and reproducible science, the building blocks of open science practice that are common across many disciplines have not yet been built into routine workflows for pharmacoepidemiology and outcomes researchers. Observational researchers should highlight the level of transparency of their studies by providing a succinct statement addressing study transparency with the publication of every paper, poster, or presentation that reports on an RWE study. In this paper, we propose a framework for an explicit transparency statement that declares the level of transparency a given RWE study has achieved across 5 key domains: (1) protocol, (2) preregistration, (3) data, (4) code-sharing, and (5) reporting checklists. The transparency statement outlined in the present paper can be used by research teams to proudly display the open science practices that were used to generate evidence designed to inform public health policy and practice. While transparency does not guarantee validity, such a statement signals confidence from the research team in the scientific choices that were made.

Development and Validation of a Claims-Based Model to Predict Categories of Obesity.

We developed and validated a claims-based algorithm that classifies patients into obesity categories. Using Medicare (2007-2017) and Medicaid (2000-2014) claims data linked to 2 electronic health record (EHR) systems in Boston, Massachusetts, we identified a cohort of patients with an EHR-based body mass index (BMI) measurement (calculated as weight (kg)/height (m)2). We used regularized regression to select from 137 variables and built generalized linear models to classify patients with BMIs of ≥25, ≥30, and ≥40. We developed the prediction model using EHR system 1 (training set) and validated it in EHR system 2 (validation set). The cohort contained 123,432 patients in the Medicare population and 40,736 patients in the Medicaid population. The model comprised 97 variables in the Medicare set and 95 in the Medicaid set, including BMI-related diagnosis codes, cardiovascular and antidiabetic drugs, and obesity-related comorbidities. The areas under the receiver-operating-characteristic curve in the validation set were 0.72, 0.75, and 0.83 (Medicare) and 0.66, 0.66, and 0.70 (Medicaid) for BMIs of ≥25, ≥30, and ≥40, respectively. The positive predictive values were 81.5%, 80.6%, and 64.7% (Medicare) and 81.6%, 77.5%, and 62.5% (Medicaid), for BMIs of ≥25, ≥30, and ≥40, respectively. The proposed model can identify obesity categories in claims databases when BMI measurements are missing and can be used for confounding adjustment, defining subgroups, or probabilistic bias analysis.

Non-benzodiazepine Hypnotics and Police-Reported Motor Vehicle Crash Risk among Older Adults: A Sequential Target Trial Emulation.

Non-benzodiazepine hypnotics ( "Z-drugs") are prescribed for insomnia, but might increase risk of motor vehicle crash (MVC) among older adults through prolonged drowsiness and delayed reaction times. We estimated the effect of initiating Z-drug treatment on the 12-week risk of MVC in a sequential target trial emulation. After linking New Jersey driver licensing and police-reported MVC data to Medicare claims, we emulated a new target trial each week (July 1, 2007 - October 7, 2017) in which Medicare fee-for-service beneficiaries were classified as Z-drug-treated or untreated at baseline and followed for an MVC. We used inverse probability of treatment and censoring weighted pooled logistic regression models to estimate risk ratios (RR) and risk differences with 95% bootstrap confidence limits (CLs). There were 257,554 person-trials, of which 103,371 were Z-drug-treated and 154,183 untreated, giving rise to 976 and 1,249 MVCs, respectively. The intention-to-treat RR was 1.06 (95%CLs 0.95, 1.16). For the per-protocol estimand, there were 800 MVCs and 1,241 MVCs among treated and untreated person-trials, respectively, suggesting a reduced MVC risk (RR 0.83 [95%CLs 0.74, 0.92]) with sustained Z-drug treatment. Z-drugs should be prescribed to older patients judiciously but not withheld entirely over concerns about MVC risk.

Influence of incomplete death information on cumulative risk estimates in US claims data.

Administrative claims databases often do not capture date or fact of death, so studies using these data may inappropriately treat death as a censoring event-equivalent to other withdrawal reasons-rather than a competing event. We examined 1-, 3-, and 5-year inverse-probability-of-treatment weighted cumulative risks of a composite cardiovascular outcome among 34 527 initiators of telmisartan (exposure) and ramipril (referent), who were aged ≥55 years, in Optum (United States) claims data from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared with treating death as a competing event (subdistribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users, 5-year cause-specific and subdistribution cumulative risk estimates per 100, respectively, were 16.4 (95% CI, 15.3-17.5) and 16.2 (95% CI, 15.1-17.3) among ages 55-64 (difference = 0.2) and were 43.2 (95% CI, 41.3-45.2) and 39.7 (95% CI, 37.9-41.4) among ages ≥75 (difference = 3.6). Plasmode simulation results demonstrated the differences in cause-specific versus subdistribution cumulative risks to increase with increasing mortality rate. We suggest researchers consider the cohort's baseline mortality risk when deciding whether real-world data with incomplete death information can be used without concern. This article is part of a Special Collection on Pharmacoepidemiology.

Separable effects for adherence.

Comparing different medications is complicated when adherence to these medications differs. We can overcome the adherence issue by assessing effectiveness under sustained use, as in usual causal 'per-protocol' estimands. However, when sustained use is challenging to satisfy in practice, the usefulness of these estimands can be limited. Here we propose a different class of estimands: separable effects for adherence. These estimands compare modified medications, holding fixed a component responsible for non-adherence. Under assumptions about treatment components' mechanisms of effect, a separable effects estimand can quantify the effectiveness of medication initiation strategies on an outcome of interest under the adherence mechanism of one of the medications. These assumptions are amenable to interrogation by subject-matter experts and can be evaluated using causal graphs. We describe an algorithm for constructing causal graphs for separable effects, illustrate how these graphs can be used to reason about assumptions required for identification, and provide semi-parametric weighted estimators.

High-dimensional Iterative Causal Forest (hdiCF) for Subgroup Identification Using Health Care Claims Data.

We recently developed a machine-learning subgrouping algorithm, iterative causal forest (iCF), to identify subgroups with heterogeneous treatment effects (HTEs) using predefined covariates. However, such predefined covariates may miss or poorly define important features leading to inaccurate subgrouping. To address such limitations, we developed a new semi-automatic subgrouping algorithm, hdiCF, which adapts methodology from high-dimensional propensity score for feature recognition in claims data. The hdiCF algorithm has 3 steps: 1) high-dimensional feature identification by International Classification of Diseases, Current Procedural Terminology, and Anatomical Therapeutic Chemical codes (in/outpatient diagnoses, procedures, prescriptions) and creation of ordinal variables by frequency of occurrence; 2) propensity score trimming and high-dimensional feature preparation; 3) iCF implementation to identify subgroups. We applied hdiCF in a 20% random sample of fee-for-service Medicare beneficiaries who initiated sodium-glucose cotransporter-2 inhibitors (SGLT2i) or glucagon-like peptide-1 receptor agonists to identify subgroups with HTEs for incidence of hospitalized heart failure. HdiCF findings were consistent with studies suggesting SGLT2i to be more beneficial for patients with pre-existing heart failure or chronic kidney disease. HdiCF is not dependent on prior hypotheses about HTEs and identifies subgroups with markers for potential HTEs in real-world evidence studies where active-comparator, new-user study designs limit the potential for unmeasured confounding.

The Healthy User Effect in Pharmacoepidemiology.

The healthy user effect is a well-recognized bias in the field of pharmacoepidemiology and can be expected to overstate the effect of a preventive intervention when comparing long term users or "adherers" to non-users. Similar to the healthy worker effect observed in occupational epidemiology, the healthy user effect can be separated into a healthy initiator effect (baseline confounding) and a healthy adherer effect (selection bias). Restriction approaches and new user designs that implicitly condition on the indication and, similar healthy behaviors or health status can often mitigate the healthy initiator effect (confounding) or healthy adherer effect (selection bias) at the start of a study. Addressing the healthy adherer effect due to continued conditioning on adherence over the duration of a study is more challenging as methods to mitigate it require the ability to predict adherence, which is often difficult using databases common in pharmacoepidemiologic research. Here, we describe the healthy user effect, with supporting examples, and describe study design approaches available to pharmacoepidemiologists to mitigate the potential for bias.

Tree-based scan statistics to generate drug repurposing hypotheses: a test case using sodium-glucose cotransporter-2 inhibitors.

Most drug repurposing studies using real-world data focused on validating, instead of generating, hypotheses. We used tree-based scan statistics to generate repurposing hypotheses for sodium-glucose cotransporter-2 inhibitors (SGLT2i). We used an active-comparator, new-user design to create a 1:1 propensity-score matched cohort of SGLT2i and dipeptidyl peptidase-4 inhibitors (DPP4i) initiators in the MerativeTM MarketScan® Research Databases. Tree-based scan statistics were estimated across an ICD-10-CM-based hierarchical outcome tree using incident outcomes identified from hospital and outpatient diagnoses. We used an adjusted P≤0.01 as the threshold for statistical alert to prioritize associations for evaluation as repurposing signals. We varied the analyses by tree size, scanning level, and clinical settings for outcomes. There were 80,510 matched SGLT2i-DPP4i initiator pairs with 215,333 outcomes among SGLT2i initiators and 223,428 outcomes among DPP4i initiators. There were 18 prioritized associations, which included chronic kidney disease (P=0.0001), an expected signal, and anemia (P=0.0001). Heart failure (P=0.0167), another expected signal, was identified slightly beyond the statistical alert threshold. Narrowing the outcome tree, scanning at different tree levels, and including outcomes from different clinical settings influenced the scan statistics. We identified signals aligning with recently approved indications of SGLT2i, plus potential repurposing signals supported by existing evidence but requiring future validation.

Comparison of caffeine consumption behavior with plasma caffeine levels as exposure measures in drug-target Mendelian randomization.

Mendelian randomization is an epidemiological technique that can explore the potential effect of perturbing a pharmacological target. Plasma caffeine levels can be used as a biomarker to measure the pharmacological effects of caffeine. Alternatively, this can be assessed using a behavioral proxy, such as average number of caffeinated drinks consumed per day. Either variable can be used as the exposure in a Mendelian randomization investigation, and to select which genetic variants to use as instrumental variables. Another possibility is to choose variants in gene regions with known biological relevance to caffeine level regulation. These choices affect the causal question that is being addressed by the analysis, and the validity of the analysis assumptions. Further, even when using the same genetic variants, the sign of Mendelian randomization estimates (positive or negative) can change depending on the choice of exposure. Some genetic variants that decrease caffeine metabolism associate with higher levels of plasma caffeine, but lower levels of caffeine consumption, as individuals with these variants require less caffeine consumption for the same physiological effect. We explore Mendelian randomization estimates for the effect of caffeine on body mass index, and discuss implications for variant and exposure choice in drug target Mendelian randomization investigations.

Cardiorenal effects of Angiotensin-converting enzyme inhibitors and Angiotensin receptor blockers in people underrepresented in trials: analysis of routinely collected data with emulation of a reference trial (ONTARGET).

Cardiovascular disease (CVD) is a leading cause of death globally. Angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARB), compared in the ONTARGET trial, each prevent CVD. However, trial results may not be generalisable and their effectiveness in underrepresented groups is unclear. Using trial emulation methods within routine-care data to validate findings, we explored generalisability of ONTARGET results. For people prescribed an ACEi/ARB in the UK Clinical Practice Research Datalink GOLD from 1/1/2001-31/7/2019, we applied trial criteria and propensity-score methods to create an ONTARGET trial-eligible cohort. Comparing ARB to ACEi, we estimated hazard ratios for the primary composite trial outcome (cardiovascular death, myocardial infarction, stroke, or hospitalisation for heart failure), and secondary outcomes. As the pre-specified criteria were met confirming trial emulation, we then explored treatment heterogeneity among three trial-underrepresented subgroups: females, those aged ≥75 years and those with chronic kidney disease (CKD). In the trial-eligible population (n=137,155), results for the primary outcome demonstrated similar effects of ARB and ACEi, (HR 0.97 [95% CI: 0.93, 1.01]), meeting the pre-specified validation criteria. When extending this outcome to trial-underrepresented groups, similar treatment effects were observed by sex, age and CKD. This suggests that ONTARGET trial findings are generalisable to trial-underrepresented subgroups.

Applying two approaches to detect unmeasured confounding due to time-varying variables in a self-controlled risk interval design evaluating COVID-19 vaccine safety signals, using myocarditis as a case example.

We test the robustness of the self-controlled risk interval (SCRI) design in a setting where time between doses may introduce time-varying confounding, using both negative control outcomes (NCOs) and quantitative bias analysis (QBA). All vaccinated cases identified from 5 European databases between 1 September 2020 and end of data availability were included. Exposures were doses 1-3 of the Pfizer, Moderna, AstraZeneca, and Janssen COVID-19 vaccines; outcomes were myocarditis and otitis externa (NCO). The SCRI used a 60-day control window and dose-specific 28-day risk windows, stratified by vaccine brand and adjusted for calendar time. The QBA included two scenarios: (i) baseline probability of the confounder was higher in the control window and (ii) vice versa. The NCO was not associated with any of the COVID-19 vaccine types or doses except Moderna dose 1 (IRR = 1.09, 95%CI 1.01-1.09). The QBA suggested even the strongest literature-reported confounder (COVID-19; RRmyocarditis = 18.3) could only explain away part of the observed effect from IRR = 3 to IRR = 1.40. The SCRI seems robust to unmeasured confounding in the COVID-19 setting, although a strong unmeasured confounder could bias the observed effect upward. Replication of our findings for other safety signals would strengthen this conclusion.

Prenatal exposure to benzodiazepine and z-hypnotics and fifth-grade scholastic skills - emulating target trials using data from the Norwegian Mother, Father and Child Cohort Study.

Evidence is limited regarding the effect of prenatal benzodiazepine and z-hypnotic exposure and long-term neurodevelopment in childhood. The objective of this study was to investigate the effects of initiating benzodiazepine or z-hypnotic treatment in early, mid and late pregnancy on fifth-grade numeracy and literacy scholastic skills in children, by emulating three target trials. The trials are identical except for the timing of enrollment and the number of eligible individuals. Eligibility to the trials required a history of anxiety and/or depression prior to pregnancy. We used data from the Norwegian Mother, Father and Child Cohort Study, linked to the Medical Birth Registry of Norway, to emulate the trials. We adjusted for baseline covariates that were available at time 0 for each trial by inverse probability of treatment weighting using the propensity score. The findings of this study did not show any effect of mothers' initiation of treatment with benzodiazepines or z-hypnotics in early, mid or late pregnancy on the children's 5th grade test scores in numeracy and literacy. The study results provide reassurance for patients in need of benzodiazepines and z-hypnotics during pregnancy; however, these findings need to be interpreted with caution due to low study power in some of the analyses.

Power to the people: why person-generated health data are important for pharmacoepidemiology.

Person-generated health data (PGHD) are valuable for studying outcomes relevant to everyday living, for obtaining information not otherwise available, for long-term follow-up, and in situations where decisions cannot wait for traditional clinical research to be completed. While there is no dispute that these data are subject to bias, insights gained may be better than having an information void, provided the biases are understood and addressed. People will share information known uniquely to them about exposures that may affect drug tolerance, safety, and effectiveness (eg, nonprescription and complementary medications, alcohol, tobacco, illicit drugs, exercise, etc). Patients may be the best source of safety information when long-term follow-up is needed (eg, the 5- to 15-year follow-up required for some gene therapies). Validation studies must be performed to evaluate what people can accurately report and when supplementary confirmation information is needed. However, PGHD has already proven valuable in quantifying and contrasting COVID-19 vaccine benefits and risks and for evaluating disease transmission and the accuracy of COVID-19 testing. Going forward, PGHD will be used for patient-measured and patient-relevant outcomes, including for regulatory purposes, and will be linked to broader health data networks using tokenization, becoming a mainstay for signals about risks and benefits for diverse populations. This article is part of a Special Collection on Pharmacoepidemiology.

Association between epidermal growth factor receptor-tyrosine kinase inhibitors and venous thromboembolism among older patients with advanced non-small cell lung cancer.

BACKGROUND: Venous thromboembolism (VTE) risk is higher among patients with non-small cell lung cancer (NSCLC) and specific subgroups, including the elderly, but little is known about the VTE risk of different generations of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) and whether the risk differs by demographic characteristics. This study aims to compare the risk of VTE (deep venous thromboembolism [DVT]; pulmonary embolism [PE]) between a third-generation EGFR-TKI and first/second-generation EGFR-TKIs and stratify VTE risk by sex, age, and race/ethnicity in third-generation EGFR-TKI users. METHODS: Via the 2006-2019 Surveillance, Epidemiology, and End Results-Medicare database, this retrospective cohort study included older patients (aged ≥65 years) with advanced NSCLC who initiated on a third-generation EGFR-TKI (n = 493) and first/second-generation EGFR-TKIs (n = 1036). We estimated the hazard ratio (HR) and its 95% confidence interval (95% CI) with the Cox proportional hazards model. RESULTS: A third-generation EGFR-TKI had a significantly higher VTE risk than first/second-generation EGFR-TKIs (HR, 1.26 [95% CI, 1.01-1.57]; p = .037), with an elevated risk in males (HR, 2.16 [95% CI, 1.47-3.19]; p < .001), patients aged ≥75 years (HR, 1.38 [95% CI, 1.04-1.83]; p = .026), and non-Hispanic Whites (HR, 1.46 [95% CI, 1.10-1.95]; p = .010). Males consistently showed a significantly higher risk of DVT (HR, 2.49 [95% CI, 1.29-4.80]; p = .007) and PE (HR, 2.00 [95% CI, 1.29-3.11]; p = .002). A significantly higher risk of DVT (HR, 1.54 [95% CI, 1.00-2.37]; p = .050) and PE (HR, 1.47 [95% CI, 1.06-2.05]; p = .021) was shown in patients aged ≥75 years and non-Hispanic Whites, respectively. Among third-generation EGFR-TKI users, non-Hispanic Whites had a significantly higher risk of VTE (HR, 2.04 [95% CI, 1.03-4.02]; p = .041) and PE (HR, 2.88 [95% CI, 1.24-6.70]; p = .014) than non-Hispanic Asian/Pacific Islanders. CONCLUSIONS: Close monitoring of VTE events in high-risk patients is essential to promote early diagnosis and treatment.

Association of Renin-Angiotensin System Inhibition With Liver-Related Events and Mortality in Compensated Cirrhosis.

BACKGROUND & AIMS: While renin-angiotensin system inhibition lowers the hepatic venous gradient, the effect on more clinically meaningful endpoints is less studied. We aimed to quantify the relationship between renin-angiotensin system inhibition and liver-related events (LREs) among adults with compensated cirrhosis. METHODS: In this national cohort study using the Optum database, we quantified the association between angiotensin-converting enzyme (ACE) inhibitor or angiotensin-receptor blocker (ARB) use and LREs (hepatocellular carcinoma, liver transplantation, ascites, hepatic encephalopathy, or variceal bleeding) among patients with cirrhosis between 2009 and 2019. Selective beta-blocker (SBB) users served as the comparator group. We used demographic and clinical features to calculate inverse-probability treatment weighting-weighted cumulative incidences, absolute risk differences, and Cox proportional hazard ratios. RESULTS: Among 4214 adults with cirrhosis, 3155 were ACE inhibitor/ARB users and 1059 were SBB users. In inverse probability treatment weighting-weighted analyses, ACE inhibitor/ARB (vs SBB) users had lower 5-year cumulative incidence (30.6% [95% confidence interval (CI), 27.8% to 33.2%] vs 41.3% [95% CI, 34.0% to 47.7%]; absolute risk difference, -10.7% [95% CI, -18.1% to -3.6%]) and lower risk of LREs (adjusted hazard ratio [aHR], 0.69; 95% CI, 0.60 to 0.80). There was a dose-response relationship: compared with SBB use, ACE inhibitor/ARB prescriptions ≥1 defined daily dose (aHR, 0.65; 95% CI, 0.56 to 0.76) were associated with a greater risk reduction compared with <1 defined daily dose (aHR, 0.87; 95% CI, 0.71 to 1.07). Results were robust across sensitivity analyses such as comparing ACE inhibitor/ARB users with nonusers and as-treated analysis. CONCLUSIONS: In this national cohort study, ACE inhibitor/ARB use was associated with significantly lower risk of LREs in patients with compensated cirrhosis. These results provide support for a randomized clinical trial to confirm clinical benefit.

Association of Proton Pump Inhibitor Use With Incident Dementia and Cognitive Decline in Older Adults: A Prospective Cohort Study.

BACKGROUND & AIMS: Prior studies have suggested that proton pump inhibitor (PPI) use is associated with increased risk of dementia; however, these have been limited by incomplete assessment of medication use and failure to account for confounders. Furthermore, prior studies have relied on claims-based diagnoses for dementia, which can lead to misclassification. We investigated the associations of PPI and histamine-2 receptor antagonist (H2RA) use with dementia and cognitive decline. METHODS: We conducted a post hoc analysis of ASPirin in Reducing Events in the Elderly (ASPREE), a randomized trial of aspirin in the United States and Australia, including 18,934 community-based adults ≥65 years of all races/ethnicities. Baseline and recent PPI and H2RA use were determined according to review of medications during annual in-person study visits. Incident dementia was defined according to Diagnostic and Statistical Manual for Mental Disorders, Fourth Edition, criteria. Secondary endpoints include cognitive impairment, no dementia (CIND) and changes in cognition. Associations of medication use with dementia and CIND outcomes were examined using Cox proportional hazards models. Changes in cognitive test scores were examined using linear mixed-effects models. RESULTS: Baseline PPI use vs nonuse was not associated with incident dementia (multivariable hazard ratio, 0.88; 95% confidence interval, 0.72-1.08), CIND (multivariable hazard ratio, 1.00; 95% confidence interval, 0.92-1.09), or with changes in overall cognitive test scores over time (multivariable B, -0.002; standard error, 0.01; P = .85). Similarly, no associations were observed between H2RA use and all cognitive endpoints. CONCLUSIONS: In adults ≥65 years of age, PPI and H2RA use were not associated with incident dementia, CIND, or decline in cognition over time. These data provide reassurance about the safety of long-term use of PPIs among older adults.

Beta-blocker use and breast cancer outcomes: a meta-analysis.

PURPOSE: Beta blockers (BBs) are commonly used cardiovascular medications, and their association with breast cancer outcomes has been examined in several previous observational studies and meta-analyses. In this study, an updated meta-analysis was undertaken to ascertain the association between BBs and both breast cancer death (BCD) and breast cancer recurrence (BCR). METHODS: Articles were sourced from various databases up until the 14th of August 2023. Effect estimates were pooled using the random effects model, and the Higgins I2 statistic was computed to ascertain heterogeneity. Subgroup analyses were conducted by the potential for immortal time bias (ITB), the exposure period (prediagnosis vs postdiagnosis), and type of BB (selective vs non-selective). Publication bias was assessed using funnel plots and Egger's regression tests. RESULTS: Twenty-four studies were included. Pooled results showed that there was no statistically significant association between BB use and both BCD (19 studies, hazard ratio = 0.90, 95% CI 0.78-1.04) and BCR (16 studies, HR = 0.87, 95% CI 0.71-1.08). After removing studies with ITB, the associations were attenuated towards the null. There was no effect modification for either outcome when stratifying by the exposure period or type of BB. There was clear evidence of publication bias for both outcomes. CONCLUSION: In this meta-analysis, we found no evidence of an association between BB use and both BCD and BCR. Removing studies with ITB attenuated the associations towards the null, but there was no effect modification by the exposure period or type of BB.