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      Five comparative cohorts to assess the risk of genital tract infections associated with sodium‐glucose cotransporter‐2 inhibitors initiation in type 2 diabetes mellitus

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          Abstract

          Aim

          To assess the association between SGLT‐2 inhibitors initiation and genital tract infections (GTIs) among patients with type 2 diabetes.

          Methods

          A population‐based cohort study using administrative healthcare data from Alberta, Canada, and primary care data from the UK’s Clinical Practice Research Datalink (CPRD). Among new metformin users, we identified new users of SGLT‐2 inhibitors and five active comparator cohorts (new users of dipeptidyl peptidase‐4 (DPP‐4) inhibitors, sulfonylureas (SU), glucagon‐like peptide‐1 receptor agonists (GLP‐1 RA), thiazolidinediones (TZD) and insulin). The outcome of interest was a composite GTI outcome. In each cohort, we used high‐dimensional propensity score matching to adjust for confounding and conditional Cox proportional hazards regression to estimate the hazard ratios (HR). We used random‐effects meta‐analysis to combine aggregate data across databases.

          Results

          The risk of GTI was higher for SGLT‐2 inhibitors users compared with DPP4inhibitor users (pooled HR 2.68, 95% CI 2.19 3.28), SU users (3.29, 2.62–4.13), GLP1‐RA users (2.51, 1.90–3.31), TZD users (4.17, 2.46–7.08) and insulin users (1.86, 1.27–2.73).

          Conclusion

          In five comparative cohorts, SGLT‐2 inhibitors initiation is associated with a higher risk of GTIs. These findings from real‐world data are consistent with placebo‐controlled randomized controlled trials.

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          Most cited references33

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          Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples

          The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile–quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.
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            Data Resource Profile: Clinical Practice Research Datalink (CPRD)

            The Clinical Practice Research Datalink (CPRD) is an ongoing primary care database of anonymised medical records from general practitioners, with coverage of over 11.3 million patients from 674 practices in the UK. With 4.4 million active (alive, currently registered) patients meeting quality criteria, approximately 6.9% of the UK population are included and patients are broadly representative of the UK general population in terms of age, sex and ethnicity. General practitioners are the gatekeepers of primary care and specialist referrals in the UK. The CPRD primary care database is therefore a rich source of health data for research, including data on demographics, symptoms, tests, diagnoses, therapies, health-related behaviours and referrals to secondary care. For over half of patients, linkage with datasets from secondary care, disease-specific cohorts and mortality records enhance the range of data available for research. The CPRD is very widely used internationally for epidemiological research and has been used to produce over 1000 research studies, published in peer-reviewed journals across a broad range of health outcomes. However, researchers must be aware of the complexity of routinely collected electronic health records, including ways to manage variable completeness, misclassification and development of disease definitions for research.
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              Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies

              In a study comparing the effects of two treatments, the propensity score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates. Propensity-score matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of propensity-score matching, pairs of treated and untreated subjects are formed whose propensity scores differ by at most a pre-specified amount (the caliper width). There has been a little research into the optimal caliper width. We conducted an extensive series of Monte Carlo simulations to determine the optimal caliper width for estimating differences in means (for continuous outcomes) and risk differences (for binary outcomes). When estimating differences in means or risk differences, we recommend that researchers match on the logit of the propensity score using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score. When at least some of the covariates were continuous, then either this value, or one close to it, minimized the mean square error of the resultant estimated treatment effect. It also eliminated at least 98% of the bias in the crude estimator, and it resulted in confidence intervals with approximately the correct coverage rates. Furthermore, the empirical type I error rate was approximately correct. When all of the covariates were binary, then the choice of caliper width had a much smaller impact on the performance of estimation of risk differences and differences in means. Copyright © 2010 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                jm.gamble@uwaterloo.ca
                Journal
                Diabet Med
                Diabet Med
                10.1111/(ISSN)1464-5491
                DME
                Diabetic Medicine
                John Wiley and Sons Inc. (Hoboken )
                0742-3071
                1464-5491
                08 May 2022
                August 2022
                : 39
                : 8 ( doiID: 10.1111/dme.v39.8 )
                : e14858
                Affiliations
                [ 1 ] School of Pharmacy University of Waterloo Waterloo Ontario Canada
                [ 2 ] Faculty of Pharmacy Université Laval Quebec QC Canada
                [ 3 ] CHU de Quebec‐Université Laval Research Center Quebec QC Canada
                [ 4 ] School of Public Health University of Alberta Edmonton AB Canada
                [ 5 ] Department of Medicine University of Toronto Toronto Ontario Canada
                [ 6 ] Division of Endocrinology Sunnybrook Health Sciences Centre Toronto Ontario Canada
                Author notes
                [*] [* ] Correspondence

                John‐Michael Gamble, School of Pharmacy, University of Waterloo, 10A Victoria Street S, Kitchener, ON N2G 2C5, Canada.

                Email: jm.gamble@ 123456uwaterloo.ca

                Author information
                https://orcid.org/0000-0002-7030-0442
                https://orcid.org/0000-0003-2197-0463
                https://orcid.org/0000-0003-3598-3628
                https://orcid.org/0000-0001-6891-8721
                Article
                DME14858
                10.1111/dme.14858
                9546240
                35460294
                4c7a838f-f11e-4294-84ba-65d592ec9e30
                © 2022 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 18 October 2021
                : 22 April 2022
                Page count
                Figures: 4, Tables: 3, Pages: 12, Words: 4981
                Funding
                Funded by: Canadian Institute of Health Research
                Award ID: FRN 156064
                Categories
                Research: Epidemiology
                Research: Epidemiology
                Custom metadata
                2.0
                August 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.0 mode:remove_FC converted:07.10.2022

                Endocrinology & Diabetes
                sglt‐2 inhibitors,genital tract infections,cohort,comparative safety

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