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Aims: Gestational diabetes (GDM) is associated with the development of postpartum (PP) glucose intolerance. Plasma glycated CD59 (pGCD59) is an emerging biomarker for the detection of hyperglycaemia. The aim of this study was to assess the ability of PP pGCD59 to predict the development of PP GI as defined by the 2 h 75 g OGTT using the ADA criteria, in a cohort of women diagnosed with prior GDM in the index pregnancy using the 2 h 75 g OGTT at 24-28 weeks of gestation according to the World Health Organization (WHO) 2013 criteria., Methods: Of the 2017 pregnant women recruited prospectively 140 women with gestational diabetes had samples for pGCD59 taken PP at the time of the OGTT. The ability of pGCD59 to predict the results of the PP OGTT was assessed using nonparametric receiver operating characteristic (ROC) curves., Results: Women with PP glucose intolerance had significantly higher PP pGCD59 levels compared to women with normal glucose tolerance PP (3.8 vs. 2.7 SPU). PP pGCD59 identified women who developed glucose intolerance PP with an AUC of 0.80 (95% CI: 0.70-0.91). A PP pGCD59 cut-off value of 1.9 SPU generated a sensitivity of 100% (95% CI: 83.9-100), specificity of 16.9% (95% CI: 9.8-26.3), positive predictive value of 22.1% (95% CI: 21.0-22.6), and negative predictive value of 100% (95% CI: 87.4-100). PP fasting plasma glucose generated an AUC of 0.96 (95% CI: 0.89-0.99) for the identification of PP glucose intolerance., Conclusion: Our study found that PP pGCD9 may be a promising biomarker to identify women not requiring PP glucose intolerance screening using the traditional OGTT. While the diagnostic accuracy of pGCD59 is good, fasting plasma glucose remains a better test for the identification of PP glucose intolerance., (© 2023 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.)

The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize the study participants to a particular treatment, therefore observational study designs may be used. There are major challenges with observational studies; one of which is confounding. Controlling for confounding is commonly performed by direct adjustment of measured confounders; although, sometimes this approach is suboptimal due to modeling assumptions and misspecification. Recent advances in the field of causal inference have dealt with confounding by building on classical standardization methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where new estimators were developed to overcome the limitations of the previous estimators (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R, and Python for researchers to adapt in their own observational study. The code can be accessed at https://github.com/migariane/Tutorial_Computational_Causal_Inference_Estimators., (© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: Numerous studies have analysed the effect of comorbidity on cancer outcomes, but evidence on the association between multimorbidity and short-term mortality among colorectal cancer patients is limited. We aimed to assess this association and the most frequent patterns of multimorbidity associated with a higher short-term mortality risk among colorectal cancer patients in Spain., Methods: Data were obtained from two Spanish population-based cancer registries and electronic health records. We estimated the unadjusted cumulative incidence of death by comorbidity status at 6 months and 1 year. We used a flexible parametric model to derive the excess mortality hazard ratios (HRs) for multimorbidity after adjusting for sex, age at diagnosis, cancer stage and treatment. We estimated the adjusted cumulative incidence of death by comorbidity status and identified multimorbidity patterns., Results: Among the study participants, 1,048 cases were diagnosed with cancers of the colon and rectum, 2 cases with cancer of the anus with overlapping sites of the rectum and 11 cases with anal adenocarcinomas but treated as colorectal cancer patients. Among 1,061 colorectal cancer patients, 171 (16.2%) died before 6 months, 246 (23.3%) died before the 1-year follow-up, and 324 (30.5%) had multimorbidity. Patients with multimorbidity had two times higher mortality risk than those without comorbidities at 6 months (adjusted HR: 2.04; 95% confidence interval [CI]: 1.30-3.20, p = 0.002). The most frequent multimorbidity pattern was congestive heart failure + diabetes. However, patients with rheumatologic disease + diabetes had two times higher 1-year mortality risk than those without comorbidities (HR: 2.23; 95% CI: 1.23-4.07, p = 0.008)., Conclusions: Multimorbidity was a strong independent predictor of short-term mortality at 6 months and 1 year among the colorectal cancer patients in Spain. The identified multimorbidity pattern was consistent. Our findings might help identify patients at a higher risk for poor cancer and treatment outcomes., Competing Interests: Conflict of interest statement The authors declare no conflict of interests., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objective: Plasma glycated CD59 (pGCD59) is an emerging biomarker in diabetes. We assessed whether pGCD59 could predict the following: the results of the glucose challenge test (GCT) for screening of gestational diabetes mellitus (GDM) (primary analysis); and the diagnosis of GDM and prevalence of large for gestational age (LGA) newborns (secondary analyses)., Research Design and Methods: Case-control study of 1,000 plasma samples from women receiving standard prenatal care, 500 women having a normal GCT (control subjects) and 500 women with a failed GCT and a subsequent oral glucose tolerance test (case patients)., Results: Compared with control subjects, the median (interquartile range) pGCD59 value was 8.5-fold higher in case patients and 10-fold higher in GDM patients, as follows: control subjects 0.33 (0.19); case patients 2.79 (1.4); GDM patients 3.23 (1.43) ( P < 0.001); area under the receiver operating characteristic curve 0.92. LGA prevalence was 4.3% in the lowest quartile and 13.5% in the highest quartile of pGCD59., Conclusions: One pGCD59 measurement during weeks 24-28 identifies pregnancy-induced glucose intolerance with high sensitivity and specificity and can potentially identify the risk for LGA., (© 2017 by the American Diabetes Association.)

Background: In highly populated African urban areas where access to clean water is a challenge, water source contamination is one of the most cited risk factors in a cholera epidemic. During the rainy season, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns where shallow wells could easily become contaminated by excretes. In cholera endemic areas, spatial information about topographical elevation could help to guide preventive interventions. This study aims to analyze the association between topographic elevation and the distribution of cholera cases in Harare during the cholera epidemic in 2008 and 2009., Methods: We developed an ecological study using secondary data. First, we described attack rates by suburb and then calculated rate ratios using whole Harare as reference. We illustrated the average elevation and cholera cases by suburbs using geographical information. Finally, we estimated a generalized linear mixed model (under the assumption of a Poisson distribution) with an Empirical Bayesian approach to model the relation between the risk of cholera and the elevation in meters in Harare. We used a random intercept to allow for spatial correlation of neighboring suburbs., Results: This study identifies a spatial pattern of the distribution of cholera cases in the Harare epidemic, characterized by a lower cholera risk in the highest elevation suburbs of Harare. The generalized linear mixed model showed that for each 100 meters of increase in the topographical elevation, the cholera risk was 30% lower with a rate ratio of 0.70 (95% confidence interval=0.66-0.76). Sensitivity analysis confirmed the risk reduction with an overall estimate of the rate ratio between 20% and 40%., Conclusion: This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Furthermore, elevation information, among other risk factors, could help to spatially orientate cholera control interventions during an epidemic.