Objective: To compare the accuracy measures of the random glucose test and the 50-g glucose challenge test as screening tests for gestational diabetes mellitus (GDM).

Research Design and Methods: In this prospective cohort study, pregnant women without preexisting diabetes in two perinatal centers in the Ebonyi State underwent a random glucose test and a 50-g glucose challenge test between 24 and 28 weeks of gestation. If one of the screening tests exceeded predefined threshold values, the 75-g oral glucose tolerance test (OGTT) was performed within 1 week. Furthermore, the OGTT was performed in a random sample of women in whom both screening tests were normal. GDM was considered present when the OGTT (reference test) exceeded predefined threshold values. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the two screening tests. The results were corrected for verification bias. Results: We included 1,301 women. The OGTT was performed in 322 women. After correction for verification bias, the random glucose test showed an area under the ROC curve of 0.69 (95% CI 0.61– 0.78), whereas the glucose challenge test had an area under the curve of 0.88 (0.83– 0.93). There was a significant difference in area under the curve of the two tests of 0.19 (0.11– 0.27) in favor of the 50-g glucose challenge test.  Conclusions: In screening for GDM, the 50-g glucose challenge test is more useful than the random glucose test.

 Sensitivity and specificity are classic parameters to assess and to compare the precision of binary diagnostic tests in relation to a gold standard. Another parameter to assess and to compare the performance of binary diagnostic tests is the weighted kappa coefficient, which is a measure of the beyond-chance agreement between the binary diagnostic test and the gold standard, and it is a function of the sensitivity and the specificity of the diagnostic test, the disease prevalence and the relative loss between the false positives and the false negatives. In this study, we carry out a review of the weighted kappa coefficient, its estimation for a single diagnostic test and the hypothesis tests to compare the

weighted kappa coefficients of two or more diagnostic tests, both when the gold standard is applied to all of the subjects in a random sample and when the gold standard is only applied to a subset of subjects in a random sample. The results were applied to different examples.

Decision analysis modeling has emerged as a powerful tool to weigh the cost-effectiveness of complex healthcare decisions. Decision analysis utilizes mathematical models to quantitatively compare multiple decisions accounting for both the monetary cost and the effect on quality of life. The current article reviews the components, statistical analyses, strengths, and limitations of decision analysis modeling for cost-effectiveness research in medicine.

We propose the instrumental variable method for consistent estimation of generalized linear mixed models with measurement error. This method does not require parametric assumptions for the distributions of the unobserved covariates or of the measurement errors, and it allows random effects to have any parametric distributions (not necessarily normal). We also propose simulation-based estimators for the situation where the marginal moments do not have closed forms. The proposed estimators are not only computationally attractive but also strongly root-n consistent. Moreover, the proposed estimators have a bounded influence function so they are robust against data outliers. The methodology is illustrated through simulation studies.

Glaucoma\'s irreversibility, lacking of glaucoma specialists and patient unawareness demand for an economic and effective glaucoma diagnosis system for screening. In this study we explore feature selection (FS) technologies to identify the most essential parameters for automatic glaucoma diagnosis. Methods: We compose feature space from heterogeneous data sources, i.e., retinal image and eye screening data. A feature selection framework is proposed by exploring multiple feature ranking schemes and a wide range of supervised learners. The optimal feature set is derived automatically from the performance curve smoothed by measurement score regression. Results: Under the proposed framework, the optimal feature set obtained using mRMR (minimum Redundancy Maximum Relevance) scheme contains only 1/4 of the original features. The classifiers trained upon the optimal feature set are more efficient with better performance in terms of Accuracy and F-score. A detailed investigation on the features in the optimal set indicates that they can be the essential parameters for glaucoma mass screening and image segmentation. Conclusions: An intelligent Computer-aid-diagnosis (CAD) model is constructed for automatic disease diagnosis. The effectiveness of the model is demonstrated in our glaucoma study based on heterogeneous data sets. The effort not only improves the discriminative power, but also facilitates a better understanding of CAD process and may ease the data collection in glaucoma mass screening.

Background: Surgical site infections can be a severe complication associated with an increase in mortality following coronary artery bypass graft surgery. We performed a study to determine risk factors for the development of SSI and to set up prevention strategies in the management of these patients. Methods: Case-control study at a 302-bed, teaching hospital. Case-patients with surgical-site infection occurring up to 1 month following coronary artery bypass grafting performed between January 2000 and December 2010 were identified prospectively by hospital epidemiologist using National Nosocomial Infections Surveillance (NNIS) System methods. Two control-patients were selected for each case-patient, matched by date of surgery. Results: Eighty-seven patients with infections (65 superficial and 22 deep) were identified. Logistic regression analysis identified tree variables independently associated with the development of infection: diabetes mellitus (OR, 1.9; 95% confidence interval [95% CI], 1.0 to 3.8.; P = 0.05), a NYHA class IV score (OR, 3.4; 95% CI, 1.8 to 8.4; P = 0.0001) and the use of surgical drains (OR, 1.2; 95% CI, 1.1 to 1.4; P = 0.0001). Factors not statistically associated with the development of infection included age, NNIS System risk index score, presence of various co morbidities, surgeon, duration and type of procedure, or other invasive procedures. Conclusion: The use of closed suction drainage, diabetes mellitus and a high NYHA score were associated with the development of surgical-site infection following coronary artery bypass grafting. Avoiding the use of surgical drains and careful monitoring of blood glucose in patients undergoing coronary artery bypass grafting should reduce the risk of infection.

Cluster detection is an important part of spatial epidemiology because it may help suggest potential factors associated with disease and thus, guide further investigation of the nature of diseases. Many different methods have been proposed to test for disease clusters. The most popular methods for detecting spatial focused clusters are circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS) and Bayesian disease mapping (BYM). The only latter approach is based on rigorous modeling approach. However, the Bayesian inference may depend on the choice of priors. We propose a frequentist approach, which yields to maximum likelihood estimation, to identify potential focused clusters. The proposed approach is based on the recent introduction of the method of data cloning. We can also provide the prediction (and prediction interval) for relative risk values. The advantages of data cloning approach are that the answers are independent of the choice of priors and non-estimable parameters are flagged automatically. We illustrate the proposed approach, and compare with aforementioned approaches, by analyzing a dataset of childhood asthma visits to hospital in the province of Manitoba, Canada, during 2000-2010. Our results showed that the potential clusters are mainly located in the north-central part of the province.