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PurposeWhole-exome (WES) and whole-genome sequencing (WGS) increase the diagnostic yield in autism spectrum disorder (ASD) compared to chromosomal microarray (CMA), but there have been no comprehensive cost analyses. The objective was to perform such an assessment of CMA, WES, and WGS and compare the incremental cost per additional positive finding in hypothetical testing scenarios.MethodsFive-year patient and program costs were estimated from an institutional perspective. WES and WGS estimates were based on HiSeq 2500 with an additional WGS estimate for HiSeq X platforms. Parameter uncertainty was assessed with probabilistic and deterministic sensitivity analysis.ResultsThe cost per ASD sample was CAD$1,655 (95% CI: 1,611; 1,699) for WES, CAD$2,851 (95% CI: 2,750; 2,956) for WGS on HiSeq X, and CAD$5,519 (95% CI: 5,244; 5,785) on HiSeq 2500, compared to CAD$744 (95% CI 714, 773) for CMA. The incremental cost was over CAD$25,000 per additional positive finding if CMA was replaced by newer technology.ConclusionWhile costs for WES and WGS remain high, future reductions in material and equipment costs, and increased understanding of newly discovered variants and variants of unknown significance will lead to improved value.

Aim: Thiopurine S-methyltransferase (TPMT) testing is used in patients receiving thiopurines to identify enzyme deficiencies and risk for adverse drug reactions. It is uncertain whether genotyping is superior to phenotyping. The objectives were to conduct a systematic review of TPMT-test performance studies. Materials & methods: Electronic and grey literature sources were searched for studies reporting test performance compared with a reference standard. Sixty-six eligible studies were appraised for quality. Results: Thirty phenotype–genotype and six phenotype–phenotype comparisons were of high quality. The calculated sensitivity and specificity for genotyping to identify a homozygous mutation ranged from 0.0–100.0% and from 97.8–100.0%, respectively. Conclusion: Clinical decision-makers require high-quality evidence of clinical validity and clinical utility of TPMT genotyping to ensure appropriate use in patients.

Background and Aims Screening and brief intervention (SBI) is a public health intervention that has been shown to be effective in reducing heavy alcohol consumption. The aim of this study is to estimate the cost-effectiveness of implementing universal alcohol SBI in primary care in Canada. Design We developed a microsimulation model of alcohol consumption and its effects on 18 alcohol-related causes of death. Setting The model simulates a Canadian population. Participants The model simulates individuals and their alcohol consumption on a continuous scale starting from age 17 years to death. Interventions The reference case assumes no SBI in Canada. The base case assumes screening was conducted using the Alcohol Use Disorders Identification Test (AUDIT) at a threshold score of 8. Additional analyses included evaluating SBI using the AUDIT at threshold scores between 4 and 8 or the Derived Alcohol Use Disorders Identification Test (AUDIT-C) at threshold scores between 3 and 7. Measurements The model estimates the direct health-care costs, life years gained and quality-adjusted life years (QALY) gained, which are then used to estimate the incremental cost-effectiveness ratio (ICER) of SBI versus no SBI. Findings SBI with AUDIT (at a threshold score of 8) had an ICER of $8729/QALY. Our results suggest that using AUDIT thresholds between 8 and 4, inclusive, would be cost-effective for the whole population, as well as for men and women individually. Our results suggest that the AUDIT-C would be cost-effective at thresholds of 7 to 3, inclusive, for men, women and the whole population. Conclusions In Canada, screening and brief intervention via Alcohol Use Disorders Identification Test (AUDIT) and Derived Alcohol Use Disorders Identification Test (AUDIT-C) to reduce heavy alcohol consumption appears to be cost-effective for men and women at Alcohol Use Disorders Identification Test (AUDIT) thresholds of 8 and lower and at Derived Alcohol Use Disorders Identification Test (AUDIT-C) thresholds of 7 and lower.

We compare two methods of training artificial neural networks (ANNs) that potentially reduce the risk of the neural network overfitting the training data set. We refer to these methods as training with jitter. In one method of training with jitter, a new random noise vector is added to each training-data vector between successive iterations. In this work, we propose a different method of training with jitter, in which instead of adding different random noise vectors between iterations, a number of random vectors are used to expand the training data set prior to training. This artificially expanded data set is then used to train the artificial neural network in the conventional manner. These two methods are compared to the conventional method of training artificial neural networks. We find that although training with a single expanded training data set does increase the performance of the neural networks, overfitting can still occur after a large number of training iterations.

Health care systems require high quality economic evaluations to support wise allocation decisions to enhance child health within the population served. LMICs face the added jeopardy of having very limited resources (human and fiscal) to produce complex economic evaluations within highly constrained healthcare budgets that demand judicious expenditure. Furthermore, conducting economic evaluations in children is challenging methodologically and this is exacerbated in LMICs where healthcare funding and health priorities are influenced by external forces. World Health Organization initiatives such as generalized cost-effectiveness analysis have addressed many of the pertinent methodological issues. However, of 49 economic evaluations of preventive interventions for children published in 2012, only 17 were relevant to LMIC. Of 25 economic evaluations of active pediatric treatments, only four targeted an LMIC population. Greater collaboration between health researchers from LMICs and high-income countries promises, in future, to improve the quality of evidence available for decision-making.

Rationale and Objectives We showed previously that maximum-likelihood (ML) and Bayesian (with a flat prior on a common parameterization of the model) estimates of “proper” binormal receiver operating characteristic (ROC) curves produce similar results. We propose a new prior that is flat over the area under the ROC curve (AUC) and investigate its effect on the Bayesian estimates. Materials and Methods In two simulation studies, we compared Bayesian estimation of the AUC with the two prior probability distributions against ML estimation in terms of root mean squared error (RMSE) and the coverage of 95% confidence (or credible) intervals (both abbreviated CIs). In the first study, we simulated categorical data that tend to be “well-behaved” and produce ROC curve estimates that most would consider reasonable. In the second study, we simulated coarsely discretized categorical data that often included so-called degenerate datasets that cause the ML estimate to be the perfect ROC curve. Results For the well-behaved datasets, all three AUC estimates were similar in terms of RMSE and 95% CI coverage. For the coarsely discretized datasets, the RMSE of ML was consistently greater than that of Bayesian estimation and the 95% CI coverage of ML estimation was consistently below nominal, whereas the 95% CI coverage of Bayesian estimation was consistently equal to, or greater than, nominal. Conclusion Bayesian estimation with a flat prior on the AUC can provide reasonable inference from datasets with coarsely categorized data that are prone to be degenerate and produce results similar to other estimation methods on well-behaved datasets.

We studied the effect of noise injection in overcoming the problem of overtraining in the training of artificial neural networks (ANNs) in comparison with other common approaches for overcoming this problem such as early stopping of the ANN training process and weight decay (which is similar to Bayesian artificial neural networks). We found from simulation studies and studies of a computer-aided diagnosis application that noise injection is effective in overcoming overtraining and is as effective as, or even more effective than, early stopping and weight decay.

Bayesian neural network (BNN) with automatic relevance determination (ARD) priors has the ability to assess the relevance of each input feature during network training. Our purpose is to investigate the potential use of BNN-with-ARD-priors for joint feature selection and classification in computer-aided diagnosis (CAD) of medical imaging. With ARD priors, each group of weights that connect an input feature to the hidden units is associated with a hyperparameter controlling the magnitudes of the weights. The hyperparameters and the weights are updated simultaneously during neural network training. A smaller hyperparameter will likely result in larger weight values and the corresponding feature will likely be more relevant to the output, and thus, to the classification task. For our study, a multivariate normal feature space is designed to include one feature with high classification performance in terms of both ideal observer and linear observer, two features with high ideal observer performance but low linear observer performance and 7 useless features. An exclusive-OR (XOR) feature space is designed to include 2 XOR features and 8 useless features. Our simulation results show that the ARD-BNN approach has the ability to select the optimal subset of features on the designed nonlinear feature spaces on which the linear approach fails. ARD-BNN has the ability to recognize features that have high ideal observer performance. Stepwise linear discriminant analysis (SWLDA) has the ability to select features that have high linear observer performance but fails to select features that have high ideal observer performance and low linear observer performance. The cross-validation results on clinical breast MRI data show that ARD-BNN yields statistically significant better performance than does the SWLDA-LDA approach. We believe that ARD-BNN is a promising method for pattern recognition in computer-aided diagnosis of medical imaging.