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The recent introductions of chikungunya virus (CHIKV) and Zika virus (ZIKV) in the Americas led to widespread epidemics with substantial health and economic effects. Small island developing states in the [...]

Randomized comparisons among several treatments give rise to an incomplete-blocks structure known as mixed treatment comparisons (MTCs). To analyze such data structures, it is crucial to assess whether the disparate evidence sources provide consistent information about the treatment contrasts. In this article we propose a general method for assessing evidence inconsistency in the framework of Bayesian hierarchical models. We begin with the distinction between basic parameters, which have prior distributions, and functional parameters, which are defined in terms of basic parameters. Based on a graphical analysis of MTC structures, evidence inconsistency is defined as a relation between a functional parameter and at least two basic parameters, supported by at least three evidence sources. The inconsistency degrees of freedom (ICDF) is the number of such inconsistencies. We represent evidence consistency as a set of linear relations between effect parameters on the log odds ratio scale, then relax these relations to allow for inconsistency by adding to the model random inconsistency factors (ICFs). The number of ICFs is determined by the ICDF. The overall consistency between evidence sources can be assessed by comparing models with and without ICFs, whereas their posterior distribution reflects the extent of inconsistency in particular evidence cycles. The methods are elucidated using two published datasets, implemented with standard Markov chain Monte Carlo software. KEY WORDS: Bayesian hierarchical model; Direct and indirect evidence; Evidence cycle; Inconsistency degrees of freedom; Inconsistency factor; Mixed treatment comparison; Random-effects model; WinBUGS.

A new method of estimating the HIV transmission rate in mother-to-child cases and the age distribution of diagnosis, otherwise known as the pediatric AIDS incubation period, is introduced. Unlike other methods which lead to biased estimates, it employs the EM algorithm to determine the maximum likelihood estimates of vertical transmission rates, sensitivity of virus tests, age distribution at antibody loss and pediatric AIDS incubation period using clinical, immunological and virological information., This article considers the estimation of the rate of vertical human immunodeficiency virus (HIV) transmission and the pediatric acquired immune deficiency syndrome (AIDS) incubation period using data from birth cohort [...]

Several countries include medium-chain acyl- CoA dehydrogenase ( MCAD) deficiency, a rare autosomal recessive disease, in their newborn screening programmes despite prevalence uncertainty. We estimated the frequency of its most common mutation, c. 985A>G, tested for regional differences and compared screening and genotype frequencies. We identified 43 studies reporting the frequency of c. 985A>G over 10 million individuals, and pooled frequency data using a novel Bayesian approach. We found significant variation in the frequency of the mutation across regions supporting a reported founder effect. The proportion of c. 985A>G homozygotes was highest in Western Europe with 4.1 (95% CI: 2.8-5.6) per 100,000 individuals, then the New World (3.2, 95% CI: 2.0-4.7), Southern (1.2, 95% CI: 0.6-2.0) and Eastern European regions (0.9, 95% CI: 0.5-1.7). No cases with the mutation were identified in Asian and Middle Eastern regions. Significant differences were found in some countries between the genotype and screening allele frequency of c. 985A>G. Our predictions could inform the frequency of the mutation by region and our approach could apply to other genetic conditions. [ABSTRACT FROM AUTHOR]