BackgroundCompared with conventional genotyping, which typically tests for a limited number of mutations, next‐generation DNA sequencing (NGS) provides increased accuracy for carrier screening. The objective of this study was to evaluate the cost effectiveness of carrier screening using NGS versus genotyping for 14 of the recessive disorders for which medical society guidelines recommend screening.MethodsData from published literature, population surveys, and expert opinion were used to develop a decision tree model capturing decisions and outcomes related to carrier screening and reproductive health.ResultsModeling a population of 1,000,000 couples that was representative of the United States population and that contained 83,421 carriers of pathogenic mutations, carrier screening using NGS averted 21 additional affected births as compared with genotyping, and reduced costs by approximately $13 million. As compared with no screening, NGS carrier screening averted 223 additional affected births. The results are sensitive to assumptions regarding mutation detection rates and carrier frequencies in multiethnic populations.ConclusionThis study demonstrated that NGS‐based carrier screening offers the greater benefit in clinical outcomes and lower total healthcare cost as compared with genotyping.

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We consider a particular instance of a common problem in recommender systems: using a database of book reviews to inform usertargeted recommendations. In our dataset, books are categorized into genres and sub-genres. To exploit this nested taxonomy, we use a hierarchical model that enables information pooling across across similar items at many levels within the genre hierarchy. The main challenge in deploying this model is computational: the data sizes are large, and fitting the model at scale using off-the-shelf maximum likelihood procedures is prohibitive. To get around this computational bottleneck, we extend a moment-based fitting procedure proposed for fitting single-level hierarchical models to the general case of arbitrarily deep hierarchies. This extension is an order of magnetite faster than standard maximum likelihood procedures. The fitting method can be deployed beyond recommender systems to general contexts with deeply-nested hierarchical generalized linear mixed models.