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Background: There is some evidence that increased maternal intake of omega-3 (n-3) long-chain polyunsaturated fatty acids (LC-PUFAs) during pregnancy may reduce the incidence of immunoglobulin E (IgE)-mediated allergic disease. Objective: We aimed to evaluate prenatal n-3 LC-PUFA dietary exposure in observational studies and n-3 LC-PUFA supplementation in randomized controlled trials (RCTs) on outcomes of IgE-mediated allergic disease. Design: We conducted searches of the Cochrane Central Register of Controlled Trials, PubMed, Ovid MEDLINE, EMBASE, CINAHL, SCOPUS, and Web of Science to 30 July 2015. We included prospective cohort studies that showed an association between maternal fish or n-3 LC-PUFA intake during pregnancy and RCTs with a prenatal intervention to modify maternal n-3 LC-PUFA intake and outcomes of allergic disease (eczema, rhino-conjunctivitis, asthma) or sensitization in the offspring. Results: A total of 13 publications from 10 prospective cohort studies and 7 publications representing 5 unique RCTs were included. Three RCTs were combined in a meta-analysis for selected outcomes. Nine of 13 observational study publications and 5 of 7 publications from RCTs found a protective association between increased prenatal n-3 LC-PUFA or fish intake and incidence of allergic disease symptoms in the child. Meta-analysis was limited because of the heterogeneity of the RCTs. Pooled results showed a significant reduction in the incidence of "atopic eczema," "any positive SPT [skin-prick test]," "sensitization to egg," and "sensitization to any food" in the first 12 mo of life [RRs (95% CIs): 0.53 (0.35, 0.81), P = 0.004; 0.68 (0.52-0.89), P = 0.006; 0.55 (0.39-0.76), P = 0.0004; and 0.59 (0.46, 0.76), P < 0.0001, respectively]. Conclusions: Our systematic review and meta-analysis was suggestive of benefits of increased n-3 LC-PUFAs in the maternal diet and outcomes of childhood allergic disease. However, due to the inconsistency in results, the hypothesis linking maternal n-3 LC-PUFA intake to childhood allergic disease cannot unequivocally be confirmed or rejected. [ABSTRACT FROM AUTHOR]

Abstract: Background: Standard approaches to estimation of Markov models with data from randomized controlled trials tend either to make a judgment about which transition(s) treatments act on, or they assume that treatment has a separate effect on every transition. An alternative is to fit a series of models that assume that treatment acts on specific transitions. Investigators can then choose among alternative models using goodness-of-fit statistics. However, structural uncertainty about any chosen parameterization will remain and this may have implications for the resulting decision and the need for further research. Methods: We describe a Bayesian approach to model estimation, and model selection. Structural uncertainty about which parameterization to use is accounted for using model averaging and we developed a formula for calculating the expected value of perfect information (EVPI) in averaged models. Marginal posterior distributions are generated for each of the cost-effectiveness parameters using Markov Chain Monte Carlo simulation in WinBUGS, or Monte-Carlo simulation in Excel (Microsoft Corp., Redmond, WA). We illustrate the approach with an example of treatments for asthma using aggregate-level data from a connected network of four treatments compared in three pair-wise randomized controlled trials. Results: The standard errors of incremental net benefit using structured models is reduced by up to eight- or ninefold compared to the unstructured models, and the expected loss attaching to decision uncertainty by factors of several hundreds. Model averaging had considerable influence on the EVPI. Conclusions: Alternative structural assumptions can alter the treatment decision and have an overwhelming effect on model uncertainty and expected value of information. Structural uncertainty can be accounted for by model averaging, and the EVPI can be calculated for averaged models. [Copyright &y& Elsevier]