Does antenatal education affect labour and birth? A structured review of the literature

ObjectiveTo undertake a structured review of the literature to determine the effect of antenatal education on labour and birth, particularly normal birth.MethodOvid Medline, CINAHL, Cochrane and Web of Knowledge databases were searched to identify research articles published in English from 2000 to 2012, using specified search terms in a variety of combinations. All articles included in this structured review were assessed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).FindingsThe labour and birthing effects on women attending antenatal education may include less false labour admissions, more partner involvement, less anxiety but more labour interventions.ConclusionThis literature review has identified that antenatal education may have some positive effects on women's labour and birth including less false labour admissions, less anxiety and more partner involvement. There may also be some negative effects. Several studies found increased labour and birth interventions such as induction of labour and epidural use. There is contradictory evidence on the effect of antenatal education on mode of birth. More research is required to explore the impact of antenatal education on women's birthing outcomes.     

The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models

Summary.  We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.