Background In high-income countries, administration of antenatal steroids is standard care for women with anticipated preterm labour. However, although >1 million deaths due to preterm birth occur annually, antenatal steroids are not routine practice in low-income countries where most of these deaths occur.Objectives To review the evidence for and estimate the effect on cause-specific neonatal mortality of administration of antenatal steroids to women with anticipated preterm labour, with additional analysis for the effect in low- and middle-income countries.Methods We conducted systematic reviews using standardized abstraction forms. Quality of evidence was assessed using an adapted GRADE approach. Existing meta-analyses were reviewed for relevance to low/middle-income countries, and new meta-analysis was performed.Results We identified 44 studies, including 18 randomised control trials (RCTs) (14 in high-income countries) in a Cochrane meta-analysis, which suggested that antenatal steroids decrease neonatal mortality among preterm infants (<36 weeks gestation) by 31% [relative risk (RR) = 0.69; 95% confidence interval (CI) 0.58–0.81]. Our new meta-analysis of four RCTs from middle-income countries suggests 53% mortality reduction (RR = 0.47; 95% CI 0.35–0.64) and 37% morbidity reduction (RR = 0.63; 95% CI 0.49–0.81). Observational study mortality data were consistent. The control group in these equivalent studies was routine care (ventilation and, in many cases, surfactant). In low-income countries, many preterm babies currently receive little or no medical care. It is plausible that antenatal steroids may be of even greater effect when tested in these settings.Conclusions Based on high-grade evidence, antenatal steroid therapy is very effective in preventing neonatal mortality and morbidity, yet remains at low coverage in low/middle-income countries. If fully scaled up, this intervention could save up to 500 000 neonatal lives annually.

In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid; and the coefficients are governed by a Bernoulli-Gaussian prior model turning model order selection into binary sequence detection. Unlike earlier works which retain only point estimates of the frequencies, we undertake a more complete Bayesian treatment by estimating the posterior probability density functions (pdfs) of the frequencies and computing expectations over them. Thus, we additionally capture and operate with the uncertainty of the frequency estimates. Aiming to maximize the model evidence, variational optimization provides analytic approximations of the posterior pdfs and also gives estimates of the additional parameters. We propose an accurate representation of the pdfs of the frequencies by mixtures of von Mises pdfs, which yields closed-form expectations. We define the algorithm VALSE in which the estimates of the pdfs and parameters are iteratively updated. VALSE is a gridless, convergent method, does not require parameter tuning, can easily include prior knowledge about the frequencies and provides approximate posterior pdfs based on which the uncertainty in line spectral estimation can be quantified. Simulation results show that accounting for the uncertainty of frequency estimates, rather than computing just point estimates, significantly improves the performance. The performance of VALSE is superior to that of state-of-the-art methods and closely approaches the Cram\'er-Rao bound computed for the true model order.Comment: 15 pages, 8 figures, accepted for publication in IEEE Transactions on Signal Processin