How reliable are self-reported estimates of birth registration completeness? Comparison with vital statistics systemsby Adair T et al
This study assesses the concordance of self-reported birth registration and certification completeness with completeness calculated from civil registration and vital statistics (CRVS) systems data for 57 countries. These findings suggest that self-reported completeness figures over-estimate completeness when compared with CRVS data, especially at lower levels of completeness, partly due to over-reporting of registration by respondents. Estimates published by UNICEF should be viewed cautiously, especially given their wide usage.
Epidemic curves are an important component of the public health and global health toolbox. Learn more about creating and interpretting them.
Become a Cochrane citizen scientist. Anyone can join their collaborative volunteer effort.
Around half of the clinical trials done on medicines we use today are not published; a tragic truth that needs to be changed.
Development of composite outcomes for individual patient data (IPD) meta-analysis on the effects of diet and lifestyle in pregnancy: a Delphi surveyby Rogozinska et al
The objective of the study was to develop maternal, fetal, and neonatal composite outcomes relevant to the evaluation of diet and lifestyle interventions in pregnancy by individual patient data (IPD) meta-analysis.A two-generational Delphi survey involving members of the i–WIP collaborative network (26 members in 11 countries) was undertaken to prioritise the individual outcomes for their importance in clinical care. The final components of the composite outcomes were identified using pre-specified criteria. The study has identified the components of maternal, fetal, and neonatal composite outcomes required for the assessment of diet and lifestyle interventions in pregnancy by IPD meta-analysis.
Professor Mike English explains how KEMRI-Wellcome are ''working with government to generate patient level data from a network of Kenyan hospitals as a platform for research'.
In celebration of Global Health Trials' fifth birthday (May 11th 2015) Professor Trudie Lang, Principal Investigator of the programme, talks to us about why Global Health Trials was started, why people should share their experience, and what the future holds.
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, the author discusses the implications of these problems for the conduct and interpretation of research.
In this article, the authors illustrate five basic statistical concepts that can significantly impact the interpretation of the medical literature and its application to the care of patients, drawing examples from the vaccine literature: (i) consider clinical and statistical significance separately, (ii) evaluate absolute risks rather than relative risks, (iii) examine confidence intervals rather than p values, (iv) use caution when considering isolated significant p values in the setting of multiple testing, and (v) keep in mind that statistically nonsignificant results may not exclude clinically important benefits or harms.