Inactivated influenza vaccine reduced proven influenza illness by 63% in infants up to 6 months of age and averted approximately a third of all febrile respiratory illnesses in mothers and young infants. Maternal influenza immunization is a strategy with substantial benefits for both mothers and infants. (ClinicalTrials.gov number, NCT00142389.)

The introduction of the RV5 vaccine was associated with a dramatic reduction in hospitalizations for acute gastroenteritis among US children during the 2008 rotavirus season.

Background: Lactational mastitis is a maternal morbidity that affects the wellbeing of women and their babies, including through breastfeeding discontinuation. Research Aim: To systematically review the available global literature on the frequency of lactational mastitis, and to summarize the evidence on risk factors for lactational mastitis. We also describe gaps in the evidence and identify priority areas for future research. Methods: We systematically searched and screened 6 databases and included 26 articles, conducted meta-analysis of disease frequency, and narratively synthesized evidence on risk factors. Results: In 11 (42%) articles researchers reported a measure of disease frequency; 5 (19%) reported risk factors, and 10 (39%) included both. Overall, the quality of studies was low, related to suboptimal measurement of disease frequency, high risk of bias, reverse causality, and incomplete adjustment for confounding. Meta-analysis was based on 3 studies (pooled incidence between birth and Week 25 postpartum: 11.1 episodes per 1,000 breastfeeding weeks; 95% CI [10.2–12.0]); with high heterogeneity across contexts and highest incidence in the first four weeks postpartum. Researchers assessed 42 potential risk factors; nipple damage was the most frequently studied and strongly associated with mastitis. There was a scarcity of studies from low-resource settings. Conclusions: Lactational mastitis is a common condition, but the wide variability in incidence across contexts suggested that a substantial portion of this burden might be preventable. Provision of care to breastfeeding women at risk for or affected by mastitis is currently constrained due to a critical lack of high quality epidemiological evidence about its incidence and risk factors.

Nearly all facility registers were available and complete. But accuracy varied, with antenatal care and HIV testing and counseling performing the best and family planning and acute respiratory infections data less well. Most facilities visibly displayed routine health data and most hospitals and district health offices had staff trained in health management information systems, but training was lacking at the facility level as were routine data quality checks and regular supervision.

Background There is growing recognition of the importance of menstruation in achieving health, education, and gender equality for all. New policies in high income countries (HICs) have responded to anecdotal evidence that many struggle to meet their menstrual health needs. Qualitative research has explored lived experiences of menstruating in HICs and can contribute to designing intervention approaches. To inform the growing policy attention to support people who menstruate, here we review and synthesise the existing research. Methods and findings Primary, qualitative studies capturing experiences of menstruation in HICs were eligible for inclusion. Systematic database and hand searching identified 11485 records. Following screening and quality appraisal using the EPPI-Centre checklist, 104 studies (120 publications) detailing the menstrual experiences of over 3800 individuals across sixteen countries were included. We used the integrated model of menstrual experiences developed from studies in low- and middle-income countries (LMICs) as a starting framework and deductively and inductively identified antecedents contributing to menstrual experiences; menstrual experiences themselves and impacts of menstrual experiences. Included studies described consistent themes and relationships that fit well with the LMIC integrated model, with modifications to themes and model pathways identified through our analysis. The socio-cultural context heavily shaped menstrual experiences, manifesting in strict behavioural expectations to conceal menstruation and limiting the provision of menstrual materials. Resource limitations contributed to negative experiences, where dissatisfaction with menstrual practices and management environments were expressed along with feelings of disgust if participants felt they failed to manage their menstruation in a discrete, hygienic way. Physical menstrual factors such as pain were commonly associated with negative experiences, with mixed experiences of healthcare reported. Across studies participants described negative impacts of their menstrual experience including increased mental burden and detrimental impacts on participation and personal relationships. Positive experiences were more rarely reported, although relationships between cis-women were sometimes strengthened by shared experiences of menstrual bleeding. Included studies reflected a broad range of disciplines and epistemologies. Many aimed to understand the constructed meanings of menstruation, but few were explicitly designed to inform policy or practice. Few studies focused on socioeconomically disadvantaged groups relevant to new policy efforts. Conclusions We developed an integrated model of menstrual experience in HICs which can be used to inform research, policy and practice decisions by emphasising the pathways through which positive and negative menstrual experiences manifest. Review protocol registration The review protocol registration is PROSPERO: CRD42019157618.

Poor nutrition contributes substantially to global disease, diminishing the wellbeing of women and children in low and middle income countries, and better nutrition must be part of the universal health coverage agenda, say Rebecca Heidkamp and colleagues

The Safety Explorer provides interactive charts that contain the same information available in standard displays, but the interactive interface allows for improved exploration of patterns and comparisons. Medical Monitors, Safety Review Boards, and Project Teams can use these tools to effectively track and analyze key safety variables and study endpoints.

BackgroundLow-income and middle-income countries (LMICs) seek to better utilize household and health facility survey data for monitoring and evaluation, as well as for health program planning. However, analysis of this complex survey data are complicated. In Tanzania, the National Evaluation Platform project sought to analyze Demographic and Health Survey (DHS) data and Service Provision Assessment (SPA) data as part of an evaluation of the national One Plan for Maternal and Child Health. To support this evaluation, we used this survey data to answer two key methodological questions: 1) what are the benefits and costs of using sampling weights in rate estimation; and 2) what is the best method for calculating standard errors in these two surveys?MethodsWe conducted a simulation study for each methodologic question. The first simulation study assessed the benefits and costs of using sampling weights in rate estimation. This simulation used weighted and unweighted estimates and examined bias, variance, and the mean squared error (MSE). The second simulation study assessed the best method for calculating standard errors comparing cluster bootstrapped variance estimation, design based asymptotic variance with one level (svy1), and design based asymptotic variance with three levels (svy3). We compared coverage probability and confidence interval length.ResultsOur results showed that although weighted estimates were less biased, unweighted estimates were less variable. The weighted estimates had a lower MSE, indicating that the effect of the bias trade-off was greater than the effect of the variance trade-off for most indicators assessed. The best performer for variance estimation was the cluster bootstrap method, followed by the svy3 method. The svy1 method was the worst performer for most indicators assessed.ConclusionsAs complex survey data become more widely used for policymaking in LMICs, there is a need for guidance on the best methods for analyzing this data. The standard of practice has been a design-based analysis using survey weights and the single-level svy method for calculating standard errors. This study puts forth an alternative approach to analysis. In addition, this study offers practical guidance on determining the best method for analysis of complex survey data.