Search

BackgroundMaternal age is increasingly recognized as a predictor of birth outcomes. Given the importance of birth and growth outcomes for children's development, wellbeing and survival, this study examined the effect of maternal age on infant birth and growth outcomes at 6 months and mortality. Additionally, we conducted quantitative bias analysis (QBA) to estimate the role of selection bias and unmeasured confounding on the effect of maternal age on infant mortality.MethodsWe used data from randomized-controlled trials (RCTs) of 21 555 neonates in Burkina Faso conducted in 2019-2020. Newborns of mothers aged 13-19 years (adolescents) and 20-40 years (adults) were enrolled in the study 8-27 days after birth and followed for 6 months. Measurements of child's anthropometric measures were collected at baseline and 6 months. We used multivariable linear regression to compare child anthropometric measures at birth and 6 months, and logistic regression models to obtain the odds ratio (OR) of all-cause mortality. Using multidimensional deterministic analysis, we assessed scenarios in which the difference in selection probability of adolescent and adult mothers with infant mortality at 6 months increased from 0% to 5%, 10%, 15% and 20% if babies born to adolescent mothers more often died during the first week or were of lower weight and hence were not eligible to be included in the original RCT. Using probabilistic bias analysis, we assessed the role of unmeasured confounding by socio-economic status (SES).ResultsBabies born to adolescent mothers on average had lower weight at birth, lower anthropometric measures at baseline, similar growth outcomes from enrolment to 6 months and higher odds of all-cause mortality by 6 months (adjusted OR = 2.17, 95% CI 1.35 to 3.47) compared with those born to adult mothers. In QBA, we found that differential selection of adolescent and adult mothers could bias the observed effect (OR = 2.24, 95% CI 1.41 to 3.57) towards the null [bias-corrected OR range: 2.37 (95% CI 1.49 to 3.77) to 2.84 (95% CI 1.79 to 4.52)], whereas unmeasured confounding by SES could bias the observed effect away from the null (bias-corrected OR: 2.06, 95% CI 1.31 to 2.64).ConclusionsOur findings suggest that delaying the first birth from adolescence to adulthood may improve birth outcomes and reduce mortality of neonates. Babies born to younger mothers, who are smaller at birth, may experience catch-up growth, reducing some of the anthropometric disparities by 6 months of age.

BackgroundImproved sanitation has been associated with improved child growth in observational studies, but multiple randomized trials that delivered improved sanitation found no effect on child growth. We assessed to what extent differences in the effect estimated in the two study designs (the effect of treatment in observational studies and the effect of treatment assignment in trials) could explain the contradictory results.MethodsWe used parametric g-computation in five prospective studies (n = 21 524) and 59 cross-sectional Demographic and Health Surveys (DHS; n = 158 439). We compared the average treatment effect (ATE) for improved sanitation on mean length-for-age z-score (LAZ) among children aged 0.15 z-scores, a clinically meaningful difference, in most prospective studies but in

The following errors were discovered in this article: The third row of Figure 1 was mislabelled; the result for the row for Ali et al., 2013 in Table 1 was mislabelled; in Table 3, the rows for Contreras and Maitra, for Avitabile 2012 and for German et al had errors; two studies were omitted from Figure 3B; the values for % cholera vaccine coverage for Emch et al., 2009 in Supplement 7, Figure 2 were incorrect; and "quality of evidence" values for Ali et al., 2013 and for Cooper and Fitch 1983 were incorrect.

Many public health interventions provide benefits that extend beyond their direct recipients and impact people in close physical or social proximity who did not directly receive the intervention themselves. A classic example of this phenomenon is the herd protection provided by many vaccines. If these 'spillover effects' (i.e. 'herd effects') are present in the same direction as the effects on the intended recipients, studies that only estimate direct effects on recipients will likely underestimate the full public health benefits of the intervention. Causal inference assumptions for spillover parameters have been articulated in the vaccine literature, but many studies measuring spillovers of other types of public health interventions have not drawn upon that literature. In conjunction with a systematic review we conducted of spillovers of public health interventions delivered in low- and middle-income countries, we classified the most widely used spillover parameters reported in the empirical literature into a standard notation. General classes of spillover parameters include: cluster-level spillovers; spillovers conditional on treatment or outcome density, distance or the number of treated social network links; and vaccine efficacy parameters related to spillovers. We draw on high quality empirical examples to illustrate each of these parameters. We describe study designs to estimate spillovers and assumptions required to make causal inferences about spillovers. We aim to advance and encourage methods for spillover estimation and reporting by standardizing spillover parameter nomenclature and articulating the causal inference assumptions required to estimate spillovers.

BackgroundMany interventions delivered to improve health may benefit not only direct recipients but also people in close physical or social proximity. Our objective was to review all published literature about the spillover effects of interventions on health outcomes in low-middle income countries and to identify methods used in estimating these effects.MethodsWe searched 19 electronic databases for articles published before 2014 and hand-searched titles from 2010 to 2013 in five relevant journals. We adapted the Cochrane Collaboration's quality grading tool for spillover estimation and rated the quality of evidence.ResultsA total of 54 studies met inclusion criteria. We found a wide range of terminology used to describe spillovers, a lack of standardization among spillover methods and poor reporting of spillovers in many studies. We identified three primary mechanisms of spillovers: reduced disease transmission, social proximity and substitution of resources within households. We found the strongest evidence for spillovers through reduced disease transmission, particularly vaccines and mass drug administration. In general, the proportion of a population receiving an intervention was associated with improved health. Most studies were of moderate or low quality. We found evidence of publication bias for certain spillover estimates but not for total or direct effects. To facilitate improved reporting and standardization in future studies, we developed a reporting checklist adapted from the CONSORT framework specific to reporting spillover effects.ConclusionsWe found the strongest evidence for spillovers from vaccines and mass drug administration to control infectious disease. There was little high quality evidence of spillovers for other interventions.

Background Biannual azithromycin distribution to children 1–59 months old reduced all-cause mortality by 18% [incidence rate ratio (IRR) 0.82, 95% confidence interval (CI): 0.74, 0.90] in an intention-to-treat analysis of a randomized controlled trial in Niger. Estimation of the effect in compliance-related subgroups can support decision making around implementation of this intervention in programmatic settings. Methods The cluster-randomized, placebo-controlled design of the original trial enabled unbiased estimation of the effect of azithromycin on mortality rates in two subgroups: (i) treated children (complier average causal effect analysis); and (ii) untreated children (spillover effect analysis), using negative binomial regression. Results In Niger, 594 eligible communities were randomized to biannual azithromycin or placebo distribution and were followed from December 2014 to August 2017, with a mean treatment coverage of 90% [standard deviation (SD) 10%] in both arms. Subgroup analyses included 2581 deaths among treated children and 245 deaths among untreated children. Among treated children, the incidence rate ratio comparing mortality in azithromycin communities to placebo communities was 0.80 (95% CI: 0.72, 0.88), with mortality rates (deaths per 1000 person-years at risk) of 16.6 in azithromycin communities and 20.9 in placebo communities. Among untreated children, the incidence rate ratio was 0.91 (95% CI: 0.69, 1.21), with rates of 33.6 in azithromycin communities and 34.4 in placebo communities. Conclusions As expected, this analysis suggested similar efficacy among treated children compared with the intention-to-treat analysis. Though the results were consistent with a small spillover benefit to untreated children, this trial was underpowered to detect spillovers. [ABSTRACT FROM AUTHOR]

Background: Improved sanitation has been associated with improved child growth in observational studies, but multiple randomized trials that delivered improved sanitation found no effect on child growth. We assessed to what extent differences in the effect estimated in the two study designs (the effect of treatment in observational studies and the effect of treatment assignment in trials) could explain the contradictory results.Methods: We used parametric g-computation in five prospective studies (n = 21 524) and 59 cross-sectional Demographic and Health Surveys (DHS; n = 158 439). We compared the average treatment effect (ATE) for improved sanitation on mean length-for-age z-score (LAZ) among children aged <2 years to population intervention effects (PIEs), which are the observational analogue of the effect estimated in trials in which some participants are already exposed.Results: The ATE was >0.15 z-scores, a clinically meaningful difference, in most prospective studies but in <20% of DHS surveys. The PIE was always smaller than the ATE, and the magnitude of difference depended on the baseline prevalence of the improved sanitation. Interventions with suboptimal coverage and interventions delivered in populations with higher mean LAZ had a smaller effect on population-level LAZ.Conclusions: Estimates of PIEs corresponding to anticipated trial results were often smaller than clinically meaningful effects. Incongruence between observational associations and null trial results may in part be explained by expected differences between the effects estimated. Using observational ATEs to set expectations for trials may overestimate the impact that sanitation interventions can achieve. PIEs predict realistic effects and should be more routinely estimated. [ABSTRACT FROM AUTHOR]