Association of Early Interventions With Birth Outcomes and Child Linear Growth in Low-Income and Middle-Income Countries:Bayesian Network Meta-analyses of Randomized Clinical Trials

Importance:The first 1000 days of life represent a critical window for child development. Pregnancy, exclusive breastfeeding (EBF) period (0-6 months), and complementary feeding (CF) period (6-24 months) have different growth requirements, so separate considerations for intervention strategies are needed. No synthesis to date has attempted to quantify the associations of interventions under multiple domains of micronutrient and balanced energy protein and food supplements, deworming, maternal education, water sanitation, and hygiene across these 3 life periods with birth and growth outcomes. Objective: To determine the magnitude of association of interventions with birth and growth outcomes based on randomized clinical trials (RCTs) conducted in low-income and middle-income countries (LMICs) using Bayesian network meta-analyses. Data Sources: MEDLINE, Embase, and Cochrane databases were searched from their inception up to August 14, 2018. Study Selection: Included were LMIC-based RCTs of interventions provided to pregnant women, infants (0-6 months), and children (6-24 months). Data Extraction and Synthesis: Two independent reviewers used a standardized data extraction and quality assessment form. Random-effects network meta-analyses were performed for each life period. Effect sizes are reported as odds ratios (ORs) and mean differences (MeanDiffs) for dichotomous and continuous outcomes, with 95% credible intervals (CrIs). This study calculated probabilities of interventions being superior to standard of care by at least a minimal clinically important difference. Main Outcomes and Measures. The study compared ORs on preterm birth and MeanDiffs on birth weight for pregnancy, length for age (LAZ) for EBF, and height for age (HAZ) for CF. Results: Among 302 061 participants in 169 randomized clinical trials, the network meta-analyses found several nutritional interventions that demonstrated greater association with improved birth and growth outcomes compared with standard of care. For instance, compared with standard of care, maternal supplements of multiple micronutrients showed reduced odds for preterm birth (OR, 0.54; 95% CrI, 0.27-0.97) and improved mean birth weight (MeanDiff, 0.08 kg; 95% CrI, 0.00-0.17 kg) but not LAZ during EBF (MeanDiff, −0.02; 95% CrI, −0.18 to 0.14). Supplementing infants and children with multiple micronutrients showed improved LAZ (MeanDiff, 0.20; 95% CrI, 0.03-0.35) and HAZ (MeanDiff, 0.14; 95% CrI, 0.02-0.25). The study found that pregnancy interventions generally had higher probabilities of a minimal clinically importance difference than the interventions for the EBF or CF in the first 1000 days of life. Conclusions and Relevance: These analyses highlight the importance of intervening early for child development, during pregnancy if possible. Results of this study suggest that there is a need to combine interventions from multiple domains and test for their effectiveness as a package

Long-Term Effect of Group Support Psychotherapy on Depression and HIV Treatment Outcomes: Secondary Analysis of a Cluster Randomized Trial in Uganda

OBJECTIVE: We aimed to determine the effect of group support psychotherapy (GSP) compared with group HIV education (GHE) on depression and HIV treatment outcomes 24 months after treatment. We further aimed to investigate the mediating role of depression and antiretroviral therapy (ART) adherence in the relationship between GSP and viral load suppression. METHODS: Thirty HIV clinics across three districts were randomly assigned to deliver either GSP or GHE for depression. Depression and optimal (≥95%) ART adherence was assessed at baseline and 6, 12, 18, and 24 months after treatment. Viral load was drawn from the medical charts at baseline and 12 and 24 months after treatment. Multilevel mixed-effects regression models and generalized structural equation modeling were used to estimate 24-month outcomes and mediation effects. RESULTS: Participants ( N = 1140) were enrolled from HIV clinics offering either GSP ( n = 578 [51%]) or GHE ( n = 562 [49%]). Fewer GSP than GHE participants met the criteria for depression at 24 months after treatment (1% versus 25%; adjusted odds ratio [aOR] = 0.002, 95% confidence interval [CI] = 0.0002-0.018). More GSP than GHE participants reported optimal (≥95%) ART adherence (96% versus 88%; aOR = 20.88, 95% CI = 5.78-75.33) and improved viral suppression (96% versus 88%; aOR = 3.38, 95% CI = 1.02-11.02). The indirect effects of GSP through sequential reduction in depression and improvement in ART adherence at 12 months may partially explain the higher viral suppression rates at 24 months in GSP than GHE groups. CONCLUSION: In settings where the HIV epidemic persists, depression treatment with GSP may be critical for optimal HIV treatment outcomes.Trial Registration: The Pan African Clinical Trials Registry, number PACTR201608001738234

Statistical Modelling of the Annual Rainfall Pattern in Guanacaste, Costa Rica

Rainfall in Guanacaste, Costa Rica, has marked wet/dry phases: the rainy season is punctuated by a short midsummer drought, and the dry season frequently has months of no rain. In this region, spring and summer rainfall peaks are important for local rain-fed agriculture and annual total for groundwater recharge and hydroelectricity production. We propose a novel model of rainfall in this region, the double-Gaussian model, which uses monthly total rainfall data collected from 1980 to 2020 from two meteorological observation stations. Our model provides an intuitive way of describing the seasonality of rainfall, the inter-annual variability of the cycle, and variability due to the monthly Oceanic Niño Index, ONI. We also consider two alternative models, a regression model with ARMA errors and a Tweedie model, as a means of assessing the robustness of our conclusions to violations of the assumptions of the double-Gaussian model. We found that the data provide strong evidence of an increase/decrease in rainfall in both temporal maxima during La Niña/El Niño (negative/positive ONI) conditions but no evidence of a decade-scale trend after accounting for ONI effects. Finally, we investigated the problem of forecasting future rainfall based on our three models. We found that when ONI is incorporated as a predictor variable, our models can produce substantial gains in prediction accuracy of spring, summer, and annual totals over naive methods based on monthly sample means or medians

Temporal and Spatial Variability of Annual Rainfall Patterns in Guanacaste, Costa Rica

We analyze a body of rainfall data covering 38 years from five meteorological stations in the Nicoya Peninsula of the Guanacaste Province, Costa Rica. The purpose of the analysis is to uncover spatial and temporal variability of rainfall in order to support research into water and sustainability under the FuturAgua project. We use a variety of statistical analysis and modelling techniques. The analysis uncovers a relatively suppressed spatial pattern of rainfall. Rainfall totals for periods shorter than two weeks are dominated by strong stochastic variability, while longer totalizing periods reveal systematic variation. Monthly totals show the strong double peak, and associated midsummer drought that has been previously reported. The annual cycle can be efficiently captured by a double Gaussian model. A simple application of this model to individual years shows large inter- annual variability, and a strong dependence of the second rainfall peak on the Oceanic Niño Index (ONI). A Bayesian analysis confirms the appropriateness of the double Gaussian model, and quantifies the strength of the dependence on ONI. We discuss the implications of our statistical analyses for research under the FuturAgua project.Science, Faculty ofNon UBCEarth, Ocean and Atmospheric Sciences, Department ofStatistics, Department ofUnreviewedFacult

Design and Analysis of Experiments on Nonconvex Regions

<p>Modeling a response over a nonconvex design region is a common problem in diverse areas such as engineering and geophysics. The tools available to model and design for such responses are limited and have received little attention. We propose a new method for selecting design points over nonconvex regions that is based on the application of multidimensional scaling to the geodesic distance. Optimal designs for prediction are described, with special emphasis on Gaussian process models, followed by a simulation study and an application in glaciology. Supplementary materials for this article are available online.</p

Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity

Aim: This research evaluated standard Weibull mixture cure (WMC) network meta-analysis (NMA) with Bayesian hierarchical (BH)WMCNMAto inform long-term survival of therapies. Materials & methods: Four trials in previously treated metastatic non-small-cell lung cancer with PD-L1 >1% were used comparing docetaxel with nivolumab, pembrolizumab and atezolizumab. Cure parameters related to a certain treatment class were assumed to share a common distribution. Results: Standard WMC NMA predicted cure rates were 0.03 (0.01; 0.07), 0.18 (0.12; 0.24), 0.07 (0.02; 0.15) and 0.03 (0.00; 0.09) for docetaxel, nivolumab, pembrolizumab and atezolizumab, respectively,with corresponding incremental life years (LY) of 3.11 (1.65; 4.66), 1.06 (0.41; 2.37) and 0.42 (-0.57; 1.68). The Bayesian hierarchical-WMC-NMA rates were 0.06 (0.03; 0.10), 0.17 (0.11; 0.23), 0.12 (0.05; 0.20) and 0.12 (0.03; 0.23), respectively, with incremental LY of 2.35 (1.04; 3.93), 1.67 (0.68; 2.96) and 1.36 (-0.05; 3.64). Conclusion: BH-WMC-NMA impacts incremental mean LYs and cost–effectiveness ratios, potentially affecting reimbursement decisions