Dataset Information

Child anthropometry data quality from Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and National Nutrition Surveys in the West Central Africa region: are we comparing apples and oranges?

ABSTRACT:

Background

There has been limited work comparing survey characteristics and assessing the quality of child anthropometric data from population-based surveys.

Objective

To investigate survey characteristics and indicators of quality of anthropometric data in children aged 0-59 months from 23 countries in the West Central Africa region.

Methods

Using established methodologies and criteria to examine child age, sex, height, and weight, we conducted a comprehensive assessment and scoring of the quality of anthropometric data collected in 100 national surveys.

Results

The Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) collected data from a greater number of younger children than older children while the opposite was found for the National Nutrition Surveys (NNS). Missing or implausible height/weight data proportions were 12% and 8% in MICS and DHS compared to 3% in NNS. Average data quality scores were 14 in NNS, 33 in DHS, and 41 in MICS.

Conclusions

Although our metric of data quality suggests that data from the NNS appear more consistent and robust, it is equally important to consider its disadvantages related to access and lack of broader socioeconomic information. In comparison, the DHS and MICS are publicly-accessable for research and provide socioeconomic context essential for assessing and addressing the burden of undernutrition within and between countries. The strengths and weaknesses of data from these three sources should be carefully considered when seeking to determine the burden of child undernutrition and its variation within countries.

SUBMITTER: Corsi DJ

PROVIDER: S-EPMC5496063 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Child anthropometry data quality from Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and National Nutrition Surveys in the West Central Africa region: are we comparing apples and oranges?

Corsi Daniel J DJ Perkins Jessica M JM Subramanian S V SV

Global health action 20170101 1

<h4>Background</h4>There has been limited work comparing survey characteristics and assessing the quality of child anthropometric data from population-based surveys.<h4>Objective</h4>To investigate survey characteristics and indicators of quality of anthropometric data in children aged 0-59 months from 23 countries in the West Central Africa region.<h4>Methods</h4>Using established methodologies and criteria to examine child age, sex, height, and weight, we conducted a comprehensive assessment a ...[more]

PMID: 28641057

Similar Datasets

Project description:Rapid advancements in sequencing technologies along with falling costs present widespread opportunities for microbiome studies across a vast and diverse array of environments. These impressive technological developments have been accompanied by a considerable growth in the number of methodological variables, including sampling, storage, DNA extraction, primer pairs, sequencing technology, chemistry version, read length, insert size, and analysis pipelines, amongst others. This increase in variability threatens to compromise both the reproducibility and the comparability of studies conducted. Here we perform the first reported study comparing both amplicon and shotgun sequencing for the three leading next-generation sequencing technologies. These were applied to six human stool samples using Illumina HiSeq, MiSeq and Ion PGM shotgun sequencing, as well as amplicon sequencing across two variable 16S rRNA gene regions. Notably, we found that the factor responsible for the greatest variance in microbiota composition was the chosen methodology rather than the natural inter-individual variance, which is commonly one of the most significant drivers in microbiome studies. Amplicon sequencing suffered from this to a large extent, and this issue was particularly apparent when the 16S rRNA V1-V2 region amplicons were sequenced with MiSeq. Somewhat surprisingly, the choice of taxonomic binning software for shotgun sequences proved to be of crucial importance with even greater discriminatory power than sequencing technology and choice of amplicon. Optimal N50 assembly values for the HiSeq was obtained for 10 million reads per sample, whereas the applied MiSeq and PGM sequencing depths proved less sufficient for shotgun sequencing of stool samples. The latter technologies, on the other hand, provide a better basis for functional gene categorisation, possibly due to their longer read lengths. Hence, in addition to highlighting methodological biases, this study demonstrates the risks associated with comparing data generated using different strategies. We also recommend that laboratories with particular interests in certain microbes should optimise their protocols to accurately detect these taxa using different techniques.

Project description:BackgroundOne of the challenges of bioinformatics remains the recognition of short signal sequences in genomic DNA such as donor or acceptor splice sites, splicing enhancers or silencers, translation initiation sites, transcription start sites, transcription factor binding sites, nucleosome binding sites, miRNA binding sites, or insulator binding sites. During the last decade, a wealth of algorithms for the recognition of such DNA sequences has been developed and compared with the goal of improving their performance and to deepen our understanding of the underlying cellular processes. Most of these algorithms are based on statistical models belonging to the family of Markov random fields such as position weight matrix models, weight array matrix models, Markov models of higher order, or moral Bayesian networks. While in many comparative studies different learning principles or different statistical models have been compared, the influence of choosing different prior distributions for the model parameters when using different learning principles has been overlooked, and possibly lead to questionable conclusions.ResultsWith the goal of allowing direct comparisons of different learning principles for models from the family of Markov random fields based on the same a-priori information, we derive a generalization of the commonly-used product-Dirichlet prior. We find that the derived prior behaves like a Gaussian prior close to the maximum and like a Laplace prior in the far tails. In two case studies, we illustrate the utility of the derived prior for a direct comparison of different learning principles with different models for the recognition of binding sites of the transcription factor Sp1 and human donor splice sites.ConclusionsWe find that comparisons of different learning principles using the same a-priori information can lead to conclusions different from those of previous studies in which the effect resulting from different priors has been neglected. We implement the derived prior is implemented in the open-source library Jstacs to enable an easy application to comparative studies of different learning principles in the field of sequence analysis.

Project description:BackgroundInfant immunization coverage worldwide has plateaued at about 85%. Using existing survey data to conduct analyses beyond estimating coverage may help immunization programmes better tailor strategies to reach un- and under-immunized children. The Demographic and Health Survey (DHS) and the Multiple Indicators Cluster Survey (MICS), routinely conducted in low and middle-income countries (LMICs), collect immunization data, yet vaccination coverage is often the only indicator reported and used. We conducted a review of published immunization-related analyses to characterize and quantify immunization secondary analyses done using DHS and MICS databases.MethodsWe conducted a systematic search of the literature, of immunization-related secondary analyses from DHS or MICS published between 2006 and August 2018. We searched 15 electronic databases without language restrictions. For the articles included, relevant information was extracted and analyzed to summarize the characteristics of immunization-related secondary analyses. Results are presented following the PRISMA guidelines.ResultsAmong 1411 papers identified, 115 met our eligibility criteria; additionally, one article was supplemented by the Pan American Health Organization. The majority were published since 2012 (77.6%), and most (68.9%) had a first or corresponding author affiliated with institutions in high-income countries (as opposed to LMICs where these surveys are conducted). The median delay between survey implementation and publication of the secondary analysis was 5.4 years, with papers with authors affiliated to institutions in LMIC having a longer median publication delay (p < 0.001). Over 80% of the published analyses looked at factors associated with a specific vaccine or with full immunization. Quality proxies, such as reporting percent of immunization data from cards vs recall; occurrence and handling of missing data; whether survey analyses were weighted; and listing of potential biases or limitations of the original survey or analyses, were infrequently mentioned.ConclusionOur review suggests that more needs to be done to increase the increase the utilization of existing DHS and MICS datasets and improve the quality of the analyses to inform immunization programmes. This would include increasing the proportion of analyses done in LMICs, reducing the time lag between survey implementation and publication of additional analyses, and including more qualitative information about the survey in the publications to better interpret the results.

Project description:BackgroundSocio-economic and demographic determinants of child growth at ages 0-5 years in developing countries are well documented. However, Precision Public Health interventions and population targeting require more finely grained knowledge about the existence and character of temporal changes in child growth associations.MethodsWe evaluated the temporal stability of associations between height-for-age z-score (HAZ) of children aged 0-59 months and child, parental, household, and community and infrastructure factors by following 25 countries over time (1991-2014) in repeated cross-sections of 91 Demographic and Health Surveys using random effect models and Wald tests.ResultsWe found that child growth displayed relatively more time stable associations with child, parental, and household factors than with community and infrastructure factors. Among the unstable associations, there was no uniform geographical pattern in terms of where they consistently increased or decreased over time. There were differences between countries in the extent of temporal instability but there was no apparent regional grouping or geographic pattern. The instability was positively and significantly correlated with annual changes in HAZ.ConclusionsThese findings inform about the generalizability of results stemming from cross-sectional studies that do not consider time variation - results regarding effects of child, parental, and household factors on HAZ do not necessarily need to be re-evaluated over time whereas results regarding the effects of infrastructure and community variables need to be monitored more frequently as they are expected to change. In addition, the study may improve the Precision Public Health population targeting of interventions in different regions and times - whereas the temporal dimension seems to be important for precision targeting of community and infrastructure factors, it is not the case for child, parental, and household factors. In general, the existence of temporal instability and the direction of change varies across countries with no apparent regional pattern.

Dataset Information

Child anthropometry data quality from Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and National Nutrition Surveys in the West Central Africa region: are we comparing apples and oranges?

Background

Objective

Methods

Results

Conclusions

Publications

Child anthropometry data quality from Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and National Nutrition Surveys in the West Central Africa region: are we comparing apples and oranges?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets