Contextual data in IPUMS DHS: physical and social environment variables linked to the Demographic and Health Surveys

Boyle, Elizabeth Heger; King, Miriam L; García, Sarah; Culver, Corey; Boudreiux, Jordan

doi:10.1007/s11111-020-00348-4

Cited by 31 publications

(45 citation statements)

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“…Data used in this analysis were obtained from the 2016 Ethiopia Demographic and Health Survey in the children's file [21]. The Ethiopian DHS was financially sponsored by the United States Agency for International Development (USAID).…”

Section: Data and Populationmentioning

confidence: 99%

“…The 2016 EDHS data were downloaded from the DHS Program website using the Integrated Public Use Microdata Series extract system [21]. The 2016 EDHS used a two-stage stratified cluster sampling method to identify a nationally representative sample of the Ethiopian population living in 9 regions and 2 city administrations of the country.…”

Section: Data Access Sample Size and Samplingmentioning

confidence: 99%

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The effect of access to water, sanitation and handwashing facilities on child growth indicators: Evidence from the Ethiopia Demographic and Health Survey 2016

2020

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Introduction Poor access to water, sanitation, and handwashing (WASH) facilities frequently contribute to child growth failure. The role of access to WASH facilities on child growth outcomes in Ethiopia is largely unknown. The aim of this study was to determine individual and combined effects of access to WASH facilities on child growth outcomes. Methods Data for this analysis was sourced from the recent Ethiopia Demographic and Health Survey (EDHS) 2016. A multivariable logistic regression model was applied to identify the separate and combined association of access to WASH facilities with child growth outcomes. Odds ratio (OR) and 95% confidence interval (CI) were estimated. Statistical significance was declared at p < 0.05. Results Included in the analyses were data for children 0-59 months of age, which amounted to valid data for 9588 children with a height-forage z-score (HAZ), 9752 children with a weight-forage z-score (WAZ) and 9607 children with a weight-for-height z-score (WHZ). Children with access to improved combined sanitation with handwashing facilities had 29% lower odds of linear growth failure (stunting) (adjusted odds ratio (AOR) = 0.71; 95% CI: 0.51-0.99) compared with those with unimproved. Children with access to combined improved WASH facilities were 33% less likely to have linear growth failure (AOR = 0.67; 95% CI: 0.45-0.98). Access to improved handwashing alone reduced the odds of being underweight by 17% (AOR = 0.83; 95% CI: 0.71-0.98) compared with unimproved. Improved water and sanitation separately as well as combined WASH were not associated with decreased odds of underweight and wasting.

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Section: Data and Populationmentioning

confidence: 99%

Section: Data Access Sample Size and Samplingmentioning

confidence: 99%

The effect of access to water, sanitation and handwashing facilities on child growth indicators: Evidence from the Ethiopia Demographic and Health Survey 2016

2020

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“…27 Combined multicountry data for this study were obtained from the IPUMS-DHS portal. 28 Combined DHS data were available for a total of 34 LMICs on the IPUMS-DHS database. We used all available data in these countries from 1987 to 2017 (see online supplementary table 1) .…”

Section: Data Source and Analytical Toolsmentioning

confidence: 99%

Efficacy of deep learning methods for predicting under-five mortality in 34 low-income and middle-income countries

2020

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ObjectivesTo explore the efficacy of machine learning (ML) techniques in predicting under-five mortality (U5M) in low-income and middle-income countries (LMICs) and to identify significant predictors of U5M.DesignThis is a cross-sectional, proof-of-concept study.Settings and participantsWe analysed data from the Demographic and Health Survey. The data were drawn from 34 LMICs, comprising a total of n=1 520 018 children drawn from 956 995 unique households.Primary and secondary outcome measuresThe primary outcome measure was U5M; secondary outcome was comparing the efficacy of deep learning algorithms: deep neural network (DNN); convolution neural network (CNN); hybrid CNN-DNN with logistic regression (LR) for the prediction of child’s survival.ResultsWe found that duration of breast feeding, number of antenatal visits, household wealth index, postnatal care and the level of maternal education are some of the most important predictors of U5M. We found that deep learning techniques are superior to LR for the classification of child survival: LR sensitivity=0.47, specificity=0.53; DNN sensitivity=0.69, specificity=0.83; CNN sensitivity=0.68, specificity=0.83; CNN-DNN sensitivity=0.71, specificity=0.83.ConclusionOur findings provide an understanding of determinants of U5M in LMICs. It also demonstrates that deep learning models are more efficacious than traditional analytical approach.

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“…Detailed survey methods and sampling procedures are found in the respective EDHS reports [ 13 , 20 – 22 ]. EDHS data were downloaded with permission from the DHS Program website using Integrated Public Use Microdata Series (IPUMS-DHS) extract system [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Trends in child growth failure among children under five years of age in Ethiopia: Evidence from the 2000 to 2016 Demographic and Health Surveys

Bekele

Rawstorne²,

Rahman³

2021

PLoS ONE

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Introduction In a majority of low- and middle-income countries (LMICs), levels of child growth failure (CGF) have steadily declined since 2000. However, some countries show a different trend. Despite continued investment from the government of Ethiopia as well as donors, CGF levels are still high in Ethiopia. This study aimed to assess trends in CGF and associated sociodemographic, economic and water, sanitation, and hygiene (WASH) factors from 2000 to 2016 in Ethiopia. Methods Data were taken from four rounds of the Ethiopia Demographic and Health Survey (EDHS). Children aged between 0 to 59 months were included. CGF indicators were categorised based on height-for-age z-score (HAZ) < -2 Standard deviation (SD), weight-for-age z-score (WAZ) < -2 SD and weight-for-height z-score (WHZ) < -2 SD. CGF trends were estimated for predicted probabilities and odds ratios (ORs) between 2000 and 2016. Results A total sample size of 31978 for HAZ, 32045 for WAZ and 32246 for WHZ were included in the current study. Stunting decreased from an adjusted odds ratio (AOR) = 0.77 (95% CI: 0.67 to 0.88) in 2005 to an AOR = 0.45 (95% CI: 0.39 to 0.53) in 2016 compared with the year 2000. Compared with data in 2000, underweight decreased from an AOR of 0.70 (95% CI: 0.61 to 0.80) in 2005 to an AOR of 0.43 (95% CI: 0.36 to 0.50) in 2016. Wasting declined from an AOR of 0.91 (95% CI: 0.75 to 1.10) in 2005 to an AOR of 0.76 (95% CI: 0.61 to 0.94) in 2016, compared with data in 2000. Conclusions Between 2000 to 2016, there was a decline in CGF levels albeit the levels are still relatively high compared with the World Health Organization (WHO) cut-off levels for public health concern. Observed rates of change varied across sociodemographic, economic and WASH factors which suggest that interventions tailored towards addressing the imbalances across those factors are required.

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Contextual data in IPUMS DHS: physical and social environment variables linked to the Demographic and Health Surveys

Cited by 31 publications

References 50 publications

The effect of access to water, sanitation and handwashing facilities on child growth indicators: Evidence from the Ethiopia Demographic and Health Survey 2016

The effect of access to water, sanitation and handwashing facilities on child growth indicators: Evidence from the Ethiopia Demographic and Health Survey 2016

Efficacy of deep learning methods for predicting under-five mortality in 34 low-income and middle-income countries

Trends in child growth failure among children under five years of age in Ethiopia: Evidence from the 2000 to 2016 Demographic and Health Surveys

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