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

BMJ Open ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. e034524
Author(s):  
Adeyinka Emmanuel Adegbosin ◽  
Bela Stantic ◽  
Jing Sun
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Low Income ◽  
Maternal Education ◽  
Household Wealth ◽  
Secondary Outcome ◽  
Cross Sectional ◽  
Middle Income ◽  
Under Five ◽  
Middle Income Countries

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.

scholarly journals Predicting Under-five mortality across 21 Low and Middle-Income Countries using Deep Learning Methods

2019 ◽  
Author(s):  
Adeyinka E Adegbosin ◽  
Bela Stantic ◽  
Jing Sun
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Child Survival ◽  
Household Wealth ◽  
Secondary Outcome ◽  
Cross Sectional ◽  
Middle Income ◽  
Under Five ◽  
Middle Income Countries ◽  
Low And Middle Income

AbstractObjectivesTo explore the efficacy of Machine Learning (ML) techniques in predicting under-five mortality in LMICs and to identify significant predictors of under-five mortality (U5M).DesignThis is a cross-sectional, proof-of-concept study.Settings and participantsWe analysed data from the Demographic and Health Survey (DHS). The data was drawn from 21 Low-and-Middle Income Countries (LMICs) countries (N = 1,048,575). Eligible mothers in each household were asked information about their children and the reproductive care they received during the pregnancy.Primary and secondary outcome measuresThe primary outcome measure was under-five mortality; 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 survival.ResultsWe found that duration of breast feeding, household wealth index and the level of maternal education are the most important predictors of under-five mortality. 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 interventions that needs to be prioritized, in order to reduce levels of U5M in LMICs. It also demonstrates that deep learning models are more efficacious than a traditional analytical approach.Strengths and limitations of this studyThe models were tested using a very large data sample, drawn from over 1 million households.The survey utilised a cluster sampling approach and are representative of each country included.Socio-economic, political and cultural differences between the included countries may limit generalisability of the results.The cross-sectional design of the study means we can only infer association and not causality.

scholarly journals Are inequities decreasing? Birth registration for children under five in low-income and middle-income countries, 1999–2016

2019 ◽  
Vol 4 (6) ◽  
pp. e001926
Author(s):  
Amiya Bhatia ◽  
Nancy Krieger ◽  
Jason Beckfield ◽  
Aluisio J D Barros ◽  
Cesar Victora
Keyword(s):  
Low Income ◽  
Maternal Education ◽  
Vital Statistics ◽  
Birth Registration ◽  
Middle Income ◽  
Children Under Five ◽  
Under Five ◽  
Rural Location ◽  
Middle Income Countries ◽  
Urban Rural

IntroductionAlthough global birth registration coverage has improved from 58% to 71% among children under five globally, inequities in birth registration coverage by wealth, urban/rural location, maternal education and access to a health facility persist. Few studies examine whether inequities in birth registration in low-income and middle-income countries have changed over time.MethodsWe combined information on caregiver reported birth registration of 1.6 million children in 173 publicly available, nationally representative Demographic Health Surveys and Multiple Indicator Cluster Surveys across 67 low-income and middle-income countries between 1999 and 2016. For each survey, we calculated point estimates and 95% CIs for the percentage of children under 5 years without birth registration on average and stratified by sex, urban/rural location and wealth. For each sociodemographic variable, we estimated absolute measures of inequality. We then examined changes in non-registration and inequities between surveys, and annually.Results14 out of 67 countries had achieved complete birth registration. Among the remaining 53 countries, 39 countries successfully decreased the percentage of children without birth registration. However, this reduction occurred alongside statistically significant increases in wealth inequities in 9 countries and statistically significant decreases in 10 countries. At the most recent survey, the percentage of children without birth registration was greater than 50% in 16 out of 67 countries.ConclusionAlthough birth registration improved on average, progress in reducing wealth inequities has been limited. Findings highlight the importance of monitoring changes in inequities to improve birth registration, to monitor Sustainable Development Goal 16.9 and to strengthen Civil Registration and Vital Statistics systems.

scholarly journals Hierarchical disentanglement of contextual from compositional risk factors of diarrhoea among under-five children in low- and middle-income countries

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adeniyi Francis Fagbamigbe ◽  
A. Olalekan Uthman ◽  
Latifat Ibisomi
Keyword(s):  
Risk Factors ◽  
National Level ◽  
Cross Sectional ◽  
Middle Income ◽  
Contextual Risk ◽  
Under Five ◽  
Factors Associated ◽  
Under Five Children ◽  
Middle Income Countries ◽  
Low And Middle Income

AbstractSeveral studies have documented the burden and risk factors associated with diarrhoea in low and middle-income countries (LMIC). To the best of our knowledge, the contextual and compositional factors associated with diarrhoea across LMIC were poorly operationalized, explored and understood in these studies. We investigated multilevel risk factors associated with diarrhoea among under-five children in LMIC. We analysed diarrhoea-related information of 796,150 under-five children (Level 1) nested within 63,378 neighbourhoods (Level 2) from 57 LMIC (Level 3) using the latest data from cross-sectional and nationally representative Demographic Health Survey conducted between 2010 and 2018. We used multivariable hierarchical Bayesian logistic regression models for data analysis. The overall prevalence of diarrhoea was 14.4% (95% confidence interval 14.2–14.7) ranging from 3.8% in Armenia to 31.4% in Yemen. The odds of diarrhoea was highest among male children, infants, having small birth weights, households in poorer wealth quintiles, children whose mothers had only primary education, and children who had no access to media. Children from neighbourhoods with high illiteracy [adjusted odds ratio (aOR) = 1.07, 95% credible interval (CrI) 1.04–1.10] rates were more likely to have diarrhoea. At the country-level, the odds of diarrhoea nearly doubled (aOR = 1.88, 95% CrI 1.23–2.83) and tripled (aOR = 2.66, 95% CrI 1.65–3.89) among children from countries with middle and lowest human development index respectively. Diarrhoea remains a major health challenge among under-five children in most LMIC. We identified diverse individual-level, community-level and national-level factors associated with the development of diarrhoea among under-five children in these countries and disentangled the associated contextual risk factors from the compositional risk factors. Our findings underscore the need to revitalize existing policies on child and maternal health and implement interventions to prevent diarrhoea at the individual-, community- and societal-levels. The current study showed how the drive to the attainment of SDGs 1, 2, 4, 6 and 10 will enhance the attainment of SDG 3.

scholarly journals Reporting of inequalities in blindness in low income and middle income countries: a review of cross sectional surveys

2017 ◽  
Vol 46 (1) ◽  
pp. 99-100 ◽  
Author(s):  
Jacqueline Ramke ◽  
Anna Palagyi ◽  
Jennifer Petkovic ◽  
Clare E Gilbert
Keyword(s):  
Low Income ◽  
Cross Sectional ◽  
Middle Income ◽  
Middle Income Countries

scholarly journals State of deworming coverage and equity in low-income and middle-income countries using household health surveys: a spatiotemporal cross-sectional study

2019 ◽  
Vol 7 (11) ◽  
pp. e1511-e1520 ◽  
Author(s):  
Nathan C Lo ◽  
Sam Heft-Neal ◽  
Jean T Coulibaly ◽  
Leslie Leonard ◽  
Eran Bendavid ◽  
...  
Keyword(s):  
Low Income ◽  
Health Surveys ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Middle Income ◽  
Middle Income Countries

Assessment of county-level poverty alleviation progress by deep learning and satellite observations

2021 ◽  
Author(s):  
Liqiang Zhang ◽  
Yanxiao Jiang ◽  
Yang Li ◽  
Alicia J Zhou ◽  
Jing Cao ◽  
...  
Keyword(s):  
Growth Rate ◽  
Deep Learning ◽  
Low Income ◽  
Poverty Alleviation ◽  
County Level ◽  
Average Growth Rate ◽  
Middle Income ◽  
Average Growth ◽  
Middle Income Countries ◽  
Per Capita

Abstract Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries. China, which had the largest rural poverty-stricken population, has made tremendous efforts in alleviating poverty especially since the implementation of the targeted poverty alleviation (TPA) policy in 2014. Yet it remains unknown about the successfulness of the policy, because the official statistics are not timely available and in some cases questionable. This study combines deep learning with multiple satellite datasets to estimate county-level economic development from 2008 to 2019 and assess the effect of the TPA policy for 592 national poverty-stricken counties (NPCs) at country, provincial and county levels. Per capita gross domestic product (GDP) is used to measure the affluence level. From 2014 through 2019, the 592 NPCs experience an average growth rate of per capita GDP at 7.6%±0.4%, higher than the average growth rate of 310 adjacent non-NPC counties (7.3%±0.4%) and of the whole country (6.3%). This indicates an overall success of TPA policy so far. We also reveal 42 counties with weak growth recently and that the average affluence level of the NPCs in 2019 is still much lower than the national or provincial averages. The inexpensive, timely and accurate method proposed here can be applied to other low-income and middle-income countries for affluence assessment.

scholarly journals A Cross-Sectional Analysis of the Association between Domestic Cooking Energy Source Type and Respiratory Infections among Children Aged under Five Years: Evidence from Demographic and Household Surveys in 37 Low-Middle Income Countries

2021 ◽  
Vol 18 (16) ◽  
pp. 8516
Author(s):  
Zubaidah Al-Janabi ◽  
Katherine E. Woolley ◽  
G. Neil Thomas ◽  
Suzanne E. Bartington
Keyword(s):  
Respiratory Infections ◽  
Population Based ◽  
Energy Sources ◽  
Shortness Of Breath ◽  
Cross Sectional ◽  
Middle Income ◽  
Under Five ◽  
Middle Income Countries ◽  
Cooking Energy ◽  
Solid Biomass

Background: In low- and middle-income countries (LMICs), household air pollution as a result of using solid biomass for cooking, lighting and heating (HAP) is associated with respiratory infections, accounting for approximately 4 million early deaths each year worldwide. The majority of deaths are among children under five years. This population-based cross-sectional study investigates the association between solid biomass usage and risk of acute respiratory infections (ARI) and acute lower respiratory infections (ALRI) in 37 LMICs within Africa, Americas, Southeast Asia, European, Eastern Mediterranean and Western Pacific regions. Materials and methods: Using population-based data obtained from Demographic and Health surveys (2010–2018), domestic cooking energy sources were classified solid biomass (wood, charcoal/dung, agricultural crop) and cleaner energy sources (e.g., Liquid Petroleum Gas (LPG), electricity, biogas and natural gas). Composite measures of ARI (shortness of breath, cough) and ALRI (shortness of breath, cough and fever) were composed using maternally reported respiratory symptoms over the two-week period prior to the interview. Multivariable logistic regression was used to identify the association between biomass fuel usage with ARI and ALRI, accounting for relevant individual, household and situational confounders, including stratification by context (urban/rural). Results: After adjustment, in the pooled analysis, children residing in solid biomass cooking households had an observed increased adjusted odds ratio of ARI (AOR: 1.17; 95% CI: 1.09–1.25) and ALRI (AOR: 1.16; 95% CI 1.07–1.25) compared to cleaner energy sources. In stratified analyses, a comparable association was observed in urban areas (ARI: 1.16 [1.06–1.28]; ALRI: 1.14 [1.02–1.27]), but only significant for ARI among those living in rural areas (ARI: 1.14 [1.03–1.26]). Conclusion: Switching domestic cooking energy sources from solid biomass to cleaner alternatives would achieve a respiratory health benefit in children under five years worldwide. High quality mixed-methods research is required to improve acceptability and sustained uptake of clean cooking energy source interventions in LMIC settings.

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