Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach.
Ontology highlight
ABSTRACT:
SUBMITTER: Bizzego A
PROVIDER: S-EPMC7908191 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
Publications
Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach.
Bizzego Andrea A Gabrieli Giulio G Bornstein Marc H MH Deater-Deckard Kirby K Lansford Jennifer E JE Bradley Robert H RH Costa Megan M Esposito Gianluca G
International journal of environmental research and public health 20210201 3
Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 distal causes of CM and identify the top 10 causes in terms of predictive potency. Based on the top 10 causes, we identified households with improved conditions. We retrospectively validated the results by investigating the associatio ...[more]
Similar Datasets
Project description: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.
| S-EPMC7430449 | biostudies-literature
Project description:INTRODUCTION:Machine learning (ML) has been used in bio-medical research, and recently in clinical and public health research. However, much of the available evidence comes from high-income countries, where different health profiles challenge the application of this research to low/middle-income countries (LMICs). It is largely unknown what ML applications are available for LMICs that can support and advance clinical medicine and public health. We aim to address this gap by conducting a scoping review of health-related ML applications in LMICs. METHODS AND ANALYSIS:This scoping review will follow the methodology proposed by Levac et al. The search strategy is informed by recent systematic reviews of ML health-related applications. We will search Embase, Medline and Global Health (through Ovid), Cochrane and Google Scholar; we will present the date of our searches in the final review. Titles and abstracts will be screened by two reviewers independently; selected reports will be studied by two reviewers independently. Reports will be included if they are primary research where data have been analysed, ML techniques have been used on data from LMICs and they aimed to improve health-related outcomes. We will synthesise the information following evidence mapping recommendations. ETHICS AND DISSEMINATION:The review will provide a comprehensive list of health-related ML applications in LMICs. The results will be disseminated through scientific publications. We also plan to launch a website where ML models can be hosted so that researchers, policymakers and the general public can readily access them.
| S-EPMC7223147 | biostudies-literature
Project description:BackgroundIncome inequality and national wealth are strong determinants for health, but few studies have systematically investigated their influence on mortality across the early life-course, particularly outside the high-income world.MethodsWe performed cross-sectional regression analyses of the relationship between income inequality (national Gini coefficient) and national wealth (Gross Domestic Product (GDP) averaged over previous decade), and all-cause and grouped cause national mortality rate amongst infants, 1-4, 5-9, 10-14, 15-19 and 20-24 year olds in low and middle-income countries (LMIC) in 2012. Gini models were adjusted for GDP.ResultsData were available for 103 (79%) countries. Gini was positively associated with increased all-cause and communicable disease mortality in both sexes across all age groups, after adjusting for national wealth. Gini was only positively associated with increased injury mortality amongst infants and 20-24 year olds, and increased non-communicable disease mortality amongst 20-24 year old females. The strength of these associations tended to increase during adolescence. Increasing GDP was negatively associated with all-cause, communicable and non-communicable disease mortality in males and females across all age groups. GDP was also associated with decreased injury mortality in all age groups except 15-19 year old females, and 15-24 year old males. GDP became a weaker predictor of mortality during adolescence.ConclusionPolicies to reduce income inequality, rather than prioritising economic growth at all costs, may be needed to improve adolescent mortality in low and middle-income countries, a key development priority.
| S-EPMC5425964 | biostudies-literature
Project description:Childhood malnutrition is associated with high morbidity and mortality globally1. Undernourished children are more likely to experience cognitive, physical, and metabolic developmental impairments that can lead to later cardiovascular disease, reduced intellectual ability and school attainment, and reduced economic productivity in adulthood2. Child growth failure (CGF), expressed as stunting, wasting, and underweight in children under five years of age (0-59 months), is a specific subset of undernutrition characterized by insufficient height or weight against age-specific growth reference standards3-5. The prevalence of stunting, wasting, or underweight in children under five is the proportion of children with a height-for-age, weight-for-height, or weight-for-age z-score, respectively, that is more than two standard deviations below the World Health Organization's median growth reference standards for a healthy population6. Subnational estimates of CGF report substantial heterogeneity within countries, but are available primarily at the first administrative level (for example, states or provinces)7; the uneven geographical distribution of CGF has motivated further calls for assessments that can match the local scale of many public health programmes8. Building from our previous work mapping CGF in Africa9, here we provide the first, to our knowledge, mapped high-spatial-resolution estimates of CGF indicators from 2000 to 2017 across 105 low- and middle-income countries (LMICs), where 99% of affected children live1, aggregated to policy-relevant first and second (for example, districts or counties) administrative-level units and national levels. Despite remarkable declines over the study period, many LMICs remain far from the ambitious World Health Organization Global Nutrition Targets to reduce stunting by 40% and wasting to less than 5% by 2025. Large disparities in prevalence and progress exist across and within countries; our maps identify high-prevalence areas even within nations otherwise succeeding in reducing overall CGF prevalence. By highlighting where the highest-need populations reside, these geospatial estimates can support policy-makers in planning interventions that are adapted locally and in efficiently directing resources towards reducing CGF and its health implications.
| S-EPMC7015855 | biostudies-literature
Project description:ImportanceThe burden of cancer falls disproportionally on low-middle-income countries (LMICs). It is not well known how novel therapies are tested in current clinical trials and the extent to which they match global disease burden.ObjectivesTo describe the design, results, and publication of oncology randomized clinical trials (RCTs) and examine the extent to which trials match global disease burden and how trial methods and results differ across economic settings.Design, setting, and participantsIn this retrospective cohort study, a literature search identified all phase 3 RCTs evaluating anticancer therapies published from 2014 to 2017. Randomized clinical trials were classified based on World Bank economic classification. Descriptive statistics were used to compare RCT design and results from high-income countries (HICs) and low/middle-income countries (LMICs). Statistical analysis was conducted in January 2020.Main outcomes and measuresDifferences in the design, results, and output of RCTs between HICs and LMICs.ResultsThe study cohort included 694 RCTs: 636 (92%) led by HICs and 58 (8%) led by LMICs. A total of 601 RCTs (87%) tested systemic therapy and 88 RCTs (13%) tested radiotherapy or surgery. The proportion of RCTs relative to global deaths was higher for breast cancer (121 RCTs [17%] and 7% of deaths) but lower for gastroesophageal cancer (38 RCTs [6%] and 14% of deaths), liver cancer (14 RCTs [2%] and 8% of deaths), pancreas cancer (14 RCTs [2%] and 5% of deaths), and cervical cancer (9 RCTs [1%] and 3% of deaths). Randomized clinical trials in HICs were more likely than those in LMICs to be funded by industry (464 [73%] vs 24 [41%]; P < .001). Studies in LMICs were smaller than those in HICs (median, 219 [interquartile range, 137-363] vs 474 [interquartile range, 262-743] participants; P < .001) and more likely to meet their primary end points (39 of 58 [67%] vs 286 of 636 [45%]; P = .001). The observed median effect size among superiority trials was larger in LMICs compared with HICs (hazard ratio, 0.62 [interquartile range, 0.54-0.76] vs 0.84 [interquartile range, 0.67-0.97]; P < .001). Studies from LMICs were published in journals with lower median impact factors than studies from HICs (7 [interquartile range, 4-21] vs 21 [interquartile range, 7-34]; P < .001). Publication bias persisted when adjusted for whether a trial was positive or negative (median impact factor: LMIC negative trial, 5 [interquartile range, 4-6] vs HIC negative trial, 18 [interquartile range, 6-26]; LMIC positive trial, 9 [interquartile range, 5-25] vs HIC positive trial, 25 [interquartile range, 10-48]; P < .001).Conclusions and relevanceThis study suggests that oncology RCTs are conducted predominantly by HICs and do not match the global burden of cancer. Randomized clinical trials from LMICs are more likely to identify effective therapies and have a larger effect size than RCTs from HICs. This study suggests that there is a funding and publication bias against RCTs led by LMICs. Policy makers, research funders, and journals need to address this issue with a range of measures including building capacity and capability in RCTs.
| S-EPMC7844695 | biostudies-literature
Project description:OBJECTIVE:Using estimates from the Global Burden of Disease (GBD) study, we examined differences in unintentional occupational injury mortality rates from 1990 to 2016 between high-income countries (HICs) and low- and middle-income countries (LMICs). METHODS:Unintentional occupational injury mortality rates were obtained through the GBD online visualization tool. We quantified mortality changes over time for common external causes of injury for ages 15?49 years and 50?69 years separately in HICs and LMICs using negative binomial regression models. RESULTS:In 2016, there were 24,396 and 303,999 unintentional occupational injury deaths among individuals aged 15 to 69 years in HICs and LMICs, respectively, corresponding to 3.1 and 7.0 per 100,000 people. Between 1990 and 2016, unintentional occupational injury mortality for people aged 15?69 years dropped 46% (from 5.7 to 3.1 per 100,000 people) in HICs and 42% in LMICs (from 13.2 to 7.0 per 100,000 people). Sustained and large disparities existed between HICs and LMICs for both sexes and both age groups during 1990?2016 (mortality rate ratio: 2.2?2.4). All unintentional occupational injury causes of death displayed significant reduction with one exception (ages 15?49 years in HICs). Country-specific analysis revealed large variations in unintentional occupational injury mortality and changes in occupational injury mortality between 1990 and 2016. CONCLUSIONS:Despite substantial decreases in mortality between 1990 and 2016 for both HICs and LMICs, a large disparity continues to exist between HICs and LMICs. Multifaceted efforts are needed to reduce the disparity.
| S-EPMC6210857 | biostudies-other
Project description:BackgroundNon-biological childhood mortality sex ratios may reflect community sex preferences and gender discrimination in health care.ObjectiveWe assessed the association between contextual factors and gender bias in under-five mortality rates (U5MR) in low- and middle-income countries.MethodsFull birth histories available from Demographic and Health Surveys and Multiple Indicator Cluster Surveys (2010-2018) in 80 countries were used to estimate U5MR male-to-female sex ratios. Expected sex ratios and their residuals (difference of observed and expected) were derived from a linear regression model, adjusted for overall mortality. Negative residuals indicate more likelihood of discrimination against girls, and we refer to this as a measure of potential gender bias. Associations between residuals and national development and gender inequality indices and with survey-derived child health care indicators were tested using Spearman's correlation.ResultsMortality residuals for under-five mortality were not associated with national development, education, religion, or gender inequality indices. Negative residuals were more common in countries where boys were more likely to be taken to health services than girls (rho -0.24, 95% confidence interval -0.45, -0.01).ConclusionsCountries where girls were more likely to die than boys, accounting for overall mortality levels, were also countries where boys were more likely to receive health care than girls. Further research is needed to understand which national characteristics explain the presence of gender bias, given that the analyses of development levels and gender equality did not discriminate between countries with or without excess mortality of girls. Reporting on child mortality separately by sex is required to enable such advances.
| S-EPMC8453971 | biostudies-literature
Project description:BackgroundThe progress to achieve the fourth Millennium Development Goal in reducing mortality rate in children younger than 5 years since 1990 has been remarkable. However, work remains to be done in the Sustainable Development Goal era. Estimates of under-5 mortality rates at the national level can hide disparities within countries. We assessed disparities in under-5 mortality rates by household economic status in low-income and middle-income countries (LMICs).MethodWe estimated country-year-specific under-5 mortality rates by wealth quintile on the basis of household wealth indices for 137 LMICs from 1990 to 2016, using a Bayesian statistical model. We estimated the association between quintile-specific and national-level under-5 mortality rates. We assessed the levels and trends of absolute and relative disparity in under-5 mortality rate between the poorest and richest quintiles, and among all quintiles.FindingsIn 2016, for all LMICs (excluding China), the aggregated under-5 mortality rate was 64·6 (90% uncertainty interval [UI] 61·1-70·1) deaths per 1000 livebirths in the poorest households (first quintile), 31·3 (29·5-34·2) deaths per 1000 livebirths in the richest households (fifth quintile), and in between those outcomes for the middle quintiles. Between 1990 and 2016, the largest absolute decline in under-5 mortality rate occurred in the two poorest quintiles: 77·6 (90% UI 71·2-82·6) deaths per 1000 livebirths in the poorest quintile and 77·9 (72·0-82·2) deaths per 1000 livebirths in the second poorest quintile. The difference in under-5 mortality rate between the poorest and richest quintiles decreased significantly by 38·8 (90% UI 32·9-43·8) deaths per 1000 livebirths between 1990 and 2016. The poorest to richest under-5 mortality rate ratio, however, remained similar (2·03 [90% UI 1·94-2·11] in 1990, 1·99 [1·91-2·08] in 2000, and 2·06 [1·92-2·20] in 2016). During 1990-2016, around half of the total under-5 deaths occurred in the poorest two quintiles (48·5% in 1990 and 2000, 49·5% in 2016) and less than a third were in the richest two quintiles (30·4% in 1990, 30·5% in 2000, 29·9% in 2016). For all regions, differences in the under-5 mortality rate between the first and fifth quintiles decreased significantly, ranging from 20·6 (90% UI 15·9-25·1) deaths per 1000 livebirths in eastern Europe and central Asia to 59·5 (48·5-70·4) deaths per 1000 livebirths in south Asia. In 2016, the ratios of under-5 mortality rate in the first quintile to under-5 mortality rate in the fifth quintile were significantly above 2·00 in two regions, with 2·49 (90% UI 2·15-2·87) in east Asia and Pacific (excluding China) and 2·41 (2·05-2·80) in south Asia. Eastern and southern Africa had the smallest ratio in 2016 at 1·62 (90% UI 1·48-1·76). Our model suggested that the expected ratio of under-5 mortality rate in the first quintile to under-5 mortality rate in the fifth quintile increases as national-level under-5 mortality rate decreases.InterpretationFor all LMICs (excluding China) combined, the absolute disparities in under-5 mortality rate between the poorest and richest households have narrowed significantly since 1990, whereas the relative differences have remained stable. To further narrow the rich-and-poor gap in under-5 mortality rate on the relative scale, targeted interventions that focus on the poorest populations are needed.FundingNational University of Singapore, UN Children's Fund, United States Agency for International Development, and the Bill & Melinda Gates Foundation.
| S-EPMC5905403 | biostudies-literature
Project description:BackgroundThe UN Sustainable Development Goals (SDGs) call for stratification of social indicators by ethnic groups; however, no recent multicountry analyses on ethnicity and child survival have been done in low-income and middle-income countries (LMICs).MethodsWe used data from Demographic and Health Surveys and Multiple Indicator Cluster Surveys collected between 2010 and 2016, from LMICs that provided birth histories and information on ethnicity or a proxy variable. We calculated neonatal (age 0-27 days), post-neonatal (age 28-364 days), child (age 1-4 years), and under-5 mortality rates (U5MRs) for each ethnic group within each country. We assessed differences in mortality between ethnic groups using a likelihood ratio test, Theil's index, and between-group variance. We used multivariable analyses of U5MR by ethnicity to adjust for household wealth, maternal education, and urban-rural residence.FindingsWe included data from 36 LMICs, which included 2 812 381 livebirths among 415 ethnic groups. In 25 countries, significant differences in U5MR by ethnic group were identified (all p<0·05 likelihood ratio test). In these countries, the median mortality ratio between the ethnic groups with the highest and lowest U5MRs was 3·3 (IQR 2·1-5·2; range 1·5-8·5), whereas among the remaining 11 countries, the median U5MR ratio was 1·9 (IQR 1·7-2·5; range 1·4-10·0). Ethnic gaps were wider for child mortality than for neonatal or post-neonatal mortality. In nearly all countries, adjustment for wealth, education, and place of residence did not affect ethnic gaps in mortality, with the exception of Guatemala, India, Laos, and Nigeria. The largest ethnic group did not have the lowest U5MR in any of the countries studied.InterpretationSignificant ethnic disparities in child survival were identified in more than two-thirds of the countries studied. Regular analyses of ethnic disparities are essential for monitoring trends, targeting, and assessing the impact of health interventions. Such analyses will contribute to the effort towards leaving no one behind, which is at the centre of the SDGs.FundingBill & Melinda Gates Foundation, UNICEF, Wellcome Trust, Associação Brasileira de Saúde Coletiva.
| S-EPMC7034191 | biostudies-literature
Project description:IntroductionFrom 1990-2010, worldwide child mortality declined by 43%, and maternal mortality declined by 40%. This paper compares two sources of progress: improvements in societal coverage of health determinants versus improvements in the impact of health determinants as a result of technical change.MethodsThis paper decomposes the progress made by 146 low- and middle-income countries (LMICs) in lowering childhood and maternal mortality into one component due to better health determinants like literacy, income, and health coverage and a second component due to changes in the impact of these health determinants. Health determinants were selected from eight distinct health-impacting sectors. Health determinants were selected from eight distinct health-impacting sectors. Regression models are used to estimate impact size in 1990 and again in 2010. Changes in the levels of health determinants were measured using secondary data.FindingsThe model shows that respectively 100% and 89% of the reductions in maternal and child mortality since 1990 were due to improvements in nationwide coverage of health determinants. The relative share of overall improvement attributable to any single determinant varies by country and by model specification. However, in aggregate, approximately 50% of the mortality reductions were due to improvements in the health sector, and the other 50% of the mortality reductions were due to gains outside the health sector.ConclusionsOverall, countries improved maternal and child health (MCH) from 1990 to 2010 mainly through improvements in the societal coverage of a broad array of health system, social, economic and environmental determinants of child health. These findings vindicate efforts by the global community to obtain such improvements, and align with the post-2015 development agenda that builds on the lessons from the MDGs and highlights the importance of promoting health and sustainable development in a more integrated manner across sectors.
| S-EPMC4718632 | biostudies-literature