scholarly journals Predictive models and under-five mortality determinants in Ethiopia: evidence from the 2016 Ethiopian Demographic and Health Survey

2020 ◽  
Author(s):  
Fikrewold Bitew ◽  
Samuel H. Nyarko ◽  
Lloyd Potter ◽  
Corey S. Sparks
Keyword(s):  
Random Forest ◽  
Health Survey ◽  
Mortality Risk ◽  
Predictive Models ◽  
Access To Health Care ◽  
Predictive Power ◽  
Learning Algorithm ◽  
Demographic And Health Survey ◽  
Prediction Ability ◽  
Under Five

Abstract Background: There is a dearth of literature on predictive models estimating under-five mortality risk in Ethiopia. In this study, we develop a spatial map and predictive models to predict the sociodemographic determinants of under-five mortality in Ethiopia. Methods: The study data were drawn from the 2016 Ethiopian Demographic and Health Survey. We used three predictive models to predict under-five mortality within this sample. The three techniques are random forests, logistic regression, and k-nearest neighbors For each model, measures of model accuracy and Receiver Operating Characteristic curves are used to evaluate the predictive power of each model. Results: There are considerable regional variations in under-five mortality rates in Ethiopia. The under-five mortality prediction ability was found to be moderate to low for the models considered, with the random forest model showing the best performance. Maternal age at birth, sex of a child, previous birth interval, water source, health facility delivery services, antenatal and post-natal care checkups, breastfeeding behavior and household size have been found to be significantly associated with under-five mortality in Ethiopia. Conclusions: The random forest machine learning algorithm produces a higher predictive power for under-five mortality risk factors for the study sample. There is a need to improve the quality and access to health care services to enhance childhood survival chances in the country.

scholarly journals Predictive models and under-five mortality determinants in Ethiopia: evidence from the 2016 Ethiopian Demographic and Health Survey

2020 ◽  
Author(s):  
Fikrewold Bitew ◽  
Samuel H. Nyarko ◽  
Lloyd Potter ◽  
Corey S. Sparks
Keyword(s):  
Random Forest ◽  
Health Survey ◽  
Mortality Risk ◽  
Predictive Models ◽  
Access To Health Care ◽  
Predictive Power ◽  
Learning Algorithm ◽  
Demographic And Health Survey ◽  
Prediction Ability ◽  
Under Five

Abstract Background: There is a dearth of literature on predictive models estimating under-five mortality risk in Ethiopia. In this study, we develop a spatial map and predictive models to predict the sociodemographic determinants of under-five mortality in Ethiopia. Methods: The study data were drawn from the 2016 Ethiopian Demographic and Health Survey. We used three predictive models to predict under-five mortality within this sample. The three techniques are random forests, logistic regression, and k-nearest neighbors For each model, measures of model accuracy and Receiver Operating Characteristic curves are used to evaluate the predictive power of each model. Results: There are considerable regional variations in under-five mortality rates in Ethiopia. The under-five mortality prediction ability was found to be moderate to low for the models considered, with the random forest model showing the best performance. Maternal age at birth, sex of a child, previous birth interval, water source, health facility delivery services, antenatal and post-natal care checkups, breastfeeding behavior and household size have been found to be significantly associated with under-five mortality in Ethiopia. Conclusions: The random forest machine learning algorithm produces a higher predictive power for under-five mortality risk factors for the study sample. There is a need to improve the quality and access to health care services to enhance childhood survival chances in the country.

scholarly journals Predictive models and under-five mortality determinants in Ethiopia: evidence from the 2016 Ethiopian Demographic and Health Survey

2019 ◽  
Author(s):  
Fikrewold Bitew ◽  
Samuel H. Nyarko ◽  
Lloyd Potter ◽  
Corey S. Sparks
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Health Survey ◽  
Mortality Risk ◽  
Predictive Models ◽  
Predictive Power ◽  
Proportional Hazard ◽  
Under Five ◽  
Cox Proportional Hazard

Abstract Background There is a dearth of literature on predictive models estimating under-five mortality risk in Ethiopia. In this study, we develop a spatial map and predictive models to predict the sociodemographic determinants of under-five mortality in Ethiopia.Methods The study data were drawn from the 2016 Ethiopian Demographic and Health Survey. We used machine learning algorithms such as random forest, logistic regression, and Cox-proportional hazard models to predict the sociodemographic risks for under-five mortality in Ethiopia. The Receiver Operating Characteristic curve was used to evaluate the predictive power of the models.Results There are considerable regional variations in under-five mortality rates in Ethiopia. The under-five mortality prediction ability was found to be 88.7% for the random forest model, 68.3% for the logistic regression model, and 68.0% for the Cox-Proportional Hazard model. Maternal age at birth, sex of a child, previous birth interval, water source, contraceptive use, health facility delivery services, antenatal and post-natal care checkups have been found to be significantly associated with under-five mortality in Ethiopia.Conclusions The random forest machine learning algorithm produces a higher predictive power for under-five mortality risk factors for the study sample. There is a need to improve the quality and access to health care services to enhance childhood survival chances in the country.

scholarly journals Machine learning approach for predicting under-five mortality determinants in Ethiopia: evidence from the 2016 Ethiopian Demographic and Health Survey

Genus ◽  
2020 ◽  
Vol 76 (1) ◽  
Author(s):  
Fikrewold H. Bitew ◽  
Samuel H. Nyarko ◽  
Lloyd Potter ◽  
Corey S. Sparks
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Health Survey ◽  
Predictive Power ◽  
Demographic And Health Survey ◽  
Learning Models ◽  
Under Five ◽  
Machine Learning Model ◽  
Machine Learning Models

Abstract There is a dearth of literature on the use of machine learning models to predict important under-five mortality risks in Ethiopia. In this study, we showed spatial variations of under-five mortality and used machine learning models to predict its important sociodemographic determinants in Ethiopia. The study data were drawn from the 2016 Ethiopian Demographic and Health Survey. We used three machine learning models such as random forests, logistic regression, and K-nearest neighbors as well as one traditional logistic regression model to predict under-five mortality determinants. For each machine learning model, measures of model accuracy and receiver operating characteristic curves were used to evaluate the predictive power of each model. The descriptive results show that there are considerable regional variations in under-five mortality rates in Ethiopia. The under-five mortality prediction ability was found to be between 46.3 and 67.2% for the models considered, with the random forest model (67.2%) showing the best performance. The best predictive model shows that household size, time to the source of water, breastfeeding status, number of births in the preceding 5 years, sex of a child, birth intervals, antenatal care, birth order, type of water source, and mother’s body mass index play an important role in under-five mortality levels in Ethiopia. The random forest machine learning model produces a better predictive power for estimating under-five mortality risk factors and may help to improve policy decision-making in this regard. Childhood survival chances can be improved considerably by using these important factors to inform relevant policies.

scholarly journals Association Between Household Air Pollution and Infant and Child Mortality in Myanmar: Evidence From The First Demographic and Health Survey

2021 ◽  
Author(s):  
Juwel Rana ◽  
Md Nuruzzaman Khan ◽  
Rakibul M Islam ◽  
Razia Aliani ◽  
Youssef Oulhote
Keyword(s):  
Air Pollution ◽  
Child Mortality ◽  
Health Survey ◽  
Rural Areas ◽  
Demographic And Health Survey ◽  
Household Air Pollution ◽  
Cluster Sampling ◽  
High Exposure ◽  
Under Five ◽  
Under Five Children

Abstract Background: Household air pollution (HAP) from solid fuel use (SFU) for cooking has been considered a public health threat, particularly for women and children in low and middle-income countries (LMICs), with limited evidence. This study was undertaken to investigate the effects of HAP on neonatal, infant, and under-five child mortality in Myanmar. Methods: This cross-sectional study employed data from the Myanmar Demographic and Health Survey (MDHS), the first nationally representative survey conducted in 2016. Data were collected from MDHS based on stratified two-stage cluster sampling design applied in urban and rural areas. The sample consists of 3249 under-five children in the household with a 98% response rate. Exposure measures were HAP (coal and biomass) and level of exposure to HAP (no exposure, moderate and high exposure). The main outcomes were neonatal, infant, and under-five child mortality reported by mothers presented in rates and risk ratios with 95% confidence intervals, accounting for survey weight and cluster variation. Results: The prevalence of SFU was 79.0%. The neonatal, infant and under-five child mortality rates were 26, 45, and 49 per 1,000 live births, respectively. The risks of infant (aRR 2.02; 95% CI: 1.01-4.05) and under-five mortality (aRR 2.16; 95% CI: 1.07-4.36) mortality were higher among children from households with SFU compared to children from households using clean fuel. When applying an augmented measure of exposure to HAP by incorporating SFU and the kitchen's location, the likelihoods of infant and under-five mortality were even higher among moderate and highly exposed children than unexposed children with similar trends. Neonatal mortality was not associated with either HAP exposure or levels of exposure to HAP.Conclusion: Infants and under-five children are at higher risk of mortality from exposure to HAP. Increasing access to cookstoves and clean fuels is imperative to reduce the risk of infant and under-five child mortality in LMICs, including Myanmar.

scholarly journals High prevalence of under-five child mortality in the pastoralist communities of Ethiopia: Evidence from the 2016 Ethiopian demographic and health survey data

2020 ◽  
Author(s):  
Mesfin Wudu Kassaw ◽  
Aele Mamo ◽  
Biruk Abate ◽  
Ayelign Kassie ◽  
Seteamlak Masresha
Keyword(s):  
Survey Data ◽  
Child Mortality ◽  
Health Survey ◽  
Wealth Index ◽  
Demographic And Health Survey ◽  
Under Five ◽  
Khat Chewing ◽  
High Prevalence ◽  
Health Survey Data ◽  
The Government

Abstract Objective: The aim of this study was to assess the prevalence and association of child mortality in the pastoralist regions of Ethiopia. The study is a further analysis from 2016 Ethiopian Demographic and Health Survey data. Results: The prevalence of under-five child mortality in the pastoralist’s regions was 23.2%, 95%CI (21.4%, 24.6%). The prevalence of mortality among daughters was 15.4%, 95%CI (14.2, 16.6%), and sons 16.8%, 95%CI (15.6, 18.1%).In logistic regression, wealth index, head of household, Khat chewing, type of child birth, husband education, and child age in months were associated with under-five mortality irrespective of the deceased children’s gender. The prevalence of under-five child mortality in the pastoralist regions of Ethiopia was high, which was far highest in relative to the national under-five mortality prevalence. In assessing the effect of variables on under-five child mortality by gender, almost all the variables that have an effect on female or male child are similar. The government should emphasize on the pastoralists’ regions to decrease the high prevalence of under-five child mortality.

Geographical disparities and determinants of childhood diarrheal illness in Ethiopia: Further Analysis of 2016 Ethiopian Demographic and Health Survey

2020 ◽  
Author(s):  
Asmamaw Atnafu ◽  
Malede Mequanent Sisay ◽  
Getu Debalkie Demissie ◽  
Zemenu Tadesse Tessema
Keyword(s):  
Health Survey ◽  
Mixed Model ◽  
Addis Ababa ◽  
Demographic And Health Survey ◽  
Sub Saharan Africa ◽  
Childhood Diarrhea ◽  
Mixed Effect ◽  
Under Five ◽  
Diarrheal Illness ◽  
Under Five Children

Abstract Background: Childhood diarrheal illness is the second leading cause of child mortality in Sub Saharan Africa, including Ethiopia. Studies hypothesized that there are regional variations. Thus, the study aimed to examine the spatial variations and to identify the determinants of childhood diarrhea in Ethiopia. Methods: Data from the 2016 Ethiopia Demographic and Health Survey (EDHS) was analyzed. This nationwide survey involved 10,337 children below 5 years old. The survey was carried out using a two-stage stratified sampling design. Moran’s I and LISA were used to detect the spatial clustering of diarrhea cases and to test for clustering in the data. Descriptive statistics followed by a mixed-effect logistic regression was used to identify the factors associated with the prevalence of diarrhea. Results: Overall, 11.87% of children were experienced childhood diarrheal illness. The study reveals high-risk areas were Southern and central Ethiopia, while eastern and west were indicated as low-risk regions. Younger children were more likely to suffer from childhood diarrhea than their older counterparts: age 6 to 12, 12 to 23, and 24 to 35 months were (AOR = 2.66, (95% CI 2.01, 3.52)), (AOR = 2.45, (95% CI 1.89, 3.17)), and (AOR = 1.53, (95% CI 1.17, 2.01)), respectively. Children living in Tigray (AOR= 1.69 (95% CI, 1.01, 2.83)), Amhara (AOR = 1.80, (95% CI, 1.06, 3.06), SNNPR (AOR = 2.04, 95% CI 1.22, 3.42), and Gambela (AOR = 2.05, (95% CI 1.22, 3.42)), faced greater risk than Addis Ababa city. The odds of getting diarrhea is decreased by 24% among households having ≥3 under-five children as compared to households having only one under-five child (AOR = 0.76 (95% CI: 0.61, 0.94)). The odds of children getting diarrheal illness among working mothers increase by 19% as compared to not working (AOR = 1.19 (95% CI 1.03, 1.38)). Conclusions: childhood diarrheal illness is highly prevalent among under-five children, particularly in SNNP, Gambella, Oromia, and Benishangul Gumuz regions. Capacity building programs with best experience sharing and better household environment may prove effective in reducing the incidence of childhood diarrhea in Ethiopia. Keywords: Spatial statistics, Ethiopia, under-five children, Diarrhea, Generalized Mixed Model

scholarly journals Under-Five Mortality and Associated Factors: Evidence from the Nepal Demographic and Health Survey (2001–2016)

2019 ◽  
Vol 16 (7) ◽  
pp. 1241
Author(s):  
Pramesh Ghimire ◽  
Kingsley Agho ◽  
Osita Ezeh ◽  
Andre Renzaho ◽  
Michael Dibley ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Health Survey ◽  
Child Survival ◽  
Common Factors ◽  
Demographic And Health Survey ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
Live Births ◽  
Under Five ◽  
Factors Associated

Child mortality in Nepal has reduced, but the rate is still above the Sustainable Development Goal target of 20 deaths per 1000 live births. This study aimed to identify common factors associated with under-five mortality in Nepal. Survival information of 16,802 most recent singleton live births from the Nepal Demographic and Health Survey for the period (2001–2016) were utilized. Survey-based Cox proportional hazard models were used to examine factors associated with under-five mortality. Multivariable analyses revealed the most common factors associated with mortality across all age subgroups included: mothers who reported previous death of a child [adjusted hazard ratio (aHR) 17.33, 95% confidence interval (CI) 11.44, 26.26 for neonatal; aHR 13.05, 95% CI 7.19, 23.67 for post-neonatal; aHR 15.90, 95% CI 11.38, 22.22 for infant; aHR 16.98, 95% CI 6.19, 46.58 for child; and aHR 15.97, 95% CI 11.64, 21.92 for under-five mortality]; nonuse of tetanus toxoids (TT) vaccinations during pregnancy (aHR 2.28, 95% CI 1.68, 3.09 for neonatal; aHR 1.86, 95% CI 1.24, 2.79 for post-neonatal; aHR 2.44, 95% CI 1.89, 3.15 for infant; aHR 2.93, 95% CI 1.51, 5.69 for child; and aHR 2.39, 95% CI 1.89, 3.01 for under-five mortality); and nonuse of contraceptives among mothers (aHR 1.69, 95% CI 1.21, 2.37 for neonatal; aHR 2.69, 95% CI 1.67, 4.32 for post-neonatal; aHR 2.01, 95% CI 1.53, 2.64 for infant; aHR 2.47, 95% CI 1.30, 4.71 for child; and aHR 2.03, 95% CI 1.57, 2.62 for under-five mortality). Family planning intervention as well as promotion of universal coverage of at least two doses of TT vaccine are essential to help achieve child survival Sustainable Development Goal (SDG) targets of <20 under-five deaths and <12 neonatal deaths per 1000 births by the year 2030.

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