Determinants of Acute Respiratory Infection Among Children in Ethiopia: A Multilevel Analysis from Ethiopian Demographic and Health Survey

Comorbidity of diarrhea and respiratory infection symptoms, and associated factors among under-five children in Gondar City, Northwest Ethiopia: a community-based cross-sectional study

BACKGROUND

Childhood morbidity is frequently characterized by more than one health condition. Children under the age of five in low- and middle-income countries including Ethiopia experience multiple episodes of diarrhea and respiratory infection symptoms. However, there have been limited studies on comorbidities of diarrhea and respiratory infection symptoms. In addition, most studies conducted in Ethiopia seek separate outcomes for diarrhea and respiratory infection symptoms. Therefore, this study aimed to determine the prevalence of diarrhea and respiratory infection symptoms comorbidity, and associated factors among under-five children in Gondar City.

METHODS

Community-based cross-sectional study was conducted in Gondar City among under-five children from April 05 - May 04, 2023. Multi-stage sampling technique was used to collect a sample of 836. A structured questionnaire was employed through an interview-administered method for data collection at participants' homes. Bivariable and multivariable binary logistic regression analyses were undertaken to identify predictors of childhood comorbidity of diarrhea and respiratory infection symptoms.

RESULTS

The comorbidity prevalence of diarrhea and respiratory infection symptoms in under-five children was 17.22% [CI: 14.8%-19.9%]. Mothers/caretaker age < 25 years (AOR = 3.52 at 95% CI:1.64,7.5), mothers/caretakers who had no formal education (AOR = 4.42 at 95% CI: 2.08,9.9.40), family size > 5 (AOR = 4.52 at 95% CI: 2.13,9.61), second birth order (AOR = 2.67 at 95% CI: 1.31,5.41), children playground not clean(AOR = 2.19 at 95% CI:1.01,4.71), started supplementary feeding at age > 6 months (AOR = 4.51 at 95% CI:1.50,13.58), mothers/caretakers who didn't wash their hands after visiting latrine (AOR = 2.03 at 95% CI: 1.03,4.03), mothers/caretakers who didn't wash their hands with soap and water (AOR = 1.92 at 95% CI: 1.00,3.69) were significantly associated factors with under five children comorbidity of diarrhea and respiratory infection symptoms.

CONCLUSIONS

According to the findings, the prevalence of diarrhea and respiratory infection symptoms comorbidity was higher and variation in the amount of comorbidity is explained by maternal and child predictors. Educating mothers/caregivers about hand washing, sanitation, hygiene, and supplementary feeding is a key approach for the prevention and control of comorbidities in children.

Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa

Background

The primary cause of death for children under the age of five is acute respiratory infections (ARI). Early predicting acute respiratory tract infections (ARI) and identifying their predictors using supervised machine learning algorithms is the most effective way to save the lives of millions of children. Hence, this study aimed to predict acute respiratory tract infections (ARI) and identify their determinants using the current state-of-the-art machine learning models.

Methods

We used the most recent demographic and health survey (DHS) dataset from 36 Sub-Saharan African countries collected between 2005 and 2022. Python software was used for data processing and machine learning model building. We employed five machine learning algorithms, such as Random Forest, Decision Tree (DT), XGBoost, Logistic Regression (LR), and Naive Bayes, to analyze risk factors associated with ARI and predict ARI in children. We evaluated the predictive models' performance using performance assessment criteria such as accuracy, precision, recall, and the AUC curve.

Result

In this study, 75,827 children under five were used in the final analysis. Among the proposed machine learning models, random forest performed best overall in the proposed classifier, with an accuracy of 96.40%, precision of 87.9%, F-measure of 82.8%, ROC curve of 94%, and recall of 78%. Naïve Bayes accuracy has also achieved the least classification with accuracy (87.53%), precision (67%), F-score (48%), ROC curve (82%), and recall (53%). The most significant determinants of preventing acute respiratory tract infection among under five children were having been breastfed, having ever been vaccinated, having media exposure, having no diarrhea in the last two weeks, and giving birth in a health facility. These were associated positively with the outcome variable.

Conclusion

According to this study, children who didn't take vaccinations had weakened immune systems and were highly affected by ARIs in Sub-Saharan Africa. The random forest machine learning model provides greater predictive power for estimating acute respiratory infections and identifying risk factors. This leads to a recommendation for policy direction to reduce infant mortality in Sub-Saharan Africa.

Prediction of acute respiratory infections using machine learning techniques in Amhara Region, Ethiopia

Many studies have shown that infectious diseases are responsible for the majority of deaths in children under five. Among these children, Acute Respiratory Infections is the most prevalent illness and cause of death worldwide. Acute respiratory infections continue to be the leading cause of death in developing countries, including Ethiopia. In order to predict the main factors contributing to acute respiratory infections in the Amhara regional state of Ethiopia, a machine learning technique was employed. This study utilized data from the 2016 Ethiopian Demographic and Health Survey. Seven machine learning models, including logistic regression, random forests, decision trees, Gradient Boosting, support vector machines, Naïve Bayes, and K-nearest neighbors, were employed to forecast the factors influencing acute respiratory infections. The accuracy of each model was assessed using receiver operating characteristic curves and various metrics. Among the seven models used, the Random Forest algorithm demonstrated the highest accuracy in predicting acute respiratory infections, with an accuracy rate of 90.35% and Area under the Curve of 94.80%. This was followed by the Decision Tree model with an accuracy rate of 88.69%, K-nearest neighbors with 86.35%, and Gradient Boosting with 82.69%. The Random Forest algorithm also exhibited positive and negative predictive values of 92.22% and 88.83%, respectively. Several factors were identified as significantly associated with ARI among children under five in the Amhara regional state, Ethiopia. These factors, included families with a poorer wealth status (log odds of 0.18) compared to their counterparts, families with four to six children (log odds of 0.1) compared to families with fewer than three living children, children without a history of diarrhea (log odds of -0.08), mothers who had occupation(log odds of 0.06) compared mothers who didn't have occupation, children under six months of age (log odds of -0.05) compared to children older than six months, mothers with no education (log odds of 0.04) compared to mothers with primary education or higher, rural residents (log odds of 0.03) compared to non-rural residents, families using wood as a cooking material (log odds of 0.03) compared to those using electricity. Through Shapley Additive exPlanations value analysis on the Random Forest algorithm, we have identified significant risk factors for acute respiratory infections among children in the Amhara regional state of Ethiopia. The study found that the family's wealth index, the number of children in the household, the mother's occupation, the mother's educational level, the type of residence, and the fuel type used for cooking were all associated with acute respiratory infections. Additionally, the research emphasized the importance of children being free from diarrhea and living in households with fewer children as essential factors for improving children's health outcomes in the Amhara regional state, Ethiopia.

Empowering child health: Harnessing machine learning to predict acute respiratory infections in Ethiopian under-fives using demographic and health survey insights

BACKGROUND

A dearth of studies showed that infectious diseases cause the majority of deaths among under-five children. Worldwide, Acute Respiratory Infection (ARI) continues to be the second most frequent cause of illness and mortality among children under the age of five. The paramount disease burden in developing nations, including Ethiopia, is still ARI.

OBJECTIVE

This study aims to determine the magnitude and predictors of ARI among under-five children in Ethiopia using used state of the art machine learning algorithms.

METHODS

Data for this study were derived from the 2016 Ethiopian Demographic and Health Survey. To predict the determinants of acute respiratory infections, we performed several experiments on ten machine learning algorithms (random forests, decision trees, support vector machines, Naïve Bayes, and K-nearest neighbors, Lasso regression, GBoost, XGboost), including one classic logistic regression model and an ensemble of the best performing models. The prediction ability of each machine-learning model was assessed using receiver operating characteristic curves, precision-recall curves, and classification metrics.

RESULTS

The total ARI prevalence rate among 9501 under-five children in Ethiopia was 7.2%, according to the findings of the study. The overall performance of the ensemble model of SVM, GBoost, and XGBoost showed an improved performance in classifying ARI cases with an accuracy of 86%, a sensitivity of 84.6%, and an AUC-ROC of 0.87. The highest performing predictive model (the ensemble model) showed that the child's age, history of diarrhea, wealth index, type of toilet, mother's educational level, number of living children, mother's occupation, and type of fuel they used were an important predicting factor for acute respiratory infection among under-five children.

CONCLUSION

The intricate web of factors contributing to ARI among under-five children was identified using an advanced machine learning algorithm. The child's age, history of diarrhea, wealth index, and type of toilet were among the top factors identified using the ensemble model that registered a performance of 86% accuracy. This study stands as a testament to the potential of advanced data-driven methodologies in unraveling the complexities of ARI in low-income settings.

Interpretable prediction of acute respiratory infection disease among under-five children in Ethiopia using ensemble machine learning and Shapley additive explanations (SHAP)

Background

Although the prevalence of childhood illnesses has significantly decreased, acute respiratory infections continue to be the leading cause of death and disease among children in low- and middle-income countries. Seven percent of children under five experienced symptoms in the two weeks preceding the Ethiopian demographic and health survey. Hence, this study aimed to identify interpretable predicting factors of acute respiratory infection disease among under-five children in Ethiopia using machine learning analysis techniques.

Methods

Secondary data analysis was performed using 2016 Ethiopian demographic and health survey data. Data were extracted using STATA and imported into Jupyter Notebook for further analysis. The presence of acute respiratory infection in a child under the age of 5 was the outcome variable, categorized as yes and no. Five ensemble boosting machine learning algorithms such as adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), Gradient Boost, CatBoost, and light gradient-boosting machine (LightGBM) were employed on a total sample of 10,641 children under the age of 5. The Shapley additive explanations technique was used to identify the important features and effects of each feature driving the prediction.

Results

The XGBoost model achieved an accuracy of 79.3%, an F1 score of 78.4%, a recall of 78.3%, a precision of 81.7%, and a receiver operating curve area under the curve of 86.1% after model optimization. Child age (month), history of diarrhea, number of living children, duration of breastfeeding, and mother's occupation were the top predicting factors of acute respiratory infection among children under the age of 5 in Ethiopia.

Conclusion

The XGBoost classifier was the best predictive model with improved performance, and predicting factors of acute respiratory infection were identified with the help of the Shapely additive explanation. The findings of this study can help policymakers and stakeholders understand the decision-making process for acute respiratory infection prevention among under-five children in Ethiopia.

Assessing the combined effect of household cooking fuel and urbanicity on acute respiratory symptoms among under-five years in sub-Saharan Africa

Background

This study sought to investigate the association between urbanicity (rural-urban residency), the use of solid biomass cooking fuels and the risk of Acute Respiratory Infections (ARIs) among children under the age of 5 in sub-Saharan Africa (SSA).

Methods

Cross-sectional data from the most recent surveys of the Demographic and Health Survey Program conducted in 31 sub-Saharan African countries were pooled for the analysis. The outcome variables, cough and rapid short breath were derived from questions that asked mothers if their children under the age of 5 suffered from cough and short rapid breath in the past two weeks preceding the survey. To examine the associations, multivariable negative log-log regression models were fitted for each outcome variable.

Results

Higher odds ratios of cough occurred among children in urban households that use unclean cooking fuel (aOR = 1.05 95% CI = 1.01, 1.08). However, lower odds ratios were observed for rural children in homes that use clean cooking fuel (aOR = 0.93 95% CI = 0.87, 0.99) relative to children in urban homes using clean cooking fuel. We also found higher odds ratios of short rapid breaths among children in rural households that use unclean cooking fuel compared with urban residents using clean cooking fuel (aOR = 1.12 95% CI = 1.08, 1.17).

Conclusion

Urbanicity and the use of solid biomass fuel for cooking were associated with an increased risk of symptoms of ARIs among children under five years in SSA. Thus, policymakers and stakeholders need to design and implement strategies that minimize children's exposure to pollutants from solid biomass cooking fuel. Such interventions could reduce the burden of respiratory illnesses in SSA and contribute to the realization of Sustainable Development Goal 3.9, which aims at reducing the number of diseases and deaths attributable to hazardous chemicals and pollution of air, water and soil.

Geographical variation of common childhood illness and its associated factors among under-five children in Ethiopia: spatial and multilevel analysis

Although substantial progress has been made in reducing child mortality over the last three decades, the magnitude of the problem remains immense. Ethiopia is one of the countries with a high under-five mortality rate due to childhood illnesses including acute respiratory infections, diarrhea, and fever that varies from place to place. It is vital to have evidence of the factors associated with childhood illnesses and the spatial distribution across the country to prioritize and design targeted interventions. Thus, this study aimed to investigate the spatial cluster distribution and associated factors with common childhood illnesses. Secondary data analysis based on the 2016 Ethiopian Demographic and Health Survey data was carried out. A total weighted sample of 10,417 children was included. The study used ArcGIS and SaTScan software to explore spatial distribution. For associated factors, a multilevel binary logistic regression model was fitted using STATA V.14 software. Adjusted Odds Ratios (AOR) with a 95% Confidence Interval (CI) and p-value ≤ 0.05 in the multivariable model were used to declare significant factors associated with the problem. ICC, MOR, PCV, and deviance (-2LLR) were used to check model fitness and model comparison. In this study, the prevalence of common childhood illnesses among under-five children was 22.5% (95% CI: 21.6-23.3%). The spatial analysis depicted that common childhood illnesses have significant spatial variation across Ethiopia. The SaTScan analysis identified significant primary clusters in Tigray and Northern Amhara regions (log-likelihood ratio (LLR) = 60.19, p < 0.001). In the multilevel analysis, being rural residence [AOR = 1.39, 95% CI (1.01-1.98)], small child size at birth [AOR = 1.36, 95% CI (1.21-1.55)], high community poverty [AOR = 1.26, 95% CI (1.06-1.52)], mothers aged 35-49 [AOR = 0.81, 95% CI (0.69-0.94)], the household had electricity [AOR = 0.77, 95% CI (0.61-0.98)], the household had a refrigerator [AOR = 0.60, 95% CI (0.42-0.87)], improved drinking water [AOR = 0.82, 95% CI (0.70-0.95)], improved toilet [AOR = 0.72, 95% CI (0.54-0.94)], average child size at birth [AOR = 0.83, 95% CI (0.75-0.94)] were significantly associated with common childhood illnesses. Common childhood illnesses had spatial variations across Ethiopia. Hotspot areas of the problem were found in the Tigray, Northern Amhara, and Northeast SNNPR. Both individual and community-level factors affected common childhood illnesses distribution and prevalence in Ethiopia. Therefore, public health intervention should target the hotspot areas of common childhood illnesses to reduce their incidence in the country.

Prevalence and associated factors of common childhood illnesses in sub-Saharan Africa from 2010 to 2020: a cross-sectional study

OBJECTIVE

This study aimed to assess the prevalence and determinants of common childhood illnesses in sub-Saharan Africa.

DESIGN

Cross-sectional study.

SETTING

Sub-Saharan Africa.

PARTICIPANTS

Under-5 children.

PRIMARY OUTCOME

Common childhood illnesses.

METHODS

Secondary data analysis was conducted using data from recent Demographic and Health Survey datasets from 33 sub-Saharan African countries. We used the Kids Record dataset file and we included only children under the age of 5 years. A total weighted sample size of 208 415 from the pooled (appended) data was analysed. STATA V.14.2 software was used to clean, recode and analyse the data. A multilevel binary logistic regression model was fitted, and adjusted OR with a 95% CI and p value of ≤0.05 were used to declare significantly associated factors. To check model fitness and model comparison, intracluster correlation coefficient, median OR, proportional change in variance and deviance (-2 log-likelihood ratio) were used.

RESULT

In this study, the prevalence of common childhood illnesses among under-5 children was 50.71% (95% CI: 44.18% to 57.24%) with a large variation between countries which ranged from Sierra Leone (23.26%) to Chad (87.24%). In the multilevel analysis, rural residents, mothers who are currently breast feeding, educated mothers, substandard floor material, high community women education and high community poverty were positively associated with common childhood illnesses in the sub-Saharan African countries. On the other hand, children from older age mothers, children from the richest household and children from large family sizes, and having media access, electricity, a refrigerator and improved toilets were negatively associated.

CONCLUSIONS

The prevalence of common illnesses among under-5 children was relatively high in sub-Saharan African countries. Individual-level and community-level factors were associated with the problem. Improving housing conditions, interventions to improve toilets and strengthening the economic status of the family and the communities are recommended to reduce common childhood diseases.

Is there any association between undesired children and health status of under-five children? Analysis of a nationally representative sample from Bangladesh

BACKGROUND

Child health, especially childhood mortality, is one of the critical indicators of human development. No child mortality is desirable, but it is still high in Bangladesh. We aimed to assess the effect of the child's desired status on childhood morbidity and mortality in Bangladesh.

METHODS

We used the data from the nationally representative cross-sectional Bangladesh Demographic and Health Survey (BDHS) 2017-18 and restricted the analyses to children born in the past five years preceding the survey. We estimated the undesired status (excess in boy, girl, both, and parity) by subtracting an ideal number of children from the total live birth. We measured childhood mortality (perinatal, early neonatal, neonatal, post-neonatal, infant, child, and under-five mortality), morbidity (fever, diarrhea, cough, and acute respiratory infectious-ARI), nutritional problems (stunting, wasting, underweight, and low birth weight), and treatments (postnatal care, treatment for fever, diarrhea/cough, and vitamin A supplementation). Finally, we utilized the chi-square test and multilevel mixed-effects logistic regression analyses.

RESULTS

The prevalence of undesired children was 19.2%, 21.5%, 3.7%, and 25.4% for boys, girls, both boys and girls, and parity, respectively. Age, education, residence, division, and wealth index were significantly associated with undesired children. The prevalence of under-five mortality was 3.3% among desired children, almost double (5.4%) among undesired children. The likelihood of under-five mortality was [adjusted odds ratio (aOR): 2.05, p ≤ 0.001] higher among undesired children. Despite lower under-five mortality among higher socioeconomic status, the relative contribution of undesired children to under-fiver mortality was substantial. The undesired girl children were associated with an increased likelihood of moderately wasting (aOR: 1.28, p = 0.072), severely underweight (aOR: 1.41, p = 0.066), and low birth weight (aOR: 1.50, p ≤ 0.05). Moreover, the undesired children were 19% (p ≤ 0.05) more likely to be infected with fever. The undesired children had lower treatment for diarrhea and fever/cough and were less likely to get vitamin A supplementation (aOR: 0.71, p ≤ 0.001).

CONCLUSIONS

The share of childhood morbidity, mortality, and malnutrition were higher among undesired children. Every child should be wanted, and no unwanted pregnancies are desirable; thereby, the government should reemphasize the proper use of family planning methods to reduce child mortality and malnutrition.

Determinants of Acute Respiratory Infection Among Children Under-Five Years in Indonesia

BACKGROUND

Acute respiratory infection (ARI) among children under five years has been identified as a risk factor for child morbidity, leading to child mortality in Indonesia. Many factors may cause ARI; however, determinants associated with ARI remain unclear in Indonesia.

OBJECTIVES

This study sought to analyze the determinants of ARI among children aged under five years in Indonesia.

METHODS

This study was cross-sectional and utilized secondary data from the 2017 Indonesian Demographic and Health Survey (IDHS). A total of 15,993 children under five years old were selected as respondents. Chi-squared test and binary logistic regression were used to examine the determinants of ARI among children under five years in Indonesia.

RESULTS

Children aged 1 year [Odds Ratio (OR) = 1.43, 95% CI = 1.04-1.97], children aged 2 years [OR = 1.54, 95% CI = 1.12-2.11], mother's occupation [OR = 1.24, 95% CI = 1.01-2.154], poorest wealth index [OR = 1.91, 95% CI = 1.26-2.89], poor [OR = 1.50, 95% CI = 1.01-2.21], region of residence: Western Indonesia [OR = 1.96, 95% CI = 1.28-2.00], Middle of Indonesia [OR = 2.19, 95% CI = 1.44-3.33] were significantly associated with ARI among children under five years in Indonesia.

CONCLUSIONS

This study revealed that the determinants of ARI among children under five years in Indonesia remain related to the socio-demographic aspect. This research highlighted that the family's and the living area's wealth index remains essential in improving children's health outcomes.

PRACTICE IMPLICATIONS

Our findings support increasing awareness of the low-income family through adequate information and health promotion. Advancing the feasibility, accessibility, and affordability of health information and health services across all Indonesian regions should be strengthened.

The triple burden of communicable and non-communicable diseases and injuries on sex differences in life expectancy in Ethiopia

BACKGROUND

Ethiopia has experienced great improvements in life expectancy (LE) at birth over the last three decades. Despite consistent increases in LE for both males and females in Ethiopia, the country has simultaneously witnessed an increasing discrepancy in LE between males and females.

METHODS

This study used Pollard's actuarial method of decomposing LE to compare age- and cause- specific contributions to changes in sex differences in LE between 1995 and 2015 in Ethiopia.

RESULTS

Life expectancy at birth in Ethiopia increased for both males and females from 48.28 years and 50.12 years in 1995 to 65.59 years and 69.11 years in 2015, respectively. However, the sex differences in LE at birth also increased from 1.85 years in 1995 to 3.51 years in 2015. Decomposition analysis shows that the higher male mortality was consistently due to injuries and respiratory infections, which contributed to 1.57 out of 1.85 years in 1995 and 1.62 out of 3.51 years in 2015 of the sex differences in LE. Increased male mortality from non-communicable diseases (NCDs) also contributed to the increased difference in LE between males and females over the period, accounting for 0.21 out of 1.85 years and 1.05 out of 3.51 years in 1995 and 2015, respectively.

CONCLUSIONS

While injuries and respiratory infections causing male mortality were the most consistent causes of the sex differences in LE in Ethiopia, morality from NCDs is the main cause of the recent increasing differences in LE between males and females. However, unlike the higher exposure of males to death from injuries due to road traffic injuries or interpersonal violence, to what extent sex differences are caused by the higher male mortality compared to female mortality from respiratory infection diseases is unclear. Similarly, despite Ethiopia's weak social security system, an explanation for the increased sex differences after the age of 40 years due to either longer female LE or reduced male LE should be further investigated.

Biomass fuel use and acute respiratory infection among children younger than 5 years in Ethiopia: a systematic review and meta-analysis

OBJECTIVES

The objective of this study was to estimate the magnitude of acute respiratory infection (ARI) among children younger than 5 years (under-five children) and its association with biomass fuel use for domestic purposes and other behavioural and housing characteristics in Ethiopia.

STUDY DESIGN

Systematic review and meta-analysis.

METHODS

PubMed, Google Scholar, MEDLINE and the Cochrane Library were systematically searched (using the Preferred Items for Systematic Review and Meta-analysis guideline) for studies that reported an association between biomass fuel use for domestic purposes and ARI. Grey literature and other sources of unpublished information, which were subjected to the same extraction methods and quality appraisal as published studies, were also identified. A meta-analysis was performed to combine the quantitative measures from eligible individual studies into a summary estimate. Quality assessment was conducted using the modified Newcastle-Ottawa Scale quality assessment tool for cross-sectional and case-control studies. Funnel and Doi plots were used to detect potential publication bias. Statistical analyses were performed using Comprehensive Meta-Analysis and MetaXL, version 5.3, software.

RESULTS

A total of 21 (18 cross-sectional and 3 case-control) eligible studies with their combined 30,013 participants were reviewed. The overall pooled prevalence of ARI among under-five children in households where biomass fuel was the main source of energy was estimated to be 22% (95% confidence interval [CI]: 17-29). In the subgroup analysis by area of residence, the highest prevalence was found in urban regions 26% (95% CI: 24-28). Based on the preceding 2 weeks before the interview, a significant association was found between biomass fuel use and ARI (odds ratio [OR] = 2.6, 95% CI: 2.05-3.30). In addition to biomass fuel use, being female (OR = 1.57, 95% CI: 1.06-2.33), absence of a window in the kitchen (OR = 2.89, 95% CI: 2.11-3.96), being carried on the back or in lap during cooking (OR = 2.76, 95% CI: 1.96-3.89) and non-separated kitchen from the main house (OR = 1.99, 95% CI: 1.49-2.68) were associated with ARI.

CONCLUSIONS

Based on the results of this review, ARI among under-five children remains high. The present study also revealed that ARI is associated with biomass fuel use, child's gender, absence of a window in the kitchen, non-separated kitchen and carrying the child while cooking. Improved solid fuel stoves, advanced combustion designs, windows or chimneys, separating the kitchen from the main house are all measures required to reduce ARI in under-five children.

SYSTEMATIC REVIEW REGISTRATION

The review has been registered with PROSPERO (registration number CRD42020181372).

Factors associated with diarrhoea and acute respiratory infection in children under-5 years old in Ghana: an analysis of a national cross-sectional survey

BACKGROUND

Diarrhoea and acute respiratory infection (ARI) are major causes of morbidity and mortality in children under-5 years old in Ghana. The aim of the study was to assess factors associated with diarrhoea and ARI in children under-5 years old.

METHODS

We analysed nationally representative data from the 2017-2018 Ghana Multiple Indicator Cluster Survey (MICS) on 8879 children under-5 years old. Multivariable logistic regression was used to assess the factors associated with diarrhoea and ARI. We applied sample weights, stratification and clustering to account for the sampling design of the MICS.

RESULTS

The prevalence of diarrhoea was 17.0% (95% CI: 15.70, 18.24%). Children aged 6-11 months [Adjusted prevalence odds ratio (aPOR): 2.06, 95% CI: 1.45, 2.92], and 12-23 months (aPOR: 2.37, 95% CI: 1.67, 3.35), had higher prevalence of diarrhoea compared to children aged 0-5 months. Children whose mothers had a college or higher education (aPOR: 0.41, 95% CI: 0.22, 0.78), and a secondary education (aPOR: 0.66, 95% CI: 0.51, 0.86), had 59% and 34% lower odds of diarrhoea respectively, compared to children whose mothers had no formal education. Children from the richest households (aPOR: 0.58, 95% CI: 0.39, 0.86), had 42% lower odds of diarrhoea compared to children from the poorest households. Children resident in rural areas had 22% lower odds of diarrhoea compared to their peers in urban areas (aPOR: 0.78, 95% CI: 0.63, 0.98). The prevalence of ARI was 33.3% (95% CI: 31.72, 34.82%). Children aged 6-11 months (aPOR: 1.43, 95% CI: 1.06, 1.93), and 12-23 months (aPOR: 1.41, 95% CI: 1.10, 1.82), had higher prevalence of ARI compared to children aged 0-5 months.

CONCLUSIONS

This study suggests that the prevalence of diarrhoea and ARI among children aged 6-11 and 12-23 months was higher compared to children aged 0-5 months. Children under-5 years old whose mothers had a secondary or higher education had a lower prevalence of diarrhoea compared to children whose mothers had no formal education.

Place of food cooking is associated with acute respiratory infection among under-five children in Ethiopia: multilevel analysis of 2005-2016 Ethiopian Demographic Health Survey data

Background

Globally, acute respiratory infections are among the leading causes of under-five child mortality, especially in lower-income countries; it is associated with indoor exposure to toxic pollutants from solid biomass fuel. In Ethiopia, 90% of the population utilizes solid biomass fuel; respiratory illness is a leading health problem. However, there is a paucity of nationally representative data on the association of household cooking place and respiratory infections. Besides, evidence on the variability in the infection based on the data collected at different times is limited. Therefore, this study is intended to assess the association of food cooking place with acute respiratory infections and the variability in households and surveys.

Methods

The current analysis is based on the Ethiopian Demographic and Health Survey data collected in 2005, 2011, and 2016 and obtained via online registration. The association of food cooking place with acute respiratory infection was assessed using multilevel modeling after categorizing all factors into child level and survey level, controlling them in a full model. The analyses accounted for a complex survey design using a Stata command “svy.”

Result

A total of 30,895 under-five children were included in this study, of which 3677 (11.9%) children had an acute respiratory infection, with 12.7% in 2005, 11.9% in 2011, and 11.1% in 2016. The risk of having an infection in under-five children in households that cooked food outdoors was 44% lower (AOR = 0.56, 95% CI = 0.40, 0.75) compared to those households that cooked the food inside the house. There was a statistically significant difference among the children among surveys to have an acute respiratory infection.

Conclusion

The risk of having children with acute respiratory infection is lower in the households of cooking food outdoor compared to indoor. The infection difference in different surveys suggests progress in the practices in either food cooking places or the fuel types used that minimize food cooking places location or the fuel types used that minimizes the risk. But, the infection is still high; therefore, measures promoting indoor cooking in a well-ventilated environment with alternative energy sources should take place.