Prevalence of malnutrition among infants in Sanaa city, Yemen

Al-Rabeei, Nabil Ahmed; Dallak, Abdulsalam M.; Al-Barakani, Mustafa Hasan

doi:10.5455/umj.2015.1.1

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…In [12], "a prevalence of malnutrition among infants in Sana'a city was determined. A descriptive cross-sectional survey was conducted among 400 Yemeni infants aged less than one year and the data was analysed using SPSS software.…”

Section: Data Mining Based Prediction Of Malnutrition Inmentioning

confidence: 99%

Predicting Malnutrition Status of Under-Five Children in Dhamar Governorate, Yemen Using Data Mining Techniques

Al-Ansi

Al-Maqaleh²

2022

AJRCoS

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Malnutrition is characterised by the insufficient intake of certain nutrients and the inability of the body to absorb or use these nutrients. This health problem keep going to be a real challenge among children under five years of age in developing countries, including Yemen, despite good aids provided. So, malnutrition is a health problem that significantly participates to child mortality rate in Yemen. The overall prevalence of malnutrition among children in Dhamar Governorate has significantly higher rates compared to other Yemeni governorates. In this paper, an intelligent predictive system using data mining classification techniques such as J48 decision tree, Bagging and Multi-Layer Perceptron Neural Network (MLPNN) for predicting malnutrition status of under-five children in Dhamar Governorate is proposed. The main objective of the present paper is to study these classification techniques to predict the 2018-2019 Dhamar Governorate, Yemen Demographic and Health Survey (DGYDHS) dataset and find an efficient technique for prediction. This dataset is imbalanced, so Synthetic Minority Over-sampling TEchnique (SMOTE) is utilised to balance the dataset. The obtained results were evaluated by the famous performance metrics like Accuracy, TP (True Positive)-rate, FP (False Positive)-rate, Precision, F-Measure, Receiver Operating Characteristics (ROC) graph and execution time. The obtained results revealed that the three classifiers with all attributes have higher predictive accuracy and are generally comparable in predicting malnutrition cases.

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Section: Data Mining Based Prediction Of Malnutrition Inmentioning

confidence: 99%

Predicting Malnutrition Status of Under-Five Children in Dhamar Governorate, Yemen Using Data Mining Techniques

Al-Ansi

Al-Maqaleh²

2022

AJRCoS

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The role of community health and nutrition volunteers in improving the health and nutrition status of infant and young children in remote areas, Hajjah, Yemen

Nassar,

Al-Haddad

2024

BMC Pediatr

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Background In Yemen, morbidity and malnutrition are major public health problems. The Community Health and Nutrition Volunteers (CHNVs) program was launched to tackle these problems through providing services to mothers and their children residing in remote villages. Since establishment of the CHNVs program in Yemen, its outcome has never been evaluated. Therefore, the aim of this study was to assess the role of CHNVs in improving the immunization, morbidity and nutritional status of infant and young children (IYC). Methods A comparative cross-sectional study design was conducted in Al-Maghrabah and Bani-Qais districts, Hajjah governorate. It was carried out between January and April 2023. A three-stage cluster sampling method was used. A total of 926 IYC with their mothers were interviewed using a pre-tested questionnaire. SPSS 26 was used for data analysis. The multinomial logistic regression and chi-square or fisher exact tests were used to compare the vaccination, morbidity and nutritional status of IYC between the volunteer and non-volunteer villages. Odds Ratio (OR) with 95% Confidence Interval (CI) were calculated. A p value < 0.05 was considered statistically significant. Results The IYC in volunteer villages were more likely to be fully or partially vaccinated compared to those in non-volunteer villages [OR = 2.3, 95% CI: 1.5–3.7, p < 0.0001, and OR = 1.9, 95% CI: 1.3–2.8, p = 0.001, respectively]. The specific coverage rates for BCG, and the 1st and 2nd doses of OPV/Pentavalent/Pneumo/Rota vaccines were significantly higher in the volunteer compared to non-volunteer villages [(OR = 1.8, 95% CI: 1.3–2.5, p < 0.0001), (OR = 1.5, 95% CI: 1.2–2.1, p = 0.003), and (OR = 1.5, 95% CI: 1.2-2.0, p = 0.002), respectively]. Moreover, the prevalence of diarrhea and fever among IYC was significantly lower in the volunteer compared to non-volunteer villages [(OR = 0.7, 95% CI: 0.5–0.9, p = 0.004) and (OR = 0.7 95% CI: 0.5–0.9, p = 0.045), respectively]. Conclusions The study found that CHNVs play a significant role in improving vaccination status and the coverage rate for BCG, and 1st and 2nd doses of OPV/Pentavalent/Pneumo/Rota vaccines, and reducing the prevalence of diarrhea and fever among IYC in their villages compared to non-volunteer villages, in Hajjah governorate. Future follow-up study and expansion to other settings in different governorates is recommended.

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Prevalence of malnutrition among infants in Sanaa city, Yemen

Cited by 2 publications

References 5 publications

Predicting Malnutrition Status of Under-Five Children in Dhamar Governorate, Yemen Using Data Mining Techniques

Predicting Malnutrition Status of Under-Five Children in Dhamar Governorate, Yemen Using Data Mining Techniques

The role of community health and nutrition volunteers in improving the health and nutrition status of infant and young children in remote areas, Hajjah, Yemen

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