Maternal factors associated with low birth weight in public hospitals of Mekelle city, Ethiopia: a case-control study

Desta, Sielu Alemayehu; Damte, Ashenafi; Hailu, Tsehay

doi:10.1186/s13052-020-00890-9

Cited by 27 publications

(27 citation statements)

References 11 publications

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“…As the feature importance rank identified, Gender of the child, marriage to birth interval, mother’s occupation and mother's age were the top four critical predictors of low birth weight in Ethiopia, according to the XGB model. This study is roughly in line with the findings research [ 6 , 10 , 14 , 16 , 22 ]. The findings of this study are similar to those of the subsequent analyses.…”

Section: Resultssupporting

confidence: 94%

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Machine learning algorithms for predicting low birth weight in Ethiopia

Bekele

2022

BMC Med Inform Decis Mak

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Background Birth weight is a significant determinant of the likelihood of survival of an infant. Babies born at low birth weight are 25 times more likely to die than at normal birth weight. Low birth weight (LBW) affects one out of every seven newborns, accounting for about 14.6 percent of the babies born worldwide. Moreover, the prevalence of LBW varies substantially by region, with 7.2 per cent in the developed regions and 13.7 per cent in Africa, respectively. Ethiopia has a large burden of LBW, around half of Africa. These newborns were more likely to die within the first month of birth or to have long-term implications. These are stunted growth, low IQ, overweight or obesity, developing heart disease, diabetes, and early death. Therefore, the ability to predict the LBW is the better preventive measure and indicator of infant health risks. Method This study implemented predictive LBW models based on the data obtained from the Ethiopia Demographic and Health Survey 2016. This study was employed to compare and identify the best-suited classifier for predictive classification among Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbor, Random Forest (RF), Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting. Results Data preprocessing is conducted, including data cleaning. The Normal and LBW are the binary target category in this study. The study reveals that RF was the best classifier and predicts LBW with 91.60 percent accuracy, 91.60 percent Recall, 96.80 percent ROC-AUC, 91.60 percent F1 Score, 1.05 percent Hamming loss, and 81.86 percent Jaccard score. Conclusion The RF predicted the occurrence of LBW more accurately and effectively than other classifiers in Ethiopia Demographic Health Survey. Gender of the child, marriage to birth interval, mother’s occupation and mother’s age were Ethiopia’s top four critical predictors of low birth weight in Ethiopia.

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Section: Resultssupporting

confidence: 94%

“…Several studies in Ethiopia have been conducted to assess the prevalence and risk factors for low birth weight and have found that low birth weight ranges from 6 to 29.1%. Furthermore, low birth weight was associated with the infant’s sex, pregnancy-induced hypertension, ANC follow-up, preterm, parity, and residence [ 14 – 16 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithms for predicting low birth weight in Ethiopia

Bekele

2022

BMC Med Inform Decis Mak

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“…Likewise, studies conducted in Hosanna town, Gondar, Mekelle, and a systematic review and meta-analysis in Ethiopia showed similar findings. 10,23,25,27 These might be because having a birth at an early age had a higher risk of delivering a baby with LBW. 28 Pregnant teens are more likely to develop pregnancy-related high blood pressure and anemia, which will lead to preterm labor and delivery.…”

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confidence: 99%

“…This result is comparable with studies conducted in Bale zone hospitals, the University of Gondar, Hosanna town, and Mekelle. 5,10,13,27 There will be a chance for monitoring of fetal wellbeing and timely intervention of feto-maternal problems when there is an ANC visit. Additionally, there will be routine nutritional and medical advice, as well as the provision of supplementations during ANC visits.…”

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confidence: 99%

<p>Low Birth Weight and Associated Factors Among Newborn Babies in Health Institutions in Dessie, Amhara, Ethiopia</p>

Jember¹,

Menji²,

Yitayew³

2020

JMDH

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Background: Globally, more than 20.5 million infants are born with low birth weight, and the majorities were from Asia and Africa. Even though efforts were made to reduce low birth weight worldwide, it remains a global public health problem, especially in sub-Saharan Africa. Objective: To assess low birth weight and associated factors among newborn babies in health institutions in Dessie, Amhara, Ethiopia. Methods: An institution-based cross-sectional study was conducted among 358 newborn/ mother pairs from March 1 to April 15, 2017, in Dessie town health institutions. The data were collected using a semi-structured interviewer-guided questionnaire. The numbers of newborn/mother pairs surveyed from each health institution were allocated proportionally, and systematic random sampling was used to select the respondents. Epi-info version 7.0 was used for data entry, and Statistical Package for Social Sciences version 20 was used for the analysis. Multivariate logistic regression with adjusted odds ratios and 95% confidence intervals were used to identify significantly associated variables with low birth weight. Results: In this study, the prevalence of low birth weight was 15.6%. Maternal age <20 years (AOR: 3.78, 95% CI, 1.02-13.97), rural residence (AOR: 3.49, 95% CI, 1.48-8.24), having antenatal care follow-up (AOR: 3.79, 95% CI, 1.08-13.23), gestational age <37 weeks (AOR: 3.82, 95% CI, 1.55-9.42), and females (AOR: 3.37, 95% CI, 1.17-9.72) were significantly associated with low birth weight. Conclusion: The proportion of LBW in this study is comparable to the estimated global prevalence. Maternal age, residence, antenatal care, gestational age, and sex were significantly associated variables with low birth weight. Therefore, special attention should be given to antenatal care services and preventive strategies for preterm delivery.

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“…VSD was the most commonly reported in 53.2% patients followed by coarctation of aorta (17%) [3], suggesting the screening of low birth weight neonates for congenital cardiac defects. Godfrey [21]. Further studies may be conducted to identify risk factors and manage accordingly.…”

Section: Discussionmentioning

confidence: 99%

Frequency of Complications in Neonates with Low Birth Weight

Shahani¹,

Khidri

Riaz

et al. 2021

JPRI

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Objective: To identify and determine the frequency of complications in neonates with low birth weight. Methodology: This cross sectional study was conducted at Khairpur Medical College and Lady Willingdon Hospital, Khairpur Mirs from January 2018 to July 2021. Two hundred (n=200) neonates with low birth weight (<2500 grams) were recruited and frequency of different complications were observed. Results: The mean age of the neonates was 10.50 ± 4.34 days. Of the 200 neonates with low birth weight, 132 neonates (66%) presented with various complications; however 68 neonates (34%) were normal and without complications. Of the 200 neonates with low birth weight, the following complications were observed, hypoglycaemia (14.5%), jaundice (12.5%), respiratory distress syndrome (8%), feeding problems (8%), congenital cardiac defects (5%), hypothermia (4%), other complications such as sepsis (2%), apnea of prematurity (2%), intraventricular haemorrhage (2%), and more than 1 complications (16 neonates, 8%). Conclusions: In conclusion, hypoglycaemia and jaundice were the common complications associated with low birth weight in neonates. Advanced maternal age during childbirth, stress, lower socioeconomic conditions, consanguineous marriages, lower body mass index and maternal illness such as preeclampsia and anaemia were the risk factors of low birth weight observed in our study. Risk factors may be identified earlier in order to lessen the morbidities and mortality in low birth weight neonates.

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Maternal factors associated with low birth weight in public hospitals of Mekelle city, Ethiopia: a case-control study

Cited by 27 publications

References 11 publications

Machine learning algorithms for predicting low birth weight in Ethiopia

Machine learning algorithms for predicting low birth weight in Ethiopia

<p>Low Birth Weight and Associated Factors Among Newborn Babies in Health Institutions in Dessie, Amhara, Ethiopia</p>

Frequency of Complications in Neonates with Low Birth Weight

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