Factors Associated with Low Birthweight in Rural Bangladesh

Hosain, G. M. Monawar; Chatterjee, Nilesh; Begum, Afroza; Saha, Subas Chandra

doi:10.1093/tropej/fmi066

Cited by 62 publications

(38 citation statements)

References 4 publications

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“…[34] The relationship between maternal hemoglobin level and birth weight was established in many studies and hemoglobin was identified as an important biomarker contributing to birth weight. [72836] In this study, information is lacking to ascertain when the hemoglobin levels were measured (e.g., during early or late pregnancy). Thus, it is not possible to draw any conclusion in relation to anemia level and LBW.…”

Section: Discussionmentioning

confidence: 99%

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Socio-economic and nutritional determinants of low birth weight in India

2014

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Background:Low birth weight (LBW) is an important risk factor for childhood morbidity and mortality, consequently an important public health concern.Aim:This study aims to identify significant socio-economic and nutritional determinants associated with LBW in India.Materials and Methods:Data from 2005 to 2006 National Family Health Survey-3 (NFHS-3) of India was analyzed. A total of 20,946 women (15-49 years) who gave birth at least once 5 years preceding the NFHS-3 were included in this study. Infant's LBW (<2500 grams) as outcome variable was examined in association with all independent predictors as infant's sex, maternal household wealth status, caste, age, education, body mass index (BMI), stature, anemia level, parity, inter-pregnancy interval, antenatal care received, and living place.Results:Almost 20% of the infants were born with LBW. Mother's low education level, BMI <18.5, short stature (height <145 centimeters) and lack of antenatal visits (<4 visits) were significant predictors of LBW. Male gender has a protective effect against LBW.Conclusion:Maternal education, nutritional status and antenatal care received are key determinants that need to be addressed to reduce prevalence of LBW in India. Continue implementation of multifaceted health promotion interventions are needed to address these factors effectively.

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Section: Discussionmentioning

confidence: 99%

“…[78910] In India, low body mass index (BMI), short stature, anemia and/or other micronutrient deficiencies are known to increase the risk of giving birth to a baby with LBW. [910] For example, low BMI is a reliable indicator for protein-energy malnutrition, which affects fetal growth during pregnancy.…”

Section: Introductionmentioning

confidence: 99%

Socio-economic and nutritional determinants of low birth weight in India

2014

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“…Each year, an estimated 21 million infants are born with low birthweight worldwide, more than half of them are in South Asia (2). In Bangladesh, the incidence of low birthweight is estimated to be 22%, which is among the highest in the world (3), and in rural areas the estimates are as high as 31–47% (4). Low birthweight has two main causal components: preterm birth (<37 weeks of gestation) and intrauterine growth restriction (IUGR), which is commonly assessed as small for gestational age (<10 th percentile of the birthweight-for-gestational age sex-specific reference population) (3).…”

Section: Introductionmentioning

confidence: 99%

Investigating causal relation between prenatal arsenic exposure and birthweight: Are smaller infants more susceptible?

Rahman¹,

Valeri

Kile

et al. 2017

Environment International

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Background Shortening of gestation and intrauterine growth restriction (IUGR) are the two main determinants of birthweight. Low birthweight has been linked with prenatal arsenic exposure, but the causal relation between arsenic and birthweight is not well understood.. Objectives We applied a quantile causal mediation analysis approach to determine the association between prenatal arsenic exposure and birthweight in relation to shortening of gestation and IUGR, and whether the susceptibility of arsenic exposure varies by infant birth sizes. Methods In a longitudinal birth cohort in Bangladesh, we measured arsenic in drinking water (n=1,182) collected at enrollment and maternal toenails (n=1,104) collected ≤1-month postpartum using inductively coupled plasma mass spectrometry. Gestational age was determined using ultrasound at ≤16 weeks’ gestation. Demographic information was collected using a structured questionnaire. Results Of 1,184 singleton livebirths, 16.4% (n=194) were low birthweight (<2500 g), 21.9% (n=259) preterm (<37 weeks’ gestation), and 9.2% (n=109) both low birthweight and preterm. The median concentrations of arsenic in drinking water and maternal toenails were 2.2 µg/L (range: bellow the level of detection [LOD]–1400) and 1.2 µg/g (range:

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“…The Low birth weight levels in sub-Saharan Africa are found to be around 15% and Central and South America was about (10%). The low birth weights in the Caribbean are found to be around (14%) and almost as high as in sub-Saharan Africa [45,46]. A significant geographical variation that characterized the incidence of low birth weight in Europe, with lower rates in the more northerly countries is reported in recent study by Skokić et al [32].…”

Section: Discussionmentioning

confidence: 99%

Semiparametric Multinomial Ordinal Model to Analyze Spatial Patterns of Child Birth Weight in Nigeria

Adeyemi

Zewotir

Ramroop

2016

IJERPH

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Abstract:Background: Birth weight is an important health parameter for obstetricians and gynaecologists. It is a good health indicator of a child-bearing mother and a strong predictor of infant morbidity and mortality. Methods: This paper utilizes data on 28,647 children born between 2003-2008 obtained from the 2008 Nigeria Demographic and Health Survey (NDHS). For a simple epidemiological convenience, the occurrence of a newborn weight can intuitively be considered to be categorical in nature and the thresholds can be put on a continuous scale. In survey reporting, the mothers frequently estimate their infant's birth weight and make a classification in ordinal category (low, normal, large) instead of actual birth weight. The study fits a multinomial regression model to analyze the relationships between the polytomous response and different kind of covariates in a unified manner. We estimate the fixed effects of bio-social covariates parametrically and the non-linear effect modeled using P-spline. The spatial component was modeled using conditional autoregressive error. A penalized maximum likelihood estimation was performed to estimate the model parameters. Results: We found risk factors that are positively associated with low birth weight, which include multiple birth, short birth interval, death of sibling, childhood diarrhea, fever, mother's smoking, firewood/dung cooking and poor household. Results further showed that iron syrup supplementation, antenatal attendance, mother literacy and household wealth had significant association with low probability of low birth weight. The finding also showed spatial patterns, which are not captured by the underlying determinants, and we produced probability predictive maps of the spatial residual effects. Conclusions: In addition to the statistical relevance of our method, the generated spatial maps identify highly endemic areas of low birth weight that can assist government agency to channel scarce health resources. A comprehensive approach which institutes a combination of interventions to improve the overall health care of the women is needed.

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Factors Associated with Low Birthweight in Rural Bangladesh

Cited by 62 publications

References 4 publications

Socio-economic and nutritional determinants of low birth weight in India

Socio-economic and nutritional determinants of low birth weight in India

Investigating causal relation between prenatal arsenic exposure and birthweight: Are smaller infants more susceptible?

Semiparametric Multinomial Ordinal Model to Analyze Spatial Patterns of Child Birth Weight in Nigeria

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