Determinants of Low Birth Weight in Malawi: Bayesian Geo-Additive Modelling

Abstract

Studies on factors of low birth weight in Malawi have neglected the flexible approach of using smooth functions for some covariates in models. Such flexible approach reveals detailed relationship of covariates with the response. The study aimed at investigating risk factors of low birth weight in Malawi by assuming a flexible approach for continuous covariates and geographical random effect. A Bayesian geo-additive model for birth weight in kilograms and size of the child at birth (less than average or average and higher) with district as a spatial effect using the 2010 Malawi demographic and health survey data was adopted. A Gaussian model for birth weight in kilograms and a binary logistic model for the binary outcome (size of child at birth) were fitted. Continuous covariates were modelled by the penalized (p) splines and spatial effects were smoothed by the two dimensional p-spline. The study found that child birth order, mother weight and height are significant predictors of birth weight. Secondary education for mother, birth order categories 2-3 and 4-5, wealth index of richer family and mother height were significant predictors of child size at birth. The area associated with low birth weight was Chitipa and areas with increased risk to less than average size at birth were Chitipa and Mchinji. The study found support for the flexible modelling of some covariates that clearly have nonlinear influences. Nevertheless there is no strong support for inclusion of geographical spatial analysis. The spatial patterns though point to the influence of omitted variables with some spatial structure or possibly epidemiological processes that account for this spatial structure and the maps generated could be used for targeting development efforts at a glance.

Modeling Maternal Risk Factors Affecting Low Birth Weight Among Infants in Kenya

“…This study found that child birth order, mother weight, mother height, mother education and family wealth are significant predictors of birth weight. The study is in consistent with the findings of the previous study done by, [11]. The positive effect of mother's education on birth weight in this study is in consistent with other studies, [22], [20] which found mother education as an important factor influencing birth weight.…”

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

“…This output on regional variation has corroborated the finding in similar research studies. Recent studies had showed remarkable variations in the incidence of low birth weight with geographical patterns [24,44]. It is been reported that over 20 million global infants, constituting 15.5 per cent of all births, are born with low birth weight, 95.6% of them in developing countries.…”

“…The prevalence of low birth weight can be attributed to poverty level in developing countries (16.5%), which is more than double the level in developed regions (7%). Half of all low birth weight babies are born in South Central Asia, where more than a quarter (27%) of all infants weights less than 2500 grams are recorded at birth [44]. The Low birth weight levels in sub-Saharan Africa are found to be around 15% and Central and South America was about (10%).…”

Assessing the Effects of Maternal HIV Infection on Child Birth Weight and Knowledge of HIV status on Breastfeeding using Observational Data in Malawi

“…One of the known factors of LBW is pre-term delivery and intrauterine growth retardation [5,6,7,8]. [c5] Other determinants of LBW are smoking [9,10], [c6] low maternal education [11] [c7] younger maternal age [11], [c8] anaemia [12,13] [c9] marital status, slight weight gain during pregnancy [11], [c10] hypertension [12], [c11] genitourinary tract infection in pregnancy, cesarean delivery [13], [c12] child birth order, mothers weight and height [14]. [c13] parity and fewer prenatal consultations [14].…”

“…[c5] Other determinants of LBW are smoking [9,10], [c6] low maternal education [11] [c7] younger maternal age [11], [c8] anaemia [12,13] [c9] marital status, slight weight gain during pregnancy [11], [c10] hypertension [12], [c11] genitourinary tract infection in pregnancy, cesarean delivery [13], [c12] child birth order, mothers weight and height [14]. [c13] parity and fewer prenatal consultations [14]. [c14] In addition, low income family, demographic and reproductive variables such as history of low birth weight infants [12] [c15] and history of miscarriage have been reported to be associated with low birth weight [15].…”