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

Ngwira A, Stanley CC - PLoS ONE (2015)

Bottom Line: The study found that child birth order, mother weight and height are significant predictors of birth weight.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.

View Article: PubMed Central - PubMed

Affiliation: Lilongwe University of Agriculture and Natural Resources, Department of Basic Sciences, Lilongwe, Malawi.

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.

No MeSH data available.


Nonlinear terms from the Gaussian and logistic model (left and right).Red band (95% CI) and green band (80% CI).
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pone.0130057.g002: Nonlinear terms from the Gaussian and logistic model (left and right).Red band (95% CI) and green band (80% CI).

Mentions: Starting with the nonlinear effects to child birth weight (Fig 2 left), children of young mothers (aged 15 to 23 years) and older mothers (aged 35 to 49 years) are more likely to have low birth weight than children of mothers aged 23 to 35 years. Furthermore as number of antenatal visits for pregnancy increase, child birth weight also increases. With regard to nonlinear effects to child size at birth (Fig 2 right), children of mothers aged 15 to 25 years and children of mothers aged 35 to 49 years are prone to have small size at birth than children of mothers aged 25 to 35 years. Children whose mothers have less prenatal visits are prone to be small at birth.


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

Ngwira A, Stanley CC - PLoS ONE (2015)

Nonlinear terms from the Gaussian and logistic model (left and right).Red band (95% CI) and green band (80% CI).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482619&req=5

pone.0130057.g002: Nonlinear terms from the Gaussian and logistic model (left and right).Red band (95% CI) and green band (80% CI).
Mentions: Starting with the nonlinear effects to child birth weight (Fig 2 left), children of young mothers (aged 15 to 23 years) and older mothers (aged 35 to 49 years) are more likely to have low birth weight than children of mothers aged 23 to 35 years. Furthermore as number of antenatal visits for pregnancy increase, child birth weight also increases. With regard to nonlinear effects to child size at birth (Fig 2 right), children of mothers aged 15 to 25 years and children of mothers aged 35 to 49 years are prone to have small size at birth than children of mothers aged 25 to 35 years. Children whose mothers have less prenatal visits are prone to be small at birth.

Bottom Line: The study found that child birth order, mother weight and height are significant predictors of birth weight.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.

View Article: PubMed Central - PubMed

Affiliation: Lilongwe University of Agriculture and Natural Resources, Department of Basic Sciences, Lilongwe, Malawi.

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.

No MeSH data available.