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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.


Residual spatial patterns from the Gaussian model.
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pone.0130057.g003: Residual spatial patterns from the Gaussian model.

Mentions: Most areas in the south are associated with increased birth weight (Fig 3) while north and central regions have a mixture of areas increasing birth weight and decreasing birth weight. Posterior probability map thoughindicates that there is no significant variation in the residual spatial effects to birth weight (Fig 4). With regard to residual spatial effects to child size at birth (Fig 5), Chitipa, Mchinji and Mangochi are associated with increasedrisk of child being small at birth while Phalombe, Mulanje and Nsanje decrease the risk of child being small at birth. Areas showing significant spatial effects to child size at birththough are Chitipa, Mchinji and Nsanje (Fig 6).


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

Ngwira A, Stanley CC - PLoS ONE (2015)

Residual spatial patterns from the Gaussian model.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130057.g003: Residual spatial patterns from the Gaussian model.
Mentions: Most areas in the south are associated with increased birth weight (Fig 3) while north and central regions have a mixture of areas increasing birth weight and decreasing birth weight. Posterior probability map thoughindicates that there is no significant variation in the residual spatial effects to birth weight (Fig 4). With regard to residual spatial effects to child size at birth (Fig 5), Chitipa, Mchinji and Mangochi are associated with increasedrisk of child being small at birth while Phalombe, Mulanje and Nsanje decrease the risk of child being small at birth. Areas showing significant spatial effects to child size at birththough are Chitipa, Mchinji and Nsanje (Fig 6).

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.