Semiparametric Multinomial Ordinal Model to Analyze Spatial Patterns of Child Birth Weight in Nigeria |
Authors: |
Rasheed A. Adeyemi,Temesgen Zewotir, and Shaun Ramroop |
Source: |
International Journal of Environmental Research and Public Health, 13: 1145; doi:10.3390/ijerph13111145 |
Topic(s): |
Birth weight Spatial analysis
|
Country: |
Africa
Nigeria
|
Published: |
NOV 2016 |
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.
Keywords: Nigeria; child birth size; cumulative multinomial model; penalized spline; spatial maps |
Web: |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5129355/ |
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