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

Abstract

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.

Socio-demographic backwardness in cyclone prone coastal villages: An Insight from Indian Sundarban

“…When the resulted variable is polychotomous, it can happen in a variety of ways. Multinomial and ordinal outcome variables are two types of outcome variables (Fahrmeir and Lang 2001 ; Rasheed et al, 2016 ). The multinomial logistic regression model cannot be used when the dependent variable is categorized by order of magnitude.…”

Association between Wood and Other Biomass Fuels and Risk of Low Birthweight in Uganda: A Cross-Sectional Analysis of 2016 Uganda Demographic and Health Survey Data

“…However, only a limited number of studies from Africa were included in the meta-analyses [ 4 , 7 , 11 , 12 ], a region with a high burden of LBW [ 10 ], and predominant use of polluting fuels [ 1 ]. The studies from Africa were limited to relatively few countries, including Ghana [ 13 ], Nigeria [ 14 , 15 , 16 , 17 ], Malawi [ 18 ], Ethiopia [ 19 , 20 , 21 ], and Zimbabwe [ 22 ]. The African studies were limited to hospital-based settings [ 21 ] with a reliance on the woman’s subjective recall of the birth weight or size of the child [ 14 , 18 , 22 ].…”

“…The studies from Africa were limited to relatively few countries, including Ghana [ 13 ], Nigeria [ 14 , 15 , 16 , 17 ], Malawi [ 18 ], Ethiopia [ 19 , 20 , 21 ], and Zimbabwe [ 22 ]. The African studies were limited to hospital-based settings [ 21 ] with a reliance on the woman’s subjective recall of the birth weight or size of the child [ 14 , 18 , 22 ]. The studies did not address the effect of outdoor or indoor place of cooking on LBW risk [ 13 , 14 , 15 , 17 , 23 ], or generally control for important confounders [ 18 ].…”

“…The African studies were limited to hospital-based settings [ 21 ] with a reliance on the woman’s subjective recall of the birth weight or size of the child [ 14 , 18 , 22 ]. The studies did not address the effect of outdoor or indoor place of cooking on LBW risk [ 13 , 14 , 15 , 17 , 23 ], or generally control for important confounders [ 18 ]. Moreover, there remains limited understanding of the relationship between different biomass fuel types and risk of LBW, which is of importance in Sub-Saharan Africa where access to cleaner fuels remains limited.…”

A Comparison of Polytomous Model With Proportional Odds and Non-Proportional Odds Model on Birth Size Case in Indonesia

“…Several LBW studies related to the development of statistical models have been carried out, such as the classification of LBW with a nonparametric approach using the Weighted Probabilistic Neural Network method (Yasin & Ispriyansti, 2017), and analysis of risk factors for LBW cases using LASSO and Fused LASSO selection techniques (Kurniawati et al, 2020). Besides, Khan et al (2018) also identified LBW in Bangladesh using a mixed logistic model and Adeyemi et al (2016) developed a spatial effect on the LBW model in Nigeria using a semiparametric multinomial ordinal model. Risk factors that affect the birth size of a baby can be analyzed by using the POM and NPOM models.…”