Abstract
This study examined comparison of the Multiple logistic regression, Linear discriminant analysis and Quadratic discriminant in estimating the infant birth outcome and misclassification error rate of birth outcomes with factors of infant mortality in Anambra State, Nigeria. The birth outcomes of interest were the Neonatal death, Still birth and Alive. Secondary source of data were obtained from the records department of General Hospital Onitsha from 2007-2016. The data comprises of Status of infant birth, Mothers parity, Age of mother, Weight of baby, Mothers Education Status, Number of Bookings before gestation and Gestation Age. The data analysis is performed using R-software. The result of the findings from the multiple logistic regression showed that Mothers Education Status (MES) and Booking contributed significantly on the logistic model while factors of Parity, Sex, Age of Mother (AOM), Year, GA and Birth Weight (BW) were found to be insignificant on birth outcomes. Also observed that the misclassification error rate for birth outcome for the said approach is found to be 0.5992 (59.92%). More so, findings of the study equally showed that the prior probabilities of the groups for the linear and quadratic discriminant analysis were 0.228503, 0.40168 and 0.36981 for Alive, Neonatal death and Still birth respectively. Further findings revealed that the Mothers Education Status and Bookings Status have the greatest impact for first and second linear function respectively. In addition, the result of the misclassification error rate for birth outcome using the linear discriminant analysis is 0.5931 (59.31%). The misclassification error rate for birth outcome based on quadratic discriminant analysis is 0.5956 (59.56%). Based on the findings of this study, linear discriminant approach is the best alternative in estimating misclassification error rate of infant birth outcome followed by quadratic discriminant analysis and the least is multiple logistic regression. The findings clearly confirmed that the linear discriminant analysis is the best with misclassification error rate of 59.31%.
Highlights
Infant mortality is a fundamental measure of a country's level of socio-economic development and the quality of life, especially of mothers
The Multiple logistic regression analysis is a statistical tool used in predicting categorical placement in a dependent variable or the probability of category membership on a dependent variable based on multiple independent variables
The result of the multiple regression analysis presented in table 2 found that Mothers Education Status (MES) and Booking contributed significantly on the logistic model with p-values of 0.0398 and 0.0237 respectively which were found to be less than critical value of 0.05
Summary
Infant mortality is a fundamental measure of a country's level of socio-economic development and the quality of life, especially of mothers. Some literature noted that it would be methodologically wrong to fit a single-level standard regression model in the analysis of child survival, because of the hierarchical nature of mortality data Such studies that consider hierarchical structure of mortality data to establish the contextual factors influencing infant and child mortality are limited in Nigeria. The validity of self-reported stillbirths and neonatal deaths in surveys is often threatened by misclassification errors between the two birth outcomes. Study such as Liu et al (2016) recommend examining the extent of stillbirths being misclassified as neonatal deaths for larger sample size in Malawi or other developing countries. There is need to determine the misclassification error rate associated with reporting stillbirths and neonatal death in Anambra State, Nigeria, the essence of this study
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