Modeling Birthweight Using Quantile Regression at the University of Maiduguri Teaching Hospital, Borno State, Nigeria


Quantile Regression
Maternal Related Factors
Infant Mortality

How to Cite

Bakari,H.R., Dibal, N.P., Falmata, A.M., & Terab, M.A. (2019). Modeling Birthweight Using Quantile Regression at the University of Maiduguri Teaching Hospital, Borno State, Nigeria. Research Journal of Science, 19(1), 1 - 10. Retrieved from


Extreme infant’s birthweights have short and long-term effects on the health of infant and mother leading to complications causing neonatal deaths and or maternal deaths. We use quantile regression analysis which is robust against extreme values to estimate the relationship of  with  conditional on quantiles  (or percentiles) of by modeling the entire distribution of the response variable. In this work, we model the birthweight of infants on related maternal factors, using quantile regression where the results show that different factor combinations  are  significant  at  different  levels  of  the  quantiles;  at maternal height, no prenatal care and pregnancy complications were all statistically significant (           ); at            , maternal weight, no prenatal care,  1st visit at 3rd trimester and pregnancy complications were found to be statistically significant with  (      );  and at      , maternal weight and     education  level  3  are  statistically  significant  with   .  These  results   indicate the performance of the quantile regression in assessing the entire distribution of the infant’s birthweight.



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