2018, Vol. 3, Issue 5, Part B
Logistic regression methodology for assessing the contributing factors of low birth weightAuthor(s):
Aboobucker Haalisha and Aboobucker JahuferAbstract:
The objective of this study was to inspect elements which impact the low birth weight in new-born babies. Low birth weight can be quantified over the maternal, nutritional and socio-economic factors, taking important properties on Low birth weight as it can lead to largest new-born sicknesses. Logistic regression model was utilized in this study to identify the influential variables in predicting Low birth weight and used babies’ histories in the Medical Officer of Health office Akkaraipattu. Medical records of 410 babies accessible over the period from January 2016 to December 2016, were used for the examination. Obligatory data analysis was completed using Minitab, Excel and SPSS software.
Based on this study, it was invented that the occurrence of Low birth weight in the designated region is 17.81% with a mean Low birth weight of 2193.01 g for the year 2016. Mothers’ weight increase throughout gestation, educational position, parity, birth intermission, hypertension, preceding Low birth weight history, history of miscarriage and passive smoking were considerably accompanying (positively / negatively) with Low birth weight (p<0.05). The fitted logistic regression model shows that the passive smoking has the uppermost odd ratio compared to other monitoring variables. Study consequences endorse that there is a vital requirement to develop health literateness of females on numerous features of pregnancy and extra training essentials to be connected to field level health employees to upsurge their communication skills as well as their competence to classify and manage high risk pregnancies Pages: 134-139 | Views: 824 | Downloads: 10Download Full Article: Click Here
How to cite this article:
Aboobucker Haalisha, Aboobucker Jahufer. Logistic regression methodology for assessing the contributing factors of low birth weight. Int J Stat Appl Math 2018;3(5):134-139.