Background

Introduction

Student: Kristoffer Seem Mathematics University of Wisconsin-Eau ClaireFaculty mentor: Dr. AzizStudent: Kristoffer Seem Mathematics University of Wisconsin-Eau ClaireFaculty mentor: Dr. Aziz

Birth weight is one of the main aspects to look at when considering infant mortality rates.

In the last 20 years there has been no real decrease in infant mortality rate in the US(Lau, 2013).

This is due to a general increase in the proportion of LBW that have been born in more recent years(Lau, 2013).

Birth weight follows a Skew-Normal distribution and thus should be analyzed as such.

Birth weights were analyzed in groupings of all infant birth weights, below 2500 g (LBW) and below 1500g (vLBW)

Risk factors associated with LBW were found and included physical and socioeconomic factors of the parents.

This research focuses on determination of risk factors that are associated with LBW, so that we as a society and especially the health care professionals can give better advice to future mothers so as to reverse the trend there has been of more LBW infants being born. This poster should give a quick look at some of the associated risk factors that are present in the US population. Studies using other countries populations would not work as well, since there are different gene pools and different optimal birth weights in other countries.

Skew-Normal Regression for Determination ofRisk Factors Associated with LBWSkew-Normal Regression for Determination ofRisk Factors Associated with LBW

Materials and Methods Data was obtained from the state of Washington, the data

included all births that had occurred in the state in 2012. Simple random sample groups were taken of 500 births from

the infant and LBW groups. From the LBW sample group there was taken another subsection of only vLBW births.

Analyzed variables included: sex, plurality, age of parents, race of father, education of parents, prior live births, start of prenatal care, number of prenatal visits, weightgain during pregnancy, weight before pregnancy, BMI before pregnancy, genital herpes and gestational age.

Normality tests of birth groupings showed that the samples did not follow a normal distribution.

The skew normal distribution is given by:

Where α is the shape parameter of the distribution. The multiple skew normal regression model is given by: It can be seen that as α changes so does the skewness and

direction of the distribution

Above figure shows the distributions of Skew-Normal distributions with the only different between them being the value of α.

As a means of comparison a regular linear normal regression analysis was also performed, this showed less significant factors than the Skew-Normal.

Discussion In summation we found that Skew-Normal regression was more

appropriate for determination of risk factors when it comes to LBW infants.

The risk factors that we found were associated with LBW were different from the factors associated with a healthy birth weight and those associated with vLBW.

Risk factors included both physical, pathological and socioeconomic factors. This helps us appreciate exactly how many things and how complex of a thing it is for a baby to develop correctly.

Some of the factors that are determined to be significant previously in literature were not found to be significant in our analysis of the data.

At the same time we found significant factors that were not significant in other articles from literature.

This knowledge on risk factors will allow people in the health care professions to give coming mothers better advice when it comes to carrying and giving birth to babies that are of a healthy weight.

Further work needs to be done with additional variables, bigger geographic area and a determination of variables effects on each other. The checking of association between variables is most important with socioeconomic factors.

Results When analyzing the different birth groupings we ended up with

different significant factors for all groupings. Factors were determined to be significant if they had a p-value of less

than 0.05 In the infant birth weight group we found the significant factors: Sex,

twins, prior live births, marital status unknown, weight gain, weight before pregnancy, BMI, herpes and gestational age.

In the LBW group the significant factors included: Triplets, prior live births, start of prenatal care, prenatalcare visits, herpes and gestational period.

In the vLBW group significant factors found were: Twins, triplets, age of father, age of mother, education of mother, prior live births, start of prenatalcare, prenatalcare visits, marital status unknown, weight gain, BMI, herpes and gestational age.

We thank the Office of Research and Sponsored Programs for supporting this research, and Learning & Technology Services for printing this poster.

Acknowledgements

Boxplots of the 3 birth groupings, full data set used for infant and LBW plots. For vLBW the 98 observations in the group were used for the boxplot.

Q-Q plot of the groupings to determine observations do not follow a normal distribution.

CitationsLau, C.A., et al (2013) Extremely Low Birth Weight and Infant Mortality Rates in the United States. Pediatrics, May 2013131(5):855-860