Final Project - Post 1

Adithya Parupudi
finalpart1
Investigating the Etiology of Low Infant Birth Weight: An Exploration of Risk Factors
Author

Adithya Parupudi

Published

March 20, 2023

Description

The birthwt dataset(part of the MASS package) is a widely-used data collection in the field of medical statistics and public health research, focusing on the factors influencing birth weight in newborns. It contains records of various factors such as maternal age, weight, race, smoking habits during pregnancy, and the number of prenatal visits, among others. By analyzing the relationships between these variables and birth weight, researchers and medical professionals can identify potential risk factors, better understand the determinants of low birth weight, and develop effective interventions to improve maternal and neonatal health outcomes.

Research Questions

  1. Does maternal smoking during pregnancy have a significant impact on newborn birth weight?

  2. Is there a correlation between maternal age and the number of prenatal visits?

  3. Do racial differences influence birth weight, when controlling for other factors such as maternal age, weight, and smoking habits?

Hypothesis

  • Null Hypothesis (H0): There is no significant relationship between maternal smoking during pregnancy and newborn birth weight, after controlling for other factors such as maternal age, weight, and race.

  • There is a significant relationship between maternal smoking during pregnancy and newborn birth weight.

  • The relationship between maternal age and newborn birth weight is moderated by the number of prenatal visits, such that the positive association between maternal age and birth weight is stronger for mothers with a higher number of prenatal visits.

Descriptive Statistics

The birthwt data frame has 189 rows and 10 columns. The data were collected at Baystate Medical Center, Springfield, Mass during 1986.

  • low: an indicator of birth weight less than 2.5 kg.
  • age: mother’s age in years.
  • lwt: mother’s weight in pounds at last menstrual period.
  • race: mother’s race (1 = white, 2 = black, 3 = other).
  • smoke: smoking status during pregnancy.
  • ptl: number of previous premature labors.
  • ht: history of hypertension.
  • ui: presence of uterine irritability.
  • iv: number of physician visits during the first trimester.
  • bwt: birth weight in grams.

Exploratory Data Analysis

Code 
# column names
colnames(birthwt)
 [1] "low"   "age"   "lwt"   "race"  "smoke" "ptl"   "ht"    "ui"    "ftv"  
[10] "bwt"
Code 
glimpse(birthwt)
Rows: 189
Columns: 10
$ low   <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ age   <int> 19, 33, 20, 21, 18, 21, 22, 17, 29, 26, 19, 19, 22, 30, 18, 18, …
$ lwt   <int> 182, 155, 105, 108, 107, 124, 118, 103, 123, 113, 95, 150, 95, 1…
$ race  <int> 2, 3, 1, 1, 1, 3, 1, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 1, 3, 1, 3, 1…
$ smoke <int> 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0…
$ ptl   <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
$ ht    <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ ui    <int> 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1…
$ ftv   <int> 0, 3, 1, 2, 0, 0, 1, 1, 1, 0, 0, 1, 0, 2, 0, 0, 0, 3, 0, 1, 2, 3…
$ bwt   <int> 2523, 2551, 2557, 2594, 2600, 2622, 2637, 2637, 2663, 2665, 2722…
Code 
head(birthwt)
   low age lwt race smoke ptl ht ui ftv  bwt
85   0  19 182    2     0   0  0  1   0 2523
86   0  33 155    3     0   0  0  0   3 2551
87   0  20 105    1     1   0  0  0   1 2557
88   0  21 108    1     1   0  0  1   2 2594
89   0  18 107    1     1   0  0  1   0 2600
91   0  21 124    3     0   0  0  0   0 2622

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer