Giving birth to healthy bouncing babies is the expectation of every pregnant mother. The baby should be bouncing from all aspects. This includes health, size, weight and any other parameter that might suit a bouncing baby. However, research has put it that this will depend on a number of factors. Some might be natural factors which are beyond human control while some are as a result of human making. For example, a baby might be born physically handicap just because it is hereditary. This is beyond human control. In some cases babies get affected while still in the womb due to the behavior of the mother. The mother might be taking some products which go along way affecting the overall development of the baby in the womb. For example, mothers who take drugs during pregnancy can really affect their unborn babies in various ways (England & Kendrick, 2011). One of the ways is affecting their birth weight, preterm delivery or even death of the baby and or gets their mental capacities affected.
This study focuses on the birth weight of babies born to mothers who smoke and those who do not smoke during pregnancy. Various health research have put it that in general, babies born to mothers who smoke during the gestation period tend to be lighter or of low birth weight compared to the babies who are born to mothers who are non-smokers (Currie & Hyson, 2009). It has been found that when you inhale cigarette so does your unborn baby. Harmful substances such as lead, carbon monoxide and even lead get into the babies system before it is still in the womb. These harmful substances go through the placenta which acts as protection to the baby. For this reason, the placenta is not able to supply the baby with enough oxygen, nutrients and be able to get rid of wastes (Conley & Bennet, 2010). This in turn affects the healthy growth of the unborn child.
Pregnant mothers who do not smoke but put themselves near second hand smoke can also be affected thus affecting the baby (Evans & Ringe, 2012). They are at a risk of also giving birth to underweight babies or babies with health problems. The effects to the baby may remain even long after they have been born. These may manifest themselves inform of persistent colds, problems with physical developments, lung problems and physical disabilities, coughs and sometimes middle-ear infections (Behrman & Rosenzweig , 2014). A recent research study has also linked babies born to smoking mothers to under the risk of getting asthma and cancer.
Due to the above extensive risks of smoking to unborn babies pregnant mothers have always been encouraged to stop smoking as this will benefit not only their unborn babies but also themselves. Some of the benefits that come along with quitting smoking during pregnancy to pregnant mothers include increased chances of going home with a live baby. It also reduces by a big percentage the chances of your baby being underweight during birth. Lastly, it ensures proper development of the baby since it is able to get all the nutrients that are requisite for growth.
Babies born with low birth weight is a common phenomenon in various hospitals across the globe. It is not known exactly what the real causes are but various hypotheses have put forward to explain the reason behind this. There are few studies that have been done to unravel or get accurate answers to the reasons of low birth-weight. Some of them have pointed smoking during pregnancy as one of the major causes of babies being born while underweight. According to them, the smoke penetrates the placenta interfering with it thus preventing required nutrients to get to the baby thus hindering proper growth. It is against this backdrop that this study was carried out to establish whether indeed smoking leads to low birth weight of babies born to smoking mothers. This study will therefore compare the birth-weight of babies born to smoking mothers during the gestation period and the weight of babies born to mothers who are non-smokers during the gestation period. The study wanted to know whether a significant difference exists between the two.
The research study objectives were as below;
This study focused on babies that were born to mothers who were smoking during pregnancy and babies born to mothers who are non-smokers. The parameter of interest here was the weights of the babies at birth. The other variables of interest were the age of the mothers, their weight and the gestation period.
Hypothesis 1
H0: There is no significant difference between the weights of babies born to smoking mothers during gestation period and non-smoking mothers.
Versus
H1: There is a significant difference between the weights of babies born to smoking mothers during gestation period and non-smoking mothers.
Hypothesis 2
H0: There is no relationship between the gestation period and the birth-weight of a child.
H1: There is a significant relationship between the gestation period and the birth-weight of a child.
Hypothesis 3
H0: There is no relationship between the weight of the mother and the birth-weight of a child.
H1: There is a significant relationship between the weight of the mother and the birth-weight of a child.
Hypothesis 4
H0: There is no relationship between the age of the mother and the birth-weight of a child.
H1: There is a significant relationship between the age of the mother and the birth-weight of a child.
Since there is information gap that exists in matters regarding to the causes of low birth-weight, this study will come in handy to provide appropriate information regarding the causes of low birth-weight. It will also delve deeper and find out whether in deed smoking during pregnancy affect the unborn child. This information will help in educating the public about the real dangers of smoking and more worse during pregnancy. It will also be a literature that will be resourceful to the health sector as it will be used as a reference material on effects of smoking to pregnant mothers. To add on, it will be a source of criticism due to various weaknesses hence prompting further research in this area. It will also help in adding up to the literature that has existed on the same topic. Lastly, this study will be resourceful to all academicians especially the students in the institutions of higher learning in the field of health sciences.
The variables in this research study were five. They included both numerical and categorical variables. Numerical variables included age, weight and gestation period. All these numerical variables can as well be classified as continuous variables as they do not assume whole numbers only. For example, one can be 5.6 kilograms or 30.5 years old. The other variable was a categorical variable. This was status. It categorized the mothers into two; non-smokers and smokers. It can also be called a dichotomous variable since it only has two sides. Age was measured in years, weight in grams and kilograms and gestation period in weeks.
The study employed a survey design. This was deemed appropriate as it would allow the researcher to meet the respondent face-to-face thus ensuring that the collected information was right. To add on, it was easier since the research concentrated on a small geographical area.
The research study applied simple random (probability sampling) sampling in selecting the respondents to be included in the sample. This method was chosen by the study as it ensured each and every member of the population had equal chance to be included in the sample thereby eliminating biasness. A sample of 30 respondents was collected.
The data collected was analyzed with the help of excel and statistical package for social sciences (SPSS).
|
Statistics |
|||||
|
Mother weight kgs |
Mothers age |
Baby weight grams |
Gestation period in weeks |
||
|
N |
Valid |
30 |
30 |
30 |
30 |
|
Missing |
0 |
0 |
0 |
0 |
|
|
Mean |
58.2000 |
34.4667 |
2895.6000 |
37.8000 |
|
|
Median |
57.5000 |
35.5000 |
2934.0000 |
38.0000 |
|
|
Mode |
44.00a |
41.00 |
3130.00a |
39.00 |
|
|
Std. Deviation |
10.02892 |
7.15702 |
336.71953 |
2.20345 |
|
|
Variance |
100.579 |
51.223 |
113380.041 |
4.855 |
|
|
Range |
31.00 |
25.00 |
991.00 |
8.00 |
|
|
Minimum |
44.00 |
20.00 |
2420.00 |
34.00 |
|
|
Maximum |
75.00 |
45.00 |
3411.00 |
42.00 |
|
|
a. Multiple modes exist. The smallest value is shown |
Table one above shows the summary statistics for the four variables of the study. It entails the measures of central tendencies and measures of dispersion. It can be observed that the mean weight for mothers was 58.2 kilograms while their mean age was 34.5 years. The mean birth-weight for the babies was 2895.6 grams while the mean gestation period in weeks was 37.8. To add on, the median weight for mothers was 57.5 kilograms while their median age was 35.5 years. The median birth-weight for the babies was 2934.0 grams while the median gestation period in weeks was 38. The youngest mother was 20 years old while the oldest mother was 45 years old. The lightest mother weighed 44 kilograms while the heaviest mother weighed 75 kilograms. The lightest baby weighed 2420 grams while the heaviest baby weighed 3411grams. The shortest gestation period was 34 weeks while the longest gestation period was 42 weeks.
It can be seen from the histogram above that the weight of the mothers was normally distributed. However, it was not a perfect normal distribution. This can be confirmed by the mean and the median which were 58.2 kilograms and 57.5 kilograms respectively. They are very close to each other indicating tendency of normality.
It can be seen from the histogram above that the age of the mothers was normally distributed. However, it was not a perfect normal distribution. This can be confirmed by the mean and the median which were 34.4 years and 35.5 kilograms respectively. They are very close to each other indicating tendency of normality.
It can be seen from the histogram above that the weight of the babies was normally distributed. However, it was not a perfect normal distribution. This can be confirmed by the mean and the median which were 2895.6 grams and 2934 grams respectively. They are very close to each other indicating tendency of normality.
It can be seen from the histogram above that the gestation period was normally distributed. However, it was not a perfect normal distribution. This can be confirmed by the mean and the median which were 37.8 weeks and 38 weeks respectively. They are very close to each other indicating tendency of normality.
An independent samples t-test was appropriate in this situation since we were comparing the equality of the mean of two independent samples
Hypothesis
H0: There is no significant difference between the weights of babies born to smoking mothers during gestation period and non-smoking mothers.
H1: There is a significant difference between the weights of babies born to smoking mothers during gestation period and non-smoking mothers
At 95% confidence level
|
Independent Samples Test |
||||||||||
|
Levene’s Test for Equality of Variances |
t-test for Equality of Means |
|||||||||
|
F |
Sig. |
t |
df |
Sig. (2-tailed) |
Mean Difference |
Std. Error Difference |
95% Confidence Interval of the Difference |
|||
|
Lower |
Upper |
|||||||||
|
Baby weight grams |
Equal variances assumed |
1.505 |
.230 |
-1.187 |
28 |
.245 |
-144.93333 |
122.09439 |
-395.03236 |
105.16570 |
|
Equal variances not assumed |
-1.187 |
27.398 |
.245 |
-144.93333 |
122.09439 |
-395.28016 |
105.41349 |
The t-test results are as observed in the table above. The value of p-value computed (0.25) is greater than the alpha value (0.05). This guides the study not to reject the null hypothesis. The conclusion therefore is, there was no significant difference between the weights of babies born to smoking mothers during gestation period and non-smoking mothers
Hypothesis
H0: There is no relationship between the gestation period and the birth-weight of a child.
Versus
H1: There is a significant relationship between the gestation period and the birth-weight of a child.
Table of result
|
Correlations |
|||
|
Baby weight grams |
Gestation period in weeks |
||
|
Baby weight in grams |
Pearson Correlation |
1 |
.895** |
|
Sig. (2-tailed) |
.000 |
||
|
N |
30 |
30 |
|
|
Gestation period in weeks |
Pearson Correlation |
.895** |
1 |
|
Sig. (2-tailed) |
.000 |
||
|
N |
30 |
30 |
|
|
**. Correlation is significant at the 0.01 level (2-tailed). |
The Pearson correlation coefficient value is 0.89. This is an indication that there is a strong relationship between birth-weight and gestation period in weeks. The relationship is also in the positive direction. The p-value calculated is 0.00 which is less than the alpha value of 0.05. This means that the null hypothesis is rejected. It is therefore concluded that there is a significant relationship between the gestation period and the birth-weight of a child.
Hypothesis 3
H0: There is no relationship between the weight of the mother and the birth-weight of a child.
H1: There is a significant relationship between the weight of the mother and the birth-weight of a child.
Table of results is as below;
|
Correlations |
|||
|
Baby weight grams |
Mother weight kgs |
||
|
Baby weight grams |
Pearson Correlation |
1 |
.003 |
|
Sig. (2-tailed) |
.988 |
||
|
N |
30 |
30 |
|
|
Mother weight in kgs |
Pearson Correlation |
.003 |
1 |
|
Sig. (2-tailed) |
.988 |
||
|
N |
30 |
30 |
The Pearson correlation coefficient value is 0.03. This is an indication that there is no relationship between birth-weight and weight of the mother. The p-value calculated is 0.99 which is more than the alpha value of 0.05. This means that the null hypothesis is not rejected. It is therefore concluded that there is no significant relationship between mothers’ weight and the birth-weight of a child.
Hypothesis 4
H0: There is no relationship between the age of the mother and the birth-weight of a child.
H1: There is a significant relationship between the age of the mother and the birth-weight of a child.
Results table
|
SUMMARY OUTPUT |
||||||
|
Regression Statistics |
||||||
|
Multiple R |
0.401339984 |
|||||
|
R Square |
0.161073783 |
|||||
|
Adjusted R Square |
0.131112132 |
|||||
|
Standard Error |
313.8702637 |
|||||
|
Observations |
30 |
|||||
|
ANOVA |
||||||
|
df |
SS |
MS |
F |
Significance F |
||
|
Regression |
1 |
529614.01 |
529614 |
5.375998 |
0.027936599 |
|
|
Residual |
28 |
2758407.2 |
98514.5 |
|||
|
Total |
29 |
3288021.2 |
||||
|
Coefficients |
Std Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
|
|
Intercept |
2244.799973 |
286.47417 |
7.83596 |
1.55E-08 |
1657.984235 |
2831.6157 |
|
Age of the mother |
18.88201239 |
8.1436428 |
2.31862 |
0.027937 |
2.200516292 |
35.563508 |
Table 5
The R2 value computed had a value of 0.16. This is an indication that there is some relationship between the dependent variable (birth-weight) and independent variable (mother age). This value indicates that the independent variable can only explain 16% of the variation in the dependent variable. The p-value calculated is 0.00 which is less than the alpha value of 0.05. This means that the null hypothesis is rejected. It is therefore concluded that there is a significant relationship between mothers’ age and the birth-weight of a child.
Conclusion
From the analysis and findings from the above section, the research study came up with several conclusions. It was concluded that smoking did not contribute to birth-weight of newborns since the results from independent sample t-test confirmed that there was no significant difference between the weights of babies born to smoking mothers during gestation period and non-smoking mothers. This means that any difference that occurred was just due to chance. The study also concluded that the gestation period plays a great role in determining the birth-weight of a newborn. This is after it was found that there was a strong correlation between gestation period in weeks and birth-weight. There is no significant relationship that exists between mothers’ weight and the weight of the newborns. This was confirmed by a correlation coefficient which was tending towards zero.
References
Behrman , J., & Rosenzweig , S. (2014). Returns to birth weight. The Review of Economics and Statistics, 86(2), 586-601.
Conley , M., & Bennet, H. (2010). Is biology destiny? Birth weight and life chances. American Sociological Review, 65(3), 458-467.
Currie , B., & Hyson, L. (2009). Is the impact of health shocks cushioned by socio-economic status? The case of low birth weight,. American Economic Review, 89(2), 245-250.
England, K., & Kendrick, W. (2011). Effects of smoking reduction during pregnancy on the birth weight of term infants. American Journal of Epidemiology, 154(8), 694-701.
Evans , R., & Ringe, J. (2012). Can cigarette taxes improve birth outcomes. American Journal of Obstetrics and Gynecology, 91(11), 1851-1856.
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