Introduction

Do adequate prenatal care check ups possess positive childbirth outcomes for unmarried mothers?
Zain & Othman(2015) in a study on Malaysian women posit that the marriage status of a women is a statistically significant determinant in the health of the unborn baby. In addition, a study conducted by Dr.Ahmed F. Ahmed, a Professor of Surgery Anesthesiology and Radiology at Howard University in the USA finds that babies of unmarried mothers have atleast a 34% higher chance of having low birth weight and high mortality rate than babies of married mothers. These studies indicate how unmarried mothers are at a disadvantage during pregnancy which motivates this paper to investigate whether more prenatal visits should be implemented in order to improve the birth outcomes for unmarried mothers.

Conceptual framework

Due to cultural and religious reasons in the USA during the 1950s to 1980s, unmarried pregnant women tended not to have a social support system because their births are rarely considered legitimate (Parnell et.al, 1994). This scenario might have negative effects on their birth outcomes. In addition to this, the absence of a good relationship with the husband also poses depressive sysmptoms especially among unmarried mothers (Bloch et.al, 2010). These mental disorders are closely linked with negative birth outcomes. So unmarried mothers need to have a support system around them to help them in having successful pregnancies. Prenatal care checks are a standard health procedure that would ensure that women have healthy newborn babies. The fact that unmarried pregnant women face challenges in getting support and acceptance, prenatal visits which include health check ups, counselling and other psychological support might offer a shield to protect the well being of the unborn child of especially unmarried mothers. This hypothesis will be tested herein.

Econometric (regression) model

An OLS model is adopted to answer this paper’s research question which is “Do prenatal visits for unmarried women improve birth outcomes?”. To proxy for birth outcomes, the variable of birth weight is used as the dependent variable in the model. A high birth weight is considered a positive birth outcome as opposed to an extremely low birth weight. The number of prenatal visits (nprevist) is used as an explanatory variable. A dummy variable for unmarried is also incorporated as an explanatory variable to determine the effect of marital status on birth outcomes. To answer the research question, an interaction term between the dummy variable unmarried and nprevist is used to investigate the partial effects of prenatal visits for unmarried mothers on birth weight. There is a need to control for other variables that determine birth weight. Wang (2020), Kariniemi (1988), Chevalier & Sullivan (2007) find age, alcohol & smoking, and education level, respectively, as key determinants in birth weight of a new born baby. So in our model, these variables are controlled for.

The model to be estimated is as follows:

\(log(birthweight)= \alpha_0 +\beta_1nprevisit +\beta_2age+\beta_3educ+\beta_4drinks+\delta_1unmarried+ \delta_2smoker+\delta_3unmarried*nprevist+\mu_0\)

Data

The data used in this paper is obtained from the 1989 National Natality-Mortality dataset for 3000 child births in Pennsylvania, USA.
A description of the 7 variables is shown in Table 1 below.

data<-read.csv("C:\\Users\\Kevin Meng\\OneDrive\\Desktop\\book2.csv")  #pulling the spreadsheet with the data 

data<-cbind(data$nprevist,data[,7:12])   #subsetting the data columns I need and dropping the rest



data$birthweight<-log(data$birthweight) #convert birthweight to log 10

colnames(data)<-c("nprevist","log_birthweight","smoker","unmarried","educ","age","drinks")   #renaming my data columns




descrip<-read.csv("C:\\Users\\Kevin Meng\\OneDrive\\Desktop\\variable.csv")

#adding some kable styles to my table

descrip %>%
  kbl(caption = "Table 1: Variable Description") %>%
  kable_classic(full_width = F, html_font = "Cambria")%>%
  kable_classic_2(full_width =F) %>%
  kable_material(c("striped", "hover"))%>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
Table 1: Variable Description
Variables Description
log_birthweight Log-transformed birth weight of baby
nprevist Total number of prenatal visits
age Mother’s age
educ Years of education (more than 16 years is 17)
drinks Number of drinks per week
unmarried Dummy: unmarried =1 / married = 0
smoker Dummy: smoker =1 / Non-smoker = 0

The Summary statistics of all the variables are shown in Table 2.

summary<-read.csv("C:\\Users\\Kevin Meng\\OneDrive\\Desktop\\summary.csv")  #pulling the spreadsheet with the data summary


#adding some kable styles to my table

summary %>%
  kbl(caption = "Table 2: Summary Statistics") %>%
  kable_classic(full_width = F, html_font = "Cambria")%>%
  kable_classic_2(full_width =F) %>%
  kable_material(c("striped", "hover"))%>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left") %>%
  footnote(general = "Number of Observations: 3000")
Table 2: Summary Statistics
Variable Mean Standard.Deviation Minimum Maximum
birthweight (grams) 3383.000 592.00 425 5755
nprevist 10.990 3.67 0 35
age 26.890 5.36 14 44
educ 12.910 2.17 0 17
drinks 0.058 0.69 0 21
unmarried 0.230 0.42 0 1
smoker 0.190 0.40 0 1
NA NA NA NA
Note:
Number of Observations: 3000

Results

To answer the research question, the foundation or baseline OLS model below ought to be run to examine the partial effects of prenatal visits, the effect of being pregnant but unmarried and lastly the effect of prenatal visits for unmarried mothers in determining the birth weight of the baby.

\(log(birthweight)= \alpha_0 +\beta_1nprevisit+\delta_1unmarried+\delta_3unmarried*nprevist+\mu_0\)

However, to control for other variables that are reported in the literature as determinants of birth weight, our final regression will include age, education, and drinks.

\(log(birthweight)= \alpha_0 +\beta_1nprevisit +\beta_2age+\beta_3educ+\beta_4drinks+\delta_1unmarried+ \delta_2smoker+\delta_3unmarried*nprevist+\mu_0\)

The results of the OLS model are reported in Table 3.

#running all the eight linear models one at a time

model1<-lm(log(log_birthweight)~nprevist,data)

model2<-lm(log(log_birthweight)~nprevist+unmarried,data)

model3<-lm(log(log_birthweight)~nprevist+unmarried+unmarried*nprevist,data)

model4<-lm(log(log_birthweight)~nprevist+unmarried+unmarried*nprevist+age,data)

model5<-lm(log(log_birthweight)~nprevist+unmarried+unmarried*nprevist+age+educ,data)

model6<-lm(log(log_birthweight)~nprevist+unmarried+unmarried*nprevist+age+educ+drinks,data)

#outputting regression results in table format

stargazer(model1,model2,model3,model4,model5,model6,type="html",title = "Table 3: OLS Regression Results",out="C:\\Users\\Kevin Meng\\OneDrive\\Desktop\\mymodel.htm")
Table 3: OLS Regression Results
Dependent variable:
log(log_birthweight)
(1) (2) (3) (4) (5) (6)
nprevist 0.002*** 0.002*** 0.001*** 0.001*** 0.001*** 0.001***
(0.0001) (0.0001) (0.0002) (0.0002) (0.0002) (0.0002)
unmarried -0.009*** -0.021*** -0.022*** -0.022*** -0.022***
(0.001) (0.003) (0.003) (0.003) (0.003)
age -0.0001 -0.0002* -0.0002*
(0.0001) (0.0001) (0.0001)
educ 0.0003 0.0003
(0.0003) (0.0003)
drinks -0.0003
(0.001)
nprevist:unmarried 0.001*** 0.001*** 0.001*** 0.001***
(0.0003) (0.0003) (0.0003) (0.0003)
Constant 2.072*** 2.077*** 2.082*** 2.085*** 2.083*** 2.083***
(0.002) (0.002) (0.002) (0.003) (0.004) (0.004)
Observations 3,000 3,000 3,000 3,000 3,000 3,000
R2 0.057 0.076 0.081 0.082 0.082 0.082
Adjusted R2 0.057 0.076 0.080 0.081 0.081 0.081
Residual Std. Error 0.027 (df = 2998) 0.027 (df = 2997) 0.027 (df = 2996) 0.027 (df = 2995) 0.027 (df = 2994) 0.027 (df = 2993)
F Statistic 182.730*** (df = 1; 2998) 124.129*** (df = 2; 2997) 88.494*** (df = 3; 2996) 66.906*** (df = 4; 2995) 53.770*** (df = 5; 2994) 44.819*** (df = 6; 2993)
Note: p<0.1; p<0.05; p<0.01

From the results in Table 3, it is observed that the birth weight increases by approximately 0.1% for every prenatal visit indicating that prenatal visits are effective in improving the birth outcomes for expecting mothers.

In addition, it is noted with 99% statistical evidence that when unmarried, the baby’s weight reduces by 2.2%. This can be attributed to the fact that single mothers experience societal hardship and might lack psychological support leading to poor birth outcomes (Parnell et.al, 1994). However, it is noticeable that with the presence of prenatal visits for unmarried mothers, birth weight increases by 0.1% which points to the fact that prenatal visits offer the support needed by unmarried mothers to have better pregnancy outcomes. Therefore, prenatal services should be extended more to expecting mothers especially the single unmarried mothers who tend to have negative birth outcomes.

Other factors that are controlled for like age present expected results. As the age of the mother increases by 1 year, the birth weight of her child reduces by 0.02%. This is at the 90% level of significance. This result is in line with medical research that states that the older the woman the higher the risk of having a negative birth outcome (Correa & Yoon, 2021).

Given the fact that the constant’s coefficient is still very high and statistically significant, it must be noted that other variables that explain birth weight ought to be further investigated and controlled for.

Conclusion

In conclusion, there is statistical evidence at the 99% level of confidence that the presence of prenatal visits for unmarried mothers significantly improves their birth outcomes even after controlling for all other variables that explain birth weight in the literature. This is so because prenatal visits offer psychological and health care support to expecting single mothers which partly compensates the difficulty they face in their society. It is because of this, therefore, that prenatal visits should be promoted to ensure better birth outcomes.

References

Ahmed F. Unmarried mothers as a high-risk group for adverse pregnancy outcomes. J Community Health. 1990 Feb;15(1):35-44. doi: 10.1007/BF01350184. PMID: 2341604.

Bloch, J.R., Webb, D.A., Mathews, L. et al. Beyond Marital Status: The Quality of the Mother–Father Relationship and Its Influence on Reproductive Health Behaviors and Outcomes Among Unmarried Low Income Pregnant Women. Matern Child Health J 14, 726–734 (2010). https://doi.org/10.1007/s10995-009-0509-7

Chevalier, A., & Sullivan, V. O. (2007, February). Mother’s Education and Birth Weight. IZA. Retrieved July 5, 2022, from https://docs.iza.org/dp2640.pdf

Correa-de-Araujo R, Yoon SSS. Clinical Outcomes in High-Risk Pregnancies Due to Advanced Maternal Age. J Womens Health (Larchmt). 2021 Feb;30(2):160-167. doi: 10.1089/jwh.2020.8860. Epub 2020 Nov 13. PMID: 33185505; PMCID: PMC8020515.

Kariniemi V, Rosti J. Maternal smoking and alcohol consumption as determinants of birth weight in an unselected study population. J Perinat Med. 1988;16(3):249-52. doi: 10.1515/jpme.1988.16.3.249. PMID: 3210110.

Parnell, A. M., Swicegood, G., & Stevens, G. (1994). Nonmarital Pregnancies and Marriage in the United States. Social Forces, 73(1), 263–287. https://doi.org/10.2307/2579926

Wang, S., Yang, L., Shang, L. et al. Changing trends of birth weight with maternal age: a cross-sectional study in Xi’an city of Northwestern China. BMC Pregnancy Childbirth 20, 744 (2020). https://doi.org/10.1186/s12884-020-03445-2

Zain, N. M., Low, W.-Y., & Othman, S. (2015). Impact of Maternal Marital Status on Birth Outcomes Among Young Malaysian Women: A Prospective Cohort Study. Asia Pacific Journal of Public Health, 27(3), 335–347. http://www.jstor.org/stable/26725703