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Impact of Prenatal Care on Birth Weight

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ABSTRACT
Over the course of years with advancement in technology, medical science has developed into a field of continuous research and advancement in mechanisms to improve the health of society. This papers tries to find the relation with a minute but important application of this advancement; Prenatal Care for better growth and development of children. It tackles the question of whether the impact of prenatal care is significant on an infant’s health or not. The data used is a cross-sectional data of 1832 observations, with 29 variables. Controlling for different relevant variables like demographics, age, education etc. we try to find a causal relation between the impact of prenatal care and birth weight. And in the end we conclude our results that a positive relationship is indeed present and significant, leaving room for data limitations and possible solutions for future research.

IMPACT OF PRENATAL CARE ON BIRTH WEIGHT

Shehryar Amin Waqas Sheikh Hafiz Moazam Ali Nasir Javed
Econometrics

2

Introduction
Birth weight is a key factor for a child’s health and wellness. Not only it is important to prevent infant mortality rate but also a healthier birth weight helps reduce the increased risks of illnesses faced by babies born with low birth weight. Also in a statistical brief for the state center for health statistics of North Carolina1, it was found that compared with children born with normal birth weight, low birth weight children were more at a risk for poorer health and also low educational outcomes through childhood and adolescence. They are more likely to have special health care needs and require additional educational services and extra medical care. So birth weight is an important measure for a child’s health. In this paper we would try to establish the relation between infant’s health (being represented by birth weight) and the impact of prenatal care that the mother received.

Literature Review
First we would like to have a theoretical as well as empirical background to establish a strong base for our hypothesis so that we can properly identify and correct the mistakes in our regression model. From the literary point of view, the main focus of prenatal care is to target three known areas to prevent low birth weight2. 1) Psychological (Preventing smoking, reducing stress to prevent LBW) 2) Nutritional (Avoiding Low weight or inappropriate gaining of weight) 3) Medical (To avoid any diseases that may affect the mother or child) A study done by Donaldson and Billy used data from an International Collaborative maternity Care monitoring project3. The study focuses on the impact of prenatal visits on birth weight in six countries. So it provides evidence from an international data set with different demographics and health conditions. The results of this study indicate that around the world birth weight have a significant relation with the number of prenatal visits.

1

Miles, Donna R. "Low Birth Weight and Children’s Health." State Center for Health Statistics 26 (2011): n. pag. Www.schs.state.nc.us. June 2011. Web. 03 Dec. 2012. .
2

Alexander, Greg R., and Carol C. Korenbrot. "The Role of Prenatal Care in Preventing Low Birth weight." The Future of Children 1st ser. 5 (1995): 103-20. JSTOR. Web. 04 Dec. 2012.
3

Donaldson, Peter J., and John O.G Billy. "The Impact of Prenatal Care on Birth Weight: Evidence from an International Data Set." Medical Care 22.2 (1984): 177-88. Print

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The study done on Uruguay by Todd and Patricia (2005) used data that represented a sample of birth from all years 1995 to 2003 in a public hospital in the Capital City of Uruguay4. The study was unique in a sense that there were no costs of the prenatal care that was provided. So in a sense it allowed to check for the impact of policy to induce prenatal care among poor women. It focused on the impact of the month of initiation of prenatal care on the birth weight. The study used 2SLS method to estimate the required Birth weight production function. And the results indicated that an increase average prenatal care usage among poor women lead to an increase in average birth weight. Study carried out by Partha and Sandra (2005) focused on whether the onset or quality of prenatal care matter more for infant health. For that they made a birth weight production function based on the data collected from a number of surveys in Mexico. Six instrumental variables were used to check for the onset and quality of prenatal care. The results of the study pointed towards a positive and significant effect, especially of quality of prenatal care, effect of impact of prenatal care on the infant’s health (measured in relative terms of birth weight)

Data Description
So to study the impact of prenatal care on birth weight, first we will describe the data and then we will select relevant dependent and independent variables based on the established findings from our above literature review and rational and logical thinking. The data set given to us is a Cross sectional data set of 1832 new born babies. Various variables relating to baby’s heath and parents have also been provided in the sample. These variables try to control for different factors that could have a different impact because of different environment or culture etc. and consequently difference in birth weights. Socio-economic factors could be seen with difference in Race. Differences in age and education could lead to difference in thinking and thus ultimately leading to different outcomes for the kind of health care preference, thus leading to difference in birth weights. The full description of each of the variable is given in the Appendix.

Model, Limitations, Results and Inferences
For ease of analysis and to convey good sense to the reader this paper will step-by-step try to develop the final model on which conclusions will be based and at the same time it will present data limitations and how possible solutions can be found based on literature or introducing new variables to make sense.

4

Jewell, R. Todd, and Patricia Triunfo. The Impact of Prenatal Care on Birthweight: The Case of Uruguay. N.p.: n.p., 2005. Print

4

For our question of interest we would like the dependent variable to be bwght. Reason being as birth weight is considered as the criterion for infant’s health5. From our data we see that bwght is almost normally distributed. (Graph shown in the Appendix) For independent variable we want a variable that could tell us about the adequacy of prenatal care but we were able to identify two variables from our data set, these are “Month prenatal care began” (monpre) and “number of prenatal visits” (npvis). Due to data limitations there were issues with both of these variables to be used for our study. For the variable “Month prenatal care began”, in our sample more than 90% of the data have in which prenatal care began on or before the three months after conception. It could be that the first visit was made in the first 3 months and we don't have enough information about what happens subsequently. This variable can be used as a good measure if we had a relatively random sampling in which there had been an equal distribution of the month in which prenatal care began for different mothers. Then by holding other variables constant, we could expect to see a causal effect between prenatal care and birth weight. For the second variable, "Total number of Prenatal Visits", it provides us with the information about the extent of the care, but it doesn't reflect anything about the timing of the visits. We wanted to estimate the APNCU index (Adequacy of Prenatal Care Utilization Index) which combined the effects of the onset of prenatal care, the prenatal care visits and the gestation period and then provided with an adequacy index. But since Gestation period was also not included in our data set, we couldn’t calculate that index. Faced with this limitation of our data we decided to go for npvis variable, as it could be assumed that the visits were spread over the course from the month the prenatal care began. It could provide us with relatively reasonable and more consistent and unbiased results. So following this model our simple population regression equation becomes
������������������ℎ������ = β0 + β1 ������������������������������ + ������

In regards to our hypothesis we want to check that β1 should be positive and it should be significant. Making our H0: β1 = 0 Carrying out the simple regression we find that there is a positive relation between our interested variables. But this result is far from conclusive. There could be many variables that affect bwght and also many correlated with our independent variable. For one we expect that as the number of prenatal visits could positively impact the weight at birth but too many visits could reflect other factors that affect the birth weight. Like women living with inferior health endowments could be more worried about the child and thus having more visits. Or the quality of care provided might not be that advanced thus requiring constant visits to the
5

OECD (2011), Health at a Glance 2011: OECD Indicators, OECD Publishing

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hospitals or same could be the case with a problematic pregnancy. Thus we should include a squared functional form of npvis to see the increasing effect of number of visits. Including that our model becomes
������������������ℎ������ = β0 + β1 ������������������������������ + β2 ������������������������������ 2 + ������

And this results in a negative sign for β2, indicating that an ever increasing number of prenatal visits leads to a decrease in birth weight. We were not able to find any literature work on the ever increasing model, but rational thinking led to our conclusion and it could be a good area to find out what other factors might be present. Doing a research on developing countries might find the squared term to be really significant. We would also like to include the monpre variable in our regression model as npvis should be correlated with monpre. The earlier the prenatal care starts, the more should be the prenatal visits. In addition to monpre being related to bwght and npvis, a very important factor (at least in literature) affecting birth weight is cigarette smoking by the mother during pregnancy. Given our data we would also like to study its effect to make our model more consistent and reliable. A research conducted at the American Institute of Obstetricians and Gynecologists showed that consuming cigarette by pregnant mother resulted in reduced body weight of infant. The effect of this source is because chemicals in cigarette smoke reduce the velocity of fetal growth 6. Not only it reduced birth weight but it reduced it in an increasing way. Thus based on that evidence we would like to include the cigs variable from our data and also include its functional form (cigs^2) and expect a negative sign with cigs and positive with the functional form. Making our regression model:
������������������ℎ������ = β0 + β1 ������������������������������ + β2 ������������������������������ 2 + β3 ������������������������������������ + β4 ������������������������ + β5 ������������������������ 2 + ������

Indeed our regression supports our view. Related with cigarette smoking is the health and physical capacity of the mother for a healthy baby weight. And this health factor can somehow be controlled for by the factor age that is easier to observe. There should be a positive relation with child birth weight and mother’s age. But as the age reaches a limit the health conditions of the mother might not be very supportive for the fetus. To check for this we would like to run the following regression,
������������������ℎ������ = β0 + β1 ������������������������������ + β2 ������������������������������ 2 + β3 ������������������������������������ + β4 ������������������������ + β5 ������������������������ 2 + β6 ������������������������������ + β7 ������������������ + β8 ������������������ 2 + ������

Based on our data, the regression results point positively to our intuitive model. Also from conducted by Samiran, Amitava et al on the effects of maternal age and parity on birth weight,

6

Bernstein, Ira .., Joan A. Mongeon, Gary J. Badger, Laura Solomon, Sarah H. Heil, and Stephen T. Higgins. "Maternal Smoking and Its Association With Birth Weight."OBSTETRICS & GYNECOLOGY 106.5 (2005): 986-91. Print.

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they found that maternal age is an important factor influencing birth weight7. They found that the rate of LBW decreases significantly after 18 years of age. The results are summarized in the following graph:

Another important finding from this study is that Parity has an important influence on birth weight. Intuitively it should be related as it can correspond to the health of the mother. So an important limitation of our data set is that we had no data on whether the child borne was a woman’s first or not. Similarly we could make an intuitive model based on the education of mother’s relation with the birth weight. By logical thinking we should expect a positive relation between a mother’s education and child’s weight. As the number of years of education of a mother increases she becomes more aware of the health problems that she should avoid during pregnancy. So based on that education should affect birth weight through other factors like smoking or quality of care. Also from a study by Amir and Mark (1999), a positive relation between mother’s education and birth weight was concluded for all age groups8.

7

Bisai, Samiran, Amitava Sen, Dilip Mahalanabis, Nandini Datta, and Kaushik Bose. "The Effect of Maternal Age and Parity on Birth Weight Among Bengalees of Kolkata, India." Human Ecology 14 (2006): 139-43. Print.
8

Shmueli, Amir, and Mark R. Cullen. "Birth Weight, Maternal Age, and Education: New Observations from Connecticut and Virginia." YALE JOURNAL OF BIOLOGY AND MEDICINE 72 (1999): 245-58. Print.

7

������������������ℎ������ = β0 + β1 ������������������������������ + β2 ������������������������������ 2 + β3 ������������������������������������ + β4 ������������������������ + β5 ������������������������ 2 + β6 ������������������������������ + β7 ������������������ + β8 ������������������ 2 + β9 ������������������������������ + ������

Running this regression with our data provides a surprising result. The co-efficient on the mother’s education is positive but highly insignificant. This is counter intuitive to the reasoning established above and from the empirical findings. This error could be because of an omitted variable bias. And indeed the Ramsay RESET test in this regard supports our claim. So including a functional form makes our result free from OV Bias.
������������������ℎ������ = β0 + β1 ������������������������������ + β2 ������������������������������ 2 + β3 ������������������������������������ + β4 ������������������������ + β5 ������������������������ 2 + β6 ������������������������������ + β7 ������������������ + β8 ������������������ 2 + β9 ������������������������������ + β10 log(������������������������������)

Not only mother’s education but we would also like to add father’s education as it could be added as a proxy for the quality of care giving easy access to resources, more resting time for the mother etc.
������������������ℎ������ = β0 + β1 ������������������������������ + β2 ������������������������������ 2 + β3 ������������������������������������ + β4 ������������������������ + β5 ������������������������ 2 + β6 ������������������������������ + β7 ������������������ + β8 ������������������ 2 + β9 ������������������������������ + β10 log(������������������������������) + β11 ������������������������������ + ������

Furthermore an important determinant of a baby’s weight is whether that baby is male or female. Empirical Evidence suggests that the weight of a male baby is more than a female’s. In a study by

8

Yoshie Yokoyama et al (2005), it was found that male babies had a higher birth weight, higher birth length and greater head circumference9. So including a dummy variable for male is important. Next we would like to include the effects of omaps and fmaps as these are important measures of a baby’s health done just after birth. Whether these are in relation with birth weight or birth weight affects them, we would like to control for them as they could also be related with npvis. So to only study the effects of prenatal care on birth weight we include them in our regression model and their functional forms. Another factor highlighted from empirical studies, which is important for birth weight is race. To make our model more consistent we add race to our regression model. But again there is a limitation to our data, the data collected includes more than 90% observations of white males and females. So the results on race could be little biased. And after controlling for all variables and including the interaction terms (Explained later) our final model becomes:
������������������ℎ������ = β0 + β1 ������������������������������ + β2 ������������������������������ 2 + β3 ������������������������������������ + β4 ������������������������ + β5 ������������������������ 2 + β6 ������������������������������ + β7 ������������������ + β8 ������������������ 2 + β9 ������������������������������ + β10 log(������������������������������) + β11 ������������������������������ + β12 ������������������������������ + β13 ������������������������������ + ������1 ������������������������������ 2 + ⋯ + ������4 log(������������������������������) + β14 ������������������������ + ������1 ������������������������������ + ⋯ + ������4 ������������ℎ������������ + ������1 ������������������. ������������������������������������ + ������2 ������������������. ������������������������������������������������ + ������

With our final model first we conduct tests for Omitted Variable bias and heteroskedasticity. The Ramsay RESET test tells us that there is no omitted variable bias and the Breusch-Pagan test for heteroskedasticity rejects the H0: Constant variance. So the model is heteroskedastic. To account for that we will use the robust standard errors to get more precise values of the t-test. With the calculated values our variable of interest (npvis) remains statistically significant at 5% significance level. Also the model as a whole remains jointly highly significant (as p-value = 0.000). We use the interaction term because we believe that both the prenatal visits and the onset of prenatal care (monpre) are related with the age of the mother. For an explanation of age.prenatal:
∆������������������ℎ������ = β3 + ������4 (������������������) ∆������������������������������������

It measures the Ceteris Paribas effect of a one unit change in age that leads to a change in relation to bwght and monpre.

9

Y, Yokoyama, Suqimoto M, and Ooki S. "Analysis of Factors Affecting Birthweight, Birth Length and Head Circumference: Study of Japanese Triplets." National Center for Biotechnology Information. U.S. National Library of Medicine, 8 Dec. 2005. Web. 07 Dec. 2012. .

9

Conclusion
As our Beta co-efficient of npvis remain significant throughout in the development of the model and also it is positive so we can conclude that in fact the relation between birth weight and number of prenatal visits is positive. But our R2 value remains very low as our data was limited. We didn’t have any information on Parity (Whether first child or not) and Gestation period (Could be that lower weight babies are actually premature ones), A measure of mother’s health could be BMI which was absent from our data, also we couldn’t set aside racial differences as our data was highly biased towards the white population. Also our data had some missing values. We can say with high surety that the impact of prenatal care on birth weight is positive and significant but we can’t tell what the optimal number of visits should be because of our data limitations that could bias our result.

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APPENDIX
Kernel density estimate
2.5 Density 1.5 0 .5 1 2

6

6.5

7 log(bwght)

7.5

8

8.5

Kernel density estimate Normal density kernel = epanechnikov, bandwidth = 0.0302

Density

.0002

.0004

.0006

.0008

Kernel density estimate

0
0

1000

2000 3000 birth weight, grams Kernel density estimate Normal density

4000

5000

kernel = epanechnikov, bandwidth = 103.0398

11

Data Description

variable name mage meduc monpre npvis fage feduc bwght omaps fmaps cigs drink lbw vlbw male mwhte mblck moth fwhte fblck foth lbwght magesq npvissq

variable label mother's age, years mother's educ, years month prenatal care began total number of prenatal visits father's age, years father's educ, years birth weight, grams one minute apgar score* five minute apgar score* avg cigarettes per day avg drinks per week =1 if bwght

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...The impact of extremely low birth weight babies on family and society is associated with more long term stress, even for well-educated nuclear families whose health care is financed by the government, the social impact and personal strain experienced is so overwhelming, the anxiety not knowing what to expect, the fear of losing the baby is a very scary and anxiety laden journey coupled with the higher financial burden; therefore the need for early intervention by provision of support services and systems to these families. I feel these support services in my communities like: Las...

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