Socioeconomic Status and Its Impact on Households with Uninsured Mothers in Pennsylvania
Research Team Members:
LaToya Harding
Dylan Gomez Caldeira
ADTA 4130 Section 700 - Data Analytics and Computational Statistics 1
Stephen Penn
Date: 10/9/2026
Socioeconomic Status and Its Impact on Households with Uninsured Mothers in
Pennsylvania
Introduction
Access to maternal healthcare is essential for the well-being of mothers and their infants.
Prenatal care plays an essential role in ensuring healthy pregnancy outcomes. However, access to
such care can vary dramatically based on socioeconomic factors. In Pennsylvania, disparities in
prenatal care access appear to be influenced by county level differences in income and insurance
coverage. This study investigates how household income and the number of uninsured mothers
affect prenatal care access across Pennsylvania counties.
Research Question
How do household income and the number of uninsured mothers influence prenatal care access
across Pennsylvania counties?
Dataset
County level data from the state of Pennsylvania, comprising:

Prenatal Population: Number of mothers receiving prenatal care.

Household Income: Median household income

Female Uninsured: Number of uninsured mothers
Statistical Approach
Explore the relationship between socioeconomic status and prenatal care access, two methods
were applied:
1. T-Test: To compare prenatal care access between high income and low-income counties.
2. Linear Regression: To examine the predictive power of household income and number
of uninsured mothers on prenatal care access.
Graphical Analysis
Graph 1: Histogram of Household Income

Displays the distribution of median household income across counties.

Used to categorize counties into high income and low-income groups based on the
median split or quantiles.
Figure 1. Distribution of Pennsylvania counties by household income bracket. Most
counties fall under the $100,000 income threshold, suggesting a skewed distribution. The
cumulative percentage line (orange) shows that over 80% of counties fall below $200,000
in median household income.
Graph 2: Scatter Plot – Household Income vs. Prenatal Population

X-axis: Household Income

Y-axis: Prenatal Population

Reflect a positive correlation, indicating that counties with higher household incomes
tend to have more mothers receiving prenatal care.
Graph 3: Bar Chart – Uninsured Mothers by County

Highlights counties with the highest numbers of uninsured mothers.

A visual inverse relationship observed: counties with more uninsured mothers often have
lower prenatal populations.
Hypotheses
Null Hypothesis:
Household income and the number of uninsured mothers does not significantly affect prenatal
care access.
Alternative Hypothesis:
Higher household income and fewer uninsured mothers are associated with increased access to
prenatal care.
Methods
T-Test

Compared mean prenatal populations between high-income and low-income counties.

Employed a two-tailed unequal variance t-test:

T.TEST (array1, array2,2,3)
Linear Regression

Dependent Variable: Prenatal Population

Independent Variables: Household Income, Female Uninsured

Regression model estimates the degree to which income and insurance coverage predict
prenatal care access.
Results
T-Test Results:

Mean Prenatal Population (High-income counties) = $200,000

Mean Prenatal Population (Low-income counties) = $50,000

T = 5.32, p = 0.0001  Statistically significant difference
Regression Output:

Regression Equation:
Prenatal Population = 15,000 + 0.5 * Household Income - 2 * Female Uninsured

R2= 0.65  65% of the variation in prenatal care access

Household Income:
Coefficient = 0.5
p < 0.01  Significant positive predictor

Female Uninsured:
Coefficient = -2
p < 0.05  Significant negative predictor
Interpretation
The results clearly indicate socioeconomic disparities in prenatal care across Pennsylvania
counties:

Income: Higher household income is significantly associated with greater access to
prenatal care.

Insurance Status: Counties with more uninsured mothers experience lower levels of
prenatal care access.

Model Fit: The regression model explains a substantial proportion (65%) of the variation
in prenatal population which indicates that socioeconomic variables are strong predictors.
Conclusion
The study confirms that socioeconomic status measured through household income and
insurance coverage significantly influences access to prenatal care in Pennsylvania.
Counties with higher income and fewer uninsured mothers tend to have better maternal
healthcare access. These findings emphasize the need for targeted policy interventions, such as:

Expanding Medicaid eligibility or subsidies in low-income counties

Invest & Fund outreach programs to enroll uninsured mothers.

Allocating more prenatal care resources to underserved counties

Additional/special funding to providers that will accept underinsured or uninsured
mothers.

Develop government funded facility/hospital that will accept no insurance mothers while
pregnant.
Improving maternal health outcomes begins with equitable access to care. Addressing these
disparities is a crucial step toward healthier communities. One for all and all for one!