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Comprehensive Data Analysis: Impact of the ACA on Health Behaviors in U.S. Counties

October 05, 2023
Yasmin Howarth
Yasmin Howarth
🇬🇧 United Kingdom
Data Analysis
Yasmin Howarth is a statistics assignment expert with an academic background from the University of York, where she obtained her Ph.D. in Statistics. With 7 years of experience, she has established herself as a leading authority in data analysis.
Key Topics
  • Problem Description
  • Question 1: Descriptive Statistics and Normality Assessment

In our comprehensive data analysis, we delve into the intricate dynamics of health behaviors in U.S. counties, specifically focusing on the influence of the Affordable Care Act (ACA). By meticulously examining factors such as smoking rates and mammography screening frequency, we provide empirical evidence that sheds light on the ACA's impact. Our findings reveal significant relationships between these health behaviors and a range of key variables, informing evidence-based recommendations for the Department of Health and Human Services (DHHS) and guiding future healthcare policy decisions.

Problem Description

The data analysis assignment focuses on analyzing the "ACS Pop Health Data File.csv" dataset, containing data on health behaviors (smoking status) and health utilization (mammography screening) in U.S. counties between 2011 and 2018. The goal is to provide evidence-based recommendations to the Department of Health and Human Services (DHHS) concerning the impact of the Affordable Care Act (ACA) on these health outcomes while controlling for confounding variables like education level, unemployment rates, insurance coverage, and primary care provider availability.

Question 1: Descriptive Statistics and Normality Assessment

To address Question 1, we performed the following steps:

  • Imported the dataset into SPSS.
  • Provided descriptive statistics for each variable.
  • Assessed the normality of continuous variables using histograms and skewness/kurtosis tests.

Below are the key findings:

Year:

  • The dataset is evenly split between 2011 and 2018.

Continuous Variables (smoking, mammography, uninsured, pcp, high school, college, unemployed):

  • Most variables met criteria for acceptable skewness and kurtosis, except for "pcp."

For a more detailed presentation, here are the tables with descriptive statistics:

Table 1: Descriptive Statistics for Continuous Variables

VariableN (Valid)MeanMedianStd. DeviationVariance
Smoking562919.6219.045.1826.79
Mammography589861.6361.978.9780.40
Uninsured628116.1115.307.0449.51
PCP614955.8649.5240.021601.83
High School570082.1585.0010.91119.07
College628354.6454.8012.14147.29
Unemployed62817.136.403.2310.44
  • Table 1: Descriptive Statistics for Continuous Variables

Question 2: Variable Recommendation

In response to Question 2, we recommended dropping the "high school" variable, as "college" represents a higher level of education and provides a more comprehensive measure of education.

Question 3: Bivariate Analysis

For Question 3, we conducted bivariate correlation analyses between the remaining predictor variables and the two outcome variables (smoking and mammography). All numeric variables exhibited significant correlations with both outcome variables.

For detailed findings, here are the tables with correlation coefficients:

Table 2: Correlation Between Predictor Variables and Smoking

VariableSmokingUninsuredPCPCollege
Uninsured0.260**
PCP-0.162**-0.137**
College-0.510**-0.413**0.386**
Unemployed0.517**0.300**-0.093**-0.455**
**. Correlation is significant at the 0.01 level (2-tailed).
  • Table 2: Correlation Between Predictor Variables and Smoking

Table 3: Correlation Between Predictor Variables and Mammography

VariableMammographyUninsuredPCPCollege
Uninsured-0.190**
PCP0.230**-0.137**
College0.353**-0.413**0.386**
Unemployed-0.075**0.300**-0.093**-0.455**
. Correlation is significant at the 0.01 level (2-tailed).
  • Table 3: Correlation Between Predictor Variables and Mammography

Question 4: Multivariate Analysis

For Question 4, we recommended multiple linear regression as the appropriate analysis method. This choice was based on the linear relationships observed between the predictor and outcome variables.

Question 5: Multivariate Analysis Results

In response to Question 5, we conducted multiple linear regression analyses for mammography and smoking.

Mammography Analysis:

  • The model explained 22.0% of the variance in mammography.
  • The regression model was statistically significant (ANOVA F-test, F(6,5371) = 253.07, p < .001).
  • Key findings included:
  • Year had a significant negative effect, indicating decreased mammography rates in 2018.
  • Uninsured had a significant negative effect, suggesting that higher uninsured rates led to lower mammography rates.
  • PCP had a significant positive effect, implying that more providers increased mammography rates.
  • College had a significant negative effect, counterintuitively, implying that higher education levels were associated with lower mammography rates.

Smoking Analysis:

  • The model explained 39.5% of the variance in smoking.
  • The regression model was statistically significant (ANOVA F-test, F(6,5009) = 545.69, p < .001).
  • Key findings included:
  • Year had a significant negative effect, suggesting decreased smoking rates in 2018.
  • Uninsured had a significant negative effect, indicating that higher uninsured rates were associated with lower smoking rates.
  • PCP had a significant negative effect, suggesting that more providers were related to lower smoking rates.
  • College had a significant positive effect, indicating that higher education levels were associated with lower smoking rates.

In conclusion, these analyses provide evidence of the impact of the Affordable Care Act on screening mammography and smoking rates. The ACA appears to have a statistically significant effect on these outcomes, with varying magnitudes of effect for each predictor variable. Recommendations to DHHS should consider these findings and their implications for health policy and interventions.

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