Health Disparities Study

A Study of Health Disparity of Dental Care Across London Boroughs in Relation to Average Household Income

This code is used in a report which examines the health disparity in dental care across various London boroughs in relation to average household income. It evaluates the changing health inequalities in London over time and the impact of UK government policies, with a particular focus on the period from 2010-2013 in which the dataset is based. It investigates the boroughs with the lowest household income and explores the factors contributing to this phenomenon, such as employment and structural disadvantages such as marginalisation and accessibility challenges. The report utilises dental data of 0-19 year olds across London boroughs, with discussion on the challenges in accessing free dental care within the NHS. Overall, the Spearman's rank correlation coefficient of 0.3647 and the statistically significant p-value of 0.0176 indicate a moderate positive relationship between income and tooth extraction decay. However, it is important to consider other factors that may influence dental health outcomes, such as oral hygiene practices, access to dental care, and language and cultural barriers.

Script Overview

1. Data Loading and Preparation

The script begins by loading the datasets for children's tooth extractions and average household income. The data is then cleaned and prepared for analysis:

• Handling Missing Values: Missing values in categorical columns are filled with empty strings, and numerical data is cleaned by removing unwanted characters (e.g., £, %).
• Data Conversion: The income data and tooth extraction percentages are converted to numerical values for analysis.

2. Chi-Squared Test

Since the data is not continuous, the script first attempts to use the Chi-Squared test to determine the independence between tooth extractions and household income:

• Contingency Tables: Contingency tables are created for the years 2011/12 and 2012/13.
• Chi-Squared Test: The Chi-Squared test is performed on these tables to calculate the p-values for each year. The average p-value is then computed.

3. Spearman's Rank Correlation

As an alternative approach, the script uses Spearman's rank correlation coefficient to measure the correlation between the two variables:

• Spearman's Correlation: Spearman's rank correlation coefficient is calculated along with the corresponding p-value.

4. Linear Regression and Visualization

The script fits a linear regression model to the data to visualize the relationship between household income and tooth extractions:

• Linear Regression: A polynomial of degree 1 (a line) is fitted to the data points.
• Scatter Plot: A scatter plot is generated to visualize the data, with the linear regression line overlaid.

5. Output

The script outputs the following:

• Spearman's rank correlation coefficient: This value indicates the strength and direction of the monotonic relationship between household income and tooth extractions.
• p-value: This value indicates the significance of the observed correlation.
• Scatter Plot: A visual representation of the correlation between average household income and tooth extractions in children.