Overview
Health inequality refers to the avoidable and unfair differences in health outcomes, access to healthcare, and wider determinants of health between different groups or areas.
I brought together demographic, socioeconomic, clinical, environmental and healthcare-access indicators to investigate how health outcomes vary between areas and how wider determinants may contribute to these differences.
The Challenge
Health outcomes are shaped by many connected factors, but the relevant data is often stored across different sources and geographical levels.
The challenge was to combine these datasets into one consistent view and use them to better understand differences between local authorities.
For example, childhood vaccination data was reported quarterly rather than cumulatively. To estimate annual coverage, I first combined the quarterly denominators:
Annual denominator = Q1 + Q2 + Q3 + Q4
I then calculated a weighted annual coverage rate:
Weighted coverage = (Q1 rate × Q1 denominator + Q2 rate × Q2 denominator + Q3 rate × Q3 denominator + Q4 rate × Q4 denominator) ÷ Annual denominator
This ensured quarters with larger eligible populations contributed proportionally to the final annual figure.
Findings
Deprivation showed the clearest relationship with life expectancy, with more deprived areas experiencing the poorer outcomes.
However, healthcare access and other local indicators did not follow one simple pattern. Some areas performed better or worse than expected, suggesting that health inequality is shaped by several overlapping social, clinical and environmental factors.
The dashboard also made it possible to compare individual local authorities and see where similar areas differed across multiple indicators.
Reflections
This project strengthened my experience in combining complex public datasets, working across different geographical levels and creating comparable measures from data reported in different formats.
It also reinforced the importance of being careful when interpreting relationships in health data — particularly the difference between identifying an association and claiming that one factor directly causes another.
Conclusions
The project showed that health inequality cannot be explained by one measure alone.
Deprivation was a major factor, but demographic, healthcare and environmental conditions also helped provide a fuller picture of why outcomes differ between areas.