Interact with the dashboard in this link: https://public.tableau.com/app/profile/mohamed.abdul.hakkam.ansar.ali.khan/viz/DiabetesdataofEngland/Dashboard2
This project aims to analyze the trend of occurrence of diabetes cases in England at district level.
Background
The occurrence of diabetes in England has been increasing [1]. There are various causes of diabetes like lifestyle changes, food habits, ethnicity and genetics. In this project we analyse the occurrence of diabetes at district level to see the pattern and incur how lifestyle or demographics in each region can contribute to the occurrence of diabetes.
Data
The datasets to do this project have been taken from the following sources:
Diabetes audit at each GP surgery: https://digital.nhs.uk/data-and-information/publications/statistical/national-diabetes-audit
Patient data: https://digital.nhs.uk/data-and-information/publications/statistical/patients-registered-at-a-gp-practice/september-2023
Data Integration
All the datasets were integrated using the common unique identifier – Practice Code which is a unique code for each GP practice. The null values were removed using Excel. The data was integrated using Excel’s Vlookup and SUMIF functions. The finished dataset consisted of the columns in the following image:
This dataset was then connected to tableau to visualize it.
Visualization
The tableau datasource was connected to this completed dataset. The latitudes and longitudes were generated using the postcode districts column. The postcodes were then plotted in the map using map function.
A new calculated field was created to calculate the percentage of population having type II diabetes in each age group. For example, for the age group 40 to 64, it was done using the following formula.
In the original dataset given by the NHS, only the distribution of occurrence of diabetes percentage between various age groups were given and not the exact numbers. So, we calculate the numbers using the percentage and the total diabetes registration. With this number we can calculate the percentage of occurrence of diabetes against the total number of patients in that age group. Through this we can get an idea how widespread the occurrence of diabetes is.
After calculating the percentage we create bins to have a better visualization in the map.
In each map, we mark the postcodes that are below national average with green colour, postcodes around national average in various shades of yellow and high percentages with red.
Different maps were created for each age group and they all were integrated in the dashboard by creating a parameter.
A bar graph was inserted to compare each postcode with the national average. A secondary map was created using the patient age demographic data to represent the population of each age group in the map of England.
Inferences
Initially, when you look at the percentage of occurrence of diabetes across all age groups then you will find a pattern where there is a high percentage of occurrence in the coastal regions of England and lower percentages in the major cities as shown in the image below. This might lead to a false conclusion that living in the coastal region might increase the chances of diabetes.
But when you compare it with the age demographic map of England, coastal regions are the regions where there is a higher percentage of older people live. And as a person ages the chances of getting diabetes increases. To know the locations where there is a higher percentage of diabetes we have to look at age specific diabetes occurrence. For example, in the age group of under 40, there is a higher percentage of occurrence of diabetes in major cities. Shown below is the map of London and the percentage of occurrence of diabetes in under 40 age group:
Note that east and west London postcodes like Hunslow and stratford have higher percentage of occurrence. The bar chart that compares the percentage of occurrence in London with national average also tells the same:
Note that this trend follows across all age groups. This is also the case with other major cities like Birmingham and Manchester. There might be various reasons for that like the lifestyle or mixed ethnicity in big cities compared to the coastal towns.