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Practical example analysis of health data

Case Study PHFI

Here we come back to the CARRS study (CArdiometabolic Risk Reduction in South Asia).

The primary aim of the CARRS- GIS study was to study the relationship between Built environment and Cardiometabolic Diseases and risk factors. An understanding of the relationship between built environment and disease would give us deeper insights into the societal and environmental determinants of disease. Such knowledge is crucial in initiating control measures. Cardiometabolic risk factors include smoking, low physical activity and obesity. For example, when people do not have good access to parks, they may do less physical exercise and increase their risk for CMDs.

Distance calculations

Distance calculations were the first group of analysis that we used in our project. We calculated the distance between participant households and features of interest. These features of interest can have either a positive or a negative impact on risk of cardiometabolic disease.

Factors that potentially lower the risk are a good access to health care facilities and locations that enhance exercise like parks. During the data collecting we already gathered point data on both private and public health facilities. We now identified zones around these facilities of 1 and 2 kilometre distance to identify which of our households are within the good access to health care zones.

Healthcare facilities mapping in rural Sonipat district, Haryana state, India

However, modelling the relationship between risk and access to health care is not easy. The fact that people have access to health care may increase the chance of disease detection. Looking at the data, it may seem that there are more disease cases near the health facility where in fact a higher percentage of the cases is detected.

When we conduct analysis at a neighbourhood level we can determine if some neighborhoods have more “exercise space” compared to other neighborhoods. A GIS allows us to calculate the relative amount of green area compared to the total area of a neighbourhood. We can identify if some households live in areas with a higher or lower percentage of green. But we can also calculate the distance from the household to the closest park.

There are also factors that have a negative impact on CMD risk. Easy access to fast food and alcohol outlets may tempt households to an un-healthy life style. This enhances the risk of cardiometabolic disease. We would like to identify clusters of CMD risk factor - e.g. fast food hotspots - and relate this to our household data.

Analysis on two levels

For us it is clear that with the data we have gathered and the software we have available we can conduct analysis on different levels:

• Household level
• Neighbourhood level

Our final analysis included cluster analysis (for example on the occurrence of diabetes) but mainly falls in the category of correlation studies. Can we relate hotspots of diabetes with any (or multiple) of our environment variables? We correlate environmental factors to our household survey data to gain an understanding of the relationships between these risk and cardiometabolic disease. Besides the analytical operations that we already mentioned, these analysis will include statistical methods for example the use of Spatial regression methods such as Geographically weighted regression (GWR).