Visualization and Analysis of Historical OHCA Occurrences and Other Risk Factors for Improved Placement of AEDs
When an out of hospital cardiac arrest (OHCA) occurs, time is of the utmost importance. For every minute that the arrest goes untreated, the chance of survival decreases rapidly. The most common treatment, that is also the most known, is Cardiopulmonary Resuscitation (CPR). Thanks to new technology, the defibrillator is no longer a tool only available to hospital personnel but to anyone who knows where they are located.
The objective of this thesis is partly to visualize OHCA occurrences as well as visualize the differences in OHCA occurrences between locations and years. The thesis will analyze where the optimal locations of AEDs are based on a number of variables such as location and year, which is referred to as risk analysis. The analysis was performed by using daytime and nighttime population data from Statistics Sweden (SCB) in combination with heart disease statistics from the national patient register of Socialstyrelsen as well as socio-economic data from SCB. Along with that data, AED locations at the end of 2013 and OHCA data from 2006 up until 2013 was used in visualizations and risk analysis. In order to determine the final optimal placement through the risk analysis, a Geographical Information System (GIS) tool named Multi-Criteria Evaluation (MCE) was used. This tool enabled the weighting of the different parameters against each other, which was integral for the final result.
In order to visualize differences, e.g. between two years, a raster was created which consisted of a density difference between the two years. This analysis method shows the spots where there is a majority of either case, e.g. if one area had a larger number of OHCA cases one year compared to previous year. Simple plots were included to show an overview of the problem e.g. where OHCA occurred between the years 2006 and 2013.
The results implied that the recommended locations of AEDs while using daytime population data were located in commercial areas. Recommended AEDs from using the nighttime population data was located differently but was located as well as clusters in residential areas.
A large source of error in the analysis was the prior heart disease data. The chosen method, an assignment of a percentage chance of heart disease per age group, is a rough and inexact approximation of the actual heart disease statistics. Had there been data about exactly where patients with prior heart disease live and work, the results would most likely be even better.
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