An extended kernel density two-step floating catchment area method to analyze access to health care

In Portugal the distribution of physicians is considered an appropriate proxy for the distribution of the actual hospital resources and additional information on hospital supply is mostly unavailable, while health care utilization data are also usually absent. A suitable method that can be used to analyze patients’ access to hospital health care in countries with such characteristics is the two-step floating catchment area (2SFCA) method, since it requires only the number of physicians to represent supply and the population size to estimate demand. An improved version of the 2SFCA method is the kernel density 2SFCA (KD2SFCA) method. However, this method was not developed to analyze access to health care and it computes scores that express only the spatial access dimensions of proximity and availability. In this paper we present a new method, based on the KD2SFCA method, which improves health care access analysis and better identifies populations that are less empowered to use health care. We adapt the KD2SFCA method for the context of health care access analysis and extend it to capture additional access dimensions. We applied the extended method to the Portuguese hospital health care sector in a case study, and compared its results with those obtained with the KD2SFCA method. Our method was able to improve the identification of the less empowered populations and discovered that they represent 8.1% of the total population, instead of 4.6%, and reside in sixteen of the eighteen Portuguese districts, instead of in thirteen, as identified by the original KD2SFCA method. By improving the KD2SFCA method for the identification of the less empowered populations, our method can be a first step in an endeavor to identify opportunities to increase the health care supply or to redistribute supply resources, with the objective of increasing the access of those deprived populations. <br> <b>Keywords:</b> accessibility, spatial analysis, socioeconomic analysis, regional analysis