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Spatial accessibility assessment of COVID-19 patients to healthcare facilities: A case study of FL

The World Health Organization (WHO) announced more than 94 million confirmed COVID-19 cases worldwide as of January 19, 2021. The U.S., with approximately 24 million COVID-19 cases and over 400,000 total deaths, ranked first compared to other countries. Among U.S. states, Florida is among the top three states regarding the high number of cases. On January 19, 2021, the Florida Department of Health announced 1,589,097 cases and 24,436 deaths from coronavirus throughout the state, which have been gradually increasing. According to the Centers for Disease Control and Prevention (CDC), the older population (65+) and those with serious medical conditions such as lung disease, diabetes, liver disease, and other chronic issues are at a higher risk of getting infected with COVID-19. Especially since Florida is a state with a substantial aging population and people living in assisted living facilities or independently, the issue becomes even more challenging. As such, understanding the extent to which Florida healthcare facilities are available to the public in both urban and rural areas is crucial.

Several studies in the literature have focused on measuring transportation-based accessibility to different public services facilities such as healthcare facilities, libraries, supermarkets, shelters, and urban parks. However, there is still a research gap in the literature regarding assessing the spatial access to healthcare facilities during a global pandemic such as the COVID-19 outbreak in which the demand for this type of facility increases dramatically. As such, my team measured the spatial accessibility of COVID-19 patients to healthcare facilities in the State of Florida (Ghanbarzadeh et al. 2021). For this purpose, the two-step floating catchment area (2SFCA) and the enhanced two-step floating catchment area (E2SFCA) methods were utilized to identify the areas with high and low levels of accessibility to healthcare services given the number of confirmed coronavirus cases (demand) and the number of ICU beds (supply). More specifically, our team aimed to answer the following research question: To what extent do potential COVID-19 patients in Florida access healthcare resources, and which areas may experience resource shortages during the pandemic?

We included four main steps to measure the spatial accessibility of Floridians to healthcare providers during the COVID-19 pandemic. In the first step, the data related to healthcare facilities with the corresponding ICU beds and the number of COVID-19 patients were extracted for the entire state. Second, the travel times between the centroids of zip codes and each healthcare facility were calculated using the O-D cost matrix function of the ArcGIS Network Analyst. The travel times in the roadway network were obtained via the Florida Standard Urban Transportation Model Structure (FSUTMS) model built-in CUBE software. The congested travel times on the roadways were used. In the next step, the 2SFCA and E2SFCA methods were applied to obtain the accessibility scores at the zip code level to identify the areas with high and low levels of accessibility to healthcare resources in Florida. Ultimately, a metric, namely the Accessibility Ratio Difference (ARD), was developed to compare the level of access obtained through the models. It is important to note that the healthcare facilities that hospitalize COVID-19 patients and are equipped with ICU beds in Florida were selected.

As stated earlier, the 2SFCA and the E2SFCA methods were utilized to measure the spatial accessibility of COVID-19 patients to healthcare services in the State of Florida. Fig. 1a and Fig. 1b show the results obtained by the 2SFCA and E2SFCA models, respectively. The green and red colors in these figures represent the higher and lower accessibility ratios obtained by the models. Both methods reveal approximately the same accessibility patterns over the entire state. As shown in Fig. 1a and Fig. 1b, those regions are mainly located in the northwest and southern portions of Florida and seem to have low spatial accessibility ratios shown in red. Note that the areas in northwest Florida are mostly considered rural areas.

In contrast to northwest Florida, there are many healthcare facilities and more ICU beds in southern Florida. However, the high number of COVID-19 patients in these areas led to findings of low access in these regions, given the low computed ratios. On the other hand, the areas with higher access are mainly located in central Florida and close to the cities of Tampa and Orlando (shown in green). Therefore, the people in northwest and southern Florida are more likely to experience resource shortages due to an imbalance between supply and demand.

Additionally, to evaluate the spatial access difference between the models, the ARD metric was used to provide a detailed comparison of the 2SFCA and E2SFCA methods. The results of this approach are presented in Fig. 2. In this figure, the higher value of difference, the higher the accessibility ratio obtained by the E2SFCA method (shown in green). Due to the negative ARD values, the 2SFCA method showed higher access ratios in most parts of the state (shown clearly with the yellow color). On the other hand, the E2SFCA model shows the higher access ratios in the regions with a higher number of ICU beds which appears in green. One explanation for this finding could be related to the distance decay effect within the catchment area, considered in the E2SFCA method. According to the results, it can be concluded that the 2SFCA method overestimates accessibility in the areas with a low number of ICU beds due to the equal access of the population within the catchment area.

From a policy perspective, exploratory analyses such as the present effort can provide critical information that health officials could use to formulate educational agendas to promote safety and well-being regarding the risks associated with COVID-19. The problem is so critical that even one or two neglected locations can have dire consequences. Specifically, the 2SFCA and E2SFCA analyses and their comparison and insights presented in this paper could be a part of efforts to raise awareness of safety issues and make health officials more aware of locations near them that require further care in providing access and support. In addition, concerning COVID-19 cases, several community-oriented organizations are charged with assisting them in meeting their daily needs. The types of insights produced may have the potential to assist them in their efforts to help people, especially those vulnerable, find the health assistance they need. The obtained knowledge and insights can be helpful for public health planners and decision-makers. This information can also help officials better identify areas with low access to healthcare resources equipped with ICU beds.


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