Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA

  1. Jeon-Young Kang
  2. Alexander Michels
  3. Fangzheng Lyu
  4. Shaohua Wang
  5. Nelson Agbodo
  6. Vincent L. Freeman
  7. Shaowen Wang

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Abstract

Background

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing the coronavirus disease 2019 (COVID-19) pandemic, has infected millions of people and caused hundreds of thousands of deaths. While COVID-19 has overwhelmed healthcare resources (e.g., healthcare personnel, testing resources, hospital beds, and ventilators) in a number of countries, limited research has been conducted to understand spatial accessibility of such resources. This study fills this gap by rapidly measuring the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA.

Method

The rapid measurement is achieved by resolving computational intensity of an enhanced two-step floating catchment area (E2SFCA) method through a parallel computing strategy based on cyberGIS (cyber geographic information science and systems). The E2SFCA has two major steps. First, it calculates a bed-to-population ratio for each hospital location. Second, it sums these ratios for residential locations where hospital locations overlap.

Results

The comparison of the spatial accessibility measures for COVID-19 patients to those of population at risk identifies which geographic areas need additional healthcare resources to improve access. The results also help delineate the areas that may face a COVID-19-induced shortage of healthcare resources. The Chicagoland, particularly the southern Chicago, shows an additional need for resources. This study also identified vulnerable population residing in the areas with low spatial accessibility in Chicago.

Conclusion

Rapidly measuring spatial accessibility of healthcare resources provides an improved understanding of how well the healthcare infrastructure is equipped to save people’s lives during the COVID-19 pandemic. The findings are relevant for policymakers and public health practitioners to allocate existing healthcare resources or distribute new resources for maximum access to health services.

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  1. Version published to 10.1186/s12942-020-00229-x
  2. ScreenIT

    SciScore for 10.1101/2020.05.06.20093534: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The road network dataset was retrieved using a Python package OSMnx (Boeing, 2017), which helped us to download and analyze street networks from the OpenStreetMap.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This study has several limitations. The accessibility measures near state boundaries might be underestimated because we did not include the hospitals in neighboring states (e.g., Indiana, Wisconsin, Iowa, Kentucky, and Missouri), which Illinois residents might visit. Because there are different population distribution patterns, the catchment size parameter needs to be chosen to account for the difference of such patterns (McGrail, 2012). Therefore, assessing the impact of varying catchment size on the spatial accessibility measure (Luo and Whippo, 2012) would be worth for future research. In addition, the COVID-19 cases may not be exhaustively counted because many cases were not confirmed due to having no or light symptoms (Bai et al., 2020). Also, our analysis did not distinguish between non-acute inpatient bed versus intensive care unit bed capacities. Given that the number of confirmed cases is expected to increase in Illinois, to assess spatial accessibility for COVID-19 patients over time is an important research topic. Then it would be interesting to explore the dynamics between the demand of COVID-19 patients and the supply of healthcare resources. Although our results may help identify the areas where resources are abundant or insufficient, we did not assess how much resources need to be allocated, which should be another future research topic. While our cyberGIS approach to parallel computing of E2SFCA achieves significant computational performance gains, future work...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.05.06.20093534 on medRxiv