The U.S. has merely 4% of the world population, but contains 25% of the world’s COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.

Defining a reliable geographic unit pertaining to cancer care is essential in its assessment, planning, and management. This study aims to delineate and characterize the cancer service areas (CSAs) accounting for the presence of major cancer centers in the USA. We used the Medicare enrollment and claims from January 1, 2014 to September 30, 2015 to build a spatial network from cancer patients to cancer care facilities that provided inpatient and outpatient care of cancer-directed surgery, chemotherapy, and radiation. After excluding those without clinical care or outside of the USA, we identified 94 National Cancer Institute designated and other academic cancer centers from the members of the Association of American Cancer Institutes (AACI). By explicitly incorporating existing specialized cancer referral centers, we refined the spatially constrained Leiden method that accounted for spatial adjacency and other constraints to delineate coherent CSAs within which the service volumes were maximal but minimal between them. The derived 110 CSAs had a high mean localization index (LI) (0.83) with a narrow variability (SD = 0.10). The variation of LI across the CSAs was positively associated with population, median household income, and area size, and negatively with travel time. Averagely, patients traveled less and were more likely to receive cancer care within the CSAs anchored by cancer centers than their counterparts without cancer centers. We concluded that CSAs are effective in capturing the local cancer care markets in the USA. They can be used as reliable units for studying cancer care and informing more evidence-based policy.

Studies on spatial accessibility to health care are well established in the US for examining disparities and inequities but lacking in Austria although both experience high health care spending and have hospital care as the largest payer. This study aims to address this gap by systematically examining multiscale spatial accessibility to acute hospitals in Carinthia, one of nine provinces in Austria. Using the most recent data, the study refines the proximity method by considering bypass behavior and the generalized two-step floating catchment area (G2SFCA) method by incorporating distance decay to examine accessibility at the census block and 250-meter grid levels while accounting for the classic Modifiable Areal Unit Problem (MAUP) and edge effects. The results reveal that, on average, travel times to the nearest acute hospitals are 16 minutes for census blocks and 21 minutes for grids, covering 58.8% and 76.2% of the population, respectively. For the three nearest acute hospitals, they increase to 25 and 31 minutes, covering slightly lower populations of 52.6% and 73.4%, respectively. The bypass behavior is more influential as 20% more population living in mountainous or rural areas need to travel more than 30 minutes. The G2SFCA method with a more pronounced distance decay tends to result in a more decentralized polycentric structure of accessibility and identify more areas with the poorest access. While the urban advantage is most evident in Klagenfurt and Villach, but not all areas close to acute hospitals enjoy the best accessibility as captured by the G2SFCA method. The two methods capture different profiles of accessibility. In combination, they can identify less accessible areas, which is a key priority for health policy to improve access. In addition, the MAUP tends to overestimate accessibility at a coarse level and in areas with less or sparsely distributed populations. The edge effects tend to occur at the border when using the proximity method, but it is more sensitive if considering bypass behavior or using the G2SFCA method with a weak decay effect. This study provides valuable insights into the spatial accessibility of acute hospitals in Carinthia and highlights the challenges faced by rural, mountainous, and other underserved areas in accessing acute care, with significant implications for health equity and resource allocation. It also underscores the importance of considering different geographic units and edge effects for health care planning and management.