Sociodemographic Characteristics

This retrospective time series analysis examined health services inequalities among the elderly population residing in urban and non-urban areas in Florida.  Specific health services factors analyzed in the study were emergency department visits, hospitalizations, and deaths related to COVID-19.  Patient records were used as the primary source of data.  Adjusted incidence of COVID-19 cases and associated rates of the health services factors were calculated using both logistic and Poisson regression models.  Below are the key findings:

• There were 13,659 COVID-19 patients (aged 65+) as of May 27 with a mortality rate of 14.9%.
• Elderly in small metropolitan or rural areas were less likely to be hospitalized compared to those living in large metropolitan areas (35% and 34% vs. 41%).
• Elderly men (aged 65-64) living in small metropolitan areas had a 43% lower adjusted incidence of COVID-19 compared to those in large metropolitan areas (56 vs 2.82 per 1000 persons; rate ratio (RR): 0.57; 95% confidence interval (CI): (0.33–0.98), p = 0.04).
• Elderly men (aged 65-74) living in rural areas had a 53% lower rate of emergency department visits compared to those living in large metropolitan areas (22% vs 41%, odds ratio (OR): 0.47 (0.29–0.75), p = 0.002).
• Elderly females (aged 75+) living in rural areas had a 79% lower rate of emergency department visits compared to those living in large metropolitan areas (13% vs 43%, OR: 0.21 (0.13–0.34), p < 0.001).
• Elderly females (aged 75+) living in rural areas had a higher adjusted incidence of COVID-19 (113%) and lower hospitalizations compared to those living in large metropolitan areas (29% vs 46%; odds ratio: 0.37 (0.25–0.54), p < 0.001).

Many studies have examined health effects of COVID-19 on the elderly population and those with comorbidities.  This study, however, examined the risk of COVID-19 in the middle-aged population (40-59 years of age) without comorbidities. Using data from three hospitals in China, 119 middle-aged patients without comorbidities were identified.  The median age of these patients was 50 years and the majority were male (64.7%).  Approximately 18 patients (15.1%) experienced severe illness and 5 (3.9%) deaths were reported. Symptoms including fever, expectoration, myalgia, and dyspnea more commonly occurred in the severe cases than with the milder cases (p<0.05).  Diarrhea, on the other hand, occurred only in the milder cases. Most complications were related to ARDS (26, 21.8%) and an elevated D-dimer (36, 31.3%).

Spatial Modeling

Spatial modeling of disease transmission can be helpful in guiding a comprehensive pandemic response.  Sociodemographic and healthcare factors can be used to examine and predict spatial transmission of the disease.

This study sought to analyze the incidence of COVID-19 cases and deaths based on county-level health factors such as race, number of hospital beds, number of ICU beds, adult obesity rate, number of uninsured, and uptake rate of flu vaccinations.  Excluding counties in Alaska and Puerto Rico, there were 3,108 counties included in the study.  For analysis, Moran’s Index (Moran’s I) was used to measure the bivariate spatial correlation between county-level health factors and incidence of COVID-19 cases.  The results indicated that geographical variations with the incidence of COVID-19 cases and mortality across counties were prevalent.

• significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran I= 0.174 and 0.264, p < 0.0001) and deaths due to COVID-19 ((Moran I = 0.264, p < 0.0001)
• higher percentage of non-Hispanic white was significantly negatively spatially correlated with cases of COVID-19 (Moran I= –0.203, p < 0.0001) and deaths due to COVID-19 (Moran I = –0.137, p < 0.0001)
• significant but weak spatial autocorrelation between the number of intensive care unit beds and COVID-19 cases (Moran I= 0.08, p < 0.0001) and deaths due to COVID-19 (Moran I = 0.15, p < 0.0001), respectively

In the Midwest counties, few spatial clusters of COVID-19 cases with a low number of ICU beds were seen.  The Midwest and South had significant clusters of COVID-19 cases/deaths and diabetes.

A similar study was conducted in Oman to determine if there was spatial variation among various sociodemographic factors and COVID-19 incidence rates.  Age demographics, number of hospital beds, long-term illness, and population density comprised the sociodemographic factors.  Key findings from this study indicated that sociodemographic factors had an impact on the incidence of COVID-19 cases and that there was geographical variation.  The results also indicated that there was considerable spatial heterogeneity in COVID-19 incidence rates with urban-rural factors.    Furthermore, those aged 65+, population density, and long-term illness were determinants of COVID-19 incidence rates.