As the world is suffering from the impact of the coronavirus disease 2019 (COVID-19) pandemic, scientists across the globe are conducting extensive research to bring about a better understanding of the disease and the transmission dynamics of its causative pathogen: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
In a short period, they have obtained vital information about the structure and genetic sequence of SARS-CoV-2. These studies have played a crucial role in deciphering the mutation pattern of the virus and, thereby, in the development of vaccines and other preventive measures (such as facemasks). The identification of the link between SARS-CoV-2, demographics, and environmental factors (e.g., air quality), is important to map the seasonal variability and infection load.
Previous researchers have reported that demographic variables, such as ethnicity, age, gender, and population density have a substantial impact on SARS-CoV-2 infection. A significant increase in the death rate from COVID-19 was linked with the older age group. This may be due to two factors, i.e., age-related low immunity levels and pre-existing health conditions such as diabetes, pulmonary disease, hypertension, etc. Although the relationship between sex and COVID-19 is not completely understood, males are reported to be more susceptible to suffer from SARS-CoV-2 complications and fatality. Scientists have also reported heightened vulnerabilities of some ethnic groups. Multiple studies have proved that environmental factors and meteorological parameters are associated with the SARS-CoV-2 disease.
A new piece of research was conducted using a wide range of variables related to demographic and environmental factors in New York State (NYS), USA. NYS consists of urban and ethnically diverse counties, some of which are population-dense while others are population-sparse. This research was published on medRxiv* preprint server.
In this study, researchers have grouped the counties based on COVID-19 infection and fatality and linked them to various demographic and environmental variables. A regression model was constructed to comprehend the relative contribution of these variables to the prevalence of SARS-CoV-2, death, and fatality.
For this study, researchers obtained data on COVID-19 infection rates, fatality, and death rates from 62 counties in NYS for the period of March 1, 2020, to May 16, 2020, from publicly available sources. All the data were grouped according to the area, population with age greater than 55 years, the Hispanic American population, poverty levels, and the African American population. The distances were calculated using ArcGIS Map 10.7.1 software and the environmental factors were obtained from the Environmental Protection Agency (EPA). Data on PM2.5 was used to gauge air quality. The statistical techniques used to conduct the comparative study between the different variables included the Kruskal-Wallis test, a non-parametric equivalent of one-way analysis of variance (ANOVA), Mann-Whitney U test, etc. An ARIMA model was used to analyze the multi-year time-series data of temperature.
After the first wave of the pandemic, researchers reported significant heterogeneity, across the counties, in terms of the rate of infection, death, and fatality. A strong correlation was observed between COVID-19 infection and the number of deaths. Regression analysis also revealed a differential contribution of the air quality and various demographic variables on the infection, death, and fatality rates. These analyses help further the understanding of the impact of demographic and environmental factors on COVID-19 in NYS.
Infection and death from COVID-19 in NYS counties (data till May 16, 2020). (A-B) Infection rates (A) and death rates (B) rates of individual counties are shown; counties are further grouped into three clusters using k-means clustering technique. (C) Maps of NYS showing the locations of counties included in each of the clusters.
Previous research has suggested that a higher risk of virus exposure is due to socioeconomic inequities such as living in densely populated areas, professional demands, and a higher prevalence of chronic comorbidity. Prior research has also reported that individuals with low socioeconomic status are more vulnerable to SARS-CoV-2 infection. The current research has shown a positive correlation between poverty and COVID-19 infection in NYS.
Interestingly, researchers observed a strong correlation between PM2.5 and the percentage of the population belonging to African American or Hispanic American ethnicity. This may be due to socioeconomic inequities. This makes the African American and the Hispanic American population more vulnerable owing to the poor air quality surrounding them.
While studying two demographic variables, namely, distance from the epicenter and age above 55 years, researchers found that the distance of NYS counties from the disease epicenter is inversely related to COVID-19 infection and death rates.
The outcome of the current study helps researchers to visualize the impact of COVID-19 on NYS counties. The regression model has helped to assess the relative contribution of environmental and demographic factors to infection. A thorough assessment of the association of these variables with the incidence of the disease would help in better understanding the impact of the disease on the population. This would also help to develop mitigative measures or preventive policies to contain the pandemic.
medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.