Standardized Incidence Ratio of the COVID-19 Pandemic in a Midwestern State
Presentation Type
Poster
Student
Yes
Track
Health Care Application
Abstract
The Coronavirus disease 2019 (COVID-19) has made a drastic impact around the world, with some communities facing harsher outcomes than others. We looked at what factors contributed to negative outcomes from the pandemic in South Dakota (SD). In addition, we sought to understand how counties in SD fared compared to expected using the SD rate as a reference population. To do this, a penalized generalized linear regression model was used to identify factors that were associated with COVID-19 hospitalization and death rates. The Standardized Incidence Ratio (SIR) values of all counties were computed three times for hospitalization rates and three times death rates to account for population, population and age, and population, age, and socio-demographic factors. With the linear model, we identified that race and education as significant factors associated with the outcomes which were confirmed by current literature. The SIR values highlighted counties that had more or less severe outcomes than expected. The counties with high non-white populations, which mostly included counties with American Indian reservations, typically had worse outcomes than other counties. Counties with high educational attainment typically had better outcomes than other counties. We believe that these results may provide useful information to improve the implementation of mitigation strategies to curb the damage of this or future pandemics by providing a way for data-driven resource allocation.
Start Date
2-8-2022 1:00 PM
End Date
2-8-2022 1:00 PM
Standardized Incidence Ratio of the COVID-19 Pandemic in a Midwestern State
The Coronavirus disease 2019 (COVID-19) has made a drastic impact around the world, with some communities facing harsher outcomes than others. We looked at what factors contributed to negative outcomes from the pandemic in South Dakota (SD). In addition, we sought to understand how counties in SD fared compared to expected using the SD rate as a reference population. To do this, a penalized generalized linear regression model was used to identify factors that were associated with COVID-19 hospitalization and death rates. The Standardized Incidence Ratio (SIR) values of all counties were computed three times for hospitalization rates and three times death rates to account for population, population and age, and population, age, and socio-demographic factors. With the linear model, we identified that race and education as significant factors associated with the outcomes which were confirmed by current literature. The SIR values highlighted counties that had more or less severe outcomes than expected. The counties with high non-white populations, which mostly included counties with American Indian reservations, typically had worse outcomes than other counties. Counties with high educational attainment typically had better outcomes than other counties. We believe that these results may provide useful information to improve the implementation of mitigation strategies to curb the damage of this or future pandemics by providing a way for data-driven resource allocation.