Objective The COVID-19 pandemic poses an unprecedented threat to the health and economic prosperity of the world's population. Yet, because not all regions are affected equally, this research aims to understand whether the relative growth rate of the initial outbreak in early 2020 varied significantly between the US states and counties. Study design Based on publicly available case data from across the USA, the initial outbreak is statistically modeled as an exponential curve. Methods Regional differences are visually compared using geo maps and spaghetti lines. In addition, they are statistically analyzed as an unconditional model (one-way random effects analysis of variance estimated with HLM 7.03); the bias between state- and county-level models is evidenced with distribution tests and Bland–Altman plots (using SPSS 26). Results At the state level, the outbreak rate follows a normal distribution with an average relative growth rate of 0.197 (doubling time 3.518 days). But there is a low degree of reliability between state-wide and county-specific data reported (Intraclass correlation coefficient ICC = 0.169, P < 0.001), with a bias of 0.070 (standard deviation 0.062) as shown with a Bland–Altman plot. Hence, there is a significant variation in the outbreak between the US states and counties. Conclusions The results emphasize the need for policy makers to look at the pandemic from the smallest population subdivision possible, so that countermeasures can be implemented, and critical resources provided effectively. Further research is needed to understand the reasons for these regional differences.
Background This study examines the association of contextual factors with the COVID‐19 outbreak rate across U.S. counties in its initial phase. Methods Contextual factors are simultaneously tested at the county‐ and state‐level with a multilevel linear model using full maximum likelihood. Results The variation between states is substantial and significant (ICC = 0.532, u0 = 8.20E−04, P < .001). At the state level, the cultural value of collectivism and the contextual factor of government spending are positively associated with the outbreak rate. At the county level, the racial and ethnic composition contributes to outbreak differences, disproportionally affecting black/African, native, Asian, and Hispanic Americans as well as native Hawaiians. Counties with a higher median age and a higher household income have a stronger outbreak. Better education and personal health are generally associated with a lower outbreak. Obesity and smoking are negatively related to the outbreak, in agreement with the value expectancy concepts of the health belief model. Air pollution is another significant contributor to the outbreak. Conclusions Because of a high variation in contextual factors, policy makers need to target pandemic responses to the smallest subdivision possible, so that countermeasures can be implemented effectively.
Background. The COVID-19 pandemic poses an unprecedented threat to the health and economic prosperity of the world's population. Yet, some countries or regions within a country appear to be affected in different ways.Objectives. This research aims to understand whether the outbreak varies significantly between U.S. states and counties. Methods.A statistical model is estimated using publicly available outbreak data in the U.S., and regional differences are statistically analyzed.Results. There is significant variance in outbreak data between U.S. states and counties.At the state level, the outbreak rate follows a normal distribution with an average relative growth rate of 0.197 (doubling time 3.518 days). But there is a low degree of reliability between state-wide and county-specific data reported (ICC = 0.169, p < 0.001), with a bias of 0.070 (standard deviation 0.062) as shown with a Bland-Altman plot. Conclusions.The results emphasize the need for policy makers to look at the pandemic from the smallest population subdivision possible, so that countermeasures can be implemented, and critical resources provided effectively. Further research is needed to understand the reasons for these regional differences.
Objectives.To examine the influence of county-and state-level characteristics on the initial phases of the COVID-19 outbreak across U.S. counties up to April 14, 2020. Methods. We used a statistical exponential growth model for the outbreak. Contextual factors at county-and state-level were simultaneously tested with a multilevel linear model. All data was publicly available. Results. Collectivism was positively associated with the outbreak rate. The racial and ethnic composition of counties contributed to outbreak differences, affecting Black/African and Asian Americans most. Counties with a higher median age had a stronger outbreak, as did counties with more people below the age of 18. Higher income, education, and personal health were generally associated with a lower outbreak. Obesity was negatively related to the outbreak. Smoking was negatively related, but only directionally informative. Air pollution was another significant contributor to the outbreak, but population density did not give statistical significance. Conclusions. Because of high intrastate and intercounty variation in contextual factors, policy makers need to target pandemic responses to the smallest subdivision possible, so that countermeasures can be implemented effectively.
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