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Who Stays Physically Active during COVID-19? Inequality and Exercise Patterns in the United States.

ABSTRACT: Exercising is crucial to keeping up physical and mental health during the coronavirus disease 2019 (COVID-19) pandemic. In this visualization, the authors consider how existing social inequalities may create unequal physical exercise patterns during COVID-19 in the United States. Analyzing data from a nationally representative Internet panel of the University of Southern California Center for Economic and Social Research Understanding Coronavirus in America project (March to December), the authors find that although all Americans have become physically more active since the outbreak, the pandemic has also exacerbated the inequality in physical exercise. Specifically, the authors show that the gaps in physical exercise have widened substantially between men and women, whites and nonwhites, the rich and the poor, and the educated and the less educated. Policy interventions addressing the widening inequality in physical activity can help minimize the disproportionate mental health impact of the pandemic on disadvantaged populations.

SUBMITTER: Sher C

PROVIDER: S-EPMC7812122 | biostudies-literature | 2021 Jan-Dec

REPOSITORIES: biostudies-literature

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Who Stays Physically Active during COVID-19? Inequality and Exercise Patterns in the United States.

Sher Chloe C Wu Cary C

Socius : sociological research for a dynamic world 20210115

Exercising is crucial to keeping up physical and mental health during the coronavirus disease 2019 (COVID-19) pandemic. In this visualization, the authors consider how existing social inequalities may create unequal physical exercise patterns during COVID-19 in the United States. Analyzing data from a nationally representative Internet panel of the University of Southern California Center for Economic and Social Research Understanding Coronavirus in America project (March to December), the autho ...[more]

PMID: 34192142

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Project description:A major challenge in managing disasters during a pandemic is assessing the inequalities in society and protecting vulnerable people. The objective of this paper is to geographically understand the discrimination and inequality against minorities by COVID-19. This study designed a locational discrimination index (LDI) to measure COVID-19 discrimination against minorities at county-level in the US. LDI is the difference between the death proportion of a minority and the proportion of a minority's population. If LDI >0 is significant, COVID-19 discrimination is identified against a minority in a county. I further developed a locational minority inequality index (LMII), and LMII of a minority is directly quantified by comparing its LDI with the LDI of the majority population (i.e., the White population in the US). If LMII>0 is significant, a significant health inequality is confirmed against a minority in a county. In the US, I found 157 counties with significant discrimination against Black people, and 103 counties with significant inequality against Black people; 58 counties with significant discrimination against the American Indian population, but 38 counties with significant inequality against the American Indian population; 17 counties with significant discrimination against Native Hawaiians, but only 8 counties with significant inequality; for Hispanic people, 47 counties had significant discrimination, and 64 counties had significant inequality; for Asians, 7 counties had significant discrimination, but 28 had significant inequality. LDI, LMII, and the thematic mapping provide novel insight into COVID-19 discrimination and inequalities. To the best of our knowledge, this is the first time anyone has quantitatively and statistically defined and mapped COVID-19 discrimination and inequality against minorities at a county-level across the US. Based on this, governments and communities could make efficient decisions and take effective action to addressthe significant discrimination and inequality against Black, American Indian, Native Hawaiian, Hispanic, and Asian people, which can be applied to other pandemics or public health disasters in the USA or other countries.

Project description:BackgroundThe emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends.ObjectiveWe aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States.MethodsWe retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data.ResultsWe observed a nonsignificant weak correlation (ρ= -0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models-for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: ρ=0.643; 2019-2020: ρ=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: ρ=0.746; 2019-2020: ρ=0.707).ConclusionsRelevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool.

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Dataset Information

Who Stays Physically Active during COVID-19? Inequality and Exercise Patterns in the United States.

Publications

Who Stays Physically Active during COVID-19? Inequality and Exercise Patterns in the United States.

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