County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States.
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ABSTRACT: Background
COVID-19 is an infectious disease that has killed more than 555,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection.Objectives
We evaluated whether greenness was related to COVID-19 incidence and mortality in the US.Methods
We downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home orders.Results
An increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density.Discussion
Exposures to NDVI were associated with reduced county-level incidence of COVID-19 in the US as well as reduced county-level COVID-19 mortality rates in densely populated counties.
SUBMITTER: Klompmaker JO
PROVIDER: S-EPMC8123933 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCOVID-19 is an infectious disease that has killed more than 246,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection.ObjectivesWe evaluated whether greenness is related to COVID-19 incidence and mortality in the United States.MethodsWe downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home order.ResultsAn increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density. Discussion: Exposures to NDVI had beneficial impacts on county-level incidence of COVID-19 in the US and may have reduced county-level COVID-19 mortality rates, especially in densely populated counties.
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Project description:Background Amid stagnating declines in national cardiovascular disease (CVD) mortality, documenting trends in county-level hypertension-related CVD death rates can help activate local efforts prioritizing hypertension prevention, detection, and control. Methods and Results Using death certificate data from the National Vital Statistics System, Bayesian spatiotemporal models were used to estimate county-level hypertension-related CVD death rates and corresponding trends during 2000 to 2010 and 2010 to 2019 for adults aged ≥35 years overall and by age group, race or ethnicity, and sex. Among adults aged 35 to 64 years, county-level hypertension-related CVD death rates increased from a median of 23.2 per 100 000 in 2000 to 43.4 per 100 000 in 2019. Among adults aged ≥65 years, county-level hypertension-related CVD death rates increased from a median of 362.1 per 100 000 in 2000 to 430.1 per 100 000 in 2019. Increases were larger and more prevalent among adults aged 35 to 64 years than those aged ≥65 years. More than 75% of counties experienced increasing hypertension-related CVD death rates among patients aged 35 to 64 years during 2000 to 2010 and 2010 to 2019 (76.2% [95% credible interval, 74.7-78.4] and 86.2% [95% credible interval, 84.6-87.6], respectively), compared with 48.2% (95% credible interval, 47.0-49.7) during 2000 to 2010 and 66.1% (95% credible interval, 64.9-67.1) for patients aged ≥65 years. The highest rates for both age groups were among men and Black populations. All racial and ethnic categories in both age groups experienced widespread county-level increases. Conclusions Large, widespread county-level increases in hypertension-related CVD mortality sound an alarm for intensified clinical and public health actions to improve hypertension prevention, detection, and control and prevent subsequent CVD deaths in counties across the nation.
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Project description:While studies indicate differences in incidence and case fatality risk of COVID-19, few efforts have shed light on regional variations in the intensity of initial community spread. We conducted a nationwide study using county-level data on COVID-19 from Center for Systems Science and Engineering at Johns Hopkins University. We characterized intensity of initial community COVID-19 attack by calculating the incidence and case fatality risk (CFR) for the first 4-week period of COVID-19 spread in each county. We used multivariate multilevel multinomial logistic regression to estimate the association of county-level characteristics with COVID-19 incidence and CFR. Of 3,143 counties, we included 1,052 with at least 100 reported cases on June 1st. Median incidence was 193.4 per 100,000 population (IQR: 94.2-397.5). Median case fatality risk was 3.6% (IQR: 1.4â€"7.3). Median age, rural population, population density, lower education, uninsured population, obesity, COPD prevalence were positively associated, while population, female sex, races (Asian, white), higher education, excessive drinking were negatively associated with initial COVID-19 incidence. Median age, female sex, Asian race, population density, higher education, excessive drinking, Intensive Care Unit beds, airborne infection isolation rooms were positively associated, while Hispanic ethnicity, lower education, obesity (paradox), uninsured population were negatively associated with initial COVID-19 CFR.
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