Article ; Online: Co-distribution of Light At Night (LAN) and COVID-19 incidence in the United States.
BMC public health
2021 Volume 21, Issue 1, Page(s) 1509
Abstract: Background: Light at night (LAN) as a circadian disruption factor may affect the human ... analysis aimed to test whether there is an association between LAN and COVID-19 cases in 4 selected ... of COVID-19 cases in ArcMap and performed a multiple linear regression model using data of LAN and COVID-19 ...
Abstract | Background: Light at night (LAN) as a circadian disruption factor may affect the human immune system and consequently increase an individual's susceptibility to the severity of infectious diseases, such as COVID-19. COVID-19 infections spread differently in each state in the United States (US). The current analysis aimed to test whether there is an association between LAN and COVID-19 cases in 4 selected US states: Connecticut, New York, California, and Texas. Methods: We analyzed clustering patterns of COVID-19 cases in ArcMap and performed a multiple linear regression model using data of LAN and COVID-19 incidence with adjustment for confounding variables including population density, percent below poverty, and racial factors. Results: Hotspots of LAN and COVID-19 cases are located in large cities or metro-centers for all 4 states. LAN intensity is associated with cases/1 k for overall and lockdown durations in New York and Connecticut (P < 0.001), but not in Texas and California. The overall case rates are significantly associated with LAN in New York (P < 0.001) and Connecticut (P < 0.001). Conclusions: We observed a significant positive correlation between LAN intensity and COVID-19 cases-rate/1 k, suggesting that circadian disruption of ambient light may increase the COVID-19 infection rate possibly by affecting an individual's immune functions. Furthermore, differences in the demographic structure and lockdown policies in different states play an important role in COVID-19 infections. |
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MeSH term(s) | COVID-19 ; Circadian Rhythm ; Communicable Disease Control ; Connecticut/epidemiology ; Humans ; Incidence ; SARS-CoV-2 ; United States/epidemiology |
Language | English |
Publishing date | 2021-08-04 |
Publishing country | England |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ISSN | 1471-2458 |
ISSN (online) | 1471-2458 |
DOI | 10.1186/s12889-021-11500-6 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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