Article ; Online: Co-distribution of Light At Night (LAN) and COVID-19 incidence in the United States
BMC Public Health, Vol 21, Iss 1, Pp 1-
2021 Volume 8
Abstract: 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 ... ...
| Abstract | 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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| Keywords | Light at night (LAN) ; COVID-19 ; Public aspects of medicine ; RA1-1270 |
| Language | English |
| Publishing date | 2021-08-01T00:00:00Z |
| Publisher | BMC |
| Document type | Article ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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