Relationship between meteorological factors and mortality in patients with coronavirus disease 2019: A cross-sectional study

Abstract

Background: Recent studies on COVID-19 have demonstrated that poverty, comorbidities, race/ethnicity, population density, mobility, hygiene and use of masks are some of the important correlates of COVID-19 outcomes. In fact, weather conditions also play an important role in enhancing or eradicating health issues. Based on Chinese experience, the development of SARS and COVID-19 is partially associated with alterations in climate that align with the seasonal shifts of the "24 solar terms." However, the applicability of this pattern to other countries, particularly the United States, which has the highest global incidence and mortality rates, remains subject to ongoing investigation. We need to find more evidence to in the U.S. states verify the relationship between meteorological factors and COVID-19 outcomes to provide epidemiological and environmental support for the COVID-19 pandemic prevention and resource preservation.

Objective: To evaluate the relationship between meteorological factors and Coronavirus Disease 2019 (COVID-19) mortality.

Methods: We conducted an ecological cross-sectional study to evaluate the relationship between meteorological factors (maximum temperature, minimum temperature, humidity, wind speed, precipitation, atmospheric pressure) and COVID-19 mortality. This retrospective observational study examines mortality rates among COVID-19 patients in the three US states, California, Texas, and New York, with the highest fatality numbers, between March 7, 2020 and March 7, 2021. The study draws upon data sourced from the publicly accessible Dryad database. The daily corresponding meteorological conditions were retrieved from the National Oceanic and Atmospheric Administration Global Meteorological website (https://www.ncei.noaa.gov/maps/hourly/). This study employed multivariate linear regression analysis to assess the correlation between six meteorological factors and COVID-19 mortality. Gaussian distribution models were utilized to generate smooth curves for examining the linear association between maximum or minimum temperature and mortality. Additionally, breakpoint analysis was conducted to evaluate the threshold effect of temperature.

Results: We found that the death toll of patients with COVID-19 decreased with an increase in the highest and lowest ambient temperatures (p < 0.001). In our study, we observed a seasonal difference in mortality rates, with a higher number of deaths occurring during winter months, particularly in January and February. However, mortality rates decreased significantly in March. Notably, we found no statistically significant correlation between relative humidity, average precipitation, and average wind speed with COVID-19 mortality (all p > 0.05). Daily COVID-19 death was negatively correlated with the maximum temperature (β = -22, 95% CI, -26.2 to -17.79 -, p < 0.01), while the maximum temperature was below 30 °C. Similarly, the number of deaths was negatively correlated with the minimum temperature (β = -27.46, 95% CI, -31.48 to -23.45, p < 0.01), when the minimum temperature was below 8 °C. Our study found a significant association between temperature and COVID-19 mortality, with every 1 °C increase in maximum or minimum temperature resulting in a decrease of 22 and 27 deceased cases, respectively. The relationship between atmospheric pressure and COVID-19 mortality was not fully elucidated due to its complex interaction with maximum temperature.

Conclusions: This empirical study adds to the existing body of research on the impact of climate factors on COVID-19 prevention and resource allocation. Policymakers and health scientists may find these findings useful in conjunction with other social factors when making decisions related to COVID-19 prevention and resource allocation.

Keywords: Air pressure; COVID-19; Meteorological concepts; Mortality; SARS-CoV-2; Temperature.

Fig. 1

The names and time divisions of Chinese “24 solar terms”.

Fig. 2

Comparative histogram of COVID-19 deaths in states of America from March 7, 2020 to March 7, 2021.

Fig. 3

Scatter chart of the number of deaths in California, Texas, and New York at one year.

Fig. 4

COVID-19 monthly distribution histogram of total death tolls in California,Texas,and New York.

Fig. 5

Death tolls comparison between the three states: California (CA), Texas (TX), and New York (NY).

Fig. 6

Linear relationships between COVID-19 death tolls and temperature values.