No Effects of Meteorological Factors on the SARS-CoV-2 Infection Fatality Rate.

Biomed Environ Sci

Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain;Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain;Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK;Centre for Psychiatric Research and Education, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.

Published: November 2021

Objective: Previous studies have shown that meteorological factors may increase COVID-19 mortality, likely due to the increased transmission of the virus. However, this could also be related to an increased infection fatality rate (IFR). We investigated the association between meteorological factors (temperature, humidity, solar irradiance, pressure, wind, precipitation, cloud coverage) and IFR across Spanish provinces ( = 52) during the first wave of the pandemic (weeks 10-16 of 2020).

Methods: We estimated IFR as excess deaths (the gap between observed and expected deaths, considering COVID-19-unrelated deaths prevented by lockdown measures) divided by the number of infections (SARS-CoV-2 seropositive individuals plus excess deaths) and conducted Spearman correlations between meteorological factors and IFR across the provinces.

Results: We estimated 2,418,250 infections and 43,237 deaths. The IFR was 0.03% in < 50-year-old, 0.22% in 50-59-year-old, 0.9% in 60-69-year-old, 3.3% in 70-79-year-old, 12.6% in 80-89-year-old, and 26.5% in ≥ 90-year-old. We did not find statistically significant relationships between meteorological factors and adjusted IFR. However, we found strong relationships between low temperature and unadjusted IFR, likely due to Spain's colder provinces' aging population.

Conclusion: The association between meteorological factors and adjusted COVID-19 IFR is unclear. Neglecting age differences or ignoring COVID-19-unrelated deaths may severely bias COVID-19 epidemiological analyses.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8690129PMC
http://dx.doi.org/10.3967/bes2021.120DOI Listing

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