Short-term effect of meteorological factors on COVID-19 mortality in Qom, Iran

ABSTRACT

The present study was conducted to assess the short-term effects of the meteorological factors on the COVID-19 mortality in Qom, Iran. The GAM with a quasi-Poisson link function was used to evaluate the impact of temperature, DTR, relative humidity, and absolute humidity on the COVID-19 mortality, controlling potential confounders such as time trend, air pollutants, and day of the week. The results showed that the risk of COVID-19 mortality was reduced, in single-day lag/multiple-day average lag, per one-unit increase in absolute humidity (percentage change in lag 0=−33.64% (95% CI (−42.44, −23.49)), and relative humidity (percentage change in lag 0=−1.87% (95% CI (−2.52, −1.22)). Also, per one-unit increase in DTR value, COVID death risk increased in single-day and multiple-day average lag. This study demonstrated a significant relationship between the four meteorological variables and the COVID-19 mortality.

KEYWORDS:

• Temperature
• meteorological factors
• COVID-19
• mortality

Acknowledgments

The authors gratefully acknowledge Qom University of Medical Sciences, Qom Meteorological Organization, and Qom Environmental Protection Agency.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

R.SH. developed the theory, performed the analyses, and wrote the paper. M.V. designed the study, collected the data, contributed to performing the analyses, and wrote the paper. All the authors discussed the results, contributed to the final manuscript, and conceived and designed the analyses.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, [R, SH], upon reasonable request.

Limitations

The current study has some limitations. First, ecological studies are insufficient to prove causation because of some ecological fallacies. The second limitation is the unavailability of medical resources because the study only contains the data of the first 4 months of the COVID-19 pandemic and does not include more data, which needs to be considered in future studies.

Notes

1. Diurnal temperature range.

2. Generalized additive model.

3. Day of week.

4. Akaike information criteria.

5. Temperature.

6. Relative humidity.

7. Absolute humidity.

Additional information

Funding

This work was financially supported by Qom University of Medical Sciences [Grant number: 99-038].