COVID-19: Mechanisms for Accelerated Diffusion in Italy

COVID-19: Mechanisms for Accelerated Diffusion in Italy Jefferson T, Heneghan C 01/06/2020.

Published on June 2, 2020

Reference Coccia M. Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to- human and human-to-human transmission dynamics. medRxiv  2020:2020.04.06.20055657. [Full text PDF] 2020
Study type
Country Italy
Setting 55 provincial capital cities
Funding Details National Research Council of Italy (CNR)
Transmission mode Pollutant borne
Exposures Temperature, wind, moisture, rain, fog, population density, location, and total days of high doses of particulate matter 10 micrometers or less in diameter (PM10) or ozone.

Bottom Line

The accelerated and vast diffusion of COVID-19 in Northern Italy was associated with the duration of cities’ exposure to polluted air.

Evidence Summary

  • Cities with an average of 125 days exceeding the limits set for particulate matter 10 micrometres or less in diameter (PM10) had more than 3,200 cases.
  • Cities with an average of fewer than 100 days exceeding the limits set for PM10 had an average of 900 cases.
  • Coastal cities with lower exposure to PM10 had fewer cases.
  • The authors recommend not exceeding 50 days of high PM10 level per year to minimise the risk of transmission.

What did they do?

The study examined environmental factors in the spread of COVID-19 in Italy up until April 2020. The spread was faster in the north and much slower in the centre and south of the country.

The authors assessed factors which might have facilitated or accelerated the spread by looking at environmental variables (temperature, wind, moisture, rain, fog – in February to March 2020), population density, location (45 cities in the hinterland and 10 on the coast) and total days of high doses of PM10 or ozone in 2018 in 55 provincial capital cities.

Data on cases for the period 17th March to the 1st April 2020 were obtained from the Italian Ministry of Health and environmental and density variables from state and web-based weather forecasting sources. Hinterland cities had a lower temperature, rainfall, wind speed and higher pollution than coastal cities. A similar gradient was observed from north to south. Inhabitant density and wind speed were also associated with a high number of cases but this correlation was weaker than air pollution.

The authors also report that air pollution had a stronger correlation with the incidence of cases before lockdown. Once lockdown occurred pollution levels decreased, and population density seemed a stronger variable, inferring a  change from pollutant-to-person to person-to-person spread. The authors report that the maximum number of days over a year during which pollution levels do not increase transmission is around 45.

Study reliability

The manuscript shows some inconsistencies in reporting and the reason for the observation windows are not always clear. The number of cases is almost certainly an underestimate.

Exposure to PM10 and ozone are fairly stable according to the author, i.e. are not likely to vary greatly from year to year.

There was no protocol defining prespecified cut-off values.

Clearly defined setting Demographic characteristics described Follow-up length was sufficient Transmission outcomes assessed Main biases are taken into consideration
Unclear * No Yes Partly ** No
* Partly – an ecological study in Italy
** COVID 19 cases were defined clinically, without a corroborative test.

What else should I consider?

This evidence from this study needs replicating. Making inferences based on one study is prone to errors. In the absence of a predefined protocol, studies are subject to significant uncertainties due to changes in the analysis that may not be clear. Studies with longer follow-up that vary across the seasons will increase our understanding of the play of pollutant.

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About the authors

Carl Heneghan

Carl is Professor of EBM & Director of CEBM at the University of Oxford. He is also a GP and tweets @carlheneghan. He has an active interest in discovering the truth behind health research findings

Tom Jefferson

Tom Jefferson, epidemiologist.