8 april 2020: Bron: CNN

De kans om te overlijden aan het corona virus (COVID) is veel groter voor wie woont in een gebied, meestal steden, waar de lucht sterk is verontreinigd door fijnstof met name. Omdat wie in zo'n gebied woont vaak al last heeft van onderliggende ziektes als COPD enz. Dit blijkt uit een grote studie uitgevoerd in 3000 verschillende gebieden in Amerika. 

"We ontdekten dat een toename van slechts 1 gram per kubieke meter fijn stof in de lucht geassocieerd was met een stijging van 15% in het sterftecijfer van Covid-19", zegt hoofdauteur Francesca Dominici, co-directeur van de Harvard Data Science Initiatief. De studie definieerde hoge vervuilingsniveaus als niveaus van fijn stof (PM 2,5) boven 13 microgram per kubieke meter lucht, veel hoger dan het Amerikaanse gemiddelde van 8,4."De resultaten suggereren dat langdurige blootstelling aan luchtverontreiniging de kwetsbaarheid voor de meest ernstige Covid-19-uitkomsten vergroot", aldus Dominici.

De nieuwe informatie moet worden gebruikt door federale, staats- en lokale functionarissen om weloverwogen beslissingen te nemen over het afdwingen van sociale afstand en het voorbereiden van ziekenhuizen en lokale gezondheidszorgsystemen op een mogelijke toestroom van ernstigere gevallen waarvoor extreme maatregelen nodig zijn, zoals ventilatoren, zei Dominici.

Het studierapport: Exposure to air pollution and COVID-19 mortality in the United States  is als PDF gratis in te zien.

Onderaan artikel het absgract.

Op de website van CNN staat een uitgebreid artikel over deze studie: Covid-19 death rate rises in counties with high air pollution, study says

The results provide "stark new information about the deadly toll of particle pollution," said American Lung Association president and CEO Harold Wimmer, who was not involved with the study.
"The nation has known for some time that long-term exposure to particle pollution can worsen symptoms of lung disease, increase susceptibility to lung infection, trigger heart attack and stroke, and can even cause lung cancer and premature death," Wimmer said in a statement.
"This new research from Harvard now links particle pollution exposure to a dramatically higher death rate from Covid-19.">>>>>>>lees meer
Hier het abstract van de studie:

Exposure to air pollution and COVID-19 mortality in the United States

Xiao WuRachel C. NetheryBenjamin M. SabathDanielle BraunFrancesca Dominici

Abstract

Background: United States government scientists estimate that COVID-19 may kill between 100,000 and 240,000 Americans. The majority of the pre-existing conditions that increase the risk of death for COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigate whether long-term average exposure to fine particulate matter (PM2.5) increases the risk of COVID-19 deaths in the United States. Methods: Data was collected for approximately 3,000 counties in the United States (98% of the population) up to April 04, 2020. We fit zero-inflated negative binomial mixed models using county-level COVID-19 deaths as the outcome and county level long-term average of PM2.5 as the exposure. We adjust by population size, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables including, but not limited to obesity and smoking. We include a random intercept by state to account for potential correlation in counties within the same state. Results: We found that an increase of only 1 μg/m3 in PM2.5 is associated with a 15% increase in the COVID-19 death rate, 95% confidence interval (CI) (5%, 25%). Results are statistically significant and robust to secondary and sensitivity analyses. Conclusions: A small increase in long-term exposure to PM2.5 leads to a large increase in COVID-19 death rate, with the magnitude of increase 20 times that observed for PM2.5 and all-cause mortality. The study results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was made possible by the support from the NIH grant R01 ES024332-01A1, P50MD010428, ES024012, ES026217, ES028033; MD012769, HEI grant 4953-RFA14-3/16-4, and USEPA grants 83587201-0, RD-83479801. The contents are solely the responsibility of the grantee and do not necessarily represent the official views of the funding agencies. Further, funding agencies do not endorse the purchase of any commercial products or services related to this publication. The computations in this paper were run on (1) the Odyssey cluster supported by the FAS Division of Science, Research Computing Group at Harvard University; (2) the Research Computing Environment supported by the Institute for Quantitative Social Science in the Faculty of Arts and Sciences at Harvard University. The authors would like to thank Lena Goodwin, for editorial assistance in the preparation of this manuscript.

Author Declarations

All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.

Yes

All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes


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