COVID-19 & Air Pollution in Chiang Mai
Both subjects are in the local news. Chiang Mai is affected by a very bad seasonal pollution and a midly bad average annual pollution (see our section on the burning season) and like so many places in the world, is not free of SARS-CoV-2 infections. There is little data available on the relationship between these two health issues but some conclusions can already be reached from larger epidemiological studies and the most recent handful of publications regarding COVID-19
Information to remember from this section are:
=> COVID-19 is very unlikely to be an airborne disease in a normal environment
=> COVID-19 does not seem to be more contagious in air heavily polluted in PM2.5
=> COVID-19 mortality rate shows a correlation with PM2.5 pollution in the air of European & American cities (other factors not taken into account)
=> COVID-19 mortality rate seems mostly unaffected by the high level of PM2.5 pollution in the air of Asian cities.
Please follow these links for specific sections:
Air Pollution & Respiratory diseases in Thailand
The relationship is known for some time and will not surprise anyone. The worse the air pollution the more respiratory diseases are observed. Both annual average and peak pollution have an impact on the morbidity and the mortality of these diseases and this effect has been shown in many countries. More detailed publications show the influence of particulate matter with a considerable emphasis on urban pollution (Bates et al., 1990; Schwartz et al., 1993; Detels et al., 1987, Wong et al., 2010, Silva et al., 2014). Quantified studies have shown that the mortality rate of cardiopulmonary diseases and lung cancer increase by 6% for each additional 10 mcg/m3 of PM10 added to the air pollution (Pope et al., 2002).
In Thailand, the relationship between seasonal air pollution and respiratory diseases is best exemplified with the standard mortality ratio (SMR) by district (Aungkulanon et al., 2016).
The annual average SMR (how more prevalent is a disease compared to national average) shows that the northern region is systematically* 113 to 154% higher than the national mortality rate (ex: 125% means that if the mortality is 4 per 1000 persons in Thailand on average, in the North the ratio will be 5/1000) and can be strongly linked to respiratory diseases.
Although the situation (inequality between the North and the rest of Thailand) will not change in the near future, there are some good news as the national health coverage implemented in the early 2000s seems to be the most effective in the North where the average mortality was 152% higher in the early 2000s and reached down to 106% in 2014 compared to the (also decreasing) national average. It is the highest lowering of relative death rate in Thailand over that period of time.
*exception of Chiang Mai western districts and Mae Hong Song which are 5 to 10% lower than the national average but have a higher children mortality.
Figure 1. A. Trends in the Standard Mortality Rate between different region of Thailand between 2001 and 2014. The northern region shows the steepest lowering of anomalies compared to the national average for the same period. B. Trends in the prevalence of diseases in the North for selected diseases. Unfortunately, respiratory diseases (COPD, lung cancer) are on the rise (modified from Aungkulanon et al., 2016).
Details on the cause of this anomaly show that lung cancer, COPD, asthma and pneumonia are respectively 175%, >175%, ~125% and ~100% SMR for Chiang Mai urban area compared to the Thai average. The authors only suggest seasonal air pollution as the cause for this anomaly (other researchers have provided alternative explanations) but the relationship will appear obvious for Chiang Mai residents and surrounding provinces. It might also indicate that seasonal peak in particulate pollution has a morbidity effect but also a possible additional effect to mortality since long term AQI averages between Bangkok and Chiang Mai are not fundamentally different (pers. hyp.).
Figure 1: Geographical distribution of SMR anomalies for various diseases in Thailand between 2001 and 2004 (Aungkulanon et al., 2016)
COVID-19 and air pollution
Most data used in this section is extracted from non-peer reviewed scientific articles. The current dynamics of the epidemic has lowered the typical rigorousness of scientific research and a lot of the data available is often processed, interpreted and published without the usual peer-review and editing process. This situation requires a more skeptical and critical approach when analyzing interpretation and conclusions made in these publications. Premature conclusions and extrapolations have already been seen in the past few months in virology (i.e. origin of the virus, strains,…), epidemiology (i.e. mortality rate, infectious vectors, R0,…), infectiology (i.e. ‘miracle’ cures & very effective antivirals, …) etc.
Airborne contagiousness of COVID-19
A couple of studies have investigated the potential for SARS-CoV-2 infection through airborne transmission (this type of transmission would exclude direct contact, fomite and proximal droplets). Although one recent report from Nebraska (Santarpia et al., 2020) warns that airborne transmission is possible within a hospital setting (but with no quantitative data proving it), other articles on the subject seems to indicate that although virions can be airborne, their concentration seems likely to be under the minimum infectious dose. The minimum infectious dose is not known at this stage for ethical reasons but likely to be around ~100 virions (Dr. Michael Skinner, UCL). Proper ventilation, disinfection, preventive behavior and physical boundaries have made all potential airborne transmission in Wuhan hospitals statistically impossible (Liu et al., 2020) while a similar study in a Singapore hospital has failed to detect an infectious level of virions in air (Wei Xiang Ong et al., 2020).
=> COVID-19 is very unlikely to be an airborne disease in a normal environment
COVID-19 infectiousness and high particulate air pollution
Based on the fact that initial large outbreaks occurred mostly in China and in Lombardy, some researchers have suggested that high level of particulate pollution might be responsible to the seemingly very high number of infected patients at early stages of the epidemic. Their reasoning was based on viruses hitchhiking on particulates, particulates creating a local suitable surface for virus survival and partly protecting it from some viricides such as UV.
A pilot study in Italy, on the basis of similar evidence for other diseases, correlated PM10 peak levels with the number of cases and found a strong correlation (Setti et al., 2020) but the quality of the data and the conclusions reached in this report have been rejected at this stage by ISPRA (Italian Environment Agency) and requires further study as other variables and confounders have largely been ignored.
In Wuhan, a study (Han et al., 2020) has shown that air pollution has an effect on COVID-19 transmissivity but only through confounding factors since the decreasing in infectiousness is concomitant with lockdown, social distancing and quarantining which have been associated with a drastic decrease in air pollution.
=> The current understanding of outbreaks in different part of the world does not support the idea that air pollution rich in PM2.5 makes COVID-19 an airborne transmissible disease
COVID-19 mortality and high particulate air pollution
A good starting point for this subject is a similar case of study on the influence of air pollution on the mortality of SARS during the 2003 epidemic. Although COVID-19 and SARS are different diseases, it is likely that some conclusions will be analogue. This particular ecological study shows a clear correlation (r2=0.86) between short term pollution (during the SARS epidemic itself) and the mortality of the disease. Long term pollution also show a positive correlation but not measurable trend. Potential confounders are possible but data is unavailable (Cui et al., 2003).
=> SARS epidemic in 2003 shows a correlation between peak pollution and mortality
Figure 3: Correlation between air pollution (PM10 is the main pollutant) and the mortality rate of SARS during the epidemic of 2003. Mortality might increases by a factor of 2 as a function of peak atmospheric pollution (modified from Cui et al., 2003)
For the current epidemic, an early study on the Chinese component of the pandemic (Ma et al., 2020) has correlated COVID-19 mortality with temperature, humidity and pollution. Results show that mortality is positively correlated with daily temperature variation and SO2 atmospheric content but negatively correlated with relative humidity, temperature and particulate matter. Although confounders are not included in this study, based only on these trends, it is good news for Thailand as the transition to the wet season is particularly hot and humid (pers. hyp.). The surprising negative correlation with particulate matter (the higher the pollution, the less deadly?!?) is a possible effect of the lock-down within the exponential phase of the outbreak (air pollution during the lock-down has decreased quicker than the daily mortality rate) as the average pollution in China has decreased by 13.9 mcg/m3 during this period (pers. hyp.), potentially averting more than 50000 premature Chinese deaths this year due to air pollution (He et al., 2020).
The most recent study on the relationship between COVID-19 mortality and air pollution (Wu et al., 2020) has been advertised and extensively covered by the media. Most general public articles summarized the findings to a single sentence expressing that 15% of mortality is added for each mcg/m3 of PM2.5 in the air. Without some rational analysis, this conclusion is particularly worrying for people living in northern Thailand where annual average PM2.5 is 10 to 15 mcg/m3 higher than the highest US values.
Figure 4: Graphical representation of PM2.5 concentration in air pollution of various cities and current available mortality rate of COVID-19 in these cities (unrealistic high numbers are likely a bias from testing and local reporting policies). The blue curve represents the increase in mortality as a function of PM2.5 according to Wu et al. (2020). Although western cities roughly follow this curve, Asian cities as a whole have high level of pollution but remarkably low mortality rate.
It appears that the trend is roughly verified for cities in the US and Europe. However, considering the very different situation between countries, some factors (population density, saturation & resilience of health systems, etc.) will overshadow the effect of small variations of PM2.5 levels (pers. hyp.). In addition, this trend does not seems to be verified at all for Asian cities affected by heavy PM2.5 pollution. Considering the levels of pollution reached in many cities, even small numbers of infected people should have a visible increased mortality (in the tens of percents) but it does not seem to be the case.
The most obvious discrepancy for residents of Thailand is that based on the annual PM2.5 of Chiang Mai and Bangkok (~30 mcg/m3) and the number of recorded COVID-19 infections (~1250 at the time of writing), we should see ~500 deaths, which is very significantly different to the 13 recorded deaths for these cities.
=> COVID-19 mortality shows a correlation with PM2.5 content in the atmosphere at low levels (i.e. EU and US cities). However, for high level of pollution (i.e. Asian cities), the trend is not verified and other undetermined factors seems to play a major role.
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