COVID-19 reduces economic activity, which reduces pollution, which saves lives.

COVID-19 is a massive global economic and health challenge, having caused >3500 global deaths as of this writing (Mar 8) and untold economic and social disruption.  This disruption is only likely to increase in coming days in regions where the epidemic is just beginning. Strangely, this disruption could also have unexpected health benefits -- and these benefits could be quite large in certain parts of the world.  Below I calculate that the reductions in air pollution in China caused by this economic disruption likely saved twenty times more lives in China than have currently been lost directly due to infection with the virus in that country.

None of my calculations support any idea that pandemics are good for health. The effects I calculate just represent health benefits from the air pollution changes wrought by the economic disruption, and do not account for the many other short- or long-term negative consequences of this disruption on health or other outcomes; these harms likely vastly exceed any health benefits from reduced air pollution.  [Edit: added this paragraph 3/16 to emphasize the lessons from my findings, in case readers don't make it further..]

Okay, on to the calculation.

A few weeks ago, NASA published striking satellite images of the massive reduction in air pollution (specifically, NO2) over China resulting from the economic slow-down in that country following it's aggressive response to COVID-19.

Source: NASA

Separate analyses indeed found that ground-based concentrations of key pollutants -- namely PM2.5 -- fell substantially across much of the country.  These reductions were not uniform.  In northern cities such as Beijing, where much of wintertime pollution comes from winter heating, reductions were absent.  But in more southern cities such as Shanghai and Wuhan where wintertime pollution is mainly from cars and smaller industry, pollution declines appeared to be dramatic.

Given the huge amount of evidence that breathing dirty air contributes heavily to premature mortality, a natural -- if admittedly strange -- question is whether the lives saved from this reduction in pollution caused by economic disruption from COVID-19 exceeds the death toll from the virus itself.  Even under very conservative assumptions, I think the answer is a clear "yes".

Here are the ingredients to doing this calculation [Edit: see additional details I added at end of this post, if really wanting to nerd out].  First we need to know how much the economic disruption caused by COVID-19 in China reduced air pollution in the country.  The estimates already discussed suggest about a 10ug/m3 reduction in PM across China in Jan-Feb of 2020 relative to the same months in the previous 2 years (I get this from eyeballing the figure mid-way down the blog post).  To confirm these results in independent data, I downloaded hourly PM2.5 data from US government sensors in four main cities in China (Beijing, Shanghai, Chengdu, and Guangzhou).  I calculated the daily average (and trailing 7-day mean) of PM2.5 since Jan 2016, and then again compared Jan-Feb 2020 to all other Jan-Feb periods in these cities.

I find results that are highly consistent with the above analysis.  Below is a plot of the data in each city. The blue line is the 7-day average in 2020, and the thick red line is the same average over the years 2016-19 (thin red lines are each individual year in that span).  On average across these four cities, I find an average daily reduction of 15-17ug/m3 PM2.5 across both Jan-Feb 2020 relative to the average in the previous four years.  This average difference is highly statistically significant.  As in the above analysis, Beijing appears to be the outlier, with no decline in PM relative to past years when analyzed on its own.

PM2.5 concentrations in four Chinese cities in Jan-Feb 2016-2019 (red lines) vs same period in 2020 (blue lines). All cities except Beijing show substantial overall reductions during the period. 

To be very conservative, let’s then assume COVID-19 reduced PM2.5 on average by 10ug/m3 for in China, that this effect only lasted 2 months, and that only some urban areas (and no rural areas) experienced this change (see below).

Next we need to know how these changes in air pollution translated into reductions in premature mortality.  Doing this requires knowing three numbers:

1. The change in the age specific mortality rate per unit change in PM2.5.  For this we use estimates from He et al 2016, who studied the effects of air quality improvements around the Beijing Olympics on mortality rates -- a quasi-experimental setting on air quality not unlike that induced by COVID-19.  He et al found large reductions in mortality for children under five and for adults over 65, with monthly under-5 mortality increasing 2.9% for every 1ug/m3 increase in PM2.5, and about 1.4% for people over 70 years old (note:  they measure impacts using PM10, and to convert to PM2.5 we use the common assumption in the literature that PM2.5 = 0.7*PM10 and that all impacts of PM10 are through the PM2.5 component). Estimates from the He et al paper are broadly consistent with other quasi-experimental studies on the effects of air pollution on infant health, including estimates from the US, Turkey, and Africa (see Fig 5b here).  As a more conservative approach, we also calculate mortality using 1% increase in mortality per 1ug increase in PM2.5 for both kids and older people.  In all cases, we very conservatively assume that no one between the ages of 5 and 70 is affected.  
2. The baseline age-specific mortality rates for kids and older people, which we take from Global Burden of Disease estimates.  I note a large apparent discrepancy between the GBD numbers, which give an under-5 mortality rate of 205 per 100,000 live births in China in the most recent year, and the WB/Unicef estimate, which puts the number at 860 per 100,000 live births (4x higher!).   I use the much smaller GBD estimate in my calculations to be conservative, but someone should reconcile these...  
3. The total population affected by the changes in air pollution.  For this we take the total Chinese population as estimated by the UN, and conservatively assume that only 50% of the population are affected by the change in air quality.  Since roughly 60% of Chinese live in urban areas, this is like assuming there is no effect in rural areas and some urban areas remain unaffected. 

Putting these numbers together [see table below for details] yields some very large reductions in premature mortality.  Using the He et al 2016 estimates of the impact of changes in PM on mortality, I calculate that having 2 months of 10ug/m3 reductions in PM2.5 likely has saved the lives of 4,000 kids under 5 and 73,000 adults over 70 in China.  Using even more conservative estimates of 10% reduction in mortality per 10ug change, I estimate 1400 under-5 lives saved and 51700 over-70 lives saved.  Even under these more conservative assumptions, the lives saved due to the pollution reductions are roughly 20x the number of lives that have been directly lost to the virus (based on March 8 estimates of 3100 Chinese COVID-19 deaths, taken from here). 

What's the lesson here?  It seems clearly incorrect and foolhardy to conclude that pandemics are good for health. Again I emphasize that the effects calculated above are just the health benefits of the air pollution changes, and do not account for the many other short- or long-term negative consequences of social and economic disruption on health or other outcomes; these harms could exceed any health benefits from reduced air pollution.  But the calculation is perhaps a useful reminder of the often-hidden health consequences of the status quo, i.e. the substantial costs that our current way of doing things exacts on our health and livelihoods.

Might COVID-19 help us see this more clearly?  In my narrow academic world -- in which most conferences and academic meetings have now been cancelled due to COVID-19, and where many folks are cancelling talk invitations and not taking flights -- it might be a nice opportunity to re-think our production function with regard to travel.  For most (all?) of us academics, flying on airplanes is by far the most polluting thing we do.  Perhaps COVID-19, if we survive it, will help us find less polluting ways to do our jobs.  More broadly, the fact that disruption of this magnitude could actually lead to some large (partial) benefits suggests that our normal way of doing things might need disrupting.

[Thanks to Sam Heft-Neal for helping me double check these calculations; errors are my own].

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Edited 3/9 to add additional details below on calculations and assumptions. 

Here are the actual values and math I used, either using He et al 2016 estimates of the PM/mortality relationship or the more conservative 1% change in mortality per 1 ug/m3 PM2.5 estimate.  To calculate the % change in mortality rate from the observed change in PM2.5, I multiply (a) by (c).  This number is then multiplied by the baseline monthly mortality rate in (d) to get the total change in the mortality rate, which is then multiplied by the total affected population in each age group (e*f).

age group	reduction in monthly pm2.5	% change in monthly mortality rate per 10 ug pm 10	% change in mortality per 1 ug PM2.5	baseline monthly mortality rate per 100,000 for each age group	population in each age group (100,000s)	percent of population affected	two month reduction in mortality
	(a)	(b)	(c)	(d)	(e)	(f)	(g)
Using He et al 2016 PM10 estimates in (b)
under 5	-10	20	2.9	17.1	839.3	0.5	-4097
over 70	-10	10	1.4	523.8	981.1	0.5	-73421
total							-77518
Using 1%/ug PM2.5 estimates in (c)
under 5	-10		1.0	17.1	839.3	0.5	-1434
over 70	-10		1.0	523.8	981.1	0.5	-51395
total							-52829

To get the estimates in (a), I observe daily data for four chinese cities back to Jan 1 2016.  I define a COVID-19 treatment dummy equal to one if the year==2020, and then run a panel regression of daily PM2.5 on the treatment dummy plus city-by-day-of-year fixed effects.  The sample is all days in Jan-early March.  Basically this calculates the daily PM2.5 difference between 2020 and 2016-19, averaged across all sites and days.  I can run this on the daily data or on the 7-day running mean, and with city-by-d.o.y fixed effects or city + d.o.y fixed effects and I get answers that are all between 15-18 ug/m3 reductions in PM2.5.  We very conservatively round this number down to 10 in column (a). 

What are some of the key assumptions in my overall analysis?  There are lots:

• This is only the partial effect of air pollution; it is by no means the overall effect of COVID-19 on mortality.  Indeed, the broader disruption caused by COVID-19 could cause many additional deaths that are not directly attributable to being infected with the virus -- e.g. due to declines in households' economic wellbeing, or to the difficulty in accessing health services for non-COVID illnesses.  Again, I am absolutely not saying that pandemics are good for health.  
• The key assumption in using the He et al 2016 estimates (or the 1%/1ug conservative summary estimate from quasi-experimental studies) is that changes in outdoor PM2.5 concentrations are a sufficient statistic for measuring health impacts.  One worry is that, instead of being at work, people are staying at home, and that home indoor air pollution is worse than what they would have been exposed to otherwise.  While it is true that indoor air pollution can be incredibly high in homes that rely on biomass burning for cooking and heating, existing evidence suggests that even in cold regions, urban Chinese residents probably have better air quality inside their home than outside it.  E.g see here.  So the key question to me is whether other behaviors changed in response to COVID-19 that made individuals exposures look a lot different than they did in the Beijing setting in He et al 2016.  Maybe there is a case to be made there but not sure what it is.  There's a prima facie case that the Beijing setting looks a lot like what we're worried about here:  an acute 2-month event that led to large but temporary changes in air pollution.  
• Is mortality from COVID-19 interacting with mortality from PM?  One possibility is that there are enough COVID-19 infections to actually make people more susceptible to the negative impacts of air pollution.  But this would increase rather than decrease deaths from PM!  The other possibility is that the people who have very sadly passed away from COVID would have been those most likely to pass away from PM exposure.  But even if this were 100% true, it would only account for ~5% of the overall predicted mortality.  
• We are pinning the entire PM2.5 difference between Jan-Feb 2020 and Jan-Feb 2016-19 on COVID-19.  (Note that we estimate a 17ug/m3 difference, and have rounded that down to 10 ug/mg, so in effect are only pinning about 60% of the change on COVID).  Without more careful analysis, we can't really know whether this is fair or not.  Maybe something else very big was going on at exactly the same time in China?  Seems unlikely but my analysis has nothing to say about that. 
• My estimates are a prediction of mortality impacts, not a measurement.  They are not proof that anything has happened.  In a few years, there will likely be enough data to actually try to measure what the overall effect was of the COVID-19 epidemic on health outcomes.  You could compare changes in mortality in high-exposure locations to changes in mortality in lower-exposure locations, for instance.  But this study is not yet possible, as the epidemic is still underway and the comprehensive all-cause mortality data not yet (to my knowledge) available. 

Are the curious trends in despair and diets related?

There’s a new working paper out by Anne Case and Angus Deaton on one of the most curious (and saddest) trends in America – mortality rates for whites have been rising. There have been various stories about these trends, such as this one that first turned me onto it. It’s clear that the proximate causes are an increase in “deaths of despair,” namely suicides, drugs, and alcohol. It’s also clear that self-reported mental health has been declining in this group. But why?

Any explanation has to account for the fact that the same trends aren’t seen in other racial groups, even though many of them have lower incomes (see figure below from Case and Deaton):

It should also account for the fact that the same trend isn’t seen in other predominantly white countries:

One explanation that seems to have gained most traction is that whites “lost the narrative of their lives.” That is, maybe rising economic inequality and other economic trends have affected all groups, but whites expected more. To me this seems plausible but not really convincing.

Ok, so let me offer another theory. I’ll first say that I think it’s a bit off the wall, but I don’t think I’m going crazy (despite Marshall’s frequent hinting that I am). The idea basically stems from another body of literature that I’ve recently been exploring, mainly because I was interested in allergies. Yeah, allergies. Specifically, a bunch of people I know have sworn that their allergies were fixed by eliminating certain foods, and given that some people in my family have bad seasonal allergies, I decided to look into it.

It turns out that wheat is thought by many to trigger inflammation and allergies. But what’s relevant here is that it’s also thought to affect mental health. More than that, there are actually clinical studies like this one showing that depression increases with gluten intake. There are only 22 subjects in that study, which seems low to me but obviously I don’t do that sort of work. A good summary of scientific and plenty of non-scientific views on the topic can be found in this article. Incredibly, there was even a study in the 1960s showing how hospital admission rates for schizophrenia varied up and down with gluten grain rations during World War 2.

So what’s the connection to the trends in deaths of despair? Well the striking thing to me is that wheat effects are generally only seen in white non-hispanics. Celiac disease, for instance, is much lower in other racial groups. Second, it’s apparently known that celiac has been rising over time, which is thought to indicate increased exposure (of all people) to gluten early in life. And the trends are most apparent in whites, such as seen in the figure below from this paper.

Just to be clear, I realize this is mostly speculation. Not only is this not my area of expertise, but I don’t have any data on the regional trends in gluten or wheat intake in the U.S. to compare to the regional trends in death. I’m not even sure that such data exist. It seems that studies like this one looking at trends in gluten consumption just assume the gluten content of foods is fixed, but it also seems a lot of products now have gluten added to make them rise quicker and better. (Some blame the obsession with whole grain foods, which don't rise as quickly.) If anyone knows of good data on trends in consumption, let me know. It would also be interesting to know if they add less gluten in other countries, where mortality rates haven’t risen.

(As an aside: there’s also a recent study looking at wheat and obesity in cross-section. Apparently country obesity rates are related to wheat availability, but not much else.)

Also to be clear, I still like wheat. Maybe having spent most of my career studying wheat producing systems has made me sympathetic. Or maybe it’s the fact that it has sustained civilization since the dawn of agriculture. But I think it’s possible that we recently have gone overboard in how much is eaten or, more specifically, in how much gluten is added to processed food in this country. And even if there’s only a small chance it’s partly behind the trends of despair (which aren’t just causing mortality, but all sorts of other damage), it’s worth looking into.