Do you believe this number?

Do you believe this number at face value and without question? “40% of the USA’s coronavirus deaths could have been prevented.”

I hope not. Even if you think you likely will agree with the headline that is all over my news feed today, this statement should give you pause. At very least, I would ask you to stop and think for just a moment. What does that number mean? How was it calculated? Should I care? Is this a call to any action?

Especially, please ask when you read such articles, where can I find more information? If you read nothing else from my blog, read the source and understand it if you are curious, I think it has some very interesting food for thought. You have to register on the site, but the article is free.

I first want to critique some of the news coverage. I was specifically disappointed in USA Today which only named the Lancet Commission report on “Public Policy and Health in the Trump Era.” I thought Newsweek was better because the article actually quoted directly the relevant section of the report. I was most pleased with ABC’s write up because they provided a link to the report and the title of the article was less sensationalizing. 

Let me just go through how I engaged with this material this evening. As I first read the news article these are the things that smell funny to me. 

  1. I was on alert that 40% is a round number not given with any uncertainty. 
  2. ‘Could have been prevented.’ I was very unsatisfied that this term was not well defined in several articles. How do they know these deaths were preventable? That doesn’t make sense. 
  3. I was also alerted because the date range over which this data was collected was not specifically mentioned. 

So, I wanted to look at the report more closely. 

The main downside is that the report is a 49 page document. While I genuinely think it has some valuable insight, this is a lot for someone to sit down and read. But, even if all you did was look at the first 2 out of 11 charts in this report and understand what they indicate, I think you would have a better understanding of the 40% number and what it really means. You might come to the same conclusion that I did: that the importance of this report is not just a critique of recent policies but rather that there are deep and ever worsening health issues for Americans as compared to the rest of the world.

The first figure shows life expectancy in the US compared to other G7 countries (Japan, Italy, France, Canada, UK, and Germany) and spoilers, it isn’t great. Starting in the mid 80’s the life expectancy has been shorter in the US than all of these other developed countries and has remained at the bottom of this list and since then, the gap has only widened. 

The second and similar figure shows the number of deaths above the average of the six other G7 countries. This plot shows that since 2014 there has been a yearly excess of 400,000 deaths in this country every year if you compare US health statistics to other parts of the world. It also shows that this is simply the state of things at the end of a 40 year cultural inheritance of poorer health. 

So after reading this far I began to understand that the “40% preventable COVID deaths” is a number which compares our loss of life to other countries and implies a claim of causation that had we enacted better policies similar to those enacted by other countries more lives would have been saved. 

This claim is suspect at best. Only by actually stopping and thinking for just a moment and reading this report one can question this conclusion. Consider the alternative, that the US was as healthy as the rest of the world prior to the pandemic but had a 40% higher death rate during the time of the pandemic. In that case, we could justifiably blame recent policy, dismissal and inaction for those deaths. But somehow we have to reconcile that health in the US has been on the decline for a long time and talk about how to change it. There are some recommendations in this report. 

The point is, one could dismiss this 40% number as politically prejudicial, one could overlook this number with a grim acceptance that this number aligns with your expectations for the policies of the former president, or one can realize that we have a problem in the culture of american health and healthcare and consider what can be done.

Please be well and stay safe,



On the Date of State Of The Union

As I sat at lunch today, I noticed that the state of the Union is tonight. I thought, ‘Wow! That’s late isn’t it?’ But I wasn’t sure.

So, I did the math. Here is the result:

If we look back to the dates from 1910, the current date is 1.3σ away from that mean. Furthermore, presently (since 1980), the later dates with smaller variance are still only 1.5σ away from the mean. If these fluctuations were random, 3σ would be within expectations. The σ of 6 for this time period is exactly what you would expect for random fluctuation about some average.

The only interesting thing I noticed was that the ~1940 lower average seemed to transition between 1960-1980 to a new later average.

Does anyone know of a reason why?

One Day Build (House Hunting)

We’re in the middle of house hunting in Nashville (which is booming).

So, I wanted a tool that would help me vet addresses as they pop-up in my feed from our realtor.
We wanted to be able to walk to some things in our neighborhood and fortunately that criterion a pretty easy thing to map out by hand in inkscape with screenshots from Google maps. I was able to produce a png map that had those regions clearly identified. For those that remember the 2010 floods, water ways are something to be wary of. So I pulled a map from FEMA ( and overlayed that with a transparency by hand, easy enough. I could also have tried to overlay crime maps and offender registries, but this was sufficient for triage.

Now I just needed a converter(mapper) between the x,y in inkscape and gps coordinates.
I used PIL (python image library, aka pillow) to draw on the picture I had created.
The converter looks like this:

def mapper(lat,long):
#scale = [4528.104575164487, -162821.9551111503, 3633.747495444875, 315924.9870280141]
#return(long*scale[2]+scale[3],flip-( lat*scale[0]+scale[1]))
scale = [-5228.758169935899, 189234.5555556009, 4041.34431458903, 351362.65809204464]

There are two attempts here because a conversion from GPS coordinates to inkscape coordinates is unfortunately not the same as GPS to pillow.

I derived this via two “calibration” points on my map and the respective coordinates in pillow.

given = [[36.039065, -86.782672],[36.042890, -86.606493]]
res = [[644, 795],[1356,775]]
a = (given[1][0] - given[0][0]) / (res[1][1] - res[0][1])
b = -a * res[0][1] + given[0][0]
c = (given[1][1] - given[0][1]) / (res[1][0] - res[0][0])
d = -c * res[0][0] + given[0][1]
scale = [1/a, -b/a, 1/c, -d/c]

This finds slope and offset for two categories , latitude and longitude, based off of four points and is exact (those potentially off by a little depending on the accuracy of my calibration points). I should’ve picked points better, because 36.039 is not very different from 36.042. Oh well. In the end it worked.
Then I just hardcoded the values of the variable scale into the function mapper.

I have my x,y coordinates from latitude and longitude. Now I want to draw on my map.

def drawer(coords):
im ="pillow.png")
draw = ImageDraw.ImageDraw(im)
flip = im.size[-1]
for pair in coords:
vec = [mapper(pair[0]+0.001,pair[1]-0.001,flip),

It makes all the points the same color which makes it difficult to judge multiple new points on a figure, but this was sufficient for my purposes.
The plus and minus 0.001 was found by trial and error to make the correct sized dots on the map.

The tool was just for me but my wife also appreciated that we could use this to quickly go through the initial barrage of home listings
and weed out the listings that were for sure not going to be of interest.

Not too bad for a few hours of work and most of that was just deciding and drawing out the regions of interest.