COVID-19 Update: Arizona Cases, Deaths, Hospitalizations by Age Grouping

I’ve shown raw numbers of cases by age demographic in the past. Here’s a different way to look at it. In all of the charts below I have normalized each age group’s numbers by the total numbers of members of that age group in the state. For instance, the 65+ line in the “Cases per 1000” chart represents the numbers of people in the 65+ category who have been confirmed as having COVID-19 divided by the number of thousands of 65+ people in the state. Therefore, you can see that as of yesterday, there have been just over 20 Confirmed Cases per 1000 persons over 65 in the state. This is the same as saying that 2% of all persons in Arizona over 65 have been formally diagnosed with COVID-19 since the start of the COVID era. This doesn’t mean that 2% have it right now, of course, as these numbers are cumulative.

Arizona COVID-19 Confirmed Cases per 1000 Age Group Members
Arizona COVID-19 Hospitalizations per 1000 Age Group Members
Arizona COVID-19 Deaths per 1000 Age Group Members

What you may notice in the top chart (“Cases”) is that three of the groups have been tracking together since about 6/11 (note that since I have to collect this data from the state dashboard by hand every day, I only have back to 6/11, the day I started this practice). These groups even seem to follow the same exponential curve. When not normalized by the age group, these three all look much differently due to the difference in the populations (there are many more 20-44 year olds). This makes the case that the virus affects this range from 20-64 in a very similar way. You’ll also note, however, that the 65+ group and the <20 group are very different. This is interesting so lets reason about this.

Why are the 65+ and >20 groups different regarding Case rates?

I think there are two different stories here. For the 65+ group, one just has to look at the deaths chart to note that they are the group that is much more at risk than any other. This is widely known by all including people in this age group! My guess is that they are the age demographic that is being the most cautious about COVID. If true, then their efforts (distancing, masks, etc.) appear to be effective. The second group that is being affected less as a proportion of their population is the under 20’s. This grouping is a bit unfortunate, as it includes both children and adults (I wish they would provide case numbers for under 13 as well as 13-20). Regardless, it does seem like this grouping is far less likely to get infected with COVID-19. My suspicion is that most of the infections that do occur in this group are in the 17-20 age range, but I can’t prove that. I would also guess that the significantly lower incidence of infection in this group is due to a combination of the lower mobility that people under 16 have and a more optimal immune system response.

Case Growth Linearity

Although the case growth appears non-linear when viewed by raw counts, only the groups between 20 and 64 appear non-linear when normalized. The 65+ and under 20 groups both appear to be mostly linear. What this means is that the rate of growth (i.e., 40 cases per day) stays consistent and doesn’t go from 40/day one week to 50/day the next week and so forth. This is interesting and probably indicates some sort of resistance to infection, either through natural causes or practical effort.

Government Measures to Slow the Rates

There have been a number of measures taken by state and local governments during this period that have attempted to slow the case growth rates. Many of these started on or around 6/19/2020 when both Maricopa and Pima Counties unveiled new mask ordinances. My expectation was that I would see case rates decrease (i.e., “flattening the curve”) about 10 days after the measures were put in place. As you can see, there is very little indication of any effect yet on case growth (or hospitalization or deaths). There is a slight slope change in Case Growth over the last 2 to 3 days, but there’s a good chance that’s just due to data collection issues. From my experience, I don’t trust any trend in the data that I can’t see over a full week.

A few reasons that might explain the lack of an effect from the recent COVID state and county ordinances:

  1. Possibly people in these regions (especially the most affected parts of these counties) are not being consistent in their compliance to the new rules. In the areas around my home (a zip code that has been very lightly affected by COVID) I observe very impressive compliance, but I’ve noticed in other regions of the state that there is significantly less diligence around COVID-19 safety measures. How to measure this kind of compliance is interesting and this would make a good social study…
  2. Perhaps it still to early to observe an effect (and hopefully the three-day trend we can start to see will become more pronounced). My assumption that the lag between infection and symptoms and the lag from positive test to data being published would sum to something less than 2 weeks may have been wrong. If so, then we should be able to measure an impact and evaluate the lag times at some point in the future.
  3. It’s possible that there is a different method of transmission that we’re not considering where people are getting infected in times when their masks are off and/or their social distancing is more lax. I’ve been thinking about heating, air conditioning, and ventilation for quite a while and was studying the aerosolization of the virus very early on. This might be one example of an unexpected transmission route. If this is true, then it might mean that the CDC and the State/County/City governments may need to re-evaluate their recommendations.

One Arizona county where there may be some measurable effect due to the Government measures is Pima County. It’s not dramatic, but it is a visible change in the slope of the 20-44 age grouping approximately two weeks after the county mask ordinance and new state measures were ordered. See chart below where I show raw case counts (not normalized… this is why it looks different). Unfortunately this effect is not seen very clearly yet in Maricopa County or Pinal County data.

Pima County Cases by Age Demographic – raw case counts – 7/13/20

Hospitalization Curves

It is interesting how the different age groups are seeing clear separation in their age-normalized hospital rates (whereas 3 of the groups had pretty identical case rates). Each age group (except the under 20 group who is seeing little growth at all) is seeing mostly-linear growth in hospitalizations, but based on evidence from people working in the hospitals (the hospitals don’t publish data they’re not required to publish, so we’re left with stories from their workers) the combination of these is enough to overwhelm the limited resource of hospital beds and staff.

Death Curves

Deaths have been increasing over the last week or so and you can see that one of the reasons is that there is a slight acceleration of deaths in the over 65 group. To date, this group has accounted for over 85% of the deaths in the state. The fact that their death rate has accelerated over the last 10 or so days is concerning. Not really sure what to attribute this acceleration to (hospitalization was pretty linear for the two-three weeks prior to the acceleration starting).

3 Replies to “COVID-19 Update: Arizona Cases, Deaths, Hospitalizations by Age Grouping”

  1. Fascinating. Do you use R to run your models? If so, I would love to take a look at your code. Trying to become more familiar with it.

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