After much delay, the New York City Department of Health recently released data on the number of tests given and the number of positive COVID-tests in each ZIP Code. And what followed was a flurry of maps and analysis by news organizations hoping to pinpoint which neighborhoods were hardest hit.

I recently pointed out that subway ridership had fallen much slower in low income neighborhoods than in higher income ones, leading me to believe that the effect of COVID may be harsher in those neighborhoods as more people continued to venture out as things worsened (likely due to the nature of their work).

A quick glance at this new dataset would at first suggest otherwise. This map shows the number of positive tests per 1K residents in each neighborhood:

It shows some wealthy areas of Manhattan having elevated levels. And when I ran the actual numbers, there was no correlation between the median income of a ZIP Code and the proportion of a population that had tested positive.

That being said, there is a problem with this metric - access to testing is quite varied. If you are wealthy, have health insurance and a primary care doctor, your ability to get tested is going to far outpace those in lower income brackets that lack the same healthcare access. Those who lack insurance might rely solely on a public hospital where they would only be tested if symptoms were severe and required hospitalization. An urgent care facility might test with lesser symptoms. Therefore, you might have to be less sick to be tested if you are wealthy than not.

If that were the case, then the proportion of tests in higher income neighborhoods that come back positive should be lower. And indeed, that is very much the case (r=-0.50):

A map of the proportion of positive cases looks like this:



This likely tells us that positive testing rates, given the disparities in testing, is likely not a good proxy for the rate of infection when comparing across income lines.

Since we can’t get the information we want from positive tests, I turned to the symptom surveillance website that shares symptom counts from those visiting Emergency Rooms.

A look at the overall number of visits to ER with respiratory symptoms over time shows a climb start on March 8th:

The same goes for Influenza like symptoms. As of April 1st, the number of visits per day with respiratory or influenza-like symptoms was 3,873. The good news is that this is trending down from Mar 25th. (Note that having less people walk in does not mean less people are in our hospitals, as many stay for a long time. So the number of people in our hospitals continues to grow at an alarming rate).

The tool allows you to see the ZIP Codes of a subset of these emergency room visitors and so I plotted the per-capita rate of visits per ZIP Code below on a map.

There are many caveats to this data of course. First, these are ER visits, not confirmed cases. So some flu and other respiratory issue visits are in there. Second, the city gives ZIP Codes for only a subset of visits, and it’s not clear if there is bias in that sample. For example, if only a few hospitals reported ZIP Codes to the city, it could cause this to over sample some areas. So the rates I provide are likely higher in each neighborhood, though the relative rankings should be reasonable. Third, lower income New Yorkers are more likely to use ER visits for non life-threatening conditions because they might lack access to other healthcare options. So this could end up over-reporting lower income communities as having more cases.

With all of those caveats aside, here is the map of ER visits with symptoms of influenza or respiratory issues per capita in each ZIP Code Mar 8-Apr 1:

A quick glance seems to show that lower income communities might have higher ER visit rates for respiratory symptoms over the last 3 weeks than higher income ones. As noted before, this could likely be due to the ER playing a different role in healthcare in lower income communities. A quick analysis seems to confirm that there is a relationship between median income in a Zip Code and the ER visit rate for respiratory symptoms (R=-0.51):



Each dot represents one Zip Code. The scatterplot shows a relationship between income and the rate of visits, where no higher income communities have high rates. The plot shows a few outliers, so I am listing them below:

And here is the striking thing - it turns out there is no mathematical correlation between hospitalization rates and positive test rates, adding the evidence that positive test rates is not a great proxy for understanding the spread.

And to bring things full circle, let’s take a look at hospitalization rates compared to the reduction in subway ridership in each ZIP Code. Again, the theory is that neighborhoods that kept taking the subway in higher numbers might be at higher risk. The scatterplot below shows that to be the case (R=0.44):

Given the caveats used to unpack this all, it would be great to see the city release data on the number of patients who have been hospitalized in each ZIP Code - not just ER visits. That number would be the cleanest to understand the potential inequities associated with this outbreak. I applaud the Department of Health in releasing this data, but would love to see more. In the end, it’s necessary that we understand how New Yorkers across income demographics may be subject to different levels of risk. And to do that better, we need more refined data.