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The Math of Reopening: Looking into Ohio’s COVID-19 Testing and Mortality Numbers

April 30, 2020
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The United States now leads the world in deaths related to COVID-19, representing one-third of all global daily deaths. While this figure is tragic, many states, including Ohio, aggressively implemented physical distancing policies to contain the spread of the virus and flatten the curve. Now, with Ohio poised to roll-back some of those public health measures, questions remain as to how we quantify and understand the impact of COVID-19, especially as Ohio’s cases, hospitalizations and number of deaths continue to rise. It is therefore paramount that data in Ohio gives decisionmakers and the public the ability to understand the prevalence of the virus, and its impact, in our state.

 The level of testing needed to re-open requires a minimum of 152 tests per 100,000 people.

According to research from the Harvard Global Health Institute, the level of testing needed to re-open requires a minimum of 152 tests per 100,000 people. As of April 15, Ohio was more than 100 tests short, coming in at 22 tests per 100,000 people. To explain it in scale, Ohio, with a population of 11.7 million, would need about 17,767 tests per day to accommodate that recommendation. On April 27, the Governor announced a new testing capacity plan, indicating Ohio should be able to accommodate 18,200 tests per day by May 13. And while testing will help better refine our understanding of transmission in Ohio, it may not accurately demonstrate the harm it has already caused.  

In addition to deaths that are directly attributable to COVID-19, there is “excess mortality,” or the number of deaths which occur above what’s expected in a given timeframe. An April 29 article in The New York Times looked at these numbers globally, and found that when they combined the historical average number of deaths with the number of deaths due to COVID-19, 40,000 additional people died over the last month than expected. That 40,000 number is about 20 percent of the total global mortality currently attributed to COVID-19. The estimates for the United States were based on a combination of reports from the local and national levels, most notably the from the Centers for Disease Control and Prevention’s (CDC) National Vital Statistics System. While the Times’ report did not include Ohio, the CDC numbers did have Ohio’s data, and the results of our analysis are interesting.  

Below compares the annual average mortality in Ohio during the first 14 weeks of the year (first week of April) between 2014 and 2019 alongside data from 2020. When comparing “all causes” of mortality, there doesn’t seem to be much variance:  

In looking at additional potential conditions not qualified as “all causes,” many of the trends demonstrate the same continuance of a trend, except for one category, “symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified.” This category can include fever, headache, fatigue and other, more diffuse symptoms. The data is as follows:  

When looking at the total number of deaths in this category, we see a three-fold increase in mortality relative to expected average.

 It’s important to realize that this data may not represent Ohio’s excess mortality.

It’s important to realize that this data may not represent Ohio’s excess mortality and, indeed, there may be multiple reasons why we are seeing the data reported this way. On the one hand, the classification of the causes, given the relative lack of clinical experience with COVID-19, may indicate symptoms tied to COVID-19 are not being qualified as COVID-19 related. Additionally, there may be data lags between Ohio and the CDC that do not occur in other states. Regardless of the reason, it will be important Ohio review its current epidemiological data collection processes to provide a better picture of the true impact on Ohio. Especially as the state contemplates reopening, knowing exactly how flat the curve is will be a clear indicator of what strategies are working and which may do more harm than good.

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