The Burden of COVID-19
Tags: Covid 19, Data Science, Dataviz

In this work we will look at how COVID-19 deaths compare with the expected number of deaths in a country, based on historical mortality data. We define the COVID-19 burden to be the number of COVID-19 deaths as a fraction of the expected number of deaths in a country over the same period of time; that is from the time of the first reported COVID-19 death.

Now, you might ask why we need yet another metric? What does this metric tell us that others, such as the more usual total number of fatalities per capita, do not? Just as the number of fatalities per capita is a better metric than a simple count of fatalities — because it normalises for population, making it easier to compare countries of different sizes — the COVID-19 burden goes one step further, because it also normalises for a country’s natural mortality rate. And by doing so, it combines a whole host of additional mortality factors, from population demographics, healthcare conditions, living standards, food and water security, conflict etc.

Thus, we present the COVID-19 burden as providing a more comprehensive account of a country’s experience under the current COVID-19 regime. Using datasets from OWID we will attempt to shed light on the following questions:

Are countries with higher mortality rates more or less likely to be associated with high numbers of COVID-19 fatalities per capita? Is it fair to compare official COVID-19 deaths between countries, given differences in reporting policies? How does the COVID-19 burden vary by country and continent? How does COVID-19 rank against other common causes of death in countries around the world?

A Major Caveat

Before going any further it is worth confronting a key caveat associated with this work, namely inconsistencies in official COVID-19 fatality statistics. It is not that reported numbers are inaccurate per se, rather they mean different things because they arise from many different reporting policies. Some countries only include deaths that arise from a confirmed COVID test, others have limited their focus to hospital settings, while yet others have been far more inclusive, counting suspected and confirmed deaths across all settings. These differences were especially common during the early stages of the pandemic, and while many countries have at least addressed some of the obvious shortcomings in their early counting (e.g. counting hospital deaths only) inconsistencies do remain.

This is problematic given the central aim of this work, because the COVID-19 burden is, after all, a relative fatility estimate that is based on these inconsistent fatality numbers. However, we can at least shed some light on the the scale of this problem, by comparing official COVID-19 fatality statistics to so-called excess deaths. Excess deaths provide an estimate of the additional deaths this year so far, regardless of cause, by comparing total numbers of deaths in 2020 to historical averages. The problem is that these data are difficult to come by and are only readily available for a small subset of the countries in our dataset. However, enough data is available to at least gain some useful insight into the degree of inconsistency that likely exists.

Ideally, we should expect to find a strong correlation between excess deaths and officially reported COVID-19 deaths. This graph shows this relationship using an OWID dataset of 27 countries with known excess death estimates. It does indicate a reasonably strong relationship with a so-called r-squared value of 0.8, which means that 80% of the variation in the official COVID deaths can be explained by the excess death estimates. Note that excess deaths is measured by OWID as a sum of the daily percentage differences, relative to historical norms. For example, Belgium has a total excess of 380% based on an average daily excess of 2.5% during the duration of its outbreak. This does not mean that there have been nearly 4x as many extra deaths in Belgium, rather it means that there have been approximately 2.5% extra deaths so far during the COVID period.

Most countries fall close to the linear regression line used to estimate the relationship between these two fatality parameters, but Belgium is the obvious outlier. It’s official fatalities (>80 per 100k) are very high in comparison to other countries with similar excess deaths. Given that Belgium is the outlier, this suggests that its official fatality estimate is too high and needs to be reduced (by almost 40%) to bring it into line with the reporting standards of others, and such an adjustment will have a corresponding effect on Belgium’s COVID-19 burden. In a similar way, the official fatality estimate from the UK may be too low and it should be increased by approximately 12%.

In summary, some variation remains in the way that official COVID-19 fatalities are recorded, and although the degree of this variation between countries is likely to be <20%, this nonetheless highlights the need for caution when comparing countries to one another. This is especially true when official fatality statistics differ by <20%, because such differences may be an artefact of reporting rather than a sign of genuine difference in the number of COVID-19 related deaths.

Defining the COVID-19 Burden

Given this caveat we can now consider the relationship between official COVID-19 fatalities and historical mortality rates for a dataset of 173 countries. Not surprisingly, there is only a very weak association between COVID-19 deaths per capita and the corresponding expected deaths over the same period —the r-squared value between these two variables is just 0.02— because the former depends on the number of COVID-19 infections in a country, which is not strongly correlated with historical mortality rates. In other words, countries with a higher historical mortality rate are not associated with a higher COVID-19 infection or fatality rate, and vice versa.

We define the COVID-19 burden to be the total number of official COVID-19 deaths divided by the historical number of (all-cause) deaths that would be expected to occur over the same period; for convenience, we will typically express the the COVID burden as a percentage rather than a fraction. For example, at the time of writing it has been 180 days since Ireland’s first reported COVID-19 fatality, and there has been 1,778 COVID deaths reported so far. Ireland’s historical number of annual deaths is just under 30,000 per year. So during the outbreak approximately 14,500 deaths might have been expected; this assumes a uniform distribution of deaths throughout the year, which is certainly not accurate, but likely good enough for our purposes. This means that Ireland’s current COVID-19 burden is just over 12% (or strictly speaking 0.12) of expected deaths over the same period.

The “Tadpole” Plot

Below, is a plot of COVID-19 deaths versus the expected (historical) deaths over the same period of time. Each country is represented by a single marker (the head) with a tail, just like a tadpole. The size of the head is proportional to the number of confirmed cases per capita, and it is coloured according to its COVID-19 burden, as per the scale shown.

The highlighted countries are also annotated with their current COVID-19 burden, as a percentage. An arrow shows whether this is increasing, stable, or falling (compared to 30 days ago) and, in brackets, a separate burden estimate calculated using the total expected number of deaths in a year, instead of the expected deaths so far during the COVID period. For example, Ireland has a current COVID-19 burden of 12%, which is falling, and its COVID deaths so far account for 5% of Ireland’s historical annual total.

The tails indicate the trajectories of COVID-19 deaths over the previous 30 days. They show whether deaths have been increasing (tails with an upward trajectory) or absent (horizontal tails). For example, upward-facing tails of countries such as Peru, the US, and Israel indicate increasing deaths — the steeper the angle the greater the rate of increase — whereas Ireland, France, and Germany, have horizontal tails, indicating no material increase in COVID deaths over the past 30 days.