What can we learn from the UK mortality data?

Data reliability has bedevilled the task of analysing and responding to the Covid-19 outbreak.  So there might be something to be gleaned from recent UK mortality data.

It captures and breaks down the weekly deaths in England and Wales, which normally average a little more 10,000.

But in the week to 3 April there were 16,387 (60% above the average of the last five years) and 18,500 in the following week (some 80% over the average). 

The data shows a strong association between Covid as a cause of death and the ‘excess mortality’ (i.e., the number of deaths above the historical trend).  There’s a good chart in this BBC reporting.  But you don’t need to be a pessimist to fear that this could understate the case.  It might be advisable to work on the basis, at least pro tem, that most, if not all, of this excess mortality is attributable to Covid.

The detailed breakdown throws up some important pointers, and in particular the enormous disparity in mortality rates according to age. Drawing from this rich data set we can see that in the week to 3 April there were 5,742 more deaths than two weeks earlier:  625 more under-65s and 5,116 more over-65s.  Yet the over-65s make up barely 18% of the total population.

The age disparity becomes even clearer when seen in terms of death rates.  In 2018, the death rate in England and Wales for under-65s was 171 per 100,000 for under-65s and 4,230 per 100,000 for over-65s.  The data from the week of 3 April showed (roughly) annualised death rates of 239 and 6,801 respectively.

And there is probably even more disparity hidden inside this data.  Specifically, there is a strong association between Covid mortality and existing ill-health:  more than 90% of those who died in March with Covid cited as a contributory factor also had an underlying health condition, such as heart disease, dementia or respiratory illness.

So if one could sort the over-65s into two groups according to health status, there would be higher – potentially much higher – death rates for those already in ill health.

A crude proxy for identifying infirm elderly might be to use the population in care homes (410,000 in England and Wales).  The actual death rates in this group may not be immediately apparent because the available data records location of death (most Covid deaths are shown occurring in hospitals) rather than place of residence before death.

The revelation that excess deaths are particularly heavily concentrated in this group would no doubt provoke necessary, and perhaps uncomfortable, scrutiny of epidemic preparedness and response. 

But the emerging conclusion from the data could be that the main social distancing requirement is essentially between two distinct but not yet fully identified risk groups.  The first, including the young (and middle aged) and healthy, would be directed towards a minimise (risk) and normalise strategy; while the second, consisting of the vulnerable, would be more intensively sequestered and protected.  Moreover, in a free society it seems essential for the individual to have as much choice as possible in choosing his or her group (and consequently the risks he or she faces).

If something like this were to emerge as the world standard, it would have implications for those governments, like New Zealand’s, which hope it might be possible to eliminate Covid.

Elimination looks a bit like sequestering the entire country inside the more-vulnerable category, at least for a time.  The prize would then be the ability to end costly social distancing inside the national borders.  But the cost of restricting the activities of those who don’t need so much protection, and of hardening the external border, may prove to be much more expensive than simply protecting the vulnerable.   

But we should be finding out more about this very soon.

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