Jonathan Portes

Analysis: what is meant by 13,000 ‘excess’ NHS deaths?

When the dust settles on the Keogh report published last week  one figure is likely to linger: the “13,000 excess deaths” in the 14 NHS hospitals. It deserves careful scrutiny – and some has been applied by Isabel Hardman here with more details about this curious notion of “Hospital Standardised Mortality Rates” in the Health Service Journal here.  But these still leave the question unanswered as to why these “extra” people are dying, and what, if anything, we can and should do about it.  Here’s my attempt. It’s fairly detailed, and it’s still a lovely day so those who don’t have an appetite for such things may not want to click on the link. But those who do want to get their heads around this may find it interesting. The figure of 13,000 excess deaths was important enough to put on front pages of newspapers and quoted on the news bulletins, so it’s worth looking a little more at what it actually means.

Simplifying somewhat, we start with data which includes information on patients’ death or survival, and correlates that, using so-called regression analysis, with some observed characteristics – mostly specific health conditions and demographic characteristics.  So we can say, for example, that a 55 year old male lung cancer patient has on average an X% chance of dying. Express that on a hospital level, and you have “expected” deaths.  “Excess” deaths (or “excess” survival) are just the difference between actual deaths and the number “predicted” by the regression model for each individual hospital.  A hospital with no excess deaths is one that performs exactly as the model predicts.

But – and this is the key point – these differences are simply the variation between hospitals that the regression model doesn’t predict. By definition, we don’t know what explains them; if we did, we’d have put it in the model in the first place as one of our explanatory variables.

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