A few weeks ago, the Wall Street Journal reported on a study suggesting that the average person who dies of Covid-19 loses tens years of life. I looked up the study, and left the following comment on the study’s site (which, to the author’s credit, they have up to get instant peer review), after noticing that the author’s seem to assume that the victims were previously as healthy as their demographic average:
Two people who are coded with the same disease could be in vastly different circumstances. We know the virus has taken a huge toll on nursing homes. An 82 year old with heart disease who lives in a nursing home is not similarly-situated, life expectancy-wise, to an 82 year old who is otherwise doing well and is self-sufficient. The former would assumedly be much more likely to succumb to Covid-19 than the latter. Similarly, “otherwise-healthy” people who succumb to Covid-19 can be expected to, on average, be more likely to have an undiagnosed health issue than those who don’t. Is that taken into account? If neither of these are taken into account, the effect on life expectancy must be reduced.
Now, I see you’ve responded [to another commenter[ that this should NOT have a major effect on life expectancy. I don’t see how you can be so confident. A *huge* percentage of deaths, wildly disproportionate, have been in nursing (“care”) homes… You simply can’t compare an otherwise healthy 82 year old with heart disease to someone whose heart disease so enfeebles him or her that they need to be in a nursing home.
A day later, another commenter wrote:
I’m perplexed by this study. How can it be assumed that the Covid victims would have lived the average life expectancy unless there’s no or minimal standard deviation around that average? Wouldn’t it be more compelling to compare to the minimum life expectancy of each cohort? Otherwise, you are implicitly assuming that the people who are dying are more or less representative of the average, which seems like a major assumption that, if untrue, would render your conclusions pretty useless. I hope I’m missing something here because it would seem far more intuitive to assume that people who are dying are the most vulnerable of their respective cohorts.
A few days later, lead author David McAllister responded:
In response to Jason Bloomberg and David Bernstein.
Our work was a response to the assertion has been that “because those dying are older and have lots of comorbidity, they probably don’t have to live”. I think JB and DB may be making a different statement that “notwithstanding the fact that the average life expectancy is still quite long among older people with comorbidity, those dying from COVID-19 are likely atypical compared to the average among older people with comorbidity”.
I think we are talking here about residual confounding, i.e., after you take into account the known/measured variables, are there remaining differences between patients on which we estimated life expectancy (the general community in Wales) and those dying of COVID-19 in the Italian data.
I think one has two options with residual confounding. Either to state this as an assumption/limitation and/or try and model it in some kind of sensitivity analysis.
Professor Andy Briggs effectively does the latter (https://avalonecon.com/estimating-qaly-losses-associated-with-deaths-in-hospital-covid-19/) looking at the effect of quite large multipliers on life expectancy, implemented via an excel tool. This would allow the commentators or others to explore the impact of different mortality rate ratios based on different assumptions as to the degree of residual confounding.
We have taken the former approach. As we are not aware of any empirical evidence to provide us with an estimate for the magnitude of the residual confounding due to unmeasured characteristics (e.g. frailty, functional limitation).
This is because, in order to make the assertion that those dying from COVID19 are atypical of their fellows who are similar in terms of age, sex and comorbidity we would argue that empirical evidence to support that claim is needed. Not least because, although we cannot know how strong they are, there may be selection pressures in the opposite direction. For example, someone with relatively mild COPD might go food shopping themselves, whereas someone with more severe disease might have someone else shop for them, thereby reducing their infection risk. Since the risk of death is the product of the risk of infection and the case fatality, this mechanism would tend to select for less severe COPD among those dying from COVID-19.
We argue that additional data, ideally on functional limitations (e.g. able to walk to shops, able to walk up stairs) and frailty measures (e.g. grip strength, lung capacity, six-minute walking distance) should be obtained to allow us to estimate the YLL more accurately using more empirical evidence.
Nonetheless, we think that this reasoning should not be applied to care home residents. Our results came out before the large numbers who were dying in care homes became apparent and this was not the focus of our work. Instead we agree that we should estimate mortality (and YLL) in care homes separately. Importantly, care home residents are a well-defined population so the task of estimating life expectancy in this group should be achievable in most settings.
Some readers may be wondering what a law professor is doing commenting on scientific papers, but I’ve been researching and writing about the reliability of scientific evidence for over thirty years, so I’m not a complete noob. And of course it goes without saying that all lives are valuable, even if scientists and policy-makers have good reasons for wanting to know how Covid-19 is affecting victims’ life spans.
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