From The Economist:
Would most covid-19 victims have died soon, without the virus?
A new study suggests not
May 2nd 2020 edition
… However, a study by researchers from a group of Scottish universities has attempted to do so. They found that the years of life lost (ylls) for the average Briton or Italian who passed away was probably around 11, meaning that few of covid-19’s victims would have died soon otherwise.
… The authors excluded the 1% of victims under 50.
I don’t see why.
Then they calculated how much longer these cohorts would normally survive. Life expectancies for old people are surprisingly high, even when they have underlying conditions, because many of the unhealthiest have already passed away. For example, an average Italian 80-year-old will reach 90. The ylls from this method were 11.5 for Italian men and 10.9 for women.
Then the authors accounted for other illnesses the victims had, in case they were unusually frail for their age. For 710 Italians, they could see how many had a specific long-term condition, such as hypertension or cancer….
Strikingly, the study shows that in this hybrid European model, people killed by covid-19 had only slightly higher rates of underlying illness than everyone else their age. When the authors adjusted for pre-existing conditions and then simulated deaths using normal Italian life expectancies, the ylls dropped just a little, to 11.1 for men and 10.2 for women. (They were slightly lower for Britons.) Fully 20% of the dead were reasonably healthy people in their 50s and 60s, who were expected to live for another 25 years on average. …
Eliminating the 1% of deaths under 50 probably lowered the estimate by about 0.4 years.
The researchers warn that their data exclude people who died in care homes, who might have been especially sickly. Nor can they account for the severity of underlying illnesses. For example, covid-19 victims might have had particularly acute lung or heart conditions. More complete data could produce a lower estimate of ylls.
A lot of the commentary by our society’s abundance of Nietzschean Supermen has been about how very few people have died who are, like them, wholly without physical flaws. But, it turns out, that having zero underlying conditions doesn’t add much to your life expectancy relative to having one. For example, 50-something men who died of CV with zero long-term conditions had an expected additional life span of 35.81 years, while those who died with 1 LTC lost an expected 35.03, while those with 5 LTCs lost 19.39.
On the other hand, these estimates are for just Years of Life Lost, not for Quality-Adjusted Life Years Lost. From Wikipedia:
To determine QALYs, one multiplies the utility value associated with a given state of health by the years lived in that state. A year of life lived in perfect health is worth 1 QALY (1 year of life × 1 Utility value). A year of life lived in a state of less than perfect health is worth less than 1 QALY; for example, 1 year of life lived in a situation with utility 0.5 (e.g. bedridden, 1 year × 0.5 Utility) is assigned 0.5 QALYs. Similarly, half a year lived in perfect health is equivalent to 0.5 QALYs (0.5 years × 1 Utility). Death is assigned a value of 0 QALYs, and in some circumstances it is possible to accrue negative QALYs to reflect health states deemed “worse than dead.”
Somebody should hurry up and estimate QALY lost. But I can see why we don’t have that number yet: a big chunk of QALY losses would be among coronavirus survivors whose long term health was damaged. Unfortunately, after a few months, we are still pretty clueless about the long-term effects on survivors.
On the other hand, what were people who died of CV living like before they got it. How close were their years to a perfect 1.0?
By the way, my suspicion is that one reason Italian fatalities skewed so old were that younger Northern Italians tend to be highly healthy compared to Americans.
Here’s the academics’ preprint:
COVID-19 – exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study [version 1; peer review: awaiting peer review]
Peter Hanlon , Fergus Chadwick , Anoop Shah , Rachael Wood ,
Jon Minton , Gerry McCartney , Colin Fischbacher , Frances S. Mair ,
Dirk Husmeier , Jason Matthiopoulos , David A. McAllister1,3
Background: The COVID-19 pandemic is responsible for increasing deaths globally. Most estimates have focused on numbers of deaths, with little direct quantification of years of life lost (YLL) through COVID-19. As most people dying with COVID-19 are older with underlying long-term conditions (LTCs), some have speculated that YLL are low. We aim to estimate YLL attributable to COVID-19, before and after adjustment for number/type of LTCs.
Methods: We first estimated YLL from COVID-19 using standard WHO life tables, based on published age/sex data from COVID-19 deaths in Italy. We then used aggregate data on number/type of LTCs to model likely combinations of LTCs among people dying with COVID-19. From these, we used routine UK healthcare data to estimate life expectancy based on age/sex/different combinations of LTCs. We then calculated YLL based on age, sex and type of LTCs and multimorbidity count.
Results: Using the standard WHO life tables, YLL per COVID-19 death was 14 for men and 12 for women. After adjustment for number and type of LTCs, the mean YLL was slightly lower, but remained high (13 and 11 years for men and women, respectively). The number and type of LTCs led to wide variability in the estimated YLL at a given age (e.g. at ≥80 years, YLL was >10 years for people with 0 LTCs, and <3 years for people with ≥6).
Conclusions: Deaths from COVID-19 represent a substantial burden in terms of per-person YLL, more than a decade, even after adjusting for the typical number and type of LTCs found in people dying of COVID-19. The extent of multimorbidity heavily influences the estimated YLL at a given age. More comprehensive and standardised collection of data on LTCs is needed to better understand and quantify the global burden of COVID-19 and to guide policy-making and interventions.