The increase in global deaths due to COVID-19 has been widely publicised, with updated mortality figures being broadcast daily. But little analysis has been carried out on whether these people died long before their time and if so, for how much longer they could have expected to live.
A political and media emphasis on ‘underlying health conditions’ linked to mortality rates has eased this burden of information, leaving an impression that these people would have died soon anyway.
New research, undertaken between Glasgow and Edinburgh Universities, Public Health Scotland and the Scottish Public Health Observatory, suggests that this may not be the case.
The study adopted the standard epidemiological measure of Years of Life Lost (YLL) to indicate the potential life expectancy of an individual had they not succumb to a single heath condition. The authors extend this measure in their analysis to include the number and type of multiple, long-term health conditions, known as multimorbidity, and apply it to COVID-19.
The researchers started by looking at the World Health Organisation’s Life Table data which indicates global life-expectancy statistics. This was modelled with published data on COVID-19 deaths in Italy, chosen due to the comprehensive reporting of multimorbidity in Covid-related deaths by the Istituto Superiore di Sanità, Italy’s National Institute for Health.
This provided a useful aggregate of data to produce an acceptable model of long-term conditions in those with the virus, but individual patient-level data was needed to improve its accuracy. The team supplemented the Italian data with a smaller subset of Scottish health records to perform complex simulations to represent a cohort of 10,000 patients.
The authors then analysed 850,000, anonymised, individual patient records within SAIL Databank to look for an association between age and survival rates against an index of comorbidities. They also examined 145 patients within SAIL Databank who had ‘influenza’ recorded as the cause of death for the year 2011.
In the highly correlated age-multimorbidity model it was estimated that the number of years of life lost was 13.3 for men and 10.9 for women, and in all the study’s models the years of life lost remained high at over 5 years, regardless of high counts of multimorbidity and even among older age groups.
The mix of direct and indirect consequences of coronavirus, and the ensuing ‘lockdown’, present a complex set of considerations for policymakers. This research seeks to provide evidence that could help strike a balance when lockdown measures are relaxed; restoring some degree of public liberties but controlling infection rates and maintaining capacity in the health service to deal with further outbreak.
In addition to providing a model template for similar analysis in other countries, the researchers hope that this work will prevent the full burden of this disease from being overlooked and will aid society’s transition back to normality, in whatever form this may take.
By Chris Roberts, Swansea University
Related News Article and Media Coverage
Read the full article on COVID-19 – exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study here.
Read a feature from The Economist here.
ACKNOWLEDGMENTS
Hanlon P, Chadwick F, Shah A et al. 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]. Wellcome Open Res 2020, 5:75 (//://doi.org/10.12688/wellcomeopenres.15849.1)
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//://twitter.com/PopDataSci_SU/status/1257253513299742720
SAIL Databank is one of the nine Centres of Excellence based in Population Data Science at Swansea University Medical School.