COVID-19 – exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study

Background: COVID-19 is responsible for increasing deaths globally. As most people dying with are older underlying long-term conditions (LTCs), some speculate that YLL low. We aim to estimate attributable COVID-19, before and after adjustment number/type of LTCs, using the limited data available early in pandemic. Methods: first estimated from WHO life tables, based on published age/sex Italy. then used aggregate LTCs a Bayesian model likely combinations among COVID-19. routine UK healthcare Scotland Wales expectancy age/sex/these Gompertz models which we YLL. Results: Using standard per death was 14 men 12 women. After number type mean slightly lower, but remained high (11.6 9.4 years women, respectively). The led wide variability at given age (e.g. ≥80 years, >10 0 <3 ≥6). Conclusions: Deaths represent substantial burden terms per-person YLL, more than decade, even adjusting typical found extent multimorbidity heavily influences age. More comprehensive standardised collection (including LTC type, severity, potential confounders such as socioeconomic-deprivation care-home status) needed optimise estimates specific populations, understand global guide policy-making interventions.