Adjusting Expected Deaths for Mortality Displacement During the COVID-19 Pandemic: A Model Based Counterfactual Approach at the Level of Individuals

Background
Near-real time surveillance of excess mortality has been an essential tool during the COVID-19 pandemic. It remains critical for monitoring mortality as the pandemic wanes, to detect fluctuations in the death rate associated both with the longer-term impact of the pandemic (e.g. infection, containment measures and reduced service provision by the health and other systems) and the responses that followed (e.g. curtailment of containment measures, vaccination and the response of health and other systems to backlogs). Following the relaxing of testing and social distancing regimes, across many countries, it becomes critical to measure the impact of COVID-19 infection. However, prolonged periods of mortality in excess of the expected across entire populations has raised doubts over the accuracy of historic estimates of the expected deaths that are needed to calculate excess deaths because many individuals died earlier than they would otherwise have done: i.e., “mortality displacement” or “harvesting”. Furthermore, unlike a community-wide event in which the whole population is ‘at risk’ (e.g., temperature shock), it is primarily people who contracted COVID-19 that are ‘at risk’ of the direct effects of the disease implying that an individual-level analytic approach to estimating mortality displacement is advantageous.

Methods
We present a novel Cox-regression-based methodology using time-dependent covariates to estimate the profile of the increased risk of death across time in individuals who contracted COVID-19 among a population of hip fracture patients in England (N=98,365).

Results
Among the exemplar population we present an end-to-end application of our methodology to estimate the extent of mortality displacement. A greater proportion of older, male and frailer individuals were subject to significant displacement while the magnitude of displacement was higher in younger females and in individuals with lower frailty: groups who, in the absence of COVID-19, should have had a substantial life expectancy.

Conclusion
Our results indicate that calculating the expected number of deaths following the first wave of the pandemic in England based solely on historical trends will result in an overestimate, and excess mortality will therefore be underestimated. Our findings, using this exemplar dataset are conditional on having experienced a hip fracture. Fractures that impede mobility in the weeks that follow the accident/surgery considerably shorten life expectancy and are in themselves markers of significant frailty. It is therefore important to apply these novel methods to the general population, among whom we anticipate strong patterns in mortality displacement – both in its length and prevalence – by age, sex, frailty and types of comorbidities. This counterfactual method may also be used to investigate a wider range of disruptive population health events. This has critical implications for public health and the interpretation of public health data in England and globally.