[W]e require the current economic shock, which is much larger than 2008, to result in much smaller life loss than was associated with 2008. Otherwise we will lose more life to the economic effects of Covid-19 suppression efforts than were ever likely to have been lost to Covid-19 itself. Of course the consequences of the 2008 crisis were amplified by the policies adopted thereafter, and perhaps those consequences could have been substantially alleviated by a more enlightened approach. But the historical record from the UK does not suggest a willingness to vote for such an approach, even if any sort of credible plan for avoiding the economic life loss were actually to be proposed. The 1945 election was perhaps the exception, but it’s unclear that several months stuck at home on your sofa really leads to the same sort of cathartic re-evaluation of life’s priorities as storming the beaches of Normandy.
The cost of adding one more year of life to someone who is dying of coronavirus is more than five times higher than the maximum the NHS can spend on other illnesses, according to a statistician.
- The cost of adding one more year of life to someone who is dying of coronavirus is more than five times higher than the maximum the NHS can spend on other illnesses.
- Professor Simon Wood has calculated that it costs approximately £180,000 per extra year of life to rescue a dying Covid-19 patient.
- NHS watchdog will only spend up to £30,000 per year of life on any new treatment, deeming any higher cost a bad cost-to-benefit ratio.
- Many people left in worse physical or mental health, or in poverty, as a result of policies brought in to slow down Covid-19 could see years chopped off their life expectancy.
- The Office for Budget Responsibility predicted the UK’s national debt would grow by £550billion next year as a result of spending during the epidemic.
- The National Institute for Health and Care Excellence (NICE), which makes decisions on which drugs are good value for the NHS, considers £30,000 to be at the upper end of its good value limit.
- Statistical organisations across the UK, meanwhile, suggest that there have been around 59,000 ‘excess deaths’ during the epidemic, which includes people who died of Covid-19 but never tested positive, as well as those who died because of indirect effects of lockdown, such as being unable to get hospital care.
The number of new infections per day is a key quantity for effective epidemic management. It can be estimated by testing of random population samples. Without such direct epidemiological measurement, other approaches are required to infer whether the number of new cases is likely to be increasing or decreasing: for example, estimating the pathogen reproductive rate, R, using data gathered from the clinical response to the disease. For COVID-19 (SARS-CoV-2) such R estimation is heavily dependent on modelling assumptions, because the available clinical case data are opportunistic observational data subject to severe temporal confounding. Given this difficulty it is useful to reconstruct the time course of infections from the least compromised available data, using minimal prior assumptions. A Bayesian inverse problem approach applied to UK data on COVID-19 deaths and the disease duration distribution suggests that infections were in decline before full UK lockdown (24 March 2020), and that infections in Sweden started to decline only a day or two later. An analysis of UK data using the model of Flaxman et al. (2020, Nature 584) gives the same result under relaxation of its prior assumptions on R.
Modelling by Professor Simon Wood, of the school of mathematics at the University of Bristol, shows that the majority of people who died at the peak would have been infected roughly five days before the lockdown was introduced.
By simply separating out weekly reporting variability, the long-term death rate profile becomes clear, and its peak can be located with confidence. Using the distribution of times from disease onset to death, it is possible to extend the model to infer the time course of fatal infections required to produce the later deaths. Because of the wide variability in onset to death times, a quite sharply peaked infection curve produces a death curve that declines only slowly. The inferred infection curve peaks a few days before lockdown, with fatal infections now likely to be occurring at a much-reduced rate.