How bad is COVID-19? That is far too complex a question for a blog post, so let’s focus on a much simpler question: What would an answer to the previous question look like? COVID-19 will cause deaths and illness, and attempts to reduce the deaths and illness will themselves impose economic costs. We could use a metric like QALYs (quality-adjusted life years) to aggregate the burden of COVID-19. Then, in assessing public policy interventions, such as closing schools and limiting travel, we could at least informally perform some sort of cost-benefit analysis, assessing the economic cost of an intervention and comparing it with the benefit in reduced deaths and illness.
Yet the QALY does not seem to enter into discussions of COVID-19. We see a great deal of discussion of mortality rates among those infected by the novel coronavirus, including reports of how those rates vary by age band. The question, though, is not just the age of those who have died, but how much longer they would have lived. I have not seen any systematic attempt to convert these mortality rates into a QALY number, let alone any formal cost-benefit analyses assessing how different interventions might save QALYs. Perhaps the government or some private citizens have created such analyses, but if so, they don’t seem to show up on Google or Twitter.
Why? Some possible explanations:
(1) Too many unknowns. Cost-benefit analysis, the argument goes, is a process that we can undertake only when we have sufficient time to accomplish it with care. There are many unknowns concerning COVID-19, including how many people carry the virus with mild symptoms or asymptomatically. It is difficult to construct a baseline case of how many people might become infected and die absent government intervention, so any cost-benefit analysis would largely be guesswork.
(2) Real option theory. A more sophisticated version of the previous hypothesis is that a cost-benefit analysis might be too simplistic and in particular might understate the value of extraordinary efforts to contain the virus while we develop better information about its dangers. For example, a cost-benefit analysis based on current assumptions of mortality might suggest that many interventions to reduce the spread of the virus are with high probability not cost-benefit justified, but that with low probability the pandemic will be so bad that such interventions are critical. In theory, we might assume some distribution of how deadly the virus is and simulate the effects of interventions now, taking into account that our information will improve in the future and thus allow better decisions then. But this means that the cost-benefit analysis must account for many more unknowns, plus it is difficult to aggregate the results into a single punchy conclusion.
(3) Lost QALYs. Discussion of QALYs almost always focuses on QALYs saved by interventions. The first step in a COVID-19 analysis would focus on the QALYs that would be lost in baseline. We frequently see reports of how many people die from cancer, but few reports of how many QALYs are lost to cancer. There are exceptions, such as this one measuring the global burden of disease with the related measure of DALYs (disability-adjusted life years). An explanation for the general lack of discussion is that the baseline burden of disease doesn’t matter for policy as much as lives saved from particular interventions. Because we are not used to thinking about disease burdens in QALYs or DALYs, we don’t seek to measure the burden of some new disease in those terms, even though we need to do that before we can assess interventions.
(4) Not medical treatments. Typically, QALYs are used to measure life-years saved by new medical treatments. Here, the question is the expected QALY savings from quarantines, canceling sporting events, and the like. We have standard methodologies for conducting randomized trials of medical treatments, and these methodologies can be used to generate QALY numbers. We may not have standard methodologies for measuring QALYs for government actions that are not medical treatments. In principle, however, we could use epidemiological models to estimate morbidity and mortality and translate that to QALYs.
(5) Prospect theory. The value of a QALY may differ based on whether we focus on a QALY saved or a QALY lost. Because our baseline is the pre-COVID-19 world, whether we are focusing on the burden of disease or the effectiveness of interventions, we frame the new suffering as losses rather than gains. Thus, arguably we should assign a higher value to a QALY with COVID-19 than when focusing on treatments of more familiar diseases. But questions of what a QALY is worth are second-order questions, worth asking when we want to compare QALYs to economic costs of particular interventions. It would be nice to have estimates of QALYs saved by interventions even if we might reasonably disagree about the trade-off between QALYs and economic costs.
I have no idea which steps to slow the spread of COVID-19 are cost-justified and which steps are not. My own ignorance doesn’t matter, but I worry that health organizations and governments may not even be trying to compare costs and benefits in any systematic fashion. Overreaction in the form of an availability cascade is especially likely at the beginning of a crisis, yet there is also a danger of complacency. There may be some good reasons not to base policy decisions solely on comparisons of QALYs and costs, but production of at least back-of-the-envelope estimates could be useful in anchoring serious policy discussion, even in or maybe especially in times of crisis.
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