Part Five – Capturing the Full Effects of Policy Interventions

How quality-inclusive metrics can help us better appraise potential policy outcomes

Part Five in Paul Dolan’s six-part series ‘Making Policy Better

Whatever our views about the policy responses to COVID-19, we surely all agree that they did not properly consider the collateral effects of mandated non-pharmaceutical interventions (MNPIs) or “lockdowns”, such as cancelled hospital appointments, closed schools and increased loneliness. One reason is that these effects cannot be quantified in the same – relatively easy – way as mortality risks. It does not follow, however, that they cannot be quantified at all.

Even if we only looked at life expectancies and ignored life experiences completely, we might have reached different conclusions about the effectiveness of the most restrictive MNPIs. Or we might have reached the same conclusions but with greater confidence in the decisions made. Appraising a policy in terms of changes to the number of life-years, rather than changes to the number of lives, forces us to think about those whose lives are immediately saved by a policy and about the lives of everyone else in the population over a longer time frame.

In this way, we can properly account for the expected effects on the life expectancies of those affected by COVID-19 as well as those who will die sooner from health services being displaced from elsewhere to treat COVID-19 patients (those missing urgent cancer diagnosis and treatment due to “stay at home” messaging, for example). At the same time, any policy appraisal would acknowledge that a reduction in educational opportunities and mental illness reduces life expectancy. Loneliness is another good example since it is a significant risk factor for all-cause mortality.

We all care about quality of life as well as quantity. To reflect this, the UK led the way in the adoption of quality-adjusted life years (QALYs). QALYs seek to combine the value of changes in quality of life and length of life into a single number, with one year of life in full health being equivalent to one QALY.

Based on the submissions made by medical device manufacturers and pharmaceutical companies about the cost-per-QALY of their therapies, and from the decisions made by the National Institute for Health and Care Excellence (NICE) about which therapies to recommend for NHS funding, it is possible to estimate the threshold at which a QALY becomes too expensive to commit pubic resources.

In the UK, the threshold value is around £30,000 per QALY, rising to up to about £50,000 for end-of-life care. Given the widespread use of QALYs in the UK, it is surprising that they have not featured prominently in appraisals of pandemic response policies.

QALYs focus on health-related life experiences. But what about the effects of being socially isolated? This might show up in QALYs, but only indirectly via its effect on health. However, it will be of direct importance to most of us. Wellbeing measures that ask people about how they are feeling daily or about their lives overall allow us to capture the full range of effects that policies have on people’s lives.

To fully appraise policy, we must seek to capture the effects of changes in life experiences and life expectancies in a single metric. Combining wellbeing measures with life-years yields a wellbeing-adjusted life-year – or WELLBY. If all benefits across different sectors could be captured in WELLBYs, then the resources devoted to the public sector could be allocated to generate the greatest number of WELLBYs. This would allow us to use resources as efficiently as possible.

The pandemic has illustrated the importance of knowing how much water is displaced when a policy intervention pebble is dropped into the water. It is not good enough to look only at the initial splash, especially when – as with MNPIs – some of the ripple effects will create much more impact.

Indeed, we are likely to be catching tidal waves of effect across many domains of human experience for years to come. WELLBYs provide us with a metric to capture these effects and build their assessment into planning for future crises and into effective decision-making at any time.

Click here to read Part Four, The Effects of Fear at a Time of Crisis

Paul Dolan is Professor of Behavioural Science at London School of Economics and Political Science He is the best-selling author of Happiness by Design and Happy Ever After, and the host of the new Duck-Rabbit podcastwww.pauldolan.co.uk.

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