Note: As many of the posts have not yet been completed, I may edit this summary to reflect the final content.
This sequence describes some of the metrics commonly used to evaluate health interventions and estimate the burden of disease, explains some problems with them, presents some alternatives, and suggests some potentially fruitful areas for further research.
It is primarily aimed at members of the effective altruism (EA) community who may wish to carry out one of the projects. Many of the topics would be suitable for student dissertations (especially in health economics, public health, psychology, and perhaps philosophy), but some of the most promising ideas would require major financial investment. Parts of the sequence—particularly the first and last posts—may also be worth reading for EAs with a general interest in evaluation methodology, global health, mental health, social care, and related fields.
I begin by looking at health-adjusted life-years (HALYs), particularly the quality-adjusted life-year (QALY) and the disability-adjusted life-year (DALY). By combining length of life and level of health in one metric, these enable direct comparison across a wide variety of health conditions, making them popular both for evaluating healthcare programmes and for quantifying the burden of diseases, injuries, and risk factors in a population. I’ve also heard EAs using these concepts informally as a generic unit of value.
However, HALYs have a number of major shortcomings in their current form. In particular:
They neglect non-health consequences of health interventions.
They rely on poorly-informed judgements of the general public.
They fail to acknowledge extreme suffering (and happiness).
They are difficult to interpret, capturing some but not all spillover effects.
They are of little use in prioritising across sectors or cause areas.
This can lead to inefficient allocation of resources, in healthcare and beyond.
Broadly, three alternative measures could be developed in order to address these limitations:
The HALY+: a tweaked version of the original QALY or DALY that captures some non-health outcomes and/or relies on more informed preferences.
The sHALY: a “subjective wellbeing-based HALY” that retains the health-focused descriptive system but assigns weights to health states using experienced wellbeing rather than preferences.
The WELBY: a wellbeing-adjusted life-year that can, in principle, capture the benefits of all kinds of intervention. A variation, the pWELBY, uses preferences to assign weights to each level of wellbeing.
After introducing these metrics, this sequence considers the additional research required to create them, and potential applications both within and outside EA. The importance, tractability, and neglectedness of each major project is briefly considered, though I do not attempt a formal priority ranking. For individual researchers, my extremely tentative view is that work to establish the “dead point” (below which are states worse than dead) and lower bound on wellbeing scales is likely to have the greatest payoff—but, as with careers in general, the best choice of project is likely to depend heavily on personal fit. For well-funded research teams, including some large EA organizations, there may be the opportunity to resolve some key uncertainties and help establish wellbeing as the unit of measurement in global health and public policy.
While the main purpose of the sequence is to raise questions rather than provide answers, I conclude with some general thoughts about the value of work to improve and apply these outcome measures. Overall, I’m increasingly skeptical that any single metric will suit all purposes, and that the outcome measure is a major source of uncertainty in the biggest decisions, such as choosing between neartermist cause areas (such as global health) and longtermist ones (such as risks from artificial intelligence). I also think that the practical and normative challenges of using wellbeing, especially subjective wellbeing, have perhaps been underestimated. That said, progress on these questions could have significant implications for certain priorities, potentially changing our views on, for example, the relative importance of physical versus mental health, healthcare versus social services, and preventing human extinction versus preventing astronomical suffering.