New metric reveals economic losses from disease and illness
Over the course of a standard working lifespan in Indonesia, their national economy will lose around 15.6 million years of productivity to smoking related illnesses among the workforce. Australia stands to lose just a fraction of this amount, with 2.5 million productive years wiped from our economy over a standard working lifetime.
In Australia, high blood pressure causes nearly 610,000 years of lost productivity over the course of a standard working lifespan. If we could help patients achieve optimal control of their blood pressure long-term, it would reduce that figure by more than half, saving our economy $76 billion in gross domestic product.
These are some impressive figures. But how do we know? And how do we best use that information?
Such estimates have been made possible by the work of A/Prof Zanfina Ademi and colleagues at the new Monash Outcomes Research and health Economics (MORE) Unit, including Prof Danny Liew, A/Prof Ilana Ackerman and Dr Ella Zomer. They’ve designed a novel metric to assess long-term economic productivity losses caused by illness or unhealthy behaviours.
The Productivity-Adjusted Life Year (PALY) has been designed to complement two widely used measures of burden and value in healthcare, Disability-Adjusted Life Years (DALYs) and Quality-Adjusted Life Years (QALYs). PALYs gauge productivity losses associated with ill health that accumulate over populations and time to have significant societal and economic consequences.
Health economists and researchers around the world have embraced the new metric. Zanfina says, “It’s been only 12 months since we released the PALY, and already 13 researchers in eight countries have applied it to their own work. I’ve also been invited to provide a commentary in the journal Pharmacoeconomics, and several editorials have been written about studies that have reported PALY data.
“International application of the PALY to date has already made revelations about the costs of diabetes, heart disease, smoking, kidney disease, pneumococcal disease, high blood pressure, occupational hearing loss, high cholesterol, epilepsy, migraine, blood cancers, streptococcal infections and depression. It just goes to show the breadth of health issues that can be assessed through this tool.”
Standardised measurements of work loss, typically measuring absenteeism (days of work missed) and presenteeism (working, but at a lower productivity rate due to illness), have long been used by health economists and researchers. However, these measures do not capture the full working life span or whole populations.
The PALY delivers a measurement of productivity loss at a population level and across working lifetimes, enabling health financiers and decision-makers to better understand long-term gains achievable by enacting preventive health strategies, and conversely, the losses incurred by inaction. It accounts for unemployment, days off work, reduced efficiency at work and premature death.
Just as in the smoking and blood pressure examples cited above, the PALY delivers a unique opportunity to easily compare productivity losses between disease states and regions. For example, the years of productivity lost per person in Australia as a result of some common conditions and behaviours are:
- Diabetes = 1.4 years (undiscounted)
- Smoking = 1 year (undiscounted)
- High blood pressure = 0.3 years (discounted)
- Epilepsy = 1.4 years (discounted)
Such information gives policy-makers explicit insights into where economic gains can be made by improving health management. It’s also something that might be used directly by employers, especially in countries where employers support employee health costs, and may influence health behaviours.
The team have already calculated unique PALY estimates for a range of illnesses and unhealthy behaviours that result in productivity loss with a high financial cost, and now they’re developing estimates for low back pain, knee osteoarthritis, cancer and COVID-19 related depression.
The team have recently advanced the model to become dynamic, allowing for people with multiple health conditions, movement in and out of the models over time (migration, death, birth etc), and changes to prevalence or population size over time.
They are also working hard to share their new model with other researchers, including those from low- and middle- income countries that would particularly benefit from its insights.
Zanfina says, “I hope we’ll see more uptake, and that PALYs will be used for health resource and reimbursement decision making, as well as for improving our understanding of the economic and productivity impacts of common diseases and risk factors across populations. We’ll also be able to evaluate the effectiveness of new treatment options in terms of their potential for work productivity gains over time.
“The coming years will see us educating and training health economists about the PALY metric, so that it will one day be used to drive policy decisions around the world.”
To read more about the PALY, check out this invited commentary in PharmacoEconomics: Productivity‑Adjusted Life‑Years: A New Metric for Quantifying Disease Burden