key: cord-0856457-okndygn5 authors: Ademi, Zanfina; Ackerman, Ilana N.; Zomer, Ella; Liew, Danny title: Productivity-Adjusted Life-Years: A New Metric for Quantifying Disease Burden date: 2021-01-11 journal: Pharmacoeconomics DOI: 10.1007/s40273-020-00999-z sha: f0b67b48c5fcc6c6f587becfce08af8c1eeec030 doc_id: 856457 cord_uid: okndygn5 nan The PALY is calculated by multiplying a 'productivity index' by years lived (Fig. 1) . The productivity index ranges from 0 (completely unproductive) to 1 (completely productive), and is estimated from data on time worked by fully healthy individuals, absenteeism, presenteeism and premature workforce exit. Data on absenteeism and presenteeism can be drawn from the use of the aforementioned validated tools. We commonly derive the productivity index through dividing the days worked in a year (maximum working days minus days missed because of the condition of interest) by the maximum working days in a year. The maximum working days in a year is obtained through combining information on the overall percentage of equivalent full-time workers, which is country specific and age and sex specific. For example, if the weighted average for full-time workers across the defined age range is 75%, the maximum working days in a year for that particular country would be 180 days (240 days multiplied by 75%). By definition, a PALY has an intrinsic economic value, and herein lies its utility. There are a number of ways to estimate this financial value. To date, we have ascribed a value to each PALY equivalent to the gross domestic product (GDP) per equivalent full-time worker for a specific country. This 'human capital' approach is simple to estimate given that GDP is an oft-reported economic metric, as are data on workforce participation. However, it is crude as GDP is generated in many ways other than people working. Another way to assign a financial value to a PALY is by using available salary estimates, which reflects both personal income for workers and income tax for governments. We recommend that PALYs only be reported as absolute values, and not be used in ratio calculations (for example, cost per PALY saved). This is because they have an inherent financial value, and hence their application to ratios will involve double counting of costs. In the last 2 years, our group has collaboratively published PALY estimates for a range of highly prevalent health conditions and risk factors, including diabetes mellitus [6] [7] [8] , coronary heart disease [9, 10] , hypertension [11] , [12, 13] , occupational-related hearing loss [14] , pneumococcal disease [15] , familial hypercholesterolaemia [16] , epilepsy [17] and migraine [18] . We are now developing PALY estimates for low back pain, cancer, COVID-19-related depression and acute myeloid leukaemia. As a generic measure, the PALY facilitates comparison of productivity burden across health conditions to inform practice and policy. For example, between-condition comparisons show that 1.4 and 1.0 PALYs (undiscounted) were lost per person in Australia as a result of diabetes [6] and smoking [12] , respectively, over the working lifetime while 0.3 and 1.4 PALYs (discounted) were lost per person as a result of hypertension [11] and epilepsy [17] , respectively. Aggregate PALY estimates can highlight not only the prevalence of a health condition and its work impacts, but also the potential for productivity gains with appropriate treatment. For example, hypertension was estimated to cause the loss of > 609,000 PALYs in Australia, but optimal blood pressure control would save > 340,000 PALYs or $76 billion in GDP [11] . Thus far, PALY estimates have been produced for Australia [6, 9, 11, 14, [16] [17] [18] , Bangladesh [8] , China [7] , Indonesia [10, 19] , Japan, Korea [20] , Malaysia [13] and Thailand [15] . Between-country comparisons of disease burden or risk factor burden can highlight significant disparities: using a consistent methodology for each country, we found that PALYs lost because of smoking totalled 15.6 million in Indonesia [19] , compared with 3.0 million in Malaysia [13] and 2.5 million [12] in Australia. We have now extensively tested and refined our PALY modelling, moving from initial 'static' life table models to 'dynamic' models that are capable of incorporating: (1) multiple health states; (2) movement of individuals in and out of the model over time; and (3) updates of prevalence, population and efficacy data. A dynamic model (accounting for future migration, births and deaths, and changes in the working age population) was most recently used to examine the impact of coronary heart disease and potential PALY gains if all new cases could be prevented over a 10-year period [9] . Importantly, we are also building researcher capacity in this new technique by training the next generation of health researchers, including those from low-and middle-income countries. Modelling support is available for researchers seeking to undertake PALY analyses within their field of interest. Like any measure, the PALY approach has inherent limitations. By definition, PALYs focus on working age populations (usually defined as those aged 16-65 years) and productivity estimates are only simulated over the working lifetime. As health economic evaluations usually adopt a lifetime horizon, this raises a potential equity issue, particularly if PALYs are used to inform reimbursement decisions. To date, PALY models have only incorporated paid workforce data but we acknowledge that unpaid work (caregiving, child rearing, household and community roles) is also impacted by ill health. Where robust population-level data on unpaid work are available, these will enable a more complete definition of 'productivity'. The PALY is not intended to replace the disabilityadjusted life-year or the quality-adjusted life-year. Instead, it offers a novel, but now well-tested, approach to quantifying the population-level impact of disease on productivity (arising from unemployment, days off work, reduced efficiency at work and premature death) and the broader economy. New treatments could be evaluated through estimation of the number of PALYs gained from their use. Beyond 'traditional' healthcare payers, PALYs could also be used by employers, particularly in settings where they support employee healthcare costs. For the first time, there exists an opportunity for decision making in health to be explicitly informed by the productivity burden of disease and the potential for productivity gain with effective intervention. Author contributions ZA and INA drafted the manuscript for intellectual content. All authors provided feedback on early drafts and revised the manuscript for intellectual content. Funding No funding was received for the preparation of this commentary. Ademi has no conflicts of interest that are directly relevant to the content of this article. Ilana N. Ackerman declares fellowship funds from the Victorian Government. Ella Zomer reports grant support from Amgen, AstraZeneca, Pfizer and Shire, outside the submitted work. Danny Liew declares grants from Abbvie, Amgen, AstraZeneca, Bristol-Myers Squibb, Edwards Lifesciences, Pfizer and Sanofi, and past participation in advisory boards and/or receipt of honoraria from Abbvie, Amgen, Astellas, AstraZeneca, Bris- Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study Health outcomes in economic evaluation: the QALY and utilities Labor and health status in economic evaluation of health care: the Health and Labor Questionnaire The Work Limitations Questionnaire The validity and reproducibility of a work productivity and activity impairment instrument The productivity burden of diabetes at a population level The impact of diabetes on productivity in China The impact of diabetes on the productivity and economy of Bangladesh The impact of coronary heart disease prevention on work productivity: a 10-year analysis Health and productivity burden of coronary heart disease in the working Indonesian population using life-table modelling Productivity burden of hypertension in Australia Productivity burden of smoking in Australia: a life table modelling study Impact of tobacco use on health and work productivity in Malaysia Productivity burden of occupational noise-induced hearing loss in Australia: a life table modelling study Estimating the productivity burden of pediatric pneumococcal disease in Thailand The economic impact of familial hypercholesterolemia on productivity The costs of epilepsy in Australia: a productivity-based analysis The health and productivity burden of migraines in Australia PNS16 Health and economic burden of smoking in Indonesia PMH2 The impact of major depression on productivity in South Korea