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Can DALYs and QALYs become the next carbon footprint? Using health impact metrics to drive investments in health

Authors: Alayna Sublette & Andrea B Feigl, PhD

What would a world and economy look like where a DALY or QALY averted was just as valuable as a unit in CO2 emissions curbed? What can the global health community learn from the climate science journey to ensure that the value of health is represented in global economic standards and decision-making?

There is nothing intrinsically economic or financial about a unit of CO2 emission. And yet, due to hard-won global consensus and the established linkage between increased CO2 emissions, environmental degradation, and economic losses, the carbon footprint model is now widely accepted as the standard measure of CO2 emissions and fully integrated into many national and international fiscal policies.

Despite facing similar financial and political barriers as the climate crisis, global health has not achieved the same success in mobilizing support. Global health is facing a significant investment gap, with the poorest 54 countries in the world expected to experience a gap of $176 billion USD annually by 2030. More specifically, investments in mental health constitute less than 2% of governments’ health budgets and represent 0.3% of the development assistance earmarked for health. With approximately 1 billion people living with a mental health condition, this crisis requires immediate action from the public, private, and philanthropic sectors. Furthermore, the COVID-19 pandemic, which has upended health systems and exacerbated the mental health burden, underscores the need for a “health footprint.” When paired with other strategies, DALYs (disability-adjusted life years) and QALYs (quality-adjusted life years) are useful tools to illustrate the investment case for mental health.[1]

Through the lens of mental health, this post will explain how health data can be leveraged to mobilize political and financial support, highlight the broader implications of good data systems, and discuss health data translation moving forward.   

How Health Data Can be Leveraged

Health data is inextricably linked to social and economic outcomes. Not only can social and economic factors determine wellbeing through policies such as housing and education, but health outcomes can have profound effects on human capital generation. In the case of mental health, 1 in 5 children and adolescents suffers from a mental health disorder, which can inhibit participation in daily life such as school. Additionally, depression and anxiety alone have led to an annual $1 trillion USD loss for the world economy. However, these data points by themselves are incomplete; they require targeted analysis that can translate into concrete political and financial actions.


Similar to the carbon footprint model for the climate crisis, DALYs and QALYs represent underleveraged building blocks for translating health data into substantive change. When valued from a health and economic lens, DALYs and QALYs can illustrate the burden of health and appeal to a wide array of public, private, and philanthropic stakeholders based on their respective incentives and priorities. Additionally, in the case of mental health, packaging data from a health and economic perspective can help remove stigma from the equation, while underscoring the real effects of these disorders. Rather than just conducting an economic exercise, this approach would illustrate mental health’s tangible value. But how can public health advocates accomplish this in practice?

Lessons from the Climate Crisis

Data translation enables advocates to mobilize political and financial support for various causes. The climate crisis has generated lessons learned that could be applied to the global mental health crisis. Between 2018 and 2020, bonds for green finance more than doubled from $200 billion USD to $491 billion USD. In June 2021, the G7 pledged to increase finance for “climate change adaptation and nature-based solutions.” The swell in support of climate-based interventions is undeniable, but how exactly have climate advocates mobilized this funding and political will?

One strategy that climate advocates have utilized is data analysis paired with tangible political and financial interventions. For example, the United Nations Intergovernmental Panel on Climate Change (IPCC) routinely shares public reports on potential climate change scenarios, known as representative concentration pathways (RCPs). These RCPs are adjusted depending on certain levels of greenhouse gas emissions and predict the “physical, biological, economic, and social effects of climate change.” The IPCC then pairs each RCP with specific policy and financial measures. These data are compelling for public, private, and philanthropic stakeholders alike. Climate advocates have achieved tangible success by linking carbon footprint to environmental and economic impacts, garnering support from high-level stakeholders like the G7, and outlining evidence-based recommendations. Perhaps most importantly, the common unit of impact – as measured by CO2 emissions – is a widely accepted standard, in a way that the health unit of impact is not (yet).

Translating Health Data into Action

Similar to the climate crisis approach, leveraging data to design evidence-based recommendations can enable health advocates to mobilize funding. Regarding non-communicable diseases (NCDs) more broadly, organizations such as the Health Finance Institute (HFI) have used health economics and innovative finance to address the NCD financing gap. For example, HFI has developed a type 2 diabetes model that explores the impacts of increasing access to, and equity of, diabetes screening and treatment in different populations. This model mimics the patient care cascade and represents the burden of disease through outcomes including costs of care, healthy days lost, and reductions in productivity. This model also draws on the amenable burden of disease or the “disease burden that could be avoided in the presence of high-quality personal health care.” With the right data, a similar model could be developed for mental health.

With these data, advocates can then forecast the health and economic impacts of mental health interventions or public health policies. These indicators can inform the potential return on investment for prospective funders. For example, it is estimated that “for every $1 invested in scaled-up treatment for depression and anxiety, there is a $4 return in better health and productivity.” From there, organizations like HFI can advise on public-private partnerships that fit the context and can unlock funding that would otherwise be hesitant to invest in mental health. 

Broader Implications of Good Data Systems

In line with PLOS’ “scientists for open science,” a standard “health footprint” would open up opportunities to improve health outcomes from an evidence-based perspective. There is a scientific and moral imperative to better understand the linkages between health and economic indicators, and the consequences of weak data systems are too far-reaching to ignore. For example, comprehensive and up-to-date health data, particularly in low- and middle-income countries (LMICs), is very limited and these limitations are amplified for mental health due to enduring stigmas. We can’t change what we can’t measure; without compelling evidence that mental health is a problem, there is little political or financial will to address it.

Health Data Translation Moving Forward

Like the climate, human health is all-encompassing and affects every dimension of our lives. A world and economy in which health is valued through a tangible “footprint” would open the door for a common health investment standard, realization of international health objectives, and tangible commitments within government budgets and strategies. While mental health is included in the UN Sustainable Development Goals (SDGs), significant commitments to address this crisis are yet to be realized. The carbon footprint model, DALYs, and QALYs offer a useful guide for public health advocates to address mental health and other significant health crises of our time.

[1] DALYs, or disability-adjusted life years, are a “time-based measure that combines years of life lost due to premature mortality (YLLs) and years of life lost due to time lived in states of less than full health, or years of healthy life lost due to disability (YLDs).”  QALYs, or quality-adjusted life years, “are measures of longevity, in units of years of life, adjusted for the quality of life during those years… often used in the denominator of an incremental cost-effectiveness ratio.”


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