**Abstract**

The cost of healthcare interventions varies greatly with age, with a significant fraction of cost being spent in the last two years of life. Treating a child can save orders of magnitude more life-years than an octogenarian treated for the same disease, such as cancer. While Quality-Adjusted Life Years (QALYs) can be used to plan a roadmap for how resources should be expended to maximize quality of life the execution of those plans often fail due to societal norms which trump the carefully measured QALYs, resulting in lowered average number and/or quality of years lived. The ethical issues concerning age, sex, lifestyle (smoking, drinking, obesity), cost transparency, and extreme examples (war, population explosion vs. collapse) will be discussed.

**Keywords:** Quality Adjusted Life Years (QALY), generation, elder care, disability-adjusted life year (DALY), fair innings, rule of rescue, standard gamble, cost transparency, organ donation, smoking, alcoholism, diabetes

### **1. Introduction**

Quality Adjusted Life Years (QALYs) provide a quantified mechanism to alot limited healthcare resources to maximize desired years and quality of life. Both the numbers of years we live and the quality of health has increased more in the last 150 years than in any prior time in human history. It is interesting that QALYs were invented at the same time the variables that go into defining QALYs are changing so rapidly. The U.S. National Council on Disability (NCD) has found sufficient evidence that QALYs are discriminatory by design, and suggested Congress should pass legislation prohibiting the use of QALYs by Medicaid and Medicare [1]. What constitutes a disability and how much should it decrease QALYs? There have been deaf families that argue deafness is not a disability, it heightens other senses, and have chosen not to have cochlear implants. What QALY hit do paraplegics receive compared to quadriplegics? The very nature of QALYs cause users to assign some agreed upon weights to life abilities.

The average human lifespan has increased 80% over the last 120 years, with a clear increase in longevity starting at the end of the 19th century (around 1890). Those living in the United Kingdom, increased average lifespan from 45.2 to 81 years from 1890 to 2015. The United States had a similar increase, doing slightly less well recently with an average lifespan of 79 in 2015. The global average lifespan started from a lower level with its significant increase delayed a decade (1900) but has paralleled the gains each year achieving even more impressive results, starting at 32 years in 1900 and rising to 71.7 years in 2015 [2] (**Figure 1**). Starting in the 20th Century infant mortality plummeted from 10% to under 1% currently, which significantly contributed to the average lifespan. However, if you look at mortality rates at later ages it is apparent that lifespan has increased after keeping infants alive [3]. There is significant scientific data now that, across the animal kingdom, caloric restriction extends life [4–9] which provided hope we could continue the trend increasing human longevity. In mice a 60% reduction in calories has been shown to increase lifespan by about a third, however in humans and primates it appears we may only be able to extend our lives 1–5 years [10, 11] though research is ongoing [12].

It has been estimated that while clinical care accounts for 15% of the quality of one's health, clinical care data only represents 0.1% of the data (0.4 terabytes) applicable to health outcomes over their lifetime (1,106 terabytes) [13]. Most of the data that affects one's health (1,100 terabytes) concerns one's social determinants of health and health behaviors which account for 40% and 20% of one's quality of health respectively. The last 25% of one's health is determined by "Nonmodifiable factors" such as genetics, but this data (6 terabytes) is still very actionable in that different actions (e.g. pharmaceuticals, diet, lifestyle interventions) can be taken based on one's genetics. While it is likely most easy to modify healthcare's actions in clinical care, because it only represents 15% of our health outcomes, in order to maximize QALYs we must invest in analyzing and modifying the other data realms that affect our lifetime biomedical health (social determinants of health, health behaviors, and nonmodifiable factors).

Medical spending has increased by an order of magnitude in the last 200 year as a proportion of GDP. The share of GDP used on healthcare in 1800, 1850, 1900, 1950, 200o was 2%, 2.1%, 2.5%, 4.5%, 13.5% respectively (**Figure 1**) [14]. There is a clear and historically long trend of healthcare accounting for larger percentages of GDP in the developing world. Despite concern that this increased expenditure is just going to fatten the profits of big pharma, the reality is more nuanced with significantly more people and services being funded. Concomitantly and unsurprisingly, in the U.S. rapid growth is projected in both health and STEM occupations

**Figure 1.** *Life Expectancy and Expenditure on Healthcare increase over time. Source: Our World in Data. https://ourworldindata.org/life-expectancy.*

*Ethical Issues Which Have Prevented the U.S. from Maximizing Quality of Life Years DOI: http://dx.doi.org/10.5772/intechopen.97561*

while office support, food service, and manufacturing production jobs will decline [1]. In order to maintain or lower the cost of healthcare, country's must either lower costs per treatment (increased efficiency) or reduce treatment provided (decreased expenditure). While everyone would like the former solution of getting the same treatment for cheaper, the continual rise in healthcare expenditures despite plateauing lifespan suggests cuts will be needed. There are large economic differences in healthcare expenditures between countries which do not translate to better care. Common examples are the United States spending 10-fold more per citizen than Cuba despite similar life expectancies. The counties of the E.U. also spend less than the United States while having the same or better life spans. The successes and failures of using QALYs to reduce healthcare costs will be discussed. Most of the QALY issues discussed apply globally. However, this chapter will focus on data and issues in the United States, which is unusual among industrialized countries because it does not have a single payer system, and therefore has uniquely heightened QALY misallocations.

#### **1.1 QALYs vs. DALYs**

In 1976 Zeckhauser and Shepard first used the term Quality-Adjusted Life Years (QALYs) to describe measurements of health outcomes which were defined by both duration and quality of life measurements [15]. Pliskin detailed the three assumptions QALYs required to act as valid metrics to assign health resources [16], namely:


While these foundational assumptions of QALYs have been questioned [17], they have been globally accepted and used by most countries for making economic decisions [18–21].

Two decades after the description of a QALY, the Disability-Adjusted Life Years (DALYs) were developed in the 1990s measuring both duration as well as quality. DALYs by definition measure disease burden but are also often used like QALYs to maximize cost-effectiveness. QALYs have a health-related quality of life weighting (Q ) that ranges from 0 to 1, with 1 representing a year of perfect health and 0 representing death. A Q measure of 0.5 has been expressed as bed ridden, and it should be noted that a state considered "worse" than death can have a negative Q rating. The quality of life each year can be added up to calculate one's quality-adjusted life expectancy (QALE). On the other hand DALYs are measured from 0 to 1 where 0 represents no disability. Therefore in QALYs the higher the weighting the better, but in DALYs the lower the weighting the better. Usually expert valuations are assigned to a universal set of weightings for DALYs, whereas QALYs use preference-based health-related measures gathered from groups of patients or the general population [22]. DALYs have an age-weighting function, and can therefore preferentially favor spending money on the young versus the old compared to QALYs.

• QALYs lived in one year = 1\*Q (where Q ≤ 1)

$$QALEt = \sum\_{i}^{\*} Qt$$

Qt = Health related quality of life weighting at year t. QALE = quality-adjusted life expectancy at a given age. RLE = Residual Life Expectancy at given age. t = individual years within residual life expectancy range.
