Perspective Chapter: Quality of Life (QoL) Calculations and Interventions across Divergent Societies

Abstract

Before the industrial revolution, living standards largely hinged on population size. With the advent of the industrial age, technological progress became the main influencer. Gross Domestic Product (GDP) initially served as a primary indicator of national well-being, tying economic growth directly to overall quality of life (QoL). Nevertheless, GDP falls short in encompassing diverse elements of QoL, such as environmental health, personal well-being, and cultural richness. Contemporary QoL indicators now encompass life expectancy, mortality rates, and satisfaction surveys. Depending on a nation’s GDP, QoL strategies vary. Lower-income countries benefit more from cost-effective public health measures, while affluent nations can invest in sophisticated biomedical research and comprehensive mental health care. The success of these interventions largely rests on the unique economic, political, and cultural landscapes of each nation. Therefore, monitoring these metrics at the national level and customizing interventions will optimize improvements in well-being.

1. Introduction

Prior to the industrial revolution, living standards were largely determined by the size of the population. With the industrial revolution, living standards became more dependent on the distribution and utilization of technological advances. In modern times, the first big global economic indicator of national well being was Gross Domestic Product (GDP) [1]. The GDP originally was a conceptual proxy that equated economic growth with national well being. It is generally defined as a measure of the total production of a region or country, the monetary value that can be assigned to all goods and services produced within a specified time period. The major methodological flaws of GDP originally was that it is difficult to equate the different prices, quality, and currencies across regions.

The first big global health indicator of national well being was mortality. In 1950, the United Nations published estimates of mortality rates and data on causes of death for member states. These were sometimes close to wild guestimates since many regions of the world did not have any organized data systems on mortality. At the same time, the United Nations, listed the reasons for mortality in each region, but these data were even more suspect due to lack of diagnoses coupled with poor to no organized mortality statistics in the many pre-industrial countries [2].

Since QoL can be defined differently by groups or people there are various methods used to calculate it. Some collect survey information from citizens to measure how people feel their future in their country will be, such as the Edelman Trust barometer (Figure 1). These survey metrics can be useful to cut through stereotypes. For example, in the 2022 Edelman Trust barometer index the top 6 countries from first to fifths were: China, UAE, Indonesia, India, Saudi Arabia, and Malaysia while more developed countries often thought of as trusted were Netherlands, Canada, France, Germany, and United States. See Figure 1 for details about what subcategories went into those trust rankings. This chapter however, will focus more on metric calculations of QoL instead of survey data.

Figure 1.

Edelman trust barometer (2022).

While the years of life (Quantity) have greatly increased over the last 60 years, the fraction of those years spent in moderate health has not changed. From 1960 to 2019 life expectancy increased from 54 to 73 years respectively while the fraction of one’s life spent in moderate health has remained at 50% [3]. This paper will review both biological & mental health, economic, environmental, and societal/cultural aspects of quality of life (QoL) across societies.

2. Quality of Life measured through different lenses

One’s QoL can be weighted more heavily by different metrics such as:

• Economic growth

• Specific aspects of a society

  • Housing

  • Income

  • Jobs

  • Environment

  • Safety

  • Education

  • Life satisfaction

  • Community

  • Civic engagement

  • Work-life balance

  • Health of Society

• Personal Health

  • Biological

  • Mental

We will discuss QoL from each of these viewpoints in turn.

3. Economic growth

By 2040 global GDP could increase by 8%, which is $12 trillion USD, just from improved health conditions. This would be due to $4.2 trillion from fewer health conditions, 4.1 trillion from expanded participation, 2 trillion from increases in productivity, and $1.4 trillion from fewer early deaths [3]. It is clearly true that as poor countries become more affluent their life expectancy increases and therefore lifetime quality of life increases due to quantity of years, even if the quality of those years remains the same. Interestingly the calculation of quality of life is more robust for wealthy first-world countries. This could be due to better overall data from those countries or overweighting of metrics that are harder to obtain from poorer countries. Figure 2 compares 27 countries’ Gross Domestic Product (GDP) per capita to the standard deviation of three quality of life measurements: Numbeo’s Quality of Life Index, CEO World Human Development Index (HDI), and the United Nations (UN) Human Development Index. Note the lower standard deviation of wealthier countries. The Numbeo metrics go past 100 so had to be scaled to compare to the CEO and UN metrics, see methods section for details.

Figure 2.

Variability in measuring quality of life (QoL) in nations with greatly varying GDP. The standard deviation was calculated between three QoL: Numbeo’s Quality of Life Index, CEO World Human Development Index (HDI), and United Nations (UN) Human Development Index. Both the HDI and UN QoL measurements are based with a maximum of 100%. To make measurements Numbeo numbers were comparable (their minimum (Numbeo_min = 76.9 and maximum Numbeo_max = 180.3) were scaled between the HDI index minimum (HDI_min = 60.1) and maximum (HDI_max = 95.5) numbers of the 27 countries investigated. Therefore each Numbeo value (Numbeo_val) underwent the following transformation: (HDI_max–HDI_min) * (Numbeo_val-Numbeo_min) / (Numbeo_max-Numbeo_min) + HDI_min = (95.5–60.1)*(Numbeo_val−76.92)/(180.27–76.92) + 60.1.

4. Weighting which QoL metric is most important

Countries around the world diverge in per capita income by orders of magnitude. In addition cultures and personal interests differ in which aspects of life they desire to optimize (e.g. community vs. income). The Organization for Economic Co-Operation and Development (OECD) has broken Quality of Life measurements per county into different aspects of life. By scaling the importance of each aspect below you can see how countries shift in their QoL ranking (Figures 3 and 4).

1. Housing

2. Income

3. Jobs

4. Community

5. Education

6. Environment

7. Civic engagement

8. Health

9. Life satisfaction

10. Safety

11. Work-life balance

Figure 3.

OECD better life index graphs. It shows how each countries QoL index would change if you weighted each aspect by 90% in turn (e.g. housing, jobs, etc).

Figure 4.

OECD Better Life Index table. This is a table showing how each countries QoL index would change if you weighted each aspect by 90% in turn (e.g. housing, jobs, etc). This table contains numerical data that is shown graphically in Figure 3.

4.1 Housing

The three countries whose QoL drops the most when housing is overweighted are Israel (50%), the Slovak Republic (−20%), and New Zealand (−20%), while gains were seen for Brazil (+35%) and Argentina (+20%). Many countries did not shift significantly when overweighting housing, showing the bulk of the population in the countries represented can afford adequate housing in their countries.

4.2 Income

While the long term trends over the last 50 years have shown increased incomes in the populous poor countries (e.g. China and more recently India), it is well known that the distribution of income has remained very uneven and increased in many countries (e.g. the United States). It is therefore not surprising that overweighting income causes many countries’ QoL to shift drastically.

Many countries saw large drops in QoL when income is overweighted such as Estonia (−40%), Latvia (−39%), Denmark (−37%), Hungary (−34%), Finland (−33%), Chile (−32%), Slovak Republic (−32%), and many others, while gains were seen for Luxenburg (+15%), and the United States (+7%).

4.3 Jobs

Three countries whose QoL drops the most when jobs are overweighted are Greece (−18%), South Africa (−18%), and Spain (−11%), while large gains were seen for many countries including Columbia (+34%), Russia (+27%), Mexico (+19%), and Germany (+15%).

4.4 Environment

Countries whose QoL had large drops when the environment is overweighted are Türkiye (−34%), Chile (−27%), Korea (−27%), Israel (−21%), while gains were seen for the socialist nordick countries which invest tax money and laws in protecting theri environment: Finland (+20%), Norway (+15%), Iceland (+12%), and Sweden (+16%).

4.5 Safety

Countries whose QoL drops the most when safety is overweighted are Mexico (−29%), Brazil (−21%), and Columbia (−14%), while gains were seen for Slovenia (+27%), Japan (+26%), Türkiye (+24%), Austria (+22%), and Luxembourg (+16%).

4.6 Education

The three countries whose QOL drops the most when education is overweighted are Costa Rica (−34%), Luxembourg (−27%), Mexico (−21%), Brazil (−20%), Columbia (−17%) while gains were seen for, Poland (+21%), and Finland (+17%).

4.7 Life satisfaction

Four countries whose QOL drops the most when life satisfaction is overweighted are Türkiye (−37%), Portugal (−33%), Russia (−30%), and Korea (−28%), while the largest gains were seen for Finland (+22%) and Israel (+11%).

4.8 Community

The three countries whose QOL drops the most when community is overweighted are Korea (−40%), Greece (−35%), and Mexico (−30%) while gains were seen for South Africa (+33%), Slovak Republic (+27%) Czech Republic (+22%), Hungary (+22%), Israel (+19%), and Poland (+19%).

4.9 Civic engagement

The three countries whose QOL drops the most when civic engagement is overweighted are Switzerland (−45%), Portugal (−39%), Ireland (−35%), Czeck Republic (−34%), Japan (−34%), Chile (−31%), and Austria (−29%), while gains were seen for Brazil (+17%), Korea (+14%), and Australia (+10%).

4.10 Work-life balance

The three countries whose QOL drops the most when work-life balance is overweighted are Japan (−50%), Australia (−39%), United States (−32%), Mexico (−30%), Korea (−27%), Costa Rico (−24%), and United Kingrom (−20%), while gains were seen for Russia (+36%), Latvia (+34%), Italy (+29%), Chile (+27%), and Lithuania (+24%).

4.11 Health of society

The three countries whose QOL drops the most when health balance is overweighted are Russia (−21%), Lithuania (−16%), and Latvia (−14%), while gains were seen for Greece (+34%), Türkiye (+22%), Chile (+20%), Italy (+20%), Canada (+16%), and Australia (+15%). Specific examples of health are discussed more in the Biological Health section.

5. Personal health

5.1 Biological health

The concept of health-related quality of life (HRQoL) was first broadly introduced in relation to the impact pathology and its treatment has on an individual’s perception of their life [4, 5], but numerous philosophical frameworks have since been developed to address the impact of a particular disease or condition on the quality of life of an individual [6, 7]. Adapting this concept to a population- rather than an individual-level, health-related quality of life encompasses diseases and health issues, resources, policies, and behaviors that impact the population’s health and perception of quality of life. Herein, health-related quality of life refers to the health of an individual or population over time, incorporating both biological and mental aspects [8]. While physical and mental health are interrelated, the most significant factors contributing to quality of life in these realms differ. For biological or physical health, the most effective cost benefit intervention is highly individualized and varies at a population-level across the globe.

Given significant health inequalities [9], many of the population-level differences in HRQoL can be summarized in the context of GDP. In low-income countries, maternal and neonatal disease, parasitic worms (e.g. malaria and gastrointestinal worms), infectious disease, and malnutrition are responsible for the greatest reduction in HRQoL. Strategies to reduce exposure to parasites, immunization against communicable disease, enhance maternal health, or improve dietary resources (e.g. food accessibility and vitamin supplementation) are prime targets to improve quality of life in these populations [10, 11, 12]. Whereas in high-income countries, an unhealthy lifestyle is the greatest contributor to reductions in HRQoL. An unhealthy diet, sedentary behaviors, and smoking contribute to chronic illnesses, such as cardiovascular disease, diabetes, and cancer that are the main causes of both death and disability in high income countries such as the United States [13, 14, 15, 16]. Targeting obesity alone would not only result in significant improvements in HRQoL in high income countries, but also would have an enormous impact on reducing medical costs [17]. For high income countries, preventing or improving unhealthy lifestyles in order to reduce cardiovascular disease and smoking-related diseases are low hanging fruit (e.g. prevention and cessation programs) allowing for 60% of health gains to be achieved for less than $1000 USD/additional healthy year [3].

Interventions to improve quality of life based upon biological health vary across populations due to both differing health targets as well as variation in resource accessibility. For instance, there is a known dose response relationship between protein intake and muscle mass [4]. Increasing protein to build muscle can improve biological health-related quality of life but is most effective in undernourished populations when substituted for carbohydrates. Populations in high-income countries are more likely to opt for more expensive interventions, such as increasing the proportion of dietary protein acquired from animal products. This example highlights how individuals in a high-income country may pursue a relatively expensive path for a targeted HRQoL outcome (e.g. increased meat consumption to augment muscle mass), whereas those in low-income countries can attain the same outcome through less expensive interventions that are more readily available (e.g. increased consumption of legumes, soy, or nuts to augment muscle mass).

Within a particular population, demographic features such as age impact an individual’s health-related quality of life. For instance, musculoskeletal and biomechanical limitations are a significant variable in the biological HRQoL of older individuals living in the United States [18, 19]. Nonetheless, in international comparisons there appears to be a strong, statistically significant relationship between an older population’s health and the health status of the overall population. However, descriptive analysis indicates that there are differences between the aggregate population compared to its older population suggesting that international comparisons should also publish separate analyses for their older populations [5].

There are individuals and charitable organizations that try to quantify the efficacy of dollars spent on biological health-related quality of life variables in order to leverage their resources and maximize their charitable benefit. This movement is called Effective Altruism (EA). One well known organization that follows these principles is GiveWell, which works to direct charitable donations to the most cost-effective interventions to “save or improve lives the most per dollar” [20] (https://www.givewell.org/). GiveWell’s top charities are (with the estimated cost in $USD to save a life): medicine to prevent malaria ($5000), nets to prevent malaria ($5500), supplements to prevent vitamin A deficiency ($3500), and cash incentives for routine childhood vaccines ($5000) [20]. Many of these charities direct their funds to cost effective measures offering a considerable “bang for the buck” in low income countries. In addition, GiveWell calculates a return on investment (ROI) but does not stop at the immediate benefit to the individual helped in life years but continues for decades of quality of life gained, though there have been those that question Givewell’s calculations [21, 22]. Indeed many in the Effective Altruism community calculate cost effectiveness generations into the future. However, this could result in overcommitting funds to the prevention of human extinction-level events (e.g. detrimental initial general articifical intelligence (GAI) instantiation, biolofigcal or atomic warfare) given the orders of magnitude greater return over benefiting the biological health of current populations. Regardless, publicly available population-level data provide a clear picture of the difference in high income versus low income populations in their health-related quality of life. While wealthy countries can afford to focus on expensive targets such as viral therapies to increase their quality adjusted life years [23], low income countries are better served by focusing on cost effective targets with a significant impact on quality of life such as mosquito nets [24, 25].

5.2 Mental health

The first global indicators of social/emotional well-being focused on the incidence and prevalence of mental disorders. Well-being was defined by the absence of psychopathology. Early data analyses focused on national suicide rates as well as anxiety and depression rates. Getting reliable cross-cultural data was and continues to be hampered by

1. different expressions of mental health problems in different cultures

2. underdiagnosis in many nations, as well as

3. underreporting.

Wealthier countries have much greater rates of diagnosis, treatment, and expenditure on mental health than poorer countries, making valid and reliable global estimates and cross-national comparisons very difficult. Until very recently there was no independent monitoring, evaluation, and accountability mechanisms for mental health and well-being.

Given these caveats, the best estimates are that around 1 in 8 people (970 million) globally (11–18%) have one or more mental or substance abuse disorders. The most prevalent diagnosis is anxiety disorders (301 million), which plagues about 4% of the world’s population. The other most frequent mental health problems include depression (280 million), bipolar disorder (240 million), schizophrenia (24 million) and eating disorders (14 million) [26]. In 2022, due to the Covid epidemic, there was a 26% and 28% increase respectively for anxiety and major depressive disorders in just 1 year. It is unclear if this effect will be transitory [27]. These global rates rely on diagnoses from the World Health Organization’s International Classification of Diseases (ICD-10) and includes mental health disorders (depression, anxiety, bipolar, eating disorders and schizophrenia), substance use (alcohol and drug use disorders), and neurodevelopmental disorders, including autism, attention-deficit hyperactivity disorders (ADHD) as well as developmental disability.

Yet, the National Institute of Mental Health (part of the National Institute of Health) estimates that 26% of Americans ages 18 and older suffer from a diagnosable mental disorder in a given year and more than half of US adults will have a mental illness or disorder at some point in their lifetime. Report criteria from NIMH include that the disorder must be of sufficient duration to meet diagnostic criteria specified within the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV); and excludes developmental and substance use disorders, both of which are included in the World Health Organization’s statistics.

The NIMH data are gathered in an intensive, multi-step manner which differentiates AMI (any mental illness) from SMI (serious mental illness): The AMI and SMI estimates are generated from a prediction model created from clinical interviews and data collected on a subset of adults who have taken the National Survey of Drug Abuse and Health (NSDAH). This subset of participants complete an adapted (past 12 month) version of the Structured Clinical Interview for DSM-IV-TR Axis I Disorders (Research Version, Non-patient Edition) [28], and is differentiated by level of functional impairment based on the Global Assessment of Functioning Scale [29]. Adult ADHD is not assessed nor are autism spectrum disorders, schizophrenia or other psychoses.

The fact that the US reports 1 in 5 adults as having a mental health disorder (excluding drug abuse and the autistic spectrum) and the worldwide rate is 1 and 8 (including drug abuse and the autistic spectrum) may or may not mean that Americans carry far more mental illness burdens than the rest of the world, due to the many cultural differences in diagnoses and reporting problems noted above.

Reporting global mental health rates have undergone two dramatic changes in recent decades. First, Global Health reports increasingly include mental health as a subset of reported medical problems. We now realize that there is no mental health silo along-side a physical health silo. There is a dynamic interaction between mental illness and other health conditions and mental health is a necessary precursor for physical health.

Second, In the past 20 years, there has been a paradigm shift where social-emotional well-being is no longer equated with lack of diagnosable mental health problems. Rather social-emotional well-being is construed more independently and includes a plethora of positive resiliency and life satisfaction factors.

In 2008, the nations of the world were made aware of the importance of a holistic approach to social-emotional well-being when Bhutan wrote a new constitution codifying that Bhutan’s national mandate is to promote conditions that enable the pursuit of Gross National Happiness (GNH).

The four pillars of GNH are:

1. Ecological Sustainability,

2. Sustainable and Equitable socio-economic development,

3. Good Governance and Equity before the Law and

4. Preservation of a Free and Resilient Culture.

Now, sustainability of the environment, economic and legal equity, policies that promote creativity and an emotionally intelligent citizenry are listed as the essential brew for happiness of the individual [30].

Four years later, in 2012, the first World Happiness Report was published by the United Nations’ Sustainable Development Solutions network [31]. Using the Gallup World Poll, it reported on the state of happiness in 155 nations as well as the primary causes of happiness and misery. Finland ranked as the world’s happiest country based on the 2021 report. The next four happiest countries were Denmark, Iceland, Netherlands, and Norway. And at the bottom of the list were Lebanon, Zimbabwe, Rwanda and Afghanistan. The rankings of the World Happiness Report use a mix of GDP, social support (as measured by someone to count on in times of trouble), healthy life expectancy, freedom to make life choices, generosity (as measured by donations) and perceptions of corruption. This combination of social-emotional measures (social support, freedom to make life choices and generosity) combined with economic well-being (GDP) and satisfaction with government (perception of corruption) seems an enlightened way to account for the moderating effects economics and national stability has on an individual’s social-emotional well-being. Interestingly, health is not a factor in this eq. [32, 33]. Psychologists and other well-being promoters have spent time operationalizing, measuring, and promoting the “Free and Resilient Culture” column of GNH in the service of advancing the systemic factors that promote said well-being.

In closing, we will briefly introduce two exemplary global projects that are pushing the possibilities in defining, measuring and using data on well-being: the first an interactive tool (Countdown Global Mental Health 2030) where global data is gathered and public health officials can interact with the data to find ways to more effectively assess and treat AMIs and SMIs and the second, is a culturally flexible, self-report scale on perceived well-being (The Flourishing Scale).

5.3 Countdown global mental health 2030

First, tuhe Countdown Global Mental Health 2030 dashboard is a tool for policy analysts and public health professionals around the world and is unique in that the databases cover different mental health clusters that are related to actionable policy decisions [34]. It has been developed by United for Global Mental Health in partnership with WHO, UNICEF, GlobalMentalHealth@Harvard, Global Mental Health Peer Network, and The Lancet. It is a free, publicly accessible, and interactive dashboard (https://unitedgmh.org/knowledge-hub/countdown2030/) that allows any person or nation to search 42 quality indicators relevant to mental health by country. The three cluster themes are: (a) the determinants of mental health (e.g., demographic, economic), (b) factors shaping the demand for mental health care (e.g., burden, financial accessibility), and (c) factors shaping the strength of mental health systems (e.g., mental health service level and quality, human resources). Not all countries have data on all the indicators and users can see how many indicators are missing in each of the three cluster themes. This helps direct future data collection efforts and tempers inter-country interpretations when widely different amounts of data are being reported. The Countdown can be used to compare countries and assess global social-emotional well-being by measuring the likely prevalence of various AMIs and SMIs, as well as the cultural and health infrastructures available (or required) to meet the need. It is the first entity to offer independent monitoring, evaluation, and accountability mechanisms for mental health and well-being.

5.4 Flourishing scale

At the opposite end of well-being measurements is the global well-being measure based on the positive psychology concept of “flourishing”. This is a true wellness measure, unrelated to health care costs, infrastructures, and mental health diagnoses. The Flourishing Scale has a humanistic focus and measures the social-emotional well-being of all people; with and without a mental illness diagnosis, in multiple countries around the world. It does not cover infrastructure or directly tie into policy but allows for keen insights into the most contemporary ideas of self-perceived well-being on a global scale. The Flourishing Scale directly measures positive well-being. Trying to define the opposite of mental illness, 10 features of positive social-emotional well-being were identified that could be assessed by self-report including:

1. Competence (Most days I feel a sense of accomplishment from what I do)

2. Emotional stability ((In the past week) I felt calm and peaceful)

3. Engagement (I love learning new things)

4. Meaning (I generally feel that what I do in my life is valuable and worthwhile)

5. Optimism (I am always optimistic about my future)

6. Positive emotion (Taking all things together, how happy would you say you are?)

7. Positive relationships (here are people in my life who really care about me)

8. Resilience (When things go wrong in my life it generally takes me a long time to get back to normal. (reverse score))

9. Self esteem (In general, I feel very positive about myself)

10. Vitality ((In the past week) I had a lot of energy)

Data from the 23 countries which participated in the European Social Survey (Round 3) reveals a four-fold difference in flourishing rate, from 41% in Denmark to less than 10% in Slovakia, Russia and Portugal. Country profiles across the 10 features can be used to gain insight into how well-being is contextualized in different cultures as well as potentially finding policy changes that may increase a country’s social-emotional well-being [35].

The reader should not despair of the many disparate approaches. Each is touching a different part of the social-emotional wellness elephant and over time the resulting mosaic will inform policy makers, public health professionals and educators, as well as individuals from all over the world.

6. Methods

While the CEO and UN metrics are in a standard percent scale to 100%, the Numbeo metrics have numbers that go past 100. To make comparisons comparable the Numbeo numbers were scaled to between the minimum and maximum numbers in the HDI index of the 27 countries investigated, namely Kenya (67.1) and Ireland (95.5) respectively (see “normalized_quality_of_life_calculations.xlsx” for data). Note that Ireland has a deceptively high GDP due to foreign companies being located there, while an average citizen makes significantly less. An analysis using Gross National Profit (GNP) differs from GDP because it is the value of all finished goods and services owned by the citizens of the country regardless of where those goods are produced. In contrast, GDP counts goods and services produced within the country by anyone in the world.

It was attempted to generate small portions of this paper using the deep learning Generative Pre-trained Transformer 3 (GPT-3) AI model using prompts as well as the counterfactual prompt such as below, similar to other recent papers [36]:

• “Write the discussion section of a scientific cited paper describing the efficacy of Quality of Life interventions is largely controlled by the GDP of the country. Include real citations”

• “Write the discussion section of a scientific cited paper describing the efficacy of Quality of Life interventions is largely controlled by the GDP of the country. Include real citations”

However, the GPT-3 results did not prove useful. The counterfactual prompts which always returned reasonable, if not as convincing, results. This highlights how studies have found conflicting results and while case examples can be cherry-picked from each bin, that does not indicate parity in the magnitude of effect, robustness, or actionability in interventions moving forward. The even more blatant failure of GPT-3 was the creation of citations that do not exist. Most often a supposedly authors last name with a year was given, but the authors of this paper could not find any such citations it could be referencing. More clearly incorrect were the times GPT-3 gave multiple authors, a journal with issue number as well as start and end pages for the article. These references were easily fact-checked and found to be complete fiction.

Other AI tools were investigated for a similar purpose. Instead of asking for creation of content, questions such as “Is the efficacy of Quality of Life interventions determined by the GDP of a country?” were asked to https://you.com/ and https://consensus.app/. These were results were found to be much more robust but were in essence seemed similar to a google result list. Note, consensus.app aims a create an AI summary of results in 2023. No AI text was used in this publication due to lack of accurate citations which was what the authors were hoping would be produced.

7. Conclusion

GDP/capita continues to be a useful gross metric to determine the resources a country has to spend on its citizens to increase its QoL. However, there are clearly more nuanced metrics that can, and are, being collected. Efforts are underway to move beyond GDP as a measure of improving one’s society. For example the Well-being Economy Governments partnership (WEGo) formed the Well-being Economy Alliance (WEAll) which strives for “economic transformation and changing the debate so that economies around the world deliver shared well-being for people and planet” [37]. Current members include Finland, Iceland, Scotland, Wales and New Zealand which aim to form an economic approach to address global crises, such as climate change, biodiversity loss, and the cost-of-living crisis [38].

While the quality of measuring QoL is significantly more variable in developing countries, this is dwarfed by the much easier ability to raise QoL per dollar compared to the cost in developed countries. The measurement of interventions on quality of life can be coarsely translated between countries, but should be tracked and verified in each country. Most importantly the most efficacious intervention for a state or territory to spend its limited resources on is not transferable internationally and is economically, geopolitically, and temporally bound.

Nomenclaure

AMI	any mental illness
HDI	CEO world human development index
GDP	gross domestic product
GNH	gross national happiness
GPT-3	generative pre-trained transformer 3
HRQoL	health related quality of life
OECD	Organization for Economic Co-Operation and Development
SMI	serious mental illness
QoL	quality of life
WEAll	well-being economy alliance
WEGo	well-being economy governments partnership