Mental
State ofthe World
2021

A publication of
the Mental HealthMillion ProjectDear Reader,
We are very happy to present our second annual Mental State of the World report for 2021
describing the mental wellbeing of Internet-enabled populations around the globe. With additionaltranslations in Spanish, French and Arabic, we expanded our reach from ~49,000 respondents in 8
English speaking countries in 2020 to 223,087 respondents in 2021 across 34 countries in Spanishspeaking Latin America, the Arab world, Spanish and French speaking continental Europe andAfrica. This now represents the largest and most comprehensive study of global mental wellbeing
currently in progress. The results provide a stark illustration of the growing magnitude of mental
health challenges around the world.
The most immediate concern raised by the data is that an alarming plummet in mental wellbeing
across successively younger generations is prevalent across every single country population that
we sampled. The reasons behind this decline are likely numerous and complex but add to theongoing debate around the consequences of growing up in an Internet-dominated and inequitableworld. We offer perspectives and hypotheses on how we might understand this better.
Another significant finding was that mental wellbeing was poorest overall in English-speaking
countries and highest in Latin American and European countries. Surprisingly, MHQ scores ofcountries were significantly negatively correlated with cultural indicators such as Performance
Orientation and Individualism as well as economic indicators such as GDP and GNI per capita. This
stands in stark contradiction to the belief that economic growth enhances societal wellbeing.
Thus we are left to ponder the following: That perhaps a system that relentlessly sorts us into
performers and non-performers in the singular service of economic growth is not the path to humanwellbeing. That a structural paradigm that connects us digitally but disconnects us physically andemotionally is antithetical to human need. And that perhaps it is not material hardship itself thatbreaks us, but the lack of belonging and being in it together. Even as we must understand these
relationships more fully, these data make clear that to nurture the human spirit we need a newparadigm.

2

Tara Thiagarajan, Ph.D.		

Jennifer Newson, Ph.D.

Founder and Chief Scientist

Lead Scientist, Cognitive and Mental HealthA report of the Mental Health Million projectsapienlabs.org/mental-health-million-project

Advisory Committee
Dr. Jennifer Newson, Lead Scientist, Cognitive and Mental Health,
Sapien Labs, USA (Project Lead)
Dr. Helen Christenson, Director and Chief Scientist, Black Dog Institute, Sydney, Australia
Dr. Pim Cuijpers, Full Professor, Faculty of Behavioural and Movement Sciences,
Clinical Psychology, VU University Amsterdam, Netherlands
Dr. Eiko Fried, Assistant Professor of Clinical Psychology, Leiden University, Netherlands
Dr. Brandon Kohrt, Associate Professor of Psychiatry and Behavioral Sciences,
George Washington University, USA
Dr. Vikram Patel, Professor, Department of Global Health and Population, Harvard University,
USA and Adjunct Professor and Joint Director, Centre for Chronic Conditions and Injuries,
Public Health Foundation of India, India
Dr. Joshua Seidman, Chief Research and Knowledge Officer, Fountain House, New York City,
USA
Take part at sapienlabs.org/mhq© Sapien Labs 2021
sapienlabs.org

3

4

Executive Summary

5

Introduction

7

Our collective mental wellbeing in 2021

7

The Mental Health Million Project7

Mental wellbeing on a spectrum from distressed to thriving

7Interpreting the MHQ

8What’s in this report8

1. The Mental State of the Internet-Enabled World

9

Mental wellbeing across world regions

10

Changes in 2021 in the English-Speaking World

11

Functional dimensions of mental wellbeing

12

2. The Mental Wellbeing of Countries

14

How countries rank

14

Explaining the differences between countries16

3. Demographic factors that impact mental wellbeing and key trends

20

A global decline in younger generations

20

A persistent gender gap most pronounced in Latin America

21

4. Education, employment and mental wellbeing

23

More education, better mental wellbeing

23

Employment, mental wellbeing and productivity

24

5. Summary and Insights

26

A comparison of demographic factors

26

Possible causes of the generational decline

27

Potential consequences

28

Looking Ahead

29

Appendix 1: Understanding the MHQ

30

Appendix 2: Data Collection & Analysis Methods

35

References

39

Executive Summary
The Mental State of the World report provides insight into the mental wellbeing of Internetenabled populations around the globe. This report for 2021 covers 223,087 respondents across
34 countries in the Core Anglosphere, Spanish-speaking Latin America, the Arab world, Spanish &French speaking continental Europe and Africa sampled in 2021 based on responses to the MHQ
assessment in English, Spanish, French and Arabic. The MHQ provides an aggregate metric ofmental wellbeing (the MHQ) as well as multiple dimensional views. Key findings this year are as
follows:
A smaller decline in mental wellbeing relative to 2020: Across the 8 English speaking countries
measured from 2019 to 2021, there was a decline in mental wellbeing of 3% in 2021, smaller than
the 8% decline in 2020. The decline across countries from 2019 to 2021 was significantly correlated
to the stringency of Government Covid-19 measures and directionally correlated to the cases anddeaths per million.
Latin America and continental Europe reported the highest mental wellbeing: 8 out of 10
countries with the highest MHQ scores were located in Spanish-speaking Latin America andcontinental Spanish and French-speaking Europe. In contrast, 8 out of 10 countries with the lowest
MHQ scores were from the English-speaking world. The relative MHQ scores of countries weresignificantly negatively correlated with cultural indicators such as Performance Orientation andIndividualism as well as with core economic indicators such as GDP per capita and GNI per capita,
correlations that were particularly strong for males aged 18-64.
The alarming decline in mental wellbeing of younger generations was a global phenomenon:
Whereas only 7% (6% to 9% across different regions) of those aged 65+ were Distressed or
Struggling with their mental wellbeing at a level that impacts functioning and would be consideredclinical, 44% (38% to 50% across different regions) of 18-24 year olds had mental wellbeing scoreswithin this Distressed or Struggling range. This represents a growing gap between generations that,while present prior to the Covid-19 pandemic, has since been exacerbated. It also stands in stark
contrast to the happiness and wellbeing patterns documented prior to 2010 across several regionsof the world, where young adults 18-24 typically had the highest wellbeing.
The gender gap was highest in Spanish-speaking Latin America: The gap between males
and females was highest in Spanish-speaking Latin America followed by West Africa and the
Middle East, but many fold smaller than the age gap, the education gap, the employment gap or5the difference between countries. In English-speaking Internet-enabled populations there wasnear parity between males and females overall, although both genders were lower than theircounterparts in other regions of the world. Across the world, those identifying as non-binary/third
gender (<1%) had the lowest mental wellbeing of any group with 51% (31% to 85% across different
regions) distressed or struggling at a clinical level.
Higher education and employment were associated with higher MHQ scores: Across all countries,
higher education was associated with higher mental wellbeing with those with Masters and PhDs
scoring highest. In addition, being employed or self-employed was associated with substantiallyhigher mental wellbeing compared to being unemployed, most significantly in the Core Anglosphere.
What are the implications? It should be of particular concern that across countries, lower MHQ
scores, and in particular the dimensional scores of Social Self (how we view ourselves and ourability to form strong, stable relationships with others) were correlated with national statistics ofrates of suicide, sexual violence and violent assault. As today’s young adults and children become
tomorrow’s older generations, the current trends suggest that aggregate societal mental wellbeing
and Social Self will decline even more substantially in the next two decades. This portends grave
challenges for the future of civil society.
Looking ahead: There is yet much we need to understand, particularly with respect to the alarming
global generational decline of mental wellbeing overall, and in particular the Social Self. We consider
and discuss various possible underlying causes from inequality to environmental causes and thegrowth of mobile phones and Internet use. In 2022, as we continue to expand our view of the mental
state of the world to 10 languages and 70 countries, we hope to bring you deeper insights into thesocietal frameworks and structures that drive our Social Self, and our mental wellbeing as a whole.

6

Introduction
Our collective mental wellbeing in 2021
The Mental State of the World Report 2021 is the annual report of the Mental Health Million projectand provides a comprehensive view of the evolving mental wellbeing of the Internet-enabled world andinsights into significant underlying trends. As of January 2021 there were 4.66 billion active Internet
users worldwide, representing 59.5% of the global population. 2021 was the second year of the projectand consolidates responses from 223,087 people across 34 countries in the Core Anglosphere, Spanish
and French-speaking Continental Europe, Spanish-speaking Latin America, West and North Africa withtranslations in English, Spanish, French and Arabic and spanning a wide range of demographic groups.
It is important to keep in mind that trends reported here are not likely to be reflective of offline
populations who typically live in a different context. This is particularly relevant for developing countries
such as those in Africa where the Internet-enabled population are a minority and generally represent
higher socioeconomic groups or those who have achieved greater levels of education.

The Mental Health Million Project
The objective of the Mental Health Million Project is to provide an evolving global map of mental
wellbeing, and enable deep insights into its drivers that can be used for more effective managementof population mental wellbeing through evidence-based social policy and interventions. This projectutilizes the Mental Health Quotient, or MHQ (Newson and Thiagarajan, 2020; Newson et al., 2022),
delivered as an open online anonymous survey that takes approximately 15 minutes to complete andreturns overall wellbeing scores as well as a comprehensive report with tailored self-care and helpseeking recommendations via email. The MHQ has been demonstrated to relate systematically to
productivity in work and life as well as clinical burden (Newson et al., 2022 , see also Appendix 1) and is afunctional reflection of how well-equipped we are to handle both life’s adversities and opportunities. Moreinformation on the MHQ can be found here. Feel free to take it yourself.

Mental wellbeing on a spectrum from distressed to thriving
The MHQ assessment captures a comprehensive spectrum of emotional, social and cognitive attributes
encompassing both problems (or symptoms) across 10 different mental health disorders (as defined7

by the DSM-5), as well as positive mental attributes. An aggregate mental wellbeing score based onthese aspects (the MHQ) positions individuals on a spectrum from Distressed to Thriving (Newson andThiagarajan, 2020; Newson et al., 2022). The scale is divided into positive and negative components.

Distressed Struggling

-100

-50

Enduring

0Managing Succeeding

50

100

150

Thriving

200

MHQ Score

The positive range of the scale represents the spectrum of normal functioning, and is a 200-point scalecalibrated to a mean of 100 based on pre-pandemic responses in 2019, similar to the IQ scale. The
negative range of the scale represents mental wellbeing scores associated with a negative impact on theability to function and is associated with clinical level risks and challenges. Five functional dimensionsof Drive & Motivation, Mood & Outlook, Cognition and Social Self and Mind-Body Connection are also
computed. In addition, the assessment captures information on demographics, lifestyle factors, andtraumas and adversities, providing a rich context for understanding key drivers of risks. You can learn
more about the MHQ and its scale in Appendix 1.

Interpreting the MHQ
Mental wellbeing, a reflection of how we ‘feel’, is by its very nature subjective. The MHQ captures theimpact of each mental aspect on a life impact scale. Thus, individual judgement of how much a mental
aspect impacts our ability to function must necessarily be within our individual context. For one person
this may mean being able to carry out a particular type of work in the day, while for another it may
mean managing a household. Furthermore, each person will have in their own mind what appropriate
functioning looks like. Thus, mental wellbeing, as we measure it, inherently reflects an individual’s senseof how their inner state impacts their ability to function within their life context rather than an absolute ofhuman mental function.

What’s in this report
In this report we show the mental wellbeing of the Internet-enabled populations of 34 countries, spanning
6 continents. In addition to comparisons of countries and regions we highlight trends in mental wellbeing
across age, gender, employment and education. Finally, we provide insights into possible key drivers of
the trends described.

8

1.

The Mental State of theInternet-Enabled WorldIn 2021, the Mental Health Million Project collected MHQ assessment data from 223,087 Internet
enabled respondents, spanning 34 countries across 6 continents in English, Spanish, French and Arabic(Figure 1.1). We show here regional views that reflect an aggregation of the countries within each region
where there was data from 4 or more countries. (For details on construction of these regional views see
Appendix 2).
Figure 1.1: Countries and regions that form part of the 2021 report

9Core Anglosphere

Latin America

Middle East

United States, Canada, United Kingdom,
Ireland, New Zealand, Australia

Argentina, Chile, Colombia, Ecuador, Guatemala,Mexico, Peru, Puerto Rico, Venezuela

Iraq, Saudi Arabia, United ArabEmirates, Yemen

French & Spanish Speaking Mainland Europe

West Africa

North Africa

Spain, France, Belgium, Switzerland

Cameroon, Cote d'Ivoire, Democratic
Republic of the Congo*, Nigeria

Algeria, Egypt, Morocco, Tunisia

South Africa, India and Singapore are notcurrently aggregated into any one region*Central Africa but grouped here due to proximity.

Mental wellbeing across world regions
Figure 1.2: Average MHQ scores across regions
The distribution of mental wellbeing across broad regions of the world shows more people distressed or
struggling, and lower MHQ score overall, in the core Anglosphere compared to all other regions of the world.

80

EuropeEuropeLatin
America75

Average MHQ Score*West
AfricaMiddleEast
North
AfricaCore
Anglosphere

0%

20%

40%

60%

80%

-50

0

50

100

150

70

65

Middle East
North Africa

60Core Anglosphere

100%

Distressed Struggling Enduring Managing Succeeding Thriving-100

Latin America
West Africa

200

55

50

MHQ Scale
Weighted by age and gender of the population. Results therefore reflect the age/gender demographic make-up. For example,
only 3% of West Africa is over age 65 compared to 20% of Europe. Regional averages are weighted by the Internet populationof each country in the region.
*Note that MHQ scores are on a 300-point scale.

Altogether 30% of respondents in the Core Anglosphere had mental wellbeing scores in the Distressed or
Struggling range, compared to 23% for the Middle East, 23% for North Africa, 24% for Latin America and
18% for Europe (Figure 1.2). Conversely, only 36% in the Core Anglosphere, Middle East and North Africa
were Thriving or Succeeding compared to 45% and 46% in Latin America and Europe, respectively.

The Core Anglosphere had the poorest
mental wellbeing of six regions worldwide.
10

On average, this translated to mental wellbeing scores being highest for Europe (78), followed by
Spanish-speaking Latin America and French-speaking West Africa (74 and 75), differences that were
not statistically significantly different from one another. In contrast, mental wellbeing scores weresignificantly lower for the Core Anglosphere (5% lower on the 300-point scale). The Middle East and North
Africa were similar to one another and fell in between. Thus, altogether the Core Anglosphere had thepoorest mental wellbeing of 6 regions worldwide.

Changes in 2021 in the English-Speaking World
Data collection for the Mental Health Million Project began in 2019 in English and was followed by an
official launch and expansion in 2020. Presently therefore, we are only able to show trends for 8 English
speaking countries which include the Core Anglosphere (the United States, United Kingdom, Canada,
Australia and New Zealand) as well as India, Singapore and South Africa (see Newson et al., 2021a for
more on the 2020 results). Altogether, compared to 2020, there was a decline in mental wellbeing in thesecountries of 3% along the MHQ scale in 2021 (Figure 1.3) Overall, this was smaller than the 8% decline
from 2019 to 2020 (Newson et al., 2021a). Correspondingly the number of respondents with MHQ scoresin the Distressed or Struggling range increased from 26% in 2020 to 30% in 2021. This 4% jump however
was smaller than the 12% increase seen from 2019 to 2020.

In English-speaking countries, the percentage of people Distressed or
Struggling increased from 26% to 30% in 2021.
These declines in mental wellbeing from 2019 to 2021 were significantly correlated with the aggregate
stringency of government Covid-19 measures across both years (Oxford COVID-19 Government
Response Tracker, 2022; r=-0.54) as well as directionally correlated with Covid-19 cases and deaths per
million (Johns Hopkins, 2021).
Figure 1.3: Mental wellbeing trend in the English-speaking world 2019-2021

11

90
8%

66

58
3%

2019

2020

% Distressed/Struggling

Average MHQ Score

Trends across 8 English speaking countries show a continued but slower
decline in mental wellbeing in 2021 relative to 2020.

2021% change in MHQ scores is based on decrease in percentage along the 300-point MHQ scale

26%

30%

14%

2019

2020

2021

Functional dimensions of mental wellbeing
The Mental Health Million Project also examines global trends across 5 different functional dimensionsof mental wellbeing: Mood & Outlook, Social Self, Drive & Motivation, Mind-Body Connection, andCognition. These dimensions represent scores constructed from subsets of elements within the MHQ toreflect specific aspects of our mental functioning (See Appendix 1 for more details).

Mood & Outlook

Social Self

The ability to manage and regulate yourHow you interact with,emotions effectively and to have a constructive

relate to, and see yourself

or optimistic outlook for the future.

with respect to others.

Drive & Motivation

Mind-Body Connection

The ability to work towards achieving yourThe regulation of thedesired goals and to initiate, persevere andbalance between yourcomplete activities in your daily life.

mind and body.

Cognition*
Your ability to perform basic cognitive functions, make senseof complex sets of events and situations and display a longerterm perspective in your thoughts and behavior.

* Two dimensions shown in 2020 – Core Cognition and Complex Cognition have now been consolidated into a single dimension of
Cognition. These dimensions are constructed on the same scale as the MHQ and represents a rescaling from the way they werepreviously represented. See appendix for details.

Within almost every region, scores for Cognition and Drive & Motivation were highest while Mood &Outlook and Social Self were the lowest. In aggregate, 30% of respondents were Distressed or Struggling
with their Social Self while 27% of respondents were Distressed or Struggling with their Mood & Outlook.
In contrast, only 17% and 18% were Distressed or Struggling with their Drive & Motivation and Cognition,
respectively.
Compared to all other regions, the Core Anglosphere scored lower on 4 out of the 5 dimensions. The
difference between the Core Anglosphere and other regions was particularly notable for the dimension ofSocial Self, where average MHQ dimension scores were 4% lower on the scale relative to all other regions,12which clustered close together. Similarly, the Core Anglosphere was lowest on the dimensions of Mood
& Outlook and Mind-Body Connection while Europe, Latin America and West Africa were highest on both.
On these dimensions the Middle East and North Africa were in between.
Along the dimensions of Cognition and Drive & Motivation, Europe and Latin America stood out for having
higher scores than all other regions (on average 2-4% higher). Cognition was the only dimension where
the Core Anglosphere did not score the lowest. Here, scores were lowest for the Middle East, and North
Africa, though not significantly so.

The Core Anglosphere scored lowest on most dimensions of mental
wellbeing relative to other regions, most notably the dimension of Social Self

Figure 1.4: MHQ dimension scores across the regions
Average scores* across five dimensions of mental wellbeing for each region.
100

Average MHQ Dimension Score

95
90
85
Core Anglosphere80

EuropeLatin America

75West Africa

70

Middle East
North Africa

65
60
55
50
Cognition

Drive &
Motivation

Mood &
OutlookSocialSelf

Mind-Body
Connection*Note that the MHQ dimension scores are computed on a 300 point scale.

13

2.

The Mental Wellbeingof Countries

How countries rank
Here we show how each of the 34 countries fared relative to one another (Figure 2.1). This includes India,
Singapore and South Africa, which were not included in the regional views. The MHQ scores of countries
represent weighted averages of the age and gender groups as they are represented in the populations(See Appendix 2 for more details). Surprisingly, Venezuela topped the list with an average MHQ of 91
while the UK and South Africa had the lowest scores at 46. This difference of 45 MHQ points represents
a 15% difference along the 300-point scale. Further, 8 out of 10 countries with the highest MHQ scores
were located in Latin America and Europe (MHQ score range of 74 to 91). In contrast, English-speaking
respondents reported the greatest challenges with their mental wellbeing. Eight out of ten countries withthe lowest MHQ scores (MHQ score range of 46 to 63) were from the Core Anglosphere or were countries
where there is a large English-speaking population (i.e. South Africa, India). Overall, most countries (15out of 34) clustered together with aggregate scores within a narrow range of 70 to 75, less than 2%
apart on the scale, a difference that is typically not statistically significant. We note that the statisticalsignificance of any difference varies with sample size, and variance, which differs by country, and caution
the reader to consult the associated statistical tables to determine if a difference between any two
countries is in fact significant.
Within the Latin American countries in our sample, Venezuela had the highest MHQ scores (91), followed
by Peru (78) and Argentina (75). Lowest within Latin America were Colombia and Guatemala (both 70).
Among the four European countries, Spain topped the list at 85, followed by Switzerland at 82, while
French speaking Belgium and France were similar at 75 and 73. Within the Core Anglosphere, the UnitedStates and Canada were the highest (both at 63) while the UK, at 46, was lowest, representing the widest
regional range. We note that among other English-speaking countries outside of the Core Anglosphere,
Singapore was highest at 74 while South Africa was lowest at 46, along with the UK. India was in thelowest 5 at 56. Among the Middle East and North Africa, the United Arab Emirates and Tunisia werehighest on the list at 73 while Iraq was lowest at 57. Within West Africa, the Democratic Republic ofCongo topped the list with an average MHQ of 82 while Nigeria and Cameroon were the lowest at 71. We
note that West African countries have the lowest Internet penetration (23-50%, Data Reportal, 2021), andthe Internet populations are typically more educated and have higher employment rates relative to theInternet populations of countries where Internet penetration is higher.

14Figure 2.1: MHQ scores across countries
Average MHQ for each of the 34 countries

Percentage of Distressed/Struggling for each of the 34 countries

Spain

85

Democratic Republicof the Congo
Venezuela

Puerto Rico
Democratic Republicof the Congo
Switzerland

84

Spain

82

Switzerland

18

82

Cote d'Ivoire

19

Venezuela

91

15
15
16

Peru

78

Cameroon

19

Argentina

75

Tunisia

19

Belgium

75

United Arab Emirates

19

Singapore74

France19

Ecuador74

Puerto Rico

20

United Arab Emirates

73

Singapore

20

Tunisia73

Belgium

20

France73

Morocco20

Cote d'Ivoire

72

Peru

21

Chile

71

Algeria

21

Nigeria71

Argentina

21

Cameroon

71

Saudi Arabia

22

Mexico

71

Yemen

23

Guatemala70

Nigeria24

Colombia

70

Chile

24

Morocco70

Ecuador25

Algeria

69

Guatemala26

Saudi Arabia

68

Egypt

26

7%

3%
1%

Yemen

64

Iraq

26

United States

63

Colombia

27

Canada63

Mexico27

Canada28

United States

29

New Zealand29

Ireland

30

Egypt

60

New Zealand

59

Iraq

57

India

56

Ireland

56

Australia
2%
3% United Kingdom
South Africa
3%

54

0%
2%

8%

3%
4%
4%
8%

46
46

0
Change in MHQ
Score compared
to 2020

3%
4%
1%

20

40

60

15%

Average MHQ Score

80

India

31

Australia

32

United Kingdom

36South Africa

36

100

0
Change in %distressed/struggling
compared to 2020

10

20

30

% Distressed / Struggling

Across the 34 countries, the percentage with scores in the negative range of Distressed or Struggling
varied from 19% to 36%. While the ordering of countries along this metric is largely similar to that of theaverage MHQ score, it is interesting to note that several West African and North African countries move
up on the list, indicating that a smaller fraction within the Internet-enabled population of these countries
had severe mental health challenges.

15

40

Finally, for the subset of English-speaking countries for which data was collected in 2020, we note in
Figure 2.1 the percentage drop of average MHQ scores along the scale relative to the previous year. Most
countries dropped by 2-3% on the MHQ scale with corresponding increases in the percentage of people
Distressed or Struggling of 3-4%. Standouts were Singapore where the MHQ drop was 7%, and South
Africa where the percentage Distressed or Struggling increased by 8% from 28.5% in 2020 to 36%.

Explaining the differences between countries
With countries such as Venezuela and Peru higher on mental wellbeing than the United States andCanada, at first glance the ordering of mental wellbeing across the Internet-enabled world does notcorrespond with country level economic prosperity. What then explains the ordering of countries? We
took a look at a number of factors; demographic, economic, cultural and Covid related in search ofanswers. The outcomes surprised us.

Demographic factors
First, there are various demographic differences among the Internet populations of these countries. For
example, West Africa’s population in general is much younger, with only 3% over age 65 (United Nations,2019). This is in stark contrast to European countries where over 20% of the population are above age 65(United Nations, 2019). Second, the Internet-enabled population of West African countries are typicallymore educated and more likely to be employed. Figure 2.3 shows the correlations between different
demographic factors and MHQ scores of countries. The percentage of older adults, the percentage with abachelors or higher and the percentage of employed are all directionally positively correlated with country
MHQ scores (although not significantly), hinting that these factors play a role, but also that they are not
the whole story (see Statistical Tables for details). Significantly correlated was only the percentage of thesample for each country with a Master’s degree or higher (r=0.4). Note that demographics of age andgender are factored into the calculation of Country MHQ scores while education and employment are not.
Mental wellbeing along these various demographic dimensions are discussed in a subsequent section.

Covid Factors
Another factor that might be expected to play a role are Covid related metrics. Here we looked at both
Cases and Deaths per 1M population in 2021 in each country (Johns Hopkins, 2021) as well as anaggregate metric of the stringency of Covid response measures in 2021 (Oxford COVID-19 Government
Response Tracker, 2022). Stringency of Covid measures were significantly negatively correlated with
MHQ scores of the populations 18-34, though not for the overall population, and directionally negatively
correlated with deaths and cases per 1M. (r=-0.4 for stringency, see Statistical Tables).

16Economic factors

Figure 2.2: Correlation of country

MHQ scores to economic indicators

Is there a relationship between
economic indicators and mental
wellbeing? We looked for correlations***

between MHQ scores of countries**

and key economic and development

Human Development
Index (HDI)

indicators including GDP per capita,**

GNI per capita, Decadal GDP growth,2020 GDP growth, Gini index (World**

Bank) and the Human Development*

Index (HDI; UNDP). Surprisingly, GDP

Gross national income(GNI) per capita*

per Capita, GNI per Capita and the

*
*
*
**

Human Development Index were all
significantly negatively correlated with
MHQ scores. While the correlation*for overall MHQ was not significant,

GDP Per Capita

when only considering those 18-64 (i.e.

*excluding the elderly), these negative**

correlations became highly significant
for Drive and Motivation, Social Self

MHQ 18-64

and Mind Body Connection. In addition,

GDP decadal change

MHQ 18-34

correlations were highly significant for

MHQ 35-64

Males 18-64 and for younger adults

MHQ Males 18-64

under 34 (both males and females) but

MHQ Females 18-64

not significant for older Females. We

Cognition 18-64
Drive motivation 18-64

note that Drive and Motivation includes

Gini Index

Mood outlook 18-64

various factors such as energy levels

Social Self 18-64

and desire to achieve goals, which

Mind body connection 18-64

-0.8

-0.6

-0.4

on the negative ends are also the key-0.2

0Correlation*** -p<0.001, ** -p<0.01, * -p<0.05

0.2

0.4

0.6

0.8

factors of burnout. As this sample
is specific to the Internet-enabled
populations, there is the possibility
that this reflects the greater prosperityof the Internet-enabled in countries

with lower per capita GDP and internet penetration. However, the negative correlations persisted

17

Country indicators of economic prosperity are negatively correlatedwith mental wellbeing, particularly for young adults and males, andbelies the commonly held belief that national economic prosperity
translates to greater social wellbeing

after removing African countries

Figure 2.3: Correlations of MHQ and dimension

scores with country level cultural indicators

where Internet penetration is lower.
Furthermore, when considering only
the countries in western Europe and

**
**
**
**
**
**
**
**
**
**

the Core Anglosphere, the direction ofcorrelation for GDP and GNI per capita

Performance Orientation

were still negative though no longer
significant given the smaller numberof countries. However, the negative*

correlations of MHQ scores to the Gini

*
**
*

*

*

*

Index and decadal changes in GDP

Institutional Collectivism

increased substantially to -0.45 and**-0.46 from -0.14 and -0.12 for all 34
countries. This belies the commonly

**
*

***

held belief that national economic

***
***
***

prosperity translates to greater social

Individualism*

wellbeing, where these correlations****

would be expected to be positive andUncertainty
Avoidance**

MHQ 18-64**

MHQ 18-34
MHQ 35-64*
PowerDistance

Cognition 18-64
Drive motivation 18-64*

Mood outlook 18-64
Social Self 18-64***

Cultural Factors

**
***
**
***

How much does culture play a role?
Here we looked at correlations
between MHQ scores and country-

**
***
**

MHQ Males 18-64
MHQ Females 18-64

not significantly negative.

**

*
**

level cultural indicators of the Globe
Project (House et al., 2004) as well**as those compiled by Geert Hofstede***

at Maastricht University (Hofstede,2013). Surprisingly, several indicators

Mind body connection 18-64**

of culture had exceptionally strong

In-group or Family Collectivism

correlations with mental wellbeing*scores of countries. At the top of thelist was the Globe Project’s indicator

-1

-0.8

-0.6

-0.4

-0.2

0

Correlation*** -p<0.001, ** -p<0.01, * -p<0.05

18

0.2

0.4

0.6

0.8

of Performance Orientation (Houseet al., 2004), a measure of how muchrewards and recognition are based on

Countries high on indicators of Performance Orientation andIndividualism had the poorest mental wellbeing across all dimensions
and age groups.

work performance. Of all indicators examined, this was the most significantly negatively correlated to
MHQ scores overall, all MHQ dimensional scores, and for all age and gender groups. Following this were
two additional metrics Institutional Collectivism (Globe Project) and Individualism (Geert Hofstede; GH),
which were also significantly negatively correlated with mental wellbeing and its various dimensions.
Institutional Collectivism reflects a country’s practice of Institutional redistribution of wealth while theindicator of Individualism reflects a social framework in which individuals are expected to take careof only themselves and their immediate families. Countries high on Performance Orientation andIndividualism also tend to practice greater Institutional Collectivism. Thus all three metrics are highly
positively correlated to one another.
On the other hand, the indicators of Power Distance (GH), Uncertainty Avoidance (GH) and In-groupor Family Collectivism (Globe Project) were positively correlated with mental wellbeing. Particularly,
these were most positively correlated for Males and for the MHQ dimensions of Drive & Motivation andSocial Self. A high Power Distance refers to cultures where there is a high acceptance of an unequal,
hierarchical distribution of power, where people understand "their place" in the system. Similarly highUncertainty Avoidance refers to the degree to which a society relies on social norms and rules to alleviate
unpredictability, and in particular, social unpredictability. Finally, In-group or Family Collectivism refers to
the degree to which societies express pride, loyalty and cohesion within their family groups.
We also note that other cultural indicators such as the Masculinity Index (GH), Gender Egalitarianism(Globe Project) and Humane Orientation (Globe Project) were not significantly correlated with any aspectof mental wellbeing and are not shown here but can be seen in the Statistical Tables.
Altogether these relationships paint a surprising but consistent picture: a culture where we are each forourselves and judged and sorted by performance may be good for economic growth but damaging toour collective mental wellbeing. What does this say about who we are and what the goals of our social,economic and political systems should be? We hope that these findings will foster deeper investigation
and active debate.

19

3.

Demographic factors that
impact mental wellbeing
and key trends

A global decline in younger generations
Last year we highlighted the trend of declining mental wellbeing with each successively younger
generation in English-speaking countries. This trend was present even in 2019 although it was amplified
in 2020 during the Covid-19 pandemic when mental wellbeing declined significantly more for younger
adults than older adults (Newson et al., 2021a). This year we show a similar decline in mental wellbeing
across successively younger generations for all 34 countries, irrespective of language or region.

Figure 3.1: MHQ score across age groups
Average MHQ scores for different
age groups across each region

140

Percentage of 18-24 year olds Distressedor Struggling across each region

120

Core
Anglosphere
EuropeAverage MHQ Score*

100

Latin
AmericaWest
Africa80

North
Africa60

MiddleEast

40

Core Anglosphere
EuropeLatin America

20West Africa
Middle East
North Africa

0

18-24 25-34 35-44 45-54 55-64 65-74 75+

Age Group

20*Note that MHQ Scores are on a 300-point scale.

0

10

20

30

40

50

60

% of 18-24 year olds Distressed/Struggling

Figure 3.1 shows a consolidated view for each region. Respondents aged 18-24 had an average MHQ
score of 29, with ~44% of respondents (50% for Core Anglosphere to 38% for Middle East) in theDistressed or Struggling range and only 19% in the Succeeding or Thriving range. In contrast, adults
aged 65 and over had an average MHQ score of 107, 26% higher on the scale than those aged 18-24.
Correspondingly, only 7% among those aged 65+ were in the Struggling or Distressed range (6% for West
Africa to 9% for Middle East), while 63% were in the Succeeding or Thriving range. While we don’t show
this here, we note that this pattern was consistent across all dimensions of mental wellbeing and inparticular the dimensions of Mood & Outlook and Social Self where the decrease in scores with younger
age was steeper. In addition, it was steeper for females than for males.
Does this suggest that mental wellbeing increases systematically as we age? The data suggests not.
In patterns of wellbeing observed prior to 2010, young people typically had the greatest happiness andwellbeing. In the United States wellbeing followed a U-shaped curve where younger and older adults
fared best with a dip among the middle age groups (Stone et al., 2010). In Latin America on the other
hand, wellbeing was highest among young adults, declining steadily with older age groups (Steptoe etal., 2015). While these older studies measured wellbeing specifically in terms of happiness and elementsof our Mood & Outlook measure, we note that Mood & Outlook now shows an even steeper decline
than other dimensions with Feelings of Sadness, Distress and Hopelessness the most prevalent mental
challenge among those aged 18-24. The profound change in the patterns of mental wellbeing by age
group, and findings that mental health challenges in young adulthood tend to persist over adult life
(Richmond-Rakerd et al., 2021), suggests a progressive decline across generations rather than a trend of
improving mental wellbeing with age.
The globally consistent nature of this phenomenon suggests a common underlying cause, particularly
since patterns of wellbeing by age were shown to differ quite significantly across regions prior to 2010
(Steptoe et al., 2015). While we are unable to present a proven explanation for this global trend, we
discuss multiple possibilities in section 5 from changes in inequality to environmental causes and theimpact of the Internet.

A persistent gender gap most pronounced in Latin America
Gender differences in mental wellbeing have been reported previously, with rates of depression, posttraumatic stress disorder (PTSD), eating disorders and generalized anxiety disorder higher for womencompared to men (Kessler et al., 1993; Van de Velde et al., 2010; McLean et al., 2011; Ditlevsen and Elklit,
2012; Salk et al., 2017; Udo and Grilo, 2018). In line with these findings, we showed that female respondents
had a slightly lower MHQ scores overall relative to male respondents in our 2020 report (Newson et al.,2021a). However, one of the most striking findings from 2020 was that those who identified as nonbinary/
third gender were experiencing substantially more serious challenges with their mental wellbeing,
consistent with other studies (Cochran et al., 2003; Russell and Fish, 2016; Trevor Project, 2021).

21

Figure 3.2: MHQ score across genders
Gender gap for different age groups across regions

10%

Core Anglosphere
EuropeRelationship between gender and average MHQ score

Males

FemalesNonbinary/
Third Gender

0

10

20

30

40

50

60

Average MHQ Score

7080

MHQ percentage difference
between Males and Females

8%

Latin America
West Africa
Middle East

6%

North Africa

4%

2%

0%

-2%
18-24

25-34

35-44

45-54

55-64

65-74

Age Group

In 2021 as well, mental wellbeing, as measured by the MHQ, was slightly higher in males (68) comparedto females (62), and dramatically lower in nonbinary/third gender respondents (25). This translates to just
a 2% difference between males and females on the MHQ scale but 12-13% between nonbinary or third
gender and either males or females (Figure 3.2). This year we extend these findings to multiple regions.
Overall it was greatest in Latin America and lowest in the Core Anglosphere, although the pattern was
distinct for young adults 18-24.
The gender gap, generally in the range of 2 to 4%, was many-fold smaller than the age gap reported
above, and the magnitude of this gap diminished with age. For example, for young adults aged 18-24
years, there was a 6% difference in mental wellbeing scores, with men having higher mental wellbeing
compared to women. However, for those aged 65+ this gap had diminished to only 1.5%. Possibly thisreflects the greater decline of mental wellbeing with age for females than for males.

22Education, Employment
and Mental Wellbeing

4.

More education, better mental wellbeing
Previous evidence suggests a bidirectional relationship between mental wellbeing and education
attainment where poor mental wellbeing can lead to lower levels of attainment and vice versa (Esch etal., 2014; Gariépy et al., 2021). Figure 4.1 demonstrates the relationship between mental wellbeing, as
measured by MHQ scores, and education level – defined as the highest level of education that a person
has achieved. The results show higher mental wellbeing scores with increasing levels of education.
Respondents with PhD or Master’s qualifications had the highest levels of mental wellbeing withaverage MHQ scores of 101 and 94, respectively. Those with Bachelor’s or Vocational certifications
had mental wellbeing scores approximately 6% lower at 78 and 79, respectively. Those individuals witheducation levels below a Bachelor’s degree (i.e. high school or below) had the lowest mental wellbeingscores (range 47-58), approximately 9% lower on the MHQ scale than those with Bachelor’s degrees
or Vocational certifications. This pattern was similar across all regions, although the magnitude of

Figure 4.1: MHQ scores across education levels
Average MHQ scores acrosseducation levels for each region

Relationship between educationlevel and average MHQ score

120

Ph.D.
or higher

100

Master's
DegreeBachelor's
Degree80

60

80

Average MHQ Score

23

100

120

Middle East
North Africa

0Ph.D.
or higher

40

West Africa

20

Master's
Degree20

Latin America

Bachelor's
Degree0EuropeVocational
certification

Primary
Education

Core Anglosphere

40

High
SchoolSome HighSchool60

Some HighSchoolHigh
SchoolPrimary
Education

Average MHQ Score*

Vocational
certificationdifference between education levels varied slightly. For example, there was a smaller difference between
those who had a high school education or less compared to those with higher degrees in Europe than allother regions, while the Core Anglosphere had the steepest difference.
This relationship of MHQ scores to education may also be a reflection of increased income associatedwith higher educational attainment, a factor we do not consider here.

Employment, mental wellbeing and productivity
Work is an integral part of life that occupies much of our waking time and can provide people with astrong sense of identity, self-worth, purpose and economic stability (Modini et al., 2016). Conversely,
unemployment has been shown to have a negative impact on mental wellbeing (Brouwers, 2020).
In aggregate, the data showed similar mental wellbeing scores for respondents who identified
themselves as employed/self-employed or homemakers, with average MHQ scores of 75 and 69,
respectively (figure 4.2). In contrast, those who were either unemployed or unable to work reported
MHQ scores that were 11% and 19% lower on the MHQ scale respectively compared to those who were
employed or homemakers. The percentage of those who were employed was highest between ages 25
and 54 in all regions and substantially lower among those 18-24 and 65+.

Figure 4.2: MHQ scores across employment groups% Employed/self-employed
across each age group and region

Unemployed
Studying
Not ableto work
0

20

40

60

80

100 120

Average MHQ Score

80

Percentage difference along MHQ scale

Homemaker

14%

70
60
50
40
30
20

10%
8%
6%
4%
2%

10

0%
75+

65-74

55-64

45-54

35-44

25-34

18-24

0

24

12%

Age groupCore Anglosphere

Europe

55-64

90

45-54

Employed /Self Employed

16%

35-44

100

Percentage Employed/Self-Employed (%)

RetiredDifference between Employed/
Self Employed and Unemployed

25-34

Relationship between
employment and MHQ score

Age group
Latin America

West Africa

Middle East

North Africa

Those retired are typically 65 and older, while those studying are typically 18-24, where the average MHQ
scores ranged from 90-120 and 20-38 across regions respectively. Thus, the higher MHQ scores of those
Retired and low MHQ scores of those Studying is likely a reflection of the decline in mental wellbeing
with younger generations than specifically to do with being Retired or Studying per se. Across the age
ranges of 25 to 54 where the majority were employed, the difference between the MHQ scores of those
Employed/Self-Employed to those Unemployed was substantially higher in the Core Anglosphere (1214% for all age groups) and lowest in North Africa and the Middle East (as low as 1% for ages 55-64 in
the Middle East to as high as 10% for ages 24-34 in North Africa). Overall, with the exclusion of the Core
Anglosphere, the difference was typically 4-10%.
Evidence has long suggested that poor mental health leads to productivity loss (Kessler and Frank,
1997; Lim et al., 2000; Hemp, 2004; Burton et al., 2008; Evans-Lacko and Knapp, 2016; Bubonya et al.,2017) and economic loss (Marcotte and Wilcox-Gök, 2001; Stewart et al., 2003; Trautmann et al., 2016;
Whiteford, 2021) indicating that the relationship between mental wellbeing and productivity is likely to
be bidirectional such that those unemployed may be so because of challenges to their mental wellbeing.
We note that there is a systematic relationship between MHQ scores and self-reported productivity(also shown previously in Sapien Labs, 2021; Newson et al., 2022). Absenteeism and presenteeism
increase substantially in the negative ranges of the MHQ scale (see Appendix 1). This both supports thebidirectional relationship between employment and productivity and demonstrates that the MHQ is aneffective measure of a person’s productive capabilities.

25

5. Summary and Insights
A comparison of key demographic factors
In Figure 5.1 we show side by side the magnitude of differences in MHQ scores (the mental wellbeinggap) within each region along each of the various demographic dimensions described in section 4.
These include age (18-24 year olds vs 65+), gender (males vs females), education (Bachelor’s degrees
or higher vs lower),
employment

Figure 5.1: Magnitude of differences by demographic factors

the biggest gap was
the generational gap
where the difference

Education

Across all of these,

MHQ Employed –MHQ Unemployedregion, and overall).

MHQ College+ MHQ No Collegescore within eachCore Anglosphere
Middle East
West Africa
Latin America
North Africa
EuropeMHQ Male MHQ Female

vs lowest country

Core AnglosphereWest Africa
Middle East
North Africa
EuropeLatin America

Latin America
West Africa
Middle East
EuropeNorth Africa
Core Anglosphere

MHQ 65+ MHQ 18-24

country (highest

Employment

unemployed) andCore Anglosphere
Latin America
West Africa
EuropeMiddle East
North Africa

MHQ Country Max MHQ Country Min(employed vsCountry Overall
Core Anglosphere
Latin America
Middle East
North Africa
EuropeWest Africa

between those age
18-24 and those 65
and older was 20-

Genderin mental wellbeing

31% across regions.
generational mental
wellbeing gap was

Age

In all regions, the2- to 3-fold larger
than that of gender,
education (notingthat employment
status is also

26

Country

employment or

0%

5%

10%

15%

20%

Difference along MHQ Scale

25%

30%

35%

influenced by age). Even the maximum difference between countries overall, although relatively large(15%), was still smaller than the generational gap within any region.
Thus, of all trends, understanding what is behind the global shift in the pattern of mental wellbeing by age
since 2010 is of profound importance.

The difference in mental wellbeing between younger and older adults
was 2- to 3-fold larger than the differences along the dimensions ofage, gender, employment or country within a region.

Possible causes of the generational decline
The rapid shift from region specific patterns of wellbeing with age where young adults were typicallyhighest on various wellbeing scales, to a consistent pattern where they are now consistently lowest
across the Internet-enabled population of every single one of the 34 countries we sampled, suggests a
common underlying cause. We considered various potential causal factors: Income Inequality, Political
Instability, factors related to the Internet, and Environmental Toxins.
Inequality as measured by the Gini index has increased in much of the developed world in the past
decade. However, in other regions such as Latin America it has decreased or remained stable. And yet
the same age dependent decline exists in every one of these countries. Similarly, while there have been
instances of political instability over the last decade, in many of the 34 countries in this report, estimatesof Political Stability and Absence of Violence/Terrorism (World Bank) have been unchanged over the past
decades. Thus, while these factors may play a role in specific countries, neither can explain the presenceof such a consistent global trend.
With respect to environmental toxins, air pollution has generally declined worldwide or remained stable
over the past decade, ruling this out as a likely cause. On the other hand, chemical contaminants andendocrine disruptors in water have grown significantly (Wee and Aris, 2019). So too has the presence of
microplastics in our food and water, which are known to induce immune and stress response and result
in reproductive and developmental toxicity in animals but are not well studied in humans (Blackburn andGreen, 2022). Unfortunately, country level data on these parameters are not available. However, this is anarea that we believe should be further explored in the context of its impact on mental health status.
Finally, one global change that is consistent across all countries, is the exponential growth of smart phones
and ubiquitous Internet access that began around 2009 and stabilized around 2016. The impact of various
aspects of Internet use such as social media and screen time on mental health have been studied with27

mixed conclusions (Wongkoblap et al., 2017; Kelly et al., 2018; Twenge and Campbell, 2018; Keles et al.,2020). However, we offer another possible perspective. Since the mobile phone became pervasive, global
statistics indicate that individuals spend an average of 7 to 10 hours online per day depending on the
country. We suggest therefore that it is perhaps not the use of the mobile phone and Internet per se thathas been damaging, but rather that it occupies such a large fraction of waking time that it crowds out timethat previously would have been spent on the in-person social interactions that are required to build andmaintain a strong Social Self, the dimension most significantly lacking in younger adults. While the present
data are only suggestive and not conclusive, this is an area that warrants immediate attention and much of
our efforts in 2022 will seek to understand these effects.

Potential consequences
While work needs to be done to understand clearly the specific causal factors, the urgency is highlighted
by some sobering statistics. Lower MHQ scores are significantly correlated with national rates of suicideand sexual violence, and directionally with rates of violent assault of countries, as reported by United
Nations Office on Drugs
and Crime (UNODC) (Figure

Figure 5.2: Correlations of country MHQ

5.2). The correlations arescores with national statistics

particularly significant foryoung men 18-34 and with****the dimension of Social**

Self. Left unmanaged, theSexual Violence

present trend suggests that*

there could be substantial**

increases in these statistics.

*

*

*

*
*

*

Suicide****MHQ 18-64*

MHQ 18-34
MHQ 35-64*

MHQ Males 18-64

Serious Assault

MHQ Females 18-64
Cognition 18-64
Drive motivation 18-64
Mood outlook 18-64
Social Self 18-64
Mind body connection 18-64

-0.7

-0.6

-0.5

-0.4

-0.3

Correlations*** -p<0.001, ** -p<0.01, * -p<0.05

28

-0.2

-0.1

0

Looking ahead
Global expansion
2021 was the second year of the Mental Health Million project and saw the addition of three languages:
Spanish, French and Arabic. This expanded the scope from 8 English-speaking countries and 49,000
responses in 2020 to 34 countries and 223,087 responses in 2021. In 2022, with the addition oftranslations in Portuguese, Russian, German, Hindi, Tamil and Swahili, we will expand our reach to 72
countries and over half a million responses as we work towards our goal of over 1 million responses eachyear. With the expansion both in breadth across the globe and depth of reach within each country, we will
be able to provide increasingly representative and localized perspectives with the potential for deeper
insights into underlying drivers.

Deeper insights
The insights in this report represent only the tip of the iceberg. Triangulating data with other data sources,
both environmental and societal, to understand relationships will enable an untangling of the causal
factors to determine what must be managed to make the biggest impact on mental wellbeing. Deeper
analysis of symptom profiles can also enable understanding of how specific clinical risk profiles differ
across geographies and demographics. The possibilities are many. Access to the real time data from the
Mental Health Million project is therefore freely available on request to researchers and organizations for
not-for-profit research. It is our hope that organizations will use this data effectively for greater impact.

Donations and partnership support
Reaching our goals of a truly global view that helps us actively and effectively manage the mental
wellbeing of society requires multifaceted support from around the world. We look to our readersand stakeholders to support our effort to expand across the globe, and also to partner with us tomake effective use of the insights this data offers. Donations can be made through our website or bycontacting us. We also welcome partnerships to expand reach to specific geographies or demographic
groups, and to help bring the insights of this data into policy and interventions that can positively impact
the future mental wellbeing of the world.

29

Appendix 1:
Understanding the MHQ
About the MHQ
Data for the Mental Health Million project is collected using an online assessment tool called the Mental
Health Quotient (MHQ) that was developed at Sapien Labs. The MHQ is a unique comprehensive
assessment of mental wellbeing comprised of 47 elements of mental function including both problems
and assets (Newson and Thiagarajan, 2020). It uses these elements to provide an aggregate score toposition individuals on a spectrum from Distressed to Thriving, as well as sub-scores across 5 broadfunctional dimensions. The MHQ is freely available online, is anonymous, and takes ~15 minutes tocomplete. It is currently available in English, Spanish, French and Arabic with additional translations
planned for 2022 and beyond. In addition to the 47 scored questions, respondents answer questions
relating to their demographics, life experience and lifestyle. To encourage thoughtful and honest
responses, respondents receive an MHQ score along with tailored feedback on completion of the MHQ
and can opt to receive a more detailed report with recommendations for action via email.

Rationale behind the MHQ
The MHQ was developed to address existing challenges with mental health assessment, and thediversity and comorbid nature of mental illness. First, no tools were available that assessed both thepositive and negative dimensions of mental function that would be appropriate to identify the full rangeof mental wellbeing across a general population. Further, available mental health assessment tools
were typically specific to particular ‘disorders’. A study by Sapien Labs of 126 commonly used mental
health questionnaires and interviews, spanning 10 disorders showed that questionnaires and interviews
assessing the same disorder were only 29-58% similar in terms of the symptoms captured, dependingon the particular disorder (Newson et al., 2020). Conversely, 60% of symptoms were assessed in atleast half of all disorders illustrating the extensive overlap between disorder-specific assessment tools.
Furthermore, no cross-disorder tools available assessed the full spectrum of symptoms or considered
positive dimensions of mental wellbeing. The MHQ was therefore born out of the need for a global

30

standard in mental health assessment which spanned the breadth of symptoms of mental healthdisorders, but was also relevant to the wider population who do not necessarily exhibit symptoms at aclinical level, but nonetheless may experience natural fluctuations in their mental wellbeing.

Development of the MHQ coded questions
The MHQ was developed based on a comprehensive coding of symptoms and mental elements across126 different mental health questionnaires and interviews spanning depression, anxiety, bipolar disorder,
attention-deficit/hyperactivity disorder (ADHD), post-traumatic stress disorder (PTSD), obsessivecompulsive disorder (OCD), addiction, schizophrenia, eating disorder, and autism spectrum disorder(ASD), and cross-disorder tools (Newson et al., 2020). A total of 10,154 questions were coded andconsolidated into a set of 43 symptom categories. The resultant items were then reviewed in the context
of other transdiagnostic frameworks including the Research Domain Criteria (RDoC) put forward by
the National Institute of Mental Health (Insel et al., 2010) and symptoms relevant to dementia, andreorganized into a set of 47 elements of mental wellbeing.

Use of a life impact rating scale
Across the 126 tools studied, the evaluation of symptoms was highly heterogeneous, ranging frompresence or frequency to severity and duration of symptoms on various time scales from days tomonths. Taking instead the position that the ultimate goal of mental health intervention is to mitigate
the impact of mental distress on one’s life experience and functioning, the MHQ uniquely captures these
symptoms and mental attributes using a 9-point life-impact rating scale. The MHQ contains two typesof mental elements. Those that could exist on a spectrum from positive to negative and those that are
problems of varying degrees of severity.

Demographic, experiential, and momentary questions
The MHQ also includes numerous un-scored questions relating to demographics, lifestyle andlife traumas and adversities that can be of value in understanding contextual triggers, drivers anddeterminants of mental wellbeing.

Validation of the MHQ
The MHQ has now been validated in multiple ways (Newson et al., 2022). First, reliability of the MHQ
has been demonstrated by showing that distinct samples collected during the same period had
indistinguishable MHQ distributions and average ratings for each of the 47 elements. In addition, MHQ

31

scores have been shown to be correlated with r=0.84 between retakes within an 8–120-day period.
Comparisons of the MHQ life impact rating against frequency and severity ratings for the MHQ element
Feelings of sadness, distress or hopelessness showed a clear linear relationship with an R2>0.99.

The MHQ scale
The MHQ positions individuals on the spectrum from Distressed to Thriving, spanning a possible range ofscores from −100 to +200 where negative scores indicate a mental wellbeing status that has significant
negative impact on the ability to function. Importantly the MHQ score is not based on a simple averaging
of question ratings but rather each individual rating is thresholded along the functional scale between
positive and negative impact to function and nonlinearly transformed based on a ranked severity of
implications. The positive range of the scale is modeled on the IQ scale. Positive scores, which are largely
normally distributed, are calibrated to a mean of 100 based on our original 2019 sample and can range
from 1 to 200. Negative scores, on the other hand, have a long-tailed distribution. In order to ensure that
overall average scores are not inordinately determined by the small number of individuals in the longtail, the negative scale was compressed to a smaller scale of 0 to -100 in order to mitigate the impact ofnegative scores on the population average. More details of this methodology are provided in (Newson
and Thiagarajan, 2020).

Functional implications of the MHQ
The MHQ has been demonstrated to relate systematically to the productive capital of an individual inwork and life (Sapien Labs, 2021; Newson et al., 2022). For example, we have shown that the average
number of days of work missed in the
Figure A1.1: Relationship of MHQ score to productivity

average (Figure A1.1). While those with thehighest MHQ scores did not often miss a

32

MHQ ScoreLines show top and bottom of the range

200

175

150

anywhere from 18-23 days per month on0
125

an overall reduction in life productivity of100

MHQ scores (between -75 and -100) had

575

productive days) those with the lowest

10

50

20% to 50% loss of productivity on less

15

25

less productive and assuming a range of

Employed0days of work missed and days that were

All respondents

20

-25

of MHQ score (taking into account both

25

-100

total loss of life productivity as a function

Weighted productivity lost (days)2022). Cumulatively, when considering the-50

as MHQ scores increase (Newson et al.,-75

past month decreases systematicallyday of work, even this group reported a few unproductive days a month. Thus, MHQ scores are a good
representation of behavioral loss of function and supports the use of the MHQ as an assessment of the
productive capacity of a population, independent of any disorder classification. It also positions the MHQ
as an important tool for companies and universities to be more strategic in their management of mental
health and wellbeing.

Relationship of the MHQ scale to clinical disorders
The MHQ elements map to diagnostic criteria for each of 10 major DSM-5 disorders (Newson et al.,2020; Newson et al., 2021b). Mapping individual profiles to these criteria has shown that MHQ scores
relate systematically to clinical burden (Newson et al., 2022). The percentage of people with clinicalsymptom profiles that aligned with any of 10 DSM-5 defined disorder criteria increased as the MHQ
score decreased, such that 89% of those with scores in the Distressed range had symptom profiles thataligned with at least one of the 10 DSM-5 defined disorder compared to 0% for those with scores in the
Succeeding or Thriving range (Newson et al., 2022). Similarly, the number of disorders per individual
decreased systematically as MHQ scores increased with the average number of disorders per person
at 3.8 for those in the Distressed group and 0.0 for those in the Succeeding and Thriving groups. Thus,
the MHQ score is also reflective of the overall clinical burden of mental health. Note that the MHQ score
range labelled as Distressed was previously referred to as Clinical, while the score range Struggling waspreviously referred to as At Risk.

Computation of MHQ dimension scores
MHQ dimension scores are computed for 5 broad dimensions of mental health: Mood & Outlook, Drive& Motivation, Social Self, Mind-Body Connection, and Cognition, that have overlap and parallels with the
RDoC principles of domains and constructs (Insel et al., 2010; Note that cognition is a composite of thetwo historical dimensions of Core Cognition and Complex Cognition). Dimension scores are computed asa weighted average of a subset of relevant items, where those core to the dimension are weighted as 1
and those secondary to the dimension are weighted 0.5. This weighting algorithm was developed basedon a review of cognitive and neuroscience models of brain functioning. For example, the item Stability
and calmness is coded with a primary 1 weighting in the Mood & Outlook dimension and a secondary
0.5 weighting in the Mind-Body dimension to reflect its dual components of emotion and physiologicalresponse, whereas the item Unwanted, strange, or obsessive thoughts is dual coded with a primaryweighting in the Cognition dimension and a secondary weighting in the Mood & Outlook dimension toreflect both the cognitive and emotional elements of this item. In this regard, an item could be assigned

33

to 2 different dimensions and occasionally to 3. Overall, each dimension comprised 10 to 24 items.
The dimension scores are reported on the same scale as the overall MHQ as described above. Positivescores are normalized to the range of 0 to 200, whereas negative scores are normalized to the range of−1 to −100 (Note that this represents a change in the dimensional scale which ranged from -50 to +100
previously. As this represents a linear scaling to avoid the use of multiple scales, reports using this earlier
scale can be compared to results in this report by simply scaling by 2).

34

Appendix 2:
Data Collection& Analysis Methods
Recruitment of respondents
223,087 respondents from around the world completed the MHQ assessment between 1st January
and December 31st 2021. Participants were recruited through advertising on Google and Facebook bytargeting a broad audience within each age-gender demographic across a wide geography within eachof 34 countries (See table A2.1 for a list of countries). Recruitment in 2021 initially focused on Englishspeaking countries, and was then expanded to Spanish, French and Arabic speaking countries with thelaunch of these translations in June, September and November, respectively.
The Google Ads outreach specifically targeted those individuals who were searching for terms relevant
to mental health (e.g. psychological test, cognitive assessment test, mental health assessment) andwere applied consistently across all countries. Those recruited through this stream may therefore have
had a specific interest or concern relating to their mental health. In contrast, Facebook outreach wasmuch broader, spanning individuals who had shown a previous interest in mental health and wellness
topics, as well as all adults in that country with the simple tagline What is your mental wellbeing score?
Those recruited through this stream were therefore not specifically searching for information relating to amental health interest or concern.

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Data distribution
The number of respondents for each targeted country is shown in Table A2.1.
Table A2.1: Sample size across countries

Country
United States
India
United Kingdom
Venezuela
Argentina
MexicoSouth Africa
CanadaColombia
Spain
Australia
Algeria
Nigeria
New Zealand
Egypt
Ireland
MoroccoTunisia

N
25074
24924
17318
13839
13043
12662
11887
10796
10385
8083
7840
7631
6468
5098
4771
3899
3695
3653

Country
Peru
FranceIraq
Singapore
Yemen
GuatemalaCameroon
Ecuador
Chile
Cote d'Ivoire
Belgium
Saudi Arabia
Democratic Republicof the Congo
Puerto Rico
United Arab Emirates
Switzerland

TOTAL

Figure A2.2: Percentage of respondents

in each age and gender group

N
3332
3093
2633
2612
2577
2459
2139
2097
2074
2050
1945
1505
1285
1095
719
406

Age profile of respondents

75+
5%

65-74

18-24
22%

14%

25-34

55-64

12%

19%

35-44
45-54

13%

15%

Gender profile of respondents

Nonbinary/
Third Gender1%

223087
Male
41%

FEMale58%

Respondents spanned all age groups roughly equally (Figure A2.2
top) while the gender split was 58% female, 41% male and 1%
nonbinary/third gender (Figure A2.2 bottom).
Respondents across countries varied in terms of their education level and employment status.
Education and employment levels are shown in the associated data tables. However, please note thatsince the MHQ Scores of countries are weighted by age and gender, the average scores do not reflect theraw distribution in the sample. For example, a smaller older population would also mean a smaller retired
population while a smaller younger population would mean a smaller population still studying. For details
see section on Data analysis.

36

Data analysis
Exclusion criteria
Only those respondents who stated that they found the MHQ easy to understand were included in theanalysis. This exclusion criterion was applied by only selecting respondents who answered “Yes” to thefinal question in the MHQ which asks them “Did you find this assessment easy to understand?”. Those
who answered no were excluded from the analysis. In addition, only respondents who were over 18 were
included. Those who responded that they were “Under 18” were unable to continue with the assessment
and so were automatically excluded. No other inclusion or exclusion criteria were applied to the data.

Computing average MHQ Scores for countries
The spread of respondents across age and gender groups was not an accurate representation of their
proportion of the population in each country. Furthermore, the proportion of respondents in each agegender group were not identical across countries. Thus, to enable a more representative view of acountry’s population, and more accurate comparisons between countries, scores were first computedfor each age-gender group and then a weighted average score was computed based on the relative
proportions of each group within individual countries. Analyses comparing age brackets were only
weighted by gender, while conversely, analyses comparing genders were only weighted by age. All
population estimates and age-gender distributions that are utilized for these weightings were taken fromthe United Nations population estimates (United Nations, 2019). As there are no reliable statistics forthe proportion of nonbinary/third gender individuals across all countries, we used a broad population
estimate weighting of 0.5% across all countries, which was decided on after reviewing a number ofsources (Flores et al., 2016; Meerwijk and Sevelius, 2017), although we acknowledge, similar to othersources, that this may be an underestimate.

Computing average MHQ Scores for regions
Computation of the regional MHQ and dimension scores were not a simple average across countries but
were additionally weighted based on the proportion of Internet users within the country (Data reportal,
2021). Thus, larger countries or countries with larger Internet populations would play a larger role in theseregional estimates. Where the Internet population may be larger than the particular language groups inthe country in which the MHQ was offered (e.g. Belgium), the proportion of those language groups wasused as the weighting factor rather than the proportion of Internet users.

Score Reporting in MHQ points and percentage differences
We typically report differences in terms of MHQ points and the corresponding percentage shift along
a 300-point scale i.e. ((Value 1 – Value 2)/300) * 100. For instance, 75 points represents 25% of the37possible length of the scale. Thus a 75-point shift or difference between groups would be a 25% shift
along this scale.

Statistical analysis
Statistics were computed by comparing groups using a standard t-test. P-values obtained were thencorrected for multiple comparisons using a Bonferroni correction. All statistical tables showing these
corrected p-values are provided in a supplementary download along with the report.

Correlations
Correlations between country level MHQ scores and economic, cultural, Covid-19 and other indicators
were computed using Pearson's correlation measure. While economic and Covid-19 indicators wereavailable for all 34 countries, the cultural indicators were available only for 18 (Globe Project) or 25 (GH)of the 34 countries. In general indicator numbers used were for the latest year available except for theGini index where a 4 year average was used.

Limitations of sampling and data interpretation
Although respondents were similarly recruited across all countries, two key caveats must be highlighted.
First, these samples may not reflect a true sample of any country’s population and will be biased bythose with language proficiency, Internet access and the willingness to spend 15 minutes completing an
online assessment. Thus, results must be interpreted strictly in this context. Second, cultural differencesin language usage and culture itself can significantly influence how people interpret and respond toeach individual question. Any individual country’s results will therefore reflect these differential effects ofculture.

38

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