Mental
State ofthe World
2020

A publication of
the Mental HealthMillion ProjectDear Reader,
Mental wellbeing extends beyond our feelings of happiness or life satisfaction to encompass thebreadth of our emotional, cognitive and social function and capabilities. Understanding where westand collectively on the spectrum of mental wellbeing serves as an important barometer of the healthof our society.
The Mental Health Million project was conceived and developed at Sapien Labs as a public interest
project to enable a comprehensive view of the evolving mental wellbeing of our world so that we canbetter manage it both individually and collectively. The project utilizes a uniquely designed online
assessment called the Mental Health Quotient, or MHQ. The MHQ scores mental wellbeing based on
a comprehensive list of capabilities or assets as well as challenges that encompass symptoms acrossten major mental health disorders, to reflect the spectrum of mental wellbeing across the general
population. Furthermore, by incorporating demographic and life experience elements it can be used togain deep insights into what drives our mental wellbeing status.
In this first year since launch of the project in English we have captured ~49,000 responses, primarily
across 8 English speaking countries enabling a profile of these countries. Over the next few years
its reach will expand to many more languages and countries. This report provides comparisonsof aggregate mental wellbeing and its six functional dimensions by countries, age groups andgender, with a particular focus this year on the impact of major lifestyle factors and Covid-19 related
adversities and traumas. While the data represented in this report was collected beginning in April
2020 following the start of the Covid-19 lockdowns, a few thousand people were surveyed in 2019
offering an approximate point of comparison. While it is clear that Covid-19 has had, and continues
to have, a significant impact on our mental wellbeing, the data tells a story of more long-term global
challenges, only exacerbated by the ongoing pandemic.
Achieving our objective of generating a truly global representation of our collective mental wellbeing
depends on public support including partnerships to spread the word as well as financial contributions.
As an individual you can contribute your own state of mind to our collective understanding of mental
wellbeing across the globe by taking the MHQ at sapienlabs.org/mhq.
Success of the Mental Health Million project ultimately lies in the utilization of insights arising fromthis project at various levels from governments, institutions and companies through to families andindividuals to better manage our collective mental wellbeing across all sectors of our society. We lookforward to engaging in these conversations.

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. Robert Latzman, Associate Professor, Department of Psychology,
Georgia State 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. Josh Seidman, Managing Director, Avalere Health, Washington DC, USA
Take part at sapienlabs.org/mhq
Cite this report as:
Newson JJ, Pastukh V, Sukhoi O, Taylor J and Thiagarajan TC, Mental State of the World 2020, Mental
Health Million Project, Sapien Labs, March 15th, 2021, doi:10.5281/zenodo.4603620© Sapien Labs 2021
sapienlabs.org
3

4

Executive Summary

6

Introduction

8

Our collective mental wellbeing in 20208

Beyond happiness and disorders

8

From silos to holistic understanding

9

The challenge of measurement

9

The Mental Health Million project

10

What’s in this report11

1. The Mental State of the (English Speaking) World12

An aggregate view

12

Functional dimensions of mental wellbeing13

2. The Mental Wellbeing of Countries

15

Aggregate mental wellbeing of countries

15

Country scores along functional dimensions

16

The distinct assets and problems of countries

17

Big cities fare better

18

The clinical burden of countries

18

Psychiatry resources and outcomes19

3. Age and Mental Wellbeing

20

Mental wellbeing is diminished in younger adults

20

Dimensions of mental wellbeing across age groups

21

Explaining the generational gap in mental wellbeing

23

4. Gender and Mental Wellbeing

24

The male-female gender gap

24

A gender gap that diminishes with age

25

The enormous challenge for nonbinary/third gender26

5. Lifestyle Factors that Impact Mental Wellbeing

27

The magnitude of influence of lifestyle factors

27

Everything is worse without sleep, socializing and exercise

28

Lifestyle factors by country

29

Lifestyle factors by age and gender demographics

30

Untangling cause and effect31

5

6. The Mental Cost of the Covid-19 Pandemic

32

What hit us the hardest in 2020?

32

Covid-19 related impact across countries

33

Differential impact of circumstances on different age groups

34

The long term impact of trauma and adversity

35

7. Conclusions & Recommendations

36

A population-based approach to mental wellbeing

36

Tackling the youth mental wellbeing crisis

37

Tackling the nonbinary/third gender crisis

37

Cracking sleep and self care

38

Looking Ahead

39

Expanding our reach39

Expanded research and insights

39

Donations and partnership support

39

Appendix 1: Understanding the MHQ

40

About the MHQ

40

Rationale behind the MHQ

40

Development of the MHQ

41

Coded questions

41

Use of a life impact rating scale

41

Demographic, experiential, and momentary questions

41

The MHQ scale

42

Computation of MHQ Subscores

42

Appendix 2: Data Collection & Analysis Methods

44

Recruitment of respondents

44

Data distribution

45

Exclusion and adjustments

45

Data analysis

46

Computing average MHQ scores

46

Weighting of MHQ data when comparing cities

47

Calculation of sleep, exercise and socializing scores

47

Computing score differences

48

Statistical analysis

48

Challenges of sampling and interpretation

48

References

49

Executive Summary
This represents the first annual report of the Mental Health Million project, an ongoing effort
to measure and track the mental wellbeing of our global population, with the goal of providing
deep insights into its drivers that can be used to guide the development of effective policy andintervention. Mental wellbeing, as measured by the Mental Health Quotient or MHQ, a free andanonymous online assessment tool, encompasses a comprehensive view of our emotional, socialand cognitive function and capability. In its first year, the Mental Health Million project obtained
data from ~49,000 people across 8 English speaking countries: United States, Canada, United
Kingdom, Australia, New Zealand and the substantial English speaking populations of South Africa,
India and Singapore. This report provides a descriptive view of this data with the caveat that the
samples may not be fully representative of a country.
Aggregate mental wellbeing shows a profound drop from 2019. In the aggregate, the overallmental wellbeing score for 2020 was 66 relative to a score of 90 obtained in 2019 from a different
and relatively smaller sample of 2000 people from the same countries, representing an 8% shiftdown the MHQ scale (which ranges from -100 to 200). The percentage of respondents withclinical level risk increased from 14% in 2019 to 26% in 2020. The drop relative to 2019 was mostpronounced for young adults aged 18-24.
The mental wellbeing of countries: United Kingdom struggling. The average mental wellbeing score
was highest for respondents from Singapore and the United States and lowest for those from theUnited Kingdom and South Africa overall, and across multiple dimensions. Respondents from India,
while in the middle overall, varied the most across dimensions of mental wellbeing. Furthermore,
respondents across countries differed in the individual elements of strength and challenge while
those living in big cities fared better than countries overall.
A crisis in young adults. MHQ scores were 86 points lower in young adults aged 18-24 comparedto older adults (65+), representing a shift of 27% along the MHQ scale, with mental wellbeing
decreasing systematically with each younger generation. With 44% of young adult respondents
reporting clinical level risk (compared to 6% of 65+) this adds to the growing alarm of a profoundsocietal mental health crisis. In particular, young adults had significantly compromised self-worth
and confidence as well as focus and concentration and struggled with feelings of sadness anddistress and unwanted and obsessive thoughts.
A country specific gender gap that diminishes with age. Mental wellbeing was statistically higher
in males than females across all countries combined, though the differences were small. However,this aggregate number reflected a larger gender gap in young adults that reduced with increasing
age and reversed in favor of females after age 65. Overall, the gender gap was largest in Singapore.

6Male and female groups also differed in their relative strengths and problem areas.
Nonbinary/Third Gender at highest risk for suicide. Nonbinary/third gender adults had significantly
compromised mental wellbeing with over 50% in the category of clinical level risk, and average
MHQ scores ~47 points lower than males and females combined (17% lower along the MHQ scale).
Particularly, they reported substantial suicidal thoughts and intentions relative to other groups.
The importance of sleep, social interaction and exercise: an unexpected magnitude. MHQ scores
were shifted 82 points (27% lower along the MHQ scale) in those who rarely had a good night’s
sleep relative to those who always did; 66 points lower on the MHQ scale in those who (before thepandemic) rarely or never engaged in face-to-face social interactions with friends and family at least
three times a week compared to those who rarely or never did; and 46 points lower on the MHQ
scale in those who never exercised compared to those who exercised at least 30 minutes a day.
The impacts for all three – sleep, socializing and exercise - were present across all facets of mental
wellbeing although specific elements of mental wellbeing that were most impacted across sleep,socializing and exercise, differed.
The mental cost of the Covid-19 pandemic on mental wellbeing. 3.9% of respondents reported
having had a Covid-19 infection of which 0.7% reported a severe case, roughly in line with the global
infection rate in 2020. On the other hand, a full 57% of people experienced a range of negative
health, financial or social consequences. Particularly, the 2% of respondents who were unable toget critical care for other existing health conditions due the pandemic reported the worst mental
wellbeing that was 61 points or 20% lower along the MHQ scale than those who experienced noneof the negative consequences. The 1.4% of people who struggled to make ends meet for basic
necessities had the second worst mental wellbeing at 18% lower along the full extent of the MHQ
scale. Having reduced household income was also associated with a shift of 4% down the MHQ
scale but impacted a full 17% of people. Social isolation shifted MHQ scores ~20 points down thescale for all age groups. However, this same shift represented a 65% drop relative to the scores ofyoung adults aged 18-24 with no Covid-19 related adversities on average but only ~17% for those
65+. We also note that greater incidence of lifetime traumas and adversities was systematicallyassociated with lower mental wellbeing scores, pointing to the potential long-term impact of these
Covid-19 related changes.
Conclusions and recommendations Altogether we advocate for embedding a population-based
approach to mental wellbeing into social and economic policy. We also join a growing call for greater
research investment into understanding the drivers of the mental health crisis of young adults
and nonbinary/third gender adults, and in understanding the factors that impact people’s lifestyle
habits relating to exercising, socializing and sleep. Finally, we encourage a greater role for schools,
universities and companies in actively enabling the mental wellbeing of their students and workforce.

7

Introduction
Our collective mental wellbeing in 2020
2020 was a year unlike any other in our life experience. The arrival of the Covid-19 pandemic touched
all corners of the globe, causing illness, distress and uncertainty along with an economic fallout that is
unrivalled in recent history. The tremendous toll it has taken on our collective mental health has beenhighlighted repeatedly in the media and scientific literature (Holmes et al., 2020; Pierce et al., 2020;
Serafini et al., 2020) and also reminds us that mental health and wellbeing isn’t just about clinicaldiagnoses. Our lives come naturally with adversity, and although not every mental challenge or momentof sadness warrants labelling as a clinical disorder, our mental wellbeing can still feel compromised attimes.

Beyond happiness and disorders
Although there are different perspectives and definitions of mental wellbeing,
here we use the term to represent the spectrum of our emotional, social andcognitive function and capability. In this regard, our mental wellbeing score is areflection of how well-equipped we feel to handle life’s challenges, rebound fromadversities and capitalize on opportunities to contribute productively to society.
Our mental wellbeing measure therefore spans a spectrum from Clinical toThriving and encompasses a host of emotional, social, cognitive, and physicalfunctions that need to operate in line with, or beyond, the demands of any
situation. These broadly span the dimensions of Mood & Outlook, Drive &Motivation, our Social Self, our Core and Complex Cognition and our Mind-bodyConnection. Importantly, therefore, mental wellbeing, although related, is not
the same as happiness or life satisfaction.
While the extreme impairment of one or more functions are consideredmental health disorders, focusing on these clinical disorders alone hinders ourunderstanding of the spectrum of mental wellbeing in the general population.
Only by considering this diversity will we be able to make progress that
improves the lives of everyone.

8“Mental healthis a state of wellbeing in which anindividual realizes
his or her own
abilities, can copewith the normal
stresses of life, canwork productively
and is able to makea contribution
to his or her
community."- WHOMental wellbeing, as measured by the MHQ, represents the spectrum of ouremotional, social and cognitive function and capability, a reflection of how
well-equipped we feel to handle both life’s adversities and opportunities.

From silos to holistic understanding
Understanding the status of mental wellbeing across populations and identifying the factors that impact
mental wellbeing systemically is of paramount importance for effective policy making and appropriate
provision of mental health resources (Patel et al., 2018). As a global population, we exhibit a hugediversity in our state of mental health and wellbeing. Although there is some data on the prevalence ofindividual disorders like depression, anxiety, and addiction (Kessler et al., 2009; Steel et al., 2014; Jameset al., 2018; Ritchie and Roser, 2018; World Health Organization, 2018) that provide an indication of how
many people are suffering from serious challenges to their mental health, there is currently no big picture
understanding of our collective mental wellbeing on the spectrum from Clinical to Thriving and along
major functional dimensions.
Although pockets of data exist within current mental health surveillance systems, and information
sources are more readily available in some countries, there are still many gaps that need to be filled.
Special Initiatives on mental health from the World Health Organization (WHO, 2019b) and the inclusionof Good Health and Wellbeing as one of the United Nations’ Sustainable Development Goals (UN, 2020)highlights the importance of plugging these gaps to facilitate the allocation of resources and support toindividuals and populations who are most at risk.

The challenge of measurement
One challenge to achieving this goal is of measurement. The assessment of mental health is fraught
with several challenges from a bias towards capturing clinical disorder rather than overall wellbeing, topoor standardization across assessment tools such that the same disorder
may be diagnosed differently with different tools (Newson et al., 2020). The
Mental Health Million project uses a unique scientifically developed tool called
the Mental Health Quotient, or MHQ, (Newson and Thiagarajan, 2020), designed
to overcome the various limitations of measurement tools today. Based on asystematic coding of 10,000 questions across 126 DSM-based assessment
tools and inclusion of additional criteria from the Research Domain Criteria(RDoC) put forth by the National Institute of Mental Health in the United States(Insel et al., 2010), the MHQ captures the full breadth of symptoms across ten

9

We can’t manage
our collective
mental wellbeing
effectively if we
don’t measure it.

different disorders as well as positive mental attributes on a life impact scale to position individuals
on the spectrum from Clinical to Thriving, in the aggregate, and across six functional dimensions (seeAppendix 1 for more details on how the MHQ was developed).

The MHQ and mental wellbeing

The MHQ is a unique tool that measures problems that map to symptom profiles across ten
common mental health disorders as well as positive mental attributes to position people on aspectrum of mental wellbeing from Clinical to Thriving.
Clinical-100

At Risk-50

Enduring

050Managing Succeeding Thriving

100

150

200

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 MHQ delivered as an open online anonymous survey that takes approximately 15 minutes
to complete and returns overall wellbeing scores aswell as a comprehensive report with tailored lifestyle
recommendations via email, encouraging honest responses.
Launched in April of 2020 in English, this first year has
focused on adults 18 and older in eight English speaking
countries that include the United States, Canada, United
Kingdom, Australia, New Zealand, and India’s, South Africa’sand Singapore’s considerable English-speaking populations.
While the Mental Health Million project will launch inmultiple languages in 2021 and beyond, here we present
Available for everyone at sapienlabs.org/mhq

insights into the mental wellbeing of the English-speaking
world in 2020. This includes data obtained from ~49,000
people across these countries, recruited through Internet

search and social media which, while similarly recruited, may not be a fully representative sample of any

10country. While this report provides a top-line description of this data, much more can be mined from it.
The Mental Health Million project is therefore structured as an open data project where researchers canaccess the data on request. Information on how this data was obtained can be found in Appendix 2.

What’s in this report.
In this report we show a comparison of mental wellbeing profiles of eight countries, as well as highlightdifferences across age and gender groups based on this data. We also report on key lifestyle factors thatare revealing themselves as important drivers of mental wellbeing and discuss the impact of adversities
and traumas associated with the Covid-19 pandemic.

CANADA

UNITED
KINGDOM

UNITED
STATESSINGAPORE

AUSTRALIA
INDIA

SOUTH AFRICA11NEWZEALAND

1.

The Mental Stateof the (English Speaking) WorldAn aggregate view
The average mental wellbeing score across 8 English speaking countries in 2020 was 66, a decline of
24 points relative to a smaller sample from 2019 that represents an 8% downward shift on the MHQ
scale. The percentage of people at risk for, or with clinical disorders was 26%, 12% higher compared to
2019. MHQ estimates of clinical burden in 2019 are broadly in line with estimates of annual prevalence
rates of mental health disorders reported from other sources such as the surveys from the World HealthOrganization and the Global Burden of Disease Study which have made estimates in the range of 9.819.1% (Kessler et al., 2009; Steel et al., 2014; James et al., 2018) while 2020 estimates are aligned withthe finding of increased prevalence of mental health challenges in 2020 (e.g. Pierce et al., 2020; Serafini etal., 2020).
Figure 1.1: Distribution of MHQ scores in 2020 across the
English speaking population and changes relative to 2019*

Change from 2019*25%

Average MHQ score

20%

2019

15%

90

2020
-24

66

10%
5%

Clinical or at Risk

0%

2019
Clinical-100-50

At Risk

Enduring
0

50

100

Average MHQ score

12

14%

Managing Succeeding Thriving
150

66

2020
+12%

26%

200

Managing* 2019 had a small sample sizeof 2000 people across mainly
US/India/UK. Numbers can beconsidered directional

These overall scores are based on averages for respondents from each country

The Six Dimensionsof Mental Wellbeingin The MHQ

weighted by their relative adult populations. For each country, average scores were firstobtained for each age and gender group and then weighted by their proportion of the
population (for more details on analysis methods, see Appendix 2). Note that 2019 data
included 2000 people primarily across United States, India and the United Kingdom.

Functional dimensions of mental wellbeing

Mood & Outlook
The ability to manage andregulate your emotions
effectively and to have aconstructive or optimistic
outlook for the future.

Within this aggregate view of mental wellbeing, what did distinct functional
dimensions of mental wellbeing look like?
Respondents felt most positive about their Complex Cognition overall with less than
15% experiencing serious risks and 50% Succeeding or Thriving on this dimension.
They were similarly confident in their Drive & Motivation and Core Cognition where

Social Self
How you interact with,relate to and see yourself
with respect to others.

about half were Succeeding or Thriving and 20% or less of the population struggledon each of these dimensions.
Drive & Motivation
The ability to work towards

Respondents felt most positive about their Complex
Cognition overall with less than 15% experiencing serious
risks and 50% Succeeding or Thriving on this dimension.

On the other hand, people struggled most in their Social Self. Over 30% of
respondents had significant risks and challenges in their Social Self, the way they
relate to, and see themselves with respect to others in the world, with only 40%
Succeeding or Thriving. Similarly, nearly a third of respondents struggled withachieving your desired goals
and to initiate, persevere andcomplete activities in yourdaily life.

Core Cognition
The health of your basic
cognitive skills which allow
you to function effectively
and independently on a
moment to moment basis.

serious challenges to their Mood & Outlook, the ability to regulate their emotions
and see the world optimistically. These scores were most different from 2019
decreasing 11 and 12 points for Social Self and Mood & Outlook (a downward shiftof 7-8% on these dimension subscales) respectively while Drive & Motivation andComplex Cognition declined relatively less (both 8 points or 5% on the subscale).

Over 30% of respondents had significant risks andchallenges in their Social Self; the way they relate to, andsee themselves with respect to others in the world.
13

Complex Cognition
The ability to synthesize
and make sense of complex
sets of events and situations
and display a longer-term
perspective in your thoughts
and behavior.

Mind-Body Connection
The regulation of thebalance between yourmind and body.

Figure 1.2: MHQ score distributions across the six functional

dimensions of mental wellbeing and changes relative to 2019*
Average Dimension Score

SocialSelf

50

Mood &
Outlook

Change from2019*

Drive &
Motivation
Core
Cognition

Complex Cognition

8

Complex
Cognition

Drive & Motivation
Core Cognition

8
10

Mind Body Connection

9

Mood & Outlook
Social Self

12
11

40

Mind Body
Connection0%
Clinical-50

20%
At Risk

-25

40%

60%

80%

100%

Enduring Managing Succeeding Thriving

0

25

50

Dimension Score Subscale

75

100
30

* 2019 had a small sample size of 2000 people across mainly
US/India/UK. Numbers can be considered directional

14

The Mental Wellbeingof Countries

2.

In this first year we have focused on English speaking people from 8 countries: United States, Canada,
United Kingdom, Australia, New Zealand, Singapore, India and South Africa. How did mental wellbeing
look across these countries overall and across different dimensions in 2020?

Aggregate mental
wellbeing of countries

Figure 2.1: Average MHQ Score by Country for 2020 and change from 2019

Change from 2019

Altogether respondents from

4*
14
31*
51*
34*
13*
23*
16

Singapore
United States
CanadaIndia
Australia
New ZealandSouth Africa
United Kingdom

0

20

40

60

Singapore had the highest mental
wellbeing score with an average MHQof 94 followed by the United Stateswith an MHQ of 72. Those from
United Kingdom and South Africa
had the poorest mental wellbeing
with scores of 54 and 56 respectively,
a 13% range on the MHQ scale. We
note that our results reflect weighted

80 100 120

Average MHQ Score
Error bars represent 95% CI of the mean

average responses by age and* 2019 samples ofn<150. Numbers can beconsidered directional

gender distributions in the country inorder to provide a truer picture of the

population mental wellbeing. However, it is important to keep in mind that the English speaking Internet
enabled populace is not necessarily representative of each country as a whole. Further, not all differences
between countries are statistically significant. For example, Singapore is not significantly different fromthe United States due to its small sample size, although both are significantly higher than the United
Kingdom. All statistics are provided in a supplementary downloadable file.

India’s English speaking more educated populace.

Figure 2.2: Proportion of respondents with
Bachelor's degree and above across countries

India’s English speaking population while substantial at ~128
represents a relatively more educated group. Reflecting this, while
the proportion of respondents with a college level education ormore were approximately 10% higher than adult demographics
for most countries, for India it was 70%, a 7-8 fold difference from15

the country proportion of <10%. This must be kept in mind wheninterpreting comparisons.

80%
Percentage of respondents

million, is still only a small proportion of the population and

60%
40%
20%
0%

Country scores along functional dimensions
Within this data, Singapore had higher scores on all dimensions, while the United Kingdom had the lowest
for four out of six dimensions, and second lowest on the other two. Singapore, United States, Canada,
Australia and New Zealand maintained their relative positions to one another across all dimensions while
India and South Africa had the greatest variability. India was relatively higher on Drive & Motivation andMind-Body Connection and lower on Core Cognition and Complex Cognition respectively. Similarly, South
Africa was relatively higher on Drive & Motivation and Complex Cognition.

Figure 2.3: Countries by functional dimensions of mental wellbeing

Social Self

50

Singapore

Singapore

United States

United States

CanadaIndia
40

Australia
New Zealand30

United Kingdom

50

Canada
India
40

30

20

40

60

Drive & Motivation

Average Dimension Score

Average Dimension Score

CanadaSouth Africa
New Zealand

50

Core Cognition
United States

40

New ZealandAustralia
South Africa

30

India
United Kingdom

0

20

40

60

0

20

Complex Cognition

60

Average Dimension Score

Average Dimension Score

United States
South Africa

40

CanadaNew Zealand
India

30

Australia

50

Mind Body Connection
India

40

CanadaAustralia
New Zealand

30

South Africa
United Kingdom

0

20

40

60

Average Dimension Score

16

60

United States

United Kingdom
20

40Singapore

Singapore50

20

Average Dimension Score

Average Dimension Score

60

60

CanadaUnited Kingdom
20

40Singapore

India

Australia
30

20

Average Dimension Score

60

United States
40

0

20

Average Dimension Score

Singapore
50

United Kingdom
South Africa

020

New ZealandAustralia

South Africa

60

Mood & Outlook

60

Average Dimension Score

Average Dimension Score

60

Note that the scale for subcategory scores extends from -50 to 100 (see Figure 1.2)

20

0

20

40

60

Average Dimension Score

Error bars represent 95% CI of the mean.

We note that differences in the average score may or may not be statistically significant depending on
relative sample sizes. All statistics are provided in a supplementary download.

The distinct assets and problems of countries
Countries with similar overall MHQ scores still differed in their specific profile of mental wellbeing. Here we
looked at the top three assets that differed for each country relative to the United States, in both negative andpositive directions and the top three problem areas that differed in either the positive or negative direction.
For instance, the biggest differences between respondents from Singapore and the United States
were that those from Singapore had better Appetite regulation, Self-image and Sleep quality but also
experienced less Pain, had less Physical health issues and Feelings of sadness, distress or hopelessness.
Indian respondents similarly had better Sleep quality, Self-image and Energy levels relative to the UnitedStates. On the other hand they had worse problems with Unwanted, strange or obsessive thoughts,
Aggression towards others and a Sense of being detached from reality.
Those from United Kingdom had poorer Ability to Learn, Self image and Self worth and confidence relative to theUnited States and also struggled more with Mood swings, Sense of being detached from reality and Feelings ofsadness, distress or hopelessness. All comparisons are in Figures 2.4 (Left: Assets; Right: Problems).

Top three differences in mental assets relative
to the USA, either negative or positive
Greater asset in USA

Figure 2.4:

Greater asset in Country

Appetite Regulation
Self Image
Sleep Quality

SINGAPORE

Top three differences in problem areas relative
to the USA, either negative or positive

Greater problem in USA

Greater problem in Country

Feelings of Sadness, Distress,
or Hopelessness

SINGAPORE

Physical Health Issues

Experience of Pain

Self Control & Impulsivity
Emotional Control
Stability & Calmness

SOUTH AFRICA

Sense of being detached from reality
Aggression Towards Others
Unwanted, Strange or Obsessive Thoughts

Emotional Control
Ability to Learn
Physical Intimacy

NEW ZEALAND

Suicidal Thoughts or Intentions
Addictions
Sense of being detached from reality

Sleep Quality
Self-Image
Energy Level

-1

-0.8

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Score difference between USA and other Country

17

AUSTRALIA

Mood Swings
Sense of being detached from reality
Feelings of Sadness, Distress, or Hopelessness

UNITED KINGDOM-0.6

CANADA

Send of being detached from reality
Mood Swings
Suicidal Thoughts or Intentions

AUSTRALIA

Ability to Learn
Self Image
Self Worth & Confidence

INDIA

Physical Health Issues
Mood Swings
Experience of Pain

CANADA

Ability to Learn
Self Worth & Confidence
Emotional ControlNEW
ZEALAND

Unwanted, Strange or Obsessive Thoughts
Aggression Towards Others
Sense of being detached from reality

INDIA

Emotional Control
Drive & Motivation
Ability to Learn

SOUTH
AFRICA

1

-1

-0.8

-0.6

-0.4

-0.2

UNITED
KINGDOM
0

0.2

0.4

0.6

0.8

Score difference between USA and other Country1Big cities fare better
Figure 2.5: Cities relative to rest of country

Big cities are a distinct ecosystem with(more positive = city has higher mental wellbeing)

higher density and faster pace. Does thismake mental wellbeing better or worse?

New York vs rest of US

Respondents from five major cities - New

London vs rest of UK

York, Los Angeles, London, Sydney, and

Los Angeles vs rest US

Mumbai had MHQ scores higher relative

Mumbia vs rest of India

to the rest of their respective countries.

Sydney vs rest of Australia

Particularly, these city folks felt greater
0

2

4

6

8

10

12

Difference in average MHQ Score

14

Drive & Motivation and Core and Complex
Cognition compared to the rest of theirrespective countries. While Singapore,

which is a city-state, still had the highest MHQ, the gap between Singapore and New York City was lessthan half the gap with the United States overall.

The clinical burden of countries
Overall, 26% of respondents were at risk for clinical-level challenges (5% Clinical and 21% At Risk)
representing a considerable clinical burden. This burden was highest in South Africa at 8% followed byUnited Kingdom, India and New Zealand ranging from 6 to 6.5% and lowest in Singapore at 2%. On theother hand, when including those at risk of clinical-level challenges the burden was highest in the United
Kingdom at 31.5% followed by New Zealand at 30% and substantially lower in Singapore at 12.4%.
Country groups also differed substantially in the percentage of the adult population with clinical-level
challenges seeking professional help. Respondents from Canada were the highest at 65.2% followed byAustralia at 58.6%. In contrast, a very low 14.5% of those with clinical-level challenges from India sought
professional help followed by Singapore at 27.3%.

Figure 2.6: Clinical burden of countries% with Clinical
MHQ Scores

18

Figure 2.7: Percentage of people with clinical MHQ

Scores seeking professional or medical help

% with At Risk
MHQ Scores

United Kingdom

CanadaNew ZealandAustralia

South Africa

New ZealandAustralia

United Kingdom

India

United States

CanadaSouth Africa

United States

Singapore

Singapore

India

0

5

10

15

20

25

Percentage of respondents (%)

30

35

0

10

20

30

40

50

Percentage of respondents (%)

60

70Psychiatry resources and outcomesHow does the mental health resource ecosystem correlate withthe percentage seeking help? Across this group of countries, theFigure 2.8: Psychiatrists per 10,000 correlateswith percentage seeking help across countries

number of psychiatrists working in the mental health sector per80

1.5 respectively where the percentage seeking help is very low

70

compared to 14.7 in Canada and 13.5 in Australia respectively

60

where it is high (WHO, 2019a). Overall, there was a generally linear
relationship between the percentage seeking help and the numberof psychiatrists, suggesting that the availability of mental healthsupport is a critical factor in help seeking. However, there are likely
to be other factors which also play a role.

% Seeking Help

100,000 people are lowest for India and South Africa at 0.3 and50
40

CanadaAustralia
South
AfricaUnitedStatesUnitedKingdomNew
Zealand30
Singapore20

10 IndiaFor example, cultural reasons may create hesitancy in seeking
help for fear of stigma or discrimination. Financial constraints incountries without free healthcare systems is also likely to be a

0

0

5

10

15

20

Psychiatrists per 10,000

factor.

Figure 2.9: Clinical burden of countries wasunrelated to psychiatry resources

On the other hand, is it the case that having more
psychiatrists results in a lower clinical burden? A
comparison of the clinical and total risk burden versusSouth
9 Africa

the number of psychiatrists per 100,000 suggests

Clinical Burden (%)

87

to the availability of psychiatrists. There are likely

India

6

Australia

New
Zealand

5
4

Canada2

Singapore

1
0

5

10

15

20

25

Psychiatrists per 10,000

19

numerous reasons for this. Among them may be thecurrent lack of effective treatments and tools available
for mental disorders.

UnitedStates30that this is not the case. Clinical burden was unrelated

UnitedKingdom

30

25

30

3. Age and Mental Wellbeing
Mental wellbeing is diminished in younger adults
Within this dataset the average MHQ score for adults aged 65 and over was 115 and decreased with eachsuccessive generation. Respondents 18-24 years old had an average score of 29, 29% lower on the MHQ
scale than those 65+. Correspondingly, the proportion of respondents in the Clinical and At risk categories
was only ~6% among the 65+ but 44% in the 18-24 group. Conversely, 70% of those aged 65+ werein the categories Succeeding or Thriving compared to only 17% of 18-24 year olds. This is a profounddifference. Along with other studies that suggest that mental health challenges are disproportionallyaffecting younger people with increasing prevalence (Twenge et al., 2019), the magnitude of differencealong such an aggregate measure must sound a loud alarm.

A full 44% of respondents aged 18-24 years were Clinical or At risk ofa clinical disorder compared to only 6% of those 65 and older. Such a
profound difference in mental wellbeing must sound a loud alarm.
Figure 3.1: Mental wellbeing is progressively worse for each younger
generation. Covid-19 also had a more dramatic impact on younger adults

Distribution of MHQ Scores

Average MHQ
Score

18-24

160

Succeeding

35-44
45-54
55-64

Managing

75-84
85+

0%
Clinical

20

-100

140

120

Age Bracket(years)Changefrom 2019*

85+
75-84

12

65-74
100

65-74

20%
At Risk

-50

40%

60%

80%

100%

Enduring Managing Succeeding Thriving

0

50

100

MHQ Score Range

150

200

Enduring

Age Bracket (years)

25-34

55-64
80
45-54
60

40

35-44
25-34
18-24

28
27
44

20

0

* 2019 had a small sample sizeof 2000 people across mainly
US/India/UK. Numbers canbe considered directional

This generational decline in mental wellbeing is a global phenomenon present in all 8 countries. While
the magnitude of this gap was large everywhere, it was greatest for respondents living in Singapore, andsmallest for those living in Canada (67 and 50 MHQ point difference between adults under age 45 andover 45 respectively).
Notably this difference between younger and older adults has been substantially exacerbated in 2020
by the Covid-19 pandemic. Mental wellbeing decreased most dramatically in 2020 (relative to 2019) for
those aged 18-24, with a decrease of 44 MHQ points (a 15% shift along the MHQ scale). For those aged
25-64, there were also decreases of smaller magnitude (27 or 28 MHQ score points), while surprisingly,
those 65 and above actually fared better in 2020, possibly reflecting a bias in this sample of older adults
with the health and faculties to complete a mental health assessment online. This finding tracks withother reports that the consequences of the pandemic are having a disproportionate impact on the young(Varma et al., 2020), even though this group is at least risk of severe illness or death from Covid-19.

Dimensions of mental wellbeing across age groups
Are these differences between age groups specific to certain dimensions? Remarkably, the same trend
is present across the 6 dimensions of wellbeing although it is particularly amplified in the dimensions ofSocial Self and Mood & Outlook.

Figure 3.2: Mental wellbeing was worse in younger adults

across all dimensions of mental wellbeing
SocialSelf

Thriving
Succeeding80

Mood &
Drive &Core
Complex
Outlook Motivation cognition cognition

Mind
Body
Age Bracket(years)60
65+

Managing
Enduring

40

20

45-64
25-44
18-24

0
Note subcategory scores range from -50 to +100.

With respect to specific elements of mental wellbeing, positive aspects that increased the mostsubstantially with age included Self-image, Self-worth and Confidence, Focus and Concentration andEmotional resilience. Conversely young adults struggled substantially more with Feelings of sadness,
distress or hopelessness, Unwanted, strange or obsessive thoughts, Mood swings and Guilt and blame,compared to those aged 65+. In contrast, the more prominent challenges for those aged above 2421and into middle age were Fear and anxiety and Avoidance and withdrawal while those older still (65+)
increasingly struggled with Physical health issues. As in this data, for those over 65, studies have shownthat lower mental wellbeing scores are more likely to be driven by physical health issues (Fiske et al.,2009; Doherty and Gaughran, 2014).

Figure 3.3:

Key differences between age groups 18-24 and 65+

Top problems for each age group

Key assets in the elderly (65+) relative to 18-24
Self Image

Age 18-24

Self-worth & Confidence

Feelings of Sadness, Distress or Hopelessness
Unwanted, Strange or Obsessive Thoughts

Focus & Concentration

Age 25-44

Emotional Resilience

0

0.5

1

1.5

2

2.5

3

Difference in rating from 18-24 to 65+

Fear & Anxiety
Avoidance & Withdrawal

31.0%
28.6%

Age 45-64

Biggest problems in the young (18-24) relative to 65+

Fear & Anxiety

Feeling of Sadness,
Distress or Hopelessness

Avoidance & Withdrawal

Unwanted, Strange
or Obsessive Thoughts

19.2%
17.9%

Age 65+

Guilt & Blame

Physical Health Issues
Fear & Anxiety

Mood Swings

0

0.5

1

1.5

2

2.5

3

Difference in rating between age 18-24 and 65+

The major challenges reported by the 18-24 year old group in this 2020 dataset are similar to thosereported in our smaller 2019 sample, although the relative ranking of these challenges has changedslightly. For 18-24 year olds, poor Self-image, Unwanted, strange or obsessive thoughts and Moodswings dominated in 2019. While almost all elements of wellbeing deteriorated in 2020 for this younger
age group relative to 2019, the biggest changes (although not the most prevalent challenges) were in
deteriorating Relationships with others, Suicidal thoughts and intentions, Fear and anxiety, Confused andslow thinking and Repetitive and compulsive actions.

22

41.0%
39.4%

11.1%
8.8%

Explaining the generational gap in mental wellbeing
What accounts for this profound difference across generations among the English speaking Internet
enabled? Is it that we become increasingly better equipped to cope with life as we age? Or is it that thechanging forces of the world have had a disproportionate impact on the younger groups? Learning theanswers to these questions is exceedingly important for us to understand the future of society.
Arguing in favor of the former is that the reductions in mental wellbeing in young adults are across theboard from Social Self to Core Cognition. This suggests that young adults may not be well equipped tocalibrate their expectations or self-perceptions relative to society as a whole. The higher self-confidence
and emotional resilience with age may also suggest a natural maturing and perspective as we age.
Indeed, despite declining abilities, older adults have more favorable perceptions of their mental status(Reed and Carstensen, 2012). Some aspects of the difference may also relate to a shadow effect, where
major issues such as Feelings of sadness, distress or hopelessness cast a shadow on how the individual
views other aspects of their functioning.

What does the future look like for a society that is profoundly compromisedin its focus and concentration, and consumed with strange and unwanted
thoughts and feelings of distress and hopelessness? The consequences ofthese differences across age groups cannot be taken lightly.
On the other hand, the vastly greater presence of strange and unwanted thoughts, substantial challenges
in focus and concentration, and feelings of hopelessness in the 18-24 age group are warning signsof more worrying forces at play. What if these challenges persist as they transition to middle age
and beyond? What are the consequences of a society that lacks focus and concentration and where
unwanted, strange and obsessive thoughts run amok? Studies suggests that 75% of adults with a mental
health disorder have experienced its onset by the age of 24 (Kessler et al., 2005; Kessler et al., 2007).
Furthermore, recent research shows that mental health struggles in early life can lead to poorer physicalhealth in middle-age and advanced aging (Richmond-Rakerd et al., 2021; Wertz et al., 2021). With 44%
of young adults having or at risk for clinical-level issues, what does this mean for the future of society?
18-24 year olds are the first generation to grow up immersed fully in an internet-connected world. Are
the major societal shifts brought about by the Internet to blame? It is imperative that we invest fully asa society to understand the drivers of this considerable generation shift in mental wellbeing and address
them at their root causes.
The Mental Health Million project will be able to look longitudinally to see how this year’s 18-24 year olds
fare in their next decade, providing increasingly deeper insight that can help drive mitigating strategies
and track their success.

23

Gender and
Mental Wellbeing

4.

The male-female gender gap
The prevalence of disorders such as depression, post-traumatic stress disorder (PTSD) and generalized
anxiety disorder, has typically been found to be higher for women compared to men (Kessler et al., 1993;
Van de Velde et al., 2010; McLean et al., 2011; Ditlevsen and Elklit, 2012; Salk et al., 2017). In contrast,
suicide rates are often higher in men compared to women (Freeman et al., 2017; Naghavi, 2019), andmen are often more reticent to speak out about mental health challenges (Galdas et al., 2005). Here we
provide insight into the difference between genders across the spectrum of mental wellbeing.
Overall, in 2020 our data shows that female respondents had a slightly lower MHQ scores overall relative
to male respondents. However, they also fared better in 2020 relative to a smaller 2019 sample with alower drop of 19 MHQ points compared to 28 in men.
Figure 4.1: There was a small difference between males and females overall whilenonbinary/third gender had substantially poorer mental wellbeing
Distribution of MHQ Scores

Average MHQ Score
80

Male

Managing

FemaleNonbinary/
Third Gender

Changefrom 2019*

Male
Female28
19

Nonbinary/
Third Gender

51

60

40

0%
Clinical50%
At Risk

100%

Enduring
20

Enduring Managing Succeeding Thriving
0

-100

-50

0

50

100

MHQ Score Range

24

150

200

* 2019 had a small sample size of 2000 people across mainly
US/India/UK. Numbers can be considered directional

This difference between men and

Figure 4.2: MHQ scores for each gender group

women predominantly reflectedacross each functional dimension
SocialSelf

Mood &
Drive &Core
Complex
Outlook Motivation Cognition Cognition

Mind
Body

60

Managing

Enduring

differences along the dimension of
Mind-Body Connection followed by
Mood & Outlook (a 3-5% differencealong these dimension scales).

40

Relative to men, women had greater

20

Experience of pain and Fear and anxiety.
Conversely men had greater issues with

0
Male

FemaleNonbinary/Third Gender

Addictions and Empathy than women.

Note that dimension scales range from -50 to +100.

A gender gap that diminishes with age
Interestingly, this gender gap between men and

Figure 4.3: Male-female gender gap by age

women diminished with age. For young adults, there(more positive = males have higher mental wellbeing)(6% of the MHQ scale) with men reporting higher
mental wellbeing compared to women. The gap was
progressively lower for older generations, reversing in
direction for those aged 65+. Notably, at all ages, thedifference between males and females were many foldsmaller than the generational gaps reported above.

65+
45-64
25-44
18-24

-5

0

5

10

15

20

Difference in Average MHQ Score

The gender gap also varied across respondents

Figure 4.4: Male-female gender gap acrossrespondents living in different countries

living in different countries with most

(more positive = males have higher mental wellbeing)

populations reporting slightly higher mental
wellbeing for men compared to women. It is

Singaporeconsiderably larger for respondents living in

India

Singapore (+16 MHQ points or 5% of the MHQSouth Africa

scale) compared to other countries. On theAustralia

other hand, respondents living in the United

CanadaKingdom had gender parity overall, and the gap

United States

was reversed for respondents living in New

United Kingdom

Zealand where women were higher overall.
In these countries, higher MHQ scores for

New Zealand

-5

0

5

10

15

Difference in Average MHQ Score

25

Age Bracket (years)

was a sizable gender gap of 17 MHQ score points

20

men persisted in the 18-24 range but quickly
reached parity thereafter and reversed in favorof women after age 55.

The enormous challenge for nonbinary/third gender
While the differences between males and females were hardly a few MHQ points overall, those
respondents who were nonbinary/third gender had strikingly lower mental wellbeing compared to either
males or females. On average, MHQ scores were 50 MHQ points lower for nonbinary/third gender
respondents (average MHQ of 22) compared to male respondents (average MHQ of 71). This is in linewith other studies that have highlighted the specific challenges faced by this population (Cochran et al.,
2003; Meyer, 2003; Russell and Fish, 2016). Sadly, those who responded as nonbinary/third gender had
consistently poorer mental wellbeing across all 6 dimensions, but in particular for the dimensions ofSocial Self and Drive & Motivation. Particularly, relative to males or females, this group had substantiallygreater troubles with Suicidal thoughts or intentions and a Sense of being detached from reality.

26

5.

Lifestyle Factors That
Impact Mental Wellbeing

We know that sleep, exercise and social contact are key influencers of our mental wellbeing. For
example, research has shown that poor sleep quality leads to impaired cognitive performance (McCoy
and Strecker, 2011; Lowe et al., 2017; Dzierzewski et al., 2018), increases stress reactivity (Meerlo et al.,2008) and is both a trigger and a consequence of poor mental and cognitive health (Krystal, 2012; Hvolby,
2015; Meerlo et al., 2015). Similarly, research is finding that exercise is essential for our mental wellbeing(Di Lorito et al., 2020; Hu et al., 2020) and that without regular contact with friends and family, people
experience social isolation and loneliness (Hawkley and Cacioppo, 2010; Lim et al., 2018; Wang et al.,2018), leading to increased stress levels (Brown et al., 2018) and poorer mental and physical health.
Here we show how lifestyle behaviors vary across different groups and highlight the surprisingly large
magnitude of their impact on mental wellbeing overall, as well as on its individual elements. We note thatdata on social interaction shown here was captured to reflect people’s typical behavior when not in lockdown,
while social isolation due to Covid-19 was separately assessed and highlighted in the section on Covid.

The magnitude of influence of lifestyle factors
The biggest surprise was the magnitude of difference in MHQ points along the scales of sleep, socializing
and exercise. Those respondents who always got a good night’s sleep had overall mental wellbeingscores that were 82 points higher than those who hardly ever did (a shift of 27% along the MHQ scale).
Similarly, those who typically socialized with friends and family several days a week had mental wellbeingscores 66 points higher than those who rarely or never did, and those who exercised at least 30 minutes
everyday had mental wellbeing scores 46 points higher than those who rarely or never exercised. Clinicalrisk too had proportionate disparities.
Over 50% of those who rarely or never got a good night’s sleep had clinical level challenges or risks; 44%of those who rarely or never socialized had clinical level challenges or risks; and 39% of those who rarelyor never exercised had clinical level challenges or risks. These are not small numbers and are a profoundtestament to the foundational importance of these elements to human mental function. Yet, almost halfof respondents struggled on each of these factors.

27

Figure 5.1: MHQ scores and prevalence in the population for different categories of sleep, socializing and exercise
Frequency of Socializing

120

80
60

All of the time
Most of the time

Some of the time

11%
43%
31%

40

100
23% of MHQ scale

27% of MHQ scale

100

120

Prevalence

80
60

0

Hardly Ever

15%

29%
23%
Once a week
1-3 times a month
25%
23%

0

Prevalence

100

Several days a week

40
20

120

Prevalence

Rarely/Never
20

Frequency of Physical Exercise

16% of MHQ Scale

Good Night's Sleep

80

Every dayFew days a week

60

Once a week
Less than once a week

50%
24%

40

Rarely/Never

26%

20
0

Note that dimension scales range from -50 to +100.

Everything is worse without sleep, socializing and exercise
Studies on sleep, exercise and social interaction have typically focused on their impact on a single
disorder or mental function. When we look across the full spectrum of mental function, however, which
dimensions are most likely to be impacted by each of these factors? Surprisingly, each factor appears to
impact not just every functional domain but virtually every element within them. In all three cases, every
asset of mental function was diminished between the lowest and highest groups on each dimension,and every problem area was magnified. Most prominently affected by inadequate sleep, were Self-image,
Appetite regulation and Outlook and optimism as well a greater Experience of pain, Guilt and blame;
and Feelings of sadness, distress or hopelessness. Those with a lack of social interaction were mostprominently compromised in their Relationships with others, Outlook and optimism and Self-image as well
as reporting challenges with Avoidance and withdrawal, Guilt and blame; and Feelings of sadness, distressor hopelessness, while those who rarely exercised were most prominently compromised in their Appetite
regulation, Energy levels and Drive and motivation, in addition to reporting more Physical health issues,
Feelings of sadness, distress or hopelessness and Avoidance and withdrawal. Only a few aspects such as
Sensory acuity or problems with Hallucinations were unchanged.

Sleep, social interaction and exercise substantially impact all facets ofmental function. Those who regularly got adequate sleep, social interaction
and physical exercise had MHQ scores 27% , 23% and 16% higher along the
MHQ scale respectively than those who rarely or never did.

28

Figure 5.2: MHQ dimension scores by frequency of sleep, socializing and exercise
Frequency of Socializing

Good Night's Sleep

Frequency of Physical Exercise

60

55
MHQ Dimension Scores

50

40

30
Complex Cognition

22

Drive & Motivation

20

Core Cognition
Mind Body

10

Mood & Outlook
Social Self
HardlyEver

Some ofthe Time

Most ofthe Time

All ofthe Time

Rarely/Never

1-3 Times
a MonthOnce aWeek

Several Daysa WeekRarely/ Less Than Once a
Never Once a Week Week

Few Daysa WeekLifestyle factors by country
The differences across respondents from each country along these lifestyle dimensions were notdramatic. However, what does stand out is that those living in India and Singapore had the best Sleep
and Socializing scores while those living in Australia and New Zealand were highest on Exercise.
Notably respondents living in South Africa were significantly lower than all other countries with respect
to Socializing and Exercise scores while those in the United Kingdom had the poorest sleep. Scores
represent an estimate of days per week (see Appendix 2).

Figure 5.3: Average frequency of sleep, socializing and exercise by country
Good Night's Sleep

Frequency of Socializing

India

Singapore

Australia

Singapore

India

New ZealandUnited Kingdom

CanadaUnited States

United States

CanadaIndia

Australia

Australia

Singapore

South Africa

United Kingdom

CanadaNew Zealand

New ZealandUnited States

United Kingdom

South Africa

South Africa

0

1

2

3

Average Sleep score*

4

0

1

2

Average Socializing score** Scores calculated by transforming answer labels to numerical values

29

Frequency of Physical Exercise

0

1

2

3

4

Average Exercise score*

Error bars represent 95% CI of the mean

Every
Day

We note that other surveys of sleep (e.g. Tozer, 2018) focusing on different aspects such as time in bed
showed different rankings between countries with Singapore having fewer hours in bed relative to mostother countries on this list. However, both hours in bed and sleep quality may vary based on numerous
factors from diet to stress levels such that hours in bed is not directly comparable to one’s sense of
having had a good night’s sleep.

Lifestyle factors by age and gender demographics
Can differences in these factors explain the differences between genders and age groups? There were no
dramatic or statistically significant differences between any of the gender groups in sleep, socializing andexercise, although the nonbinary/third gender group had slightly poorer sleep scores and lower exercisescores than either males or females.
Figure 5.4: Average frequency of good sleep, socializing and exercise by gender groups
Good Night's Sleep

Frequency of Socializing

Frequency of Physical Exercise

Nonbinary/Third Gender
Female
Male

0

1

2

3

4

5

0

Average Sleep score*

1

2

0

Average Socializing score** Scores calculated by transforming answer labels to numerical values

1

2

3

4

Average Exercise score*

Error bars represent 95% CI of the mean

On the other hand, age differences were more apparent. Those 65 and above slept well, exercised andsocialized most regularly. Both sleep and exercise frequency were progressively worse for each younger
age group, although not significantly so. On the other hand, respondents aged 18-24 had as much social
interaction as those aged 65 and older while those aged 25-64 socialized substantially less.
Figure 5.5: Average frequency of good sleep, socializing and exercise by age groups
Good Night's Sleep

Frequency of Socializing

Frequency of Physical Exercise

65+
45-64
25-44
18-24

0

2
Average Sleep score*

4

0

1
Average Socializing score** Scores calculated by transforming answer labels to numerical values

30

2

0

1

2

3

4

Average Exercise score*
Error bars represent 95% CI of the mean

Altogether, while these factors impact all age groups, the differences between age and gender groups are
fairly small and are not likely the core drivers of gender and generational disparities.

Untangling cause and effectSleep, exercise and social interaction are all highly correlated lifestyle factors that feedback on one
another. Those who exercise more sleep better and those who sleep better exercise more. Similarly,
those who interact socially more frequently, exercise more regularly on average and vice versa.
Conversely those who sleep poorly tend to exercise less on average and have less social interaction.
Thus, untangling the impact of each relative to the other requires deeper analysis. More important
however, is understanding what can help the large proportion of the population (~50% in this dataset)
who are struggling with sleep, exercise and social interaction. Not all aspects relating to sleep andsocial interaction can be fully controlled by us individually. One can go to bed fully hoping for a goodnight’s sleep but not achieve it while others may want social interaction but be unable to find it. The bestsolutions therefore may not be individual in nature. The challenge is to understand the causes of thewidespread compromise of these fundamental human needs and design interventions that can result in
fundamental societal shifts.

31

The Mental Cost of theCovid-19 Pandemic

6.

It has now been established that beyond physical illness, the economic uncertainty and social isolation of
the Covid-19 pandemic has taken a substantial and unprecedented toll on our physical and mental health(Holmes et al., 2020; Pierce et al., 2020; Serafini et al., 2020). How have these different consequences
impacted us, and how does it differ across the globe and for different age and gender groups?

What hit us the hardest in 2020?
3.9% of respondents reported having had a Covid-19 infection of which 0.7% reported a severe case,roughly reflecting the global infection rate in 2020. On the other hand, a full 57% experienced a range ofnegative health, financial and/or social consequences. Figure 6.1 shows the MHQ scores of respondents
who reported each of a list of health, financial or social challenges arising from the pandemic.
Figure 6.1: Average MHQ scores for adults impacted by each of various health,financial and social challenges due to Covid-19
Average MHQ Scores

Financial Impacts

No health, social or financial impacts

Health Impacts

We have reduced household income

Social Impacts

Prevalence

43%
17%
20%
2%
15%
3%
6%
8%
5%
1.4%
0.7%
2%
1.4%
2%

I am isolated at home with my family
My partner has lost their job

My current job demands have become excessive or stressful
I currently have or have had mild Coronavirus infection
I have lost my job
I am isolated at home on my own
A close family member currently has or has had severe Coronavirus infection
Someone in my immediate family can't receive the critical healthcare support
they need for an existing condition
I currently have or have had severe Coronavirus infection
A close family member has died from Coronavirus
We are unable to make ends meet for basic necessities of rent, food etc.
I can't receive the critical healthcare support I need for an existing condition

0

20

40

Average MHQ Score

32

60

80

The 2% of respondents who were unable to get critical care for other existing health conditions due to thepandemic reported the poorest mental wellbeing at 61 MHQ points lower than those who experienced
none of the key listed consequences (20% along the MHQ scale). In contrast, the 0.7% who reported
having had a severe case of Covid-19 had lower mental wellbeing scores by 35 MHQ points and the ~2%
who lost a close family member to Covid-19 had lower mental wellbeing scores by 37 MHQ points.
After those who could not receive critical care for other conditions, the 1.4% of people who struggled tomake ends meet for basic necessities had the worst mental wellbeing at 55 MHQ points lower than those
who experienced none of the listed consequences. Those with reduced household income were 13 MHQ
points lower but constituted a full 17% of the sample. Social isolation along with family, while only 14
points lower, impacted the largest number of people at 20%. It is also worth noting that even those who
were not impacted by any of the specific challenges listed in Fig. 6.1 nonetheless had a reduced MHQ
score of 76 compared to the mean of 90 in the smaller 2019 sample.
Thus, the toll of the pandemic and the measures taken to control it have had a multifaceted impact thatmust be taken into consideration. However, more analysis will be necessary to specifically identify theindividual impact of each factor, as many people experienced multiple challenges.

Covid-19 related impact across countries
Overall, respondents from South Africa followed by those from the United Kingdom experienced the greatest
health and financial impacts perhaps underpinning their substantially lower mental wellbeing overall relative
to other countries (see Fig. 2.1). On the other hand, those in the United States and Canada saw the biggest
impact with respect to social isolation. In contrast, the impact of Covid-19 was lowest for those in New
Zealand across all dimensions overall and those living in Singapore had the smallest health impact at <1%.

Figure 6.2: Prevalence of health, financial and social impacts of Covid-19 across countries% of respondents reportingsocial impacts% of respondents reporting
financial impacts

United States

South Africa

South Africa

CanadaUnited Kingdom

United Kingdom

United Kingdom

India

CanadaAustralia

United States

Singapore

India

CanadaIndia

South Africa

New ZealandUnited States

Singapore

Australia

Australia

New ZealandSingapore

New Zealand

0%33% of respondents reporting
health impacts

10% 20% 30%

40% 50%0% 5% 10% 15%

0% 10% 20% 30% 40% 50% 60%

Differential impact of circumstances on different age groups
Crucially, circumstances surrounding the pandemic impacted different age groups differently. Here weshow, as an example, a comparison of respondents aged 18-24 relative to those aged 65 and above.
For those aged 18-24 who had had a mild coronavirus infection, mental wellbeing was 16 MHQ points
better than those who had faced no direct health, social or financial consequences from the pandemic(5% shift on the MHQ scale but a 50% improvement in score). On the other hand, those who had had
a severe Covid-19 infection had mental wellbeing scores 95% lower than those who had experienced
no adverse impact of Covid-19. In contrast, respondents 65+ who had experienced a severe case of
Covid-19 had only 10% lower mental wellbeing scores. Similarly, 18-24 year olds who had lost a family
member to Covid-19 had 33% lower mental wellbeing scores while those 65+ who had lost a family
member to Covid-19 were lower by only 12%.

Figure 6.3: Differing mental wellbeing impact of Covid-19 related challenges by age group

Ages 18-24

Prevalence

I currently have or have had
mild Coronavirus infection

3.7%
42%
5%
18%
6%
32%
12%
4%
2%
1.3%
0.5%
1.2%
1.7%
1.2%

None of these impacts
I have lost my job
We have reduced household income
A close family member currently has or
has had severe Coronavirus infection
I am isolated at home with my family
My current job demands havebecome excessive or stressful
I am isolated at home on my own
A close family member has
died from Coronavirus
My partner has lost their job
I currently have or have had
severe Coronavirus infection
Someone in my immediate family can't
receive the critical healthcare support
they need for an existing condition
We are unable to make ends meet forbasic necessities of rent, food etc.
I can't receive the critical healthcare
support I need for an existing condition

-10

0

10

20

30

40

Average MHQ Score

50

Ages 65+

Prevalence

51%
0.5%
1.6%
10%
20%
2%
4%
15%
3%
1.4%
1%
2%
2%
0.7%

None of these impacts
I currently have or have had
severe Coronavirus infection
A close family member has
died from Coronavirus
We have reduced household income
I am isolated at home with my family
I currently have or have had
mild Coronavirus infection
A close family member currently has or
has had severe Coronavirus infection
I am isolated at home on my own
My current job demands havebecome excessive or stressful
Someone in my immediate family can't
receive the critical healthcare support
they need for an existing condition
My partner has lost their job
I can't receive the critical healthcare
support I need for an existing condition
I have lost my job
We are unable to make ends meet forbasic necessities of rent, food etc.

020 40 60 80 100 120

Average MHQ Score

The impact of social isolation also differed substantially. For those aged 18-24, social isolation, withfamily or alone, had an enormous toll with mental wellbeing at 35% and 69% lower respectively compared34

to those who had no Covid-19 related adversities. In contrast, social isolation had a milder impact inthose aged 65+ with mental wellbeing scores only 14-20% lower in our 2020 dataset, although a roughly
equal proportion of both groups experienced social isolation.
On the other hand, severe issues such as not being able to make ends meet or being unable to receive
critical care for a pre-existing condition had a dramatic toll on both age groups. It is also worth notingthat the financial impacts had more severe differential in mental wellbeing scores overall in the 2564 age group (not shown here) compared to both of these age groups and also that these profiles
differed across respondents living in different countries. It will be of considerable interest to probe thesedifferential effects and impacts in more depth. This will require further analysis in order to untangle
the relative impact of individual factors as people with a particular financial impact may also haveexperienced a health or social challenge from the pandemic and vice versa.
As data collection continues into 2021, the health and economic fallouts of Covid-19, and the eventual
post-Covid-19 recovery, will be monitored throughout the project.

The long-term impact of trauma and adversity
Life comes with adversity and hardship. Few manage to sail through life untouched by the experience of thedeath of a close family member, the breakdown of a relationship or family or financial hardships. On the other
hand, some experience horrific traumas from abuse and assault, death of family members in war, devastationfrom natural disasters and displacement from their homes. Naturally, trauma and adversity have an impact on
cumulative trauma and adversity take their toll on mental
wellbeing (Turner and Lloyd, 1995) with early childhood
being an especially vulnerable period (Green et al., 2010;
Kessler et al., 2010; McLaughlin et al., 2010; McGrath et al.,2017). This goes beyond post-traumatic stress disorder,
with other disorders also being associated with, or triggeredby, traumatic or adverse life experiences and resulting
in trauma becoming a significant public health concern(Kleber, 2019). It is therefore important to put the impact of
the Covid-19 pandemic into this larger context.

Figure 6.4: Impact of cumulative trauma andadversity on mental wellbeing

No traumas or adversities
Cumulative number oftrauma or adversities

mental wellbeing. Research shows that across the lifespan,

1
2
3
4
5
6

6+ traumas or adversities-40 -20
At risk

0

20 40
Enduring

60 80 100 120
Managing

Average MHQ score

We have often heard the saying that what doesn’t kill you makes you stronger with some studies showing
evidence of a benefit of moderate adversity in enhancing resilience (Seery et al., 2010). However, in thisdata, mental wellbeing scores declined systematically with the experience of more adversities and traumas
over the lifetime, consistent with other reports (Fernandez et al., 2020). One might consider the analogy tophysical injury where repeated injuries over a lifetime from broken bones to muscle tears might heal but
leave the body physically compromised and less capable than before the injury. Thus, the consequences of
the Covid-19 pandemic and their impact on mental wellbeing may reverberate for years to come.

35

7.

Conclusions &Recommendations

The two overarching messages of this data are the enormous impact of societal circumstance andbehavior on mental wellbeing and the alarming and all-encompassing crisis of mental wellbeing in young
people and those who identify as nonbinary or third gender.

A population-based approach to mental wellbeing
The magnitude of the impact of the health, financial and social strains of the Covid-19 pandemic havehighlighted just how much societal circumstances dictate our mental wellbeing. Not just our feelingsof distress or mood and outlook, but every aspect from our cognition to our drive and motivation. In
addition, the unexpectedly high magnitude of differences along the dimensions of sleep, social interactionand exercise tells us that our lifestyle and social behaviors impact our mental wellbeing far more than we
may have appreciated. While much of the focus in the mental health arena has been on self-care throughapps, therapy and other programs, social and economic policy and institutional culture may have a largerole to play in the mitigation of our present mental health crisis and prevention of future crises.
For instance, the data indicates that government policies that force social isolation will have a greater
impact on the mental wellbeing of young people than older people, pushing many into a zone of clinicalrisk and raising the risk of suicide. We also now know that lack of access to critical care for other existing
health conditions during the pandemic has created a clinical level mental health crisis in an alreadyvulnerable population. Such factors may be considered when formulating policy in the management of
the pandemic as we move forward. Finally, this data adds to evidence that economic circumstances are
not divorced from our mental wellbeing but are central to it. Jobs and financial stability may play a keyrole in preventing a shift of more people into a zone of clinical mental health disorders. As such, taking
into consideration mental wellbeing as one of the key objectives of social and economic policy can help
ensure a stronger populace.
Another important rationale for a population-based approach to mental wellbeing is the lack of effective
treatments for mental health disorders. Although access to psychiatric resources helps, it is not apanacea. While we found a strong correlation between help seeking behavior and the availability ofpsychiatrists, there was no correlation with clinical burden.

36

Many others have called for a population-based approach to mental wellbeing arguing that it has thepotential to induce large-scale shifts in population wellbeing which could benefit the lives of hundreds,
thousands, or millions of people (Huppert, 2009; Sampson and Galea, 2018; Fuhrer and Keyes, 2019).
However, to bring such an approach to fruition, what is needed is large scale population data thatmeasures and tracks changes in mental wellbeing across the population beyond just the prevalence ofclinical disorders, as well as providing the necessary insights to inform decision making. Thus far such aperspective has been lacking and we offer this project as a starting point for this ambitious goal.

Tackling the youth mental wellbeing crisis
The mental wellbeing of young adults is a global societal crisis that needs immediate attention. Witha full 44% of respondents aged 18-24 exhibiting clinical level risk, this puts the future of humanity atrisk if not understood and mitigated. With research suggesting that 75% of adults with a mental healthdisorder had experienced its onset by the age of 24 (Kessler et al., 2005; Kessler et al., 2007) and that
poorer mental health during youth has long-term effects on physical health and quality of life in middle
age and beyond (Richmond-Rakerd et al., 2021), this points towards a crisis that this generation will
carry into its future. Improvements in sleep and exercise habits may help but are not likely to be at
the root of the large differential between younger and older adults which was present even before thepandemic. It is imperative that the causes are fully understood so that solutions can be formulated ata policy level and at the level of institutions such as schools and universities. While this data does notprovide any direct evidence of a root cause, the key challenges of Self-Image, Self-worth & confidence
and Focus & concentration lend to the long existing hypothesis of the negative impacts of the Internet
and in particular social media. Adults 18-24 years old are the first generation to grow up fully Internet
immersed. It is crucial that countries make it a priority to invest in research in this area to untangle root
causes and identify solutions. For a crisis of this scale and magnitude with such potentially far-reachingconsequences, recommendations for individual behaviors are not enough.

Tackling the nonbinary/third gender crisis
Those who identified as nonbinary/third gender had the poorest mental wellbeing of any demographic
group with over 50% in a clinical risk category and the highest level of Suicidal thoughts andintentions. While the fraction of the global population who are nonbinary or third gender is estimatedat approximately 0.5%, estimates are ambiguous. In this data 0.9% identified as being nonbinary.
Furthermore, studies suggest that it is rising over time, which is also borne out in our results where there
is a higher fraction in each younger decade.
The challenges that this group faces are many, and working to understand and mitigate both the social
and biological traumas that they experience is essential. A substantial difference in the mental wellbeing

37

status of this group across geographies points to a substantial social component. The challenge isunderstanding key drivers that can be addressed, perhaps through early intervention and support.

Cracking sleep and self care
The large difference in mental wellbeing between always having a good night’s sleep and rarely having
good sleep underscores its core position as a driver of mental wellbeing. However, sleep has strong
correlations to various other factors including social interaction and exercise. Thus, understanding what
helps drive sleep is crucial to successful functioning as a society. This goes beyond measuring andtracking our sleep to gaining deeper understanding of how wake-time behaviors from diet and exercise tosocial behaviors and technology impact the quality of our sleep.
We recommend that the importance of the triad of sleep, socializing and exercise, as well as strategies tomanage them, should be taught from an early age to build self-care skills for life. However, importantly,
beyond teaching these elements, schools and colleges may have an important role to play in facilitatingthese aspects as part of their structure such that children grow up with the strong social connections,
sleep and exercise habits for a successful future. We also recommend that companies pay more active
attention to policies and work culture that impact these core areas as part of a key strategy to support
the mental wellbeing of their workforce.
Altogether we advocate for embedding a population-based approach to mental wellbeing into socialand economic policy. We also join a growing call for greater research investment in understanding
the drivers of the mental health crisis of young adults and those who are nonbinary/third gender, andin understanding the wake-time factors that impact sleep. Finally, we encourage a greater role forschools, universities and companies in actively managing the mental wellbeing of their students andworkforce.

38

Looking ahead
2020 was the first year of the Mental Health Million project. We have achieved the milestone of ~49,000
responses over a period of 9 months, spanning primarily 8 countries but with a reach that extended toEnglish speakers in over 130 countries. This is the beginning of an ambitious journey that aims to map
the evolving mental wellbeing of the world, track the impact of both global and local societal changes andprovide deep insights to help better guide our course. Fortuitously, the project launched to provide a crucial
view into the impact of the Covd-19 pandemic and will help us understand, track and manage our recovery
over the coming years.

Expanding our reach
Today we have a steady addition of 15,000+ new MHQ respondents each month as we work towards
our goal of over 1 million responses each year from across the globe. With the addition of translationsin Spanish and Arabic we will expand our reach to 30 countries in 2021. Translations in French, German,
Russian, Hindi, Tamil, Swahili and Japanese will follow thereafter with further expansion across the globe.
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.

Expanded research and insights
The insights in this report represent only the tip of the iceberg. We can dig deeper to untangle the causal
factors that can make the biggest impact if managed. We can triangulate data with other data sources,
both environmental and societal, to understand relationships. We can understand how specific clinicalrisk profiles differ across geographies and demographics. The possibilities are many. Access to the realtime data from the Mental Health Million project is therefore freely available on request to researchers andorganizations 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 wellbeingof society requires multifaceted support from around the world. We look to our readers and stakeholders
to support our effort to expand across the globe and also to partner with us to make effective use of theinsights this data offers. Donations can be made through our website or by contacting us. We also welcome
partnerships to expand reach to particular geographies or demographic groups, and to help bring the insightsof this data into policy and interventions that can positively impact the future mental wellbeing of the world.

39

Appendix 1:
Understanding the MHQ
About the MHQ
Data for the Mental Health Million project is collected using an online assessment tool called theMental Health Quotient (MHQ) that was developed by scientists at Sapien Labs. The MHQ is a uniquecomprehensive 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 anaggregate score positioned on a spectrum from Clinical to Thriving as well as sub-scores across six
broad functional dimensions.
The MHQ is freely available online, is anonymous and takes ~15 minutes to complete. It is currently
available in English with additional translations planned for 2021 and beyond. In addition to the 47 scoredquestions, respondents answer questions relating to their demographics, life experience, lifestyle andcurrent situation. To encourage thoughtful and honest responses, respondents receive an MHQ scorealong with tailored feedback on completion of the MHQ and can opt to receive a more detailed reportwith 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. 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
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.

40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 demographic, life experience, lifestyleand situational information that can be of value in understanding contextual triggers, drivers anddeterminants of mental wellbeing.

41

The MHQ scale
The MHQ positions individuals on the spectrum from Clinical to Thriving, spanning a possible rangeof scores from −100 to +200 where negative scores indicate clinical risk. Importantly the MHQ scoreis not based on a simple averaging of question ratings but rather each individual rating is nonlinearlytransformed such that someone whose symptoms map to one or more clinical disorder are scored in
the negative range of clinical risk (elements of the MHQ map to DSM-based symptom criteria, so for
example, a rating of severity of negative life impact of a problem at 8 or 9 is considered a symptom).
The thresholds between negative and positive are optimized and calibrated such that <1% of thosein the positive range have severe problems that map to any clinical disorder and >99% of those in theClinical category map to at least one disorder. Thus someone with three very significant issues whichhave a significant negative impact to their life and meet a diagnostic criteria based on symptom severity
would be classified as Clinical even though they may have a high average rating score overall on otherdimensions. On the other hand, someone with a low average rating overall but no individual item meetinga threshold of severity for clinical diagnosis would not be classified as Clinical but rather in a normal,
positive range.
Modeling on the IQ scale, positive scores, which are largely normally distributed, are calibrated to a meanof 100 based on our 2019 sample and can range from 1 to 200. Negative scores, on the other hand, havea long-tailed distribution due to the nonlinear transformations required for clinical identification. In orderto ensure that overall average scores are not inordinately determined by the small number of individuals
in the long tail, the negative scale was compressed to a smaller scale of 0 to -100 in order to mitigate theimpact of negative scores on the population average.
More details of this methodology are provided in (Newson and Thiagarajan, 2020) .

Clinical

-100

-50

At Risk

Enduring

0Managing Succeeding Thriving

50

100

150

200

MHQ Score Range

Computation of MHQ dimension scores
MHQ dimension scores are computed for 6 broad dimensions of mental health: Core Cognition, Complex
Cognition, Mood & Outlook, Drive & Motivation, Social Self, and Mind-Body Connection, that have overlap
and parallels with the RDoC principles of domains and constructs (Insel et al., 2010). To compute these
dimension scores, a weighted average of items for each dimension is calculated by weighting spectrum
or problem items core to the dimension as 1 and spectrum or problem items secondary to the dimension42

as 0.5. This weighting algorithm was developed based on a review of cognitive and neuroscience modelsof 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 physiological response, whereas the item Unwanted, strange,or obsessive thoughts is dual coded with a primary weighting in the Core Cognition dimension anda secondary weighting in the Mood & Outlook dimension to reflect both the cognitive and emotional
elements of this item. In this regard, an item could be assigned to 2 different dimensions andoccasionally to 3. Overall, each dimension comprised 10 to 24 items. The dimension scores were thennormalized to constrain them to a smaller scale than the overall MHQ to distinguish them from the overall
score. Positive scores are normalized to the range of 0 to 100, whereas negative scores are normalized to
the range of −1 to −50.

Clinical-50-25

At Risk

Enduring

025Managing Succeeding Thriving

50

Dimension Score Range

43

75

100

Appendix 2:
Data Collection& Analysis Methods
Recruitment of respondents
48,808 respondents from around the world completed the MHQ assessment between April 9th andDecember 31st 2020. Participants were recruited through advertising on Google and Facebook bytargeting a broad audience within each age-gender demographic across a wide geography within each of
8 English speaking countries (United States, United Kingdom, Canada, South Africa, Singapore, Australia,
New Zealand and India). Recruitment from April 2020 initially focused on the United States, India and theUnited Kingdom, initially with Google Ads alone, and was later expanded in September 2020 to include
Canada, South Africa, New Zealand, Australia and Singapore.
Overall, a greater proportion of respondents were recruited
though Facebook (78%) compared to Google Ads (19%) withan additional 3% arriving at the MHQ landing page from othersources (e.g. social media shares, blog links). The Google
Ads outreach specifically targeted those individuals who weresearching for terms relevant to mental health (e.g. psychologicaltest, cognitive assessment test, mental health assessment) andwere applied consistently across all countries. Those recruited
through this stream may therefore have had a specific interestor concern relating to their mental health. In contrast, Facebook
outreach was much broader, spanning individuals who had showna previous interest in mental health and wellness topics, as well
as all adults in that country with the simple tagline What is yourmental wellbeing score? Those recruited through this stream were
therefore not specifically searching for information relating to amental health interest or concern.

44

Table A2.1: Number of respondents

from each country

Countries (n)United States (14582)
Canada (2668)
United Kingdom (6616)
India (14411)*
Australia (3694)
New Zealand (1239)
South Africa (1957)*
Singapore (374)*
Other ES Respondents (998)* English speaking population

Due to these differing strategies (inherent to the different structure of the two platforms) those recruited
though Google Ads had a greater bias towards mental health difficulties compared to those recruited
though Facebook. To take into account potential biases in these two recruitment streams, we balanced
the numbers of respondents arising from these two streams across countries where possible. This
involved re-balancing presence across the two platforms where necessary and excluding a portion of
respondents from India (5%) and the United Kingdom (5%) where a greater number of respondents wererecruited via Google Ads early on. However, it should be noted that New Zealand, Australia and South
Africa had a greater number of respondents recruited through Facebook (range 90% to 93%) compared tomost of the other countries (range 71% to 87%).
We also note that while the numbers for Singapore were small in 2020, Singapore was included in the
country comparison as significantly higher numbers obtained in January 2021 reinforced the trends inthe 2020 data.

Data distribution
The number of respondents for each targeted country is shown in Table A2.1. Respondents outside ofthese countries grouped into “Other English speaking (ES) respondents” spanned 125 countries and were
included in global analyses where appropriate.
Figure A2.2: Age (left) and gender (right) distributions of all respondents

Age Distribution of Respondents

Gender Distribution of Respondents

25-34
11.3%

35-44

18-24
22.7%

8.7%

Male

85+

75-84

45-54

5.75%

14.4%

39.3%

0.7%

FEMale
59.1%

65-74
55-64

17.1%

19.4%

Prefer not to say
0.7%

Nonbinary/Third Gender
0.9%

Respondents spanned all age groups roughly equally (Figure A2.2 left) while the gender split was 59.1%
female, 39.3% male and 0.9% nonbinary/third gender (Figure A2.2 right).

45

Exclusions and adjustments
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.
After these exclusions and rebalancing of responses through different channels, a total of 46,539
respondents were included in the final analysis.

Data analysis
Computing average MHQ Scores
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. In specific instances where analyses compared
between cities rather than countries, a similar weighting strategy was applied, but here weighting values
were based on demographic distributions of the individual cities, rather than countries.
Computation of the global overall MHQ and dimension scores (Section 1) were not a simple average
across countries but were additionally weighted based on the overall population aged over 18 of eachcountry. This was done to ensure the global score was reflective of the country population distributions
across the globe. The global scores were computed based on the 8 countries of interest and excluded the“Other ES speaking respondents” as reliable estimates of the global second-language English speaking
population for those aged over 18 are not available.
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 are takenfrom the latest governmental statistics or where that was not available, data from the latest census.
For South Africa and Singapore data alignment was not fully possible for the youngest age bracket(18-24 years) due to the way that the population statistics were estimated and so were aligned with theclosest available age bracket for which there was data (20-24 years). As there are no reliable statistics
for the proportion of nonbinary/third gender individuals across all countries, we used a broad population46

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.

Weighting of MHQ data when comparing cities
There were 6 cities of interest that included New York, Los Angeles, London, Mumbai, Sydney - as wellas Singapore (a city state). Data on city location was extracted from a question which asked about the
respondent’s zip code, or where that was not available, the name of the closest town or city to where they
live (with the exception of Singapore where data was extracted from answers to the country question).
Data from respondents living in each city of interest was weighted according to the age and gender
profile of that city. These weightings were extracted from population estimates taken from the latest
governmental statistics or where that was not available, data from the latest census. Weighting values
were applied to the average of each age and gender group separately for each city and these values wereaveraged together to create a weighted average for each city.

Calculation of sleep, exercise and socializing scores
Three lifestyle questions are included in the MHQ that ask about how frequently respondents get a goodnight’s sleep, exercise, and socialize face to face (the latter based on pre-Covid-19 habits) based on broadgroupings such as ‘Everyday’, ‘Several days a week’, to Rarely/Never’. To facilitate analysis, these textbased multiple choice answers were transformed into numerical values (Section 5). This transformation
allowed us to compare answers more easily between countries, age groups and genders. The
transformation was done by assigning a number to each answer that was roughly equivalent to the text
description. For example, the “Every day” answer option for the question “How regularly to you engage inphysical exercise (30 minutes or more)?” was assigned a value of 7 to reflect 7 days a week, while “Lessthan once a week” was assigned a value of 0.5.

Table A2.2: Scoring of categories for computation of average category scores

Sleep

47

Value

Exercise

Value

Face to Face Socializing Value

All of the time7

Every day7

Several days a week

3.5

Most of the time5

Few days a week

3.5

Once a week

1

Some of the time3

Once a week1

1-3 times a month

0.5

Hardly ever1Less than once a week

0.5

Rarely/Never0

Rarely/Never0Computing score differences
We typically report differences in terms of MHQ points and the corresponding % shift along a 300-point
scale i.e. ((Value 1 – Value 2)/300) * 100. For instance, 75 points represents 25% of the possible length of
the scale. Thus a 75-point shift or difference between groups would be a 25% shift along this scale.
In some cases where this is relevant, we additionally report the percentage drop of one number relative to
one another ((Value 1 – Value 2)/Value 1) * 100 where Value 1 is the higher number.

Statistical analysis
Statistics were computed for 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.

Challenges 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 by thosewith English speaking proficiency, Internet access and the willingness to spend 15 minutes completing an
online assessment. For instance, India’s Internet-enabled English-speaking populace, while substantial, isa minority with significantly higher education levels relative to the rest of the country. Thus results must
be interpreted strictly in this context. Second, cultural differences in language usage and culture itself
can significantly influence how people interpret and respond to each individual question. Any individual
country’s results will therefore reflect these differential effects of culture, compromising the ability for a
direct comparison.

48

References:
Brown, E.G., Gallagher, S., and Creaven, A.M. (2018). Loneliness and acute stress reactivity: A systematic
review of psychophysiological studies. Psychophysiology 55(5), e13031. doi: 10.1111/psyp.13031.
Cochran, S.D., Mays, V.M., and Sullivan, J.G. (2003). Prevalence of mental disorders, psychological distress, andmental health services use among lesbian, gay, and bisexual adults in the United States. Journal of consulting
and clinical psychology 71(1), 53-61. doi: 10.1037//0022-006x.71.1.53.
Di Lorito, C., Long, A., Byrne, A., Harwood, R.H., Gladman, J.R.F., Schneider, S., et al. (2020). Exercise
interventions for older adults: A systematic review of meta-analyses. Journal of Sport and Health Science10(1), 29-47. doi: https://doi.org/10.1016/j.jshs.2020.06.003.
Ditlevsen, D.N., and Elklit, A. (2012). Gender, trauma type, and PTSD prevalence: a re-analysis of 18 nordic
convenience samples. Annals of general psychiatry 11(1), 26-26. doi: 10.1186/1744-859X-11-26.
Doherty, A.M., and Gaughran, F. (2014). The interface of physical and mental health. Social Psychiatry andPsychiatric Epidemiology 49(5), 673-682. doi: 10.1007/s00127-014-0847-7.
Dzierzewski, J.M., Dautovich, N., and Ravyts, S. (2018). Sleep and Cognition in Older Adults. Sleep Med Clin
13(1), 93-106. doi: 10.1016/j.jsmc.2017.09.009.
Fernandez, C.A., Choi, K.W., Marshall, B.D.L., Vicente, B., Saldivia, S., Kohn, R., et al. (2020). Assessing therelationship between psychosocial stressors and psychiatric resilience among Chilean disaster survivors. The
British Journal of Psychiatry 217(5), 630-637. doi: 10.1192/bjp.2020.88.
Fiske, A., Wetherell, J.L., and Gatz, M. (2009). Depression in older adults. Annu Rev Clin Psychol 5, 363-389. doi:
10.1146/annurev.clinpsy.032408.153621.
Flores, A., Herman, J., Gates, G., and Brown, T. (2016). “How Many Adults Identify as Transgender in the UnitedStates?”, The Williams Institute.
Freeman, A., Mergl, R., Kohls, E., Székely, A., Gusmao, R., Arensman, E., et al. (2017). A cross-national study on
gender differences in suicide intent. BMC Psychiatry 17(1), 234. doi: 10.1186/s12888-017-1398-8.
Fuhrer, R., and Keyes, K.M. (2019). Population Mental Health in the 21st Century: Time to Act. American
Journal of Public Health 109(S3), S152-S153. doi: 10.2105/AJPH.2019.305200.
Galdas, P.M., Cheater, F., and Marshall, P. (2005). Men and health help-seeking behaviour: literature review. J
Adv Nurs 49(6), 616-623. doi: 10.1111/j.1365-2648.2004.03331.x.

49

Green, J.G., McLaughlin, K.A., Berglund, P.A., Gruber, M.J., Sampson, N.A., Zaslavsky, A.M., et al. (2010).
Childhood adversities and adult psychiatric disorders in the national comorbidity survey replication I:
associations with first onset of DSM-IV disorders. Archives of general psychiatry 67(2), 113-123. doi: 10.1001/
archgenpsychiatry.2009.186.
Hawkley, L.C., and Cacioppo, J.T. (2010). Loneliness matters: a theoretical and empirical review ofconsequences and mechanisms. Annals of behavioral medicine : a publication of the Society of Behavioral
Medicine 40(2), 218-227. doi: 10.1007/s12160-010-9210-8.
Holmes, E.A., O’Connor, R.C., Perry, V.H., Tracey, I., Wessely, S., Arseneault, L., et al. (2020). Multidisciplinary
research priorities for the COVID-19 pandemic: a call for action for mental health science. The Lancet
Psychiatry 7(6), 547-560. doi: 10.1016/S2215-0366(20)30168-1.
Hu, M.X., Turner, D., Generaal, E., Bos, D., Ikram, M.K., Ikram, M.A., et al. (2020). Exercise interventions for theprevention of depression: a systematic review of meta-analyses. BMC Public Health 20(1), 1255. doi: 10.1186/
s12889-020-09323-y.
Huppert, F.A. (2009). A New Approach to Reducing Disorder and Improving Well-Being. Perspectives on
Psychological Science 4(1), 108-111. doi: 10.1111/j.1745-6924.2009.01100.x.
Hvolby, A. (2015). Associations of sleep disturbance with ADHD: implications for treatment. Attention deficit
and hyperactivity disorders 7(1), 1-18. doi: 10.1007/s12402-014-0151-0.
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D.S., Quinn, K., et al. (2010). Research Domain Criteria(RDoC): Toward a New Classification Framework for Research on Mental Disorders. American Journal of
Psychiatry 167(7), 748-751. doi: 10.1176/appi.ajp.2010.09091379.
James, S.L., Abate, D., Abate, K.H., Abay, S.M., Abbafati, C., Abbasi, N., et al. (2018). Global, regional, andnational incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries
and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet
392(10159), 1789-1858. doi: 10.1016/S0140-6736(18)32279-7.
Kessler, R.C., Aguilar-Gaxiola, S., Alonso, J., Chatterji, S., Lee, S., Ormel, J., et al. (2009). The global burden ofmental disorders: An update from the WHO World Mental Health (WMH) Surveys. Epidemiologia e Psichiatria
Sociale 18(1), 23-33. doi: 10.1017/S1121189X00001421.
Kessler, R.C., Amminger, G.P., Aguilar-Gaxiola, S., Alonso, J., Lee, S., and Ustün, T.B. (2007). Age of onsetof mental disorders: a review of recent literature. Curr Opin Psychiatry 20(4), 359-364. doi: 10.1097/
YCO.0b013e32816ebc8c.
Kessler, R.C., Berglund, P., Demler, O., Jin, R., Merikangas, K.R., and Walters, E.E. (2005). Lifetime prevalence
and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen
Psychiatry 62(6), 593-602. doi: 10.1001/archpsyc.62.6.593.
Kessler, R.C., McGonagle, K.A., Swartz, M., Blazer, D.G., and Nelson, C.B. (1993). Sex and depression in the
National Comorbidity Survey I: Lifetime prevalence, chronicity and recurrence. Journal of Affective Disorders50

29(2), 85-96. doi: https://doi.org/10.1016/0165-0327(93)90026-G.
Kessler, R.C., McLaughlin, K.A., Green, J.G., Gruber, M.J., Sampson, N.A., Zaslavsky, A.M., et al. (2010).
Childhood adversities and adult psychopathology in the WHO World Mental Health Surveys. The British journalof psychiatry : the journal of mental science 197(5), 378-385. doi: 10.1192/bjp.bp.110.080499.
Kleber, R.J. (2019). Trauma and Public Mental Health: A Focused Review. Frontiers in psychiatry 10, 451-451.
doi: 10.3389/fpsyt.2019.00451.
Krystal, A.D. (2012). Psychiatric disorders and sleep. Neurol Clin 30(4), 1389-1413. doi: 10.1016/j.
ncl.2012.08.018.
Lim, M.H., Gleeson, J.F.M., Alvarez-Jimenez, M., and Penn, D.L. (2018). Loneliness in psychosis: a systematic
review. Soc Psychiatry Psychiatr Epidemiol 53(3), 221-238. doi: 10.1007/s00127-018-1482-5.
Lowe, C.J., Safati, A., and Hall, P.A. (2017). The neurocognitive consequences of sleep restriction: A meta-ana–
lytic review. Neuroscience and Biobehavioral Reviews 80, 586-604. doi: 10.1016/j.neubiorev.2017.07.010.
McCoy, J.G., and Strecker, R.E. (2011). The cognitive cost of sleep lost. Neurobiology of learning and memory
96(4), 564-582. doi: 10.1016/j.nlm.2011.07.004.
McGrath, J.J., McLaughlin, K.A., Saha, S., Aguilar-Gaxiola, S., Al-Hamzawi, A., Alonso, J., et al. (2017). The
association between childhood adversities and subsequent first onset of psychotic experiences: a crossnational analysis of 23 998 respondents from 17 countries. Psychol Med 47(7), 1230-1245. doi: 10.1017/
s0033291716003263.
McLaughlin, K.A., Green, J.G., Gruber, M.J., Sampson, N.A., Zaslavsky, A.M., and Kessler, R.C. (2010).
Childhood adversities and adult psychiatric disorders in the national comorbidity survey replication II:
associations with persistence of DSM-IV disorders. Arch Gen Psychiatry 67(2), 124-132. doi: 10.1001/
archgenpsychiatry.2009.187.
McLean, C.P., Asnaani, A., Litz, B.T., and Hofmann, S.G. (2011). Gender differences in anxiety disorders:prevalence, course of illness, comorbidity and burden of illness. Journal of psychiatric research 45(8), 10271035. doi: 10.1016/j.jpsychires.2011.03.006.
Meerlo, P., Havekes, R., and Steiger, A. (2015). Chronically restricted or disrupted sleep as a causal factor in thedevelopment of depression. Curr Top Behav Neurosci 25, 459-481. doi: 10.1007/7854_2015_367.
Meerlo, P., Sgoifo, A., and Suchecki, D. (2008). Restricted and disrupted sleep: effects on autonomic function,neuroendocrine stress systems and stress responsivity. Sleep Med Rev 12(3), 197-210. doi: 10.1016/j.
smrv.2007.07.007.
Meerwijk, E.L., and Sevelius, J.M. (2017). Transgender Population Size in the United States: a Meta-Regressionof Population-Based Probability Samples. American journal of public health 107(2), e1-e8. doi: 10.2105/
AJPH.2016.303578.
Meyer, I.H. (2003). Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations:51

conceptual issues and research evidence. Psychological bulletin 129(5), 674-697. doi: 10.1037/00332909.129.5.674.
Naghavi, M. (2019). Global, regional, and national burden of suicide mortality 1990 to 2016: systematic
analysis for the Global Burden of Disease Study 2016. Bmj 364, l94. doi: 10.1136/bmj.l94.
Newson, J.J., Hunter, D., and Thiagarajan, T.C. (2020). The Heterogeneity of Mental Health Assessment.
Frontiers in Psychiatry 11(76). doi: 10.3389/fpsyt.2020.00076.
Newson, J.J., and Thiagarajan, T.C. (2020). Assessment of Population Well-Being With the Mental Health
Quotient (MHQ): Development and Usability Study. JMIR Ment Health 7(7), e17935. doi: 10.2196/17935.
Patel, V., Saxena, S., Lund, C., Thornicroft, G., Baingana, F., Bolton, P., et al. (2018). The Lancet Commission on
global mental health and sustainable development. The Lancet 392(10157), 1553-1598. doi: 10.1016/S01406736(18)31612-X.
Pierce, M., Hope, H., Ford, T., Hatch, S., Hotopf, M., John, A., et al. (2020). Mental health before and during the
COVID-19 pandemic: a longitudinal probability sample survey of the United Kingdom population. The Lancet
Psychiatry 7(10), 883-892. doi: 10.1016/S2215-0366(20)30308-4.
Reed, A.E., and Carstensen, L.L. (2012). The theory behind the age-related positivity effect. Frontiers in
psychology 3, 339-339. doi: 10.3389/fpsyg.2012.00339.
Richmond-Rakerd, L.S., D’Souza, S., Milne, B.J., Caspi, A., and Moffitt, T.E. (2021). Longitudinal Associations of
Mental Disorders With Physical Diseases and Mortality Among 2.3 Million New Zealand Citizens. JAMA Netw
Open 4(1), e2033448. doi: 10.1001/jamanetworkopen.2020.33448.
Ritchie, H., and Roser, M. (2018). “Mental Health”. Published online at OurWorldInData.org [Online]. Available:
https://ourworldindata.org/mental-health.
Russell, S.T., and Fish, J.N. (2016). Mental Health in Lesbian, Gay, Bisexual, and Transgender (LGBT) Youth.
Annual review of clinical psychology 12, 465-487. doi: 10.1146/annurev-clinpsy-021815-093153.
Salk, R.H., Hyde, J.S., and Abramson, L.Y. (2017). Gender differences in depression in representative national
samples: Meta-analyses of diagnoses and symptoms. Psychol Bull 143(8), 783-822. doi: 10.1037/bul0000102.
Sampson, L., and Galea, S. (2018). An Argument for the Foundations of Population Mental Health. Frontiers in
Psychiatry 9(600). doi: 10.3389/fpsyt.2018.00600.
Seery, M.D., Holman, E.A., and Silver, R.C. (2010). Whatever does not kill us: cumulative lifetime adversity,vulnerability, and resilience. J Pers Soc Psychol 99(6), 1025-1041. doi: 10.1037/a0021344.
Serafini, G., Parmigiani, B., Amerio, A., Aguglia, A., Sher, L., and Amore, M. (2020). The psychological impact
of COVID-19 on the mental health in the general population. QJM : monthly journal of the Association of
Physicians 113(8), 531-537. doi: 10.1093/qjmed/hcaa201.
Steel, Z., Marnane, C., Iranpour, C., Chey, T., Jackson, J.W., Patel, V., et al. (2014). The global prevalence ofcommon mental disorders: a systematic review and meta-analysis 1980–2013. International Journal of52Epidemiology 43(2), 476-493. doi: 10.1093/ije/dyu038.
Tozer, J. (2018). Which countries get the most sleep? [Online]. Available: https://www.economist.
com/1843/2018/03/01/which-countries-get-the-most-sleep.
Turner, R.J., and Lloyd, D.A. (1995). Lifetime traumas and mental health: the significance of cumulative
adversity. J Health Soc Behav 36(4), 360-376.
Twenge, J.M., Cooper, A.B., Joiner, T.E., Duffy, M.E., and Binau, S.G. (2019). Age, period, and cohort trends inmood disorder indicators and suicide-related outcomes in a nationally representative dataset, 2005-2017. J
Abnorm Psychol 128(3), 185-199. doi: 10.1037/abn0000410.
UN (2020). Sustainable Development Goals [Online]. Available: https://www.un.org/sustainabledevelopment/sustainable-development-goals/.
Van de Velde, S., Bracke, P., and Levecque, K. (2010). Gender differences in depression in 23 European
countries. Cross-national variation in the gender gap in depression. Social Science & Medicine 71(2), 305-313.
doi: https://doi.org/10.1016/j.socscimed.2010.03.035.
Varma, P., Junge, M., Meaklim, H., and Jackson, M.L. (2020). Younger people are more vulnerable
to stress, anxiety and depression during COVID-19 pandemic: A global cross-sectional survey. Prog
Neuropsychopharmacol Biol Psychiatry 109, 110236. doi: 10.1016/j.pnpbp.2020.110236.
Wang, J., Mann, F., Lloyd-Evans, B., Ma, R., and Johnson, S. (2018). Associations between loneliness andperceived social support and outcomes of mental health problems: a systematic review. BMC Psychiatry
18(1), 156. doi: 10.1186/s12888-018-1736-5.
Wertz, J., Caspi, A., Ambler, A., Broadbent, J., Hancox, R.J., Harrington, H., et al. (2021). Associationof History of Psychopathology With Accelerated Aging at Midlife. JAMA Psychiatry. doi: 10.1001/
jamapsychiatry.2020.4626.
WHO (2019a). Psychiatrists working in Mental Health Sector [Online]. Available: http://gamapserver.who.int/
gho/interactive_charts/mental_health/psychiatrists_nurses/atlas.html.
WHO (2019b). Special initiative for mental health (2019-2023) Universal Health Coverage for Mental Health.
WHO (2018). Mental health atlas 2017. Geneva: World Health Organization.

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