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# Scaling-up treatment of depression and anxiety: A global return on investment analysis

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Background: Depression and anxiety disorders are highly prevalent and disabling disorders, which result not only in an enormous amount of human misery and lost health, but also lost economic output. Here we propose a global investment case for a scaled-up response to the public health and economic burden of depression and anxiety disorders. Methods: In this global return on investment analysis, we used the mental health module of the OneHealth tool to calculate treatment costs and health outcomes in 36 countries between 2016 and 2030. We assumed a linear increase in treatment coverage. We factored in a modest improvement of 5% in both the ability to work and productivity at work as a result of treatment, subsequently mapped to the prevailing rates of labour participation and gross domestic product (GDP) per worker in each country. Findings: The net present value of investment needed over the period 2016-30 to substantially scale up effective treatment coverage for depression and anxiety disorders is estimated to be US$147 billion. The expected returns to this investment are also substantial. In terms of health impact, scaled-up treatment leads to 43 million extra years of healthy life over the scale-up period. Placing an economic value on these healthy life-years produces a net present value of$310 billion. As well as these intrinsic benefits associated with improved health, scaled-up treatment of common mental disorders also leads to large economic productivity gains (a net present value of $230 billion for scaled-up depression treatment and$169 billion for anxiety disorders). Across country income groups, resulting benefit to cost ratios amount to 2·3-3·0 to 1 when economic benefits only are considered, and 3·3-5·7 to 1 when the value of health returns is also included. Interpretation: Return on investment analysis of the kind reported here can contribute strongly to a balanced investment case for enhanced action to address the large and growing burden of common mental disorders worldwide. Funding: Grand Challenges Canada.
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415
Scaling-up treatment of depression and anxiety: a global
return on investment analysis
Dan Chisholm, Kim Sweeny, Peter Sheehan, Bruce Rasmussen, Filip Smit, Pim Cuijpers, Shekhar Saxena
Summary
Background Depression and anxiety disorders are highly prevalent and disabling disorders, which result not only in
an enormous amount of human misery and lost health, but also lost economic output. Here we propose a global
investment case for a scaled-up response to the public health and economic burden of depression and anxiety
disorders.
Methods In this global return on investment analysis, we used the mental health module of the OneHealth tool to
calculate treatment costs and health outcomes in 36 countries between 2016 and 2030. We assumed a linear increase
in treatment coverage. We factored in a modest improvement of 5% in both the ability to work and productivity at
work as a result of treatment, subsequently mapped to the prevailing rates of labour participation and gross domestic
product (GDP) per worker in each country.
Findings The net present value of investment needed over the period 2016–30 to substantially scale up eﬀ ective
treatment coverage for depression and anxiety disorders is estimated to be US$147 billion. The expected returns to this investment are also substantial. In terms of health impact, scaled-up treatment leads to 43 million extra years of healthy life over the scale-up period. Placing an economic value on these healthy life-years produces a net present value of$310 billion. As well as these intrinsic beneﬁ ts associated with improved health, scaled-up treatment of
common mental disorders also leads to large economic productivity gains (a net present value of $230 billion for scaled-up depression treatment and$169 billion for anxiety disorders). Across country income groups, resulting
beneﬁ t to cost ratios amount to 2·3–3·0 to 1 when economic beneﬁ ts only are considered, and 3·3–5·7 to 1 when the
value of health returns is also included.
Interpretation Return on investment analysis of the kind reported here can contribute strongly to a balanced
investment case for enhanced action to address the large and growing burden of common mental disorders worldwide.
Introduction
Worldwide, investments in mental health are very
meagre. Data from WHO’s Mental Health Atlas 2014
survey1 suggest that most low-income and middle-income
countries spend less than US$2 per year per person on the treatment and prevention of mental disorders compared with an average of more than$50 in high-
income countries. As a result of this limited investment
in public mental health, a substantial gap exists between
the need for treatment and its availability. This large
treatment gap aﬀ ects not just the health and wellbeing of
people with mental disorders and their families, but also
has inevitable consequences for employers and
governments as a result of diminished productivity at
work, reduced rates of labour participation, foregone tax
receipts, and increased health and other welfare
expenditures. Findings of several national and
international studies2–5 have shown the enormous
economic challenge these disorders pose to communities
and society at large as a result of foregone production
and consumption opportunities as well as health and
social care expenditures. In 2010, worldwide, an
estimated US$2·5–8·5 trillion in lost output was attributed to mental, neurological and substance use disorders, depending on the method of assessment used.2 This sum is expected to nearly double by 2030 if a concerted response is not mounted.2 In view of this concern, the promotion of mental health and wellbeing have been explicitly included in the United Nations 2015–30 Sustainable Development Goals.6 Cost-eﬀ ectiveness studies have largely restricted themselves to a consideration of the speciﬁ c implementation costs and health outcomes of an intervention, and have typically not extended to a full estimation of the wider socioeconomic value of investment in mental health innovation and service scale-up. As shown in the Lancet’s Commission on Investing in Health, elucidation and enumeration of these wider economic and social beneﬁ ts provides a more comprehensive assessment of the returns on investment.7 In particular, increasing attention and emphasis is being given to extending valuation to also include the intrinsic value of improved health (a so-called full income approach to national accounting).7 Lancet Psychiatry 2016; 3: 415–24 Published Online April 12, 2016 http://dx.doi.org/10.1016/ S2215-0366(16)30024-4 See Comment page 390 Department of Mental Health and Substance Abuse, WHO, Geneva, Switzerland (D Chisholm PhD, S Saxena MD); Victoria Institute of Strategic Economic Studies, Melbourne, VIC, Australia (K Sweeny PhD, Prof P Sheehan PhD, Prof B Rasmussen PhD); Trimbos Institute (Netherlands Institute of Mental Health and Addiction), Utrecht, Netherlands (Prof F Smit PhD); and Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Netherlands (Prof F Smit, Prof P Cuijpers PhD) Correspondence to: Dr Dan Chisholm, Department of Mental Health and Substance Abuse, WHO, Geneva 1211, Switzerland ChisholmD@WHO.int Articles 416 www.thelancet.com/psychiatry Vol 3 May 2016 Here we did a global return on investment analysis for mental health in people aged 15 years and older focusing on depression and anxiety disorders, which are the most prevalent mental disorders. These disorders lead to large losses in work participation and productivity, and yet lend themselves to eﬀ ective and accessible treatment as part of an integrated programme of chronic disease management.8–10 Methods Analytical framework Because depression and anxiety disorders represent a public health challenge worldwide, we did a global investment appraisal in low-income, middle-income, and high-income countries. The 36 countries for which we modelled costs and beneﬁ ts of scaled-up treatment, which span all six of WHO’s major regions, account for 80% of the world’s population and 80% of the global burden of depression and anxiety disorders (appendix p 1). Results for these countries were aggregated and reported by income level (low, lower-middle, upper-middle, high). We set the scale-up period at 2016–30, in line with the timeline of the post-2015 Sustainable Development Goals. The economic and social beneﬁ ts of good mental health include both its intrinsic value (improved mental health and wellbeing) and also its instrumental value, in terms of being able to form and maintain relationships, to work or pursue leisure interests, and to make decisions in everyday life. To assess the value of these beneﬁ ts, ﬁ rst we estimated the population in need in each country, then established the health eﬀ ects of scaled-up coverage of eﬀ ective intervention, and ﬁ nally calculated the economic eﬀ ect of improved mental health outcomes in terms of enhanced labour participation and productivity. Panel 1 provides more detail on the health and economic beneﬁ ts captured in, and omitted from, the analysis. The key outputs of the model are year-on-year estimates of the total costs of treatment scale-up and system strengthening (ie, the investment), increased healthy life-years gained as a result of treatment (ie, health return), the value associated with better health (ie, the value of health returns), and enhanced levels of productivity (ie, economic return). The stream of costs incurred and beneﬁ ts obtained between 2016 and 2030 were discounted at a rate of 3%, to give a net present value. All costs and monetised beneﬁ ts were expressed in constant US$ for the year 2013.
Population and disease modelling
We used the mental health module of the inter-UN
agency OneHealth tool to estimate the number of people
with depression and anxiety disorders living in the
36 large countries until 2030. Estimates are based on UN
population projections and Global Burden of Disease
prevalence estimates for 2010.11,12 The global point
prevalence rate for anxiety disorders is 7·3%;13 for
depression it is 3·2% for men, and 5·5% for women.14
The OneHealth tool also links the epidemiology of
depression and anxiety disorders (prevalence, incidence,
remission, excess mortality, and disability weight)12–14 to
country-speciﬁ c life tables, so that cases averted and
Research in context
Systematic review
We did a systematic review of studies published until Jan 1,
2015, of the eﬀ ect of treatment of depression and anxiety
and presenteeism rates). We searched an existing database on
psychological treatments of depression, which has been
described in detail by Cuijpers and colleagues, and has been
used in a series of earlier published meta-analyses. We
identiﬁ ed abstracts by combining terms indicative of
psychological treatment and depression (both medical subject
headings terms and text words; search terms listed in appendix
p 4). For this database, we examined 17 061 abstracts from
PubMed (4007 abstracts), PsycINFO (3147 abstracts), Embase
(5912 abstracts), and the Cochrane Central Register of
Controlled Trials (3995 abstracts). We included all randomised
trials comparing a psychological treatment with a control
condition (waiting list, care as usual, placebo), another
psychological treatment, pharmacotherapy, or combined
treatment. We excluded studies in adolescents, children, and
inpatients, and maintenance trials. We scrutinised all
440 studies identiﬁ ed in this database for economic outcome
data. Although four studies had data for functioning at work,
they did not report suﬃ cient detail. Accordingly, we did an
additional search on May 21, 2105, to widen our search to
include anxiety disorders with greater emphasis on economic
outcomes in PubMed, EMBASE, PsycINFO and the Cochrane
Library (search terms listed in appendix p 5). We found few
useful data and these could not be synthesised
meta-analytically. The same conclusion was made in a similar
review of the scientiﬁ c literature.
Interpretation
This analysis sets out a model linking the prevalence of
depression and anxiety disorders with expected health and
economic beneﬁ ts of scaled-up treatment, including restored
labour participation and productivity. Results from the analysis
suggest that monetised beneﬁ ts of better health and labour
force outcomes outweigh the costs of achieving them by
2·3–3·0 to 1 when economic beneﬁ ts only are considered, and
3·3–5·7 to 1 when the value of health returns is also included.
Treatment of common mental disorders leads to improvements
in economic production and health outcomes. Clinicians should
increase the detection and management of people with
depression and anxiety disorders.
For the published
meta-analyses see http://www.
evidencebasedpsychotherapies.
org
For the OneHealth tool see
http://www.who.int/choice/
onehealthtool
See Online for appendix
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healthy life-years gained over time at the population level
can be estimated. Healthy life-years reﬂ ect time spent by
the population in a particular state of health with a
known degree of disability. Estimation of healthy life-
years for depression took into account its association
with excess mortality (due to suicide and other causes of
death).14
Intervention eﬀ ects, costs, and coverage
Intervention eﬀ ects
We restricted the analysis of interventions within the
OneHealth tool to treatment because the evidence on
prevention of depression and anxiety is quite weak and of
uncertain generalisability to low-income and middle-
income country settings.15 In line with WHO’s Mental
Health Gap Action Programme (mhGAP) intervention
guide, modelled interventions included basic psycho-
social treatment for mild cases, and either basic or more
intensive psychosocial treatment plus anti depressant
drug for moderate to severe cases.16 Moderate to severe
cases of depression were split into ﬁ rst-episode and
recurrent episode cases. We calculated the health eﬀ ect
of treatment in terms of a proportionate improvement in
the rate of remission, equivalent to a shortening of the
duration of an episode of illness, and also, up to the point
of recovery, an improvement in the average level of
functioning as reﬂ ected in the disability weight for the
disorder.8,10 The appendix shows the eﬀ ect size estimates
and their derivation (appendix p 2); these take into
account partial response, the lag time between onset of
the disorder and treatment, and expected levels of non-
Intervention costs
We worked out total costs in a given year for a country
by multiplying resource use needs by their respective
unit costs to give a cost per case, which was then
multiplied by the total number of cases expected to
receive a particular intervention. Country-speciﬁ c unit
costs of inpatient and outpatient care were taken from a
WHO database, adjusted to 2013 price levels.17 Treatment
costs relied on previous cost-eﬀ ectiveness studies and
resource need proﬁ les garnered from existing treatment
guidelines and costing studies.10,16,18,19 Key categories of
resource use were: medication: 6 months continual
antidepressant drug (generically produced ﬂ uoxetine)
was included for moderate to severe cases; outpatient
and primary care: regular visits were needed for all
cases, ranging from four per case per year for basic
psychosocial treatment, up to 14–18 visits for moderate
to severe cases receiving antidepressant drug and
intensive psychosocial treatment (half of whom are
assumed to receive this on an individual basis, the other
half in groups); in line with the mhGAP intervention
guide, it is envisaged that this care and follow-up would
largely be undertaken in non-specialist health care
settings by doctors, nurses and psychosocial care
providers trained in the identiﬁ cation, assessment, and
management of depression and anxiety disorders; and
inpatient care: few cases are expected to be admitted to
hospital (2–3% of moderate to severe cases only, for an
average length of stay of 14 days).
Additionally, we included an estimate of the expected
level of programme costs and shared health system
resources needed to deliver interventions as part of an
Panel 1: Health, economic, and social beneﬁ ts of scaled-up treatment for depression
and anxiety disorders
Health eﬀ ects
To establish the eﬀ ect of treatment, we used rates of improved recovery or remission and
levels of functioning. Improved functioning translates into fewer life-years spent by the
population in a state of diminished health, whereas an increased rate of remission leads
to a decrease in the prevalence of these disorders over time. Depression is also associated
with an excess risk of premature mortality because of suicide and other causes of death.
We projected a reduction in excess mortality, amounting to an increase in healthy life
expectancy, as a result of averting cases of depression in the population. Although
depression and anxiety disorders are often comorbid with each other, and with a range of
other health disorders (eg, substance use disorder, other non-communicable diseases and,
in certain populations, in people with HIV/AIDS) we were not able to account for these
comorbidities in the analysis. Additionally, we were unable to capture the positive eﬀ ect
of treatment on the mental and physical health of close family members, including
infants of mothers with perinatal depression, despite robust evidence that depression can
adversely aﬀ ect infant attachment and subsequent child growth and cognitive
development.
Economic eﬀ ects
A direct potential beneﬁ t of successfully treating common mental disorders is a decrease in
overall health-care costs. Although interventions have their own costs, these can be more
than oﬀ set by a reduction in other services, notably hospital-based inpatient episodes or
outpatient visits. Reduced use of informal and indigenous health-care providers, such as
faith healers or traditional healers, is a further expected source of cost savings in many
countries. Estimation of the predicted extent of these cost oﬀ sets is very challenging at the
international level because it requires detailed information about both the varying level of
comorbidity across diverse populations and the typical use of non-intervention related
services. Accordingly, we did not explicitly consider such eﬀ ects in our analysis. Similarly, we
did not have suﬃ cient information across countries to model the reduced need for other
welfare-related services potentially available to people with depression and anxiety
disorders, including unemployment beneﬁ t or income support and social or disability
assistance. In the mainly high-income countries where such welfare support is widely
available, depression and other common mental disorders account for a signiﬁ cant
proportion of overall payments.5 Instead, the analysis focused on the ﬁ nancial beneﬁ ts
owing from increased rates of workforce participation and productivity. The analysis only
considers the contribution to the economy as a whole through increased economic output;
it does not estimate the various income shares of this output.
Social eﬀ ects
Conceptually distinct from improvements in clinical functioning (health eﬀ ect) and the
restored ability to do paid work (economic eﬀ ect), the successful treatment of depression
and anxiety disorders leads to improved opportunities for individuals and households to
pursue their leisure interests, participate more in social and community activities, and
carry out household production roles. The economic worth of these non-market
production and welfare gains is incorporated into our estimate of the intrinsic value of
mental health.
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integrated model of chronic disease management. These
training and supervision, drug safety monitoring, health
promotion and awareness campaigns, and strengthened
logistics and information systems. We expressed
estimates as an on-cost to the estimated direct health-
care costs. The baseline value for this on-cost was 10%
(and therefore grows in absolute terms during scale-up).
Intervention coverage
The appendix provides coverage rates used for each
individual intervention at diﬀ erent levels of national
income (appendix p 3). Summing across all interventions
and their respective populations in need, it is estimated
that—depending on the income level of the country—
between 7% and 28% of all people with depression
currently receive treatment, equivalent to a treatment gap
of 72–93% (table 1). A gradual, linear increase in treat-
ment coverage to a third of all cases in low-income
countries and to more than half of cases in high-income
countries would close the current gap by 29–39%; the use
of separate target coverage rates for low-income, middle-
income, and high-income countries reﬂ ects diﬀ erences
in which they stand now with respect to treatment
coverage, and are intended to reﬂ ect what has been
achieved through programme scale-up eﬀ orts in countries
such as Chile and the UK.20 Because of even lower starting
coverage levels, the modelled gap reduction for anxiety
disorders is lower than for depression (16–25%).
Eﬀ ect of labour force on treatment
We modelled the economic eﬀ ect of decreased morbidity
in terms of increased participation in and increased
productivity of the workforce. With regards to labour
force participation, very few studies have assessed the
extent to which eﬀ ective depression treatments get
people back into work, and when measured, estimates
have been subject to local factors such as prevailing levels
of unemployment in the economy (panel 2).21–24 For our
base case, we conservatively modelled a 5% restored
ability to work as a result of treatment, with half and
double that rate used under pessimistic and optimistic
scenario analyses. Impaired productivity was assessed
both with respect to whole days oﬀ work (absenteeism)
and also partial days of impaired activity while an
individual is at work (presenteeism). Compared with
adults without common mental disorders in a range of
low-income, middle-income, and high-income countries
participating in the World Mental Health Survey,
4–15 more days out of role per year were recorded
because of depression and 8–24 days because of
generalised anxiety disorders; additional time lost per
year due to presenteeism was 11–25 partial disability days
for depression and 12–26 for generalised anxiety
disorders.25,26 Again, there are few empirical studies upon
which to base estimates of the eﬀ ect of eﬀ ective treatment
of depression and anxiety on productivity, and these
point towards small diﬀ erences between intervention
and control groups (panel 2).27–33 Expressed as a proportion
of total working days per year (220 days), and allowing
for both the onset of eﬀ ect as well as the time lag between
increase in working days as a result of reduced
absenteeism, and a 5% increase through reduced
presenteeism. Again, these baseline values were varied
up and down by a factor of 2 and 0·5, respectively, in an
uncertainty analysis. These losses in and returns to
productivity were linked to the prevailing rates of labour
participation in the working age population (age
15–65 years) and gross domestic product (GDP) per
worker in each of the 36 assessed countries34,35 to calculate
productivity losses at current levels of treatment coverage
and productivity gains after scaled-up treatment. The
model does not account for potential changes in
retirement age or working patterns over time, although
an increase in retirement age and more ﬂ exible working
patterns might enhance the overall productivity gains by
people with depression and anxiety with treatment.
Economic value of health beneﬁ ts
Improvements in labour force outcomes represent the
instrumental value of improved mental health after
eﬀ ective treatment of common mental disorders.
Independent of this instrumental value, being alive and
healthy is also valuable in itself. For this analysis, we
followed the approach adopted by Stenberg and
colleagues,36 who divided the overall value of a life-year
into its economic (instrumental) and health (intrinsic)
elements. For the Lancet Commission on Investing in
health, the value of a 1 year increase in life expectancy in
low-income and middle-income countries was estimated
to be 2·3 times per person national income, and
1·6 times per person national income worldwide (using a
discount rate of 3%).7 Stenberg and colleagues36 attributed
two-thirds of that derived value to the instrumental
components, which are measured here directly via the
Current
coverage
Target
coverage
Current gap Reduced gap % gap
reduction
Depression
Low-income countries 7% 34% 93% 66% 29%
Lower middle-income countries 14% 42% 86% 58% 32%
Upper middle-income countries 21% 49% 79% 51% 35%
High-income countries 28% 56% 72% 44% 39%
Anxiety disorders
Low-income countries 5% 20% 95% 80% 16%
Lower middle-income countries 10% 30% 90% 70% 22%
Upper middle-income countries 15% 35% 85% 65% 24%
High-income countries 20% 40% 80% 60% 25%
*Treatment coverage was modelled to increase from current to target rates linearly.
Table 1: Current and target levels of scaled-up treatment coverage for depression and anxiety disorders
(all interventions combined), by country income level*
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labour force outcomes, leaving the remaining third for
the intrinsic beneﬁ ts of health, which is equivalent to
0·5 times per person income.
Uncertainty analysis
We assessed the sensitivity of results to plausible
variations around these and other key input parameters
by constructing optimistic and pessimistic scale-up
scenarios. For the upper estimate: total investment costs
were assumed to be 20% lower than baseline, as a result
of lower than expected use of expensive hospital
outpatient and inpatient care or the development of more
eﬃ cient interventions, including internet-based treat-
ments; and productivity eﬀ ects were set at double their
baseline rate (10% rather than 5%); the intrinsic value of
a year of health life was set at 0·7 times GDP per person
(rather than 0·5). For the lower estimate: total investment
costs were assumed to be 20% higher than baseline, as a
result of higher than expected drug prices, service use
and programme management; productivity eﬀ ects were
set at half their baseline rate (2·5% rather than 5%); and
the intrinsic value of a year of health life was set at
0·3 times GDP per person (rather than 0·5).
Role of the funding source
The funders of the study had no role in study design,
data collection, data analysis, data interpretation, or
writing of the report. The corresponding author had full
responsibility for the decision to submit for publication.
Results
Across the 36 largest countries in the world, in the absence
of scaled-up treatment, it is projected that more than
12 billion days of lost productivity (equivalent to more than
50 million years of work) are attributable to depression and
anxiety disorders every year, at an estimated cost of
US$925 billion. Assuming the same distribution of costs across lower-income and higher-income countries holds for all other countries (representing 20% of the world’s population), the global cost per year is$1·15 trillion.
Compared with people without these disorders, 4·7 billion
extra days are lost, at a cost of $592 billion (36% of the total cost); this ﬁ gure can be termed the excess productivity loss of these disorders (ﬁ gure 1). Table 2 shows the estimated cost of scaling up treatment for depression and anxiety, expressed as the net present value of the total expenditure required over the scaling-up period between 2016 and 2030 (ie, the cumulative cost over 15 years of steady scale-up, but discounted at a rate of 3%). These costs relate to incremental treatment coverage in the population over and above current levels of coverage. For all 36 countries, the total cost amounts to US$91 billion for depression and $56 billion for anxiety disorders. Treatment of mild cases accounts for less than 10% of total costs for depression and 20% for anxiety Panel 2: Labour force eﬀ ects of treatment Labour force participation There are very few studies showing the extent to which eﬀ ective depression treatments get people back into work. Two studies undertaken in the USA reported a 6% increase in employment retention in patients with depression whose care was monitored and managed closely.20,21 Findings of another US study22 of patients in primary care showed that, at 6 months, employment rates were 52·5% for patients with no care versus 72·2% for patients with care. For low-income and middle-income countries, programme evaluation data for livelihoods from four countries—China, India, Ghana, and Pakistan—were made available by BasicNeeds, which showed that the proportion of people with depression undertaking income-generating activities increased by more than 50%, and in those with anxiety by more than 30% (Chris Underhill, BasicNeeds, personal communication). These estimates are in line with the assessment of the BasicNeeds programme in Kenya, which for a more mixed caseload showed an 43% improvement in the proportion of enrollees in income generation or productive work.23 Because these data are based on observation rather than under controlled trial conditions, we can infer only a clear association between exposure to treatment and subsequent earnings rather than a deﬁ nitive eﬀ ect of intervention. For our base case, we therefore conservatively modelled a 5% restored ability to work as a result of treatment, with half and double that rate used under pessimistic and optimistic scenario analyses. Labour force productivity A comprehensive review of 440 published trials in an existing database of psychological and pharmaceutical interventions in depression24 was speciﬁ cally undertaken for this project (by researchers at the Vrije Universiteit Amsterdam, Amsterdam, Netherlands, and the Trimbos Institute, Utrecht, Netherlands) to identify the eﬀ ect of eﬀ ective treatment on productivity; unfortunately, very few trials reported these eﬀ ects. However, some treatment trials done in the USA, Korea, and India have estimated the eﬀ ect of intervention on productivity loss. The decrease in absenteeism reported in these studies was close to 1 day per month.20,29–32 Only two studies reported the ﬁ ndings for presenteeism separately from days lost because of absenteeism: in the Korean study, treated patients had 24 more productive hours per month,29 whereas in the Indian study, patients receiving the collaborative care had 4 fewer partial days lost than controls.30 By conservatively assuming that 1 partial day is equivalent to a third of a whole day, we estimate that almost 1 complete day of unimpaired work is restored per month through reduced presenteeism. Expressed as a proportion of total working days per year (220 days), and allowing for both the onset of eﬀ ect and the time lag between improved health and return to work, a 5% increase in working days is gained through reduced absenteeism, and a 5% increase through reduced presenteeism. For more on BasicNeeds see http://www.basicneeds.org Articles 420 www.thelancet.com/psychiatry Vol 3 May 2016 disorders. After standardising for population size, the cost is actually quite low; for depression treatment, the average annual cost during 15 years of scaled-up investment is$0·08 per person in low-income countries,
$0·34 in lower middle-income countries,$1·12 in upper
middle-income countries and $3·89 in high-income countries (table 2). Per person costs for anxiety disorders are nearly half that of depression. Table 2 shows results for two key health outcomes: cases averted (reduced prevalence) and healthy life-years gained (equivalent to disability-adjusted life-years averted). Across the 36 countries represented in the analysis, we recorded a small decrease in the estimated prevalence of depression and anxiety disorders as a result of treated cases recovering from illness more quickly; in the next 15 years, this gradual decrease in prevalence translates into millions of averted cases (73 million fewer cases of depression, and 45 million fewer cases of anxiety disorder). Weighting these averted prevalent cases by the average level of improved functioning or reduced disability provides a measure of healthy life-years gained. For depression and anxiety disorders combined, the cumulative number of healthy life-years gained over 15 years is 43 million. Table 2 also shows the diﬀ erence in aggregate GDP between a continued current coverage scenario and one reﬂ ecting scaled-up treatment and enhanced productivity; again, this and the total economic return for the entire period of scale-up has been discounted at 3% to give a net present value. For all 36 countries combined, the net present value is$399 billion ($230 billion for depression and$169 billion for anxiety disorders). The intrinsic
value of health returns show a net present value of more
than $250 billion for scaled-up depression treatment and more than$50 billion for anxiety disorders (table 2).
Low-income
countries (N=6)
Lower
middle-income
countries (N=10)
Upper
middle-income
countries (N=10)
High-income
countries (N=10)
All countries
(N=36)
Total population of countries analysed (millions, 2013) 443 2215 2101 992 5751
Depression
Total investment (net present value, US$million) 517 7164 20 338 63 503 91 522 Average annual investment (net present value, US$
per person)
0·08 0·34 1·12 3·89 1·50
Health returns (averted prevalent cases) 6 150 311 25 989 404 25 607 740 15 750 268 73 497 723
Health returns (healthy life-years gained) 2 234 781 15 692 290 11 414 429 7 567 211 36 908 711
Economic returns (US$millions) 1190 18 799 52 732 157 022 229 744 Value of health returns (US$ millions)* 991 21 679 56 435 178 588 257 694
Beneﬁ t to cost ratio (economic returns) 2·3 2·6 2·6 2 ·5 2· 5
Beneﬁ t cost ratio (economic and value of health
returns)
4·2 5·7 5·4 5·3 5·3
Anxiety disorders
Total investment (net present value, US$millions) 30 4 3797 8966 42 668 55 735 Average annual investment (net present value, US$
per person)
0·05 0·16 0·52 2·44 0·88
Health returns (averted prevalent case) 3 395 363 16 59 719 12 980 180 12 077 053 45 052 316
Health returns (healthy life-years gained) 416 232 2 220 716 1 711 767 1 604 069 5 952 783
Economic returns (US$millions) 824 11 578 26 691 129 705 168 797 Value of health returns (US$ millions)* 18 1 2966 8453 40 409 52 009
Beneﬁ t cost ratio (economic returns) 2·7 3·0 3·0 3· 0 3·0
Beneﬁ t cost ratio (economic and value of health
returns)
3·3 3·8 3·9 4·0 4·0
*Healthy life-years gained multiplied by GDP per person multiplied by 0·5.
Table 2: Costs and beneﬁ ts of scaled up treatment of depression and anxiety disorders, 2016–30
Figure 1: Lost productivity attributable to depression and anxiety disorders
at current treatment coverage, by country income level (US$billion, 2013) 0$50
$100$150
$200$250
$300$350
$400$450
$500 Productivity loss (US$ billion, 2013)
Low-income and middle-
income countries (n=26)
High-income
countries (n=10)
Excess loss
Comparative loss
$185$276
$148$316
Excess loss Comparative loss
Articles
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421
By summing the discounted costs and beneﬁ ts for all
countries in an income group, we derived a summary
measure of the relationship between the beneﬁ ts of
scaled-up treatment and the associated costs of
investment (table 2, ﬁ gure 2). Restricting assessment to
the economic returns to investment, beneﬁ t to cost
ratios for scaled-up depression treatment across
country income groupings were in the range of
2·3 to 2·6. For anxiety disorders the ratios were slightly
higher (range 2·7–3·0). Extension of the beneﬁ t–cost
analysis to include the estimated value of health returns
increased the ratio of beneﬁ t to cost, especially for
depression because of the higher health returns for this
disorder compared with anxiety disorders. Beneﬁ t to
cost ratios for depression now exceed those for anxiety
disorders (range 4·2–5·7), and were more than double
the ratio when only economic beneﬁ ts of depression
treatment scale-up were considered. Beneﬁ t to cost
ratios for anxiety disorders increased by a third
(range 3·3–4·0).
We did uncertainty analysis to ascertain the sensitivity
of results to plausible changes in key study parameters.
Beneﬁ t to cost ratios fell to or almost reached parity
under the more pessimistic scenario when only economic
beneﬁ ts were considered, and did not exceed 3 even
when the value of health beneﬁ ts was included (ﬁ gure 2).
By contrast, the more optimistic scenario produces
beneﬁ t to cost ratios of 5·5–7·2 (economic beneﬁ ts only)
and 7·5–11·3 when the value of health beneﬁ ts was
added in. As expected, results were quite sensitive to the
estimated rate of enhanced labour participation and
productivity. We also assessed the eﬀ ect of changing the
rate used to discount future costs and beneﬁ ts to the
present time. At a discount rate of 6%, the net present
value of total investments and returns would be 25%
less; with no discounting, they would be 35% higher in
absolute terms. Because such a change in discount rate
was applied to both costs and beneﬁ ts, the ratio of beneﬁ t
to cost, our summary return on investment metric, is not
aﬀ ected.
Discussion
This analysis sets out, for the ﬁ rst time, a global
investment case for a scaled-up response to the massive
public health and economic burden of depression and
anxiety disorders. Previous international economic
studies of mental health have assessed the economic
eﬀ ect of these disorders,2,3 the cost-eﬀ ectiveness of
diﬀ erent intervention strategies,8,10 and the cost of scaling
up care,18,19 but not the value of both economic and health
beneﬁ ts of intervention scale up.
Notwithstanding the general limitations of any
projection modelling study, the analysis suggests that the
investment needed to substantially scale up eﬀ ective
treatment coverage for depression and anxiety disorders
in the 36 countries included in this analysis is substantial;
the net present value of all investments between 2016
and 2030 is $147 billion, equivalent to less than$10 billion
per year on average. Extending the scope to the 20% of
the world’s population not living in the 36 countries
represented in the study would increase the cost by about
25% to $184 billion. However, the returns to this Figure 2: Baseline, upper, and lower beneﬁ t to cost ratios for scaled-up treatment of depression and anxiety disorders, by country income group 0 2 4 6 8 10 12 Beneﬁt:cost ratio 0 2 4 6 8 10 12 Beneﬁt:cost ratio 0 2 4 6 8 10 12 Beneﬁt:cost ratio 0 2 4 6 8 10 12 Beneﬁt:cost ratio Economic beneﬁts: depression Economic and value of health beneﬁts: depression Economic beneﬁts: anxiety Economic and value of health beneﬁts: anxiety Low-income countries (n=6) Lower middle- income countries (n=10) Upper middle- income countries (n=10) High-income countries (n=10) 5·5 6·0 6·2 5·9 2·3 2·6 2·6 2·5 1·0 1·1 1·0 1·0 6·4 7·0 7·2 7·2 2·7 3·0 3·0 3·0 1·0 1·1 1·0 1·0 8·8 11·3 11·1 10·9 4·2 5·7 5·4 5·3 1·9 3·0 2·2 2·6 7·5 8·3 8·9 8·9 3·3 3·8 3·9 4·0 1·5 1·7 1·6 1·8 Articles 422 www.thelancet.com/psychiatry Vol 3 May 2016 investment are also substantial, with beneﬁ t to cost ratios of 2·3–3·0 when economic beneﬁ ts only are considered, and 3·3–5·7 when the value of health returns are also included. To put these ﬁ ndings into context, any beneﬁ t to cost ratio exceeding 1 provides a rationale for investment. Compared with some other potential investments in health, ratios of the order reported here can be deemed relatively modest. For example, a return on investment analysis for malaria, also for 2016–30, but using the full value of a statistical life-year, estimated beneﬁ t to cost ratios in the range of 28:1 to 40:1.37 An investment case done for maternal, reproductive, neonatal, and child health obtained a beneﬁ t to cost ratio of less than 10:1 for 2013–35,36 which is closer to the results obtained in this study. Inclusion of other beneﬁ ts arising from scaled-up treatment of common mental disorders that could not be captured though the present modelling exercise, notably reduced welfare support payments, and improved outcomes for other aﬀ ected people (eg, partners and children of women with perinatal depression) would generate higher ratios of beneﬁ t to cost. Set against that, treatment programmes might cost more or achieve less than anticipated, as highlighted by the uncertainty analysis. One limitation of our study is that although the projected level of overall prevalence of depression and anxiety disorders is quite well-established,12-14 the same cannot be said for treated prevalence. The analysis done here allows for a gradual linear increase in eﬀ ective service coverage for depression and anxiety disorders in all parts of the world in the next 15 years. However, for this to happen, not only will a new level of political commitment and resource mobilisation be required, but also a signiﬁ cant reorientation of public health systems towards chronic disease identiﬁ cation and management.9 Partial or weak implementation of envisaged treatment programmes, including appropriate management of recurrent cases of depression or insuﬃ cient promotion and awareness programmes, will inevitably reduce the number of cases eﬀ ectively reached and therefore the health and other beneﬁ ts obtained. It is also possible that as treatment coverage in the population increases substantially, the average cost per case might go up, for example as a result of reaching out to more remote or less well-served parts of a country. Target coverage rates were accordingly set at a modest level in this analysis (an upper value of 56% of depression cases in high-income countries). Aside from projected treatment coverage and eﬀ ectiveness, a further crucial parameter for this analysis concerns the eﬀ ect of treatment on labour force participation and productivity, for which there remains a paucity of evidence. As concluded by a systematic review, such data are not hard to collect alongside clinical trials and other studies, and need to be uniformly measured more often.27 More generally, population health models (eg, the OneHealth tool) rely on many input parameters, data sources, and assumptions regarding expected rates of disease, demographic change, and intervention eﬀ ects in the future, which limits their precision. Several eﬀ ects were not included in the analysis. One was the negative eﬀ ect of maternal depression on early child development, for which there is clear evidence;38 the health, social, and economic beneﬁ ts of eﬀ ective treatment of maternal depression on the cognitive and physical development of newly born babies was not assessed, but there is some evidence that this could be substantial over the longer term.39 Likewise, the monetary and non-monetary impact of eﬀ ective treatment on family and other caregivers has not been factored in. Additionally, no account has been taken of the substantial eﬀ ect of depression and its treatment on physical health outcomes; depression is a risk factor for disorders such as hypertension, stroke, coronary heart disease, and substance use disorders (just as these conditions are risk factors for depression), and adversely aﬀ ects outcomes through reduced help-seeking and adherence.40 Inclusion of these additional eﬀ ects of treatment would bolster identiﬁ ed economic returns. Taking appropriate account of the regular co-occurrence of depression and anxiety in individuals would be expected to lead to strong synergies on the treatment side, leading to potentially reduced investment costs, but health and economic outcomes for these comorbid cases might be slower or harder to achieve. Although the analysis accounted for age and sex (eg, in terms of disease prevalence, labour force participation and treatment eligibility), it was not possible to consider the eﬀ ect of socioeconomic status as a mediator and predictor of good health and economic outcomes. Poverty has an adverse eﬀ ect on the risk of depression and anxiety disorders through higher levels of stress, social exclusion, violence and trauma, but the evidence base for the mental health eﬀ ect of interventions targeted at the poor remains insubstantial.41 In many countries, poor people face signiﬁ cant barriers to accessing services, including the ﬁ nancial cost of seeking and paying towards health care. Finally, it should be acknowledged that the workplace itself can be a source of stress for many people, and that there is a consequent need to integrate mental health and wellbeing into new or existing employee support programmes. A crucial issue related to but outside the scope of this return on investment analysis is the source of ﬁ nancing for investments required to scale-up services for depression and anxiety disorders. As previously noted, the absolute amount needed for investment (eg, on a per person basis) is modest, but because existing service coverage level is so low in most countries, the gap between current and required spending can be large.18,19 Accordingly, both rich and poor countries need to carefully consider the merits of diﬀ erent health ﬁ nancing mechanisms. For many countries, the ﬁ rst question to address concerns the extent to which domestic ﬁ nancing Articles www.thelancet.com/psychiatry Vol 3 May 2016 423 represents a feasible and suﬃ cient method for ﬁ nancing mental health services, perhaps as part of a package of measures to be paid for from enhanced revenue generation. For low-income countries eligible for oﬃ cial development assistance, a second question might be to what extent external funding can complement domestically generated resources to catalyse service development. In countries where domestic or external funding mechanisms are expected to fall short of requirements or pose a risk to ﬁ scal stability, a further question relates to the extent to which market-based nancing options such as bonds oﬀ er a suitable and feasible approach to generating and providing funds for outcomes-based scale-up for mental health services. The pursuit of any of these methods of ﬁ nancing will be aﬀ ected by other factors, including the amount of investment needed, the level of political will and also scal space for raising new resources for health, and eligibility of the country for bilateral or multilateral funding. Faced with a new and broad development agenda,6 governments need to assure themselves that investment in the mental health of their populations represents a sound and equitable investment of society’s resources that leads to clear and deﬁ nable health, economic, and social beneﬁ ts. Our return on investment analysis, coupled with an assessment of health-system needs and priorities, and the broader macro-ﬁ scal situation, can contribute to a balanced investment case for common mental disorders and the health sector more generally. Contributors DC and SS conceived, planned, and oversaw the study. DC led the analysis of treatment costs and health outcomes, and drafted the paper. KS led the development of the methodology and model for estimating productivity eﬀ ects. PS and BR contributed to the conceptual development of the return on investment model and its constituent parts. PC and FS led the systematic review of productivity eﬀ ects of treatment. All authors reviewed, commented on, and approved the report. Declaration of interests We declare no competing interests. Acknowledgments We thank the contribution of Chris Underhill, Jess McQuail, and Uma Sunder of BasicNeeds, who provided country-level data for productivity outcomes for people enrolled into their mental health and development programme; colleagues in the Department of Health System Governance and Financing (Jeremy A Lauer and Melanie Bertram) regarding conceptualisation and review of return on investment frameworks in health, and Eirini Karyotaki at VU Amsterdam University for her contribution to a systematic review; Shelly Chopra for her contribution to a background literature search and to the development of the conceptual framework used in this study . Development of the mental health module of the OneHealth tool, which was used in this analysis for estimating health impacts, was made possible through the EMERALD project on mental health system strengthening in low-income and middle-income countries, which is funded by the European Union under the 7th Framework programme (Grant agreement 305968). DC and SS are staﬀ members of WHO. All opinions expressed in this report rest with the authors; they do not necessarily represent the decisions, policy, or views of WHO. References 1 WHO. Mental health ATLAS 2014. Geneva: World Health Organization, 2015. 2 Bloom DE, Caﬁ ero E, Jané-Llopis E, et al. The global economic burden of noncommunicable diseases. Geneva: World Economic Forum, 2011. 3 Hu TW. Perspectives: an international review of the national cost estimates of mental illness, 1990–2003. J Ment Health Policy Econ 2006; 9: 3–13. 4 Gustavsson A, Svensson M, Jacobi F, et al, and the CDBE2010 Study Group. 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The eﬀ ects of psychological treatment of maternal depression on children and parental functioning: a meta-analysis. Eur Child Adolesc Psychiatry 2015; 24: 237–45. DOI:10.1007/s00787-014-0660-6. 40 Prince M, Patel V, Saxena S, et al. No health without mental health. Lancet 2007; 370: 859–77. 41 Lund C, De Silva M, Plagerson S, et al. Poverty and mental disorders: breaking the cycle in low-income and middle-income countries. Lancet 2011; 378: 1502–14. ... DALYs are transformed into monetary values (=costs) by attaching a threshold based on a proportion of per capita income of 0.5 (typically per capital gross domestic product; GDP). This approach is used in the World Health Organization's Choosing Interventions that are Cost-Effective (WHO-CHOICE) project and applied in several cost studies (Chisholm et al., 2016). For GDP per capita data we use the most recent World Bank estimates (https://data.worldbank.org), ... ... Our findings suggest that the lifetime costs linked to perinatal anxiety and depression in Brazil are very high, thus adding to growing evidence about the substantial costs of mental health problems in low-and middle-income countries. Globally, the costs of anxiety and depression across the whole adult population have been estimated at USD 1.15 trillion per year (Chisholm et al., 2016). The cost of maternal depression because of the impact on just a single indicator, child stunting, has been estimated at USD 14.5 billion worldwide (Smith Fawzi et al., 2019). ... ... Innovatively, it includes the economic consequences linked to negative effects of maternal depression on children. By including the long-term and intergenerational effects on costs, we sought to address limitations of past cost studies of mental health problems that have been highlighted by the World Health Organization (Chisholm et al., 2016). It is possible to include those impacts because of the high-quality longitudinal evidence available in Brazil, which includes a large birth cohort study, the Pelotas study, that followed up mothers and children over time and thus allowed examination of the long-term impacts of maternal depression and anxiety during the perinatal period and beyond on the child. ... Article Full-text available Background Each year, an estimated 860,000 Brazilian women experience depression and anxiety perinatally. Despite well-known devastating impacts of these conditions on mothers and children, they remain neglected in low- and middle-income countries. Knowing the costs of untreated perinatal depression and anxiety can inform decision-making. Methods Simulation modelling is used to examine lifetime costs of perinatal depression and anxiety for a hypothetical cohort of women and their children, followed until children are aged 40 years. Costs are measured from a societal perspective, including healthcare expenditure, productivity and health-related quality of life losses; 2017 data are taken from country-specific sources. Present values are calculated using a discount rate of 3 %. Results Lifetime cost of perinatal depression and anxiety in Brazil are USD 4.86 billion or R$ 26.16 billion, including costs linked to poorer quality of life (USD 2.65 billion), productivity loss (USD 2.16 billion) and hospital care (USD 0.05 billion). When the costs associated with maternal suicide are included, total costs increase to USD 4.93 billion. Limitations Several costs could not be included in the analysis because of a lack of data. The study is reliant of longitudinal data on associations between perinatal depression and anxiety and impacts on mothers and children. Therefore, no causality can be inferred. Conclusion Our findings illustrate the economic rationale for investment in this area. This is the first study that estimates the costs of perinatal mental health problems in a low- or middle-income country setting.
... Evaluating the economic burden of mental illness is a critical part in making the investment case for global mental health, informing public health decision-making, and guiding priority-setting and the scale up of much-needed interventions. 11 At the global level, however, the most recent estimate of the economic impact of mental disorders was published in 2011, using burden of disease estimates from 2004. 12 This study used three distinct approaches to quantify the economic burden of non-communicable diseases (NCDs), including mental illnesses. 12 The first is a cost-of-illness (COI) analysis, which includes the direct costs of illness as well as the indirect costs (e.g., lost productivity). ...
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Background Epidemiological and economic estimates suggest that the global burden of mental disorders is considerable, both in its impacts on human health and losses to societal welfare. The availability of additional data and the emergence of new approaches present an opportunity to examine these estimates, which form a critical part in making the investment case for global mental health. Methods This study reviews, develops, and incorporates new estimates and methods in quantifying the global burden of mental illness. Using a composite estimation approach that accounts for premature mortality due to mental disorders and additional sources of morbidity and applying a value of a statistical life approach to economic valuation, we determine global and regional estimates of the economic cost that can be associated with mental disorders, building on data from the 2019 Global Burden of Disease study. Findings We estimate that 418 million disability-adjusted life years (DALYs) could be attributable to mental disorders in 2019 (16% of global DALYs)—a more than three-fold increase compared to conventional estimates. The economic value associated with this burden is estimated at about USD 5 trillion. At a regional level, the losses could account for between 4% of gross domestic product in Eastern sub-Saharan Africa and 8% in High-income North America. Interpretation The burden of mental illness in terms of both health and economic losses may be much higher than previously assessed. Funding None.
... On a global scale, there have been claims of consistently high or even rising incidences of mental disorders over the last decades (World Health Organization, 2019), resulting in an increasing financial burden on the global economy (Chisholm et al., 2016;World Health Organization, 2016). Survey-based epidemiological studies suggest a lifetimeprevalence of nearly 50% for a mental disorder among the US-population (Kessler et al., 2005;NIMH, 2019), while a meta-analysis across 63 countries identified an average 12-month prevalence of 17.6% for common mental disorders (Steel et al., 2014). ...
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Background In the light of high incidences of diagnosed mental disorders and the growing utilization of mental healthcare services, a progressing psychiatrization of society has been hypothesized as the underlying dynamic of these developments. Mental healthcare institutions, such as psychiatric hospitals, may play a decisive role in this. However, there is a scarcity of research into how psychiatrization emerges in hospital settings. This paper explores whether the emergency department (ED) can be considered as a site where psychiatrization happens, becomes observable, and which factors in the context of the ED may be its potential drivers. Methods Two cases as encountered in an interdisciplinary ED will be presented in the following in an anonymized way. Although the cases originate from individual consultations, they can be considered as prototypical. The cases were collected and discussed using the method of interactive interviewing. The results will be analyzed against the backdrop of current theoretic concepts of psychiatrization. Findings The ED can be seen as an important area of contact between society and psychiatry. Decisions whether to label a certain condition as a “mental disorder” and to therefore initiate psychiatric treatment, or not, can be highly difficult, especially in cases where the (health) concerns are rather moderate, and clearly associated with common life problems. Psychiatrists' decisions may be largely influenced in favor of psychiatrization by a wide array of disciplinary, institutional, interpersonal, personal, cultural, and social factors. Conclusions The ED appears to be a promising field for research into the mechanisms and motives through which psychiatrization may emerge in mental healthcare settings. Psychiatrists in the ED work within a complex sphere of top-down and bottom-up drivers of psychiatrization. Encounters in the ED can be an important step toward adequate support for many individuals, but they also risk becoming the starting point of psychiatrization by interpreting certain problems through the psychiatric gaze, which may induce diagnoses of questionable validity and treatment of little use.
... Depression is a leading cause of disability worldwide and is estimated to affect over 320 million people. 1 The burden of depression negatively affects role functioning and quality of life, 2 and is estimated to cost over US920 billion due to lost productivity alone. 3 Depression has a lifetime prevalence of up to 19% and is highly associated with the onset of other somatic and psychiatric disorders. 4 5 Currently, second-generation antidepressant medications are one of the first-line treatments for depression. ... Article Full-text available Objective To assess the comparative effectiveness of exercise, antidepressants and their combination for alleviating depressive symptoms in adults with non-severe depression. Design Systematic review and network meta-analysis. Data sources Embase, MEDLINE, PsycINFO, Cochrane Library, Web of Science, Scopus and SportDiscus. Eligibility criteria Randomised controlled trials (1990–present) that examined the effectiveness of an exercise, antidepressant or combination intervention against either treatment alone or a control/placebo condition in adults with non-severe depression. Study selection and analysis Risk of bias, indirectness and the overall confidence in the network were assessed by two independent investigators. A frequentist network meta-analysis was performed to examine postintervention differences in depressive symptom severity between groups. Intervention drop-out was assessed as a measure of treatment acceptability. Results Twenty-one randomised controlled trials (n=2551) with 25 comparisons were included in the network. There were no differences in treatment effectiveness among the three main interventions (exercise vs antidepressants: standardised mean differences, SMD, −0.12; 95% CI −0.33 to 0.10, combination versus exercise: SMD, 0.00; 95% CI −0.33 to 0.33, combination vs antidepressants: SMD, −0.12; 95% CI −0.40 to 0.16), although all treatments were more beneficial than controls. Exercise interventions had higher drop-out rates than antidepressant interventions (risk ratio 1.31; 95% CI 1.09 to 1.57). Heterogeneity in the network was moderate (τ ² =0.03; I ² =46%). Conclusions The results suggest no difference between exercise and pharmacological interventions in reducing depressive symptoms in adults with non-severe depression. These findings support the adoption of exercise as an alternative or adjuvant treatment for non-severe depression in adults. Systematic review registration PROSPERO CRD4202122656. ... Furthermore, mental health disorders increase vulnerability and mortality, reducing life expectancy (5), mainly due to cardiovascular disease risk (6). Thus, an increased economic burden is expected due to treatment costs and productivity loss (7). Therefore, finding new strategies to prevent mental health disorders must be a priority of public health authorities. ... ... Evidence increasingly suggests that empirically-based psychotherapies have resulted in positive outcomes in lowresource communities (Bass et al., 2013). Additionally, literature on the cost effectiveness of treatment has shown the economic value of preventing and treating mental disorders in such communities (Chisholm et al., 2016). The clear need for additional mental healthcare services in lowresource communities, coupled with the ease of access to such services afforded by media and technology, indicates the imperative to decrease the enormously high percentage (~70%) of the world's population that does not have access to mental health care (Henderson et al., 2017). ... Article p>COVID-19 has ushered in a new chapter of counseling in the United States and throughout the world. Counselors’ responses to the pandemic have been fundamentally reshaped by universal elements of the information age, including high-speed internet, smartphones, and computerbased technologies such as synchronous meeting software and collaboration tools. Now, clinicians can use technology to ally with clients, deliver psychoeducational media, and open new categories of intervention and engagement that alter the size, shape, and availability of the “counseling room” by extending it into a virtual space. The immediate investment in information technology demanded by the pandemic highlights an increasing need to deepen clinicians’ awareness of the psychology of cyberspace, the clinical applications of technological capabilities, and the use of synchronous online video counseling, all of which can directly increase quality of care, strengthen the therapeutic bond, and improve clinical outcomes. This manuscript explores the pairing of technology and counseling, outlining an open, integrative approach to counseling with updated practice and ethical competence. Properly conceived and combined, technical innovation and advanced counseling strategies developed in response to the COVID-19 pandemic will lead to updated practices of technology-assisted counseling that offer a new modality of care as fundamental and as potentially impactful as talk therapy was over a century ago.</p Article Background Evidence on the association of low physical activity (PA) with depression or anxiety is well established. Yet, evidence on the association between PA and comorbid anxiety/depression remains scarce, especially in low- and middle-income countries (LMICs). Thus, this study explored this relationship among adults aged ≥18 years from 46 LMICs. Methods Cross-sectional, community-based data were analyzed from the World Health Survey. Multivariable multinomial logistic regression analysis was conducted to examine the association between low PA and comorbid anxiety/depression with no anxiety or depression as the base category. Results 237,023 participants [mean (SD) age = 38.4 (16.0) years; 50.8 % female] were included in the analysis. Low PA was significantly associated with depression alone (OR = 1.33; 95%CI = 1.12–1.57) and anxiety alone (OR = 1.37; 95%CI = 1.23–1.53), while the OR was highest among those with comorbid anxiety/depression (OR = 1.75; 95%CI = 1.52–2.01). Conclusion Low PA is associated with particularly increased odds for comorbid anxiety/depression. Increasing PA may have a beneficial effect on the prevention of comorbid anxiety/depression. However, future longitudinal research establishing the direction of this relationship is warranted. Article Full-text available Introduction: Empirical research on the burden and determinants of common mental disorders (CMDs), especially depression and anxiety, among older adults living with HIV (OALWH) in sub-Saharan Africa is inadequate. To bridge the gap in Kenya we: (1) determined the prevalence of CMDs among OALWH on routine HIV care compared to HIV-negative peers; (2) investigated HIV status as an independent predictor of CMDs in older adults; and (3) investigated CMD determinants. Methods: In a cross-sectional study conducted between 2020 and 2021, the prevalence of CMDs and associated determinants were investigated at the Kenyan coast among 440 adults aged ≥50 years (257 OALWH). The Patient Health Questionnaire and Generalized Anxiety Disorder scale were administered alongside measures capturing biopsychosocial information. Logistic regression was used to examine the correlates of CMDs. Results: No significant differences were found in the prevalence of mild depressive symptoms, 23.8% versus 18.2% (p = 0.16) and mild anxiety symptoms, 11.7% versus 7.2% (p = 0.12) among OALWH compared to HIV-negative peers, respectively. HIV status was not independently predictive of CMDs. Among OALWH, higher perceived HIV-related stigma, ageism, increasing household HIV burden, loneliness, increasing functional disability, sleeping difficulties, chronic fatigue and advanced age (>70 years) were associated with elevated CMDs. Among HIV-negative older adults, loneliness, increased medication burden and sleeping difficulties were associated with elevated depressive symptoms. Easier access to HIV care was the only factor associated with lower CMDs among OALWH. Conclusions: On the Kenyan coast, the burden of moderate and severe CMDs among older adults is low; however, both OALWH and their HIV-negative peers have a similar relatively high burden of mild depressive and anxiety symptoms. Our results also suggest that determinants of CMDs among OALWH in this setting are predominantly psychosocial factors. These results highlight the need for psychosocial interventions (at the family, community and clinical levels) to mitigate the risks of mild CMDs as they are known to be potentially debilitating. Article Background: Public resources to answer pertinent research questions about the impact of illness and treatment on people with mental health problems are limited. To target funds effectively and efficiently and maximize the health benefits to populations, prioritizing research areas is needed. Research agendas are generally driven by researcher and funder priorities, however, there is growing recognition of the need to include user-defined research priorities to make research more relevant, needs-based and efficient. Objective: To gain consensus on top priorities for research into early intervention in psychosis through a robust, democratic process for prioritization enlisting the views of key stakeholders including users, carers and healthcare professionals. We also sought to determine which user-prioritized questions were supported by scientific evidence. Design and methods: We used a modified nominal group technique to gain consensus on unanswered questions that were obtained by survey and ranked at successive stages by a steering group comprising users, carer representatives and clinicians from relevant disciplines and stakeholder bodies. We checked each question posed in the survey was unanswered in research by reviewing evidence in five databases (Medline, Cinahl, PsychInfo, EMBASE and Cochrane Database). Results: Two hundred and eighty-three questions were submitted by 207 people. After checking for relevance, reframing and examining for duplicates, 258 questions remained. We gained consensus on 10 priority questions; these largely represented themes around access and engagement, information needs before and after treatment acceptance, and the influence of service-user (SU) priorities and beliefs on treatment choices and effectiveness. A recovery SUtheme identified specific self-management questions and more globally, a need to fully identify factors that impact recovery. Discussion and conclusions: Published research findings indicated that the priorities of service users, carers and healthcare professionals were aligned with researchers' and funders' priorities in some areas and misaligned in others providing vital opportunities to develop research agendas that more closely reflect users' needs. Patient and public contribution: Initial results were presented at stakeholder workshops which included service-users, carers, health professionals and researchers during a consensus workshop to prioritize research questions and allow the opportunity for feedback. Patient and public representatives formed part of the steering group and were consulted regularly during the research process. Article Full-text available An essential element of mental health service scale up relates to an assessment of resource requirements and cost implications.AimsTo assess the expected resource needs of scaling up services in five districts in sub-Saharan Africa and south Asia.Method The resource quantities associated with each site's specified care package were identified and subsequently costed, both at current and target levels of coverage.ResultsThe cost of the care package at target coverage ranged from US0.21 to 0.56 per head of population in four of the districts (in the higher-income context of South Africa, it was US$1.86). In all districts, the additional amount needed each year to reach target coverage goals after 10 years was below$0.10 per head of population.Conclusions Estimation of resource needs and costs for district-level mental health services provides relevant information concerning the financial feasibility of locally developed plans for successful scale up.
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Full-text available
Background An essential element of mental health service scale up relates to an assessment of resource requirements and cost implications. Aims To assess the expected resource needs of scaling up services in five districts in sub-Saharan Africa and south Asia. Method The resource quantities associated with each site's specified care package were identified and subsequently costed, both at current and target levels of coverage. Results The cost of the care package at target coverage ranged from US$0.21 to 0.56 per head of population in four of the districts (in the higher-income context of South Africa, it was US$1.86). In all districts, the additional amount needed each year to reach target coverage goals after 10 years was below $0.10 per head of population. Conclusions Estimation of resource needs and costs for district-level mental health services provides relevant information concerning the financial feasibility of locally developed plans for successful scale up. Article Background: Mental illness is a major group of disorder that can lead to both physical and emotional disability. Policymakers need to learn not only the epidemiological indicators of mental illness, such as prevalence rate and incidence rate, but also the size of its negative impact on the economy. Aims of the Study: This study is to review international publications on cost of major mental illness literature, from 1990 to 2003, focusing on the concepts, methods, and future perspective of cost illness studies. Reviewing the status quo on costs of mental illness can provide further information about gaps, limitations, and future needs on this topic. Method: This review searched all major international journals in psychiatry, clinical psychology, health economics, and mental health policy published since 1990. All national or aggregate cost of mental illness studies were included in the review. All were individually reviewed using a conceptual framework of cost of illness methodology. Results: A large majority of published cost of mental illness studies were conducted in the US and UK. Cost of illness studies were lacking from Africa, Asia, Eastern Europe, and Latin America. Empirical results from the reviewed studies indicate that the negative economic consequences of mental illness far exceed the direct costs of treatment, thus making it important to treat mental illness. Direct treatment costs for each mental disorder (i.e. depression, schizophrenia, dementia, etc.) is between 1% and 2% of total national health care costs. Discussion: The studies reviewed indicate great variation in cost estimates even for the same mental disorder during the same time period within a country. These wide variations may be due to differences in disorder classification, definition of cost categories, sample populations, data sources, and discounting rate. Given the limitations of the cost of illness studies reviewed, one should be careful in interpreting and using these estimated results. Implications for Health Services: These cost studies can be useful for understanding the magnitude of treating an illness of economic consequences or economic consequences of an illness for purposes of planning or budgeting. Such studies are one way to inform policymakers about economic consequences of mental illness. Article United Nations (UN) adopted 17 global sustainable development agenda to the year 2030 in the 68th general assembly on september, 2015. The global agendas and goals are important for 3 reasons: (1) to adopt the international standard for determining the health status; (2) to identify areas in need of attention; and (3) to advance international cooperation regarding health issues. In the area of infectious diseases, our goals include the eradication of human immunodeficiency virus infection and acquired immune deficiency syndrome, tuberculosis, and malaria as well as a substantial reduction of hepatitis by the year 2030. In the area of non-communicable diseases, our goal is to reduce premature mortality ({\leq}70years) at least 30% by the year 2030. Preventive activities such as smoking cessation, alcohol abstinence, nutritional measures, and physical activities, should also be promoted intensively nationwide. It is also necessary to establish stringent policies for control hypertension, diabetes, obesity, and hypercholesterolemia. Additionally, environmental health, injury by traffic accident, mental health, and drug and alcohol abuse are important health policies. Furthermore, in the area of international health and cooperation, maternal and child health remain important areas of support for underdeveloped countries. Education and training towards the empowerment of health professionals in underdeveloped countries is also an important issue. The global agenda prioritize resources(manpower and budget) allocation of international organizations such as UN, World Health Organization, United Nations Development Programme, and World Bank. The global agenda also sets the contribution levels of Official Developmental Assistance donor countries. Health professionals such as professors and researchers will have to turn their attention to areas of vital international importance, and play an important role in implementation strategies and futhermore guiding global agenda. Article The burden of mental, neurological, and substance use (MNS) disorders increased by 41% between 1990 and 2010 and now accounts for one in every 10 lost years of health globally. This sobering statistic does not take into account the substantial excess mortality associated with these disorders or the social and economic consequences of MNS disorders on affected persons, their caregivers, and society. A wide variety of effective interventions, including drugs, psychological treatments, and social interventions, can prevent and treat MNS disorders. At the population-level platform of service delivery, best practices include legislative measures to restrict access to means of self-harm or suicide and to reduce the availability of and demand for alcohol. At the community-level platform, best practices include parenting programmes in infancy and life-skills training in schools to build social and emotional competencies. At the health-care-level platform, we identify three delivery channels. Two of these delivery channels are especially relevant from a public health perspective: self-management (eg, web-based psychological therapy for depression and anxiety disorders) and primary care and community outreach (eg, non-specialist health worker delivering psychological and pharmacological management of selected disorders). The third delivery channel, hospital care, which includes specialist services for MNS disorders and first-level hospitals providing other types of services (such as general medicine, HIV, or paediatric care), play an important part for a smaller proportion of cases with severe, refractory, or emergency presentations and for the integration of mental health care in other health-care channels, respectively. The costs of providing a significantly scaled up package of specified cost-effective interventions for prioritised MNS disorders in low-income and lower-middle-income countries is estimated at US$3–4 per head of population per year. Since a substantial proportion of MNS disorders run a chronic and disabling course and adversely affect household welfare, intervention costs should largely be met by government through increased resource allocation and financial protection measures (rather than leaving households to pay out-of-pocket). Moreover, a policy of moving towards universal public finance can also be expected to lead to a far more equitable allocation of public health resources across income groups. Despite this evidence, less than 1% of development assistance for health and government spending on health in low-income and middle-income countries is allocated to the care of people with these disorders. Achieving the health gains associated with prioritised interventions will require not just financial resources, but committed and sustained efforts to address a range of other barriers (such as paucity of human resources, weak governance, and stigma). Ultimately, the goal is to massively increase opportunities for people with MNS disorders to access services without the prospect of discrimination or impoverishment and with the hope of attaining optimal health and social outcomes.