Introduction

One argument for prioritizing treatment of mental illness is that the secondary effects (such as higher productivity and improved health-related behavior) may be especially impactful. Illnesses like depression and addiction are incredible drains on productivity, which can be reversed with treatment. In this essay I investigate the productivity cost of untreated (or unsuccessfully treated) mental illness and the impact of treatment on productivity.

How Bad is it?

World Health Organization Data

Alonso, et al. (2011) surveyed workers to determine how many days they missed work due to a variety of chronic illnesses, including depression and anxiety. Their sample included 63,000 people spread across 24 countries, with a range of cultures and income levels. Across all countries, the following disorders caused the average person with that disorder to lose the following days of work. Note that comorbidity is common and days-missed are additive- e.g. a person with depression and generalized anxiety in a lower income country would miss 26.6 days of work.
Days of Productivity Lost to Illness

	Lower income countries		Medium income countries		Higher income countries		All countries
	Additional days		Additional days		Additional days		Additional days
	Mean	s.e	Mean	s.e.	Mean	s.e.	Mean	s.e.
Depression	13.1	5	14.7	4.1	4.1	3.2	9	2.5
Bipolar disorder	36.5	15	23.2	9.6	9.6	5.8	17.3	4.9
Panic disorder	24.3	12.9	17.7	5.5	11.7	4.1	14.3	3.5
Specific phobia	−6.6	5.2	4.2	4.7	6.7	3.3	3.9	2.5
Social phobia	5.7	10	9	8.4	7.5	2.9	7.3	2.8
GAD	13.5	9.1	24.6	8.4	7.6	4.9	7.7	3.6
Alcohol abuse	−2.8	7.2	8.2	5	−0.3	4.5	1.9	3.2
Drug abuse	14.7	13.9	3.9	12.2	1.2	5.5	2.5	4
PTSD	15.3	11.3	−1.1	9.5	16.2	4	15.2	3.5
Insomnia	5.7	5.3	4.6	5.4	9.4	3.2	7.9	2.7
Headache or migraine	10	3.6	6.5	3.3	4.5	2.1	7.1	1.5
Arthritis	6.1	4.4	0.8	5	1.8	2.4	2.7	1.8
Pain	0.9	3.1	11	2.4	19.6	2.1	14.3	1.5
Cardiovascular	2.7	6.7	1	3.6	7.2	2.7	5.7	2.1
Respiratory	10.7	3	−1.1	2.6	0.9	1.4	2.6	1.3
Diabetes	4	6.4	0.5	5.6	9.6	3.8	8.6	2.8
Digestive	−4.3	4.8	−0.4	4	16.6	4.8	7.6	3
Neurological	33.7	23	18.6	7	15.3	7.4	17.4	5.8
Cancer	19.4	17.9	−4.2	12.9	6.9	3.6	5.5	3.5

 

[Note that negative numbers mean the condition is associated with an increase in number of days worked.]

Alonso, et al (2011) did not attempt to measure workers who attended work but were less productive due to illness (presenteeism), or control for average number of days of work for a given country.

Chrisholm, et al. (2016) attempted to estimate the economic impact of depression and anxiety, including the cost of lost productivity, using primarily the data above. They estimate that treatment for depression leads to a 5% increase in attendance (in any country) and 5% increase in productivity while present. This implies a normal worker has 180 working days in high income countries and 260 in low income countries, which is low (see OECD data), meaning the 5% estimate for absenteeism is too high. However I believe their estimate for presenteeism is much too low. Just the diagnostic criteria of depression suggests more than a 5% drop in productivity.

 

Comparison to Sleep Deprivation

The effects of depression can be similar to sleep deprivation, in part because depression can cause either insomnia or a need for excess sleep, and in part because both produce a “brain fog” (weirdly, sleep deprivation may also treat depression). Given the paucity of information on the relationship between depression and productivity and the abundance of information on the relationship between sleep and productivity, I turned to sleep deprivation as a model for the effects of depression on productivity, contingent on a given a worker making it to their job. The following are mostly small studies but unsurprisingly all show sleep deprivation having a large negative impact on productivity.

 

Kessler, et al. (2011) estimate that insomnia causes presenteeism equivalent to 7.8 days of missed work per year, an estimated financial loss of $2,280 per person. This used the WHO Health and Work Performance Questionnaire, which relies entirely on workers self-reports of their productivity relative to co-workers. It is also designed only to measure whether someone is more or less productive than average, not the magnitude of the difference.

 

Gibson & Shrader (2014) estimated that a one hour increase in average nightly sleep led to a 16% increase in wages (on average, $6,000). I will use that as my lower bound for the benefits of treating depression. I assume the actual increase productivity is larger than the increase in wages, because some of the benefit is captured by the employer. If we assume the employer and employee capture equal value, this implies an actual productivity increase of 32%. And if we assume depression is equivalent to 2-3x the cost of missing one hour of sleep, that is almost a halving of productivity (note that for actual sleep, the costs of missed sleep probably increase exponentially). This study is especially promising because it is rather large and used a natural experiment (distance from timezone line) to establish study conditions.

 

What Does Treatment Accomplish?

Strong Minds

[When not otherwise stated, data comes from Strong Mind’s 2015 report.]

Strong Minds is an NGO in Africa that runs 12 week group therapy classes in Uganda. Their three month month program produces a noticeable drop in depression.

Strong Minds monitors its effect on depression using a modified version of the PHQ-9 (Patient Health Questionnaire- 9). The scale of this test is unknown, making it hard to evaluate the absolute improvement, but lower scores are relatively better (less depression) than higher scores. This questionnaire is an accepted tool for monitoring severity of depression.

Of women participating in Strong Mind’s 12 week pilot program, 92% had reduced scores on the PHQ-9; 11% of the control group had reduced scores. Most of the other effects reported in Strong Mind’s report are given in absolute terms, with no reference to the control group. Based on the reduction in PHQ-9 scores, I will assume 88% of any result is due to participation in the program. Key results:

• 15 percentage point increase in participation in primary occupation (79% -> 94%).
• 40 percentage point reduction in families going 24 hours without a meal (53% -> 13%).
• 17 percentage point reduction in medical care visits (58% -> 41%). This is likely to understate the improvement in health, as some participants probably had physical problems they had previously been too depressed to treat.
• 18 percentage point increase in families sleeping in protected shelters (65% -> 83%).
• 10 percentage point increase in school attendance (33% -> 43%).

Income is not reported in this study. The authors do not say this explicitly, but it is common in developing world studies to examine consumption, because income is so variable.

Qualms about data: the study recorded 46 variables, of which less than 10 were reported in their report (not all of which made it into this report). The report included different metrics from phase one studies (eating 3 meals/day, ability to save any amount of income).  Given that it appears this data was still collected in phase two, the absence of results in the report raises concerns about cherry picking. I included this study despite my qualms because so little data was available about the effect of treatment of depression in developing countries.

Cost: $240/12 women in the program = $20/person. This is almost certainly an underestimate of even the marginal cost of the program.

Schoenbaum, et al.

In The Effects of Primary Care Depression Treatment on Patients’ Clinical Status and Employment, researchers reported that six months after their intervention (treatment for depression by a primary care physician, in the USA), 24% (vs 70% in control group) were depressed, and 72% (vs 54%) were employed.

Summary

Translating these productivity impacts into dollars is difficult because we can’t assume they hit all incomes equally, however the WHO estimates that in aggregate depression and anxiety together cost one trillion dollars US/year in lost productivity worldwide, slightly more than 1% of total GDP. On an individual level, there is no satisfying answer here. Depression has a very broad definition: the worst cases can destroy all productivity. The typical case destroys somewhere between 5% and 50% of productivity. Treatment of depression can restore that lost productivity in some but not 100% of participants.  

 

Areas for Further Investigation

I used sleep deprivation to generate heuristics for how damaging depression might be, with the answer being “quite bad”. Those numbers are even more accurate for estimating the effect of sleep deprivation. Because the scope of this paper was limited to economic effects stemming from workplace productivity, I have left out many other costs of sleep deprivation, including health costs and developmental damage to children. Given the costs and prevalence of sleep deprivation, sleep-promoting interventions, especially in children and adolescents, may be a promising area for intervention.