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Presenteeism, Absenteeism, and Lost Work Productivity among Depressive Patients from Five Cities of Colombia

Open ArchivePublished:May 11, 2017DOI:https://doi.org/10.1016/j.vhri.2017.03.001

      Abstract

      Objectives

      To estimate productivity losses due to absenteeism and presenteeism and their determinants in patients with depression from five Colombian cities.

      Methods

      We used data from a multicenter, mixed-methods study of adult patients diagnosed with major depressive disorder or double depression (major depressive disorder plus dysthymia) during 2010. The World Health Organization’s Health and Work Performance Questionnaire was used to assess absenteeism and presenteeism. We explored the determinants of productivity losses using a two-part model. We also used a costing model to calculate the corresponding monetary losses.

      Results

      We analyzed data from 107 patients employed in the last 4 weeks. Absenteeism was reported by 70% of patients; presenteeism was reported by all but one. Half of the patients reported a level of performance at work at least 50% below usual. Average number of hours per month lost to absenteeism and presenteeism was 43 and 51, respectively. The probability of any absenteeism was 17 percentage points lower in patients rating their mental health favorably compared with those rating it poorly (standard error [SE] 0.09; P < 0.10) and 19 percentage points higher in patients with at least one comorbidity compared with patients with none (SE 0.10; P < 0.10). All other covariates showed no significant associations on hours lost to absenteeism. Patients with favorable mental health self-ratings had 16.4 fewer hours per month of presenteeism compared with those with poor self-ratings (SE 4.52; P < 0.01). The 2015 monetary value of productivity losses amounted to US $840 million.

      Conclusions

      This study in a middle-income country confirms the high economic burden of depression. Health policies and workplace interventions ensuring adequate diagnosis and treatment of depression are recommended.

      Keywords

      Introduction

      Major depressive disorder (MDD) is an important cause of burden of disease worldwide because of its high prevalence, chronic course, and association with other medical problems [
      • Kessler R.C.
      • Bromet E.J.
      The epidemiology of depression across cultures.
      ]. Globally, in 2013 it was the fourth leading cause of disability-adjusted life-years in the population aged 18 to 59 years. MDD also has a large negative socioeconomic impact related to the condition’s adverse effect on educational attainment, life-cycle events (marital timing and stability, childbearing, and parenting), and role performance [
      • Kessler R.C.
      The costs of depression.
      ,
      • Butterworth P.
      • Rodgers B.
      Mental health problems and marital disruption: Is it the combination of husbands and wives’ mental health problems that predicts later divorce?.
      ,
      • Kessler R.C.
      • Foster C.L.
      • Saunders W.B.
      • Stang P.E.
      Social consequences of psychiatric disorders, I: educational attainment.
      ,
      • Kessler R.C.
      • Walters E.E.
      • Forthofer M.S.
      The social consequences of psychiatric disorders, III: probability of marital stability.
      ]. MDD is responsible for 5.1% of the population attributable fraction of days completely out of role, a combined measure of not being able to work or carry out normal activities [
      • Alonso J.
      • Petukhova M.
      • Vilagut G.
      • et al.
      Days out of role due to common physical and mental conditions: results from the WHO World Mental Health surveys.
      ], ranking fourth after pain disorders, headache/migraine, and cardiovascular disease. The impairment levels associated with MDD are higher compared with those associated with other severe chronic conditions such as cancer, diabetes, and heart disease [
      • Kessler R.C.
      The costs of depression.
      ].
      The economic consequences of occupational impairment due to depression are substantial. Fifty to 60% of the total economic burden of depression [
      • Greenberg P.E.
      • Kessler R.C.
      • Birnbaum H.G.
      • et al.
      The economic burden of depression in the United States: How did it change between 1990 and 2000?.
      ,
      • Löthgren M.
      Economic evidence in affective disorders: a review.
      ] is a result of patients not going to work (absenteeism) or from a decrease in their performance at work (presenteeism) [
      • Kessler R.C.
      The costs of depression.
      ]. Studies in developed countries have found that employees with depression lose 20% of total work time; 81% of these losses are accrued to presenteeism and 19% to absenteeism [
      • Stewart W.F.
      • Ricci J.A.
      • Chee E.
      • et al.
      Cost of lost productive work time among US workers with depression.
      ,
      • Kessler R.C.
      • Akiskal H.S.
      • Ames M.
      • et al.
      Prevalence and effects of mood disorders on work performance in a nationally representative sample of U.S. workers.
      ,
      • Lagerveld S.E.
      • Bültmann U.
      • Franche R.L.
      • et al.
      Factors associated with work participation and work functioning in depressed workers: a systematic review.
      ].
      In 2013, MDD accounted for 6.24% of the total disability-adjusted life-years in the population aged 18 to 59 years in Latin America and the Caribbean, ranking as the third leading cause of burden of disease [

      Institute for Health Metrics and Evaluation. GBD compare. 2015. Available from: http://vizhub.healthdata.org/gbd-compare. [Accessed February 1, 2016].

      ]. To our knowledge, no studies have quantified the impact of depression in the workplace in Latin America [
      • Hu T.
      Perspectives: an international review of the national cost estimates of mental illness, 1990–2003.
      ]. This information is key to strengthen the case for increasing prevention and access to better mental health services in the health policy agenda and in the initiatives to improve labor productivity. This study uses data from the Economic Burden of Depression Study (Carga Económica de la Depresión [CED]) conducted in Colombia [
      • Pinto-Masís D.
      • Gómez-Restrepo C.
      • Uribe-Restrepo M.
      • et al.
      La carga económica de la depresión en Colombia: costos directos del manejo intrahospitalario.
      ] to estimate productivity losses in employed patients with MDD, identify clinical and sociodemographic characteristics that may be correlated with these losses, and calculate their monetary value.

      Methods

       The CED Study

      The CED sought to estimate the economic costs of MDD and double depression (MDD plus dysthymia) in a convenience sample of six mental health facilities located in medium to large cities of Colombia [
      • Pinto-Masís D.
      • Gómez-Restrepo C.
      • Uribe-Restrepo M.
      • et al.
      La carga económica de la depresión en Colombia: costos directos del manejo intrahospitalario.
      ]. The CED was designed as a cost-of-illness, mixed-methods, multicenter study. It took place between June 2008 and June 2010 and was approved by the Ethics Committee of the Universidad Javeriana School of Medicine in Bogotá, Colombia. Methods and results of the CED are reported elsewhere [
      • Pinto-Masís D.
      • Gómez-Restrepo C.
      • Uribe-Restrepo M.
      • et al.
      La carga económica de la depresión en Colombia: costos directos del manejo intrahospitalario.
      ].
      The principal investigator coordinated all fieldwork. Patients who attended inpatient or outpatient services at each site were invited to participate in the study if they met the following eligibility criteria: they were aged between 18 and 65 years and were diagnosed with MDD or double depression according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders (4th ed., Text Revision) and the International Classification of Diseases, Tenth Revision. For diagnosis of MDD, the criteria require the presence of a core set of symptoms such as depressed mood, loss of interest or pleasure, and fatigue or low energy for at least 2 weeks, most of the time and on most of the days, and also a change from previous functioning [
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders.
      ,
      World Health Organization
      The ICD-10 Classification of Mental and Behavioural Disorders: Diagnostic Criteria for Research (DCR 10).
      ]. An additional set of symptoms defines the severity of the illness, such as changes in appetite, feelings of worthlessness, difficulty concentrating or indecisiveness, and thoughts of death. Dysthymia is characterized by chronic and persistent low mood that is not severe enough to meet the criteria for an MDD, although both conditions frequently occur simultaneously.
      Study objectives and procedures were fully explained and informed consent was obtained. Patients who were not able to answer the questionnaire (patients with mental retardation, active psychosis, or brain disorders compromising memory or cognition), retired persons, or patients not giving informed consent were excluded. Participants were recruited sequentially to reach a target sample of 295 patients (64 per site) divided into equal groups by sex and age, 18 to 45 years old and older than 45 years.
      Patients responded to a structured orally delivered questionnaire about socioeconomic characteristics, clinical outcomes, labor outcomes, and out-of-pocket costs of illness, applied by a trained medical resident, nurse, or psychologist. Responses were recorded in a paper form and subsequently entered by a survey technician into an electronic database. Data quality was ensured by a supervisor available on phone on a continuous basis to assist with fieldwork, who also conducted monthly site visits and checked questionnaires and the database for errors and inconsistencies.
      The questionnaire included the World Health Organization’s Health and Work Performance Questionnaire (HPQ), a short instrument assessing three main domains of workplace performance: absenteeism, presenteeism, and critical incidents [
      • Kessler R.C.
      • Ames M.
      • Hymel P.A.
      • et al.
      Using the World Health Organization Health and Work Performance Questionnaire (HPQ) to evaluate the indirect workplace costs of illness.
      ,
      • Kessler R.C.
      • Barber C.
      • Beck A.
      • et al.
      The World Health Organization Health and Work Performance Questionnaire (HPQ).
      ]. The HPQ has been shown to be a reliable and valid instrument [
      • Scuffham P.A.
      • Vecchio N.
      • Whiteford H.A.
      Exploring the validity of HPQ-based presenteeism measures to estimate productivity losses in the health and education sectors.
      ]. Absenteeism is measured by asking about hours and days of work the respondent missed because of illness during the past month. To measure absolute presenteeism, respondents rate their overall work performance during the previous 4 weeks on a 0 to 10 scale, 0 being “worst possible work performance” and 10 the “top work performance” a person could have in his job. Relative presenteeism is the ratio of the value of own performance and the value of the respondent’s rating of the usual performance of most workers in a similar occupation. An overall measure of lost work productivity is then calculated by summing absenteeism and absolute presenteeism converted into lost hours and days equivalents (percent of productivity multiplied by hours/days worked).

       Statistical Analysis of HPQ Data

      This study used data from the HPQ baseline interview, which was only for patients who reported being employed in the last 4 weeks (n = 133 out of 295). Of these, 26 observations were excluded, either because they had missing data about presenteeism or absenteeism (14 cases) or because of extreme values for expected or worked hours (12 cases). Thus, the final study sample comprises 107 patients. The sociodemographic characteristics of the excluded cases were very similar to the rest of the sample, and so we do not expect selection issues to arise.
      We conducted descriptive statistics for dependent and independent variables. To examine the relationship of health and sociodemographic characteristics with absenteeism and presenteeism, we used two separate analytic approaches. For absenteeism, we used a two-part model given that 30% of the sample did not report any absenteeism and that the distribution was right-skewed. The first part of the model consisted of a probit regression model with marginal effects on whether the individual experienced any absenteeism; the second part was an ordinary least-squares regression on the number of hours of absenteeism conditional on having any nonzero hours of absenteeism. This is a conventional approach to estimate costs and demand for health services (i.e., outcomes with censored distributions) allowing the researcher to use parametric models with a seemingly normal conditional distribution (the second part) [
      • Pohlmeier W.
      • Ulrich V.
      An econometric-model of the 2-part decision-making process in the demand for health-care.
      ]. The effect of our set of independent variables on hours of presenteeism was analyzed using linear regression, because only one individual reported 0 hours of presenteeism, and therefore the distribution of presenteeism was approximately normal (skewness = 0.04). To ensure that the distributional assumptions of the model were not driving the results, we ran sensitivity analyses using a generalized linear model specification for the second part of the absenteeism model and for the presenteeism model. Results from the generalized linear model specification are not different from those of the linear specification. These are available from the authors on request.
      The regressions included two different sets of independent variables. The first set included demographic and health characteristics (age, sex, and mental health self-rating as average to very good/poor or very poor) and a binary variable accounting for having at least one nonmental comorbidity. The second set accounted for socioeconomic variables: employment status (full-time/part-time/self-employed) and education level (less than university or technical education/incomplete university or technical education/university-level education).

       Monetary Value of Lost Productivity

      We calculated the possible annual monetary value of productivity losses (A, in Colombian pesos [Col$]) due to absenteeism and presenteeism for the five cities in the study by constructing the following costing model:
      Aijk=TWPDijk×Wij×Eik×(Hijk×Sik)×P


      For every age group i, sex j, and city k, TWPD denotes the total working population with moderate to severe depression on the basis of the 2010 census projections of population for each city and estimates of prevalence of moderate to severe depression from a National Mental Health Survey [
      • Gómez-Restrepo C.
      • Bohórquez A.
      • Pinto Masis D.
      • et al.
      Prevalencia de depresión y factores asociados con ella en la población colombiana.
      ,

      Ministerio de la Protección Social. Estudio Nacional de Salud Mental-Colombia 2003. 2005. Available from: http://onsm.ces.edu.co/uploads/files/1243030_EstudioNacionalSM2003.pdf. Accessed April 18, 2014.

      ,

      DANE (Departamento Administrativo Nacional de Estadística de Colombia) (Colombian National Administrative Department of Statistics). Gran Encuesta Integrada de Hogares. 2010. Available from: http://www.dane.gov.co/index.php/ocupacion-y-empleo/gran-encuesta-integrada-de-hogares. [Accessed April 18, 2014].

      ]. W represents the employment rate and E the proportion of workers in the formal/informal sector reported in labor market household surveys representative for each city [

      DANE (Departamento Administrativo Nacional de Estadística de Colombia) (Colombian National Administrative Department of Statistics). Gran Encuesta Integrada de Hogares. 2010. Available from: http://www.dane.gov.co/index.php/ocupacion-y-empleo/gran-encuesta-integrada-de-hogares. [Accessed April 18, 2014].

      ]. H stands for total hours of work lost due to absenteeism and presenteeism per month estimated in this study, S represents the mean value of labor earnings per hour [

      DANE (Departamento Administrativo Nacional de Estadística de Colombia) (Colombian National Administrative Department of Statistics). Gran Encuesta Integrada de Hogares. 2010. Available from: http://www.dane.gov.co/index.php/ocupacion-y-empleo/gran-encuesta-integrada-de-hogares. [Accessed April 18, 2014].

      ], and P introduces to the equation the average number of days per year with depression [
      • Kessler R.C.
      • Akiskal H.S.
      • Ames M.
      • et al.
      Prevalence and effects of mood disorders on work performance in a nationally representative sample of U.S. workers.
      ]. The A values were converted to US dollars at the 2015 exchange rate. Detailed calculations and parameters are available from the corresponding author on request.

      Results

       Study Sample

      Absenteeism was reported by 70% of the sample, and 5% reported working extra hours. Absolute presenteeism was reported by all but one patient. More than half the sample (50.4%) perceived their own performance at work during the last month at least 50% below the usual level, and only 9.7% reported a performance of 80% or more. Regarding relative presenteeism, 73.6% of the patients reported performance levels being inferior to average workers in the same job. The mean number of hours of work lost in the last month by patients with absenteeism was 43. Almost 40% of these patients missed 7 or more days of work. On average, patients with presenteeism lost 51 hours of work in the last month, and more than half of expected work hours (out of 165 expected hours per month) were lost because of both absenteeism and presenteeism.
      Table 1 provides summary statistics of the sociodemographic and health characteristics of the variables involved in the regression models. Table 2, Table 3, Table 4 present results of the two-part model for absenteeism. The first part of the model including the whole set of independent variables showed that the probability of absenteeism is 17 percentage points lower (standard error [SE] 0.09; P < 0.10) in patients self-rating their mental health as very good/good compared with those rating it as fair/poor. Also, the probability of absenteeism was 19 percentage points higher in patients with at least one comorbidity compared with patients with none (SE 0.10; P < 0.10). The second part of the absenteeism regression model showed no significant relationships of the covariates.
      Table 1Sample descriptive variables
      Dependent variablesPercentage/N (n = 107)
      Patients reporting absenteeism70%
      Number of hours of absenteeism per month43
      Patients reporting presenteeism99%
      Number of hours of presenteeism per month55
      Demographic and health variablesPercentage/N (n = 107)
      Female57%
      Age (y)42
      Mental health
       Poor or very31%
       Average to very good69%
      Comorbidity
       No comorbidity39%
       At least one comorbidity61%
      Socioeconomic variablesPercentage/N (n = 107)
      Employment
       Full-time54%
       Part-time7%
       Self-employed39%
      Education
       Less than university or technical education54%
       Incomplete university/technical education24%
       University-level education22%
      Table 2Marginal effects on the probability of absenteeism during the last month
      VariablesControlling for demographics and healthAll control variables
      CoefficientStandard errorCoefficientStandard error
      Female−0.050.09−0.060.10
      Age0.000.000.000.00
      Mental health average to very good−0.170.08
      Significance at 5.00%.
      −0.170.09
      Significance at 10.00%.
      At least one comorbidity0.190.10
      Significance at 10.00%.
      0.190.10
      Significance at 10.00%.
      Full-time employmentReferenceReference
      Part-time employment0.080.18
      Self-employed−0.020.10
      Less than university or technical educationReference
      Incomplete university/technical education−0.150.12
      University-level education−0.150.13
      Significance at 1.00%.
      low asterisk Significance at 5.00%.
      Significance at 10.00%.
      Table 3OLS on the hours of absenteeism during the last month
      VariablesControlling for demographic characteristics and healthAll control variables
      CoefficientStandard errorCoefficientStandard error
      Female−4.4811.99−1.1212.38
      Age−0.140.53−0.370.59
      Mental health average to very good8.2312.292.1613.39
      At least one comorbidity11.3912.5610.0412.98
      Full-time employmentReferenceReference
      Part-time employment34.6027.64
      Self-employed7.6013.46
      Less than university or technical educationReferenceReference
      Incomplete university/technical education8.3115.25
      University-level education4.6916.36
      Constant63.5026.28
      Significance at 1.00%.
      72.1429.30
      Significance at 1.00%.
      Significance at 10.00%.
      Significance at 5.00%.
      low asterisk Significance at 1.00%.
      Table 4OLS on the hours of presenteeism during the last month
      VariablesControlling for demographic characteristics and healthAll control variables
      CoefficientStandard errorCoefficientStandard error
      Female−1.684.29−0.324.48
      Age0.030.190.000.20
      Mental health average to very good−17.124.51
      Significance at 1.00%.
      −16.444.74
      Significance at 1.00%.
      At least one comorbidity3.974.345.474.64
      Full-time employmentReferenceReference
      Part-time employment0.909.44
      Self-employed2.994.62
      Less than university or technical educationReferenceReference
      Incomplete university/technical education-6.015.33
      University-level education8.585.44
      Constant40.249.41
      Significance at 1.00%.
      37.3310.02
      Significance at 1.00%.
      Significance at 10.00%.
      Significance at 5.00%.
      low asterisk Significance at 1.00%.
      The results from the presenteeism model are presented in Table 4. Ordinary least-squares models of hours lost to presenteeism showed that patients self-rating their mental health favorably lose 16 fewer hours per month (SE 4.74; P < 0.01) than patients rating it as fair/poor.
      Our estimations of the value of the productivity losses due to depression in the five study cities show that the workforce losses reach 259 million work hours per year, equivalent to 32 million 8-hour work days per year. The monetary value of these losses is Col$1.6 trillion, equivalent to US $840 million when converted at the 2015 exchange rate.

      Discussion

      Our sample of depressed employed patients lost at least half of their work hours. The results also show that the relative contribution of absenteeism and presenteeism in terms of lost hours is equivalent. This differs from previous studies that find that presenteeism can contribute from 27% to 81% of total indirect costs of depression [
      • Greenberg P.E.
      • Kessler R.C.
      • Birnbaum H.G.
      • et al.
      The economic burden of depression in the United States: How did it change between 1990 and 2000?.
      ,
      • Stewart W.F.
      • Ricci J.A.
      • Chee E.
      • et al.
      Cost of lost productive work time among US workers with depression.
      ,
      • Schultz A.B.
      • Edington D.W.
      Employee health and presenteeism: a systematic review.
      ]. Overall, in our study the percentage of total hours lost is also higher compared with other settings, perhaps reflecting the fact that the sample included more severely depressed patients, as assessed by the Beck Depression Inventory. Similar results have, however, been reported by Bouwmans et al. [
      • Bouwmans C.A.
      • Vemer P.
      • van Straten A.
      • et al.
      Health-related quality of life and productivity losses in patients with depression and anxiety disorders.
      ] who found that in a working population with diagnosis of depression, anxiety, or both, more than half of the patients had long-term absenteeism (more than 2 weeks absent from work) and 31% had presenteeism. Absenteeism in Bouwmans’ study was on average 109 calendar days (3.6 months) per year. The higher losses in our study might relate to less regulated sick leaves and lower salaries, and hence more opportunity costs of absence in the Colombian labor market compared with higher income countries.
      The potentially large value of the productivity losses due to depression derived from our estimates in five cities provides an argument to seek opportunities for savings and to attenuate the negative economic impact of depression through adequate prevention, provision of full access to good-quality mental health services, and measures to decrease mental illness stigma. MDD is undertreated worldwide, even more so in low- and middle-income countries (LMICs) [
      • Wang P.S.
      • Aguilar-Gaxiola S.
      • Alonso J.
      • et al.
      Use of mental health services for anxiety, mood, and substance disorders in 17 countries in the WHO world mental health surveys.
      ]. In Colombia, the National Mental Health Survey found that only 14.2% of patients with mood disorders had used some kind of mental health service in the last year and only 2.2% consulted a psychiatrist [

      Ministerio de la Protección Social. Estudio Nacional de Salud Mental-Colombia 2003. 2005. Available from: http://onsm.ces.edu.co/uploads/files/1243030_EstudioNacionalSM2003.pdf. Accessed April 18, 2014.

      ].
      Studies in developed settings have also found that poor mental health and comorbidity are factors positively related to losses in productivity. For example, using a community health survey in Canada, Bielecky et al. [
      • Bielecky A.
      • Chen C.
      • Ibrahim S.
      • et al.
      The impact of co-morbid mental and physical disorders on presenteeism.
      ] found that comorbid physical and mental disorders had an additive effect on presenteeism. In LMICs, where an important percentage of the population is self-employed and earns low wages, the presence of depression may increase the vulnerability to economic hardship and may contribute to the cycle of poverty, as has been the case for people suffering from mental illness elsewhere [
      • Patel V.
      • Kleinman A.
      Poverty and common mental disorders in developing countries.
      ]. The association between poverty and common mental disorders, including depression, has been a strong finding across numerous studies in LMICs [
      • Lund C.
      • Breen A.
      • Flisher A.J.
      • et al.
      Poverty and common mental disorders in low and middle income countries: a systematic review.
      ].
      Although the factors mediating the mutual influence of poverty and mental illness are complex, the inter-relation of work and mental health makes a case to complement mental health policies aimed at the general population with workplace interventions. Economic evaluations in developed countries show that treatment for depression in the workplace with pharmacotherapy, psychotherapy, or their combination is cost-effective from a societal perspective as well as cost-saving to employers [
      • Wang P.S.
      • Patrick A.
      • Avorn J.
      • et al.
      The costs and benefits of enhanced depression care to employers.
      ,
      • Evans-Lacko S.
      • Koeser L.
      • Knapp M.
      • et al.
      Evaluating the economic impact of screening and treatment for depression in the workplace.
      ]. In addition, a randomized clinical trial showed that a work-focused intervention that included telephone-based counseling, integrating care coordination, cognitive-behavioral therapy strategy development, and work coaching was more effective than usual care in decreasing productivity losses in workers with moderate and severe depression [
      • Lerner D.
      • Adler D.A.
      • Rogers W.H.
      • et al.
      A randomized clinical trial of a telephone depression intervention to reduce employee presenteeism and absenteeism.
      ]. The benefit-to-cost ratio of the intervention was $6.19 for every $1 spent.
      From a research perspective, the study supports including patient-reported outcomes, such as work productivity, as an additional aspect to evaluate treatment effectiveness. These outcomes may also be an important input for more comprehensive economic evaluations of depression management.
      Several limitations of this study should be noted. First, because the findings are derived from a convenience sample, they may not be representative of the working population in the five cities in which the study took place. Nevertheless, our sample’s sociodemographic and employment characteristics are similar to those reported by national labor market surveys. Second, the data on presenteeism and absenteeism are self-reported, although validation studies of the HPQ have documented strong relationships of HPQ measures with independent payroll records and supervisor evaluations of job performance [
      • Scuffham P.A.
      • Vecchio N.
      • Whiteford H.A.
      Exploring the validity of HPQ-based presenteeism measures to estimate productivity losses in the health and education sectors.
      ]. Third, our patients were selected from secondary-level mental health facilities and may have a more severe illness than those consulting first-level care services and therefore higher absenteeism and presenteeism. Fourth, the relatively small sample of the study restricts its power to identify significant differences in productivity losses associated with characteristics expected to have an effect. Finally, the accuracy of our calculation of the monetary value of lost productivity in the five study sites is subject to the limitations of the parameter data sources and thus provides a preliminary approximation that should motivate further efforts toward quantifying the economic burden of depression.

      Conclusions

      Given that MDD was the second leading cause of burden of disease in 2013 [

      Institute for Health Metrics and Evaluation. GBD compare. 2015. Available from: http://vizhub.healthdata.org/gbd-compare. [Accessed February 1, 2016].

      ] in Colombia, our results indicate that the negative impact of depression on labor productivity in Colombia can be substantial. From a public health perspective, improving access to care and increasing the detection and management of depression in primary care settings are key strategies that need to be implemented on a larger scale in countries such as Colombia. Finally, workplace interventions targeting depression may complement primary care strategies to decrease the personal and economic negative impact of depression.
      Source of financial support: The original study was funded by Colciencias, Colombia (grant no. 326-2007).

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