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The American Journal of Managed Care August 2014
Personalized Preventive Care Reduces Healthcare Expenditures Among Medicare Advantage Beneficiaries
Shirley Musich, PhD; Andrea Klemes, DO, FACE; Michael A. Kubica, MBA, MS; Sara Wang, PhD; and Kevin Hawkins, PhD
Impact of Hypertension on Healthcare Costs Among Children
Todd P. Gilmer, PhD; Patrick J. O'Connor, MD, MPH; Alan R. Sinaiko, MD; Elyse O. Kharbanda, MD, MPH; David J. Magid, MD, MPH; Nancy E. Sherwood, PhD; Kenneth F. Adams, PhD; Emily D. Parker, MD, PhD; and Karen L. Margolis, MD, MPH
Tracking Spending Among Commercially Insured Beneficiaries Using a Distributed Data Model
Carrie H. Colla, PhD; William L. Schpero, MPH; Daniel J. Gottlieb, MS; Asha B. McClurg, BA; Peter G. Albert, MS; Nancy Baum, PhD; Karl Finison, MA; Luisa Franzini, PhD; Gary Kitching, BS; Sue Knudson, MA; Rohan Parikh, MS; Rebecca Symes, BS; and Elliott S. Fisher, MD
Potential Role of Network Meta-Analysis in Value-Based Insurance Design
James D. Chambers, PhD, MPharm, MSc; Aaron Winn, MPP; Yue Zhong, MD, PhD; Natalia Olchanski, MS; and Michael J. Cangelosi, MA, MPH
Massachusetts Health Reform and Veterans Affairs Health System Enrollment
Edwin S. Wong, PhD; Matthew L. Maciejewski, PhD; Paul L. Hebert, PhD; Christopher L. Bryson, MD, MS; and Chuan-Fen Liu, PhD, MPH
Contemporary Use of Dual Antiplatelet Therapy for Preventing Cardiovascular Events
Andrew M. Goldsweig, MD; Kimberly J. Reid, MS; Kensey Gosch, MS; Fengming Tang, MS; Margaret C. Fang, MD, MPH; Thomas M. Maddox, MD, MSc; Paul S. Chan, MD, MSc; David J. Cohen, MD, MSc; and Jersey Chen, MD, MPH
Potential Benefits of Increased Access to Doula Support During Childbirth
Katy B. Kozhimannil, PhD, MPA; Laura B. Attanasio, BA; Judy Jou, MPH; Lauren K. Joarnt; Pamela J. Johnson, PhD; and Dwenda K. Gjerdingen, MD
Synchronization of Coverage, Benefits, and Payment to Drive Innovation
Annemarie V. Wouters, PhD; and Nancy McGee, JD, DrPH
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The Effect of Depression Treatment on Work Productivity
Arne Beck, PhD; A. Lauren Crain, PhD; Leif I. Solberg, MD; Jürgen Unützer, MD, MPH; Michael V. Maciosek, PhD; Robin R. Whitebird, PhD, MSW; and Rebecca C. Rossom, MD, MSCR
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Lindsay B. Kimbro, MPP; Jinnan Li, MPH; Norman Turk, MS; Susan L. Ettner, PhD; Tannaz Moin, MD, MBA, MSHS; Carol M. Mangione, MD; and O. Kenrik Duru, MD, MSHS
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The Effect of Depression Treatment on Work Productivity

Arne Beck, PhD; A. Lauren Crain, PhD; Leif I. Solberg, MD; Jürgen Unützer, MD, MPH; Michael V. Maciosek, PhD; Robin R. Whitebird, PhD, MSW; and Rebecca C. Rossom, MD, MSCR
This study demonstrated that reduction of depression symptoms following routine treatment in primary care is significantly associated with improvements in productivity at work.


Depression is associated with lowered work functioning, including absence, productivity impairment at work, and decreased job retention. Although high-quality depression treatment provided in clinical trials has been found to reduce symptoms and improve work function, the effectiveness of routine treatment for depression in primary care has received less attention.

Study Design 
This prospective cohort study investigated the relationship between improvements in both depression symptoms and productivity in outpatients from 77 clinics in Minnesota following routine depression treatment.


Data were obtained from patients receiving usual care for depression prior to initiation of a statewide quality improvement collaborative called DIAMOND (Depression Improvement Across Minnesota: Offering a New Direction). Patients started on antidepressants were surveyed on depression symptom severity (Patient Health Questionnaire [PHQ-9]), productivity loss (Work Productivity and Activity Impairment questionnaire [WPAI]), health status, and demographics. Data were collected again 6 months later to assess changes in depression symptoms and productivity.


Data from 432 employed patients with complete baseline and outcome data showed significant reductions in depression symptoms and increases in productivity (P < .0001) over 6 months. Greater improvements in productivity at 6 months were associated with greater improvement in depression symptoms as well as with greater depression severity (P < .0001) and poorer productivity (P < .0001) at baseline.

This study demonstrated a significant relationship betweenimprovement in depression symptoms and improvements in productivity following routine primary care depression treatment. These findings underscore the benefit of depression care to improve work outcomes and to yield a potential return on healthcare investment to employers.

Am J Manag Care. 2014;20(8):e294-e301

Routine treatment of depression in primary care settings is effective in significantly reducing depression symptoms and improving productivity at work.

  • Although high-quality depression treatment provided in clinical trials has been found to reduce symptoms and improve work function, the effectiveness of routine treatment for depression in primary care has received less attention.
  • Patients with more significant baseline depression symptoms and productivity loss improved the most on these measures 6 months after treatment.
  • Productivity improvements at 6 months were greatest for patients showing response or remission following depression treatment.
  •  Employers may realize a positive return on investment for depression care based on productivity gains following depression treatment
Depression is prevalent and is associated with such indirect costs as increased work absence, impaired productivity while at work, and decreased job retention across a wide variety of occupations.1-4 In addition, several studies have shown that even minor or subthreshold depression (including dysthymia) is related to lowered work performance.5-7

Fortunately, high-quality depression treatment has been found to reduce symptoms, to improve work function, and to be cost-effective.8-14 Much of this evidence comes from clinical trials or cross-sectional studies of the effectiveness of antidepressants15,16 or depression-care management interventions.17 Aikens et al18 analyzed trajectories of improvement in depressive symptoms and work function (among other patient-reported outcomes) following antidepressant treatment and found that work performance improves in proportion to depression symptom remission. Results from the study by Woo et al of Korean employees diagnosed with major depressive disorder showed that their depressive symptoms and lost productive time decreased significantly after 8 weeks of antidepressant treatment.19 Randomized trials of non-pharmacologic enhanced depression-care management also demonstrated improved symptom and work function following the interventions.8,10

Despite the encouraging findings of work function improving with depression symptom remission, less is known about this relationship in primary care settings that are not involved in clinical trials, though recently published work does suggest that collaborative care for depression is associated with symptom remission and improvement in work function.20 The goal of the present study was to investigate the relationship between changes in depression symptom severity and changes in productivity loss following routine outpatient depression treatment provided to a large sample of patients receiving care at 77 clinics in Minnesota.



Data were obtained from patients participating in the DIAMOND (Depression Improvement Across Minnesota, Offering a New Direction) Study, an evaluation of a statewide depression quality improvement initiative in Minnesota that included 88 clinics from 23 medical groups. Details on the study design and methods have been published elsewhere.21 The results presented here represent baseline and 6-month outcome data for patients who received usual care for depression at 77 of the these clinics prior to implementation of the DIAMOND program.

Patient Recruitment and Enrollment

All patients with health plan claims data showing them to be newly started on antidepressant medications at one of the participating clinics were identified on a weekly basis by the health plans and sent a letter about the study, providing a 1-week opportunity to opt out before being called by the research survey center to determine eligibility for participation and to complete a baseline survey by phone. Patients were eligible if they were 18 years or older, had filled a new antidepressant prescription (and none in the prior 4 months) from a primary care clinician at one of the participating clinics for the treatment of depression, and had a depression symptom severity score of 7 or greater on the Patient Health Questionnaire 9-item screen (PHQ-9).22 Employment was not an eligibility criterion for patient participation in the larger DIAMOND Study, so for the purpose of this analysis, we included only the subset of patients employed for wages at least part-time at baseline and 6 months, and who had baseline and 6-month data on both the PHQ-9 and the Work Productivity and Activity Impairment Questionnaire (WPAI),2 the measure used to assess productivity loss. Data from the baseline and 6-month surveys were analyzed to assess changes in depression symptoms and productivity loss following treatment. The study protocol was reviewed, approved, and monitored by the HealthPartners Institutional Review Board.


Patient self-report surveys were used to provide information on depression severity, work absence, productivity impairment, and health status (a single item asking patients to rate their overall health), as well as demographic characteristics including employment status. The PHQ-9, widely accepted as a valid measure of depression severity, was used to measure the severity of depression symptoms.22,24-26 The PHQ-9 yields a continuous score from 0-27 with cut points representing mild (5), moderate (10), moderately severe (15), and severe (20) depression, respectively.

Questions about work function were obtained from the (WPA), a self-report measure of the amount of absence from work due to health problems, as well as productivity impairment while at work (“presenteeism”) during the previous 7 days.23,27,28 Percentage of work time missed due to health, a measure of absenteeism, was calculated as the hours missed during the previous 7 days divided by the hours missed plus the hours worked during this period. Percentage of impairment while working due to health, a measure of presenteeism, was calculated as a 10-point rating of degree of impairment while at work divided by 10. The number of hours of productivity impairment at work was calculated as the hours actually worked multiplied by the percent impairment while at work. The proportion of expected work time that was missed or affected by health problems over the previous 7 days (productivity loss) was calculated as the percent of work time missed plus the percent of time at work multiplied by impairment while there. Note that this value is not the sum of absenteeism plus presenteeism, because the latter only includes hours actually at work.

Statistical Analysis

Descriptive measures of central tendency and dispersion characterized participants included in the analytic data set. Within-person change from baseline to 6 months in productivity and depression symptoms was estimated by fitting 2 linear mixed models (PROC MIXED, SAS version 9.1.3, that used stabilized inverse probability weights to control for differences between characteristics of the study-eligible sample and the analytic dataset (details below). Each model predicted baseline and 6 month PHQ-9 (or WPAI) observations from each participant with the significance of an indicator variable for time denoting whether depression symptoms (or productivity) were significantly different at 6 months relative to baseline.

The primary analyses for this study examined the relationship between change in depression symptoms and change in productivity loss from baseline to 6 months after treatment initiation for depression. A variance components model nested patients within clinics to estimate the clinic intraclass correlation (ICC) of WPAI change scores. The clinic ICC solved to zero, indicating no significant clustering of patients within clinics. The primary analysis was then carried out using a weighted general linear model (PROC GLM) in which WPAI change was predicted from PHQ-9 change, baseline WPAI and PHQ- 9, self-reported functional health status and demographic characteristics (age, sex, racial or ethnic minority status, education, part-time employment status, and marital status). Omitting the functional health status and demographic covariates resulted in a pattern of results similar to those reported. Interaction terms between the productivity loss and depression symptom's main effects, and between main effects and covariates, were also estimated. None of these terms were found to be significant, and they were eliminated from the reported model. To be included in the primary analysis, participants had to have completed both the baseline and 6-month survey, be employed at least part-time at baseline and 6 months, and have provided depression and employment data at both time points. Chi-square statistics revealed significant differences in the demographic characteristics of the study-eligible participants who met and failed to meet each of these criteria.

Three-nonparsimonious logistic regression models (propensity models) were estimated in order to derive each participant’s likelihood of 6-month survey completion (n = 537 completed both surveys, n = 234 completed baseline only), of being employed at 6 months (n = 491 employed at baseline and 6 months, n = 46 employed at baseline only), and of providing complete depression and employment data at baseline and 6 months (n = 432 provided both, n = 59 had missing data), given demographic characteristics. The first propensity model revealed that participants who were older, in better health, had attained more education, and were currently married were more likely to complete the 6-months survey, while those who had never been married or were separated were less likely to complete the 6-month survey. The second propensity model found that participants with commercial insurance were more likely, and participants with 6-month PHQ-9 scores of 15 to 19 were less likely, to still be employed at 6 months. The third model found that participants with lower PHQ-9 scores at 6 months, no additional depression treatment in the past 6 months, and having less than a high school education or having a college education were more likely to provide complete data in both surveys.

One set of stabilized inverse probability weights (IPWs) was calculated based on the propensity scores derived from each of these models. The product of 3 IPWs was used as a weight in the primary analytic models so that the participants included in the analytic data set (n = 432) would be representative of those who were study-eligible (n = 771).


During a 36-month period, from February 2008 to January 2011, 161 patients were screened for study eligibility. The reasons for ineligibility were having a PHQ-9 score less than 7 (n = 723) self reporting that the antidepressant fill was not for the treatment of depression (n = 481), inability to complete the screener (n = 420), being treated in a nonstudy clinic (n = 247), and not recalling an antidepressant fill (n = 110). The study enrollment data are shown in Table 1, indicating that 168 patients receiving usual care for their depression were contacted, assessed for eligibility,the patients were not consented, and enrolled.

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