ArticlePDF Available

Cost of Lost Productive Work Time Among US Workers With Depression

Authors:

Abstract and Figures

Evidence consistently indicates that depression has adversely affected work productivity. Estimates of the cost impact in lost labor time in the US workforce, however, are scarce and dated. To estimate the impact of depression on labor costs (ie, work absence and reduced performance while at work) in the US workforce. All employed individuals who participated in the American Productivity Audit (conducted August 1, 2001-July 31, 2002) between May 20 and July 11, 2002, were eligible for the Depressive Disorders Study. Those who responded affirmatively to 2 depression-screening questions (n = 692), as well as a 1:4 stratified random sample of those responding in the negative (n = 435), were recruited for and completed a supplemental interview using the Primary Care Evaluation of Mental Disorders Mood Module for depression, the Somatic Symptom Inventory, and a medical and treatment history for depression. Excess lost productive time (LPT) costs from depression were derived as the difference in LPT among individuals with depression minus the expected LPT in the absence of depression projected to the US workforce. Estimated LPT and associated labor costs (work absence and reduced performance while at work) due to depression. Workers with depression reported significantly more total health-related LPT than those without depression (mean, 5.6 h/wk vs an expected 1.5 h/wk, respectively). Eighty-one percent of the LPT costs are explained by reduced performance while at work. Major depression accounts for 48% of the LPT among those with depression, again with a majority of the cost explained by reduced performance while at work. Self-reported use of antidepressants in the previous 12 months among those with depression was low (<33%) and the mean reported treatment effectiveness was only moderate. Extrapolation of these survey results and self-reported annual incomes to the population of US workers suggests that US workers with depression employed in the previous week cost employers an estimated 44 billion dollars per year in LPT, an excess of 31 billion dollars per year compared with peers without depression. This estimate does not include labor costs associated with short- and long-term disability. A majority of the LPT costs that employers face from employee depression is invisible and explained by reduced performance while at work. Use of treatments for depression appears to be relatively low. The combined LPT burden among those with depression and the low level of treatment suggests that there may be cost-effective opportunities for improving depression-related outcomes in the US workforce.
Content may be subject to copyright.
ORIGINAL CONTRIBUTION
Cost of Lost Prod uctive W ork Time
Among US W orkers With Depression
Walter F. Stewart, PhD, MPH
Judith A. Ricci, ScD, MS
Elsbeth Chee, ScD
Steven R. Hahn, MD
David Morganstein, MS
H
EALTH CONDITIONS THAT
affect ability to work are
costly to employers (unpub-
lished data; an electronic
manuscript, Lost Productive Work Time
Costs From Health Conditions in the US:
Results From the American Productivity
Audit, is available from W.F.S. on
request). Evidence consistently indi-
cates that common conditions includ-
ing migraine,
1-8
low back pain,
9,10
dia-
betes,
11,12
allergic rhinitis,
5,13-18
gastroesophageal reflux,
19-21
and de-
pression
5,10,22-25
dominate health-
related lost labor time costs. Among
these, depression is among the most
costly because it is highly prevalent and
comorbid with other conditions. Fur-
thermore, although workers with de-
pression are usually present at work,
their performance can be substan-
tially reduced.
Model-based estimates indicate that
depression costs US employers $24 bil-
lion annually in lost productive work
time.
23
However, some notable limita-
tions challenge the relevance of this and
other estimates. Using a human capi-
tal approach, this model makes impor-
tant assumptions regarding the preva-
lence of depression in the workforce,
the duration of depressive episodes,
their imputed impact on productive
time at work, and the cost to employ-
ers. Furthermore, although stated in
1990 terms, the cost estimate is based
on data collected in the early to mid-
1980s. The management and treat-
ment of depression has changed sub-
stantially since the 1980s; use of
pharmaceutical care and, more gener-
ally, access to care have increased
26
and
may have influenced disability status
and how work time is lost.
Author Affiliations: AdvancePCS Center for Work and
Health, Hunt Valley, Md (Drs Stewart, Ricci, and Chee);
Outcomes Research Institute, Geisinger Health Sys-
tems, Danville, Pa (Dr Stewart); Albert Einstein Col-
lege of Medicine, Bronx, NY (Dr Hahn); WESTAT,
Rockville, Md (Mr Morganstein).
Corresponding Author and Reprints: Walter F. Stew-
art, PhD, MPH, Outcomes Research Institute, Geis-
inger Health Systems, 100 N Academy Ave, Danville,
PA 17822-3030 (e-mail: wfstewart@geisinger.edu).
Context Evidence consistently indicates that depression has adversely affected work
productivity. Estimates of the cost impact in lost labor time in the US workforce, how-
ever, are scarce and dated.
Objective To estimate the impact of depression on labor costs (ie, work absence
and reduced performance while at work) in the US workforce.
Design, Setting, and Participants All employed individuals who participated in
the American Productivity Audit (conducted August 1, 2001–July 31, 2002) between
May 20 and July 11, 2002, were eligible for the Depressive Disorders Study. Those
who responded affirmatively to 2 depression-screening questions (n=692), as well as
a 1:4 stratified random sample of those responding in the negative (n=435), were
recruited for and completed a supplemental interview using the Primary Care Evalu-
ation of Mental Disorders Mood Module for depression, the Somatic Symptom In-
ventory, and a medical and treatment history for depression. Excess lost productive
time (LPT) costs from depression were derived as the difference in LPT among indi-
viduals with depression minus the expected LPT in the absence of depression pro-
jected to the US workforce.
Main Outcome Measure Estimated LPT and associated labor costs (work
absence and reduced performance while at work) due to depression.
Results Workers with depression reported significantly more total health-related LPT
than those without depression (mean, 5.6 h/wk vs an expected 1.5 h/wk, respec-
tively). Eighty-one percent of the LPT costs are explained by reduced performance while
at work. Major depression accounts for 48% of the LPT among those with depres-
sion, again with a majority of the cost explained by reduced performance while at work.
Self-reported use of antidepressants in the previous 12 months among those with de-
pression was low (30%) and the mean reported treatment effectiveness was only
moderate. Extrapolation of these survey results and self-reported annual incomes to
the population of US workers suggests that US workers with depression employed in
the previous week cost employers an estimated $44 billion per year in LPT, an excess
of $31 billion per year compared with peers without depression. This estimate does
not include labor costs associated with short- and long-term disability.
Conclusions A majority of the LPT costs that employers face from employee
depression is invisible and explained by reduced performance while at work. Use of
treatments for depression appears to be relatively low. The combined LPT burden
among those with depression and the low level of treatment suggests that there
may be cost-effective opportunities for improving depression-related outcomes in
the US workforce.
JAMA. 2003;289:3135-3144 www.jama.com
©2003 American Medical Association. All rights reserved. (Reprinted) JAMA, June 18, 2003—Vol 289, No. 23 3135
Downloaded From: http://jama.jamanetwork.com/ by a Albert Einstein College of Medicine User on 02/20/2015
The American Productivity Audit was
initiated to more directly understand the
relation between health and lost pro-
ductive time (LPT) in the US work-
force (W.F.S., unpublished data). We de-
scribe the results of a supplemental study
to the productivity audit that focused on
depression in the US workforce.
METHODS
The productivity audit was completed
using the Work and Health Interview
(Ricci et al
27
and unpublished data; 2
electronic manuscripts, The Work and
Health Interview and Validation of the
Work and Health Phone Interview, are
available from W.F.S. on request). A
supplemental study, the Depressive Dis-
orders Study, was conducted in a ran-
dom sample of audit participants to
more accurately estimate the LPT costs
due to depression.
Work and Health Interview
The Work and Health Interview, a com-
puter-assisted telephone interview, cap-
tures data on work absence, reduced
performance while at work, and health-
related causes. The recall period is 2
weeks. The interview comprises 8 mod-
ules. The first 3 capture detailed data
on employment status, occupation and
usual work time, and the presence of
22 different health conditions. The
health assessment includes 1 of 2 de-
pression screening questions (ie, “In the
past 2 weeks, did you feel sad, blue, or
down in the dumps?”) that were used
to identify random samples of individu-
als with and without probable depres-
sion for the Depressive Disorders Study.
A module for missed days of work cap-
tured missed workdays and the re-
lated cause. A module for job visual-
ization asked about activities performed
at work and about job demand and con-
trol.
28
The module for LPT on days at
work asked about missed hours and re-
duced performance on workdays and
the related cause. The demographics
module gathered additional informa-
tion, including annual salary.
Health-related LPT, described in de-
tail elsewhere (Ricci et al
27
and unpub-
lished data, available from W.F.S. on
request), was defined as the sum of
hours per week absent from work for
a health-related reason (“absentee-
ism”) and the hour-equivalents per
week of health-related reduced perfor-
mance on workdays (“presentee-
ism”). Absenteeism was calculated as
the sum of missed workdays (ie, mul-
tiplied by average number of hours
worked per day) and reduced work
hours on workdays (ie, late start, early
departure, or missed time during the
workday) during the recall period. Pre-
senteeism was defined as reduced work
performance during the recall period.
It was quantified by responses to 6 ques-
tions on specific work behaviors.
For 5 of the 6 questions, respon-
dents were asked how often on average
during the recall period they lost con-
centration, repeated a job, worked more
slowly than usual, felt fatigued at work,
and did nothing at work on days when
they were at work not feeling well. Re-
sponses were “all of the time,” “most of
the time,” “half of the time,” “some of
the time,” and “none of the time.” A sixth
question asked respondents about the
average amount of time it took them to
start working after arriving at work on
days not feeling well during the recall
period. The aggregate measure of re-
duced performance was then derived in
4 steps: (1) convert the categorical re-
sponse options for 5 of the 6 questions
into percentages as follows: all of the
time (100%), most of the time (75%),
half of the time (50%), some of the time
(25%), and none of the time (0%); (2)
average the responses to the 5 categori-
cal behavior questions to yield the av-
erage percentage of lost productive work
time and multiply this percentage by the
number of hours worked per day to yield
its hour equivalent; (3) add the hours
of lost productive work time to the re-
ported average amount of time it took
to start working after arriving at work;
and (4) divide by the number of weeks
per recall period for the hours per week
of LPT on days at work.
American Productivity Audit
The productivity audit, the parent sur-
vey for the Depressive Disorders Study,
is a national survey of the US popula-
tion, with 30523 interviews com-
pleted between August 1, 2001, and July
31, 2002 (W.F.S., unpublished data).
The Depressive Disorders Study and all
related estimates are based on the sub-
sample of 3351 productivity audit in-
terviews completed between May 20
and July 11, 2002, and the 1190 indi-
viduals selected from this subsample to
complete the supplemental interview.
Audit households were selected as a
random sample of residences within the
continental United States with a tele-
phone and at least 1 permanent adult
(ie, aged 18-65 years) resident. Resi-
dents who reported affirmatively to the
Current Population Survey (CPS)
29
question on employment status (ie,
“Last week, did you do any work for ei-
ther pay or profit?”), and a 10% ran-
dom sample of those who responded in
the negative, were invited to partici-
pate. Up to 2 eligible members were in-
terviewed per household. Oral in-
formed consent was obtained from each
participant before initiating the inter-
view. Audit participation was 66.2%.
A 2-step weighting method ac-
counted for selective participation. One
weight was applied to individuals as the
inverse of the number of phone num-
bers available for incoming calls to
account for the unequal probability
of selecting households. Second, a popu-
lation weighting adjustment ac-
counted for selection bias due to incom-
plete coverage of the US population and
ensured that estimates of certain sample
demographic subgroups’ totals con-
formed to known values. The CPS was
used as the external reference database
because it provides high-quality data on
a nationally representative sample of the
US workforce. A raking method was
used for population weighting adjust-
ment, benchmarking to 4 variables com-
mon to both the productivity audit and
the CPS. Raking uses an iterative pro-
portional fitting procedure to ensure that
the weights assigned to individual re-
spondents lead to marginal distribu-
tions on auxiliary variables that are
equivalent to the CPS.
30
Wesvar ver-
sion 4 statistical software (Westat, Rock-
COST OF LOST WORK TIME AMONG WORKERS WITH DEPRESSION
3136 JAMA, June 18, 2003—Vol 289, No. 23 (Reprinted) ©2003 American Medical Association. All rights reserved.
Downloaded From: http://jama.jamanetwork.com/ by a Albert Einstein College of Medicine User on 02/20/2015
ville, Md), was used to perform the rak-
ing adjustments.
The Depressive Disorders Study
The FIGURE describes participant iden-
tification and selection for the Depres-
sive Disorders Study. Between May 20
and July 11, 2002, 3351 audit partici-
pants were asked 2 questions: “In the
past 2 weeks, did you feel sad, blue, or
down in the dumps?” and “In the past
2 weeks, did you have little interest or
pleasure in doing things?” All partici-
pants who responded affirmatively to
at least 1 of these 2 questions were in-
vited to participate (n=865) in the De-
pressive Disorders Study. A group-
matched stratified (by age, sex,
occupation eligibility, and date of in-
terview) random sample (1:4) of those
reporting “no” to both questions was
also invited to participate (n =602). A
total of 733 (692 met employment cri-
teria) respondents who screened posi-
tive and 457 (435 met employment cri-
teria) who screened negative completed
the extended interview. Respondents re-
ceived a $10 incentive. Participation in
the extended interview was 86%. The
study protocol and informed consent
statement were approved by the Essex
institutional review board, Lebanon, NJ.
The extended interview included the
Primary Care Evaluation of Mental Dis-
orders (PRIME-MD) Mood Module
31
along with the module for most recent
depressive episode (ie, time since last de-
pressive episode and the duration of
episode). The PRIME-MD is a vali-
dated diagnostic interview. The mood
module contained 9 items to identify in-
dividuals with depression using Diag-
nostic and Statistical Manual of Mental
Disorders, Revised Third Edition (DSM-
III-R) criteria.
32
Subsequently, the 26-
item Somatic Symptom Inventory (SSI)
33
was administered. For each of 26 physi-
cal symptoms, respondents reported the
extent to which they were bothered by
each symptom in the past month (“not
at all,” “a little bit,” “moderately,” “quite
a bit,” and “a great deal”). The associa-
tion between physical symptoms and de-
pression was assessed by calculating the
prevalence of depression for each symp-
tom cluster and for respondents who did
not meet criteria for any symptom clus-
ter. Finally, 11 questions were asked
about medical care and treatment for
depression (ie, frequency of talking
with a physician about depression, phy-
sician’s diagnosis, whether or not a
medication was prescribed, which medi-
cation was prescribed, use and effective-
ness of the medication, and rating of
medication adverse effects).
PRIME-MD diagnostic criteria based
on the DSM-III-R were used to assign
the diagnosis of a specific depressive
disorder. A total of 29.8% (n=206) of
those who screened positive for depres-
sion met diagnostic criteria for major
depressive disorder, dysthymia, or par-
tial remission or recurrence of major de-
pressive disorder. Only 3.0% (n=13) of
those who answered in the negative to
both screening questions met diagnos-
tic criteria for depression. All 13 met
criteria for dysthymia and 1 also met
criteria for major depressive disorder.
Clustering of physical symptoms us-
ing factor analysis was evaluated to pro-
vide a structured summary of SSI data.
Factor solutions differed for respon-
dents with and without depression; we
used the factor solutions for those with
depression. Orthogonal and oblique ro-
tation models did not differ; we relied
on the oblique models. An item was in-
cluded in a factor if its absolute value
was greater than 0.4 and it did not load
significantly on more than 1 factor. The
number of factors was defined from
scree plots and limited to those with an
eigenvalue 1.0. A 7-factor solution
was deemed optimal among respon-
dents with depression: (1) pain, weak-
ness, and fatigue (7 items, 27.3% of vari-
ance); (2) gastrointestinal complaints
(3 items, 13.5% of variance); (3) panic
or anxiety (3 items, 15.4% of vari-
ance); (4) faintness or dizziness (4
Figure. Identification and Selection of the Analytic Sample for the Depression Disorders Study
865 Screened Positive for Depression 602 Matched Random Sample of Those Who Screened
Negative for Depression
733 Agreed to Participate and Completed Extended Interview 457 Agreed to Participate and Completed Extended Interview
41 Not Working
692 Working
22 Not Working
435 Working
486 Did Not Meet Diagnostic
Criteria for Depression
206 Met Diagnostic Criteria
for Depression
422 Did Not Meet Diagnostic
Criteria for Depression
13 Met Diagnostic Criteria
for Depression
219 With Depression Included in Analysis 908 Without Depression Included in Analysis
American Productivity Audit Survey (11/20/01-7/11/02)
3351 Interviews
COST OF LOST WORK TIME AMONG WORKERS WITH DEPRESSION
©2003 American Medical Association. All rights reserved. (Reprinted) JAMA, June 18, 2003—Vol 289, No. 23 3137
Downloaded From: http://jama.jamanetwork.com/ by a Albert Einstein College of Medicine User on 02/20/2015
items, 15.6% of variance); (5) auto-
nomic instability with anxiety (2 items,
12.4% of variance); (6) ringing in the
ears, or head or nose fullness (2 items,
9.4% of variance); and (7) sensory or
nerve impairment (2 items, 9.4% of
variance). Only gastrointestinal com-
plaints and panic or anxiety were com-
mon to individuals with and without
depression. A dichotomous variable de-
fined the presence of a factor-based
symptom cluster. For each factor, the
cutpoint was defined at the 10th
percentile of respondents without de-
pression.
Analyses were completed to estimate
the prevalence of depression in the US
workforce and to estimate LPT and as-
sociated costs among individuals with de-
pression compared with those without
depression. Three mutually exclusive cat-
egories were defined: major depressive
disorder (ie, major depressive disorder
only and major depressive disorder plus
dysthymia), dysthymia (ie, any dysthy-
mia excluding major depressive disor-
der with dysthymia), and partial remis-
sion or recurrence of major depressive
disorder (ie, excluding partial remis-
sion or recurrence of major depressive
disorder with dysthymia). Depression
prevalence was estimated in 2 steps. First,
age- and sex-specific prevalences were
calculated based on the sampling frac-
tion of those responding in the positive
and in the negative to the 2 depression
screening questions. Second, using di-
rect adjustment, age and sex stratum–
specific prevalence estimates were mul-
tiplied times the corresponding age- and
sex-specific population size of the US
workforce. Lost productive time in re-
spondents with and without depres-
sions were calculated as total LPT for any
health-related reason. Excess LPT was
defined as the difference in mean LPT in
respondents with depression compared
with an expected value in those with-
out depression. The expected value was
estimated by applying rates of LPT from
specific age and sex groups without de-
pression to the same demographic sub-
groups of individuals who met criteria
for depression. This same method was
used to estimate mean LPT for depres-
sion with and without a specific symp-
tom cluster and for the corresponding ex-
pected value among respondents without
depression. Lost labor costs were esti-
mated from lost productive hours and
self-reported annual income (ie, hourly
wage equaled annual income divided by
the mean number of hours worked per
week times 52 weeks). Lost dollars were
calculated by multiplying lost hours by
the hourly wage.
Benchmarking and weighting vari-
ables with missing data (ie, 0.9%) were
imputed using the age- and sex-
specific mode for categorical variables,
and the age- and sex-specific median for
continuous variables. If only 1 of the 5
variables used to calculate presentee-
ism was missing, the mean value of the
remaining 4 variables was substituted,
reducing the proportion with missing
presenteeism estimates from 4.5% to
3.3%. Salary information was missing for
18.7% of all respondents. Missing sal-
ary data were modeled using multiple
linear regression. SAS version 8.2 was
used for all analyses (SAS Institute Inc,
Cary, NC) and P.05 was used to de-
termine statistical significance.
RESULTS
Participation in the American Produc-
tivity Audit has been described in de-
tail elsewhere (unpublished data avail-
able from W.F.S. on request). Among
audit participants, women made up
56.1% of the sample and respondents
were equally distributed across the 4 age
groups. A majority of respondents were
white (77.0%), formally educated be-
yond high school (66.6%), and work-
ing more than 30 hours per week
(82.9%) with an annual income less
than $40000 (51.3%). During the
2-week recall period, 10.0% of work-
ers were absent from work for a per-
sonal health reason and 38.1% re-
ported unproductive time due to
personal health on at least 1 workday.
Overall, workers lost a mean of 1.89
hours per week of productive work time
for either a personal or family health
reason. Reduced performance at work
due to personal health accounted for
65.3% (1.32 h/wk) of the lost time.
Respondents who met PRIME-MD
criteria for any depressive disorder with
a treatment indication (ie, major de-
pressive disorder, dysthymia, or par-
tial remission or recurrence of major de-
pressive disorder) were of similar sex,
age, race, annual salary, and employ-
ment status as those without a depres-
sive disorder (T
ABLE 1). Among all par-
ticipants, the majority were women
(65.6%), between 35 and 65 years of age
(66.0%), white (76.2%), earning less
than $40 000 annually (66.2%), and
working more than 30 hours per week
(80.7%). In contrast, respondents with
a depressive disorder were signifi-
cantly more likely than those without
depression to have a lower education
level (43.0% vs 33.7% with a high
school education or less; P = .01), and
to have at least 1 of the 7 physical symp-
tom clusters derived from factor analy-
sis (78.1% vs 41.4%; P.001) (Table 1).
Compared with those with other de-
pressive disorders, those with major de-
pression were significantly more likely
to have a lower educational level
(P.01 for all comparisons), earn less
than $20000 annually (P.01), and re-
port physical symptoms associated with
pain, weakness, and fatigue (P.001),
panic or anxiety (P.001), and auto-
nomic instability (P.001) (Table 1).
In contrast, those with dysthymia were,
on average, significantly more likely to
have attained a higher level of formal
education (P.01), report a higher an-
nual salary (P.01), and work more
than 30 hours per week (Table 1).
Prevalence of Depressive Disorders
The 2-week prevalence of any depres-
sive disorder in the US workforce was
estimated at 9.4% (T
ABLE 2). Dysthy-
mia was the most prevalent (3.6%), fol-
lowed by major depression (3.4%), and
partial remission or recurrence of ma-
jor depressive disorder (2.4%) (Table 2).
Any depression was close to 2 times
more prevalent in women than in men,
with a marked difference in the preva-
lence of major depression (women,
5.3%; men, 1.6%). Other notable pat-
terns included a strong inverse gradi-
ent with increasing education level and,
COST OF LOST WORK TIME AMONG WORKERS WITH DEPRESSION
3138 JAMA, June 18, 2003—Vol 289, No. 23 (Reprinted) ©2003 American Medical Association. All rights reserved.
Downloaded From: http://jama.jamanetwork.com/ by a Albert Einstein College of Medicine User on 02/20/2015
Table 1. Distribution of Employed American Productivity Audit Respondents, by Selected Demographic, Employment, and Health
Characteristics
Characteristic
Depression, No. (%)
P
Value
*
Depressive Disorder, No. (%)
P
Value†
Any
(n = 219)
None
(n = 908)
Major Depression
(n = 87)
Dysthymia
(n = 74)
Partial Remission
of Major Depression
(n = 58)
Sex
Men 64 (29.2) 324 (35.7)
.07
20 (23.0) 24 (32.4) 20 (34.5)
.12
Women 155 (70.8) 584 (64.3) 67 (77.0) 50 (67.6) 38 (65.5)
Age, y
18-34 74 (33.8) 309 (34.0)
30 (34.5) 25 (33.8) 19 (32.8)
35-49 95 (43.4) 384 (42.3) .95 37 (42.5) 33 (44.6) 25 (43.1) .99
50-65 50 (22.8) 215 (23.7) 20 (23.0) 16 (21.6) 14 (24.1)
Sex and age, y
Men, 18-34 23 (10.5) 126 (13.9)
7 (8.1) 9 (12.2) 7 (12.1)
Men, 35-49 28 (12.8) 132 (14.5) 9 (10.3) 11 (14.9) 8 (13.8)
Men, 50-65 13 (5.9) 66 (7.3)
.59
4 (4.6) 4 (5.4) 5 (8.6)
.96
Women, 18-34 51 (23.3) 183 (20.2) 23 (26.4) 16 (21.6) 12 (20.7)
Women, 35-49 67 (30.6) 252 (27.7) 28 (32.2) 22 (29.7) 17 (29.3)
Women, 50-65 37 (16.9) 149 (16.4) 16 (18.4) 12 (16.2) 9 (15.5)
Race
White 160 (73.0) 693 (76.2)
57 (65.5) 57 (77.0) 46 (79.3)
Black 28 (12.8) 84 (9.3)
.29
14 (16.1) 5 (6.8) 9 (15.5)
.06
Other 30 (13.7) 125 (13.8) 16 (18.4) 11 (14.9) 3 (5.2)
Not stated 1 (0.5) 6 (0.7) 0 1 (1.4) 0
Education
12th grade; no diploma 18 (8.2) 40 (4.4)
11 (12.6) 2 (2.7) 5 (8.6)
High school graduate or GED 76 (34.8) 266 (29.3) 32 (36.8) 26 (35.1) 18 (31.0)
Some college; no degree 59 (26.9) 218 (24.0) 20 (23.0) 19 (25.7) 20 (34.5)
Associates degree 18 (8.2) 79 (8.7) .01 8 (9.2) 7 (9.5) 3 (5.2) .01
Bachelors degree 36 (16.4) 217 (23.9) 12 (13.8) 12 (16.2) 12 (20.7)
Graduate degree 12 (5.5) 87 (9.6) 4 (4.6) 8 (10.8) 0
Not stated 0 1 (0.1) 0 0 0
Annual salary, $
10 000 26 (11.9) 84 (9.3)
7 (8.1) 11 (14.9) 8 (13.8)
10 000-19 999 38 (17.4) 127 (14.0) 25 (28.7) 5 (6.8) 8 (13.8)
20 000-29 999 39 (17.8) 162 (17.8) 12 (13.8) 17 (23.0) 10 (17.2)
30 000-39 999 41 (18.6) 127 (14.0) .10 18 (20.7) 7 (9.5) 16 (27.6) .01
40 000-49 999 21 (9.6) 90 (9.9) 9 (10.3) 9 (12.2) 3 (5.2)
50 000 30 (13.7) 188 (20.7) 7 (8.1) 15 (20.3) 8 (13.8)
Not stated 24 (11.0) 130 (14.3) 9 (10.3) 10 (13.5) 5 (8.6)
Employment status, h/wk
30 177 (80.8) 686 (75.5)
70 (80.4) 64 (86.5) 43 (74.1)
20-30 18 (8.2) 116 (12.8)
.16
7 (8.1) 4 (5.4) 7 (12.1)
.49
20 14 (6.4) 58 (6.4) 6 (6.9) 5 (6.8) 3 (5.2)
Not stated 10 (4.6) 48 (5.3) 4 (4.6) 1 (1.4) 5 (8.6)
Depression screening status‡
Positive 206 (94.1) 486 (53.5)
.01
86 (98.9) 62 (83.8) 58 (100)
.01
Negative 13 (5.9) 422 (46.5) 1 (1.1) 12 (16.2) 0
Presence of physical symptom clusters
Pain, weakness, or fatigue 107 (48.9) 121 (13.3) .01 51 (58.6) 30 (40.5) 26 (44.8) .01
Gastrointestinal complaints 66 (30.1) 92 (10.1) .01 30 (34.5) 18 (24.3) 18 (31.0) .01
Panic or anxiety 49 (22.4) 70 (7.7) .01 28 (32.2) 12 (16.2) 9 (15.5) .01
Faintness or dizziness 45 (20.6) 39 (4.3) .01 20 (23.0) 11 (14.9) 14 (24.1) .01
Autonomic instability 71 (32.4) 100 (11.0) .01 43 (49.4) 13 (17.6) 15 (25.9) .01
Ears ringing, head or nose fullness 83 (37.9) 156 (17.2) .01 35 (40.2) 28 (37.8) 20 (34.5) .01
Sensory or nerve impairment 92 (42.0) 176 (19.4) .01 43 (49.4) 26 (35.1) 23 (39.7) .01
None present 48 (21.9) 532 (58.6) .01 16 (18.4) 20 (27.0) 12 (20.7) .01
Abbreviation: GED, General Educational Development (test).
*
Not stated category excluded from calculation of
2
statistic between “any” and “none” groups.
†Not stated category excluded from calculation of
2
statistic among depressive disorder and “none” groups.
‡See “Methods” section for description of measurement of depression screening status.
COST OF LOST WORK TIME AMONG WORKERS WITH DEPRESSION
©2003 American Medical Association. All rights reserved. (Reprinted) JAMA, June 18, 2003—Vol 289, No. 23 3139
Downloaded From: http://jama.jamanetwork.com/ by a Albert Einstein College of Medicine User on 02/20/2015
in general, higher prevalence of any de-
pression in relation to lower annual sal-
ary levels. Prevalence appears to be low-
est among those working 20 to 30 hours
per week (6.8%) compared with those
working more (9.5%) or fewer (10.9%)
hours. The greatest difference in preva-
lence of depression was observed in re-
lation to physical symptom status. Preva-
lence of major depression was
particularly elevated among those re-
porting symptoms of autonomic insta-
bility (19.8%), pain, weakness, or fa-
tigue (14.9%), and panic or anxiety
(14.1%) (Table 2).
Average LPT and
National Cost Estimates
Lost productive time was expressed as
an average across all individuals who met
criteria for depression (T
ABLE 3). On av-
erage, workers with depression re-
ported significantly more total health-
related LPT than those without
depression (mean, 5.6 h/wk vs an ex-
pected value of 1.5 h/wk in the absence
of depression) (Table 3). A total of 77.1%
of individuals with depression re-
ported some LPT during the 2-week re-
call period. The expected number of LPT
hours was estimated by applying rates
of LPT for those without depression
from specific age and sex groups to the
same demographic subgroups of indi-
viduals who met criteria for a depres-
sive disorder. Overall, LPT among de-
pressed individuals was primarily
explained by LPT while at work (82.1%).
Average total LPT per week was con-
siderably higher for major depression
(mean [SE], 8.4 [1.3] h/wk), followed
by total LPT for partial remission of ma-
jor depression (5.3 [1.1] h/wk), and dys-
thymia (3.3 [0.6]h/wk) (Table 3).
Physical symptom clusters were com-
mon among individuals with depres-
sion. Pain, weakness, or fatigue was the
most common cluster (49%), followed
by sensory or nerve impairment (40%),
and ringing ears or head fullness (38%).
Individuals with major depression con-
sistently reported the most LPT when it
co-occurred with a physical symptom
cluster—in particular, when it co-
occurred with pain, weakness, or fa-
Table 2. Prevalence of Any Depression and Specific Depressive Disorders in the US
Workforce, by Selected Demographic, Employment, and Health Characteristics
*
Characteristic
Any
Depression
Depressive Disorder
Major
Depression Dysthymia
Partial Remission
or Recurrence of
Major Depression
Overall 2-week prevalence 9.4 3.4 3.6 2.4
Sex
Men 6.8 1.6 3.1 2.1
Women 12.2 5.3 4.2 2.7
Age, y
18-34 8.2 3.1 3.1 2.0
35-49 10.7 3.5 4.5 2.7
50-65 9.1 3.5 3.0 2.6
Sex and age, y
Men, 18-34 5.4 1.8 2.0 1.6
Men, 35-49 8.7 1.5 4.5 2.7
Men, 50-65 6.5 1.5 2.7 2.3
Women, 18-34 12.5 5.1 4.7 2.7
Women, 35-49 12.6 5.5 4.4 2.7
Women, 50-65 11.4 5.4 5.3 2.8
Race
White 9.2 2.9 3.8 2.5
Black 10.6 4.3 2.2 4.1
Other 9.2 5.5 2.9 0.9
Not stated 13.5 0.0 13.5 0.0
Education
12th grade; no diploma 13.9 8.9 0.7 4.3
High school graduate or GED 10.9 4.3 3.7 2.8
Some college; no degree 10.8 3.6 3.8 3.5
Associates degree 7.5 2.8 3.9 0.8
Bachelors degree 7.3 1.5 3.9 1.9
Graduate degree 4.9 1.3 3.6 0.0
Not stated 0.0 0.0 0.0 0.0
Annual salary, $
10 000 12.6 3.1 5.0 4.5
10 000-19 999 15.2 9.7 2.6 2.9
20 000-29 999 8.3 2.1 3.8 2.4
30 000-39 999 11.6 5.0 2.2 4.5
40 000-49 999 6.5 2.6 2.7 1.2
50 000 7.5 0.9 5.1 1.5
Not stated 7.1 2.7 3.2 1.2
Employment status, h/wk
30 9.5 3.4 4.0 2.1
20-30 6.8 1.9 1.3 3.6
20 10.9 5.1 4.1 1.8
Not stated 10.1 2.9 1.9 5.3
Depression screening status†
Positive 27.4 11.2 7.9 8.2
Negative 1.8 0.1 1.8 0.0
Presence of physical symptom clusters
Pain, weakness or fatigue 32.0 14.9 8.0 9.1
Gastrointestinal complaints 26.9 11.6 6.9 8.3
Panic or anxiety 23.9 14.1 5.9 3.9
Faintness or dizziness 34.9 12.5 9.5 12.9
Autonomic instability 30.8 19.8 5.2 5.9
Ears ringing, head or nose fullness 23.9 9.2 8.5 6.2
Sensory or nerve impairment 18.6 8.1 4.8 5.7
None 4.0 1.0 2.3 0.7
Abbreviation: GED, General Educational Development (test).
*
An iterative proportional fitting procedure (ie, raking) was used to perform the population weighting adjustment, bench-
marking to 4 variables common to both the American Productivity Audit and the Current Population Survey (CPS).
Raking ensured that the weights assigned to individual respondents led to marginal distributions on auxiliary vari-
ables that were equivalent to the CPS.
†See “Methods” section for description of measurement of depression screening status.
COST OF LOST WORK TIME AMONG WORKERS WITH DEPRESSION
3140 JAMA, June 18, 2003—Vol 289, No. 23 (Reprinted) ©2003 American Medical Association. All rights reserved.
Downloaded From: http://jama.jamanetwork.com/ by a Albert Einstein College of Medicine User on 02/20/2015
tigue (mean [SE], 10.0 [1.2] h/wk), gas-
trointestinal complaints (10.7 [1.5]
h/wk), and sensory or nerve impair-
ment (10.0 [1.4] h/wk) (Table 3). In the
absence of depression, autonomic in-
stability was associated with the most
LPT (6.5 h/wk), and gastrointestinal
complaints were associated with the least
LPT (2.0 h/wk) (Table 3).
Physical symptom clusters often co-
occurred and were moderately corre-
lated. We used ordinary least-squares re-
gression to simultaneously estimate the
association of each symptom cluster with
LPT, adjusting for depression status, sex,
and age. In this model, significant asso-
ciations were observed for only 3 symp-
tom clusters, the most common being
pain, weakness, or fatigue ( = 3.0;
SE = 0.5); the least common being faint-
ness or dizziness ( = 2.1; SE = 0.7); and
autonomic instability (=2.9; SE=0.5).
Coefficients for the other symptom clus-
ters were close to zero.
United States workers with depres-
sion are estimated to cost employers
$44.01 billion per year in LPT, an ex-
cess of $30.94 billion per year when com-
pared with an expected cost in workers
without depression (T
ABLE 4). A total of
81.1% of the LPT costs are explained by
reduced performance while at work. Ma-
jor depression accounts for almost half
(48.5%) of the LPT among workers with
depression, again with the majority of the
cost explained by reduced performance
while at work (Table 4).
Treatment Status
We examined self-reported treatment
(T
ABLE 5) for depression in the 12
months prior to interview. Individuals
with depression were dichotomized by
symptom burden (ie, 2 or 3 vs 0 or 1 of
the symptom clusters significantly asso-
ciated with LPT [pain, weakness, or fa-
tigue; autonomic instability; faintness or
dizziness]). For any depression, we ob-
served overall that less than one third of
workers with depression reported re-
ceiving a prescription drug in the past 12
months for depression or anxiety. Most
workers reported taking the medica-
tion in the past 12 months, with 69% to
81% reporting taking it in the past 2 days.
Overall, self-reported treatment effec-
tiveness was moderate (5 on a 0-10 an-
chored continuous scale) and appeared
to be lower for workers with a high
symptom burden compared with those
with a low symptom burden. The differ-
ences, however, were not statistically sig-
nificant (P.05).
COMMENT
Our estimate of the LPT cost due to de-
pression offers unique information re-
garding hidden costs that is consistent
with the widely held notion that de-
pression is a leading cause of disabil-
ity.
34
Our estimate of $31 billion in ex-
cess LPT refers to time lost among
individuals actively engaged in work (ie,
worked at least 1 day in the previous
week). It does not include labor costs
associated with disability leave.
Previous studies consistently indi-
cate that the lost work-time cost from
Table 3. Average Lost Productive Time (LPT), in Hours per Worker per Week, Among US Workers With Depression and Expected LPT in the
Absence of Depression, by Type of LPT and Presence of Physical Symptoms
*
Type of LPT
Any Depression,
Mean (SE)
Depressive Disorder, Mean (SE)
Expected Mean LPT in
the Absence of DepressionMajor Depression Dysthymia
Partial Remission
or Recurrence
of Major Depression
Absenteeism 1.0 (0.2) 1.2 (0.4) 0.5 (0.2) 1.5 (0.5) 0.4
Presenteeism 4.6 (0.5) 7.2 (1.3) 2.7 (0.6) 3.8 (0.7) 1.1
Total LPT 5.6 (0.6) 8.4 (1.3) 3.3 (0.6) 5.3 (1.1) 1.5
Pain, weakness or fatigue 7.8 (0.7) 10.0 (1.2) 4.6 (1.0) 7.4 (1.8) 5.1
Gastrointestinal complaints 7.1 (0.9) 10.7 (1.5) 2.8 (1.0) 5.8 (2.2) 2.0
Panic or anxiety 6.9 (1.3) 9.3 (1.7) 3.7 (1.9) 3.2 (0.8) 4.1
Faintness or dizziness 6.1 (1.3) 8.9 (2.4) 2.9 (1.8) 6.1 (1.9) 4.5
Autonomic instability 8.1 (1.1) 9.5 (1.9) 3.2 (1.6) 7.8 (3.1) 6.5
Ears ringing, head or nose fullness 5.5 (0.8) 8.1 (1.2) 4.1 (1.0) 3.6 (0.8) 2.8
Sensory or nerve impairment 6.8 (0.7) 10.0 (1.4) 4.1 (1.2) 4.5 (1.0) 3.2
None 4.0 (1.3) 5.8 (3.7) 3.0 (1.2) 5.3 (3.0) 0.8
*
See Table 2 footnote for description of population weighting adjustment.
Table 4. Total Cost of Lost Productive Time (LPT), in $Billion per Year (2002 Dollars), in US Workers With Depression, and Expected Cost of
LPT in the Absence of Depression, by Type of LPT
*
Type of LPT
Any Depression,
Mean (SE)
Depressive Disorder, Mean (SE)
Expected Total Cost in
the Absence of DepressionMajor Depression Dysthymia
Partial Remission
or Recurrence
of Major Depression
Absenteeism 8.27 (2.3) 3.18 (1.1) 2.54 (1.3) 2.55 (1.0) 3.90
Presenteeism 35.73 (5.3) 18.18 (4.0) 10.29 (2.5) 7.27 (2.2) 9.17
Total LPT 44.01 (6.3) 21.36 (4.1) 12.83 (3.3) 9.82 (3.0) 13.07
*
See Table 2 footnote for description of population weighting adjustment.
COST OF LOST WORK TIME AMONG WORKERS WITH DEPRESSION
©2003 American Medical Association. All rights reserved. (Reprinted) JAMA, June 18, 2003—Vol 289, No. 23 3141
Downloaded From: http://jama.jamanetwork.com/ by a Albert Einstein College of Medicine User on 02/20/2015
depression is substantial,
22,23,35-42
ex-
ceeding direct medical costs. How-
ever, estimates based on studies of spe-
cific employers
38,42
pose challenges in
extrapolating to the US workforce.
Prevalence and impact of depression ap-
pear to vary by occupation.
43
More-
over, employer-specific studies often
underestimate lost labor costs be-
cause they usually focus only on ab-
sence time and rely on medical claims
data to identify employees with depres-
sion. Costs of LPT are not captured for
depressed individuals who have not
sought care or who have sought care for
other reasons (eg, physical symptoms).
Previous national projections of the
labor cost of depression were based pri-
marily on the Epidemiologic Catch-
ment Area (ECA) studies
23,35-37,39
com-
pleted in the 1980s. Greenberg et al
23
estimated lost labor costs of $24 bil-
lion in 1990 and of $33.5 billion in 2002
after adjusting for inflation. Five im-
portant differences distinguish our
study from that by Greenberg et al.
First, Greenberg et al captured some of
the costs due to disability (ie, hospital-
ization, bed-days), but the LPT cost
from reduced performance at work is
incomplete. Second, the estimate of
Greenberg et al includes major depres-
sion (1-year prevalence), dysthymia
(lifetime prevalence), and bipolar dis-
orders, but not partial remission of ma-
jor depression. Third, in using ECA
data, Greenberg et al made assump-
tions about the average number of hos-
pital (ie, treated patients) and bed-
ridden days (untreated individuals), the
number of days used for outpatient care,
the average impact of depression out-
side of inpatient care and days at work,
and other factors. In contrast, our ques-
tionnaire specifically captured LPT due
to both work absence and reduced per-
formance while at work. Fourth, in the
ECA study by Greenberg et al, details
regarding episodes of depression were
recalled over a 1-year period
35
vs the
2-week period in our study. Finally,
clinical care and management of de-
pression has changed substantially since
the 1980s.
26,44-46
Our study indicates that 81% of the
LPT costs from depression were ex-
plained by reduced performance while
at work. This finding is consistent with
observations for numerous other con-
ditions. A substantial share of the LPT
costs are explained by reduced work
performance, not work absence.
*
Using the PRIME-MD, we estimated
depression prevalence to be 9.4%. Com-
paring our estimate with those from
other studies is difficult. For example,
the ECA studies used the Diagnostic In-
terview Schedule. There is no formal link
to the PRIME-MD. Prevalence esti-
mates are not usually confined to indi-
viduals working for pay. Nonetheless, for
comparison with other studies we have
focused on major depression because the
criteria have not changed and because
major depression accounts for a sub-
stantial share of the LPT costs from de-
pressive disorders.
Based on ECA data, the 1-year preva-
lence of major depression among work-
*References 3, 4, 7, 12, 16, 17, 20, 23, 47-51.
Table 5. Medical Treatment in US Workers With Depression, by Presence or Absence of 2 or More Symptom Clusters
*
Characteristic
Any Depression
Depressive Disorder
Major Depression Dysthymia
Partial Remission
or Recurrence
of Major Depression
2-3 Clusters
(n = 62)
0-1 Cluster
(n = 157)
2-3 Clusters
(n = 33)
0-1 Cluster
(n = 54)
2-3 Clusters
(n = 12)
0-1 Cluster
(n = 62)
2-3 Clusters
(n = 17)
0-1 Cluster
(n = 41)
Received a prescription medication
for depression or anxiety in the
past 12 mo, No. (%)
28.2 30.7 43.2 30.5 18.4 35.1 8.3 23.2
Received different types
of prescription
medications, No. (%)
Anxiolytic only 1.1 2.1 2.2 2.0 0 3.3 0† 0†
Antidepressant only 5.8 6.5 6.4 10.7 12.9 7.1 0 0.3
Antidepressant and
anxiolytic
21.3 22.1 34.6 17.8 5.5 24.7 8.3 22.9
Received a medication, No. (%)
Took medication in past 12 mo 100 95.4 100.0 90.2 100 98.9 100 95.0
Took medication in past 2 d 69.0 80.7 62.1 77.1 85.0 87.9 79.0 66.6
Took a medication in past 12 mo
Mean (SE) effectiveness
of medication
(10-point scale)‡
4.4 (0.7) 6.0 (0.6) 4.2 (0.9) 4.2 (0.7) 6.5 (1.7) 7.6 (0.5) 1.8 (0.6) 4.0 (1.9)
Mean (SE) adverse effects
rating (10-point scale)§
2.2 (0.9) 2.6 (0.5) 1.7 (1.0) 3.5 (0.8) 1.1 (1.5) 2.2 (0.8) 7.1 (3.2) 2.2 (1.6)
*
See Table 2 footnote for description of population weighting adjustment. Symptom clusters were those significantly associated with LPT (pain, weakness, or fatigue; autonomic
instability; faintness or dizziness). Ns refer to study sample size on which population weighting adjustments were made.
†Projection to the US population was close to but not equal to zero.
‡0 indicates that “medication does not work at all”; 10, that “medication completely relieves symptoms.”
§0 indicates “no adverse effects”; 10, “adverse effects so bothersome that you won’t take the medication.”
COST OF LOST WORK TIME AMONG WORKERS WITH DEPRESSION
3142 JAMA, June 18, 2003—Vol 289, No. 23 (Reprinted) ©2003 American Medical Association. All rights reserved.
Downloaded From: http://jama.jamanetwork.com/ by a Albert Einstein College of Medicine User on 02/20/2015
ing populations is 4%, excluding symp-
toms attributable to alcohol, drugs,
physical injury, and illness.
43
This
prevalence is comparable with our
prevalence estimates of 3.4% for ma-
jor depression and 2.4% for partial re-
mission. We followed existing criteria
and did not make the same exclusions
as Eaton et al.
43
Broader population-
based estimates of the 1-year preva-
lence of major depression in adults aged
18 years to 54 years range from 6.5%
(ECA studies) to 11.1%.
52
Our esti-
mate of major depression prevalence
should be lower than previous esti-
mates (ie, those with a 12-month time
frame) since we capture data only from
individuals who are currently experi-
encing a depressive episode and are ac-
tively working. We do not capture data
from individuals who experience re-
current episodes of depression
53-56
but
from those who are between episodes.
Enumerating these cases is not essen-
tial to an accurate estimate of LPT from
depression. In contrast, we are likely to
have relatively complete capture of dys-
thymia, since it is an inherently chronic
condition by definition, lasting 2 years
or longer.
Workers with major depression and
physical symptoms account for a dis-
proportionate share of the LPT due to
depression. While physical symptoms
in some individuals with depression are
due to other conditions (eg, diabetes)
comorbid with depression, there is
growing recognition that physical
symptoms are often directly associ-
ated with depression. More than 80%
of patients with depression who seek
care present with physical symp-
toms.
57
Moreover, disability from de-
pression appears to be correlated with
number of physical symptoms. The
strong relation between depression and
physical symptoms is thought to be a
common product of dysfunctional se-
rotonergic and noradrenergic path-
ways that project throughout the cen-
tral nervous system and spinal cord.
58
It is noteworthy that our data suggest
that individuals with depression and an
elevated symptom burden (ie, at least
2 symptom clusters significantly asso-
ciated with LPT) appear to report the
lowest treatment effectiveness. If this
relationship is real, workers with de-
pression and a high physical symptom
burden are likely to be an important tar-
get for intervention to reduce both di-
rect medical costs and LPT. However,
larger studies of depression treatment
status in the US workforce are re-
quired to accurately determine whether
this relationship is real and whether the
relatively low proportion of workers
prescribed a treatment is an indica-
tion of unmet need.
The association of physical symp-
toms and mood disorders also may be
sustained because individuals who are
impaired by an illness are entitled to the
dispensations of the “sick role”
59
that
includes a reduction in expected per-
formance of normal role functions. To
establish the sick role, the patient must
be perceived as having a legitimate
medical condition beyond their con-
trol. Compared with physical symp-
toms, it is more difficult to establish the
sick role for depression. Stigmatiza-
tion is associated with mental disor-
ders, physicians often fail to detect
mood disorders, and individuals may
doubt whether depression is truly be-
yond personal control. Therefore, even
when role impairment is linked to a
mood disorder, the sick role is often
constructed on the basis of physical
symptoms that also may have a direct
pathophysiological relationship with
the mood disorder,
58
even though the
symptoms themselves may not cause
impairment.
Our study has several potential limi-
tations that could influence the accu-
racy of estimated LPT attributable to de-
pression. First, the Work and Health
Interview was designed to focus only
on estimating work loss incurred by in-
dividual workers reporting a health con-
dition during the recall period. Al-
though this is the primary driver of
employer costs associated with lost pro-
ductive work time, we recognize that
health-related LPT estimates could be
refined by considering other factors
such as the hiring and training of re-
placement workers or the concomi-
tant impact among coworkers.
60
These
other factors could increase, decrease,
or have no net effect on health-related
LPT cost estimates. Second, depression-
related LPT costs could be overesti-
mated because of the predisposition of
individuals to overstate work impair-
ment when in the acute phase of a de-
pressive episode.
61
While data on this
issue are limited, we cannot exclude the
possibility of reporting bias leading to
an overestimate of LPT costs among in-
dividuals with depression. Finally, our
US population estimate of LPT is based
on a strategically selected but modest
sample size of 219 employed individu-
als who met PRIME-MD criteria for de-
pression, of whom 87 had symptoms
of major depression. The uncertain-
ties inherent in the sample size for this
study must be considered when inter-
preting our cost estimates and espe-
cially when considering self-reported
treatment data.
Author Contributions: Dr Stewart, as principal
investigator of the Depressive Disorders Study, had
full access to all of the data and takes responsibility
for the integrity of the data and the accuracy of the
data analysis.
Study concept and design; drafting of the manu-
script: Stewart, Ricci, Hahn.
Acquisition of data: Stewart, Ricci.
Analysis and interpretation of data; critical revision
of the manuscript for important intellectual content;
statistical expertise: Stewart, Ricci, Chee, Hahn,
Morganstein.
Obtained funding; study supervision: Stewart.
Administrative, technical, or material support: Stewart,
Ricci, Chee, Hahn.
Role of Sponsor: Eli Lilly and Co provided financial
support for the American Productivity Audit Supple-
mentary Study on Depressive Disorders. Lilly also con-
tributed to the study design and research protocol and
provided minor comments on, although it did not au-
thorize, the manuscript. AdvancePCS provided finan-
cial support for the parent study, the American Pro-
ductivity Audit.
Acknowledgment: This work was supported in part
by an educational grant from Eli Lilly and Co. We thank
Sofia Chaudhry, MPH, Carol Leotta, PhD, Brent Man-
cha, and Bernita Brown at AdvancePCS for their im-
portant contributions to this research.
REFERENCES
1. Dasbach EJ, Carides GW, Gerth WC, Santanello NC,
Pigeon JG, Kramer MS. Work and productivity loss in
the rizatriptan multiple attack study. Cephalalgia. 2000;
20:830-834.
2. Davies GM, Santanello N, Gerth W, Lerner D, Block
GA. Validation of a migraine work and productivity
loss questionnaire for use in migraine studies. Cepha-
lalgia. 1999;19:497-502.
3. Gerth WC, Carides GW, Dasbach EJ, Visser WH,
Santanello NC. The multinational impact of migraine
symptoms on healthcare utilisation and work loss. Phar-
macoeconomics. 2001;19:197-206.
COST OF LOST WORK TIME AMONG WORKERS WITH DEPRESSION
©2003 American Medical Association. All rights reserved. (Reprinted) JAMA, June 18, 2003—Vol 289, No. 23 3143
Downloaded From: http://jama.jamanetwork.com/ by a Albert Einstein College of Medicine User on 02/20/2015
4. Hu XH, Markson LE, Lipton RB, Stewart WF, Berger
ML. Burden of migraine in the United States: disabil-
ity and economic costs. Arch Intern Med. 1999;159:
813-818.
5. McCunney RJ. Health and productivity: a role for
occupational health professionals. J Occup Environ
Med. 2001;43:30-35.
6. Schulman EA, Cady RK, Henry D, et al. Effective-
ness of sumatriptan in reducing productivity loss due
to migraine: results of a randomized, double-blind pla-
cebo-controlled clinical trial. Mayo Clin Proc. 2000;
75:782-789.
7. Stang PE, Osterhaus JT. Impact of migraine in the
United States: data from the national health inter-
view survey. Headache. 1993;33:29-35.
8. Stang P, Cady R, Batenhorst A, Hoffman L. Work-
place productivity: a review of the impact of mi-
graine and its treatment. Pharmacoeconomics. 2001;
19:231-244.
9. Fautrel B, Guillemin F. Cost of illness studies in
rheumatic diseases. Curr Opin Rheumatol. 2002;14:
121-126.
10. Riedel JE, Baase C, Hymel P, Lynch W, Dreis-
bach S, Peterson KW. Preventing Disease and Pro-
moting Health: Impact on Productivity. Scottsdale, Ariz:
Institute for Health and Productivity Management;
2001.
11. Burton WN, Connerty CM. Evaluation of a work-
site-based patient education intervention targeted at
employees with diabetes mellitus. J Occup Environ
Med. 1998;40:702-706.
12. Ng YC, Jacobs P, Johnson JA. Productivity losses
associated with diabetes in the US. Diabetes Care.
2001;24:257-261.
13. Blaiss MS. Cognitive, social, and economic costs of
allergic rhinitis. Allergy Asthma Proc. 2000;21:7-13.
14. Blanc PD, Trupin L, Eisner L, et al. The work
impact of asthma and rhinitis: findings from a
population-based survey. J Clin Epidemiol. 2001;54:
610-618.
15. Burton WN, Conti DJ, Chen C-Y, Schultz AB, Ed-
ington DW. The impact of allergies and allergy treat-
ment on worker productivity. J Occup Environ Med.
2001;43:64-71.
16. Crystal-Peters J, Crown WH, Goetzel RZ,
Schutt DC. The cost of productivity losses associ-
ated with allergic rhinitis. Am J Manage Care. 2000;
6:373-378.
17. Kessler RC, Almeida DM, Berglund P, Stang P. Pol-
len and mold exposure impairs the work perfor-
mance of employees with allergic rhinitis. Ann Al-
lergy Asthma Immunol. 2001;87:289-295.
18. Slavin RG. Occupational and allergic rhinitis: im-
pact on worker productivity and safety. Allergy Asthma
Proc. 1998;19:277-284.
19. Borchardt PJ. Employee productivity and gastro-
esophageal reflux disease: the payer’s viewpoint. Am
J Gastroenterol. 2001;96(suppl 8):S62-S63.
20. Henke CJ, Levin TR, Henning JM, Potter LP.
Work loss costs due to peptic ulcer disease and gas-
troesophageal reflux disease in a health mainte-
nance organization. Am J Gastroenterol. 2000;95:
788-792.
21. Wahlqvist P. Symptoms of gastroesophageal re-
flux disease, perceived productivity, and health-
related quality of life. Am J Gastroenterol. 2001;96
(suppl 8):S57-S61.
22. Berndt ER, Bailit HL, Keller MB, Verner JC, Finkel-
stein SN. Health care use and at-work productivity
among employees with mental disorders. Health Aff
(Millwood). 2000;19(4):244-256.
23. Greenberg PE, Stiglin LE, Finkelstein SN, Berndt
ER. The economic burden of depression in 1990. J Clin
Psychiatry. 1993;54:405-418.
24. Simon GE, Revicki D, Heiligenstein J, et al. Re-
covery from depression, work productivity, and health
care costs among primary care patients. Gen Hosp Psy-
chiatry. 2000;22:153-162.
25. Simon GE, Barber C, Birnbaum HG, et al. Depres-
sion and work productivity: the comparative costs of
treatment versus nontreatment. J Occup Environ Med.
2001;43:2-9.
26. Olfson M, Marcus SC, Druss B, Elinson L, Tanie-
lian T, Pincus HA. National trends in the outpatient
treatment of depression. JAMA. 2002;287:203-209.
27. Ricci JA, Stewart WF, Leotta C, Chee E. A com-
parison of six phone interviews designed to measure
health-related lost productive work time. Value Health.
2001;4:460.
28. Karasek R, Gordon G, Pietrokovsky C, Frese M,
Pieper C. Job Content Instrument: Questionnaire and
User’s Guide, Version 1.1. Los Angeles: University of
Southern California; 1986.
29. Bureau of Labor Statistics, Bureau of the Census.
Current Population Survey. Available at: http://www
.bls.census.gov/cps/cpsmain.htm. Accessibility veri-
fied May 19, 2003.
30. Brick JM, Kalton G. Handling missing data in sur-
vey research. Stat Methods Med Res. 1996;5:215-
238.
31. Spitzer RL, Williams JBW, Kroenke K, et al. Util-
ity of a new procedure for diagnosing mental disor-
ders in primary care: the PRIME-MD 1000 study.
JAMA. 1994;272:1749-1756.
32. American Psychiatric Association. Diagnostic and
Statistical Manual of Mental Disorders, Revised Third
Edition. Washington, DC: American Psychiatric As-
sociation; 1987.
33. Barsky AG, Wyshak G, Herman GL. Hypochon-
driasis: an evaluation of the DSM-III criteria in medi-
cal outpatients. Arch Gen Psychiatry. 1986;43:493-
500.
34. Murray CJL, Lopez AD, eds. The Global Burden
of Disease: A Comprehensive Assessment of Mortal-
ity and Disability From Diseases, Injuries, and Risk Fac-
tors in 1990 and Projected to 2020. Cambridge, Mass:
Harvard University Press; 1996.
35. Stoudemire A, Frank R, Hedemark N, Kamlet M,
Blazer D. The economic burden of depression. Gen
Hosp Psychiatry. 1986;8:387-394.
36. Broadhead WE, Blazer DG, George LK, Tse CK.
Depression, disability days, and days lost from work
in a prospective epidemiologic survey. JAMA. 1990;
264:2524-2550.
37. Johnson J, Weissman MM, Klerman GL. Service
utilization and social morbidity associated with de-
pressive symptoms in the community. JAMA. 1992;
267:1478-1483.
38. Conti DJ, Burton WN. The economic impact of
depression in a workplace. J Occup Med. 1994;36:
983-988.
39. Judd FK, Mijch AM. Depressive symptoms in pa-
tients with HIV infection. Aust N Z J Psychiatry. 1996;
30:104-109.
40. Finkelstein SN, Berndt ER, Greenberg PE, Pars-
ley RA, Russell JM, Keller MB, Chronic Depression
Study Group. Improvement in subjective work per-
formance after treatment of chronic depression:
some preliminary results. Psychopharmacol Bull. 1996;
32:33-40.
41. Zhang M, Rost KM, Fortney JC, Smith GR. A com-
munity study of depression treatment and employ-
ment earnings. Psychiatric Services. 1999;50:1209-
1213.
42. Goetzel RZ, Hawkins K, Ozminkowski RJ, Wang
S. The health and productivity cost burden of the “Top
10” physical and mental health conditions affecting
six large US employers in 1999. J Occup Environ Med.
2003;45:5-14.
43. Eaton WW, Anthony JC, Mandel W, Garrison R.
Occupations and the prevalence of major depressive
disorder. J Occup Med. 1990;32:1079-1087.
44. Goff VV. Depression: A Decade of Progress, More
to Do. Washington, DC: National Health Policy Fo-
rum; November 22, 2002. Issue brief 786.
45. Graves EJ. Detailed diagnoses and procedures, Na-
tional Hospital Discharge Survey 1987. National Cen-
ter for Health Statistics, Vital Health Stat 13.1989;
100:1-304.
46. Kozak LJ, Hall MJ, Owings MF. National Hospi-
tal Discharge Survey: 2000 annual summary with de-
tailed diagnosis and procedure data. Vital Health Stat
13. 2002;153:1-194.
47. Cockburn IM, Bailit HL, Berndt ER, Finkelstein SN.
When antihistamines go to work. Bus Health. 1999;
17:49-50.
48. Cull RE, Wells NEJ, Miocevich ML. The economic
cost of migraine. Br J Med Econ. 1992;2:103-115.
49. Kessler RC, Barber C, Birnbaum HG, et al. De-
pression in the workplace: effects on short-term dis-
ability. Health Aff (Millwood). 1999;18(5):163-171.
50. Merkesdal S, Ruof J, Schoffski O, Bernitt K, Zeidler
H, Mau W. Indirect medical costs in early rheuma-
toid arthritis: composition of and changes in indirect
costs within the first three years of disease. Arthritis
Rheum. 2001;44:528-534.
51. Osterhaus JT, Guterman DL, Piachetka JR. Health-
care resource and lost labour costs of migraine head-
ache in the US. Pharmacoeconomics. 1992;2:67-76.
52. Kessler RC, McGonagle KA, Zhao S, et al. Life-
time and 12-month prevalence of DSM-III-R psychi-
atric disorder in the United States. Arch Gen Psychia-
try. 1994;51:8-19.
53. Keller MB, Lavori PW, Mueller TI, et al. Time to
recovery, chronicity, and levels of psychopathology in
major depression: a 5-year prospective follow-up of
431 subjects. Arch Gen Psychiatry. 1992;49:809-
816.
54. Frank E, Prien RF, Jarrett RB, et al. Conceptual-
ization and rationale to consensus definitions of terms
in major depressive disorders: response, remission, re-
covery, relapse, and recurrence. Arch Gen Psychia-
try. 1991;48:851-855.
55. Katon W, Von Korff M, Lin E, et al. Collabora-
tive management to achieve treatment guidelines: im-
pact on depression in primary care. JAMA. 1995;273:
1026-1031.
56. Schulberg HC, Block MR, Madonia MJ, et al. Treat-
ing major depression in primary care practice: eight-
month clinical outcomes. Arch Gen Psychiatry. 1996;
53:913-919.
57. Kirmayer LJ, Robbins JM, Dworkind M, Yaffe MJ.
Somatization and the recognition of depression and
anxiety in primary care. Am J Psychiatry. 1993;150:
734-741.
58. Stahl SM. Does depression hurt? J Clin Psychia-
try. 2002;63:273-274.
59. Parsons T. The Social System. New York, NY: The
Free Press; 1950.
60. Berger ML, Murray JF, Xu J, Pauly M. Alterna-
tive valuations of work loss and productivity. J Oc-
cup Environ Med. 2001;43:18-24.
61. Morgado A, Raoux N, Smith M, Allilaire JF, Wid-
locher D. Subjective bias in reports of poor work ad-
justment in depressed patients. Acta Psychiatr Scand.
1989;80:541-547.
COST OF LOST WORK TIME AMONG WORKERS WITH DEPRESSION
3144 JAMA, June 18, 2003—Vol 289, No. 23 (Reprinted) ©2003 American Medical Association. All rights reserved.
Downloaded From: http://jama.jamanetwork.com/ by a Albert Einstein College of Medicine User on 02/20/2015
Comment. We found that healthy, young, nonobese women
with Turner syndrome exhibit an atherogenic lipid profile com-
pared with 46,XX women of the same age and body composi-
tion with premature ovarian failure. Since advancing age is as-
sociated with decreasing levels of HDL-C and increasing levels
of LDL-C, this unfavorable lipid profile may contribute to the
excess mortality in women with Turner syndrome.
1
Interest-
ingly, in contrast to the obesity-related “metabolic syn-
drome,” insulin sensitivity measured by fasting levels of insu-
lin and glucose appears to be normal in these young women
with Turner syndrome. Because the 2 groups in this study are
similar in gonadal status, adiposity, and lifestyle factors influ-
encing lipid metabolism, the atherogenic lipid profile in Turner
syndrome may be caused by haploinsufficiency for as-yet un-
known X-chromosome gene(s). Given that a number of X-
chromosome genes escape inactivation yet do not have a Y-
chromosome homologue,
7
some of these genes may contribute
to the the more favorable lipid profiles of healthy 46,XX women
compared with men.
Margaret Cooley, BA
Vladimir Bakalov, MD
Carolyn A. Bondy, MD
National Institute of Child Health and Human Development
National Institutes of Health
Bethesda, Md
Acknowledgment: We thank Lawrence Nelson, MD, for allowing us access to his
sample of patients with premature ovarian failure, and Vien Vanderhoof for help-
ing organize the data. We are grateful to the nurses and staff of the National In-
stitutes of Health Clinical Center for supporting our work.
1. Gravholt CH, Juul S, Naeraa RW, Hansen J. Morbidity in Turner syndrome.
J Clin Epidemiol. 1998;51:147-158.
2. Elsheikh M, Conway GS. The impact of obesity on cardiovascular risk factors in
Turner’s syndrome. Clin Endocrinol (Oxf ). 1998;49:447-450.
3. Manson JE, Hsia J, Johnson KC, et al. Estrogen plus progestin and the risk of
coronary heart disease. N Engl J Med. 2003;349:523-534.
4. Bakalov VK, Vanderhoof VH, Bondy CA, Nelson LM. Adrenal antibodies de-
tect asymptomatic auto-immune adrenal insufficiency in young women with spon-
taneous premature ovarian failure. Hum Reprod. 2002;17:2096-2100.
5. Bakalov V, Chen M, Baron J, et al. Bone mineral density and fractures in Turner
syndrome. Am J Med. 2003;115:259-264.
6. Katz A, Nambi SS, Mather K, et al. Quantitative insulin sensitivity check index:
a simple, accurate method for assessing insulin sensitivity in humans. J Clin En-
docrinol Metab. 2000;85:2402-2410.
7. Brown CJ, Greally JM. A stain upon the silence: genes escaping X inactivation.
Trends Genet. 2003;19:432-438.
CORRECTION
Incorrect Data in Abstract and Table and Incorrect Text and Date in Figure: In
the Original Contribution entitled “Cost of Lost Productive Work Time Among
US Workers With Depression” published in the June 18, 2003, issue of T
HE JOUR-
NAL
(2003;289:3135-3144), incorrect data appeared on pages 3135 and 3142.
On page 3135, in the “Results” section of the abstract, the self-reported use of
antidepressants should be 33% [not 30%]. In Table 5 on page 3142, the val-
ues in the “Received a prescription medication for depression or anxiety in the
past 12 mo” row, from left to right, should be 32.4, 33.0, 43.1, 34.9, 36.6, 36.4,
10.9, and 24.7. In the Figure on page 3137, the study referred to in the title should
be the Depressive Disorders Study [not the Depression Disorders Study], and the
text and dates in the topmost box should read “Survey of American Productivity
Audit Subsample (5/20/02-7/11/02)” [not “American Productivity Audit Survey
(11/20/01-7/11/02)”].
LETTERS
2128 JAMA, October 22/29, 2003—Vol 290, No. 16 (Reprinted) ©2003 American Medical Association. All rights reserved.
Downloaded From: http://jama.jamanetwork.com/ by a Albert Einstein College of Medicine User on 02/20/2015
... A systematic review of studies examining risk and protective factors among disaster responders found that occupational factors, such as duration of disaster-related employment, yielded mixed findings, suggesting the need to further study the effects of work-related factors on disaster recovery. 10 Lower work productivity (measured as presenteeism; eg, reduced concentration, working more slowly) 11 has been associated with adverse outcomes, including PTSD. 10,[12][13][14] In contrast, social support has been found to have a protective role during disasters and may be predictive of shorter recovery time. ...
... Work productivity at T1 was assessed using a component of the Work and Health Interview, which specifically assesses presenteeism. 11,24 Participants indicated the percentage of time that work performance was reduced within the past 2 wk on each of 5 items using a 5-point scale: (1) losing concentration; (2) repeating a job; ...
Article
Objective: In times of repeated disaster events, including natural disasters and pandemics, public health workers must recover rapidly to respond to subsequent events. Understanding predictors of time to recovery and developing predictive models of time to recovery can aid planning and management. Methods: We examined 681 public health workers (21-72 y, M(standard deviation [SD]) = 48.25(10.15); 79% female) 1 mo before (T1) and 9 mo after (T2) the 2005 hurricane season. Demographics, trauma history, social support, time to recover from previous hurricane season, and predisaster work productivity were assessed at T1. T2 assessed previous disaster work, initial emotional response, and personal hurricane injury/damage. The primary outcome was time to recover from the most recent hurricane event. Results: Multivariate analyses found that less support (T1; odds ratio [OR] = .74[95% confidence interval [CI] = .60-.92]), longer previous recovery time (T1; OR = 5.22[95%CI = 3.01-9.08]), lower predisaster work productivity (T1; OR = 1.98[95%CI = 1.08-3.61]), disaster-related personal injury/damage (T2; OR = 3.08[95%CI = 1.70-5.58]), and initial emotional response (T2; OR = 1.71[95%CI = 1.34-2.19]) were associated with longer recovery time (T2). Conclusions: Recovery time was adversely affected in disaster responders with a history of longer recovery time, personal injury/damage, lower work productivity following prior hurricanes, and initial emotional response, whereas responders with social support had shorter recovery time. Predictors of recovery time should be a focus for disaster preparedness planners.
... Yet, the relationship between economic outcomes and mental health is bi-directional as mental disorders can also be a cause of important economic outcomes. For instance, low mental health limits human capital accumulation (Currie and Stabile, 2006;Fletcher, 2010) and reduces employment and earnings (Bartel and Taubman, 1986;Frank and Gertler, 1991;Ettner, Frank, and Kessler, 1997;Stewart et al., 2003;Fletcher, 2014;Biasi, Dahl, and Moser, 2021). We contribute to these previous studies by highlighting the consequences of the peer composition in schools for adolescents' psychological well-being in the short-and long-run. ...
Article
This paper studies how peers in school affect students’ mental health. Guided by a theoretical framework, we find that increasing students’ relative ranks in their cohorts by one standard deviation improves their mental health by 6% of a standard deviation conditional on own ability. These effects are more pronounced for low-ability students, persistent for at least 14 years, and carry over to economic long-run outcomes. Moreover, we document a pronounced asymmetry: Students who receive negative rather than positive shocks react more strongly. Our findings therefore provide evidence on how the school environment can have long-lasting consequences for individuals’ well-being.
... Since a substantial part of the working population suffers from mental disorders (9,10), mental illnesses furthermore interfere with the functioning of employees and organizations as well: Several studies provide evidence for a significant relationship between employees' mental health status and their performance (11)(12)(13). Thus, mental disorders contribute to a substantial amount of indirect costs organizations spend, arising from reduced productivity or increased absenteeism of their employees with a mental illness (14,15). ...
Article
Full-text available
Although a substantial part of employees suffers from a mental illness, the work situation of this population still is understudied. Previous research suggests that people with a mental illness experience discrimination in the workplace, which is known to have detrimental effects on health. Building on the stereotype content model and allostatic load theory, the present study investigated whether employees with a mental illness become socially excluded at the workplace and therefore show more days of sick leave. Overall, 86 employees diagnosed with a mental disorder were interviewed and completed online-surveys. Path analyses supported the hypotheses, yielding a serial mediation: The interview-rated severity of the mental disorder had an indirect effect on the days of sick leave, mediated by the symptomatic burden and the social exclusion at the workplace. In the light of the costs associated with absenteeism the present paper highlights the harmfulness of discrimination. Organizations and especially supervisors need to be attentive for signs of exclusion within their teams and try to counteract as early as possible.
... Hakulinen et al. 2019). Other studies focus on the economic consequences and find significantly decreased income levels and production loss among workers having depressive symptoms or disorders-with additionally subsequent risk of unemployment (Stewart et al. 2003;Whooley et al. 2002). ...
Article
Full-text available
Objective Depressive and anxiety disorders are prevalent among employees in general. Still, knowledge regarding the contribution of these disorders to the dynamics of the labor market in terms of working time, sickness absence, and unemployment is scarce. We aim to quantify the linkage of depressive and anxiety disorders with labor market participation using the expected labor market affiliation method (ELMA), in a large sample of Danish employees. Methods We combined three survey waves on occupational health with six high-quality national registers in N = 43,148 Danish employees, of which the 2012 survey contributed 29,665 person years, the 2014 survey 33,043 person years, and the 2016 survey 35,375 person years. We used the new ELMA method to estimate the multi-state transition probabilities and 2-year expected time in work, sickness absence, and unemployment. Depressive and anxiety disorders were assessed by the Major Depression Inventory and the SCL-ANX4 scales, respectively. We adjusted for multiple variables by applying inverse probability weighting in groups of gender and age. Results Depressive and anxiety disorders among employees link to reduced labor market affiliation by significantly changed transitions probabilities between the labor markets states, viewed as reduced working time by 4–51 days (in two years), increased time in sickness absence by 6–44 days (in two years), and unemployment by 6–12 days (in two years) when compared to employees without depression or anxiety disorders. The results were most pronounced for women employees and for employees with both depression and anxiety disorders. Conclusions The study reveals detailed insight into what extent depression and anxiety disorders influence the labor market affiliation, in terms of the complex interrelation between working time, sickness absence, and unemployment. The study emphasizes the importance of preventing and handling depressive and anxiety disorders among employees for strengthening work participation.
... 2 Corresponding figures for Europe and the United States alone are €76 bn and $31 bn respectively (Sobocki et al., 2006;Stewart et al., 2003). ...
Article
Full-text available
Mental health disorders are among the leading causes of disease burden worldwide. Recently, attention has been drawn to the Internet and social media as determinants of the increase in mental health conditions in recent years. In this paper, I analyze the causal effect of broadband Internet access on the mental health of adults. I leverage confidential information on the coordinates of respondents to the German Socio‐Economic Panel (GSOEP) and exploit technological features of the German telecommunication network to instrument for broadband Internet access. The results are suggestive that broadband Internet leads to worse mental health for women (primarily those aged 17–30) but not for men, thus widening the gender gap in mental disorders. Looking at sub‐facets of mental health, broadband access leads to a worsening of socializing behavior and ability to cope with emotional problems. The fact that the results are concentrated among the younger cohorts of women is suggestive that high Internet usage intensity amplifies the negative effect of broadband internet access on mental health.
... The consequences of not achieving functional recovery are hard to ignore (Ekman et al., 2013;James et al., 2018;Kessler et al., 2008;Stewart et al., 2003;Trivedi et al., 2013). Patient-reported sub-optimal functioning is predictive of relapse (IsHak et al., 2013) with far reaching consequences for families, society and healthcare systems. ...
Article
Background Patients with MDD may experience diverse residual symptoms after clinical response to antidepressant treatment. Among these symptoms, cognitive problems in executive functioning are prominent and make functional recovery largely an unmet need for MDD patients. In this study we assessed cognitive symptoms and functional impairment in patients with MDD responding to antidepressant treatment. Methods This was a national, multi-site, non-interventional, cross-sectional study of depressive symptomatology, cognitive performance and psychosocial functioning in Greek outpatients with MDD who had clinically responded to antidepressant treatment. Both clinician- and patient- rated measures were employed. Symptom remission was assessed with the Montgomery Asberg Depression Rating Scale (MADRS) total score (≤12) and functional recovery was assessed with the Sheehan Disability Scale (SDS) score (<6). Results 335 MDD patients participated in the study. After antidepressant monotherapy approximately 60 % of responders and 40 % of remitted patients did not meet the functional recovery criterion. More than 60 % of responders had concentration difficulties as assessed by MADRS item. Patient reported cognitive symptoms were statistically significantly associated with functionality (β coefficient = 0.126, p-value = 0.027). Limitations Non-interventional study design and lack of a control group or active comparator/reference. Conclusions This study highlights the persistence of decreased cognitive performance, particularly in executive functioning in patients with MDD who have shown response and/or remission to antidepressant treatment. This appears to contribute to psychosocial functional impairment. Patient-reported cognitive and psychosocial functioning impairment should be included in routine clinical monitoring of outcomes in MDD treatments.
... The co-occurrence of psychiatric disorders with somatic symptoms is associated with increased somatic severity, more functional disability, higher medical care utilisation, and higher costs than the pathologies apart [3,28]. Each of these disorders is associated with enormous functional impairment, increased disability days and high disease burden [28,29], but the contribution of these disorders when comorbid exceeds that of its separate parts on functional impairment [22]. ...
Article
Full-text available
Introduction: Unexplained distressing bodily complaints like localised heaviness in the body, tingling, heat, pain and crawling sensations, unattributable to physical pathology and psychiatric morbidity, are common among patients that attend Family Medicine Clinic. Objectives: The study assessed patterns of psychiatric morbidity and somatisation symptoms Family Medicine Clinic of a University Teaching Hospital in Enugu, Southeast Nigeria. Methods: A cross-sectional survey of 81 somatising patients were part of a case-control study, selected by a consecutive sampling of 89 patients at the Family Medicine Clinic of the University of Nigeria Teaching Hospital, Enugu. Data was collected using the PHQ-15 and MINI plus English Version 6.0 and analysed with SPSS 21. Results: Seventy-six (93.3%) of the participants had psychiatric diagnoses. The most prevalent symptoms were heat sensations (75.3%), pain sensations (61.7%), crawling sensations (51.9%), heaviness (46.9%) and tingling/paresthesia (29.6%). The mean age at onset was 32.99 years. The mean duration was 6.07 years (±7.58). The study revealed that 76 (93.3%) participants had psychiatric diagnoses, and somatisation disorder was the most prevalent psychiatric disorder 71(87.7%). Conclusion: Knowledge of the patterns of somatisation symptoms and comorbid psychiatric conditions is vital for the effective management of these patients.
Article
Background Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS. Methods Self-identifying black and white American women and men ( n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (⩾33, Impact of Events Scale – Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample). Results Twenty-five percent ( n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 ± 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms. Conclusions These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.
Article
Background: Comorbidity between musculoskeletal (MSK) pain and depression is highly common, and is associated with a greater symptom burden and greater loss of work productivity than either condition alone. Multimodal care programs tackling both physical and mental health components may maximize productivity recovery and return to work. Digital delivery of such programs can facilitate access, ensure continuity of care, and enhance patient engagement. Objective: The aim of this study was to assess the impact of a completely remote multimodal digital care program (DCP) for MSK pain on mental health and work-related outcomes stratified by baseline depression levels. Methods: Ad hoc analysis of an interventional, single-arm, cohort study of individuals with MSK pain undergoing a DCP was performed. Three subgroups with different baseline depression severity levels were established based on responses to the Patient Health Questionnaire (PHQ-9): cluster 1 (score<5: minimal depression), cluster 2 (scores 5-10: mild depression), and cluster 3 (score≥10: moderate depression). The mean changes in depression, anxiety, fear-avoidance beliefs, work productivity, and activity impairment and adherence between baseline and end of program (8-12 weeks) were assessed across subgroups by latent growth curve analysis. Results: From a total of 7785 eligible participants, 6137 (78.83%) were included in cluster 1, 1158 (14.87%) in cluster 2, and 490 (6.29%) in cluster 3. Significant improvements in depression and anxiety scores were observed in clusters 2 and 3 but not in cluster 1, with average end-of-the program scores in clusters 2 and 3 below the initially defined cluster thresholds (score of 5 and 10, respectively). All clusters reported significant improvements in productivity impairment scores (mean changes from -16.82, 95% CI -20.32 to -13.42 in cluster 1 to -20.10, 95% CI -32.64 to -7.57 in cluster 3). Higher adherence was associated with higher improvements in depression in clusters 2 and 3, and with greater recovery in activities of daily living in cluster 3. Overall patient satisfaction was 8.59/10.0 (SD 1.74). Conclusions: A multimodal DCP was able to promote improvements in productivity impairment scores comparable to those previously reported in the literature, even in participants with comorbid depression and anxiety. These results reinforce the need to follow a biopsychosocial framework to optimize outcomes in patients with MSK pain. Trial registration: ClinicalTrials.gov NCT04092946; https://clinicaltrials.gov/ct2/show/NCT04092946.
Article
Objective. —To assess the validity and utility of PRIME-MD (Primary Care Evaluation of Mental Disorders), a new rapid procedure for diagnosing mental disorders by primary care physicians.Design. —Survey; criterion standard.Setting. —Four primary care clinics.Subjects. —A total of 1000 adult patients (369 selected by convenience and 631 selected by site-specific methods to avoid sampling bias) assessed by 31 primary care physicians.Main Outcome Measures. —PRIME-MD diagnoses, independent diagnoses made by mental health professionals, functional status measures (Short-Form General Health Survey), disability days, health care utilization, and treatment/ referral decisions.Results. —Twenty-six percent of the patients had a PRIME-MD diagnosis that met full criteria for a specific disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition. The average time required of the primary care physician to complete the PRIME-MD evaluation was 8.4 minutes. There was good agreement between PRIME-MD diagnoses and those of independent mental health professionals (for the diagnosis of any PRIME-MD disorder, κ=0.71; overall accuracy rate=88%). Patients with PRIME-MD diagnoses had lower functioning, more disability days, and higher rates of health care utilization than did patients without PRIME-MD diagnoses (for all measures, P<.005). Nearly half (48%) of 287 patients with a PRIME-MD diagnosis who were somewhat or fairly well-known to their physicians had not been recognized to have that diagnosis before the PRIME-MD evaluation. A new treatment or referral was initiated for 62% of the 125 patients with a PRIME-MD diagnosis who were not already being treated.Conclusion. —PRIME-MD appears to be a useful tool for identifying mental disorders in primary care practice and research.(JAMA. 1994;272:1749-1756)
Article
Asthma and rhinitis are common chronic conditions that affect adults of working age. Little is known about their relative impacts on work loss and decreased productivity. Using random digit telephone dialing, we carried out a population-survey of adults in Northern California aged 18–50 years. We interviewed 125 persons with asthma (with or without concomitant rhinitis) and 175 persons with rhinitis alone. Study eligibility was based on subject report of a physician's diagnosis of asthma and/or a rhinitis-related condition. Any adult labor force participation since condition onset was lower among those with asthma (88%) than among those with rhinitis alone (97%) (P = 0.002). In contrast, among those still employed, decreased job effectiveness was more frequently reported in the rhinitis group (43 of 121; 36%) compared to those with asthma (14 of 72; 19%) (P = 0.02). Condition-attributed lost work was common in both groups, with more than 20% reporting one or more complete or partial work days lost in the 4 weeks previous to interview. Taking into account age, gender, race, and smoking status, those with asthma were more likely to have no labor force participation after diagnosis (OR = 3.0; 95% CI 1.1–7.7) and less likely to report decreased job effectiveness among those remaining employed (OR = 0.4; 95% CI 0.2–0.9). Excluding subjects from the rhinitis group most likely to have unreported asthma based on past medication use had little impact on these associations. Both asthma and rhinitis negatively affect work productivity. Those with asthma are less likely to be employed at all, while among those remaining on the job, rhinitis is a more potent cause of decreased work effectiveness. The economic impact of asthma and rhinitis and related conditions may be under-appreciated.
Article
The prevalence of depression has rarely been studied in a manner permitting comparisons across a range of occupations. This analysis reveals considerable range in prevalence in 104 occupations of major depressive disorder as defined by the Diagnostic and Statistical Manual (ed 3), and measured by the National Institute of Mental Health's Diagnostic Interview Schedule. Five occupations had prevalence rates above 10%. When adjusted for sociodemographic factors, three occupations yield prevalences with statistically significant elevations in the rate, compared with employed persons generally. The three are lawyers, with an odds ratio of 3.6; other teachers and counselors, with an odds ratio of 2.8; and secretaries, with an odds ratio of 1.9. Exploration of possible sources of these differences concludes the paper. (C)1990 The American College of Occupational and Environmental Medicine
Article
Allergic disorders are a chronic and highly prevalent condition in the general population and the workforce. Their effect on workers and corporate costs go beyond the direct cost of treatment, as the condition can lower a worker's productivity. Previous research includes estimates of the decrease in productivity associated with allergic disorders. None of these studies, however, offered an objective measure of how worker productivity is affected by allergic disorders. In the present study, the productivity of telephone customer service representatives suffering from allergic disorders is examined before, during, and after the ragweed pollen season. In addition, these workers were surveyed as to the type of medication they used in response to their condition. A significant correlation was observed between an increase in pollen counts and a decrease in productivity for workers with allergies. Compared with workers without allergies, employees with allergies who reported using no medication showed a 10% decrease in productivity. No differences were observed among workers with allergies using different types of medications, although the medication groups had significantly higher productivity than the no-medication group. The expected lowered productivity of those workers with allergies who used sedating antihistamines may have been offset by their relatively lower level of symptom severity and by the nature of the job and the productivity measures used.