INSOMNIA AND PERFORMANCE OF US WORKERS
`Insomnia and the Performance of Us Workers: Results from the America
`Insomnia survey
`
`doI: 10.5665/slEEP.1230
`
`Ronald C. Kessler, Phd1; Patricia A. Berglund, MBA2; Catherine Coulouvrat, Md3; Goeran Hajak, Md4; Thomas Roth, Phd5; Victoria shahly, Phd1;
`Alicia C. shillington, Phd6; Judith J. stephenson, sM7; James K. Walsh, Phd8
`1Department of Health Care Policy, Harvard Medical School, Boston, MA; 2Institute for Social Research, University of Michigan, Ann Arbor, MI;
`3Sanofi-Aventis, Paris, France; 4Department of Psychiatry and Psychotherapy, University of Regensburg, Germany; 5Sleep Disorders and Research
`Center, Henry Ford Health System, Detroit, MI; 6Epi-Q, Inc., Oak Brook, IL; 7HealthCore, Inc., Wilmington, DE; 8Sleep Medicine and Research Center,
`St. Luke’s Hospital, St. Louis, MO
`
`Study Objectives: To estimate the prevalence and associations of broadly defined (i.e., meeting full ICd-10, dsM-IV, or RdC/ICsd-2 inclusion
`criteria) insomnia with work performance net of comorbid conditions in the America Insomnia survey (AIs).
`Design/Setting: Cross-sectional telephone survey.
`Participants: National sample of 7,428 employed health plan subscribers (ages 18+).
`Interventions: None.
`Measurements and Results: Broadly defined insomnia was assessed with the Brief Insomnia Questionnaire (BIQ). Work absenteeism and
`presenteeism (low on-the-job work performance defined in the metric of lost workday equivalents) were assessed with the WHo Health and Work
`Performance Questionnaire (HPQ). Regression analysis examined associations between insomnia and HPQ scores controlling 26 comorbid condi-
`tions based on self-report and medical/pharmacy claims records. The estimated prevalence of insomnia was 23.2%. Insomnia was significantly
`1 = 39.5, P < 0.001) but not absenteeism (χ2
`1 = 3.2, P = 0.07), with an annualized
`associated with lost work performance due to presenteeism (χ2
`individual-level association of insomnia with presenteeism equivalent to 11.3 days of lost work performance. This estimate decreased to 7.8 days
`when controls were introduced for comorbid conditions. The individual-level human capital value of this net estimate was $2,280. If we provision-
`ally assume these estimates generalize to the total Us workforce, they are equivalent to annualized population-level estimates of 252.7 days and
`$63.2 billion.
`Conclusions: Insomnia is associated with substantial workplace costs. Although experimental studies suggest some of these costs could be re-
`covered with insomnia disease management programs, effectiveness trials are needed to obtain precise estimates of return-on-investment of such
`interventions from the employer perspective.
`Keywords: Insomnia, epidemiology, employment, absenteeism, presenteeism, comorbidity
`Citation: Kessler RC; Berglund PA; Coulouvrat C; Hajak G; Roth T; shahly V; shillington AC; stephenson JJ; Walsh JK. Insomnia and the perfor-
`mance of Us workers: results from the America Insomnia survey. SLEEP 2011;34(9):1161-1171.
`
`INTRODUCTION
`The societal burden of insomnia in the United States is
`substantial, with an estimated one-third of all US adults expe-
`riencing weekly difficulties with nighttime sleep1 and an es-
`timated 50-70 million people complaining of nighttime sleep
`loss associated with daytime impairment.2 As experimental
`studies increasingly link insomnia with a range of negative
`effects on functioning, from increased sleepiness and fatigue3
`to reduced psychomotor performance,4 memory consolida-
`tion,5 and affect regulation,6 it is unsurprising that insomnia
`has been associated with significant workplace deficits. In-
`deed, adverse effects on work performance are consistently
`ranked among the most prominent components of the over-
`all societal burden of insomnia,7,8 with estimates of annual
`insomnia-related workplace costs due to excess sickness ab-
`sence, reduced work productivity, and workplace accidents-
`
`A commentary on this article appears in this issue on page 1151.
`Submitted for publication December, 2010
`Submitted in final revised form May, 2011
`Accepted for publication May, 2011
`Address correspondence to: Ronald C. Kessler, Phd, department of
`Health Care Policy, Harvard Medical school, 180 longwood Ave., Boston,
`MA 02115; Tel: (617) 432-3587; Fax: (617) 432-3588; E-mail: kessler@
`hcp.med.harvard.edu
`
`SLEEP, Vol. 34, No. 9, 2011
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`1161
`
`injuries in the US civilian workforce ranging between $15
`billion and $92 billion.9,10
`Although such large effects might justify the implementation
`of workplace insomnia screening and intervention programs, ac-
`curate estimates of the workplace costs of insomnia would be
`needed to justify such programs. Estimates of this sort currently
`do not exist, as most available studies are based either on medical/
`pharmacy claims databases that only study treated insomnia10,11
`or on consumer panels that have very low response rates and
`suboptimal measures of insomnia.12 Samples that define insom-
`nia based on treatment risk particularly strong sample bias given
`epidemiologic evidence that only a small minority of Americans
`with chronic insomnia symptoms seek formal medical attention13
`and that few insomniacs receive prescription hypnotics14 or for-
`mal diagnoses due to prominent comorbid conditions.15
`We address the limitations of currently available estimates of
`the workplace costs of insomnia in the current report by using sur-
`vey data collected in the America Insomnia Survey (AIS),1 a na-
`tional survey of employed subscribers to a very large US national
`health plan (over 34 million members) who were selected using
`probability methods that did not oversample subscribers with a
`diagnosis of or treatment for insomnia. We estimate the associa-
`tions of insomnia with work performance controlling for a wide
`range of comorbid conditions. Insomnia was assessed with a clini-
`cally validated fully structured diagnostic screening scale.16 Work
`performance was assessed with a validated questionnaire that has
`The Effects of Insomnia on Work Performance—Kessler et al
`
`Page 1 of 11
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`EISAI EXHIBIT 1036
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`

`

`been widely used in studies of health and work performance.17,18
`Comorbid conditions were assessed using both a series of validat-
`ed self-report screening scales and medical/pharmacy claims data.
`
`METHODS
`
`The Sample
`The AIS was carried out between October 2008 and July 2009
`in a stratified probability sample of 10,094 adult (ages 18 and
`older) members of a large (over 34 million members) national
`US commercial health plan. The sample was restricted to fully
`insured members enrolled for ≥ 12 months to allow medical
`and pharmacy claims data to be used in substantive analyses.
`Sample eligibility was also limited to members who provided
`the plan with a telephone number, could speak English, and
`had no impairment that limited their ability to be interviewed
`by telephone. The sample was selected with stratification to
`match the US national Census population distribution on the
`cross-classification of age (18-34, 35-49, 50-64, 65-74 and
`75+), sex, urbanicity (Census Standard Metropolitan Statistical
`Areas [SMSA], non-SMSA urbanized areas, and rural areas),
`and Census Region (Northeast, South, Midwest, and West). In-
`formation about diagnoses or treatment of sleep disorders was
`ignored in sample selection to make the sample representative
`of all plan subscribers.
`An advance letter was sent to target respondents explain-
`ing that the survey was designed “to better understand how
`health and health problems affect the daily lives of people,”
`that respondents were selected randomly, that participation
`was voluntary, that responses were confidential, that participa-
`tion would not affect health care benefits, and that a $20 in-
`centive was offered for participation. A toll-free number was
`included for respondents who wanted more information or to
`opt out. Once respondents were contacted by telephone, verbal
`informed consent was obtained before beginning interviews.
`The Human Subjects Committee of the New England Insti-
`tutional Review Board approved these recruitment, consent,
`and field procedures. The cooperation rate (the rate of survey
`completion among target respondents with known working
`telephone numbers, including respondents who were never
`reached) was 65.0%. The 10,094 interviews were weighted for
`residual discrepancies between the joint distribution of the so-
`ciodemographic and geographic selection criteria in the sample
`compared to the Census population. A total of 7,428 AIS re-
`spondents were either employed or self-employed.
`In addition to assessing insomnia, the AIS included many
`questions about the correlates of insomnia. In order to reduce
`respondent burden, some questions were administered only to
`probability subsamples. One such set concerned physical and
`mental conditions found in previous research to be comorbid
`with insomnia. Self-report questions about these conditions
`were administered to all AIS respondents reporting any sleep
`problems plus a random 50% of other respondents. The ran-
`dom subsample was assigned a weight of 2.0 (multiplied by the
`weight described in the previous paragraph) in the comorbidity
`sample to adjust for the fact that they represent only half of
`those without sleep problems in the full sample. A total of 4,991
`AIS respondents in this comorbidity subsample were either em-
`ployed or self-employed.
`SLEEP, Vol. 34, No. 9, 2011
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`Measures
`
`Insomnia
`Insomnia in the 30 days before interview was assessed with
`the Brief Insomnia Questionnaire (BIQ), a 32-question fully
`structured interviewer-administered questionnaire developed
`for the AIS to generate insomnia diagnoses according to the
`definitions and inclusion criteria of the 3 major insomnia classi-
`fication systems: the Diagnostic and Statistical Manual, Fourth
`Edition, Text Revision (DSM-IV-TR), International Classifi-
`cation of Diseases-10 (ICD-10), and research diagnostic cri-
`teria/International Classification of Sleep Disorders-2 (RDC/
`ICSD-2) systems. The BIQ allows diagnoses to be generated
`for any one of these systems alone (i.e., ignoring whether or
`not full inclusion criteria are also met for any of the other sys-
`tems). It is also possible, as with the analyses reported here, to
`combine cases that meet criteria for an insomnia diagnosis in
`any of the 3 systems into a single category of broadly defined
`insomnia. Since RDC and ICSD-2 general criteria for insom-
`nia were explicitly developed to be identical, excepting that the
`former are intended for research applications and the latter are
`reserved for clinical assessments,19 we refer to RDC/ICSD-2
`criteria throughout this report. The full text of the BIQ along
`with diagnostic algorithms is available at (http://www.hcp.med.
`harvard.edu/wmh/affiliated_studies.php).
`As noted above, the BIQ was designed to operationalize
`inclusion criteria of DSM-IV-TR, ICD-10, and RDC/ICSD-
`2 diagnoses of general insomnia. The cases considered here,
`hereafter referred to as broadly defined insomnia or insomnia,
`meet full inclusion criteria in at least one of these systems, all
`of which required respondents to report one or more nighttime
`symptom(s) (difficulty initiating sleep, difficulty maintain-
`ing sleep, early morning awakening, or non-restorative sleep)
`in addition to daytime distress/impairment and other criteria
`that vary across systems. The operational definitions of crite-
`ria across systems were harmonized to require that nighttime
`symptoms occur ≥ 3 times per week, continue for ≥ 30 min
`(with the exception of non-restorative sleep [NRS]), and persist
`for a minimum duration of one month.
`Due to familiar difficulties involved with distinguishing pri-
`mary insomnia from insomnia comorbid with physical/mental
`disorders or substance/medication use, no attempt was made
`in the BIQ to operationalize diagnostic hierarchy or organic
`exclusion rules in DSM-IV-TR Criteria C-E or to distinguish
`DSM-IV-TR Primary Insomnia, RDC/ICSD Insomnia Dis-
`order or ICD-10 Non-organic Insomnia from other insomnia
`phenotypes. This decision is consistent with the most recent
`recommendations of the task force revising the DSM criteria.20
`However, medical and pharmacy claims data for the 12 months
`before interview were obtained from the health plan for all AIS
`respondents to study the effects of diagnosed and treated co-
`morbid conditions on correlates of insomnia. The AIS interview
`also obtained self-report assessments of chronic conditions
`known to be associated with insomnia for the same purpose
`(see below). These were introduced as controls in regression
`analyses to adjust for the effects of comorbid conditions. This
`approach is consistent with the recommendations of both the
`2005 NIH State-of-the-Science Conference21 and the 2006 Rec-
`ommendations for Research Assessment of Insomnia.22
`The Effects of Insomnia on Work Performance—Kessler et al
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`

`

`A clinical reappraisal study was carried out with a subsample
`of AIS respondents that oversampled those who screened posi-
`tive in the BIQ. Blinded clinical interviewers who were highly
`experienced sleep medicine experts carried out semi-structured
`clinical interviews to make diagnoses of insomnia accord-
`ing to the definitions and criteria of the 3 systems considered
`here. Psychometric analyses documented good individual-level
`concordance of diagnoses based on the BIQ with these inde-
`pendent hierarchy-free clinical diagnoses.16 Sensitivity of BIQ
`diagnoses based on any of the diagnostic systems (i.e., meeting
`criteria either for a DSM-IV, ICD-10, or RDC/ICSD-2 diag-
`nosis) compared to clinical diagnoses was 72.6%, specificity
`was 98.9%, and area under the receiver operating characteristic
`curve (a measure of classification accuracy insensitive to dis-
`order prevalence) was 0.86. Cohen’s κ was 0.77, a value at the
`upper end of the range conventionally judged to represent sub-
`stantial agreement with clinical diagnoses.23 A more detailed
`description of the BIQ, its validation, and the qualifications of
`the clinical interviewers is presented elsewhere.16
`
`Other Physical and Mental Disorders
`As noted above, medical and pharmacy claims data for the
`12 months before interview along with self-report data on un-
`treated conditions were obtained for disorders and syndromes
`documented in the literature to be associated with elevated rates
`of insomnia.24 A total of 26 such disorders were considered.
`These included cardiometabolic disorders (congestive heart
`failure, diabetes, hypertension), musculoskeletal conditions
`(chronic back or neck pain, osteoarthritis, rheumatoid arthritis),
`respiratory disorders (chronic obstructive pulmonary disease,
`seasonal allergies, chronic bronchitis, emphysema, or other)
`digestive disorders (gastroesophageal reflux disease, irritable
`bowel syndrome, urinary or bladder problems), other sleep
`disorders (sleep apnea, restless leg syndrome), neuropathic
`pain, other chronic pain, migraine, other frequent or severe
`headaches, emotional disorders (major depression, generalized
`anxiety disorders, and a summary measure of any other emo-
`tional disorder), obesity, and climacteric symptoms common to
`perimenopausal women. Diagnoses were obtained from ICD-9
`codes in medical claims and inferred from pharmacy claims.
`Diagnoses based on self-reports were obtained in 2 ways. First,
`a chronic conditions checklist was used based on the list in the
`US National Health Interview Survey25 (http://www.hcp.med.
`harvard.edu/ncs/replication.php). Such checklists have been
`widely used in epidemiological studies and yield more complete
`and accurate reports than estimates derived from responses to
`open-ended questions.26 Methodological studies have docu-
`mented good concordance between such checklists and medi-
`cal records.27-29 Second, a series of validated disorder-specific
`self-report scales was used to detect untreated symptom-based
`conditions.30-34
`
`Absenteeism and work performance
`Work performance was assessed with the WHO Health and
`Work Performance Questionnaire (HPQ).17,18 The HPQ uses
`self-reports about absenteeism (missed days of work) and pre-
`senteeism (low performance while at work transformed to lost
`workday equivalents) to generate measures of lost workdays in
`the month before the interview. Absenteeism was defined on a
`SLEEP, Vol. 34, No. 9, 2011
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`1163
`
`0-100 scale for percent of workdays the respondent missed in
`the past 30 days, while presenteeism was defined on a separate
`0-100 scale, where 0 means doing no work at all on days at
`work and 100 means performing at the level of a top worker.
`Information about salary was used to transform the measures
`of lost work performance from a time metric to a salary metric
`for purposes of estimating human capital loss associated with
`insomnia. Salary was incremented by 30% to estimate fringe
`benefits. Validation studies have documented significant asso-
`ciations (r = 0.61-0.87) of HPQ absenteeism reports with em-
`ployer payroll records18 and significant associations of HPQ
`work performance reports with both supervisor assessments
`(r = 0.52)17 and other administrative indicators of performance
`(0.58-0.72).18
`
`Employment and other sociodemographic variables
`All AIS respondents were asked if they were employed,
`self-employed, unemployed and looking for work, a student,
`homemaker, retired, or something else. All respondents who re-
`ported they were either employed or self-employed (henceforth
`referred to as employed) were asked how many hours they were
`supposed to work (or, if self-employed, how many hours were
`necessary to complete their work) in a typical week. Respon-
`dents who reported that the number of hours varied from week
`to week were asked for an average. Respondents who reported
`they were expected to work as many or few hours as necessary
`to complete their work were asked the average number of hours
`it takes to get their work done in a typical week. Additional
`sociodemographics used as controls included respondent age,
`sex, and years of education.
`
`Analysis Methods
`Linear regression analysis was used to estimate associa-
`tions of insomnia with work performance controlling for so-
`ciodemographics and comorbid conditions. Given that the
`outcome variables are highly skewed with a large proportion
`of respondents having 0 values (i.e., reporting no absenteeism
`and no decrements in work performance), 2-part models and
`generalized linear models (GLMs) were used to investigate
`a number of different functional forms and error structures.35
`Standard model comparison procedures were used to select a
`best-fitting model.36 (Detailed results are available on request.)
`The best-fitting model to predict absenteeism was the GLM
`that assumed a square root link function and a constant error
`variance, while the best-fitting model to predict presenteeism
`was the GLM that assumed a square root link function and a
`gamma error distribution.
`Simulation was used to estimate the individual-level as-
`sociation of insomnia with the outcomes from the GLM
`models. This was required as the GLM model coefficients
`have no obvious substantive interpretation. The simulation
`was carried out by estimating the predicted values of the out-
`comes twice: once based on the parameters from the model
`and considering the actual characteristics of the respondents,
`and the second time based on the assumption that no one had
`insomnia. Individual-level differences between these 2 esti-
`mates were then transformed into the metrics of either days
`or dollars (daily salary plus fringe multiplied by days) and
`then averaged across all respondents with insomnia to obtain
`The Effects of Insomnia on Work Performance—Kessler et al
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`
`

`

`Table 1—Prevalence and sociodemographic distribution of BIQ/broadly defined insomnia1
`among employed AIs respondents (n = 7,428)
`Insomnia
`Prevalence
`%
`(SE)
`
`Multivariate
`OR
`(95% CI)
`
`Bivariate
`OR
`(95% CI)
`
`Age
`18-29
`30-44
`45-64
`65+
`χ2
`
`3
`
`Sex
`Male
`Female
`χ2
`1
`Education
`less than high school
`High school
`some college
`College graduate
`χ2
`Total
`
`3
`
`23.9
`24.2
`23.5
`14.3
`
`(1.1)
`(0.8)
`(0.8)
`(1.5)
`
`19.7
`27.1
`
`(0.6)
`(0.7)
`
`19.9
`25.3
`26.4
`21.5
`
`(4.4)
`(0.9)
`(1.5)
`(0.6)
`
`23.2
`
`(0.5)
`
`(1.4-2.5)
`(1.5-2.5)
`(1.4-2.4)
`
`1.9*
`1.9*
`1.8*
`1.0
`
`25.0*
`
`1.0
`1.5*
`
`
`(1.4-1.7)
`57.1*
`
`(0.5-1.6)
`(1.1-1.4)
`(1.1-1.5)
`
`0.9
`1.2*
`1.3*
`1.0
`
`(1.2-1.6)
`(1.3-1.7)
`(1.2-1.6)
`
`27.5*
`
`(1.3-1.6)
`59.5*
`
`(0.8-1.7)
`(1.1-1.4)
`(1.1-1.5)
`
`1.4*
`1.5*
`1.4*
`1.0
`
`1.0
`1.5*
`
`1.2
`1.2*
`1.3*
`1.0
`
`18.0*
`
`22.0*
`
`*significant association between insomnia and the sociodemographic variable at the
`0.05 level, 2-sided test. 1The Brief Insomnia questionnaire (BIQ) is a validated self-report
`measure of insomnia.16 BIQ/broadly defined insomnia includes cases meeting full criteria for
`insomnia in the BIQ according to ≥ 1 of the following 3 diagnostic systems: dsM-IV, ICd-10,
`and RdC/ICsd-2. diagnoses were made without organic exclusions or diagnostic hierarchy
`rules. see the text for more details.
`
`number of workers in the US civilian labor force
`reported in the most recent (August 2010) US Bu-
`reau of Labor Statistics Current Population Survey
`(www.bls.gov/cps). Although we have no way of
`knowing if the implicit assumption in making these
`calculations that the AIS results apply to the total
`US labor force are correct, we nonetheless believe
`that this exercise is useful in providing a societal
`perspective on the meaning of the results. We then
`examined the population attributable risk propor-
`tion (PARP) of insomnia predicting work perfor-
`mance, which is defined as the incremental (i.e.,
`controlling for all comorbid conditions) propor-
`tion of observed decrements in work performance
`that would not have occurred under the regression
`model if insomnia were eradicated and the insom-
`nia coefficient were due to causal effects of insom-
`nia. So, for example, a PARP of 0.07 would mean
`that 7% of all the work impairment observed in the
`population would be predicted not to occur if all
`cases of insomnia were effectively treated. PARP
`was calculated using the same simulation methods
`described above for estimating the individual-lev-
`el effects of insomnia, except that the mean of the
`discrepancy of predicted HPQ scores is divided by
`the mean in the unrestricted model among people
`with insomnia to define PARP. Statistical signifi-
`cance was consistently evaluated using 0.05-level
`2-sided tests. As the AIS data are weighted, the
`design-based Taylor series method37 implemented
`in a SAS macro38 was used to estimate standard
`errors and evaluate statistical significance.
`
`Table 2—distributions of HPQ absenteeism and presenteeism1 scores
`among employed AIs respondents (n = 7,428)
`Absenteeism
`Est
`(SE)
`7.1
`(0.3)
`0.5
`(1.0)
`
`Presenteeism
`Est
`(SE)
`14.2
`(0.2)
`17.7
`(0.2)
`
`−20.0
`−10.0
`0.5
`6.4
`100.0
`
`(0.6)
`(1.0)
`(1.0)
`
`0.0
`9.9
`17.7
`26.2
`100.0
`
`(0.2)
`(0.2)
`(0.3)
`
`Mean
`Median
`Percentile scores
`99
`75
`50
`25
`0
`
`1The WHo Health and Work Performance Questionnaire (HPQ) is a
`validated self-report measure of work performance.17,18 HPQ absenteeism
`is a continuous 0-100 scale.
`
`estimates of the individual-level associations of insomnia
`with the outcomes.
`In an effort to obtain a rough approximation of the popu-
`lation-level implications of the individual-level results, the in-
`dividual-level estimates were then multiplied by the estimated
`number of US workers with insomnia, which we defined as the
`prevalence of insomnia estimated in the AIS multiplied by the
`SLEEP, Vol. 34, No. 9, 2011
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`1164
`
`RESULTS
`
`Prevalence and Sociodemographic Correlates of Insomnia
`The estimated prevalence of insomnia in the total AIS sub-
`sample of working people was 23.2%. (Table 1) Insomnia
`prevalence was significantly lower among working people who
`were aged 65+ (14.3%) than those who were younger (23.5-
`3 = 25.0, P < 0.001), higher among women than men
`24.2%; χ2
`1 = 57.1, P = 0.001), and higher among
`(27.1% vs. 19.7%; χ2
`respondents with high school (25.3%) or some college (26.4%)
`education than those either with less than high school education
`3 = 18.0, P < 0.001).
`(19.9%) or college graduates (21.5%; χ2
`These associations all persisted in multivariate analyses.
`
`Distributions and Sociodemographic Correlates of Absenteeism
`and Presenteeism
`Absenteeism has a mean value of 7.1% and an inter-quartile
`range (IQR; 25th-75th percentiles) between −10.0% and 6.4%.
`The mean of 7.1% is equivalent to somewhat less than one and
`a half days of absence in a 20-day work month. The negative
`value at the lower end of the IQR represents the fact that some
`workers work more hours than required by their job descrip-
`tion. Comparable values for presenteeism are a mean of 14.2%
`and an IQR between 9.9% and 26.2% (Table 2). That the mean
`is higher for presenteeism than absenteeism suggests that the
`majority of lost work performance in the US civilian workforce
`The Effects of Insomnia on Work Performance—Kessler et al
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`

`

`actually occurs during days when workers are on the job instead
`of absent. If we think of the presenteeism score as a percentage
`of lost work performance, then overall lost work performance
`in the sample as a whole is 20.3%, which is the sum of 7.1%
`due to absenteeism and 13.2% (i.e., 14.2 × [100 – 7.1]) due to
`presenteeism. This means that presenteeism accounts for about
`two-thirds of all lost work performance and absenteeism for
`about one-third.
`Absenteeism was significantly higher and presenteeism
`significantly lower among workers aged 65+ than those aged
`18-64 (10.5% vs. 5.8% to 7.7%; F3,7424 = 4.1, P = 0.006 for ab-
`senteeism; 11.8% vs. 12.9% to 16.4%, F3,7424 = 29.1, P < 0.001
`for presenteeism) (Table 3). Women had slightly higher ab-
`senteeism but lower presenteeism than men (7.8% vs. 6.5%,
`F1,7426 = 3.3, P = 0.07 for absenteeism; 13.4% vs. 15.0%,
`F1,7426 = 29.4, P < 0.001 for presenteeism). Absenteeism was
`higher for workers with no more than a high school educa-
`tion (8.7% to 9.3%) compared to those with at least some
`college education (5.5% to 6.7%, F3,7424 = 3.5, P = 0.015).
`Conversely, presenteeism was lower among workers with
`less than a high school education compared to those with at
`least a high school education (10.0% vs. 13.1% to 14.5%,
`F3,7424 = 7.8, P < 0.001).
`
`Associations of Insomnia with Work Performance
`
`than among workers with either more or less than a high school
`education. (Detailed results of the interactive models are avail-
`able on request.) Based on the implausibility of this interac-
`tion, we chose to focus on the additive specification in further
`analyses (Table 4). The annualized individual-level association
`of insomnia with the composite HPQ measure controlling for
`sociodemographics was 11.3 days of lost work performance for
`each worker with insomnia before controlling for comorbidity
`and 7.8 days after controlling for comorbidity. These individ-
`ual-level decrements in work performance had human capital
`values of $3,274 before controlling for comorbidity and $2,280
`after controlling for comorbidity.
`
`Societal-level associations
`The population-level projections of the individual-level co-
`efficients amount to annual losses in work performance associ-
`
`Table 3—Associations of sociodemographics with absenteeism and presentee-
`ism among AIs employed respondents (n = 7,428)
`Absenteeism
`Mean
`Median
`Est
`(SE)
`Est
`(SE)
`
`Presenteeism
`Mean
`Median
`Est
`(SE)
`Est
`(SE)
`
`Age
`18-29
`30-44
`45-64
`65+
`F3,7424
`
`Sex
`Male
`Female
`F1,7426
`Education
`0-11
`12
`13-15
`16+
`
`(0.8)
`7.7
`(0.5)
`5.8
`(0.6)
`7.4
`(1.4)
`10.5
`4.1*
`
`6.5
`7.8
`
`(0.5)
`(0.5)
`3.3
`
`(3.2)
`9.3
`(0.7)
`9.3
`(0.9)
`5.5
`(0.5)
`6.7
`F 3,7424 = 3.5*
`
`1.0 (1.1)
`0.6 (1.1)
`0.8 (1.6)
`0.6 (3.3)
`
`0.7 (0.9)
`0.5 (1.3)
`
`0.9 (8.7)
`0.0 (1.3)
`1.0 (1.7)
`0.5 (1.1)
`
`16.4 (0.4)
`14.9 (0.3)
`12.9 (0.2)
`11.8 (0.7)
`29.1*
`
`15.0 (0.2)
`13.4 (2.0)
`29.C4*
`
`10.0 (1.2)
`15.5 (0.3)
`13.1 (0.4)
`14.5 (0.2)
`F3,7426 = 7.8*
`
`20.3 (0.6)
`18.7 (0.3)
`16.1 (0.3)
`13.2 (0.9)
`
`18.6 (0.3)
`16.8 (0.3)
`
`12.5 (2.3)
`17.5 (0.4)
`16.6 (0.5)
`18.1 (0.2)
`
`*significant association between mean scores of either absenteeism or
`presenteeism across categories of the sociodemographic variable and at the
`0.05 level, 2-sided test.
`
`Individual-level associations
`As noted above in the section on analysis methods, ex-
`amination of GLM models with a variety of link functions
`and error distributions showed that the best functional forms
`to describe the joint associations of insomnia and sociode-
`mographic variables in predicting absenteeism and presen-
`teeism were a square root link function for both outcomes, a
`normal error distribution for absenteeism, and a gamma er-
`ror distribution for presenteeism. (A table of detailed model
`comparison statistics is available on request.) Results for
`these best-fitting models showed that the net association of
`1 = 39.5, P < 0.001), but not
`insomnia with presenteeism (χ2
`absenteeism (χ2
`1 = 3.2, P = 0.07), was significant after con-
`trolling comorbid conditions.
`Interaction analyses found no significant interactions of
`insomnia with any of the sociodemographics in predicting
`3 = 4.3-7.4, P = 0.06-0.23 for age and educa-
`absenteeism (χ2
`1 = 0.3, P = 0.59 for sex) and no significant interac-
`tion; χ2
`tions of insomnia with either age (χ2
`3 = 3.3, P = 0.34) or sex
`1 = 1.2, P = 0.28) in pre-
`(χ2
`dicting presenteeism. A sig-
`nificant interaction was found
`between insomnia and educa-
`tion in predicting presentee-
`3 = 11.8, P = 0.008),
`ism (χ2
`but inspection of this interac-
`tion showed that it was due
`to a substantively implau-
`sible nonlinear specification
`in which the association of
`insomnia with presenteeism
`was weaker among workers
`with a high school education
`SLEEP, Vol. 34, No. 9, 2011
`
`Table 4—Associations of BIQ/broadly defined insomnia with annualized work loss days due to presenteeism with and
`without controls for comorbidity among employed AIs respondents in the comorbidity subsample (n = 4,991)
`Individual Level
`Aggregate Level (Total US Labor Force)1
`Days/year
`Dollars/year
`Million days/year
`Million dollars/year
`Est
`(SE)
`Est
` (SE)
`Est
` (SE)
`Est
` (SE)
`11.3*
`(0.1)
`3,274*
`(66)
`367.0*
`(4.2)
`91,733.2*
`(8,967)
`7.8*
`(0.1)
`2,280*
`(48)
`252.7*
`(3.0)
`63,157.9* (10,001)
`
`Without control
`With controls
`
`*significant at the 0.05 level, 2-sided test. 1These results are based on a projection to the total civilian Us labor force
`from population estimates in the August 2010 Current Population survey (www.bls.gov/cps).
`
`1165
`
`The Effects of Insomnia on Work Performance—Kessler et al
`
`Page 5 of 11
`
`

`

`ated with insomnia equal to 367.0 million days and $91.7 billion
`before controlling for comorbidity, and 252.7 million days and
`$63.2 billion after controlling for comorbidity. (Table 4) If we
`assume that these associations represent causal effects of in-
`somnia, then complete eradication of insomnia would lead to
`proportional reductions of between 5.4% (0.2) and 7.8% (0.2)
`(standard errors of estimated proportions in parentheses) of
`all the population-level lost work performance due to presen-
`teeism. The lower of these 2 PARP estimates is based on the
`assumption that indirect associations of insomnia with presen-
`teeism associated with comorbid conditions are completely due
`to spurious effects of the comorbid conditions or their causes on
`insomnia and work performance. The higher of the two propor-
`tions is based on the assumption that indirect associations of
`insomnia with presenteeism associated with comorbid condi-
`tions are completely due to indirect causal effects of insomnia
`through the comorbid conditions. Using AIS data, we cannot
`determine which of these two assumptions is the more accurate.
`
`DISCUSSION
`Given the enormous personal and health care policy implica-
`tions associated with insomnia among workers, it is important
`to establish accurate estimates of insomnia occurrence and con-
`sequences in the workplace. Unfortunately, there has been little
`consensus among previous epidemiological studies regarding
`these estimates. The AIS results are consequently valuable in
`providing estimates based on validated measures used in a na-
`tional sample of workers. Results suggest that insomnia is both
`very common in the US workforce and that insomnia is associ-
`ated with substantial lost work performance even after control-
`ling for a wide range of comorbid conditions. Before taking
`these results at face value, though, they have to be evaluated in
`comparison to previous results in the insomnia epidemiology
`literature.
`
`Insomnia Prevalence
`The most striking discrepancy is that the AIS insomnia prev-
`alence estimate is much hig