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Original Contribution |

Sleep Disorders, Health, and Safety in Police Officers FREE

Shantha M. W. Rajaratnam, PhD; Laura K. Barger, PhD; Steven W. Lockley, PhD; Steven A. Shea, PhD; Wei Wang, PhD; Christopher P. Landrigan, MD, MPH; Conor S. O’Brien, BA; Salim Qadri, BS; Jason P. Sullivan, BS; Brian E. Cade, PhD; Lawrence J. Epstein, MD; David P. White, MD; Charles A. Czeisler, PhD, MD, FRCP; for the Harvard Work Hours, Health and Safety Group
[+] Author Affiliations

Author Affiliations: Division of Sleep Medicine (Drs Rajaratnam, Barger, Lockley, Shea, Wang, Landrigan, Cade, Epstein, White, and Czeisler and Messrs O’Brien, Qadri, and Sullivan), and Channing Laboratory (Dr Wang), Department of Medicine, Brigham and Women's Hospital, and Division of General Pediatrics, Department of Medicine, Children's Hospital Boston (Dr Landrigan), and Division of Sleep Medicine (Drs Rajaratnam, Barger, Lockley, Shea, Wang, Landri gan, Epstein, White, and Czeisler), Harvard Medical School; and Sleep HealthCenters (Drs Shea, Epstein, and White), Boston, Massachusetts; and School of Psychology and Psychiatry, Monash University, Clayton, Victoria, Australia (Dr Rajaratnam).


JAMA. 2011;306(23):2567-2578. doi:10.1001/jama.2011.1851.
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Published online

Context Sleep disorders often remain undiagnosed. Untreated sleep disorders among police officers may adversely affect their health and safety and pose a risk to the public.

Objective To quantify associations between sleep disorder risk and self-reported health, safety, and performance outcomes in police officers.

Design, Setting, and Participants Cross-sectional and prospective cohort study of North American police officers participating in either an online or an on-site screening (n=4957) and monthly follow-up surveys (n=3545 officers representing 15 735 person-months) between July 2005 and December 2007. A total of 3693 officers in the United States and Canada participated in the online screening survey, and 1264 officers from a municipal police department and a state police department participated in the on-site survey.

Main Outcome Measures Comorbid health conditions (cross-sectional); performance and safety outcomes (prospective).

Results Of the 4957 participants, 40.4% screened positive for at least 1 sleep disorder, most of whom had not been diagnosed previously. Of the total cohort, 1666 (33.6%) screened positive for obstructive sleep apnea, 281 (6.5%) for moderate to severe insomnia, 269 (5.4%) for shift work disorder (14.5% of those who worked the night shift). Of the 4608 participants who completed the sleepiness scale, 1312 (28.5%) reported excessive sleepiness. Of the total cohort, 1294 (26.1%) reported falling asleep while driving at least 1 time a month. Respondents who screened positive for obstructive sleep apnea or any sleep disorder had an increased prevalence of reported physical and mental health conditions, including diabetes, depression, and cardiovascular disease. An analysis of up to 2 years of monthly follow-up surveys showed that those respondents who screened positive for a sleep disorder vs those who did not had a higher rate of reporting that they had made a serious administrative error (17.9% vs 12.7%; adjusted odds ratio [OR], 1.43 [95% CI, 1.23-1.67]); of falling asleep while driving (14.4% vs 9.2%; adjusted OR, 1.51 [95% CI, 1.20-1.90]); of making an error or safety violation attributed to fatigue (23.7% vs 15.5%; adjusted OR, 1.63 [95% CI, 1.43-1.85]); and of exhibiting other adverse work-related outcomes including uncontrolled anger toward suspects (34.1% vs 28.5%; adjusted OR, 1.25 [95% CI, 1.09-1.43]), absenteeism (26.0% vs 20.9%; adjusted OR, 1.23 [95% CI, 1.08-1.40]), and falling asleep during meetings (14.1% vs 7.0%; adjusted OR, 1.95 [95% CI, 1.52-2.52]).

Conclusion Among a group of North American police officers, sleep disorders were common and were significantly associated with increased risk of self-reported adverse health, performance, and safety outcomes.

Figures in this Article

Sleep disorders affect 50 to 70 million US residents.1 Most are undiagnosed and remain untreated, resulting in adverse health, safety, and performance outcomes that have important economic ramifications.1 Obstructive sleep apnea (OSA)2 is associated with hypertension,3,4 cardiovascular disease,3,5,6 cognitive impairment,7 and increased risk of motor vehicle crashes.8 Insomnia is a risk factor for depression9 and hypertension10 and causes daytime functional impairments3 leading to absenteeism and productivity losses.11 Shift work disorder, affecting approximately 10% of night and rotating shift workers,12 is associated with social disturbances and higher rates of ulcers, unintentional injury, absenteeism, and depression.12 Untreated sleep disorders and chronic sleep deficiency1 increase the risk of unintential motor vehicle crashes and injuries.3 These problems are exacerbated in shift workers, who experience circadian rhythm disruption and chronic sleep deficiency due to their work schedules.13

Police officers frequently work extended shifts and long work weeks,14 which in other occupations are associated with increased risk of errors, unintended injuries, and motor vehicle crashes.15 According to data through the year 2003, more officers are killed by unintended adverse events than during the commission of felonies.14 It has been hypothesized that fatigue—likely due to reduced duration and quality of sleep16 and untreated sleep disorders17—may play an important role in police officer unintentional injuries and fatalities.14 To date, the effect of sleep disorders on police officer health, safety, and performance has not been systematically investigated.

We examined, among North American police officers, the risk of major sleep disorders, including those described above as well as restless legs syndrome and narcolepsy.2 This prospective cohort study examined the association between screening positive for a sleep disorder and self-reported adverse health, safety, and performance outcomes.

Study Population and Recruitment

A total of 4957 sworn police officers in North America (United States 97%, Canada 3%) volunteered to participate in either the online or on-site study component (Figure 1). The on-site portion of the study included intense investigations of a municipal police department serving 1 of the 10 largest US cities and a state police department serving 1 of the 10 most densely populated states. The participating police departments were not from the same state. The on-site cohort was included to achieve a high cooperation rate within those departments and to compare characteristics of responders and nonresponders (in the municipal police department). The online cohort was included to provide a comparison group of police officers from across North America. Both cohorts included monthly follow-up surveys.

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Figure 1. Flow of Participants in the Study in the On-site and Online Cohorts
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To recruit for the online cohort, we corresponded with large law enforcement agencies across North America to solicit participation and placed advertisements in police magazines and newsletters and on police-focused Web sites. For the on-site cohorts, we solicited participation from several municipal and state police departments. Ultimately a municipal department and a state department were selected and agreed to participate in the study protocol. Of the 3329 potential attendees from the departments participating in the on-site study, 2004 participated in informational sessions. In addition, 4018 registered for the online study (Figure 1). Of these police officers, 4957 completed the baseline survey, which included a sleep disorders screening: 1264 (63.1%, cooperation rate18) in the on-site cohort and 3693 (91.9%, participation rate18) in the online cohort, which made them eligible for the cross-sectional analyses.18 In both cohorts, the 3545 officers who had completed at least 1 of the monthly follow-up surveys were eligible for the prospective analyses: 1264 in the on-site and 2281 in the online cohort. This represents a 63.1% cooperation rate and 56.8% participation rate, respectively; these were deemed the prospective study cohort.

The study protocol was approved by the Partners Human Research Committee and was conducted between July 2005 and December 2007. Participants provided written or electronic informed consent and were not informed about study hypotheses. For reporting purposes to the human research committee and to funding agencies, participants were required to self-report race and ethnicity using prespecified categories (Table 1). Participants selected for polysomnography studies received up to $440, and survey participants were eligible for a prize drawing valued at up to $1000.

Table Graphic Jump LocationTable 1. Self-reported Participant Characteristics (N = 4957)
Design

In a cross-sectional baseline survey, we assessed demographics, physical and mental health status, and the risk of sleep disorders. For up to 2 years following the baseline screening, each month we sent all participants an e-mail with a link to a short online survey assessing work-related performance, work hours, and safety. Reminder e-mails were sent to those who did not immediately complete the survey. (The baseline screening questions and follow-up surveys are available from the authors.)

Survey Instruments

The sleep disorders screening questionnaire used validated, self-report screening tools for OSA (Berlin Questionnaire; sensitivity 0.86, specificity 0.77),19 moderate to severe insomnia (Athens Insomnia Scale: sensitivity 0.93, specificity 0.85),20 restless legs syndrome (RLS Epidemiology, Symptoms, and Treatment questionnaire: sensitivity 0.82, specificity 0.90),21 and narcolepsy with cataplexy (Cataplexy Questionnaire: sensitivity 0.92, specificity 0.9522 and Epworth Sleepiness Scale [ESS]23). For shift work disorder, we created a screening tool based on the International Classification of Sleep Disorders, Second Edition (ICSD-2) diagnostic criteria2 (criteria are provided in the eMethods). The municipal police department allowed that only the OSA risk could be assessed for its 659 participants. Excessive sleepiness was assessed using the ESS (sensitivity 0.94, specificity 1.00)23 to examine prevalence of this symptom and to compare across positive and negative sleep disorder groups.

In the baseline survey, participants reported current health status (poor to excellent); previous diagnoses of sleep and other medical disorders (eg, diabetes, cardiovascular disease, gastrointestinal tract disorder, depression, anxiety); likelihood of falling asleep while driving after work; and use of sleeping medications (never or nearly never to nearly every day), caffeine (0->8 servings/d), and alcohol (0- >14 servings/wk). The Maslach Burnout Inventory was used to assess 2 subscales of burnout: emotional exhaustion and depersonalization.24

In the monthly surveys, participants were asked about work and sleep hours, likelihood of falling asleep in various situations and outcome measures of work performance, such as administrative errors, injuries, uncontrolled anger toward suspects or citizens, absenteeism, citizen complaints, and safety violations. The work-hours instrument was previously validated against daily work diaries (for monthly work hours,  = 0.76; for extended shifts, r = 0.94; P < .001),25 which in themselves were validated by direct observation (r = 0.98, P < .001).26

Polysomnographic Assessment

To compare questionnaire OSA screening outcome with polysomnography, full, attended polysomnography studies were performed for 126 participants from the state police department. Of these, 63 had screened positive on the Berlin questionnaire and were the first from the overall study to agree to have sleep studies performed and agree to provide the investigators with access to relevant medical records; 61 had screened negative on the Berlin questionnaire and were selected randomly from the group of participants who screened negative; and 2 had invalid Berlin screening outcomes. Obstructive sleep apnea severity was classified by a sleep specialist, blind to questionnaire outcome, and was made on the basis of both the respiratory disturbance index and the minimal arterial oxyhemoglobin saturation level associated with respiratory disturbances (eMethods).

Data Analyses

Multiple logistic regression models were used for cross-sectional analysis of the associations between health outcomes at baseline and sleep disorder screening outcomes (for positive screening for any sleep disorder, or positive OSA screening). For the prospective analysis, the generalized estimating equations (GEE) method27 was used to assess performance and attentional failures in those who screened positive and those who did not screen positive for any of the sleep disorders because these outcomes were measured monthly over the course of this study and hence likely to be correlated. For performance and attentional failures, we used a logit link in the GEE model for dichotomized outcomes (1 for any number of the performance and attentional failures and 0 for no performance and attentional failures). Supplemental analysis was conducted on the continuous version of each outcome, assuming that the actual number of events follow a Poisson distribution and using a log link in the GEE model.

For the cross-sectional analysis, we adjusted for the potentially confounding effects of age, sex, body mass index (BMI), hypertension, cigarette smoking, alcohol consumption, primary police activity, second job, usual shift length, night shift work, and shift rotation. For the prospective analysis, we adjusted for age, sex, BMI, primary police activity, second job, night shift work, shift rotation, mean total work hours per week, and monthly sleep time. We tested these confounders individually and only included those that were significant (P < .10) in the initial multivariable models. We then applied a backward elimination method to remove those that were not significant in the final model. Only significant variables of P < .05 were left in the final model. Because there were some missing data for some of the confounders, we repeated the analysis using the missing-indicator method.28 Unadjusted and adjusted odds ratios (ORs) with 95% confidence intervals are reported for the complete-case analysis, and adjusted ORs are reported when the missing-indicator method was applied.

For the cross-sectional analysis, we deemed the following primary outcomes: diabetes, depression, burnout (emotional exhaustion), and falling asleep while driving. For the prospective analysis, we deemed the following as the primary outcomes: falling asleep while driving, making a serious administrative error, making fatigue-related errors or safety violations, and having an occupational injury. P < .0125 was considered statistically significant for the 4 primary outcomes of interest in each analysis with Bonferroni adjustment. P < .05 was considered statistically significant in all secondary analyses. Statistical analyses were conducted using SAS for Windows (version 9.2, SAS Institute Inc).

Sample size and power calculations are based on our previous studies examining the effects of extended duration shifts on performance and attentional failures. A similar magnitude of difference was expected in the present study because both sleep disorders and extended durations of wakefulness have been reported to induce neurobehavioral performance impairments comparable with that of alcohol intoxication.3 For the sample size and power calculation, we considered 3 outcomes: falling asleep while driving, having an occupational injury, and making an error or committing a safety violation attributed to fatigue. Assuming that one-third of our sample would screen positive for a sleep disorder, with 15 000 person-months of data, we estimated 90% power to detect the following differences from those who had screened negative vs those who had screened positive for a sleep disorder: rate of falling asleep while driving, 0.06625 vs 0.083 (OR, 1.28); rate of occupational injury, 0.01029 vs 0.017 (OR, 1.71); and rate of error or safety violation due to fatigue, 0.03830 vs 0.051 (OR, 1.36), with a 2-sided z test at P <.05/3 = .0167.

Sample Characteristics

A total of 4957 police officers (mean [SD] age: 38.5 [8.3] years; years of police service: 12.7 [8.1]) completed the baseline survey. All participants reported being 18 years or older and sworn police officers. A total of 3930 officers (79.3%) were overweight or obese and 1663 (33.5%) were obese (Table 1). In 256 participants who attended a follow-up clinic visit, BMI was measured and found to be highly correlated with self-reported BMI (R = 0.912, P < .001).

As is the case nationally, white males made up the majority of officers. Based on the national average, women were slightly overrepresented in our sample (17.4% [95% CI, 16.3%-18.4%] vs 13.7% nationwide) whereas racial/ethnic minorities were somewhat underrepresented (7.9% [95% CI, 7.2-8.7] vs 12.7% for black officers nationwide; 5.9% [95% CI, 5.2-6.5] vs 9.1% for Hispanic officers nationwide31; Table 1). Our sample of onsite participants from the municipal police department (responders) was generally consistent in age, sex, and rank to characteristics of the entire police department, although the responders were slightly younger and had fewer high-ranking officers (eTable 1).

A total of 15 735 monthly surveys were completed during the 2-year follow-up, with a mean (SD) of 684.2 (254.1) completed monthly. Each participant completed a mean of 4.4 (5.2) surveys (median [interquartile range], 2 [1-6]). Officers who participated in the follow-up survey were similar to those who did not in terms of demographics and risk of sleep disorders (eTable 2).

Sleep Disorder Screening

A total of 2003 of 4957 participants (40.4%) screened positive for at least 1 sleep disorder (eTable 3 for demographic comparisons). One thousand six hundred sixty-six participants (33.6%) screened positive for OSA, the most common disorder, followed by 281 (6.5%) with moderate to severe insomnia; 269 (5.4%) with shift work disorder—those reporting excessive wake time sleepiness and insomnia associated with night work—representing 14.5% of those who work night shifts in the cohort; 70 (1.6%) with restless legs syndrome; and 16 (0.4%) with narcolepsy with cataplexy (Table 2). Applying the ICSD-22 criteria for shift work disorder, (ie, excessive wake time sleepiness or insomnia), 1004 (53.9%) of the police officers who work night shifts screened positive. Considering only those who showed both insomnia and night work–associated drowsiness (defined as a moderate to high chance of falling asleep while driving after working nights compared with never or a slight chance during days off, and moderate to high chance of falling asleep during night shift compared with never or slight chance during day shift), 47 (2.5%) on the night shift screened positive. To examine associations between positive sleep disorder screening and health and safety outcomes, the last definition of shift work disorder was used.

Table Graphic Jump LocationTable 2. Sleep Disorder Screening Outcomes for All Participants by Subgroups

Higher proportions of participants in the online and in the on-site municipal police department cohorts screened positive for OSA than in the onsite state police department cohort: 36.0% (95% CI, 34.5%-37.6%) for the online cohort, 32.2% (95% CI, 28.6%-35.7%) for the municipal police cohort vs 20.3% (95% CI, 17.1%-23.5%) for the state police cohort. The state police cohort also reported a significantly lower mean (SD) BMI of 27.7 (3.6) than the municipal police cohort of 28.9 (4.6; P <.001) and the online cohort of 28.8 (4.8; P <.001). (BMI is calculated as weight in kilograms divided by height in meters squared.) Most participants who screened positive did not report a current diagnosis of the disorder (Table 2).

Positive Sleep Disorder Screening and Self-Reported Comorbid Conditions: Cross-Sectional Analysis

Positive screening for any sleep disorder was associated with increased risk of reported health- and safety-related outcomes: 203 (10.7%) of those who tested positive for a sleep disorder reported having depression vs 37 (4.4%) of those who did not screen positive (adjusted OR, 2.20; 95% CI [1.52-3.19]); 399 (34.1%) of the positive-screen group reported burnout (emotional exhaustion) vs 89 (17.9%) in the negative-screen group (adjusted OR, 2.85 [95% CI, 2.16-3.77]), and 388 (20.0%) in the positive-screen group reported falling asleep while driving vs 66 (7.7%) in the negative-screen group (adjusted OR, 3.79 [95% CI, 2.79-5.14]). Several of the secondary outcomes were also significantly associated with positive screening for sleep disorder, specifically gastrointestinal tract disorder, anxiety disorder, pharmacotherapy for insomnia, health status, and burnout (depersonalization) (Table 3 and eTable 4).

Table Graphic Jump LocationTable 3. Comorbidities and Adverse Health Outcomes Associated With Positive Sleep Disorder Screening Result (N = 4957)

Because OSA was the most prevalent disorder reported, we examined associations between positive OSA screening results and risk of reported health- and safety-related outcomes. In addition to the risks observed with positive screening results for any sleep disorder, positive OSA screening was also associated with a diagnosis of diabetes, 84 (5.3%) of the positive-screen group vs 46 (1.5%) in the negative-screen group (adjusted OR, 1.61 [95% CI, 1.05-2.47]; of cardiovascular disease: 54 participants (3.4%) vs 30 (1.0%; adjusted OR, 1.95 [95% CI, 1.20-3.18]); and high caffeine consumption: 141 (12.6%) vs 164 (7.3%; adjusted OR, 1.94 [95% CI, 1.32-2.85]) (Table 4). All of these outcomes were statistically significant when adjusted for potential confounders; however, when adjusted for missing values using the missing indicator method, the significance level for diabetes (P = .03) fell short of statistical significance when accounting for multiple comparisons (P =.0125).

Table Graphic Jump LocationTable 4. Comorbidities and Adverse Health Outcomes Associated With Positive Obstructive Sleep Apnea Screening Result (N = 4957)
Excessive Sleepiness

At baseline, 1312 of 4608 participants (28.5%) who completed the ESS had scores of 11 or higher, which means that they experienced excessive sleepiness.23 Of the total 4957 baseline survey respondents, 2276 (45.9%) reported having nodded off or fallen asleep while driving; 1294 of these (56.9%, 26.1% of the total cohort) reported falling asleep while driving at least 1 to 2 times a month; and 307 (13.5%, representing 6.2% of the total cohort) reported falling asleep while driving at least 1 to 2 times a week.

Those who screened positive for a sleep disorder also had a higher mean (SD) ESS score than those who did not (9.65 [4.25] vs 7.63 [3.80]; P < .001, Figure 2). Furthermore, 38.6% (95% CI, 36.4%-40.8%) of those who screened positive for any sleep disorder had an ESS score of 11 or higher vs 21.5% (95% CI, 19.9%-23.0%; P < .001) of those who did not.

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Figure 2. Epworth Sleepiness Scale Scores
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Epworth sleepiness scale scores range from 0 to 24, with the higher values representing increasing sleepiness. The vertical dashed line represents the cut off for excessive sleepiness. Positive sleep disorder screening result was defined as meeting criteria for any of the sleep disorders assessed. A screening result negative for a sleeping disorder was defined as not meeting criteria for any of the sleep disorders assessed.

Sleep Disorder Risk and Self-Reported Performance: Prospective Analysis

During the 2-year follow-up, 15 735 monthly surveys were collected, 6587 person-months with positive screens for sleep disorders and 9148 with negative screens for a sleep disorder. Of the participants who screened positive for any sleep disorder and responded to the question, 17.9% reported making important administrative errors vs 12.7% of those who screened negative (adjusted OR, 1.43; 95% CI, 1.23-1.67), 14.4% vs 9.2% reported falling asleep while driving (adjusted OR, 1.51; 95% CI, 1.20-1.90), and 23.7% vs 15.5% reported making errors or committing safety violations due to fatigue (adjusted OR, 1.63; 95% CI, 1.43-1.85; Table 5 and eTable 5). Similarly, 34.1% vs 28.5% reported having uncontrolled anger toward a citizen or suspect (adjusted OR, 1.25; 95% CI, 1.09-1.43), 11.2% vs 9.4% incurring citizen complaints (adjusted OR 1.35; 95% CI, 1.13-1.61), 26.0% vs 20.9% absenteeism (adjusted OR, 1.23; 95% CI, 1.08-1.40), and 14.1% vs 7.0% falling asleep during meetings (OR, 1.95; 95% CI, 1.52-2.52). When these outcomes were analyzed as continuous variables rather than as dichotomized outcomes), the results were similar (eTable 6 and eTable 7).

Table Graphic Jump LocationTable 5. Self-reported Performance and Safety Outcomes and Attentional Failures Associated With Positive Sleep Disorder Screening (N = 3545)a

To address possible reporting bias, we calculated these ratios for the 459 police officers who completed at least 1 year of monthly surveys (5508 person-months; eTable 8). The results were comparable, although falling asleep while driving and occupational injury did not reach statistical significance in the smaller sample.

Two hundred eighty-seven participants reported being in a motor vehicle crash during the follow-up period. Motor vehicle crashes were more likely to be reported by those who reported falling asleep while driving (29 of 180 person-months) than by those who did not (1132 of 9980 person-months; risk ratio [RR], 1.49 [95% CI, 1.01-2.20]; P < .05) or by those who reported falling asleep while stopped in traffic (25 of 177 person-months) than by those who did not (872 of 9898 person-months; RR, 1.68 [95% CI, 1.11-2.55], P = .01).

Polysomnography Assessment

Of the 126 completed polysomnograph studies, 116 had complete data available. Forty-four of 54 participants who underwent polysomnography assessment and who screened positive for OSA on the survey (positive predictive value, 81.5%) were classified by the assessment as having mild to moderate, moderate to severe, or severe OSA compared with 28 of 60 (46.7%) of those who screened negative. Eleven of 60 (18.3%) of those who screened negative were found to have moderate to severe or severe OSA (eTable 9). We found that 35 of 44 (positive predictive value, 79.5%) of participants with a BMI of 30 or higher and 11 of 12 (positive predictive value, 91.6%) of participants with BMI of 35 or higher had mild to moderate, moderate to severe, or severe OSA (eTable 9).

Sleep disorders are common and are largely undiagnosed and untreated in North American police officers. Our comprehensive sleep disorders screening program found that 40.4% of police officers reported symptoms consistent with at least 1 sleep disorder. The majority reported not having been diagnosed in the past or not taking regular treatment. Obstructive sleep apnea was the most prevalent disorder, with one-third of officers screening positive. Given that obesity is a major risk factor for OSA3 and that one-third of our sample had a BMI of 30 or higher, the high prevalence of OSA could be anticipated but is nevertheless a concern. There are several factors that might account for the lower prevalence of OSA in the state police department cohort, including their lower reported BMI and the department's physical fitness program, which provides fitness facilities and the opportunity to exercise at all stations during paid work time and job performance standards associated with physical ability.

The OSA prevalence in our sample is comparable with recent reports,32,33 but higher than reported 2 decades ago.34 Although only a subset of patients with OSA report excessive sleepiness, many describe related symptoms (eg, fatigue, nonrestorative sleep, inattention). The prevalence of OSA without a complaint of excessive sleepiness, even in 1993, was 24% in men and 9% in women.34 Today, it is likely even higher, given that the prevalence of some of the major risk factors for OSA (eg, aging and obesity) is increasing. In 2009, self-reported obesity prevalence in the United States was 26.7%, up from 19.8% in 2000.35 In addition, recent improvements in diagnostic technology would likely yield higher apnea prevalence estimates.

Although in-the-line-of-duty death rates in police have decreased by almost half since 1972, the proportion of deaths due to unintentional injury have shown little change and in 2003 were greater than the rate of felonious deaths.14 Across 2009-2010, more than one-third of in-the-line-of-duty deaths were due to motor vehicle crashes.36 Driver sleepiness is a major cause of motor vehicle crashes,3 and excessively sleepy individuals have an increased risk of having more crashes and more serious crashes.37 Obstructive sleep apnea exposes individuals to increased sleepiness and a 2- to 3-fold higher risk of motor vehicle crashes.8 We found that excessive sleepiness is common in police officers, with almost half reporting having fallen asleep while driving and about one-quarter reporting that this occurs 1 to 2 times per month. This is despite police officers apparently recognizing the dangers associated with drowsy driving; in a survey of North American police officers, almost 90% regarded drowsy driving to be as dangerous as drunk driving.38

Police officers who screened positive for a sleep disorder were likely to report more actual and near-miss administrative errors and safety violations. The loss of even 2 hours of nightly sleep for 1 week is associated with decrements in performance comparable with those seen after 24 hours of continuous wakefulness.3 Sleep disorders resulting in chronic sleep deficiency may therefore adversely affect on-the-job performance. Because long work hours are also associated with decrements in performance and attentional failures,13,14 we adjusted for mean work hours in our analysis (eTable 5).

There may be a biological basis to our finding that those who screened positive for a sleep disorder were significantly more likely to report displaying uncontrolled anger toward a citizen or suspect. Yoo et al39 studied changes in the amygdala with functional magnetic resonance imaging and reported that those in a sleep deprived state were unable to appropriately govern behavioral responses to negative emotional stimuli. This may also explain the self-reported increased number of citizen complaints filed against those officers who screened positive for a sleep disorder, although further studies are required to test this potential mechanism.

Cardiovascular disease–related morbidity and mortality and vascular markers associated with these are increased in police officers, and these increases are not fully explained by traditional risk factors.40,41 We found significantly increased risk of diagnosed cardiovascular disease and diabetes in those who screened positive for OSA, which is recognized as a major risk factor for cardiovascular disease, diabetes, hypertension, and stroke, independent of obesity.3,6,42,43 Untreated OSA may therefore contribute to the increased prevalence of cardiovascular disease and diabetes in police. Furthermore, sleep loss is associated with metabolic abnormalities and may be the pathway contributing to sleep-related increases in the risk of obesity and diabetes.3 Many police officers are at an even greater risk of these outcomes because they are often required to work overnight, on rotating shifts, or both. Impaired cardiometabolic responses are observed in healthy volunteers scheduled to eat and sleep out of phase from their habitual times,44,45 and night work greatly increases the risk of progression to diabetes.46 These findings may at least in part explain the increased risk of cardiovascular disease and diabetes in shift workers,47 in particular police officers.48 We note, however, that although we adjusted our analysis for many of the possible variables that influence cardiovascular disease risk, behavioral factors such as physical activity and diet were not assessed and may also account for the observed increased risk.

Our finding of increased risk of adverse mental health outcomes (diagnosed depression and risk of burnout) with positive screening for sleep disorders has significant implications, given the reported increased risk for suicide in police officers.49 Obstructive sleep apnea, in particular the excessive sleepiness symptoms, and insomnia are associated with depression.9 Research has also demonstrated relationships between sleep loss and mental health problems. Resident physicians, who work protracted hours as do police in many municipalities,14 have been found to have rates of depression and burnout roughly double those in the general population.50 Depression and burnout typically develop over the first several months of residency as sleep loss increases51 and are associated with making more errors.50 By analogy, officers who are burned out may be at increased risk of making mistakes on the job, which could compromise public safety; however, further studies will be needed to determine whether this is in fact the case.

This study had several limitations. By collecting data on a monthly basis, we attempted to reduce, but could not eliminate, the effect of recall bias. Performance outcomes and attentional failures were self-reported and therefore may be overreported or underreported. Although participants were assured of confidentiality, underreporting of work-related outcomes may have occurred, possibly due to the stigma associated with these. Additionally, reporting bias could have confounded the results if participants preferentially completed monthly surveys after having had a negative health or safety outcome. However, analysis of a subgroup that completed a full year of monthly surveys yielded a similar pattern of results. Because response rate could not be determined for this study, it is possible that those who participated may not be representative of all North American police officers, although demographic characteristics in municipal police department responders were similar to the entire department. The magnitude of difference in absolute risk of some of the outcomes we assessed was relatively small, and the clinical significance of such differences is unknown. Although we observed a positive relationship between sleep disorders screening result and health and safety outcomes (Table 3), the cross-sectional analysis cannot determine causality. Hypertension and BMI are used in the Berlin questionnaire to identify those at high risk of OSA, which could have contributed to the observed association between OSA risk and cardiovascular disease, even though we adjusted for these variables in our analyses. Finally, the shift work disorder questionnaire remains to be validated.

Questionnaire screening instruments are inherently less precise than objective tests. Although the Berlin questionnaire had a positive predictive value of 81.5% for detecting mild to moderate, moderate to severe, or severe OSA, and negative predictive value of 81.7% for those with moderate to severe or severe OSA, its performance in predicting OSA in our study did not match that reported initially.19 Several factors likely account for this difference. First, the initial validation of the Berlin quesionnaire was conducted among individuals preselected with high pretest probability for OSA, such as sleep clinic populations.19 Second, the techniques we used to record breathing abnormalities are much more sensitive than those used in many previous studies on the prevalence of sleep apnea,34,52 including the initial studies used to validate the Berlin questionnaire in a sleep clinic population.19 Third, different criteria are now used for the definition of hypopnea,53 influencing respiratory disturbance index values.54 As a result of these increases in sensitivity, sleep apnea would now be detected in many individuals who were classified as not having sleep apnea in the initial validation studies for the Berlin questionnaire.19 This may explain why in our study the positive predictive value remained high for all levels of sleep apnea severity, whereas the negative predictive value was high only for those with more severe illness. Moreover, excessive sleepiness is a criterion for positive screening on the Berlin questionnaire, and given that our sample had high incidences of night shift work and chronic sleep loss, it is possible that excessive sleepiness associated with behaviorally induced insufficient sleep syndrome,55 shift work disorder, or OSA were being detected. The utility of the Berlin questionnaire in predicting outcomes in our study may thus have been due in part to the clinical consequences of all 3 of these sleep disorders; for this reason, we extended our prospective analysis to all sleep disorders, rather than to OSA alone. Research to develop more specific diagnostic questionnaires that distinguish OSA from behaviorally induced insufficient sleep syndrome and shift work disorder would be useful because the therapeutic interventions differ.

In conclusion, a large proportion of police officers in our sample showed a positive sleep disorder screening result, which was associated with adverse health, safety, and performance outcomes. Further research is needed to determine whether sleep disorder prevention, screening, and treatment programs in occupational settings will reduce these risks.

Corresponding Author: Charles A. Czeisler, PhD, MD, Division of Sleep Medicine, Brigham and Women's Hospital, Harvard Medical School, 221 Longwood Ave, Boston MA 02115 (charles_czeisler@hms.harvard.edu).

Author Contributions: Drs Rajaratnam and Barger had full access to all of the data and take responsibility for integrity of the data and the accuracy of the data analysis. Drs Rajaratnam and Barger as co-first authors contributed equally to this article.

Study concept and design: Rajaratnam, Barger, Lockley, Landrigan, O’Brien, Cade, White, Czeisler.

Acquisition of data: Rajaratnam, Barger, Lockley, Landrigan, O’Brien, Qadri, Sullivan, Cade, Czeisler.

Analysis and interpretation of data: Rajaratnam, Barger, Shea, Wang, Landrigan, Qadri, Sullivan, Epstein, Czeisler.

Drafting of the manuscript: Rajaratnam, Barger, Shea, Wang, O’Brien, Qadri, Czeisler.

Critical revision of the manuscript for important intellectual content: Rajaratnam, Barger, Lockley, Shea, Landrigan, Sullivan, Cade, Epstein, White, Czeisler.

Statistical analysis: Rajaratnam, Barger, Wang, Landrigan, Qadri, Sullivan.

Obtained funding: Rajaratnam, Lockley, Landrigan, Czeisler.

Administrative, technical, or material support: Rajaratnam, Barger, Shea, Landrigan, O’Brien, Qadri, Cade, Epstein, Czeisler.

Study supervision: Rajaratnam, Landrigan, O’Brien, Epstein, White, Czeisler.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Rajaratnam reported that he has served as a consultant through his institution to Vanda Pharmaceuticals, Philips Respironics, EdanSafe, the Australian Workers' Union, and National Transport Commission, and has through his institution received research grants and unrestricted educational grants from Vanda Pharmaceuticals, Takeda Pharmaceuticals North America, Philips Lighting, Philips Respironics, Cephalon, and ResMed Foundation, and reimbursements for conference travel expenses from Vanda Pharmaceuticals. His institution has received equipment donations or other support from Optalert, Compumedics, and Tyco Healthcare. He has also served as an expert witness and consultant to shift work organizations. Dr Barger reported receiving a research grant through her institution from Cephalon, receiving payment for lectures from Vital Issues in Medicine and National Sleep Foundation, and serving as a consultant for Alertness Solutions. Dr Lockley reported that he received 2 investigator-initiated research grants from the ResMed Foundation and an unrestricted equipment gift from ResMed Inc, in support of the studies described in this article; receiving consulting fees from Apollo Lighting, Naturebright, Sound Oasis, and Wyle Integrated Science and Engineering, and federally funded projects at Brigham and Women's Hospital, Thomas Jefferson University, and Warwick Medical School; lecture fees from Takeda Pharmaceuticals North America, I Slept Great/Euforma, LLC, and Emergency Social Services Association Conference, UK; unrestricted equipment gifts from Philips Lighting and Bionetics Corporation; an unrestricted monetary gift to support research from Swinburne University of Technology, Australia; a fellowship gift from Optalert, Pty Ltd, Melbourne, Australia; advance author payment and royalties from Oxford University Press, and honoraria from Servier Inc for writing an article for Dialogues in Clinical Neuroscience and from AMO Inc, for writing an educational monograph, neither of which refer to the companies' products; honoraria or travel and accommodation support for invited seminars, conference presentations or teaching from the Second International Symposium on the Design of Artificial Environments, Eighth International Conference on Managing Fatigue, American Academy of Sleep Medicine, American Society for Photobiology, Apollo Lighting, Bar Harbor Chamber of Commerce, Bassett Research Institute, Canadian Sleep Society, Committee of Interns and Residents, Coney Island Hospital, FASEB, Harvard University, Illinois Coalition for Responsible Outdoor Lighting, International Graduate School of Neuroscience, Japan National Institute of Occupational Safety and Health, Lightfair, National Research Council Canada, New York Academy of Sciences, North East Sleep Society, Ontario Association of Fire Chiefs, Philips Lighting, Thomas Jefferson University, University of Montreal, University of Tsukuba, University of Vermont College of Medicine, Utica College, Vanda Pharmaceuticals, Velux, Warwick Medical School, Woolcock Institute of Medical Research, and Wyle Integrated Science and Engineering (NASA); investigator-initiated research grants from Respironics Inc, Philips Lighting, Apollo Lighting, and Alcon Inc; and a service agreement and sponsor-initiated research contract from Vanda Pharmaceuticals. Dr Lockley also holds a process patent for the use of short-wavelength light for resetting the human circadian pacemaker and improving alertness and performance which is assigned to the Brigham and Women's Hospital per Hospital policy and has received revenue from a patent on the use of short-wavelength light, which is assigned to the University of Surrey. Dr Lockley has also served as a paid expert witness on behalf of 2 public bodies on arbitration panels related to sleep, circadian rhythms, and work hours. Dr Shea reported receiving fees for interpretation of polysomnographic sleep studies of participants in this study from Sleep HealthCenters, a sleep medicine specialty practice group; receiving fees for attendance at directors meeting of the American Academy of Sleep Medicine; and lecture fees from Tufts University School of Dental Medicine. Dr Landrigan reported serving as a consultant for Vital Issues in Medicine, developing an educational course for physicians on Shift Work Disorder (supported by an unrestricted educational grant from Cephalon Inc to Vital Issues in Medicine); and AXDev, to assist in the development of a study of Shift Work Disorder (supported by an unrestricted research grant from Cephalon Inc to AXDev). Dr Landrigan also reported receiving monetary awards, honoraria, and travel reimbursement from multiple academic and professional organizations for delivering lectures on sleep deprivation and safety. Mr O’Brien reported receiving support for travel to meetings for the study or other purposes from National Institute of Justice and Centers for Disease Control and Prevention. Dr Cade reported receiving a monetary award from the World Sleep Federation. Dr Epstein reported being employed by Sleep HealthCenters, a sleep medicine specialty practice group. Dr White reported being employed as the chief medical officer for Philips Respironics, for which he owns stock. Dr Czeisler reported that he has received consulting fees from or served as a paid member of scientific advisory boards for: Actelion Ltd; Bombardier Inc; Boston Celtics, Celedon Trucking, Cephalon, Inc, Delta Airlines, Eli Lilly and Co, Garda Síochána Inspectorate, Gerson Lehrman Group, Global Ground Support, Johnson & Johnson, Koninklijke Philips Electronics, NV, Minnesota Timberwolves, Norfolk Southern, Novartis, Portland Trail Blazers, Philips, Respironics Inc, sanofi-aventis Group, Sepracor Inc, Sleep Multimedia Inc, Somnus Therapeutics Inc, Vanda Pharmaceuticals, Inc, and Zeo Inc. Dr Czeisler reported that he owns an equity interest in Lifetrac Inc, Somnus Therapeutics Inc, Vanda Pharmaceuticals Inc, and Zeo Inc; that he has received royalties from the Massachusetts Medical Society/ New England Journal of Medicine, McGraw Hill, the New York Times, Penguin Press, and Philips Respironics Inc. Dr Czeisler reported receiving lecture fees from the Accreditation Council of Graduate Medical Education, Alliance for Epilepsy Research, American Academy of Sleep Medicine, Cephalon Inc, Duke University School of Medicine, Harvard School of Public Health, Hokkaido University Graduate School of Medicine, Japan Aerospace Exploration Agency (JAXA), LOTTE Health Products, Mount Sinai School of Medicine, National Academy of Sciences, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK/NIH), National Sleep Foundation, New England College of Occupational and Environmental Medicine (NECOEM), North East Sleep Society, Office of Rare Diseases Resarch (NIH), Rockpointe, sanofi-aventis Inc, Sleep Research Society, Society for Obstetric Anesthesia and Perinatology (SOAP), St Lukes Roosevelt Hospital, University of Chicago, University of Colorado, University of Pittsburgh, University of Virginia Medical School, University of Washington Medical Center, University of Wisconsin Medical School, and the World Federation of Sleep Research and Sleep Medicine Societies. Dr Czeisler reported that he has received research prizes with monetary awards from the American Academy of Sleep Medicine and Sleep Research Society, a clinical trial research contract from Cephalon Inc, and an investigator-initiated research grant from Cephalon Inc and that his research laboratory at the Brigham and Women's Hospital has received unrestricted research and education funds and support for research expenses from Cephalon Inc, Koninklijke Philips Electronics NV, ResMed, and the Committee for Interns and Residents and the Brigham and Women's Hospital. The Harvard Medical School Division of Sleep Medicine (HMS/DSM), which Dr Czeisler directs, has received unrestricted research and educational gifts and endowment funds from Boehringer Ingelheim Pharmaceuticals Inc, Cephalon Inc, George H. Kidder Esq, Gerald McGinnis, GlaxoSmithKline, Herbert Lee, Hypnion, Jazz Pharmaceuticals, Jordan's Furniture, Merck & Co Inc, Peter C. Farrell, PhD, Pfizer, ResMed, Respironics Inc, sanofi-aventis Inc, Sealy Inc, Sepracor Inc, Simmons, Sleep Health Centers LLC, Spring Aire, Takeda Pharmaceuticals, and Tempur-Pedic. The HMS/DSM has received gifts from many outside organizations and individuals including: Brigham and Women's Hospital (Development Office), Catalyst Group, Cephalon Inc, Committee for Interns and Residents, Eisai Inc, Farrell Family Foundation, Fisher & Paykel Healthcare Corp, Jordan's Furniture, Lilly USA, LLC, Neurocare Center for Sleep, Philips-Respironics Inc, Praxair US Homecare, sanofi-aventis Inc, Select Comfort Corp, Sepracor Inc, Sleep HealthCenters LLC, Somaxon Pharmaceuticals, Synchrony Healthcare Communications, Vanda Pharmaceuticals Inc, Wake Up Narcolepsy Inc, Watermark Medical, and Zeo Inc. The HMS/DSM Sleep and Health Education Program has received Educational Grant funding from Cephalon Inc, Takeda Pharmaceuticals, sanofi-aventis Inc, and Sepracor Inc. Dr Czeisler is the incumbent of an endowed professorship provided to Harvard University by Cephalon Inc and holds a number of process patents in the field of sleep and circadian rhythms (eg, photic resetting of the human circadian pacemaker). Since 1985, Dr Czeisler has also served as an expert witness on various legal cases related to sleep, circadian rhythms, or both.

Funding/Support: This study was supported by grant 2004-FS-BX-0001 and grant 2010C-10002 from the National Institute of Justice, Office of Justice Programs, US Department of Justice; grants R01 OH008496 and R01 OH009403 from the Centers for Disease Control and Prevention; and grants from the ResMed Foundation.

Role of the Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.

Disclaimer: Points of view in this document are those of the authors and do not necessarily represent the official position or policies of the US Department of Justice.

Online-Only Material: The Author Video Interview is available here.

Additional Contributions: We thank Richard Coleman, PhD, and Frank Speizer, MD, for study design; Jeffrey Ellenbogen, MD, MMSc, for clinical investigations; and Mary Kay Landon, PhD, Sarah Edwards, MD, Marissa Moritz, PA-C, Erin Flynn-Evans, PhD, Jeff Tarpy, ALM, Amy Hallal, and Sean Bendix for Study coordination and assistance, all of which was performed as part of of their regular duties at the Division of Sleep Medicine, Brigham and Women’s Hospital. We thank Richard Allen, PhD, Johns Hopkins University, Murray Johns, PhD, Optalert Ltd, Emmanuel Mignot, MD, PhD, Stanford School of Medicine, and Kingman Strohl, MD, Case Western Reserve University, for their advice about the selection of sleep disorder screening questionnaires, and Peter Brown, Brigham and Women's Hospital, and Chief Inspector Kathleen M. O’Toole, Garda Siochana Inspectorate, for assistance in recruitment of participating police department, all without compenstion from the study sponsor. We also thank the police departments, and police unions for their participation.

This article was corrected for errors on December 20, 2011.

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Figures

Place holder to copy figure label and caption
Figure 1. Flow of Participants in the Study in the On-site and Online Cohorts
Graphic Jump Location
Place holder to copy figure label and caption
Figure 2. Epworth Sleepiness Scale Scores
Graphic Jump Location

Epworth sleepiness scale scores range from 0 to 24, with the higher values representing increasing sleepiness. The vertical dashed line represents the cut off for excessive sleepiness. Positive sleep disorder screening result was defined as meeting criteria for any of the sleep disorders assessed. A screening result negative for a sleeping disorder was defined as not meeting criteria for any of the sleep disorders assessed.

Tables

Table Graphic Jump LocationTable 1. Self-reported Participant Characteristics (N = 4957)
Table Graphic Jump LocationTable 2. Sleep Disorder Screening Outcomes for All Participants by Subgroups
Table Graphic Jump LocationTable 3. Comorbidities and Adverse Health Outcomes Associated With Positive Sleep Disorder Screening Result (N = 4957)
Table Graphic Jump LocationTable 4. Comorbidities and Adverse Health Outcomes Associated With Positive Obstructive Sleep Apnea Screening Result (N = 4957)
Table Graphic Jump LocationTable 5. Self-reported Performance and Safety Outcomes and Attentional Failures Associated With Positive Sleep Disorder Screening (N = 3545)a

References

National Center on Sleep Disorders.  Research: NIH Sleep Disorders Research Plan. Bethesda, MD: National Institutes of Health; 2011. NIH publication 11-7820. http://www.nhlbi.nih.gov/health/prof/sleep/sleep_splan.htm. Accessed November 28, 2011
American Academy of Sleep Medicine.  International Classification of Sleep Disorders: An Unmet Public Health Problem. Revised: Diagnostic and Coding Manual. 2nd ed. Westchester, IL: AASM; 2005
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PubMed   |  Link to Article
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