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. Author manuscript; available in PMC: 2022 Feb 1.
Published in final edited form as: Sleep Health. 2020 Oct 6;7(1):118–122. doi: 10.1016/j.sleh.2020.08.002

Association between Obstructive Sleep Apnea and Multiple Involuntary Job Loss History Among Recently Unemployed Adults

Graciela E Silva 1, Stuart F Quan 2,3, Taryn McMorrow 4, Rueben Bautista 5, Melanie L Bell 5, Patricia L Haynes 5
PMCID: PMC7925345  NIHMSID: NIHMS1635598  PMID: 33036951

Abstract

Background:

Obstructive sleep apnea (OSA) has been associated with negative occupational outcomes including absenteeism and poor work productivity. This analysis explored whether the severity of OSA was associated with multiple involuntary job loss history among recently unemployed adults.

Methods:

This is a cross-sectional analysis of data from the screening visit of the ADAPT study. Information was collected from 261 participants who recently involuntarily lost their jobs. Data included demographics, employment, medical history, and results from a limited channel home sleep apnea test. The respiratory event index (REI) was categorized as <5 events per hour (no-OSA), 5 - < 15 (mild OSA) and ≥ 15 (moderate to severe OSA). Logistic regression and propensity score matching were used to identify factors associated with multiple involuntary job loss.

Results:

A total of 44.8% of participants reported multiple involuntary job loss. Those with mild OSA had 1.85 (95% CI: 1.04, 3.28) increased odds of reporting multiple involuntary job loss as compared to participants with no OSA in the unadjusted model; while participants with moderate-to-severe OSA had 2.71 (95% CI: 1.33, 5.70) increased odds. After adjusting for age, sex, ethnicity, beginning work between 7–9 am, job type, and, compensation type, the odds of involuntary job loss among participants with moderate-severe OSA were 2.46 (95% CI: 1.13, 5.52) as compared to having no OSA.

Conclusions:

In a sample of recently unemployed adults, having OSA significantly increased the odds of reporting previous involuntary job losses. This study suggests OSA could be a risk factor for job loss.

Keywords: Obstructive Sleep Apnea, unemployment, job loss

Unstructured Abstract:

Obstructive sleep apnea (OSA) has been associated with negative occupational outcomes including absenteeism and poor work productivity. This analysis explored whether the severity of OSA was associated with multiple involuntary job loss history among recently unemployed adults. It was a cross-sectional analysis of data from the screening visit of the ADAPT study, an ongoing 18-month closed cohort examining sleep and daily activity following involuntary job loss. Information was collected from 261 participants who recently involuntarily lost their jobs. Data included demographics, employment, medical history, and results from a limited channel home sleep study. Logistic regression and propensity score matching were used to identify factors associated with multiple involuntary job loss. After adjusting for age, sex, ethnicity, beginning work between 7–9 am, job type, and, compensation type, the odds of involuntary job loss among participants with moderate-severe OSA were 2.46 (95% CI: 1.13, 5.52) as compared to having no OSA. In a sample of recently unemployed adults, having OSA was significantly associated with increased odds of reporting previous involuntary job losses. This study suggests OSA could be a risk factor for job loss.

Introduction

Sleep disorders are a significant and growing health burden worldwide 1. As many as 23% of Americans and 18% of Europeans suffer from chronic sleep conditions putting them at-risk for serious health outcomes including cardiovascular disease, cognitive impairment, and metabolic dysfunction 2,3. Occupational health studies have also identified sleep problems as major contributors to lost productivity, rising healthcare costs, and excessive rates of workplace accidents and injuries 4. One study examining sleep disorders in older workers found that sleep disorders were strongly associated with difficulties coping with work demands, job insecurity, conflict with colleagues, and lack of workplace social interactions 5.

Obstructive sleep apnea (OSA) is characterized by upper airway blockage leading to repeated episodes of partial reductions in breathing (hypopneas) or complete pauses (apneas) occurring during sleep 6. It is estimated that the US prevalence of moderate to severe OSA (apnea-hypopnea index, measured as events/hour, ≥15) is 17% among 50 – 70-year-old men and 9% among women of the same age range 7. Health outcomes associated with OSA include excessive daytime sleepiness, fatigue, cognitive dysfunction and a variety of chronic diseases 8,9. Similarly, OSA has been associated with adverse work performance and is a contributing factor to workplace accidents, early deaths, and motor vehicle accidents 8–10.

Studies have found that OSA incidence and severity rank this disease amid the more expensive 11. The economic burden from SDB is not only related to the disease itself and its complications, but from other social consequences, the reduced ability to work and hold employment 12. For instance, a large study in Denmark 13 utilized the National Patient Registry and other public databases to compare the social consequence of health costs and lost wages of 530,278 patients with OSA to age-, sex-, and community location-matched controls. A significantly larger proportion of patients with OSA received social services, and fewer patients with OSA received income from employment as compared to the control subjects. In addition, OSA patients had significantly lower income from employment over the 12 years prior to being diagnosed and subsequently after diagnoses. Excess yearly direct net health and foregone earnings (indirect costs) were €2,174 prior to diagnosis and €3,988 after diagnosis for sleep apnea patients compared to controls.

Despite what we know about the negative effects of OSA on job performance, there is a scarcity of research on the relationship between OSA and job loss. Job loss is a stressful, involuntary life event that can lead to substantial reduction in long-term earnings, lower job quality, risk for future displacement, and declines in psychological and physical well-being for both the individual and family 14. Unemployment is a broader category that encompasses individuals who have lost their jobs as well as individuals who elect not to work for a variety of reasons such as caretaking and disability. The cost of being diagnosed with OSA may average between $2,105 - $ 1,190 per person per year 15. However, health insurers and employers have been slow to adopt aggressive OSA diagnosis and treatment plans that could ameliorate the worsening of comorbidities over time. Early diagnosis and treatment of OSA can help avoid the inexorable increase of long-term healthcare costs, preserve worker health, and maintain employee productivity 16. Patients diagnosed with OSA require a significant financial and personal time investment associated with treatment of this condition. However, diagnosis with OSA may be perceived as inconsequential by many individuals, who may be unaware of the long-term impact on quality of life, job performance, or experienced fatigue, as they may have become unaware or accustomed to it 17.

The prevalence of OSA has been associated with comorbidities such as diabetes, asthma, hypertension, stroke, and heart disease, which can increase the risk of fatal and non-fatal cardiovascular events, affecting worker productivity 18–20. Chronic diseases, such as OSA, often affect patients’ physical and mental quality of life, energy levels, and interpersonal relationships, conceivably having a direct impact on their job productivity, including underperformance, absenteeism, and adverse workplace behavior 21. Individuals with low quality of life, may in turn resort to substance abuse, including sleep medication 22. All of these aforementioned factors lead to combinations of suboptimal job performance, excessive absenteeism or presenteeism, or excessive employer medical expenses which eventually lead to the outcome of job loss.

The purpose of this study was to evaluate whether undiagnosed OSA is associated with a history of multiple job loss among individuals recently displaced from their jobs. In combination with what we know about the effect of OSA on job performance, support for this hypothesis would indicate that OSA may be a risk factor for job loss.

Methods

Study Design

The current project is a cross-sectional analysis of screening visit data from the Assessing Daily Activity Patterns Through Occupational Transitions (ADAPT) Study; design and methods for the ADAPT study are described in detail elsewhere 23. The overall objective of the ADAPT study is to explore whether social rhythms and sleep are risk factors for weight gain following involuntary job loss. In brief, it is an 18-month cohort research design which examined social rhythms, sleep, dietary intake, energy expenditure, waist circumference, and weight gain in individuals who had sustained involuntary job loss. Six assessment visits were completed approximately every three months. Recruitment for this study began in October 2015 primarily through flyers sent weekly by the Arizona Department of Economic Security to individuals applying for unemployment insurance in Tucson, Arizona. Participants in the current analysis included those who completed the screening visit for ADAPT. Participants were eligible to participate in the screening visit if they were between 25 and 60 years of age and if they had experienced involuntary job loss in the prior 90 days from a previous position where they worked 30 or more hours per week for a minimum of six months. During the screening visit, participants were administered a screening interview and questionnaire ascertaining demographic information. After enrollment, study staff collected demographic data, medical and employment histories and gave participants instructions on the use of a portable sleep testing device 24. Participants were included in the analysis if they completed the entire screening visit evaluation, complied with the inclusion criteria, and completed an overnight limited channel home sleep study. Participants were excluded if they were pregnant or less than three months postpartum; reported a diagnosed eating disorder, current substance use disorder, severe mental illness, history of sleep disorder diagnosis other than insomnia; or participated in a weight loss program or had or were planning weight loss surgery. At the time of analysis for this manuscript 404 participants had been screened during the baseline visit, of these, 143 did not meet inclusion criteria, met the exclusion criteria, or withdrew after consent. The other 261 participants completed a home sleep apnea test (ApneaLink Plus™), a semi-structured interview, and questionnaires and were included in the study. We compared demographic characteristics between these two groups and found no statistically significant differences by age, gender, race, or ethnicity, and therefore we presume no selection bias was present. A study flow diagram has been previously published 25. The study protocol was approved by the University of Arizona Human Subjects Protection Program prior to data collection.

Variables

The outcome variable was ‘multiple involuntary job loss.’ Participants were asked: “Have you ever been laid off from other jobs in the past?” A positive response indicated a history of multiple involuntary job losses, and a negative response signified that the current job loss was the only involuntary job loss participants had experienced26.

Sleep data were obtained from recordings taken using an ApneaLink Plus™ Monitor (ResMed), a validated Type III portable sleep testing device, administered at participants’ homes over one night 24. The ApneaLink Plus™ records respiratory effort via an inductance chest belt, oxygen saturation and heart rate by pulse oximetry, and air flow by a nasal cannula connected to a pressure transducer to generate a respiratory event index (REI). Apnealink data were scored by a registered polysomnographic technologist and interpreted by a physician board-certified in sleep medicine according to American Academy of Sleep Medicine (AASM) guidelines 27,28. The REI was categorized as <5 events per hour (no-OSA), 5 – <15 (mild OSA) and ≥15 (moderate to severe OSA).

Additional explanatory variables were selected a priori to be included in the analysis. They included employment classification based on the 2010 Department of Labor Standard Occupational Classification with employment type dichotomized as 1) management, healthcare or sales occupations or, 2) other. In addition, covariates included shift schedule (whether a participant usually began their shift between 7–9am), compensation type as hourly or salaried, age, gender (male, female), and ethnicity (White, non-White).

Statistical analysis

Descriptive statistics were used to characterize participants by OSA categories. Pearson’s chi-squared tests (χ2) and analysis of variance F-tests (ANOVA) were employed to assess associations between OSA categories and explanatory variables. Crude and adjusted odds ratios and 95% confidence intervals were calculated from logistic regression models to relate OSA categories to reported multiple involuntary job loss using no OSA as the reference category. The adjusted model controlled for age, gender, ethnicity, shift schedule, employment type, and compensation type. Covariates were selected prior to analysis and consisted of variables associated with OSA and employment status in previous research 4.

Because this was an observational study, we used propensity score matching to conduct sensitivity analysis. Propensity score matching is a statistical approach that is used to approximate experimental designs by estimating the participant’s probability of risk factor group (OSA, in this study) using logistic regression 30. Participants are matched so that differences between groups are more reflective of the true effect of multiple job losses in the population. We used age, gender, ethnicity, shift schedule, job type, and payment type as predictors in the propensity score model.

A nearest-neighbor method of matching was used to pair observations with similar propensity scores. To assess the performance of the matching, balance diagnostics were performed on the matched data set. Visual inspection of the propensity score against the variable broken down by OSA status and two-sample t-tests for difference in mean between OSA groups were performed on continuous predictors. For all predictors, the absolute standardized deviance (ASD) was calculated. A visual inspection with overlapping ASDs (or close to overlapping), a non-significant p-value from the t-tests, and ASD close to zero indicated balance in the matched data. After propensity score matching and balance assessment, a univariate logistic regression model was fitted on the matched data with history of multiple involuntary job loss as the response variable and OSA as the predictor. The odds ratio for history of multiple involuntary job loss for OSA and its 95% confidence interval were determined. Statistical analyses were conducted in R [version 3.5.1 (2018–07-02)] and ‘MatchIt’ (Version 3.0.2). All tests were two-sided and performed at a significance level of α=0.05.

Results

Demographics

There were 261 participants who completed the screening visit and had complete data available for analysis. The average age of the participants was 41 years. The majority were female (57.9%) and white ethnicity (66.3%), and 44.8% had a history of multiple job loss. They started work between 7–9 am (74.7%) and were compensated hourly (73.2%). Most participants had jobs in fields other than management, healthcare, and sales (64%) (Table 1). There were significant differences in age across OSA categories (p-value < 0.001). Sex, ethnicity and history of multiple involuntary job loss were significantly associated with OSA categories in the preliminary analysis (p<0.01). The mean REI for no OSA was 2.0 (SD = 1.3), mild (m = 8.9, SD = 2.7), moderate (m = 21.4, SD = 4.3), and severe OSA was 41.6 (SD = 11.4) (data not shown).

Table 1:

Demographic characteristics of participants by obstructive sleep apnea (OSA) categories.

Variable Overall (N = 261) No OSA (n = 151) Mild OSA (n = 71) Moderate-Severe OSA (n = 39) p-value
Age (years) 41.0 ± 10.4 38.7 ± 9.80 44.2 ± 10.9 43.7 ± 9.98 < 0.001
REI 7.8 ± 10.5 1.9 ± 1.3 8.9 ± 2.7 28.6 ± 12.3 < 0.001
Sex
 Female 151 (57.9%) 101 (66.9%) 36 (50.7%) 14 (35.9%) 0.001
 Male 110 (42.1%) 50 (33.1%) 35 (49.3%) 25 (64.1%)
Ethnicity
 White 173 (66.3%) 109 (72.2%) 42 (59.2%) 22 (56.4%) 0.06
 Non-white 88 (33.7%) 42 (28.8%) 29 (40.8%) 17 (43.6%)
Hispanic
Yes 92 (35.3) 46 (30.5) 28 (39.5) 18 (46.2)
No 169 (64.7) 105 (69.5) 43 (60.5) 21 (53.8) 0.13
Race
AI or AN 10 (3.8) 1 (0.7) 6 (8.5) 3 (7.7)
B or AA 23 (8.8) 15 (9.9) 5 (7.0) 3 (7.7)
More than one 16 (6.1) 9 (6.0) 5 (7.0) 2 (5.10)
NH or OPI 1 (0.5) 1 (0.7)
Other 23 (8.8) 9 (5.9) 8 (11.3) 6 (15.4)
Unknown 15 (5.7) 7 (4.6) 5 (7.0) 3 (7.7)
White 173 (66.3) 109 (72.2) 42 (59.2) 22 (56.4) 0.14
History of multiple job loss
 Yes 117 (44.8%) 56 (37.1%) 37 (52.1%) 24 (61.5%) 0.01
 No 144 (55.2%) 95 (62.9%) 34 (47.9%) 15 (38.5%)
Shift start, 7–9am
 Yes 195 (74.7%) 115 (76.2%) 49 (69.0%) 31 (79.5) 0.39
 No 66 (25.3%) 36 (23.8%) 22 (31.0%) 8 (20.5)
Payment type
 Salary 70 (26.8%) 44 (29.1%) 16 (22.5%) 10 (25.6%) 0.58
 Hourly 191 (73.2%) 407 (70.9%) 55 (77.5%) 29 (74.4%)
Job Type
 Management, Healthcare, and Sales 94 (36.0%) 62 (41.1%) 19 (26.8%) 13 (33.3%) 0.11
 Other 167 (64.0%) 89 (59.9%) 52 (73.2%) 26 (66.7%)
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Note: OSA was defined using the respiratory event index (REI) as: none to minimal (REI <5), mild (5 < REI ≤ 15), and moderate-severe (REI >15). AI (American Indian), AN (Alaska Native), B (Black), AA (African American), NH (Native Hawaiian), OPI (Other Pacific Islander).

Primary analyses

The unadjusted odds for having a history of multiple involuntary job loss for those with mild OSA was 1.85 (95% CI: 1.04, 3.28) and 2.71 (95% CI: 1.33, 5.70) for moderate-to-severe OSA, compared to participants without OSA (Table 2). In the adjusted model, the odds of multiple involuntary job loss for those with mild OSA was nonsignificant (1.55; 95% CI: 0.83, 2.90). The adjusted odds ratios were 1.55 (95% CI: 0.83, 2.90) and 2.46 (95% CI: 1.13, 5.52) for mild and moderate-to-severe OSA, respectively, compared to no OSA after controlling for age, sex, ethnicity, beginning work between 7–9 am, job type, and compensation type.

Table 2:

Unadjusted and adjusted odds ratios (OR) and 95% confidence intervals (CI) for each classification of obstructive sleep apnea (OSA) based on the respiratory event index (REI).

Multiple involuntary job loss
OSA/OSA Category Unadj. OR 95% CI Adj. OR 95% CI
No OSA Ref - Ref -
Mild 1.85 1.04, 3.28 1.55 0.83, 2.90
Moderate-Severe 2.71 1.33, 5.70 2.46 1.13, 5.52
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Note: OSA was defined using the respiratory event index as none/minimal (REI <5), mild (5 < REI ≤ 15), moderate-severe (REI >15). Adjusted model for age, sex, ethnicity, shift schedule, job type, and payment type.

The sensitivity analysis showed that after propensity score matching, a total of 39 matched pairs were identified (N = 78). Matched data appeared visually balanced; the variable with the maximum absolute standardized difference was gender (ASD = 0.11) (see Table 3). The balance diagnostics indicated good balance in matched data and univariate logistic regression model fit with a history of multiple involuntary job loss as the response variable and SDB severity as a determinant. The odds ratio for reporting a history of multiple involuntary job loss for participants with OSA (AHI ≥ 15) was 3.20 (95% CI: 1.29, 8.29).

Table 3:

Absolute standardized differences (ASD) of variables included in the propensity model broken down by whether a matched participant had obstructive sleep apnea (OSA).

Variable No OSA (N = 39) OSA (N = 39) ASD
Age (years) 42.9 43.7 0.06
Sex
 Female 27 25 0.11
 Male 12 14 0.11
Ethnicity
 White 22 22 0.0
 Non-White 17 17 0.0
Payment Type
 Salary 10 10 0.0
 Hourly 29 29 0.0
Job Type
 Social 13 13 0.0
 Non-social 26 26 0.0
First shift, 7–9am
 Yes 30 31 0.06
 No 9 8 0.06
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Note: OSA was defined as Moderate-to-Severe OSA (REI ≥15).

Discussion

In this sample of adults who experienced involuntary job loss, having OSA significantly increased the odds of reporting multiple involuntary job loss. There appears to be a trend in the relationship; mild OSA nearly doubled the odds (1.85; 95% CI: 1.04, 3.28) of multiple involuntary job loss, moderate-to-severe OSA almost tripled those odds, (2.71; 95% CI: 1.33, 5.70). For those with moderate to severe OSA, effects were independent of age, gender, ethnicity, shift work, job type, and compensation type.

Findings from previous research have shown that sleep disorders are associated with a variety of factors associated with increased risk for involuntary employment termination including absenteeism 31,32, poor work performance and productivity; and increased healthcare costs for employers 33. Reynolds and colleagues found that sickness absenteeism was independently associated with OSA (OR=9.8, CI=4.7,20.7) compared to people without OSA symptoms 34. Hui and colleagues found that disordered sleep was associated with both greater likelihood of lower self-ratings of work performance and significantly higher healthcare costs 35. To our knowledge, this is one of the first studies to examine OSA and involuntary job loss. Involuntary job loss places a large economic burden on the individual as well as on the overall economy 33,35.

This study suggests a pressing public health need for sleep health promotion and sleep disorder screening among individuals who have experienced involuntary job loss. Sleep apnea may be associated with job loss, thus limiting the ability to acquire and retain employment. In addition to loss of earnings, the personal costs of job loss may include poor mental and physical health, adverse family effects, higher rates of alcoholism and drug abuse, and premature death 14,36. Overall, these findings are consistent with a growing literature demonstrating that OSA has significant direct and indirect social costs.

This study has several important strengths. First, it used strict inclusion and exclusion criteria, thus minimizing risk for unmeasured or unknown confounders. Study data were gathered using well-validated scales and instruments administered by highly trained staff, reducing the risk of measurement bias. Presence of OSA was assessed objectively using a portable sleep testing device 24. Sleep data were scored by a registered polysomnographic technologist and interpreted by a physician board-certified in sleep medicine. Finally, the sensitivity analysis from propensity score matching provides further support for primary findings.

The study also had some limitations. First, data on employment were self-reported and there is a risk for memory and social acceptability bias in responses. These analyses also used data from the screening visit; therefore, it was not possible to include covariates such as body mass index, which was not collected at the screening visit in the early portion of the study. Thus, there may be an overestimation of the association between OSA and multiple involuntary job loss since obesity has been previously associated with both conditions 37. OSA is associated with multiple health conditions that are also associated with unemployment (e.g., cardiovascular disease, diabetes)38,39. Although few studies have assessed the prospective relationship between health conditions and job loss specifically, it is feasible that the presence of an unaccounted-for comorbid mental or physical condition could be influencing the relationship between OSA and history of multiple job loss. On the other hand, a significant body of research supports the negative effects of OSA on job performance, suggesting a lesser likelihood of this interpretation. Data on body mass index or obesity were not collected at this timepoint. Similarly, information about insomnia, alcohol use, or smoking history, were not collected during this visit; each of these variables has associations with OSA and unemployment. The study population included individuals who recently experienced involuntarily job loss; therefore, results may not be generalizable to individuals who are voluntarily unemployed. Further, the home sleep apnea test has lower sensitivity and may miss mild OSA that leads to sleep disruption. Subjects without high REI but with poor sleep may have severe daytime symptoms that may limit job performance. Economic crises such as the Great Recession not only increased the unemployment rate but also had a negative impact on the physical and mental health of workers 40. Thus, it is possible that participants in this study were affected by such economic recession and thus this factor contributed to their loss of employment. A final limitation is the cross-sectional design and the inability to assess temporal precedence. With this in mind, job loss is very difficult to predict and, ethically, OSA must be treated if identified. Thus, there is limited ability to assess the effect of undetected OSA on job loss prospectively. Long-term retrospective cohort studies could help assess both short-term and long-term effects of OSA on both work performance and job loss.

Conclusion

Obstructive sleep apnea is a significant public health issue. Recent research has focused on chronic disease outcomes associated with OSA, but less attention has been paid to its social and economic effects. Findings from this study suggest that OSA has a significant association with past multiple involuntary job loss. In combination with previous research, results from this study support the possibility that undetected OSA may contribute to long-term vocational instability.

Acknowledgements

The authors would like to thank the staff and participants of the Assessing Daily Activity Patterns Through Occupational Transitions Study (ADAPT). The authors would like to gratefully acknowledge the assistance of the Arizona Department of Economic Security in study recruitment, and the support of the University of Arizona Collaboratory for Metabolic Disease Prevention and Treatment.

Funding

This work is supported by the US National Institute of Health, National Heart, Lung, and Blood Institute (NHLBI,1R01HL117995-01A1).

Footnotes

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