Hoffman, Benjamin MD, MPH; Wingenbach, Dustin D. MBA; Kagey, Amy N. MS; Schaneman, Justin L. MS; Kasper, David
In response to the proposed regulation prepared for the Medical Review Board (MRB) of the US Department of Transportation's Federal Motor Carrier Safety Administration and increasing literature on sleep disorders and driver safety, this analysis is designed to assess the long-term financial impact of treatment of obstructive sleep apnea (OSA) in a commercial motor vehicle (CMV) driver population. In January 2008, the MRB reviewed a proposal that suggested all drivers with a body mass index ≥33 (or 30 as proposed by MRB member Dr Allan Pack) be required to participate in OSA testing (and treatment, if applicable) to maintain licensing.1 As such, this analysis estimates the related cost implications of treatment of employees afflicted with OSA.
By definition, OSA is a medical condition characterized by repetitive episodes of airflow cessation during sleep, often because of collapse of the upper airway. OSA has a variety of adverse consequences, the most common of which are daytime sleepiness that leads to a tendency to fall asleep while driving.2–4
The accepted standard to measure the presence and severity of OSA is the apnea-hypopnea index and is defined as the number of apneas and hypopneas per hour of sleep. Although measurement techniques to help and understand the prevalence of OSA have varied, a published study by Pack et al5 estimated the percent of commercial drivers in the United States suffering from at least mild OSA (apnea-hypopnea index ≥5) to be roughly 28%. OSA affects men more often than women and at least 85% of those with clinically significant OSA remain undiagnosed.6 Untreated OSA, according to the US Department of Transportation's Task Force on Pulmonary Disorders and Commercial Drivers,7 is an important preventable cause of motor vehicle accidents. Studies have suggested an increased risk for accidents for those with OSA, and estimates have ranged from an astounding 200% to 700%.8–12
An overnight study in a sleep laboratory, called a polysomnogram, or an at-home testing device are two of the common testing procedures used to clinically diagnose OSA. Once diagnosed, two devices have been confirmed to be safe and effective at treating OSA: continuous positive airway pressure (CPAP) treatment and bi-level positive airway pressure (BiPAP) treatment.13,14 Both machines apply pressure inside the throat to keep it from collapsing; however, unlike the continuous pressure delivered from the CPAP machine, the BiPAP does so at variable levels.
Numerous studies exist in the literature suggesting the effectiveness of CPAP/BiPAP treatment in reducing the number of auto-related accidents for drivers.15–19 As such, this study expands further into the economic implications of treating diagnosed OSA by examining the associated health plan and disability cost impact in a population of CMV drivers.
A retrospective study was performed using a comprehensive database containing medical claims, pharmacy claims, disability claims, demographic, employment, and compensation information from the nation's largest waste removal company, Waste Management. The subset of data used for this analysis was their population of CMV operators in the United States.
This analysis focuses on employees who were diagnosed with OSA and received CPAP or BiPAP treatment. A list of durable medical equipment codes for the CPAP or BiPAP machine was used to identify the population. The following procedure codes identified treatment: E0601, W9415, Z0613, and 94,660 (CPAP) and E0470, E0471, and E0472 (BiPAP). The study includes individuals with CPAP or BiPAP treatment between January 1, 2004, and March 31, 2007, using their earliest date of treatment as their index date. Individuals must have been employed and enrolled in medical, pharmacy, and disability benefit plans with available data for the entire 12-month period preceding the index date and the 24-month period after the index date. For this article, the 12-month preindex date period will be referred to as the preperiod, the 12-month postindex date period will be referred to as the first postperiod, and the second 12-month postindex date period will be referred to as the second postperiod. The cost of the CPAP/BiPAP and other related treatment costs are included in the first postperiod.
A nontreated OSA control group using the CMV driver population was constructed using the diagnosis codes from January 1, 2004, through March 31, 2007. The following OSA International Classification of Diseases, 9th revision diagnosis codes were used to identify this group: 327.2x, 780.51, 780.53, and 780.57. This group did not have evidence of CPAP/BiPAP treatment or treatment maintenance (including CPAP/BiPAP device accessories) in all of the available claims data that spans from January 1, 2001, through June 30, 2009. The day after the first diagnosis date of OSA was used as the index date in an effort to be consistent with the study group and include costs for the sleep apnea test in the preperiod when this test occurred on the same date as the first diagnosis. As in the study group, individuals must have been employed and enrolled in medical, pharmacy, and disability benefit plans with available data for the entire 12-month period preceding the index date and the 24-month period after the index date.
Medical and drug claims incurred through March 31, 2009, with health plan payments through June 30, 2009, were used. All cost metrics are adjusted for inflation to June 2009 dollars. It is worth noting that an individual in the study group with >$300,000 in health plan cost in the preperiod was excluded based on an outlier analysis.
1. A demographic comparison was made between the study group and the control group.
2. A cost savings analysis compared total health plan costs (medical services and prescription drug costs) and short-term disability costs in the preperiod to the first and second postperiods. Short-term disability costs represent income replacement costs paid to workers when they are unable to be on the job. The short-term disability claimant rate and average lost workdays are also compared between the preperiod and the first and second postperiods. These comparisons were made for both the study and the control groups.
3. A study and control group comorbidity analysis examined the cost of services within various coexisting condition categories. These conditions were grouped according to the 17 Agency for Healthcare Research and Quality Major Diagnostic Categories (MDCs) that group International Classification of Diseases, 9th revision diagnosis codes into major organ-, disease-, and therapeutic-specific categories.20 The average annual medical cost paid by the health plan was calculated and compared for each of the MDCs with a breakdown of other conditions to include sleep apnea and other major cost drivers.
Data analysis was generated by the SAS software platform version 9.1.3. By using the study group and comparison group of drivers in analytic comparison 1, two-sample t tests were used to assess differences in age and tenure, a χ2 test was used to address differences in union status, and Fishers exact test was used to assess differences in gender. To address analytic comparison 2, paired t tests were used to test the significance of the difference in average total health plan costs, average short-term disability costs and lost workdays from the preperiod to first and second postperiods. Demographic differences across time periods are controlled for by using a paired test where each individual in the preperiod is compared with himself in the postperiod. To test the significance of the difference in short-term disability claimant rates from the preperiod to the first and second postperiods McNemar's test was used. To identify the MDCs with health plan cost savings, paired t tests were used to test the significance of the difference in average health plan costs by MDC for the preperiod versus the first and second postperiods (analytic comparison 3). A significance level of 5% was used for all tests.
Analytic Comparison 1
The study group consisted of 156 drivers identified using the previously mentioned procedure codes for having a CPAP or BiPAP treatment. The control group consisted of 92 drivers identified using the previously mentioned diagnosis codes for having OSA, but no claims based evidence of CPAP or BiPAP treatment. The study group and control groups had similar demographic profiles with no significant differences (Table 1).
Analytic Comparison 2
By using the 1 year before treatment as a baseline for the study group, the first postperiod health plan costs decreased by an average of $2727 (P = 0.002). Costs were even lower in the second postperiod resulting in an additional $3086 (P = 0.008) in health plan cost savings when compared with the baseline. Average short-term disability costs also decreased in both the first post and second postperiods, yielding a total 2-year savings of $528. The control group exhibited insignificant differences from the preperiod to both postperiods for all cost metrics (Table 2).
Lost workdays resulting from short-term disability episodes in the treated cohort were significantly lower in the first postperiod (4.4 fewer days, P = 0.004). The second postperiod had 2.5 fewer days, though not statistically significant when compared with the preperiod. There were three outliers in the second postperiod with non–OSA-related claims, two of which had claims progressing to long-term disability (>180 days). The percent of drivers with at least one short-term disability claim was reduced considerably as well (Table 3). For the control group, lost workdays and the percent of drivers with a short-term disability claim changed insignificantly from the preperiod to both of the postperiods.
Analytic Comparison 3
To determine the distribution of the cost savings for the study group, health plan costs were stratified by MDC for each of the time periods. Cost savings was greatest in the other conditions category (first postperiod: $1137, P < 0.0001; second postperiod: $2282, P < 0.0001) with the greatest cost savings in sleep apnea-related costs. Circulatory system cost savings were significant in the second postperiod ($667, P = 0.026), and nervous system cost savings were significant in the first postperiod ($173, P = 0.009; Table 4). The control group had significant cost savings in the second postperiod in the sleep apnea category ($499, P < 0.0001) that led to significance in the cost savings for the other conditions category in the second postperiod ($617, P < 0.0001; Table 5).
Although the 1 year prior and 2 years posttreatment benefit enrollment criteria is desirable for assessing long-term cost effectiveness of the treatment devices, the size of the study group is reduced as a result. Significant effort was made to ensure that the control group was as comparable as possible to the study group; however, average total costs were still lower for the control group in the preperiod. A side-by-side comparison of the comorbidity analyses (Tables 4 and 5) revealed that the study group had higher sleep apnea-related costs in the preperiod. This difference is likely attributable to higher average sleep apnea diagnostic fees, a higher rate of tests given in the hospital (as opposed to at a sleep center or at-home), and more severe cases of sleep apnea in the study group. This difference in preperiod total cost was not statistically significant (P = 0.245). Although initial results display favorable outcomes, a follow-up analysis with even more longitudinal data may be able to confirm that the cost differences seen in this study persist into future years.
Drivers who were treated for OSA with either a CPAP or BiPAP device exhibited lower total health plan costs, fewer missed workdays because of short-term disability, and a lower rate of short-term disability claims during the 24 months after the initiation of the treatment, resulting in >$6000 in total health plan and disability cost savings per treated driver. The control group of drivers with OSA who were not treated for their condition did not experience any significant changes in health plan cost, short-term disability claimant rates or lost workdays in the postperiods. In contrast, the remainder of the driver workforce (N = 7303) without evidence of OSA experienced a significant increase in average total cost over the same time period when compared with baseline (pre: $1923; post 1: $2170 [P = 0.013]; post 2: $2573 [P < 0.0001]). This group had the same eligibility criteria as the study group and used an average index date of the study group. Comparing total cost in the preperiod to the second postperiod, the study group experienced a 41% decrease, the control group remained constant with an 8% decrease, and the rest of drivers without OSA increased on average 34% in the 2 years. Addressing OSA in the workplace offers the possibility of early identification and intervention for a chronic disease that is associated with increased health benefit utilization. Providing treatment for those identified with OSA may significantly reduce health care costs and disability rates.
This study was supported by Waste Management, Houston, TX.
2. Kribbs NB, Pack AI, Kline LR, et al. Effects of one night without nasal CPAP treatment on sleep and sleepiness in patients with obstructive sleep apnea. Am Rev Respir Dis. 1993;147:1162–1168.
3. Naegele B, Thouvard V, Pepin JL, et al. Deficits of cognitive executive functions in patients with sleep apnea syndrome. Sleep. 1995;18:43–52.
4. Rakel RE. Clinical and societal consequences of obstructive sleep apnea and excessive daytime sleepiness. Postgrad Med. 2009;121:86–95.
5. Pack AI, Dinges D, Maislin G. A Study of Prevalence of Sleep Apnea Among Commercial Truck Drivers. Washington, DC: Federal Motor Carrier Safety Administration Publication No. DOT-RT-02-030; 2002.
6. Somers VK, White DP, Amin R, et al; American Heart Association Council for High Blood Pressure Research Professional Education Committee, Council on Clinical Cardiology; American Heart Association Stroke Council; American Heart Association Council on Cardiovascular Nursing; American College of Cardiology Foundation. Sleep apnea and cardiovascular disease: an American Heart Association/American College of Cardiology Foundation scientific statement from the American Heart Association Council for High Blood Pressure Research Professional Education Committee, Council on Clinical Cardiology, Stroke Council, and Council on Cardiovascular Nursing. In collaboration with the National Heart, Lung, and Blood Institute National Center on Sleep Disorders Research (National Institutes of Health). Circulation. 2008;118:1080–1111.
7. Office of Motor Carriers. Conference on Pulmonary/Respiratory Disorders and Commercial Drivers. Washington, DC: Office of Motor Carriers; 1991.
8. George CF. Sleep. 5: driving and automobile crashes in patients with obstructive sleep apnoea/hypopnoea syndrome. Thorax. 2004;59:804–807.
9. Howard ME, Desai AV, Grunstein RR, et al. Sleepiness, sleep-disordered breathing, and accident risk factors in commercial vehicle drivers. Am J Respir Crit Care Med. 2004;170:1014–1021.
10. Kingshott RN, Cowan JO, Jones DR, et al. The role of sleep-disordered breathing, daytime sleepiness, and impaired performance in motor vehicle crashes-a case control study. Sleep Breath. 2004;8:61–72.
11. Mulgrew AT, Nasvadi G, Butt A, et al. Risk and severity of motor vehicle crashes in patients with obstructive sleep apnoea/hypopnoea. Thorax. 2008;63:536–541.
12. Stoohs RA, Guillenminault C, Itoi A, Dement W. Traffic accidents in commercial long-haul truck drivers: the influence of sleep-disordered breathing and obesity. Sleep. 1994;17:619–623.
13. Sullivan CE, Issa FG, Berthon-Jones M, Eves L. Reversal of obstructive sleep apnoea by continuous positive airway pressure applied through the nares. Lancet. 1981;1:862–865.
14. Basner RC. Continuous positive airway pressure for obstructive sleep apnea. N Engl J Med. 2007;356:1751–1758.
15. Sassani A, Findley LJ, Kryger M, Goldlust E, George C, Davidson TM. Reducing motor-vehicle collisions, costs, and fatalities by treating obstructive sleep apnea syndrome. Sleep. 2004;27:453–458.
16. Krieger J, Meslier M, Lebrun T, et al. Accidents in obstructive sleep apnea patients treated with nasal continuous positive airway pressure: a prospective study. The Working Group ANTADIR, Paris and CRESGE, Lille, France. Association Nationale de Traitement à Domicile des Insuffisants Respiratoires. Chest. 1997;112:1561–1566.
17. Cassel W, Ploch T, Becker C, Dugnus D, Peter J, Wichert P. Risk of traffic accidents in patients with sleep disordered breathing: reduction with nasal CPAP. Eur Respir J. 1996;9:2606–2611.
18. Findley L, Smith C, Hooper J, Dineen M, Suratt PM. Treatment with nasal CPAP decreases automobile accidents in patients with sleep apnea. Am J Respir Crit Care Med. 2000:161:857–859.
19. George CF. Reduction in motor vehicle collisions following treatment of sleep apnoea with nasal CPAP. Thorax. 2001;56:508–512.