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Objective To evaluate the effect of an employer-mandated obstructive sleep apnea (OSA) diagnosis and treatment program on non-OSA-program trucker medical insurance claim costs. Methods Retrospective cohort analysis; cohorts constructed by matching (randomly, with replacement) Screen-positive Controls (drivers with insurance screened as likely to have OSA, but not yet diagnosed) with Diagnosed drivers (n = 1,516; cases = 1,224, OSA Negatives = 292), on two factors affecting exposure to medical claims: experience level at hire and weeks of job tenure at the Diagnosed driver’s polysomnogram (PSG) date (the “matching date”). All cases received auto-adjusting positive airway pressure (APAP) treatment and were grouped by objective treatment adherence data: any “Positive Adherence” (n = 932) versus “No Adherence” (n = 292). Bootstrap resampling produced a difference-in-differences estimate of aggregate non-OSA-program medical insurance claim cost savings for 100 Diagnosed drivers as compared to 100 Screen-positive Controls before and after the PSG/matching date, over an 18-month period. A two-part multivariate statistical model was used to set exposures and demographics/anthropometrics equal across sub-groups, and to generate a difference-in-differences comparison across periods that identified the effect of OSA treatment on per-member per-month (PMPM) costs of an individual driver, separately from cost differences associated with adherence choice. Results Eighteen-month non-OSA-program medical claim costs savings from diagnosing (and treating as required) 100 Screen-positive Controls: $153,042 (95% CI: −$5,352, $330,525). Model-estimated effect of treatment on those adhering to APAP: −$441 PMPM (95% CI: −$861, −$21). Conclusions Results suggest a carrier-based mandatory OSA program generates substantial savings in non-OSA-program medical insurance claim costs.

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ForewordThe 100-Car Naturalistic Driving Study was undertaken with the goal of obtaining data on driver performance and behavior in the moments leading up to a crash. This type of data is not available from either of the traditional methods of studying driver behavior in regards to crashes and traffic safety, such as empirical studies and crash databases (e.g., General Estimates System and the Fatality Analysis Reporting System). Crash databases are derived from police accidents reports (PARs) and contain a wealth of data describing the non-controversial facts of the crash such as location, number of vehicles involved, type of crash, and time of day. For a variety of reasons, however, these databases do not provide good insight into the driver behavior and performance leading up to the crash. The empirical method provides a different approach to investigating driver behavior by studying how people drive under various conditions. These studies are usually conducted as highly controlled experiments using instrumented vehicles to obtain a variety of vehicle and driver performance data. Typically, these studies involve drivers operating study test vehicles for a short period of time (i.e., a few hours) in a contrived environment (i.e., either simulator or closed test track).Naturalistic studies can be used to understand crash causation and driver behavior and supplement information learned through epidemiological and empirical approaches. Naturalistic studies include driver/subjects operating vehicles in their daily lives (e.g., commuting to work) for an extended period of time (e.g., one year).