Cognitive skills affect economic preferences, strategic behavior, and job attachment.

Economic analysis has so far said little about how an individual's cognitive skills (CS) are related to the individual's economic preferences in different choice domains, such as risk taking or saving, and how preferences in different domains are related to each other. Using a sample of 1,000 trainee truckers we report three findings. First, there is a strong and significant relationship between an individual's CS and preferences. Individuals with better CS are more patient, in both short- and long-run. Better CS are also associated with a greater willingness to take calculated risks. Second, CS predict social awareness and choices in a sequential Prisoner's Dilemma game. Subjects with better CS more accurately forecast others' behavior and differentiate their behavior as a second mover more strongly depending on the first-mover's choice. Third, CS, and in particular, the ability to plan, strongly predict perseverance on the job in a setting with a substantial financial penalty for early exit. Consistent with CS being a common factor in all of these preferences and behaviors, we find a strong pattern of correlation among them. These results, taken together with the theoretical explanation we offer for the relationships we find, suggest that higher CS systematically affect preferences and choices in ways that favor economic success.

Employer-mandated obstructive sleep apnea treatment and healthcare cost savings among truckers.

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.

Nonadherence with Employer-Mandated Sleep Apnea Treatment and Increased Risk of Serious Truck Crashes.

STUDY OBJECTIVES: To evaluate the effect of an employer-mandated obstructive sleep apnea (OSA) program on the risk of serious preventable truck crashes. METHODS: Data are from the first large-scale, employer-mandated program to screen, diagnose, and monitor OSA treatment adherence in the US trucking industry. A retrospective analysis of cohorts was constructed: polysomnogram-diagnosed drivers (OSA positive n = 1,613, OSA negative n = 403) were matched to control drivers unlikely to have OSA (n = 2,016) on two factors affecting crash risk, experience-at-hire and length of job tenure; tenure was matched on the date of each diagnosed driver's polysomnogram. Auto-adjusting positive airway pressure (APAP) treatment was provided to all cases (i.e. OSA positive drivers); treatment adherence was objectively monitored. Cases were grouped by treatment adherence: "Full Adherence" (n = 682), "Partial Adherence" (n = 571), or "No Adherence" (n = 360). Preventable Department-of-Transportation-reportable crashes/100,000 miles were compared across study subgroups. Robustness was assessed. RESULTS: After the matching date, "No Adherence" cases had a preventable Department of Transportation-reportable crash rate that was fivefold greater (incidence rate ratio = 4.97, 95% confidence interval: 2.09, 10.63) than that of matched controls (0.070 versus 0.014 per 100,000 miles). The crash rate of "Full Adherence" cases was statistically similar to controls (incidence rate ratio = 1.02, 95% confidence interval: 0.48, 2.04; 0.014 per 100,000 miles). CONCLUSIONS: Nontreatment-adherent OSA-positive drivers had a fivefold greater risk of serious preventable crashes, but were discharged or quit rapidly, being retained only one-third as long as other subjects. Thus, the mandated program removed risky nontreatment-adherent drivers and retained adherent drivers at the study firm. Current regulations allow nonadherent OSA cases to drive at another firm by keeping their diagnosis private. COMMENTARY: A commentary on this article appears in this issue on page 961.

Obesity is associated with the future risk of heavy truck crashes among newly recruited commercial drivers.

This study estimates the dose-response relationship between body mass index (BMI) and crash risk in operators of heavy commercial motor vehicles. Intake data were collected during the first two weeks of instruction from 744 new truck drivers training for their commercial driver's licenses at a school operated by the cooperating trucking firm. Drivers were then followed prospectively on the job using the firm's operational data for two years, or until employment separation, whichever came first. Multivariate Poisson regression and Cox proportional hazards models were used to estimate the relationship between crash risk and BMI, controlling for demographic characteristics and for variations in the exposure to risks on the road. Results from the Poisson regression, which used cumulative miles driven as an exposure control, indicated that compared to normal BMI (18.5