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A retrospective data analysis using 2000-2008 three state Medicaid Analytic eXtract was conducted to examine the prevalence and association of comorbidities (psychiatric and non-psychiatric) with healthcare utilization and expenditures of fee-for-service enrolled adults (22-64 years) with and without autism spectrum disorders (International Classification of Diseases, Ninth Revision-clinical modification code: 299.xx). Autism spectrum disorder cases were 1:3 matched to no autism spectrum disorder controls by age, gender, and race using propensity scores. Study outcomes were all-cause healthcare utilization (outpatient office visits, inpatient hospitalizations, emergency room, and prescription drug use) and associated healthcare expenditures. Bivariate analyses (chi-square tests and t-tests), multinomial logistic regressions (healthcare utilization), and generalized linear models with gamma distribution (expenditures) were used. Adults with autism spectrum disorders (n = 1772) had significantly higher rates of psychiatric comorbidity (81%), epilepsy (22%), infections (22%), skin disorders (21%), and hearing impairments (18%). Adults with autism spectrum disorders had higher mean annual outpatient office visits (32[subscript ASD] vs 8[subscript noASD]) and prescription drug use claims (51[subscript ASD] vs 24[subscript noASD]) as well as higher mean annual outpatient office visits (US$4375[subscript ASD] vs US$824[subscript noASD]), emergency room (US$15,929[subscript ASD] vs US$2598[subscrpt noASD]), prescription drug use (US$6067[subscript ASD] vs US$3144[subscript noASD]), and total expenditures (US$13,700[subscript ASD] vs US$8560[subscript noASD]). The presence of a psychiatric and a non-psychiatric comorbidity among adults with autism spectrum disorders increased the annual total expenditures by US$4952 and US$5084, respectively.

Objective: We tested the hypothesis that automation errors on tasks easily performed by humans undermine trust in automation.Background: Research has revealed that the reliability of imperfect automation is frequently misperceived. We examined the manner in which the easiness and type of imperfect automation errors affect trust and dependence.Method: Participants performed a target detection task utilizing an automated aid. In Study 1, the aid missed targets either on easy trials (easy miss group) or on difficult trials (difficult miss group). In Study 2, we manipulated both easiness and type of error (miss vs. false alarm). The aid erred on either difficult trials alone (difficult errors group) or on difficult and easy trials (easy miss group; easy false alarm group).Results: In both experiments, easy errors led to participants mistrusting and disagreeing more with the aid on difficult trials, as compared with those using aids that generated only difficult errors. This resulted in a downward shift in decision criterion for the former, leading to poorer overall performance. Misses and false alarms led to similar effects.Conclusion: Automation errors on tasks that appear "easy" to the operator severely degrade trust and reliance.Application: Potential applications include the implementation of system design solutions that circumvent the negative effects of easy automation errors. [ABSTRACT FROM AUTHOR]

Automation users often disagree with diagnostic aids that are imperfectly reliable. The extent to which users' agreements with an aid are anchored to their personal, self-generated diagnoses was explored. Participants (N = 75) performed 200 trials in which they diagnosed pump failures using an imperfectly reliable automated aid. One group (nonforced anchor, n = 50) provided diagnoses only after consulting the aid. Another group (forced anchor, n = 25) provided diagnoses both before and after receiving feedback from the aid. Within the nonforced anchor group, participants' self-reported tendency to prediagnose system failures significantly predicted their tendency to disagree with the aid, revealing a cognitive anchoring effect. Agreement rates of participants in the forced anchor group indicated that public commitment to a diagnosis did not strengthen this effect. Potential applications include the development of methods for reducing cognitive anchoring effects and improving automation utilization in high-risk domains. [ABSTRACT FROM AUTHOR]