Sleep disturbances across two weeks predict future mental healthcare utilization.

Abstract

Insufficient sleep costs the U.S. economy over $411 billion per year. However, most studies investigating economic costs of sleep rely on one-time measures of sleep, which may be prone to recall bias and cannot capture variability in sleep. To address these gaps, we examined how sleep metrics captured from daily sleep diaries predicted medical expenditures. Participants were 391 World Trade Center responders enrolled in the World Trade Center Health Program (mean age = 54.97 years, 89% men). At baseline, participants completed 14 days of self-reported sleep and stress measures. Mean sleep, variability in sleep, and a novel measure of sleep reactivity (i.e., how much people's sleep changes in response to daily stress) were used to predict the subsequent year's medical expenditures, covarying for age, race/ethnicity, sex, medical diagnoses, and body mass index. Mean sleep efficiency did not predict mental healthcare utilization. However, greater sleep efficiency reactivity to stress (b=$191.75, p=.027), sleep duration reactivity to stress (b=$206.33, p=.040), variability in sleep efficiency (b=$339.33, p=.002), variability in sleep duration (b=$260.87, p=.004), and quadratic mean sleep duration (b=$182.37, p=.001) all predicted greater mental healthcare expenditures. Together, these sleep variables explained 12% of the unique variance in mental healthcare expenditures. No sleep variables were significantly associated with physical healthcare expenditures. People with more irregular sleep, more sleep reactivity, and either short or long sleep engage in more mental healthcare utilization. It may be important to address these individuals' sleep problems to improve mental health and reduce healthcare costs.