Sleep disturbances across 2 weeks predict future mental healthcare utilization

Area of Science:

• Sleep science
• Health economics
• Psychiatry

Background:

• Insufficient sleep incurs significant economic costs, exceeding $411 billion annually in the US.
• Previous studies often used single sleep measures, risking recall bias and failing to capture sleep variability.
• Daily sleep diaries offer a more robust method to assess sleep patterns and their impact.

Purpose of the Study:

• To investigate the predictive relationship between daily sleep metrics and subsequent medical expenditures.
• To examine how sleep variability and sleep reactivity to stress influence healthcare costs.

Main Methods:

• 391 World Trade Center responders underwent 14 days of daily sleep and stress monitoring.
• Sleep metrics included mean sleep, sleep variability, and sleep reactivity to stress.
• These metrics predicted subsequent year's medical expenditures, controlling for demographic and health factors.

Main Results:

• Greater sleep efficiency and duration reactivity to stress predicted higher mental healthcare expenditures.
• Increased variability in sleep efficiency and duration also correlated with greater mental healthcare costs.
• No sleep variables were significantly associated with physical healthcare expenditures.

Conclusions:

• Individuals with irregular sleep, high sleep reactivity, or extreme sleep durations utilize more mental healthcare services.
• Targeting sleep problems in these individuals could enhance mental well-being and lower healthcare spending.
• Sleep quality and consistency are crucial factors in mental health and healthcare resource utilization.