Multidimensional sleep health and all-cause mortality: Counterfactual risk estimation and component-specific contributions in the sleep heart health study.

Background

Multidimensional sleep health has been linked to mortality, but most evidence is based on conventional association models and provides limited information on population-level risk under hypothetical sleep-health improvement.

Methods

We included 4382 adults from the Sleep Heart Health Study with baseline polysomnography, questionnaire-based sleep assessments, covariates, and mortality follow-up. Six sleep-health domains were assessed: duration, continuity, timing, sleepiness/alertness, architecture, and sleep disorders. A composite sleep health score ranging from 0 to 6 was calculated, with higher scores indicating better sleep health. Cox model-based g-computation estimated 1-, 5-, and 10-year population standardized mortality risks. Pragmatic scenarios increased each participant's observed score by up to 1 or 2 points, capped at 6; component-specific analyses set individual domains to the healthy level while other domains remained observed.

Results

During a median follow-up of 4327 days, 926 deaths occurred. Each 1-point higher score was associated with lower all-cause mortality (HR, 0.924; 95% CI, 0.875-0.977). The standardized 10-year risk under observed scores was 16.78%. Increasing scores by up to 1 and 2 points yielded risks of 15.82% (RD, -0.96 percentage points; 95% CI, -1.63 to -0.34) and 15.06% (RD, -1.72 percentage points; 95% CI, -2.92 to -0.61), respectively. Flexible modeling indicated modest nonlinearity and attenuated incremental estimates. Continuity and timing had the largest isolated component-specific risk differences, but neither remained significant after Holm correction.

Conclusions

Better multidimensional sleep health was associated with lower mortality. Pragmatic hypothetical improvements were associated with modest reductions in standardized 10-year mortality risk.