A cost-effectiveness analysis of alternative at-home or in-laboratory technologies for the diagnosis of obstructive sleep apnea syndrome.
BACKGROUND
Obstructive sleep apnea syndrome (OSAS) is a common disorder that affects 2% to 9% of the population. Health care policy makers have noted increased referrals for sleep studies.


OBJECTIVE
In this article, the authors conduct a cost-effectiveness analysis to determine the optimal technology for the diagnosis of OSAS using polysomnography (PSG) or partial sleep monitoring (PSM).


METHODS
The target population was a hypothetical cohort of patients suspected of having OSAS. A 2-level decision tree wasformulated that reflects all possible steps of OSAS diagnosis and therapy. The method represents a comprehensive strategy to determine which of the 2 systems-PSG or PSM-has cost advantages. The financial and operational aspects of OSAS diagnosis and therapy were analyzed. A sensitivity analysis was performed over all uncertain parameters (i.e., diagnostic agreement, data loss, and referral to therapy).


RESULTS
Unattended at-home sleep monitoring was the most expensive method. The combination of 1:2 PSG and attended PSM strategy was the optimal strategy with respect tofinancing and operations. Compared to the PSG-only strategy, this combination may lead to a 10% reduction of the annual expenditure.


CONCLUSIONS
This study provides proof of concept (under a wide range of sensitivity assumptions) that the cost of sleep study techniques can be modeled. It rejects the assumption that athome portable sleep monitoring is cost advantageous. The combination of PSG and attended PSM OSAS is the most cost-effective approach to sleep evaluation.
Reuven
H
H
Department of Health Policy and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Schweitzer
E
E
Tarasiuk
A
A
D003362:Cost-Benefit Analysis; D003661:Decision Support Techniques; D006801:Humans; D017286:Polysomnography / Q000191:economics*; D012680:Sensitivity and Specificity; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis*