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| Journal of the New Zealand Medical Association,
27-August-2010, Vol 123 No 1321
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|
Societal costs of obstructive sleep apnoea syndrome
Philippa Gander, Guy Scott, Kara Mihaere, Helen Scott
Obstructive sleep apnoea syndrome (OSAS) is a progressive
disease that forms part of a spectrum of sleep-related breathing disorders. It
is characterised by the occurrence of repetitive episodes of airflow reduction
(hypopnoea) or cessation (apnoea) due to upper airway obstruction during sleep,
accompanied by excessive daytime sleepiness.1
Untreated OSAS is recognised as an independent risk factor
for hypertension, cardiovascular disease, stroke, and motor vehicle accidents
(due to excessive sleepiness).2,3 Evidence for
OSAS as an independent risk factor for Type 2 diabetes is less clear. However
evidence is converging from experimental sleep restriction studies,
epidemiological studies, and intervention studies, to support the conclusion
that OSAS exerts independent adverse effects on glucose regulation, through
multiple mechanisms.4 Adults with undiagnosed
OSAS are high users of health care
services.5
In New Zealand, there are marked regional variations in
funding for diagnosis and treatment of OSAS,6,7
which most commonly involves a device to maintain continuous positive airway
pressure during sleep (a “CPAP machine”). In the age range 30-59
yrs, Māori are more likely than non-Māori to report OSAS symptoms and
risk factors and to have OSAS.8-10 However,
ethnicity is not an independent risk factor after controlling for body mass
index (BMI) or neck circumference.8–10
There is also evidence that Māori seen at sleep clinics have more severe
OSAS than non-Māori, suggesting that there may be barriers for Māori
in accessing specialist services.11,12
The present study was undertaken to estimate the societal
costs of OSAS among people aged 30–60 years, and to develop a simulation
tool that could be used to evaluate treatment options, and to estimate the
economic impact of OSAS on different population groups.
MethodsStudy design—This study was
undertaken in Wellington in 2005 and is a combination of cost of illness (COI),
cost-benefit analysis (CBA), and cost utility analysis (CUA). The approach was
based on an outcome tree and decision analytic model developed using three sets
of information:
The prevalence of OSAS
in the Wellington region was estimated to be 5.61% (95%CI
2.62–8.60%).9,10 The economic analysis
was based on 1,692,260 people in the New Zealand population aged 30–59
years.13
Pathways through the healthcare
system—The pathways in the outcome tree were based on services in
the Wellington region (Drs Alister Neill and Angela Campbell, personal
communication, 2005. unless otherwise referenced). The first point of contact
with the healthcare system for a person seeking treatment for OSAS was a general
practitioner (GP), who could recommend no further action, conservative
therapies, or provide a referral to the respiratory medicine clinic at the
Wellington public hospital. The number of patients who sought treatment for OSAS
from their GP was unknown, and capped funding for treatment services beyond
primary care made it impossible to estimate the proportion of OSAS sufferers who
accessed such services. We have assumed that about 20% of people with OSAS
sought treatment from their general
practitioner,14 and of these 50% were referred
on to the respiratory medicine clinic at Wellington Hospital.
At the respiratory medicine clinic, patients were
triaged based on a clinical evaluation that did not include sleep monitoring.
This lead to three possible outcomes: no further treatment recommended; referral
back to their GP; or referral to the local specialist sleep clinic (WellSleep).
At the time of this study, about 70% of patients seen at the respiratory
medicine clinic for suspected OSAS were referred on to the sleep clinic for
polysomnographic evaluation.
At the sleep clinic, patients underwent a clinical
evaluation followed by an overnight polysomnographic sleep evaluation either in
the clinic or at home. About 70% of patients evaluated had a diagnosis of OSAS
confirmed. Treatment was initiated according to the severity of OSAS, and
consistent with other aspects of the patient’s health and life
circumstances. An estimated 9% of patients seen at the sleep clinic were
recommended conservative therapies for OSAS, 1% underwent surgical treatments,
and the remaining 60% were treated with CPAP therapy or dental appliances.
The final version of the model (Figure 1) represents a simplification, with some very low
probability outcome pathways excluded and others collapsed (for example,
home-based and clinic-based polysomnographic monitoring are not considered
separately). This was done in the interests of keeping a manageable level of
complexity.
To account for uncertainties in the estimates of the
proportion of patients following each trajectory, high and low probabilities
were calculated as ±25% of the base case rate. For example, the base case
estimate of the proportion of people with OSAS who sought GP treatment was 20%,
with a low estimate of 15% and a high estimate of 25%.
Risk estimates—People with OSAS
who did not seek treatment in the mainstream healthcare system were considered
to be at increased risk for a number of adverse health and safety outcomes.
Attributable fractions for each outcome were based on odds ratios from published
meta-analyses of case-control studies, or from longitudinal studies. To account
for uncertainties, base case and low and high estimates were calculated for each
attributable fraction (Table 1).
Table 1: Attributable fractions for health and
safety outcomes associated with untreated OSAS; base case (low estimate-high
estimate)
MVAs=motor vehicle accidents.
a Pooled OR (95%CI)
from a meta-analysis of studies comparing MVA rates for people with and without
OSAS.15
b Independent OR
(95%CI) for being involved in a workplace accident over a 10-year period, for
men who reported snoring and workplace sleepiness at the start of the study,
compared to those who did not. Adjusted for age, BMI, weight gain, years at
work, and other workplace exposures.16
c Independent OR
(95%CI) for developing diabetes mellitus at 4-year follow-up, based on AHI at
study start, (AH≥15 compared to reference group AHI<5 at study start).
Adjusted for age, sex, and body
habitus.17
d Independent ORs for
the presence of hypertension at 4-year follow-up, based on AHI at study start,
(reference group AHI=0 at study start). Low estimate is iOR for
AHI=0.1–4.9, base case estimate is iOR for AHI=5–14.9, high estimate
is iOR for AHI≥15. Adjusted for baseline hypertension status, BMI, neck
and waist circumference, age, sex, and weekly use of alcohol and
cigarettes.18 These values were used in the
absence of reliable estimates of the increased risk of CVD associated with OSAS.
The incidence of motor vehicle accidents on the road,
and of other accidents, was estimated from 2005 Accident Compensation
Corporation claims,19 together with the
national population data for 2005.13 The
prevalence of diabetes was taken from the data for New Zealand adults in
2002/03.20 The base case was taken as the
average prevalence for males and females in the total population (4.1%). The
high value was the average prevalence for Pacific people (10.0%) and the low
value was the average prevalence for non-Pacific, non-Māori people (2.9%).
The prevalence of cardiovascular disease (CVD) was derived from the data for New
Zealand adults in 2003/03.20 The base case was
taken as the average prevalence for males and females in the total population
(9.0%). The high value was the average prevalence for Māori (12.1%) and the
low value was the average prevalence for Pacific people (6.9%).
Resource utilisations—At each
node in the outcome tree, events occur and resources are consumed. For example,
a person with diabetes may consult a general practitioner, have prescriptions
dispensed, and incur loss of earnings and transport costs. These resource
utilisations are summarised in Table 2.
Cost estimates—Costs were
categorised as direct medical, direct non-medical, indirect, and intangible
costs.21 Both human capital and willingness to
pay approaches were used to place a value on a human life. In addition, we
calculated a direct medical cost per quality of life year (QALY) gained if OSAS
was successfully treated.22
Only incremental costs (compared with the counterfactual)
were included. For example, if a particular medical cost would have been
incurred whether or not an accident happened, it was not included in the
analyses. The timeframe was one year, so that discounting of costs and effects
was not required. Unit cost estimates are summarised in Table 3 (excluding GST
of 12.5%).
Table 3. Unit cost estimates
a The cost of
counselling or conservative therapy was assumed to be included in the
consultation costs for the GP or at the respiratory medicine clinic.
b Surgical treatments
for OSAS are changing, with tracheotomy becoming increasingly rare in this age
group and gastric reduction becoming more common, particularly among patients
who have private health insurance. Changes in the costings here do not make
major differences to the total costings because of the very small proportion of
patients receiving surgical treatments.
High and low values for
each unit cost (except intangibles) were calculated as ± 25% of the base
case rate. The high estimates for intangible costs associated with accidents
were calculated by multiplying the willingness to pay for a statistical life
($2,830,000) 30 by the proportion of accidents
causing death (for motor vehicle accident high=$28,239.93; for other accidents
high=$1,235.92). The low estimates for intangible costs associated with
accidents were calculated by multiplying one year’s average earnings (lost
due to death) by the proportion of accidents causing death (for motor vehicle
accident low=$444.66; for other accidents low=$19.46).
Sensitivity analysis—The
structure outlined in Figure 1 was represented in a spreadsheet model and 10,000
Monte Carlo simulations were run using randomly generated values between the
high and low estimates for each model parameter. 31
Multiple linear regression was then used to evaluate the effects of each
model parameter (independent variables) on the total direct and indirect costs,
and the total costs calculated by the model.
ResultsTable 4 summarises the estimated total base case societal
costs of OSAS in New Zealand, for people aged 30–60 years.
Table 4. Total societal costs (base case)
generated by OSAS (cost of illness)
The incremental costs associated with untreated OSAS among
people aged 30-60 years were estimated at $25.9 million per annum ($341 per
case). The total costs of treatment were estimated at $13.9 million per annum
($730 per case). The total costs of OSAS were thus estimated at $39.8 million
per annum (averaging $419 per case).
For 90% of the Monte Carlo simulations, the estimated total
cost fell in the range $32.9-89.8 million, with the top three cost determinants
being the prevalence of OSAS, and the cost and incidence of motor vehicle
accidents. Figure 2 illustrates the breakdown of total base case costs. Lost
productivity was the largest contributor to indirect costs, while the 3% of
intangible costs relate to loss of life. Costs associated with accidents (motor
vehicle and other) contribute 59% of the estimated total costs.
Figure 2. Breakdown of total base case costs of
OSAS
 Table 5 shows the cost benefit and cost utility analysis for
treating OSAS. It assumes a per case QALY gain of 5.4, with a low estimate of
0.10 and a high estimate of 8.00,22,32 and that
20% of people with OSAS are treated (low value 15%, high value 25%), for a total
gain of 102,531 QALYs per year.
DiscussionPrevious studies that have estimated the societal costs of
sleep disorders have generally taken a top-down
approach.33,34 In contrast, the approach taken
here using an outcome tree and decision analytic model yields a simulation tool
that can be used to evaluate treatment options, and to estimate the economic
impact of OSAS on different population groups. This is of particular interest,
given the disproportionate burden of OSAS among
Māori.8–9
For 2005, the estimated total annual societal costs of OSAS
among New Zealanders aged 30–60 yrs was $39.8 million, with 90% of the
Monte Carlo simulations in the range $32.9–$89.8 million. Limitations of
the data used to inform these cost estimates need to be considered.
OSAS prevalence is a key determinant of total costs and the
base case prevalence used (5.6%) was higher than the estimated 4% in an
Australian community study of men 35 and
2–4% in the Wisconsin Sleep Cohort.36
Using much more restrictive criteria for the definition of OSAS (RDI ≥5
plus ESS >10), we have recently estimated that the population prevalence of
OSAS is 4.4% for Māori men, 4.1% for non-Māori men, 2.0% for
Māori women, and 0.7% for non-Māori
women.8 However, we expect that these estimates
are very conservative. The Monte Carlo
simulations in the present study included prevalence estimates of
2.6–8.6%.
Estimates of the increased risks of comorbidities and
accidents associated with untreated OSAS are based, in the main, on studies that
have focused on populations with severe OSAS. These are the only data available
to inform these estimates and their applicability to the general New Zealand
population is unknown. The focus on severe OSAS could have resulted in
over-estimation of the costs associated with untreated OSAS in the present
study.
On the other hand, a number of factors would have tended to
make our estimates of the costs of untreated OSAS conservative. Medical costs
would have been higher if hospital inpatient costs of cardiovascular disease and
diabetes were included, and if a broader range of diseases had been included for
which OSAS is a possible risk factor. Our estimate of indirect costs included
lost productivity for time off work but not absenteeism or low productivity
while working.
Indirect and intangible cost estimates were based on ACC
payments.19 However, the ACC database includes
only those accidents for which claims were lodged, and which were judged as
compensable under the scheme, so the incidence estimates are conservative and
the costs represent the standards applied by the scheme, not necessarily all
costs resulting from accidents.
The estimate of intangible costs is likely to be low because
only those accidents causing death were quantified in dollar values. No attempt
was made to quantify additional costs borne by family members as a result of
living with an untreated OSAS sufferer (for example reduced productivity
associated with having their sleep disturbed, or additional caregiving).
The outcome tree was based on patient trajectories in the
Wellington region and may not be fully applicable to the variety of urban and
rural settings in New Zealand, particularly since decisions on service provision
are made at the level of district health boards, and services are not
homogeneous nationwide.7 The profile of
patients referred by GPs to the hospital screening clinic may be
unrepresentative, since GPs who are better informed about OSAS may be more
likely to make referrals.
The incremental direct medical cost per QALY gained by OSAS
treatment was estimated to be $94 (5th
percentile $56, 95th percentile $310). From
1998–2005, decisions made by PHARMAC reflected an average cost per QALY
gained of $6865.37 Thus, this economic analysis
strongly supports the cost effectiveness of OSAS treatment by comparison with
pharmaceutical treatments that the government already funds for other
conditions.
A survey in late 2006 found that 12 of the 21 District
Health Boards had a specified budget for the management of sleep-related
breathing disorders, with the remaining 9 DHBs having referral pathways to other
DHBs.7 The estimated number of sleep studies
conducted (including laboratory-based and home-based polysomnographic studies
and partial sleep studies without polysomnography), totalled 50/100,00 per year.
By comparison, for Australia the average was estimated at
282/100,000, for Canada 370/100,000 and for the USA 427/100,000.
Competing interests: None.
Author information: Philippa Gander,
Director, Sleep/Wake Research Centre, Research School of Public Health; Guy
Scott, Senior Lecturer, Department of Economics and Finance, College of
Business; Kara Mihaere, Research Officer, Sleep/Wake Research Centre, Research
School of Public Health; Helen Scott, Research Assistant, Sleep/Wake Research
Centre, Research School of Public Health, Massey University, Wellington, and
Partner (Scott Economics, Wellington)
Acknowledgements: We are very much indebted
to Drs Angela Campbell and Alister Neill of the WellSleep Clinic for their
generous provision of information on the pathways of patients through the
healthcare system, and the costs associated with different diagnosis and
treatment options. This research was funded by a grant from the Massey
University Wellington Strategic Research Fund (WSRF 04/03).
Correspondence: Philippa Gander, Sleep/Wake
Research Centre, Massey University, Private Box 756 Wellington, New Zealand.
Fax: +64 (0)4 3800629; email: swrc@massey.ac.nz
References:
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