Sleep apnea in a high risk population: a study of veterans health administration beneficiaries

Sleep Medicine 5 (2004) 345–350 www.elsevier.com/locate/sleep

Original article

Sleep apnea in a high risk population: a study of veterans health administration beneficiaries Amir Sharafkhaneh*, Peter Richardson, Max Hirshkowitz VAMC Sleep Center 111i, Baylor College of Medicine, 2002 Holcombe Blvd., Houston, TX 77030, USA Received 20 November 2003; received in revised form 7 January 2004; accepted 20 January 2004

Abstract Background and purpose: In the present study we attempt to determine the prevalence of International Classification of Disease-ninth revision, Clinical Modification (ICD-9 CM) coded sleep apnea with cardiovascular and metabolic co-morbidities in Veterans Health Administration (VHA) beneficiaries. Patients and methods: Using VHA administrative databases, we gathered available medical information on more than 4 million veterans using the VHA during the period between 1998 and 2001. We identified database entries for codes indicating sleep apnea using the ninth revision of the Clinical Modification of the International Classification of Diseases (ICD-9 CM); and tabulated demographic data including age, gender, ethnicity, and cardiovascular and metabolic co-morbidities. Results: We found 118,105 unique cases (out of 4,060,504) with sleep apnea ICD-9 CM codes (prevalence of 2.91%). Mean age at diagnosis was 57.6 with more than 38% older than 65 years. Comorbid diagnoses in this group included hypertension (60.1%), obesity (30.5%), diabetes mellitus (32.9%), cardiovascular disease (including MI and angina) (27.6%), heart failure (13.5%), and cerebrovascular accident (including Transient Ischemic Attack (TIA)) (5.7%). Conclusions: We found a high prevalence of diagnosed sleep apnea among VHA beneficiaries. Additionally, cardiovascular and metabolic conditions were common in these patients. Published by Elsevier B.V. Keywords: Obstructive sleep apnea/hypopnea syndrome; Hypertension; Congestive heart failure; Cardiovascular disease; Cerebrovascular accident; Diabetes mellitus; Obesity

1. Introduction Symptomatic obstructive sleep apnea/hypopnea (OSAH) afflicts an estimated 4% of men and 2% of women ages 30 – 70 years [1]. OSAH is characterized by repeated pharyngeal obstructions during sleep that cause airflow cessation (apnea) or reduction (hypopnea). OSAH events produce arousals, fragment sleep, and are often accompanied by oxygen desaturations. Common symptoms include daytime sleepiness, fatigue, irritability, disturbed sleep, memory problems, and diminished quality of life [2,3]. Recent epidemiological studies link untreated OSAH to hypertension, heart disease, stroke, and increased risk for motor vehicle accidents [4 – 8]. Although OSAH epidemiology is well studied in the general population [9], data from high-risk populations are * Corresponding author. Tel.: þ 1-713-794-7318; fax: þ1-713-794-7558. E-mail address: [email protected] (A. Sharafkhaneh). 1389-9457/$ - see front matter Published by Elsevier B.V. doi:10.1016/j.sleep.2004.01.019

limited. Although risk factors and comorbidities associated with OSAH are common among VHA beneficiaries, and one might expect a high prevalence in this population, only a few small studies have been reported. Ancoli-Israel and colleagues [10], in a study of inpatient VHA beneficiaries 65– 91 years of age, reported a sleep apnea (apnea index . 5) prevalence of 36%. Kreis and colleagues [11] in their study of VHA beneficiaries reported a 27% prevalence of sleep apnea (7 out of 26) defined as more than 30 episodes of apnea per night. Similar prevalence is reported for outpatients; AncoliIsrael and colleagues, in a study of 117 subjects referred to their outpatient sleep clinic at a Veterans Affairs Medical Center, found 51 patients (44%) to have sleep apnea as defined by 30 or more apneic events for the study night [12]. The US Veterans Health Administration (VHA) provides health care to more than 4 million veterans. In 1970, the VHA began developing centralized databases to monitor the care provided and store related information for inpatient

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and outpatient visits. These databases are available for research and have previously been used for study of different diseases, and in a recent report on hospital use and survival among VHA beneficiaries by the Houston VA Medical Centers Health Services Research and Development [13]. The present study used the VHA databases to (a) determine the prevalence and incidence of diagnosed sleep apnea, (b) identify co-morbidities associated with sleep apnea, and (c) characterize demographics for VHA beneficiaries with sleep apnea.

2. Methods This is a retrospective, cross-sectional, database review of all VHA Outpatient Clinic Files and Patient Treatment Files (PTF) from the beginning of fiscal year 1998 to the end of fiscal year 2001. The study was approved by the local institutional review board and the local VA research and development committee. 2.1. Data bases 2.1.1. The patient treatment file During the study period (1998 –2001), each annual PTF file contained approximately one half million hospitalization records among more than 300,000 US military veterans. The PTF was first established in 1970 and registers all hospitalizations from 172 VA hospitals throughout the US. Diagnoses recorded by practitioners at each VHA facility are entered by trained coders into the local computer system and then automatically transferred to the central VA database in Austin, TX. Each hospitalization has a primary discharge diagnosis and up to nine secondary diagnoses, encoded according to the ninth revision of the Clinical Modification of the International Classification of Diseases [ICD-9 CM]. 2.1.2. The outpatient file In 1997, the VHA began recording the outpatient file (OPC) diagnoses made during each outpatient encounter. Using unique identifiers (social security numbers), an individual can be tracked through the different files of the PTF and OPC to obtain a complete record of encounters in the system. 2.2. Procedures We searched the VHA’s inpatient hospitalization (PTF) and outpatient clinic visit (OPC) database files for the appearance of ICD-9 CM diagnosis codes for sleep apnea, including: (a) insomnia with sleep apnea (780.51), (b) hypersomnia with sleep apnea (780.53), and (c) other unspecified sleep apnea (780.57). The database showed 146,548 patients identified with these codes during

the fiscal years from 1992 to 2001 (1992 –2001 for PTF and 1997 –2001 for OPC). The study cohort is restricted to 118,105 patients, following two criteria: (i) the patient’s first coded occurrence of sleep apnea fell into the period October 1, 1998 to September 30, 2001 (VA fiscal years 1998 –2001) and (ii) the patient’s age at that first occurrence was between 21 and 85. 2.3. Collected information SAS v 8 (SAS institute, Inc., NC, USA) was used to extract demographic variables and diagnosis history (ICD-9 CM codes) for the study cohort and to construct datasets suitable for our analyses of these extracts. For this purpose, patient records were selected by scrambled social security numbers and the records in the analysis datasets were then assigned unique identifiers that could not be decoded to the patient’s social security number. The demographic variables in the analysis dataset included age, gender and ethnicity. Comorbid condition variables were defined on the basis of occurrence of ICD-9 CM diagnostic codes: (a) obesity (278.0), (b) diabetes mellitus (DM) (250), (c) hypertension (401, 402, 403 and 404), (d) heart failure (428), (e) cardiovascular disease (CVD) (412 – 414, 429.2 and 429.3), and (f) cerebrovascular disease (430 – 437). 2.4. Data analysis SAS (version 8) was used to perform the data analyses. Mean, standard deviation, and frequency distribution for age, tabulation of ethnicity and sex, and diagnostic codes for sleep apnea were calculated, the number of new cases per year were determined, and comparisons with the parent population were performed. Comparisons were performed by calculating P-values for chi-square, Student’s t and Wilcoxon test statistics. We calculated diagnosed sleep apnea incidence (person –year) and prevalence, assuming each patient to be present in the VA healthcare system for that entire fiscal year (i.e. to contribute one full patient – year to the incidence calculation). The incidence denominator is thus somewhat inflated, in that some patients will in fact contribute less than one full patient – year, resulting in an underestimation of diagnosed sleep apnea incidence in our reported figures. To assess the prevalence of comorbid conditions, we selected all diagnosed sleep apnea cases (n ¼ 98,635) and the parent population for the fiscal year 2000 (with n ¼ 3,647,328), tabulating comorbid ICD-9 codes from clinic visits during that year and hospital stays whose discharge dates fell within 2000. Two-sided 95% confidence intervals for unadjusted odds ratios and P-values for chi-square test statistics were calculated to compare the comorbidity rates for these two subpopulations. Odds ratios adjusted for demographic variables were also produced by

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way of multivariate logistic regression models with the comorbid conditions as outcomes, diagnosed sleep apnea as explanatory variable, and continuous age and categorical indicators for age, gender and ethnicity as controlling covariates.

3. Results Table 1 represents demographics of sleep apnea patients and the parent population. Mean age at diagnosis was 57.6 (SD ^ 12.47) and 38.4% were more than 65 years old. The sleep apnea patients were slightly younger (1.37 years) than the parent population. The majority of sleep apnea patients were aged 35 – 64 years. In addition, a higher percentage of sleep apnea patients were male compared to the parent population. Ethnicity was unknown for a large section of the parent population; therefore, comparison with the sleep apnea patients is not reliable. Nonetheless, the majority of diagnosed patients were Caucasian. Table 2 contains the epidemiological data for VHA beneficiaries. New cases are first appearances of an ICD-9 code for sleep apnea (780.51, 780.53, and 780.57). We identified a total of 122,052 patients with sleep apnea codes. Cumulative cases are those sleep apnea cases whose observation period (ranging from the first occurrence of sleep apnea to the last year in which any records appear in the VHA data from 1998 to 2001) spans the given fiscal year. The following equation holds: ðcumulative cases for year N þ 1Þ ¼ ðcumulative cases for year NÞ þ ðnew cases for year N þ 1Þ

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Table 2 Prevalence of diagnosed SLEEP APNEA in VA health beneficiary Fiscal year

New cases

Cumulative cases

Population

Incidencea

Prevalence (%)

1998 1999 2000 2001

22,495 23,497 26,798 30,716

54,016 73,317 94,868 118,105

3,243,039 3,397,693 3,647,328 4,060,504

0.00694 0.00692 0.00735 0.00756

1.77 2.25 2.60 2.91

a

Person–year.

apnea in their medical records. This number increased to 73,317 (out of 3,397,693) in 1999, 94,868 (out of 3,647,328) in 2000, and 118,105 (out of 4,060,504) in 2001. Sleep apnea prevalence and number of newly diagnosed cases increased steadily over these four consecutive years. Table 3 presents prevalence of cardiovascular and metabolic comorbidities in sleep apnea patients and the parent population and adjusted odds ratio for each comorbid condition. Hypertension (60.1%), obesity (30.5%), DM (32.9%), symptomatic CVD (27.6%), heart failure (13.5%), and cerebrovascular accident (CVA) (5.7%) were more prevalent in sleep apnea patients than in the parent population. Table 4 shows prevalence of cardiovascular comorbidities among patients with different sleep apnea ICD-9 codes. Prevalence of comorbidities was systematically lower among individuals with 780.51 sleep apnea ICD-9 (insomnia with sleep apnea) compared to the two other sleep apnea ICD-9 codes (hypersomnia with sleep apnea, and other unspecified sleep apnea).

4. Discussion

2 ðpatients who dropped out after year NÞ The data set contained 4,060,504 million veterans from 1998 to 2000. Of these, 2.91% (118,105 patients) were diagnosed with sleep apnea. A total of 54,016 (from 3,243,039 individuals) in 1998 had a diagnosis of sleep Table 1 Demographic characteristics of sleep apnea patients and the parent population Characteristics

Sleep apnea

Population

Age Gender (male) Ethnicity White Black Hispanic Unknown Age distribution , 34 35–64 . 65

57.6 (12.47) 96%

59.03 (15.58) 90%

59% 11% 4% 26%

40.5% 9.6% 3.5% 45.75

4.24% 57.37% 38.4%

7.22% 49.99% 42.79%

Almost 3% of VHA beneficiaries are coded with sleep apnea. Such individuals are referred on the basis of high clinical suspicion and are subsequently diagnosed. Published population based prevalence estimates (confirmed with polysomnography) are derived from both symptomatic and asymptomatic individuals. It is widely accepted that 4 and 2% of middle-aged men and women, respectively, have OSAH [1]. The 2.9% prevalence in our study represents only the clinically diagnosed sleep apnea. Several smaller studies have reported much higher prevalence in VHA beneficiaries [10 –12]. For example, Ancoli-Israel and colleagues [10], in a study of more than 400 inpatient VHA beneficiaries, reported a sleep apnea prevalence of 36%, defining sleep apnea as apnea index of . 5 per hour of sleep. Such higher prevalence presumably relates to greater self-selection and referral bias, age difference of the study cohorts, and the inpatient setting [1]. For instance, patients who interface more frequently with the healthcare system are more likely to obtain diagnoses, including those of sleep apnea and comorbid conditions. However, our prevalence may be an

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Table 3 Cardiovascular, respiratory and renal comorbidities in all sleep apnea patients and parent population Comorbidity

SRBD

Rest of VA

Odds ratio (95% CI)

x2 P-value

N Hypertension Obesity Diabetes Heart failure Cardiovascular disease (CVD) Cerebrovascular accident (CVA)

98,735 59,362 (60.1%) 30,082 (30.5%) 32,448 (32.9%) 13,281 (13.5%) 27,247 (27.6%) 5631 (5.7%)

3,548,593 1,391,965 (39.2%) 239,460 (6.8%) 577,512 (16.3%) 151,155 (4.4%) 596,358 (16.8%) 132,039 (3.7%)

2.34 (2.31– 2.37) 6.06 (5.97– 6.14) 2.52 (2.48– 2.55) 3.40 (3.34– 3.46) 1.84 (1.82– 1.87) 1.57 (1.52– 1.61)

,0.0001 ,0.0001 ,0.0001 ,0.0001 ,0.0001 ,0.0001

Odds ratio adjusted for age, gender, and ethnicity.

underestimate, and the likely number of subjects with sleep apnea, therefore, appreciably higher than our figure. Furthermore, the number of newly diagnosed cases increased over the study period. This change no doubt reflects increasing awareness of OSAH among practitioners, establishment of OSAH related guidelines within VHA, and availability of sleep facilities and professionals. While the mean age for patients with sleep apnea in our population was comparable with other studies, age distribution differed [14 – 16]. Diagnosed sleep apnea prevalence was 1.76% in the age range 35 –64 years and 1.38% for patients aged 65 years and more. By contrast, studies that prospectively define OSAH with polysomnography show increased OSAH prevalence as a function of age. This difference may arise from case definition. Generally, OSAH in older individuals is characterized by more central events and less severe disease. Bixler and colleagues [14] found that, although polysomnographically diagnosed OSAH is greater in older individuals, prevalence of symptomatic OSAH (defined as an apnea – hypopnea index (AHI) $ 10 with symptoms) is lower (4.7% for age , 65 years versus 1.7% for age $ 65 years). Furthermore, notwithstanding higher prevalence of OSAH in elders, most of the increase occurs before age 65 [14,17 – 19]. By contrast, our study included only patients referred for evaluation on the basis of clinical suspicion and who were subsequently diagnosed with sleep apnea. As expected, we found a strong association between obesity and DM with sleep apnea; this is consistent with published literatures [20 – 23]. DM is a common complication of obesity [24]. Prevalence for obesity and DM were similar among individuals with sleep apnea; however, the odds ratio for obesity in sleep apnea is more than twice that for DM. This pattern may derive from more consistent criteria and greater sensitivity for DM than for obesity. Clinical criteria for obesity tend to be less sensitive (and often are based on clinical judgment rather than body mass index). Thus, patients classified as obese are more likely to be extreme cases. These classification differences can introduce differential bias. Our data analysis revealed a high association between cardiovascular comorbid conditions and sleep apnea (Table 3). Not surprisingly, the association between

sleep apnea and hypertension was strong. This association is consistent with published cross-sectional studies [6,15,25– 27]. Furthermore, Young and colleagues [28] in a prospective analysis showed that an even minimally elevated AHI was associated with increased odds of developing hypertension. As is presented in Table 3, CVD is common in VHA patients with sleep apnea diagnosis. This relationship is supported by other observational studies. For example, in the Sleep Heart Health study, Shahar and colleagues reported 42% greater odds of prevalent coronary heart disease in individuals with AHI . 11 events per hour of sleep compared to individuals with AHI , 1.3 events per hour of sleep [29]. We also found a high prevalence of congestive heart failure (CHF) in our population. In our cohort more than 13.5% of patients with sleep apnea had a CHF diagnosis, in contrast to the parent population, in which only 4.4% were diagnosed with CHF. Published epidemiological studies report associations between OSAH and CHF. Furthermore, the Sleep Health Heart Study showed that subjects with OSAH and AHI of $ 11 per hour of sleep were more likely to have heart failure, independent of other known risk factors [29 –31]. Furthermore, elderly veterans (age . 60 years) with CHF and sleep apnea have shorter survival compared to those with CHF but without sleep apnea and those with sleep apnea but without CHF [32]. Cerebrovascular accident (CVA), another systemic vascular comorbid condition, was likewise more prevalent in patients with sleep apnea than in our parent population. Association Table 4 Frequency of ICD-9 codes consistent with SLEEP APNEA and prevalence of comorbidities ICD-9 Code Frequency

78051 7384 (7.47%)

78053 18,275 (18.50)

78057 72,103 (72.99)

Multiple 1029 (1.04)

Obesity DM HTN CHF CVD CVA

22.20% 24.29% 55.42% 9.76% 22.69% 5.08%

29.45% 29.00% 59.18% 10.20% 25.58% 5.53%

29.41% 29.33% 58.97% 12.22% 26.57% 5.30%

33.23% 32.58% 62.12% 1.26% 27.16% 5.63%

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between CVA and OSAH is well documented in other published literature [29,33 –35]. In this study we relied on available centralized administrative VHA databases to study sleep apnea epidemiology. We did not validate cases by examining the patients. The accuracy of diagnosis in VHA administrative databases has been studied by comparing data from these databases with written medical records. In one such study, Szeto and colleagues [36] concluded that the administrative database across several visits is accurate and efficient in determining chronic medical diagnoses. However, the accuracy of sleep apnea diagnosis has not been studied in the VHA, and there are a variety of limitations associated with this research methodology, perhaps the most important being the variable precision and techniques with which diagnoses are confirmed. VA programs relying on cardiopulmonary or overnight oximetries are liable to under-diagnosis because nonpolysomnographic techniques are not as sensitive and likely to miss a less severe condition [37,38]. On the other hand, physician diagnosis is likely to overestimate sleep apnea prevalence [39]. Other sources of error include coding inaccuracies and variance across multiple sites and coders. In 2002, there were 163 hospitals and 913 outpatient clinics providing health services. However, there is no reason to believe that systematic miscoding occurred in the sleep apnea group compared to the parent population. Another concern is database integrity and continuity when data codes are altered or updated. To our knowledge, there has not been any significant coding change across the years of data entry. ICD-9 codes have been stable and non-ambiguous for sleep apnea and the comorbid conditions we analyzed. Databases are notorious for having repeat entries; therefore, we checked and only accepted one data record per social security number. Our epidemiologic measurement is limited to the veterans who use the VHA system. Our prevalence figures may underestimate to the extent that we could not capture our cohort members’ use of non-VA services. Generally, veterans who use the VHA system are more likely to be male, middle-aged or older, and often have multiple comorbid conditions. Therefore, it may not be possible to generalize our findings to the general population. In summary, diagnosed sleep apnea is common (2.9%) among VHA beneficiaries, but further studies are needed to establish the true prevalence of sleep apnea in this population. Accurate information is required for appropriate resource allocation to meet health care needs of VHA beneficiaries. Not surprisingly, cardiovascular and metabolic comorbidities are common in patients with sleep apnea. The results of therapeutic interventions for these patients in terms of health care utilization and cost remain to be determined.

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Acknowledgements This work is supported by office of Research & Development of Department of Veterans Affairs.

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