COPO Prevalence in Southeastem Kentucky* The Burden of Lung Disease Study

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1 Original Research COPD COPO Prevalence in Southeastem Kentucky* The Burden of Lung Disease Study Jennifer N. Methvin, MPH; David M. Mannino, MD, FCCP; and Baretta R. Casey, MD Background: The Burden ofobstructive Lung Disease (BOLD) initiative provides a standardized way of measuring the prevalence of COPD. MethodB: We used the BOLD survey to estimate the prevalence of COPD in adults aged ~ 40 years in a target population of 325,000 in Southeastern Kentucky. Testing was done at survey centers and homes and included questionnaires on respiratory symptoms, risk factors for COPD, and health status. Postbronchodilator spirometry was used to classify subjects. We determined the prevalence ofcopd along with the relation ofcopd and comorbid disease and physical and mental quality of life measures. Results: The final study population was 508, with a participation response rate of25.2%. Overall, 19.6% of subjects met criteria for Global Initiative for Chronic Obstructive Lung Disease stage 1 or higher COPD, and an additional 17.6% met criteria for restriction. Diabetes, heart disease, and hypertension were significantly increased in subjects with restriction. Physical quality of life was significantly decreased in all respiratory impairment categories, compared to normal subjects, whereas mental quality of life measures were not affected. Conclusions: In this population, respiratory impairment is highly prevalent and associated with comorbid disease and physical, but not mental, dysfunction. (CHEST 2009; 135: ) Key words: COPD; epidemiology; prevalence Abbreviations: BMI = body mass index; BOLD = Burden of Lung Disease; CI = confidence interval; GOLD = Global Initiative for Chronic Obstructive Lung Disease; NHANES III = third US National Health and Nutrition Examination Survey; SF-12 = Short Form-12 c OPO is a preventable and treatable disease, characterized by lung function impairment with airway obstruction, that is currently the fourthleading cause of death in the United States and a major cause of morbidity. 1.2 Individuals with COPO have recurringacute exacerbations, frequent hospital admissions, poor survival, significant depressive symptoms (as well as physical symptoms of cough, sputum production, and shortness of breath), and impairedphysical functioning and quality of life. The third US National Health and Nutrition Examination Survey (NHANES III), conducted from 1988 to 1994, reports the prevalence of diagnosed COPO in the United States at 7%, and estimates that there are a Significant number of undiagnosed cases," A limitation of NHANES III was that spirometry was not "From the University of Kentucky College of Public Health (Ms. Methvin), Lexington; Division of Pulmonary and Critical Care Medicine (Dr. Mannino), University of Kentucky College of Medicine, Lexington; and Department of Family Medicine (Dr. Casey), Center for RuralHealth;University of Kentucky, Hazard,KY. This research was conducted at the University of Kentucky. Dr. Mannino has received research grants from G1axoSmithK1ine. Pfizer. and Novartis, and serves as a consultant to GlaxoSmith- Kline. Pfizer. Boehringer-Ingelheim, Astra-Zeneca, Dey, Sepracor, and Novartis. Ms. Methvin and Dr. Casey have no conflicts of interest to disclose.. Manuscript received May 22, 2008; revision accepted July 16, Reproduction of this article is prohibited without written pennission from the American College of Chest Physicians( orglmisc/reprints.shtml). Correspondence to: David M. Mannino, MD, FCCP, Associate Professor, Director, Pulmnnary Epidemiology Research Laboratory, University of Kentucky College of Public Health. 121 Washington Ave, Suite 220, 740 S Limestone, K.528, Lexinf.,1f;on, KY 40536; DrtuJnnino@Uky.edu DOl: lo.13781chest Original Research

2 performed after bronchodilation, which is the current recommendation for classifying patients with COPD.4,5 The aims of this study were to determine the prevalence of obstructive and restrictive lung disease in Southeastern Kentucky; the relation between objective measures of lung disease (as determined using spirometry) and prevalent cardiovascular disease, hypertension, and diabetes mellitus; the relation between spirometrically determined lung disease and diagnosed lung disease; and the relation between lung disease and quality of life measures. We used the Burden of Lung Disease (BOLD) protocol to complete the study." MATERIALS AND METHODS A simple random sample of noninstitutionalized adults (age ~ 18 years) from 26 counties with a target population of 325,000 in Southeastern Kentucky was surveyed. Initially, participants were contacted by telephone through random-digit dialing. Answers to a minimal data questionnaire were obtained, and site or home visits were scheduled (if applicable) for adults aged ~ 40 years to complete questionnaires and perform prebronchodilator and postbronchodilator spirometry, All participants gave written informed consent, and the study was approved by the University of Kentucky Institutional Review Board, Study Measures Trained and certified technicians administered spirometry to participants in a seated position, Measurements were made before and at least 15 min after two puffs of albuterol (200 mg) administered via a metered-dose inhaler with a spacer. Spirometry data were sent electronically to the Pulmonary Function Quality Control Center in Salt Lake City, UT, where each spirogram was reviewed and graded using American Thoracic Society guidelines. Usable spirograms met American Thoracic Society acceptability and reproducibility criteria (at least two acceptable and reproducible tests for both FEV, and FVC). Acceptable test results were those free from artifact, sudden stops, and back-extrapolated volumes of < 5.0% of FVC. Reproducible tests meant that the difference between the largest and second-largest values was < 200 ml. To calculate the percentage of predicted values for the analysis, reference equations from the NHANES III were used.' Questionnaire Data The BOLD core questionnaire contained information on health status, medication use, comorbidities, respiratory symptoms and diseases, risk factors for COPD, health-care utilization, and activity limitation. Health-related quality of life was determined based on the results of the Short Form-12 (SF-12) questionnaire." This self-report questionnaire is designed to reproduce the physical component summary and the mental component summary scores. We used the recommended scoring scheme to calculate scores for both the physical component and the mental component summaries. Definitions Demographic data included in this analysis were age, sex, and educational status (the studied population was 100% white). The study participants were classified into six lung function categories based on Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria and postbronchodiiator spirometry: normal (no respiratory symptoms or airflow obstruction or restriction); GOLD stage 0 (the presence of symptoms of cough, sputum, wheeze, or breathlessness without airflow obstruction or restriction); GOLD stage 1 (FEV/FVC < 70% and FEV, ~ 80% of predicted); GOLD stage 2 (FEV/FVC < 70% and FEV, 50 to < 80% of predicted); GOLD stage 3 or 4 (FEV/FVC < 70% and FEV, < 50% of predicted); and restricted (FEV/FVC > 0.70 and FVC < 80% of predicted). GOLD stage 0 is not in the most recent guidelines, but we have included this stage because previous work 9 10 has demonstrated worse outcomes in this group of patients. Our study differs from others by not including the restriction group in GOLD O. For some analyses, we grouped GOLD stage 1 and higher into a single "obstruction" group. We included in the analysis participants' responses to the questions "Has a doctor or other health-care provider ever told you that you have... heart disease; diabetes; hypertension; asthma; chronic bronchitis; emphysema; COPD?" We considered subjects reporting a prior diagnosis of asthma, chronic bronchitis, emphysema, or COPD as having "any diagnosed respiratory disease." We used the height and weight to calculate the body mass index (BMI) and stratified subjects as having BMI ~ 30 kglm 2 and < 30 kglm 2. Finally, we classified subjects as current smokers, former smokers, or never-smokers based on selfreported history. Further information regarding the rational and protocol of the BOLD Study have been previously published.'! Statistical Analysis Data analysis was completed using statistical software (Statistical Analysis Software, version 9.1; SAS Institute; Cary, NC) using SAS-callable SUDAAN (SUDAAN; Research Triangle Park, NC). Descriptive statistics and frequency distributions were calculated for the eligible and the studied population. We used X 2 analysis on the unweighted data to determine if there were differences between the study population and the eligible cohort. Sample weights were then applied to the study population to reflect the target population (26 Southeastern Kentucky counties). We determined the relation between age group, sex, smoking, education, BMI, and lung respiratory impairment (GOLD 1 or higher, GOLD 0, and restriction). We then determined the relation between respiratory impairment and comorbid disease or diagnosed respiratory using logistic regression (PROC RLOGIST; SUDAAN) models that adjusted for age, sex, smoking status, education, and BMI. Differences in health-related quality of life based on GOLD COPD stages were evaluated with linear regression (PROC REGRESS; SUDAAN), and also adjusted for age, sex, smoking status, education, and BMI. RESULTS A total of 15,148 different telephone numbers were dialed. In 7,073 calls, individuals refused even minimal participation in the study, could not be reached, or hung up. Another 6,011 telephone numbers dialed were ineligible due to an invalid tele-. phone number with no forwarding information, no one in the household of eligible age to participate in the study, the individuals were institutionalized, or they did not speak English. A total of 1,046 individuals were eligible and provided at least part of the CHEST/135/1/ JANUARY,

3 Table l-characteristic8 of Study Population V8 Eligible Population Study Population Eligible Population Characteristics 'No. %' I No. %1 p Value Age group. yr < :2: Sex Male Female , Smoking status Current Former Never Total 508 2,017 minimal data but refused full participation in the study; 971 individuals responded to the minimal data questionnaire and were willing to participate in the full protocol, but 575 actually participated. Acceptable postbronchodilator spirometry and full data were collected from 508 participants (206 male and 302 female subjects), which comprised the final study population.!' Our participation response rate was 508 of 2,017 subjects known to be eligible (25.2%). Table 1 displays the frequency distribution of the age, sex, smoking status, and education level of the final cohort and eligiblepopulation. Comparing the eligible population to the final cohort, the only significant difference arose in the age distributions, with a smaller proportion of elderly subjects participating. Overall, 19.6% (95% confidence interval [CI], 16.1 to 23.6%) of study participants have evidence of GOLD stage 1 or higher COPD (Table 2). As would be expected, this proportion was higher among older subjects, current or former smokers, and those with fewer years of education. The proportion of men and women with obstruction was similar (18.3% [95% CI, 13.4 to 24.0%]; vs 20.8% [95% CI, 16.0 to 26.6%]); 17.6% (95% CI, 14.3 to 21.6%) of subjects had restriction, and this proportion also increased with advancing age but was not clearly related to smoking status or educational level; 36.3% (95% CI, 31.8 to 40.7%) of subjects had respiratory symptoms with normal spirometry results. The relation between respiratory impairment, comorbid disease, and diagnosed respiratory disease is displayed in Table 3. Every category of impairment had a higher proportion of subjects with comorbid disease and, as would be expected, diagnosed respiratory disease. A large proportion of people with moderate or severe COPD, however, did not have any diagnosis of lung disease reported. After adjusting for age, sex, smoking status, and BMI, only restriction remained a significant predictor of comorbid heart disease, hypertension, and diabetes, (Table 4). The odds of any diagnosed respiratory disease Table 2-Study Population and Weighted Proportion With Respiratory Impairment by Age, Sex, Smoking, Education Level, and BMI* Study Population GOLD 1 or Higher: GOLD 0: Restricted: I, Variables No. Weighted % Weighted % (SE) Weighted % (SE) Weighted % (SE) Age group, yr (2.0) 48.2 (4.4) 10.5 (2.7) (3.0) 41.1 (3.6) 16.3 (2.7) (4.0) 23.0 (3.8) 21.4 (3.6) :2: (6.6) 23.2 (5.6) 28.0 (6.3) Sex Male (2.6) 41.0 (3.6) 15.0 (2.6) Female (2.7) 32.4 (2.9) 19.9 (2.6) Smoking status Current (4.2) 33.6 (4.6) 13.0 (2.6) Former (3.6) 27.5 (3.6) 19.6 (3.1) Never (2.2) 38.9 (3.6) 18.9 (3.2) Education, yr < (4.9) 41.2 (5.1) 15.0 (3.5) (3.2) 34.4 (3.9) 18.3 (3.3) > (2.4) 35.3 (3.4) 18.5 (2.8) BM!, kglm 2 < (2.8) 30.2 (3.0) 15.3 (2.5) :2: (2.4) 43.2 (3.4) 20.4 (2.7) Total (1.9) 36.3 (2.3) 17.6 (1.8) *GOLD classifications are defined in the "Materials and Methods" section. 104 Original Research

4 Table 3-Comorbidities and Diagnosed Respiratory Diaeate by Respiratory Status* Patients, Heart Any Diagnosed COPD or Chronic Variables No. Disease Hypertension Diabetes Respiratory Disease Asthma Bronchitis Emphysema Normal (17.4) 51 (36.7) 11 (8.5) 10(6.7) 4 (2.7) 5 (2.8) 1 (0.5) GOLD (23.5) 91 (48.6) 34 (17.3) 69 (34.1) 53 (28.4) 38 (18.9) 9 (4.4) GOLD (.'33.7) 13 (49.1) 3 (17.3) 9 (38.1) 4 (16.6) 4 (14.0) 4 (18.1) GOLD (30.3) 27 (53.7) 8(14.6) 31 (59.8) 17 (30.7) 21 (41.3) 16 (29.2) GOLD 3/ (41.3) 10 (53.9) 6(34.7) 11 (43.2) 7(29.7) 6 (20.1) 7 (33.4) Restricted (48.0) 58 (66.1) 26 (28.4) 38 (42.4) 28 (28.8) 17 (21.2) 9 (8.9) Total (28.2) 250 (49.3) 88 (17.5) 168 (31.2) 113 (21.3) 91 (17.1) 46 (8.6) *Data are presented as No. of patients (weighted percentage) unless otherwise indicated. GOLD classifications are defined in the "Materials and Methods" section. was increased in all impairment categories, although, somewhat surprisingly, there was significant overlap between all of the categories. In regression models adjusting for age, smoking status, and sex, all respiratory impairment categories were associated lower scores on the physical component of the SF-12 questionnaire, but not the mental component (Figs 1,2). DISCUSSION We found in this population-based survey of adults in Southeastern Kentucky a high prevalence of both obstruction and restriction on spirometry, along with a high prevalence of respiratory symptoms in the absence oflung function abnormality. We also found that restriction was significantly associated with heart disease, diabetes, and hypertension, and that any degree of respiratory impairment was associated with lower reported physical function on the SF-12 questionnaire. The prevalence of both obstruction and restriction is higher in this study than what has been reported in other studies.v' Ll2 While this may have been related to a sampling bias, it may also reflect the high prevalence of risk factors such as smoking, poor diet, and comorbid disease that is also common in this population. Southeastern Kentucky has had, historically, a very high prevalence of obesity, diabetes, and cigarette smoking, and much of its population has been employed in dusty trades, such as the mining or timber industries.is-is For example, the prevalence of obesity (BMI 2= 30 kg/m 2 ) in our population is 45.3%, which is much higher than the US estimate of obesity prevalence in 2005 of 23.9%.1 6 We also found that the presence of respiratory impairment was associated with a higher prevalence of heart disease, hypertension. and diabetes. While this finding was only significant in the restricted categories, the obstructive categories showed evidence ofan effect also that may have been limited by the size of the sample. These findings suggest that either the development of respiratory impairment leads to other diseases, that these other diseases may become manifest in the lungs, or that risk factors may lead to the development of both diseases.p-!" For example, others have shown a link between restriction on spirometry and the metabolic syndrome.l" and we have previously shown that obstruction and restriction is a risk for cardiovascular events.!? Our analyses, which controlled for BMI, suggest that BMI alone does not explain these findings. Our analysis also showed that the presence of respiratory symptoms in the absence of lung function impairment is an important correlate of diagnosed lung disease and lower quality of life. While the proportion of people in this category decreased with age (Table 2), this actually reflect people with symptoms preferentially moving into either the restricted or obstructed categories, as the proportion of people Table 4-Adjusted OR and 95% CIs ofcomorbid Disease or Any Diagnosed Respiratory Diseoee" Variables Heart Disease Hypertension Diabetes Any Diagnosed Respiratory Disease Normal GOLD ( ) 1.6 (0.9, 2.6) 1.8(0.8, 3.8) 8.5 (4.0, 18.0) GOLD (0.5, 4.0) 1.2 (0.4, 3.5) 1.1 (0.3,4.1) 8.8 (2.8, 27.8) GOLD (0..5,2.7) 1.6 (0.8, 3.5) 1.0 (0.3, 2.8) 17.4 (6.7, 45.1) GOLD 3/ (0.4, 4.9) 1.6 (0.6, 4.4) 2.4 (0.4, 13.0) 16.1 (5.0,51.7) Restricted ,6.3) 2.7 (1.4,.5.2) 2.8 (1.2, 6.7) 11.6 (5.0, 26.9) *Adjusted for age, sex, education, smoking status, and BMI. GOLD classifications are defined in the "Materials and Methods" section. CHEST/135/1 / JANUARY,

5 IS 20 0t-----t-----II---;i----t , GOLD. t GOLD. GOLD I t GOLDl GOLD 2 j GOlD2 ReopII1ltorY ImpairmentStage GOlD3/" I GOLD 3 I " Reltrictlltd FIGURE 1. Physical component scores of SF-12 questionnaire by respiratory impairment stage (compared to normal subjects), adjusted for age, education, smoking status, sex, and BMI. reporting symptoms is approximately 60% in each age stratum (data not shown). We also found that diagnosed respiratory disease, either asthma, COPD, or emphysema, was fairly common in this population. However, a relatively high proportion of the population with evidence of lung function impairment has never had lung disease diagnosed (Table 3), which mirrors findings from other studies. 3,20-22 Finally, we found that respiratory impairment negatively affects the physical, but not the mental, component of the SF-12 questionnaire in our survey. These findings are similar to those found in other studies of respiratory impairment, with physical functioning being more affected than mental function Conversely, the association of COPD with anxiety and depression has been well established.w which suggests that either the SF-12 questionnaire does not completely measure depression and anxiety or that our sample was biased. This study has some important limitations. The participation rate among people known to be eligible was 25.2%. Studies with relatively low participations are more subject to bias, in that the actual participants may not fully represent the target population. FIGURE 2. Mental component scores of SF-12 questionnaire by respiratory impairment stage (compared to normal subjects). Adjusted for age, education, smoking status, sex, and BMI. Although we used weights to adjust the demographics of the study sample to that of the target population, weights do not eliminate nonparticipation bias. While these findings are generalizable to the target population of Southeastern Kentucky, they may not reflect findings for other parts of Kentucky or the United States. Many of our outcomes were based on self-report and were not independently validated. Even with these important limitations, however, we believe these findings represent important new knowledge in understanding respiratory disease at the population level. In conclusion, we found that the prevalence of both obstructive and restrictive pulmonary function impairment affects a large portion of the population in our Southeastern Kentucky population, and that respiratory abnormalities are associated with comorbid disease and impaired physical functioning. REFERENCES 1 Global strategy for diagnosis, management, and prevention of COPD. Geneva, Switzerland: World Health Organization, Mannino OM, Homa DM, Akinbami LJ, et al. Chronic obstructive pulmonary disease surveillance-united States, MMWR Surveill Summ 2002; 51: Mannino DM, Gagnon RC, Petty TL, et al, Obstructive lung disease and low lung function in adults in the United States: data from the National Health and Nutrition Examination Survey, Arch Intern Med 2000; 160: Celli BR, MacNee W. Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ ERS position paper. Eur Respir J 2004; 23: Rabe KF, Hurd S, Anzueto A, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 2007; 176: Buist AS, Vollmer WM, Sullivan SD, et al. The Burden of Obstructive Lung Disease Initiative (BOLD): rationale and design. COPD J Chron Obst Pulm Dis 2005; 2: Hankinson JL, Odencrantz JR, Fedan KB. Spirometric reference values from a sample of the general U.S. population. Am J Respir Crit Care Med 1999; 159: Ware J Jr, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care 1996; 34: Stavem K, Sandvik L, Erikssen J. Can Global Initiative for ChronicObstructive Lung Disease stage 0 provide prognostic information on long-term mortality in men? Chest 2006; 130: Mannino DM. GOLD stage 0 COPD: is it real? Does it matter? Chest 2006; 130: Buist AS, McBurnie MA, Vollmer WM, et ai. International variation in the prevalence of COPD (the BOLD Study): a population-based prevalence study. Lancet 2007; 370: Menezes AM, Perez-Padilla R, Jardim JR, et al. Chronic obstructive pulmonary disease in five Latin American cities (the PLATINa study): a prevalence study. Lancet 2005; 366: Advanced pneumoconiosis among working underground coal miners: Eastern Kentucky and Southwestern Virginia, MMWR Morb Mortal WkIy Rep 2007; 56: Original Research

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