Coexistent Chronic Conditions and Asthma Quality of Life*

Original Research ASTHMA Coexistent Chronic Conditions and Asthma Quality of Life* A Population-Based Study Robert J. Adams, MD; David H. Wilson, PhD; Anne W. Taylor, MPH; Alison Daly, BA; Edouard Tursan d Espaignet, PhD; Eleonora Dal Grande, MPH; and Richard E. Ruffin, MD Objective: Reports of the prevalence and impact of comorbid conditions among people with asthma have been limited to certain population groups or convenience samples. Our aim was to examine the prevalence of major comorbidity in asthma and associations with quality of life and functional status in the general population. Study design/setting: The WANTS Health and Well-being Survey is a cross-sectional representative population household telephone interview survey in three Australian states. Participants: Representative sample of noninstitutionalized adults in three Australian states. Measurement and results: From the available sample of 10,080 patients, 7,619 interviews were completed (participation rate, 74.8%), with 834 people reporting current doctor-diagnosed asthma (11.2%). People with asthma were more likely to report one of the selected comorbid conditions: diabetes, arthritis, heart disease, stroke, cancer, osteoporosis (adjusted odds ratio, 1.9; 95% confidence interval, 1.5 to 2.2). Among people with asthma, there were statistically and clinically significant decreases in usual activity levels and in Short Form-12 physical component summary scores when another chronic condition was also present. For those with any of the chronic conditions, the additional presence of asthma was associated with significant further impairment in quality of life in those aged > 35 years but not in younger adults. Conclusion: The significant reduction in quality of life associated with comorbidity in asthma has implications for disease management and organization of care, as well as for the design and external validity of single-disease clinical trials. (CHEST 2006; 129:285 291) Key words: asthma; comorbidity; population study; quality of life Abbreviations: CI confidence interval; PCS physical component summary; SF-12 Short-Form 12 Asthma in adults accounts for significant morbidity 1 and cost to the community. 2 However, there is minimal published information on comorbid conditions associated with asthma. Such information that is available has mostly come from convenience *From the Health Observatory (Drs. Adams, Wilson, and Ruffin), Queen Elizabeth Hospital Campus, University of Adelaide, South Australia; Population Research and Outcome Studies Unit (Ms. Taylor and Ms. Dal Grande), Department of Health, South Australia; Health Outcomes Assessment Unit (Ms. Daly), Department of Health, Western Australia; and Population Health Division (Dr. Tursan d Espaignet), Newcastle University, NSW. Funding was provided by the Commonwealth of Australia Department of Health and Aged Care; Department of Human Services South Australia; Department of Health Western Australia; and Northern Territory Department of Health. samples or clinic populations, making conclusions as to the generalizability of the results limited. In a study from primary care, Ben-Noun 3 found that the prevalence of comorbidities differed between people with and without asthma. While gastric ulcers, sinusitis, and glaucoma were seen more frequently, other major chronic conditions such as diabetes and car- Manuscript received March 29, 2005; revision accepted June 30, 2005. Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (www.chestjournal. org/misc/reprints.shtml). Correspondence to: Robert J. Adams, MD, Department of Medicine, University of Adelaide, The Queen Elizabeth Hospital, Woodville Rd, Woodville, SA 5011, Australia; e-mail: robert.adams@nwahs.sa.gov.au www.chestjournal.org CHEST / 129 / 2/ FEBRUARY, 2006 285

diovascular disorders were not more common among people with asthma. 3 Diette et al 4 reported that comorbid conditions were more common in older than younger asthma patients but were unable to compare asthma patients with asthma with patients without asthma. The influence of comorbidity on quality of life and functional status has also not been extensively studied in asthma. Asthma has been associated with a greater likelihood of preventable hospitalization in people with diabetes. 5 Wijnhoven et al 6,7 reported that comorbidity was a determinant of quality of life in adults with asthma and has been linked to asthma deaths. 8 These studies were unable to compare asthma with a nonasthma population; thus, the relative effect of comorbidity in asthma compared with others remains unclear. Xuan et al 9 examined data from controlled clinical trials to find that comorbid conditions significantly affected scores on generic quality of life measures and estimation of treatment effect in asthma. The authors 9 noted that these findings had significant practical implications for the estimation of treatment effects, and design of trials. The extent of this problem, particularly in the elderly, is unknown. Many asthma clinical trials 10,11 exclude people with comorbid conditions. Consequently, the degree to which clinical trials are reflective of the actual population with asthma is also unclear. This has major potential implications for the external validity of single-disease intervention trials. 12 The objective of this study was to compare the prevalence of common, significant medical conditions in adults with and without asthma in the general population. We also compared the influence of comorbid chronic health conditions on physical health quality of life and functional status for those with and without asthma. We used data from the Collaborative Health and Well-being Survey, a large population survey of well-being among adults living in Western Australia, the Northern Territory, and South Australia. Study Population Materials and Methods All households in the three jurisdictions with a telephone connected and the number listed in the current version of the electronic white pages were eligible for selection in the sample. Samples were drawn separately for each state. The target number of interviews for each state was 2,500. A stratified sampling technique was used with the distribution of these interviews planned: n 900 in the metropolitan area, n 800 in rural areas, and n 800 in remote areas. The minimum sample size of 800 was necessary to enable population estimates to be made with reasonable confidence intervals (CIs) for the less populated areas of each state. This was particularly so in the rural and remote areas of South Australian and Western Australia, where a random sample of each state would have resulted in a small number of respondents for these areas. As a consequence of the need to oversample nonmetropolitan areas, separate samples were drawn for each of the three geographic regions (metropolitan/rural/remote) for each of the states. These samples represented increasing proportions of the population as remoteness increased. The definitions of remoteness were based on Australian Bureau of Statistics Accessibility/Remoteness Index of Australia codes. 13 Survey Method Within each household, the person aged 18 years who had their birthday last was selected for interview. There was no replacement for persons who could not be contacted or refused to participate. An introductory letter was sent to each selected household from each respective state s health department. Interviews were conducted during 2000 by trained health interviewers using a computer-assisted telephone interview system, with at least six callbacks as needed. The validity of these methods to produce unbiased samples while maximizing response rates has been previously described. 14,15 Demographic data on people who refused participation was also obtained to compare with participants and allow for appropriate weighting in the analyses. The survey was conducted with the oversight of a management group comprising a member from Health Departments of Western Australia, the Northern Territory, and South Australia, and the Commonwealth of Australia. The introductory letter sent to all potential respondents included a toll-free telephone number to contact with any queries. At the time of the interview all respondents were informed they could refuse to take part without any penalty and were able to terminate the interview at any time. All data were de-identified, and all information was kept strictly confidential. In line with other population surveys, no institutional review board approval was sought. Survey Items Current doctor-diagnosed asthma was determined by positive responses to both items asking participants if they had ever been told by a doctor they had asthma and if they still had asthma. No data on clinical asthma variables were available. Quality of life was measured using the Short Form-12 (SF- 12). 16 The SF-12 is a subset of the Short Form-36 and is a valid measure of health status in Australia. 17 Respondents were asked if they had ever been told by a doctor that they have any of the following chronic health conditions: diabetes, arthritis, heart disease, stroke, cancer, osteoporosis (not osteoarthritis). Other items included questions on demographics and number of days unable to do work or usual activities due to health over the past 4 weeks. Statistical Analysis Data were analyzed using statistical software (Version 11.0; SPSS; Chicago, IL). Data have been weighted by age, gender, state, and probability of selection in the household. 18 The data were weighted using Australian Bureau of Statistics national population estimates for 1999 so that the health estimates calculated would be representative of the adult population. This was necessary to correct for the disproportionality of the sample with respect to the population of interest. The weights reflect unequal sample inclusion probabilities and compensate for differential nonresponse. This resulted in occasional minor rounding effects for the numbers. 286 Original Research

Differences in proportions were assessed for significance by 2 tests and Mantel-Haenszel methods for analysis of 2 k tables. Logistic regression was used to examine the association between asthma and the presence of comorbid conditions, adjusted for age and gender. The SF-12 was scored as specified in the SF-12 scoring manual. 19 We present the results of the items aggregated into the physical component summary (PCS) scale. 19 Analysis of covariance was used to calculate mean SF-12 scores, adjusting for baseline covariates of age and gender. We used multivariate analysis of variance with an interaction term to examine for effect modification for PCS scores in order to determine if PCS scores were lower in those with both asthma and comorbid conditions together than would be expected from the main effects of asthma and comorbid conditions alone. Table 2 Odds Ratios (Logistic Regression) for Association of Individual Selected Chronic Conditions and Asthma, Adjusted for Age and Gender Variables Odds Ratio 95% CI Diabetes 1.2 0.9 1.7 Arthritis 1.8 1.5 2.2 Heart disease 2.2 1.6 3.0 Stroke 2.5 1.6 4.1 Cancer 1.5 1.1 2.1 Osteoporosis 1.7 1.2 2.5 Any of above conditions 1.9 1.5 2.2 Results From the available sample of 10,080, 7,619 interviews were completed (participation rate, 74.8%), with 834 people reporting current doctor-diagnosed asthma (11.2%). The demographic characteristics of people with and without asthma are shown in Table 1. People with asthma were significantly more likely to also report other chronic conditions. Table 2 shows odds ratios from logistic regression analysis for the association between asthma and individual chronic conditions, adjusted for age and gender. Table 3 shows the reported prevalence of various chronic conditions in people with and without Table 1 Proportion of People in Differing Demographic Categories Among People With and Without a Self-Reported Current Asthma Diagnosis* Variables Asthma (n 834) No Asthma (n 6,609) Male gender 40.9 52.2 Age group, yr 18 34 45.1 31.5 35 54 35.4 40.4 55 23.6 28.1 Education Primary school 4.6 4.7 Secondary/high school 54.3 54.4 Trade/apprenticeship/certificate 16.2 20.5 University degree 23.7 19.9 Work status Unemployed 2.3 2.6 Retired/student/home duties 31.9 30.3 Part-time 19.9 17.1 Full-time 45.9 49.9 Income $20,000 18.0 16.9 $20,000 to 40,000 15.3 15.6 $40,000 to 80,000 33.5 34.2 $80,000 14.6 20.3 Department of Social Security benefit 47.9 44.4 *Data are presented as %. p 0.01. p 0.05 asthma across age groups. For those aged 55 years, each chronic condition except diabetes was significantly more common in those with asthma. For people with asthma aged 35 to 54 years, arthritis was substantially increased in frequency. Overall, individuals with asthma were more likely to report days over the past 4 weeks when they had impairment in work or usual activities due to illness. This was most clearly seen in older people, in whom 31% compared with 14.9% reported one or more days partly unable to do usual activities (p 0.001), although people with asthma aged 35 to 54 years were also more likely to report one or more days partly unable to do usual activities (16.3% vs 13.2%, p 0.01). Similar differences were seen for days totally unable to do usual activities in the age group 35 to 54 years (21.0% vs 12.0%, p 0.01) and in older persons (20.0% vs 11.9%, p 0.01). No differences were seen between people with and without asthma in the 18- to 34-year age group. Among people with asthma, those who also reported one or more other chronic health conditions were significantly more likely to have days with some limitations in usual activities in the past 4 weeks, compared to those without other chronic health problems (Table 4). People with asthma reported significant impairment in physical aspects of quality of life. Mean PCS scores, adjusted for age and gender, were 47.0 (95% CI, 46.4 to 47.6) in people with asthma, compared to 50.1 (95% CI, 49.9 to 50.3) in those without asthma (p 0.01). Table 5 shows the comparison for quality of life scores for people with and without other chronic health conditions and asthma. Among people with asthma, there are statistically and clinically significant decreases in PCS scores when another chronic condition is also present. Similarly, for those with any of the chronic conditions, the additional presence of asthma was associated with significant further impairment in quality of life. For each of the chronic conditions, PCS scores were significantly lower in people with both asthma and the chronic condition than would be expected from the main www.chestjournal.org CHEST / 129 / 2/ FEBRUARY, 2006 287

Table 3 People With and Without Asthma Who Report Other Selected Chronic Conditions, by Age Group* Age Group, yr Diabetes Arthritis Heart Disease Stroke Cancer Osteoporosis Any 18 34 Asthma (n 376) 3.7 2.7 0.3 0 1.3 0 8.0 No asthma (n 2,110) 2.0 3.2 0.2 0.3 0.8 0.1 6.4 35 54 Asthma (n 295) 2.7 21.7 2.0 1.4 3.1 2.4 28.6 No asthma (n 2,671) 3.0 12.3 1.8 0.4 3.2 1.2 18.6 55 Asthma (n 197) 12.1 56.9 26.9 9.1 17.7 17.3 78.7 No asthma (n 1,853) 11.7 42.4 14.4 4.5 11.0 10.9 62.6 Total Asthma (n 834) 5.3 21.4 6.9 2.5 5.5 4.7 31.1 No asthma (n 6,609) 5.1 17.8 4.8 1.5 4.6 3.6 27.1 *Data are presented as %. p 0.001 for comparison of asthma and no asthma. p 0.01. p 0.05. effects of asthma and the chronic condition alone. When considered by age group, the negative impact of combining asthma and other chronic conditions was not seen in younger people but became clinically and statistically significant in people 35 years (Table 6). This difference became more marked in those aged 55 years: the mean PCS score in the 55-year age group in those with asthma and other conditions was 35.9 (95% CI, 34.4 to 37.8) vs asthma-alone mean PCS score of 46.8 (95% CI, 43.9 to 49.8), compared with 43.4 (95% CI, 41.4 to 45.4) and 48.2 (95% CI, 46.9 to 49.4), respectively, in the 35- to 54-year-old subjects. Among people with asthma who reported 1 to 7 days of inability to do usual activities in the past 4 weeks, physical health was also significantly further adversely affected in those with at least one of the other chronic health conditions, compared to those without any chronic health conditions. This was not seen in asthma sufferers with more frequent ( 7 days impaired in the past month) days of being limited with usual activities. Discussion In a large population-based sample, we found that selected major chronic conditions occur more frequently in people with asthma, particularly in older age groups. The coexistence of these conditions is associated with greater functional impairment in terms of activity limitation and quality of life than if they are found alone. Again, the effect was seen more clearly in older age groups. Although arthritis was the most common comorbid condition with asthma, the greatest decrements in quality of life were seen with coexistent stroke, heart disease, and osteoporosis. Unlike Ben-Noun, 3 we found major chronic conditions, such as heart disease, stroke, and cancer, to Table 4 People With and Without Asthma Who Reported Days Totally or Partly Unable To Do Work or Usual Activities Over the Past 4 Weeks, Categorized by the Presence of Any Selected Chronic Health Condition* Days Totally Unable To Do Usual Activities Due to Health, No. Days Partly Unable To Do Usual Activities Due to Health, No. Variables 1 7 7 1 7 7 With asthma Any condition (n 268) 16.0 7.8 17.3 10.9 No condition (n 594) 11.3 3.2 10.6 5.1 Without asthma Any condition (n 1,769) 9.2 6.5 11.2 8.0 No condition (n 4,786) 10.7 2.0 10.7 2.3 *Data are presented as %. At least one chronic condition (diabetes, arthritis, heart disease, stroke, cancer, osteoporosis). p 0.001 for difference of chronic condition present vs absent. p 0.001. 288 Original Research

Table 5 SF-12 PCS Scores, Adjusted for Age and Gender, Among Those With and Without Asthma, According to the Presence of Other Selected Chronic Conditions Variables Asthma No Asthma n Mean (95% CI) n Mean (95% CI) Diabetes Yes 46 43.0 (40.3 45.7) 336 45.7 (44.7 46.7) No 822 47.2 (46.6 47.9) 6,243 50.3 (50.1 50.6) Arthritis Yes 186 41.1 (39.8 42.4) 1,174 44.5 (43.9 45.0) No 682 48.8 (48.1 49.4) 5,405 51.3 (51.1 51.6) Osteoporosis Yes 41 37.4 (34.7 40.2) 235 45.2 (43.9 46.4) No 826 47.5 (46.9 48.1) 6,345 50.3 (50.1 50.5) Heart disease Yes 60 37.0 (34.7 39.0) 318 44.7 (43.7 45.8) No 807 47.9 (47.2 48.5) 6,261 50.4 (50.1 50.6) Stroke Yes 22 36.6 (33.0 40.3) 98 43.2 (41.4 45.1) No 845 47.3 (46.7 47.9) 6,481 50.2 (50.0 50.4) Cancer Yes 48 44.0 (41.3 46.7) 305 48.3 (47.2 49.4) No 820 47.2 (46.5 47.8) 6,274 50.2 (50.0 50.4) Any of above chronic health conditions Yes 270 41.6 (40.5 42.7) 1,783 45.7 (45.2 50.5) No 598 49.7 (49.0 50.5) 4,797 51.7 (51.4 52.0) be more common in people with asthma, particularly in older people. It is likely that different sampling frames may account for these differences. Similarly to previous authors 6,7,9 who used clinic or specific sample groups, we found comorbidity influenced quality of life. The implications for the external validity and generalizability of clinical trials conducted in single diseases are potentially large. Major asthma clinical trials often exclude people with significant concurrent diseases 10,11 or older people. 11 Trials using quality of life as an outcome and that do not achieve and report adequate randomization for comorbidities, or exclude people with comorbid conditions, particularly in those aged 55 years, may become difficult to interpret as guides to clinical practice. Another related issue is that of implementing disease-specific guidelines for patients with several coexisting conditions, particularly in elderly receiving multiple medications. Decisions for such individuals require tradeoffs between harms and benefits within the context of patient s priorities and values. 20 These decisions are made more difficult as the evidence base is rarely available where multidrug regimens have been compared to simpler approaches in the full spectrum of patients with multiple conditions. 20 It is also likely that we are observing a more generic comorbidity effect on functional status rather than one unique or specific to asthma. Comorbid conditions tend to cluster together, particularly in older people. Therefore, the implications of these findings extend beyond asthma clinical trial design and clinical management. As the population ages, managing any chronic condition will require attention to this fact, and research in many of chronic conditions needs to account for this situation in both design and analysis. A number of possible factors may contribute to the co-occurrence of asthma and other chronic conditions. Smoking is associated with more severe, and therefore more recognizable, asthma as well as having strong links to cardiac and cerebrovascular disease and diabetic complications. Diagnostic misclassification may play a part, for instance, with breathlessness being a common symptom of both asthma and heart disease. Our results have implications for the organization of care. Druss et al 21 reported that in five chronic conditions (mood disorders, diabetes, heart disease, asthma, and hypertension) in the United States, only about one fourth of costs were incurred in treating the conditions themselves, with the remainder spent on coexistent illnesses. In chronic conditions such as asthma, it has been advocated that rather than considering each condition in isolation, a chronic disease model of care based on better integrated service delivery systems can be more effective. 22 Both the high prevalence and significant impact on health status with comorbidity seen in our population lends support to this view. However, this is Table 6 SF-12 PCS Scores, Adjusted for Gender, Among People With Asthma and Without Asthma, According to the Presence of Any Other Selected Chronic Conditions, by Age Group* Age Group, yr Other Chronic Condition Asthma No Other Chronic Conditions Other Chronic Condition No Asthma No Other Chronic Conditions 18 34 49.9 (46.5 53.2)/28 53.3 (52.4 54.3)/324 49.3 (47.9 50.8)/132 53.4 (53.0 53.7)/1,840 35 54 43.4 (41.4 45.4)/78 48.2 (46.9 49.4)/197 46.3 (45.6 47.1)/443 51.9 (51.5 52.3)/1,960 55 35.9 (34.4 37.8)/137 46.8 (43.9 49.8)/37 42.0 (41.5 42.5)/1,054 50.5 (49.8 51.2)/611 *Data are presented as mean (95% CI)/No. of subjects. www.chestjournal.org CHEST / 129 / 2/ FEBRUARY, 2006 289

complicated by the age variations demonstrated. In older groups it seems likely an integrated approach would be successful, both in managing morbidity and in secondary prevention of comorbidities. However, in those aged 35 years, in whom asthma is common but comorbidity rare, a targeted focus on asthma care may be the more useful approach, given the ongoing high levels of asthma morbidity in the community. 23,24 An alternative view may see young people with asthma as an opportunity for targeted primary or early secondary prevention, given the likelihood of a high rate of developing multiple chronic illnesses in this group. Managing this sort of patient-centered health-care system remains a challenge for policy makers. Due to the cross-sectional nature of our study, we are unable to determine a causal relation between asthma, comorbid illness, and impaired quality of life. We are also unable to examine the relationship between differing levels of asthma severity and comorbid conditions as data on clinical variables were not collected. Our study is also limited by relying on self-report of a doctor s diagnosis of asthma and other conditions. This raises the question of selection bias in that people with asthma may seek medical attention more often resulting in other conditions being detected earlier. However, diabetes, probably the condition that most requires clinical input for early diagnosis, was not associated with asthma, suggesting any effect of selection bias is likely to be small. In addition, asthma tends to be underdiagnosed in older persons, where the association with other comorbidities was most common. 25,26 The strength of the study lies in the community sampling with adequate numbers from urban, rural, and remote populations to provide an accurate picture of health across the spectrum of sociodemographic and geographic conditions. 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