Health and Economic Burden of Obesity in Elderly Individuals with Asthma in the United States

Abstract

The health and economic burden of obesity among elderly individuals with asthma has not been adequately studied. This study assessed the association between obesity and asthma among the elderly and examined the impact of obesity on asthma-related and total health care costs among elderly individuals with asthma. This was a retrospective analysis of the 2006–2010 Medical Expenditure Panel Survey (MEPS) data. Individuals aged 65 years or older were included in the study. Individuals with asthma were identified by an International Classification of Diseases, Ninth Revision code of 493 or a Clinical Classification Code of 128. Individuals with a self-reported body mass index ≥30 kg/m² were considered to be obese. Logistic regression was used to assess the relationship between obesity and asthma. Generalized linear models with gamma distribution and log link were used to assess the relationship between obesity and asthma-related and total direct medical costs. All analyses were conducted while accounting for the complex survey design of MEPS. In all, 675 elderly individuals were identified as having asthma, 292 of whom were obese. Obese elderly individuals were more likely to suffer from asthma as compared to the nonobese (odds ratio, 1.71; 95% confidence interval [CI],1.37–2.12). Obesity was a significant predictor of asthma-related costs (β: 0.537; 95% CI: 0.18–0.89; P= 0.003) and total health care costs (β: 0.154; 95% CI: 0.08–0.23; P=0.001) among elderly individuals with asthma after controlling for sociodemographics and comorbidities. Appropriate weight management measures should be recommended to obese elderly individuals with asthma to improve asthma control and reduce health care costs. (Population Health Management 2015;18:186–191)

Introduction

Asthma is a chronic inflammatory disease of the airways affecting 25.7 million people of all ages in the United States in 2010. Among those, 3.1 million were elderly individuals aged 65 and older; this number is expected to double over the next 2 decades. The clinical and economic consequences of asthma are significant. It is estimated that asthma caused 3388 deaths, 479,300 hospitalizations, 1.9 million emergency room visits, and 8.9 million physician visits in 2009, resulting in a total cost of $56 billion in the United States.¹ This impact was compounded for the elderly, who had the highest rate of asthma-related mortality.²

Obesity is another serious and costly chronic condition. The cost of obesity has risen from $78.5 billion in 1998 to an estimated $147 billion in 2008.³ Obesity is found to be related to a host of chronic conditions such as diabetes mellitus, cancer, and certain respiratory conditions such as sleep apnea.^3
–5 According to data from the Centers for Disease Control and Prevention (CDC), more than one third of adults aged 65 and older were obese in 2007–2010.⁶

Research has suggested that obesity is a risk factor for asthma.^7

–11 Studies have shown that obese children are more likely to have asthma than nonobese children during childhood and as young adults.^8,9 A greater risk of adult-onset asthma also has been found in obese adults versus those who are nonobese.^7,10,11 Limited information exists regarding the relationship between obesity and asthma and the incremental burden of obesity on asthma-related costs in the elderly population.

Asthma and obesity are both significant public health problems in the elderly population. A better understanding of the relationship between obesity and asthma and the economic burden of obesity among elderly asthmatic individuals is important for effective asthma management and proper resource allocation in this population. A recent study found that individuals aged 65 and older who were obese were 5 times more likely to have poorly controlled asthma than nonobese elders.¹² This led to poorer quality of life, higher morbidity and mortality, greater health care resource utilization, and higher cost of care for asthma among the elderly.¹² However, this study was conducted among asthma patients from community clinics in greater Cincinnati, Ohio and therefore the results are likely to have limited generalizability to elderly asthma patients in other geographic locations. The objectives of the present study were to assess the association between obesity and asthma and to examine the impact of obesity on asthma-related and total health care costs in a nationally representative sample of elderly individuals in the United States.

Methods

Study design and data

This study employed a retrospective cross-sectional nonexperimental design. Data for this study were obtained from the pooled 2006–2010 Medical Expenditure Panel Survey (MEPS) sponsored by the Agency for Healthcare Research and Quality (AHRQ). MEPS is a nationally representative survey of the US civilian noninstitutionalized population that was started in 1996 to collect detailed information on health status, health insurance, health care utilization and expenditures, and a variety of demographic, clinical, and economic characteristics.¹³ For the purposes of this study, the Household Component (HC) of the MEPS data, which is administered to both an individual of the selected household and his or her medical providers, was used. Because the MEPS data are de-identified and publicly available to researchers, a review by the Institutional Review Board was waived.

Study population

The study population consisted of individuals who were 65 years of age or older at the beginning of each survey year from 2006 to 2010. The analysis file was created by pooling the 2006–2010 MEPS HC data in order to obtain an adequate sample size. When the data were pooled, appropriate weighting procedures were applied to account for the complex design of MEPS and to account for individuals who appeared more than once in the analytic data set. In MEPS, medical condition-related data are collected based on respondent self-report. Subsequently, specified International Classification of Diseases, Ninth Revision (ICD-9) codes are assigned to each self-reported condition by a professional coder. In addition, each self-reported health condition is verified by contacting the medical providers who provided the care. To protect the confidentiality of respondents, MEPS collapses 5-dight ICD-9 codes into 3-digit codes. The 3-digit ICD-9 codes are subsequently coded into 260 clinically relevant medical conditions by AHRQ staff using the AHRQ Clinical Classification Software.¹⁴

Study variables

Asthma status

Elderly individuals were classified as having asthma or not based on either an ICD-9 code of 493, a Clinical Classification Code of 128, or a confirmed diagnosis of asthma by a physician using the MEPS prescribed medicines file, hospital inpatient stays file, emergency room visits file, or outpatient visits file for the study period.

Obesity status

Obesity was defined by a body mass index (BMI)≥30 kg/m².^3,15 Individuals' BMI information was obtained from the MEPS full-year consolidated data files. Based on BMI, elderly individuals were classified as being obese (BMI ≥30 kg/m²) or nonobese (BMI <30 kg/m²).

Asthma-related and total (all-cause) health care costs

Both asthma-specific and total (all-cause) direct health care costs for the study sample were calculated using the MEPS event-level files. Direct health care costs for asthma were calculated by summing the costs for all asthma-related health care services (ie, inpatient, outpatient, emergency department, physician office visits, prescription drugs) utilized. Total (all-cause) health care costs referred to the costs associated with the use of health care services or prescription drugs for any diseases and/or injuries for the study sample. All costs were reported in 2010 US dollars, adjusting for medical price indices.

Sociodemographics covariates and comorbidities

Data on individual sociodemographic and clinical characteristics were obtained from MEPS full-year consolidated data files and medical conditions files for each study year. Individual sociodemographic covariates included in the study were age, race, sex, education level, insurance coverage status, and household income. Individual cardiovascular and respiratory comorbidities including diabetes mellitus, lipid disorders, hypertension, coronary atherosclerosis, smoking status, emphysema, chronic obstructive pulmonary disease (COPD), and bronchitis were identified as comorbidities.

Statistical analyses

Bivariate analyses were conducted using t tests for continuous variables and chi-square tests for categorical variables. Multivariate logistic regression was used to assess the relationship between obesity and asthma in the elderly using PROC SURVEYLOGISTIC in SAS. Individual sociodemographic characteristics and comorbidities were adjusted for in the model. Generalized linear models with a gamma distribution and a log link function were employed, using the SVY GENMOD procedure in STATA, to assess whether obesity was a significant predictor of asthma-related and total health care costs. The aforementioned individual sociodemographic and clinical covariates also were controlled for in the cost analyses. In addition, the Charlson comorbidity index (CCI) was computed and used in modeling the relationship between obesity and total health care costs among elderly individuals with asthma. Data management and statistical analyses were carried out using SAS version 9.3 (SAS Institute Inc., Cary, NC) and STATA version 11 (StatCorp LP, College Station, TX). All analyses accounted for the complex sampling design of MEPS.

Results

Of 11,265 elderly individuals in the MEPS 2006–2010 pooled data set, 675 (weighted sample: 2,473,731) were identified as having asthma. Among those, 292 (weighted sample: 1,066,010) were obese based on self-reported BMI. The sociodemographic and comorbidity characteristics for the study sample are reported in Table 1.

Table 1.

Sociodemographic Characteristics and Comorbidities among Obese and Nonobese Elderly Individuals with Asthma, Medical Expenditure Panel Survey 2006–2010

Characteristic^a	Total, No. (%)	Obese, No. (%) (n=292)	Nonobese, No. (%) (n=383)	P Value
Sex				.159
Male	222 (32.9)	83 (28.4)	139 (36.3)
Female	453 (67.1)	209 (71.6)	244 (63.7)
Age (Mean±SE)	74.56 (0.1)	71.69 (0.4)	75.03 (0.4)	<.0001
Race				.231
White	513 (76.0)	217 (74.3)	296 (77.3)
Black	111 (16.4)	58 (19.9)	53 (13.8)
Asian	34 (5.0)	10 (3.4)	24 (6.3)
Other^b	17 (2.5)	7 (2.4)	10 (2.6)
Ethnicity				.019
Hispanic	74 (7.0)	39 (13.4)	35 (9.1)
Non-Hispanic black	109 (8.0)	56 (19.2)	53 (13.8)
Non-Hispanic Asian	33 (3.0)	9 (3.1)	24 (6.3)
Non-Hispanic other	459 (82.0)	188 (64.3)	271 (70.8)
Education level
Less than high school	442 (59.0)	200 (68.5)	242 (63.5)	.173
High school graduate	182 (31.5)	70 (24.0)	110 (28.9)
College graduate	51 (9.5)	22 (7.5)	29 (7.6)
Smoking status				.429
Yes	63 (8.45)	20 (7.1)	43 (11.65)
No	589 (89.07)	263 (92.9)	326 (88.35)
Income level				.991
Poor	80 (11.9)	35 (12.0)	45 (11.8)
Near poor	56 (8.3)	25 (8.6)	31 (8.1)
Low income	130 (19.3)	58 (19.9)	72 (18.8)
Medium income	202 (29.9)	86 (29.5)	116 (30.3)
High income	207 (30.7)	88 (30.1)	119 (31.1)
Insurance status				.025
Any private	175 (27.4)	67 (22.9)	108 (28.2)
Public	498 (72.4)	224 (76.7)	274 (71.5)
Uninsured	2 (0.2)	1 (0,4)	1 (0.3)
Comorbidities
Diabetes mellitus	210 (31.1)	132 (45.2)	78 (20.4)	<.0001
Lipid disorders	393 (58.2)	187 (64.0)	206 (53.8)	.001
Hypertension	476 (70.5)	223 (76.4)	253 (66.1)	.002
Coronary atherosclerosis	152 (41.1)	74 (25.4)	78 (20.4)	.205
Emphysema	139 (20.6)	53 (18.2)	86 (22.5)	.953
COPD	92 (13.6)	37 (12.7)	55 (14.4)	.434
Bronchitis	146 (21.6)	61 (20.9)	85 (22.2)	.706
CCI (Mean±SE)	1.53 (0.1)	1.99 (0.1)	1.39 (0.1)	.003

CCI, Charlson comorbidity index; COPD, chronic obstructive pulmonary disease; SE, standard error.

Values are expressed as % unless otherwise indicated. Percentages and standard errors are based on weighted data to represent the civilian noninstitutionalized US population.

Data source: Medical Expenditure Panel Survey, 2006–2010.

Other races include American Indians, Native Hawaiians, and individuals who report multiple races.

The results of the unadjusted analysis of the association between obesity and asthma suggest that obese individuals were almost twice as likely as the nonobese elderly to have asthma (odds ratio [OR]: 1.96; 95% confidence interval [CI]: 1.54–2.35). The results for the adjusted analysis have been reported in Table 2. After accounting for the individual sociodemographic and clinical covariates, obese elderly individuals were 67% more likely to be asthmatic compared to the nonobese elderly (OR: 1.67; 95% CI: 1.35–2.10). Additionally, elderly women were 87% more likely to have asthma compared to elderly men. Individuals with other chronic respiratory conditions including COPD, emphysema, and bronchitis also were significantly more likely to have asthma. The analysis was repeated using CCI as a covariate instead of individual cardiovascular and respiratory comorbidities and the relationship between obesity and asthma for the given study sample remained unchanged (OR: 1.67; 95% CI: 1.35–2.06).

Table 2.

Multivariable Logistic Regression Results Assessing the Relationship Between Obesity Status and Asthma among Elderly Individuals, Medical Expenditure Panel Survey 2006–2010

Characteristic	Odds Ratio (95% Confidence Interval)
Obesity Status
Nonobese	Reference
Obese	1.672 (1.35–2.10)
Sex
Male	Reference
Female	1.869 (1.51–2.31)
Age	0.973 (0.96–0.99)
Race
White	Reference
Black	0.951 (0.74–1.22)
Asian	1.247 (0.84–1.86)
Other^a	1.154 (0.51–2.62)
Education Level	0.951 (0.74–1.22)
Less than high school	Reference
High school graduate	1.203 (0.96–1.52)
College graduate	1.374 (0.95–1.99)
Smoking status
No	Reference
Yes	1.576 (1.06–2.34)
Income level
Poor	Reference
Near poor	1.065 (0.64–1.77)
Low income	1.189 (0.77–1.84)
Medium income	0.881 (0.60–1.29)
High income	0.828 (0.56–1.23)
Insurance status
Any private	Reference
Public only	0.998 (0.78–1.27)
Uninsured	0.234 (0.05–1.11)
Comorbidities	Reference
Diabetes mellitus	1.064 (0.86–1.32)
Lipid disorders	1.043 (0.82–1.33)
Hypertension	0.901 (0.69–1.18)
Coronary atherosclerosis	1.05 (0.78–1.41)
COPD	2.439 (1.60–3.71)
Emphysema	4.001 (2.86–5.60)
Bronchitis	5.709 (4.05–8.05)

COPD, chronic obstructive pulmonary disease.

Other races include American Indians, Native Hawaiians, and individuals who report multiple races.

The unadjusted cost analyses (asthma-related and total health care costs) results for the study sample are reported in Table 3. The results suggest that both asthma-specific and total health care costs were significantly higher for obese asthmatics compared to nonobese asthmatic elderly individuals. On an individual basis, the incremental cost of obesity among elderly asthmatics was found to be $5395 (P<0.001) in terms of total health care costs and $136 for asthma-related costs (P=0.004). The majority of the cost differences between obese and nonobese elderly asthmatics arise because of differences in drug-related costs. Results for the adjusted cost analyses using generalized linear models suggest that obesity was a significant predictor of both asthma-related costs (β: 0.537; 95% CI: 0.18–0.89; P=0.003) and total health care costs (β: 0.154; 95% CI: 0.08–0.23; P=0.001) among elderly individuals with asthma after controlling for individual sociodemographics and comorbidities. The results of adjusted cost analysis are reported in Table 4.

Table 3.

Asthma-related and Total (All-cause) Health Care Costs for Elderly Individuals with Asthma, Medical Expenditure Panel Survey 2006–2010

Costs ^a , Mean (SE)	Nonobese (n=383)	Obese (n=292)	P Value
Asthma-related cost	2641.1 (234.9)	2776.6 (385.2)	.004
Drug costs	1995.9 (141.7)	2029.2 (168.5)	.0003
Emergency room costs	24.6 (8.9)	23.2 (10.7)	.423
Inpatient costs	420.5 (160.3)	556.9 (250.5)	.221
Outpatient costs	38.8 (16.5)	15.8 (6.8)	.632
Office-based visit costs	161.3 (25.4)	151.5 (34.3)	.112
Total health care cost	28,955 (2174.6)	34,350 (2758.1)	<.001
Drug costs	7459.1 (498.4)	9527.7 (551.4)	<.001
Emergency room costs	642.0 (92.1)	457.9 (85.8)	.674
Inpatient costs	10,754 (1475.5)	12,334 (1841.7)	.133
Outpatient costs	1768.4 (336.9)	2238.7 (461.6)	.014
Office-based visit costs	5645.2 (491.9)	6082.7 (726.7)	<.001
Other medical costs	460.2 (52.41)	899.1 (203.4)	.416
Dental visit costs	844.5 (132.1)	645.1 (103.2)	.573

SE, standard error

All costs are reported in 2010 US dollars after adjusting for medical price index.

Table 4.

Generalized Linear Model Results Assessing the Relationship Between Obesity Status, Asthma-related Costs, and Total (All-cause) Health Care Costs among Elderly Individuals with Asthma, Medical Expenditure Panel Survey 2006–2010.

	Beta Coefficient (95% Confidence Interval)
Characteristic	Asthma-related costs	Total health care costs
Obesity status	0.537 (0.18–0.89)^*	0.154 (0.08–0.23)^*
Sex	0.839 (0.47–1.21)^*	0.059 (−0.00–0.12)
Age	0.001 (−0.03–0.03)^*	0.023 (0.02–0.03)^*
Race	0.089 (−0.16–0.34)	−0.046 (−0.10–0.01)
Education Level	0.144 (−0.15–0.44)	0.073 (0.02–0.12)
Smoking status	0.124 (0.02–0.23)^*	0.046 (0.021–0.07)^*
Income level	−0.214 (−0.37–−0.06)^*	0.015 (−0.01–0.04)
Insurance status	0.0145 (−0.36–0.39)	0.048 (−0.02–0.11)
Comorbidities^a
Diabetes mellitus	0.103 (0.51–0.51)	-
Lipid disorders	0.176 (−0.20–0.55)^*	-
Hypertension	−0.437 (−0.84–0.03)	-
Coronary atherosclerosis	0.208 (−0.31–0.72)^*	-
COPD	1.884 (1.40–2.37)^*	-
Emphysema	1.646 (1.06–2.23)^*	-
Bronchitis	1.029 (0.69–1.37)^*	-
CCI^a	-	0.273 (0.25–0.29)^*

CCI, Charlson comorbidity index; COPD, chronic obstructive pulmonary disease.

Individual comorbidities were adjusted for in the asthma-related cost model while the Charlson comorbidity index was adjusted for in the total health care cost model.

P≤0.05

Discussion

Asthma and obesity are common, serious, and costly chronic conditions among the elderly. A better understanding of this relationship and the economic burden of obesity among elderly asthmatic individuals is critical for effective asthma management and proper resource allocation in this population. Using a nationally representative sample of the pooled 2006–2010 MEPS data, this study assessed the relationship between obesity and asthma among elderly individuals in the United States. In addition, this study provides national estimates of asthma-specific and all-cause health care costs for obese and nonobese elderly individuals with asthma. The results of this study show that obesity is a significant risk factor for asthma and a significant predictor of asthma-specific and total health care costs even after accounting for individual demographics and comorbidities in the elderly population.

Asthma represents a significant financial burden to patients, their families, and society. Over the past decade, clinical research has indicated that asthma is generally underdiagnosed and undertreated in the elderly population.¹⁶ The burden of asthma is heightened by obesity. In fact, the CDC has identified obesity to be one of the most significant risk factors for asthma.⁵ A recent study by Epstein and colleagues found that individuals aged 65 and older who were obese were 5 times more likely to have poorly controlled asthma than nonobese elders.¹² Another study, by Forte et al, suggested a higher prevalence of obesity among older adults (mean age 51.1±16.5 years) suffering from asthma in an outpatient asthma clinic in Brazil. However, no difference was found between obese and nonobese individuals in terms of asthma severity and asthma control.¹⁷ The results of the current study, in line with the existing literature, suggest that the obese elderly are 67% more likely (OR: 1.67; 95% CI: 1.35–2.10) to suffer from asthma compared to the nonobese elderly. Population health management programs should consider including a weight management component for obese individuals that aims to achieve better asthma control among elderly individuals. It also should be noted that better weight management will not only decrease the burden of asthma but also the burden of many other chronic conditions such as diabetes and other respiratory disorders in this population.³

The economic burden of asthma is compounded by the presence of obesity and other chronic conditions. To the best of the authors' knowledge, the current study is the first to demonstrate the significant economic impact of obesity on asthmatic elderly individuals in the United States. A study conducted by Suh and colleagues reported the incremental cost of obesity among asthmatics to be $1807, but they excluded elderly individuals from their sample and only focused on younger adults with asthma.¹⁸ It is important to understand the economic burden of obesity on asthma in the elderly population because elderly individuals with asthma have a significantly higher comorbidity profile compared to non-elderly asthmatics.¹⁹ The current study estimated that the incremental economic burden associated with obesity among elderly asthmatics was $5395 in terms of total health care costs and $136 for asthma-related costs. In addition, the current study found that obesity was a significant predictor of asthma-related costs and total health care costs among elderly individuals with asthma even after controlling for sociodemographic covariates and comorbidities. Similar findings have been documented in the literature among both adults and children with asthma.^12,20 A weight control program designed for this patient population may help achieve better disease control and reduce health care costs. In addition, cheaper steroid-sparing agents such as leukotriene blockers or inhaled steroids could be made preferred formulary options so that patients have better access to these more affordable drugs.

The estimates from this study can be generalized to the elderly population across the United States because MEPS employs a sample that is representative of the US civilian noninstitutionalized population. However, the results of this study also should be examined in light of certain limitations. First, there might be diagnostic misclassifications between asthma and COPD among elderly individuals in the MEPS data set. Studies have suggested that because of the similarity of symptoms of these airflow obstruction conditions, despite there being separate diagnostic recommendations for them, there is a chance that COPD can be misdiagnosed as asthma, especially among people older than 40 years of age.²¹ Also, because of the lack of clinical details, such as lung function test results in the MEPS data, the authors could not verify the diagnosis of asthma and obtain asthma severity measures. Second, given the cross-sectional nature of this study, causal inferences between obesity and asthma and health care costs should be made with caution; however, this study shows that there is an association between obesity and asthma in elderly individuals and that obesity shouldn't be overlooked in asthma management. Third, the analyzable sample size for this study was small in spite of pooling 5 years of MEPS data. Future studies should assess this relationship using data sets with a larger sample size, such as electronic health record-linked administrative claims data, which also would contain more detailed clinical information. Nonetheless, this study provides useful insights into the relationship between obesity and asthma, and asthma-related and total health care costs among elderly individuals in the United States.

Conclusions

Obesity was found to be associated with asthma in elderly individuals in the United States. Asthma-related and total health care costs were found to be significantly higher for obese elderly individuals with asthma compared to those who are not obese. Clinicians and population health management professionals should be aware of these findings and appropriate weight management measures should be recommended to obese elderly individuals with asthma to improve asthma control and reduce health care costs.

Footnotes

Author Disclosure Statement

Mr. Shah and Dr. Yang declared no conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors received no financial support for the research, authorship, and/or publication of this article.

Acknowledgments

The authors would like to acknowledge the efforts of Tasneem Lokhandwala, PhD, and Manasi Datar, MS, for input regarding statistical analysis.

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