Abstract
Introduction
Monitoring the level of asthma control is important in determining the effectiveness of current treatment which may decrease the frequency and intensity of symptoms and functional limitations. Uncontrolled asthma has been associated with decreased quality of life and increased health care use. The objectives of this study were to assess the level of asthma control and identify related risk factors among persons with current asthma.
Methods
Using the 2006 to 2010 BRFSS child and adult Asthma Call-back Survey, asthma control was classified as well-controlled or uncontrolled (not-well-controlled or very-poorly-controlled) using three impairment measures: daytime symptoms, night-time symptoms, and taking short-acting β2-agonists for symptom control. Multivariate logistic regression identified predictors of asthma control.
Results
Fifty percent of adults and 38.4% of children with current asthma had uncontrolled asthma. About 63% of children and 53% of adults with uncontrolled asthma were on long-term asthma control medications. Among children, uncontrolled asthma was significantly associated with being younger than 5 years, having annual household income <$15 000, and reporting cost as barriers to medical care. Among adults, it was significantly associated with being 45 years or older, having annual household income of <$25 000, being “other” race, having less than a 4-year college degree, being a current or former smoker, reporting cost as barriers, being obese, and having chronic obstructive pulmonary disease or depression.
Conclusion
Identifying and targeting modifiable predictors of uncontrolled asthma (low educational attainment, low income, cigarette smoking, and co-morbid conditions including obesity and depression) could improve asthma control.
Keywords: Asthma, comorbidity, environment, risk factors, obesity
Introduction
Asthma is a chronic inflammatory disease of the airways that causes recurring episodes of shortness of breath, tightness in the chest, coughing, and wheezing. Asthma affects nearly 26 million people, including 7.0 million children [1]. Although asthma cannot be cured, with effective asthma care and management, most persons with asthma can be free of symptoms and have a better quality of life. One of the key components of effective asthma care and management is monitoring the level of asthma control periodically and adjusting medical treatment accordingly [2].
Level of asthma control (the frequency and intensity of symptoms and functional limitations) is a function of underlying severity, responsiveness to treatment, and the adequacy of asthma care and management [2]. Multiple socioeconomic and environmental factors contribute to the exacerbation of asthma symptoms [3–8]. Uncontrolled asthma is associated with significant decreased quality of life and increased health care use [9,10]. These, in turn, increase the economic burden of asthma [11].
Monitoring the level of asthma control is important in determining the effectiveness of current treatment which may decrease the frequency and intensity of symptoms and functional limitations. Unlike the assessment of asthma severity, asthma control can be assessed while the patient is being treated and, therefore, it is easier to understand and incorporate asthma control assessment into individual asthma-management plans. Asthma control can also be evaluated using population-based survey data. A number of validated instruments for assessing asthma control among children and adults are currently available. These include the Asthma Control Questionnaire (ACQ) [12], the Asthma Control Test (ACT) [13], the Asthma Therapy Assessment Questionnaire (ATAQ) [14], and the guideline-based control measures [2]. For this study, we used impairment measures adapted from the NAEPP 2007 guidelines to determine the level of asthma control [2,8,15] and identify the factors associated with it among children and adults with asthma in the states that participated in the Centers for Disease Control and Prevention's (CDC) Behavioral Risk Factor Surveillance System (BRFSS) Asthma Call-back Survey (ACBS).
Methods
Survey data description
We combined 5 years of data to produce stable estimates. We analyzed the 2006 to 2010 child ACBS data and the 2006 to 2010 adult ACBS data from the CDC's BRFSS separately because CDC's BRFSS collects and maintains them separately. Our study includes children (aged 0–17 years) from the 35 states and adults (aged 18 years and older) from the 40 states and the District of Columbia.
The BRFSS ACBS is developed and funded by the Air Pollution and Respiratory Health Branch (APRHB) of the CDC's National Center for Environmental Health (NCEH). It has been implemented as a follow-up survey to the BRFSS since 2006. The BRFSS ACBS is conducted approximately 2 weeks after the BRFSS telephone interview. Although BRFSS is a state-based, random-digit-dialed telephone survey of non-institutionalized U.S. adults, the BRFSS survey contains a Random Child Selection module and a Child Asthma Prevalence module, both of which were used in participating states to identify households with a child who had asthma in order to administer the child ACBS. BRFSS respondents who report them or proxy servers who report child ever being diagnosed with asthma are eligible for the ACBS. Only one adult or one child per household could participate in the ACBS. An adult family member serves as a proxy respondent for the selected child. The ACBS collects in-depth information about asthma symptoms and episodes/attacks, self-management education, healthcare utilization and access, medication use, comorbidities, and environmental allergens and irritants [16]. The ACBS response rate for children and adults varies by state and year. The median ACBS response rates for children (via adult proxies) ranged from 47.6% to 53.7% and for adults ranged from 47.5% to 54.3% during 2006–2010. The data include sample weights to adjust for the unequal probability of selection, the disproportionate selection of population subgroups relative to the state's population distribution, and disproportionate non-response [16]. More information on participating states, weight calculation, and the response rate can be found in the ACBS Summary Data Quality Report for each year at http://www.cdc.gov/brfss/acbs [16].
Variables
We evaluated asthma control status among children and adults with current asthma. Consistent with the methodology used with previous CDC publications, respondents were considered to have current asthma if they answered “yes” to both questions “Have you ever been told by a doctor, nurse, or other health professional that you (the child) had asthma?” and “Do you (the child) still have asthma?” [1].
We used three impairment measures: daytime symptoms, night-time symptoms, and use of short-acting (β2-agonists (SABA) for symptom control (not for prevention of exercise-induced bronchospasm) to create an asthma control variable. We created a control variable with three mutually exclusive categories: well-controlled asthma, not-well-controlled asthma, and very-poorly-controlled asthma. Respondents were assigned to each category based on the most impaired level across the three impairment measures (Table 1) [2,17]. In addition, we used the term “controlled asthma” instead of well-controlled asthma, and “uncontrolled asthma” instead of not-well-controlled or very-poorly-controlled asthma. This is an adaptation of the 2007 NAEEP guidelines because the ACBS did not include all required measures for current impairment (e.g. pulmonary function measures) and for future risk assessment (e.g. asthma exacerbations, progressive decline in lung function in adults, or reduced lung growth in children) [2,17,18]. Also, we did not include one of the impairment measures, interference with normal activities, because the reference time for the question was the past 12 months and should only include more recent experiences.
Table 1.
Classification of asthma control among adults with asthma adopted from the National Asthma Education and Prevention Program Expert Panel Report 3 Guidelinesa.
| Measures for current impairment | Controlled asthma | Uncontrolled asthma | |
|---|---|---|---|
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| Well-controlled asthma | Not-well-controlled | Very-poorly-controlled | |
| Symptoms | ≤2 d a week | >2 d a week | Throughout the day |
| Night-time awakenings | |||
| Ages 0–4 years | ≤1 time a month | >1 time a month | >1 time a week |
| Ages 5–11 years | ≤1 time a month | ≥2 times a month | ≥2 times a week |
| Ages 12 years or older | ≤2 times a month | 1–3 times a week | ≥4 times a week |
| Short-acting β2-agonists used for symptom control | ≤2 d a week | > 2d a week | Several times a day |
Based on the most impaired level across the three variables, asthma control was classified into three mutually exclusive categories: well-controlled asthma, not-well-controlled asthma, and very-poorly-controlled asthma.
For both children and adults, the variables included in the analysis were demographic characteristics (age, sex, race/ethnicity), annual household income, cost as barriers to medical care (being unable to see a primary care physician or specialist for asthma care or unable to buy medication for asthma in the past 12 months), long-term control medications (inhaled corticosteroids, systemic corticosteroids, long-acting beta2 agonist, leukotriene receptor antagonists, methylxanthines, and immunomodulators), and environmental factors (secondhand smoke (SHS) [environmental tobacco smoke[, pets allowed in bedroom, saw cockroach inside home in past 30 d, and saw or smelled mold in the past 30 d). In addition for adults, body mass index (BMI) (defined as weight in kilograms divided by height in square meter; obese = BMI ≥ 30), the presence of chronic obstructive pulmonary disease (COPD), depression, and smoking status were included in the analysis. The COPD variable includes responses to questions asking: “have you ever been told by a doctor or other professional that you have emphysema/chronic bronchitis/COPD?”
Statistical analysis
We used SAS-callable SUDAAN (version 10.0.0, RTI International, NC) to account for the complex sampling design of the BRFSS ACBS. Data from the participating states for each year were proportionately reweighted to account for the differences in sample size by year and the number of years each state participated. We used sample weights to produce estimates that were generalizable to a participating state's population. We used the chi-square test to test for group differences and multivariate logistic regression to test for association between asthma control status (dependent variable) and independent variables. We presented weighted percent estimates, adjusted prevalence ratios (aPR) (predicted marginal risk ratio), and 95% confidence intervals (CI). Adjusted PR is considered statistically significant if 95% CI does not overlap the null value of one. All prevalence ratios for both children and adults were adjusted (aPR) for sex, age, annual household income, cost as a barrier, long-term control medication status, and environmental factors. In addition, prevalence ratios for adults were adjusted for BMI, COPD, and depression. We did not find any multicollinearity between independent variables since all tolerance estimates were ≥0.70, and only tolerance estimates below 0.40 are a concern [19]. Statistical significance was determined as a p value <0.05 by a non-directional z-test or by non-overlapping 95% CIs. Relative standard error (RSE = standard error/prevalence estimate) was used as a measure of an estimate's reliability (a RSE of <0.30 indicates a “reliable” estimate) [20].
Results
Children with current asthma
Characteristics
In the combined 2006 through 2010 BRFSS ACBS sample, 9697 children had current asthma. Among children with current asthma, 61.6% had well-controlled asthma, 38.4% had uncontrolled asthma (21.5% not- well-controlled; 16.9% very-poorly-controlled asthma) and 46.0% were on long-term control medications (Table 2). Among children with uncontrolled asthma, 62.8% were on long-term control medications and among children who were taking long-term control medications, 52.4% had uncontrolled asthma (data are not shown).
Table 2.
Characteristics and level of asthma controla among children (aged 0–17 years) with current asthmab: Behavioral Risk Factor Surveillance System Asthma Call-back Survey, 2006–2010.
| Characteristics | Level of asthma control among children with current asthma | |||||||
|---|---|---|---|---|---|---|---|---|
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| Controlled asthma | Uncontrolled asthma | |||||||
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| Survey respondents | Well-controlled asthma | Not-well-controlled | Very-poorly-controlled | |||||
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| No.c | %d (95% CId) | No.c | %d (95% CId) | No.c | %d (95% CId) | No.c | %d (95% CId) | |
| Total | 9697 | 6144 | 61.6 (59.3–63.9) | 2065 | 21.5 (19.7–23.4) | 1453 | 16.9 (15.1–18.9) | |
| Sex | ||||||||
| Male | 5536 | 57.0 (54.7–59.3) | 3559 | 63.5 (60.5–66.4) | 1151 | 19.6 (17.3–22.0) | 826 | 16.9 (14.7–19.4) |
| Female | 4126 | 43.0 (40.7–45.4) | 2585 | 59.0 (55.4–62.5) | 914 | 24.1 (21.2–27.2) | 627 | 16.9 (14.1–20.2) |
| Age, year rangee | ||||||||
| 0–4 | 1213 | 18.3 (16.4–20.4) | 655 | 52.7 (46.7–58.8) | 265 | 21.4 (17.1–26.4) | 293 | 25.9 (21.2–31.2) |
| 5–11 | 3746 | 43.5 (41.2–45.9) | 2307 | 62.0 (58.6–65.3) | 835 | 22.0 (19.3–25.0) | 604 | 15.9 (13.6–18.6) |
| 12–17 | 4738 | 38.2 (36.0–40.4) | 3201 | 65.2 (61.6–68.7) | 975 | 20.8 (18.1–23.9) | 562 | 13.9 (11.0–17.4) |
| Race/ethnicitye,f | ||||||||
| White | 6472 | 57.2 (54.7–59.6) | 4218 | 62.9 (60.3–65.5) | 1354 | 21.4 (19.3–23.6) | 900 | 15.7 (13.8–17.9) |
| Black | 991 | 15.9 (14.2–17.9) | 537 | 53.7 (47.0–60.2) | 233 | 20.1 (16.3–24.5) | 221 | 26.3 (19.9–33.8) |
| Hispanic | 971 | 16.9 (15.0–19.1) | 591 | 59.0 (51.9–65.9) | 219 | 25.7 (19.7–32.8) | 161 | 15.3 (11.1–20.7) |
| Other race | 904 | 10.0 (8.5–11.6) | 597 | 69.3 (62.0–75.7) | 184 | 16.5 (12.4–21.7) | 123 | 14.2 (09.5–20.8) |
| Household incomee | ||||||||
| <$15 000 | 811 | 11.7 (9.9–13.9) | 420 | 49.2 (40.1–58.5) | 192 | 23.3 (16.3–32.1) | 199 | 27.5 (18.7–38.5) |
| $15 000–$24 999 | 1223 | 14.5 (12.9–16.3) | 707 | 59.0 (52.5–65.3) | 272 | 21.0 (16.3–26.6) | 244 | 20.0 (15.4–25.5) |
| $25 000–$49 999 | 2103 | 21.0 (19.3–22.9) | 1329 | 61.4 (56.9–65.7) | 445 | 19.9 (16.7–23.5) | 329 | 18.7 (15.5–22.4) |
| $50 000–$74 999 | 1684 | 16.6 (14.9–18.5) | 1107 | 69.1 (64.1–73.7) | 370 | 19.4 (15.8–23.7) | 207 | 11.5 (08.8–14.8) |
| ≥$75 000 | 3351 | 36.1 (34.0–38.3) | 2275 | 63.1 (59.5–66.6) | 676 | 22.4 (19.4–25.7) | 400 | 14.5 (12.1–17.3) |
| Health care coverage in past 12 months | ||||||||
| No insurance | 332 | 3.6 (2.9–4.4) | 203 | 62.0 (50.8–72.0) | 72 | 19.8 (12.7–29.6) | 57 | 18.2 (11.2–28.2) |
| Partial year coverage | 476 | 5.9 (4.4–7.8) | 257 | 55.8 (40.9–69.7) | 103 | 17.2 (11.2–25.6) | 116 | 27.0 (14.5–44.5) |
| Full year coverage | 8856 | 90.6 (88.6–92.2) | 5679 | 61.8 (59.5–64.1) | 1894 | 21.8 (20.0–23.9) | 1283 | 16.3 (14.6–18.1) |
| Cost as barriere, g | ||||||||
| Yes | 891 | 10.8 (9.3–12.4) | 404 | 46.3 (38.9–53.8) | 236 | 31.9 (24.5–40.5) | 251 | 21.8 (17.2–27.2) |
| No | 8746 | 89.2 (87.6–90.7) | 5711 | 63.3 (60.9–65.6) | 1830 | 20.3 (18.5–22.1) | 1205 | 16.5 (14.5–18.6) |
| Long-term control medicationse | ||||||||
| Yes | 4332 | 46.0 (43.7–48.3) | 2150 | 47.6 (44.2–51.0) | 1221 | 28.2 (25.4–31.3) | 961 | 24.2 (21.4–27.2) |
| No | 5365 | 54.0 (51.7–56.3) | 4013 | 73.5 (70.3–76.4) | 854 | 15.7 (13.5–18.1) | 498 | 10.8 (08.6–13.6) |
| Anyone smoked inside child's homeh | ||||||||
| Yes | 877 | 9.9 (8.3–11.8) | 527 | 57.3 (47.6–66.5) | 198 | 21.0 (15.6–27.6) | 152 | 21.7 (13.5–33.1) |
| No | 8796 | 90.1 (88.2–91.7) | 5615 | 62.0 (59.6–64.3) | 1875 | 21.6 (19.7–23.6) | 1306 | 16.5 (14.8–18.3) |
| If pets allowed in child's bedroom | ||||||||
| Pets allowed | 3754 | 33.3 (31.2–35.5) | 2518 | 66.9 (63.4–70.3) | 779 | 20.8 (18.0–23.8) | 457 | 12.3 (10.3–14.7) |
| Pets not allowed | 2226 | 22.7 (20.9–24.7) | 1345 | 59.8 (55.1–64.4) | 514 | 24.6 (20.5–29.3) | 367 | 15.5 (12.9–18.6) |
| No pets | 3710 | 44.0 (41.7–46.3) | 2295 | 58.4 (54.7–62.1) | 781 | 20.3 (17.7–23.2) | 634 | 21.3 (18.0–25.0) |
| Saw cockroach inside home past 30 d | ||||||||
| Yes | 721 | 8.0 (6.9–09.3) | 452 | 58.6 (50.6–66.2) | 148 | 23.3 (17.2–30.8) | 121 | 18.1 (13.1–24.5) |
| No | 8945 | 92.0 (90.7–93.1) | 5684 | 61.7 (59.3–64.1) | 1924 | 21.4 (19.5–23.3) | 1337 | 16.9 (15.0–19.0) |
| Saw or smelled mold past 30 d | ||||||||
| Yes | 929 | 9.1 (7.8–10.6) | 547 | 56.1 (47.8–64.1) | 235 | 30.4 (22.6–39.5) | 147 | 13.5 (09.6–18.7) |
| No | 8720 | 90.9 (89.4–92.2) | 5578 | 62.0 (59.6–64.4) | 1836 | 20.7 (18.9–22.6) | 1306 | 17.3 (15.4–19.4) |
CI, confidence interval.
Defined as the most impaired level from the 1 or more individual elements (i.e. daytime and night time symptoms in past 30 d and rescue medication use in past 3 months).
Persons who answered “yes” to the question “Have you ever been told by a doctor or other health professional that you had asthma?” or “Has a doctor.”
Unweighted pooled sample size, 2006–2010. Due to item non-response, individual characteristic categories may not sum to total.
Weighted prevalence and 95% confidence interval.
p Values <0.05 for the chi-square test of association between asthma controlled status and all selected variables.
Race categories “white, non-Hispanic”, “black, non-Hispanic”, include persons who indicated only a single race group. “Other races, non-Hispanic” includes Asian, American Indian Alaskan Native, Native Hawaiian and Other Pacific Islander, persons reporting more than one race, and persons reporting their race as something other than those listed here.
Cost as a barrier to primary care doctor, specialist, and medicine.
Indicates secondhand smoke exposure.
Most of the children with current asthma were non-Hispanic white (57.2%) and male (57.0%). By age, 18.3% of children with asthma were aged 0–4 years, 43.5% were aged 5–11 years and 38.2% were aged 12–17 years. Nearly 12% (11.7%) of the children with asthma were in homes with annual household incomes of less than $15 000. A majority of children had health insurance (90.6%). Less than 10% had no insurance (3.6%) or partial year insurance (5.9%) and 10.8% reported cost as a barrier to medical care. Fifty-six percent of children with asthma had pets and 33.3% of them allowed pets in their bedroom. Nearly 10% of the children with asthma were exposed to SHS (9.9%), 8.0% lived where a cockroach was seen, or 9.1% lived where mold was seen or smelled inside the home in the past 30 d (Table 2).
Asthma control and associated risk factors among children with asthma
Multiple factors (age, race/ethnicity, annual household income, cost as a barrier to medical care, and long-term control medication use) were significantly associated with level of asthma control (p values <0.05) (Table 2); however, after adjusting for other variables in the regression model, some of these associations were no longer statistically significant (Table 3).
Table 3.
Association between level of asthma controla and selected characteristics: Behavioral Risk Factor Surveillance System Asthma Call-back Survey, 2006–2010.
| Characteristics (reference levels) | Children with current asthma | Adults with current asthma | ||
|---|---|---|---|---|
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| Not well-controlled asthma | Very-poorly-controlled asthma | Not well-controlled asthma | Very-poorly-controlled asthma | |
| aPRb (95% CIb) | aPRb (95% CIb) | aPRb (95% CIb) | aPRb (95% CIb) | |
| Sex (male) | ||||
| Female | 1.2 (1.1–1.5) | 1.0 (0.9–1.3) | 1.1 (1.0–1.2) | 0.9 (0.9–1.0) |
| Age, year range (ref: 12–17) | ||||
| 0–4 | 0.9 (0.7–1.1) | 1.6 (1.2–2.1) | N/A | N/A |
| 5–11 | 0.9 (0.8–1.1) | 1.1 (0.8–1.3) | N/A | N/A |
| Age, year range (ref: 18–34) | ||||
| 35–44 | N/A | N/A | 0.9 (0.8–1.0) | 1.2 (1.0–1.3) |
| 45–54 | N/A | N/A | 0.8 (0.7–0.9) | 1.4 (1.2–1.5) |
| 55–64 | N/A | N/A | 0.8 (0.7–0.9) | 1.4 (1.2–1.6) |
| 65+ | N/A | N/A | 0.8 (0.8–0.9) | 1.5 (1.3–1.7) |
| Race/Ethnicityc (ref: White) | ||||
| Black | 1.0 (0.8–1.3) | 1.3 (1.0–1.7) | 0.9 (0.8–1.1) | 1.1 (1.0–1.3) |
| Hispanic | 1.2 (0.9–1.5) | 0.8 (0.6–1.1) | 0.9 (0.7–1.0) | 1.2 (1.0–1.4) |
| Other race | 0.8 (0.6–1.1) | 0.8 (0.6–1.2) | 0.8 (0.6–0.9) | 1.3 (1.1–1.4) |
| Education level (ref: College 4 years or more) | ||||
| High School graduate or less | N/A | N/A | 1.0 (0.9–1.1) | 1.4 (1.3–1.5) |
| Some college | N/A | N/A | 1.0 (0.9–1.1) | 1.2 (1.1–1.3) |
| Household Income (ref: ≥$75 000) | ||||
| <$15 000 | 1.1 (0.8–1.5) | 1.6 (1.1–2.4) | 1.0 (0.8–1.1) | 1.5 (1.4–1.8) |
| $15 000–$24 999 | 1.0 (0.7–1.3) | 1.2 (0.9–1.7) | 1.0 (0.9–1.1) | 1.4 (1.3–1.6) |
| $25 000–$49 999 | 0.9 (0.7–1.1) | 1.2 (0.9–1.5) | 1.0 (0.9–1.1) | 1.2 (1.0–1.3) |
| $50 000–$74 999 | 0.9 (0.7–1.1) | 0.7 (0.5–1.0) | 1.1 (1.0–1.2) | 1.0 (0.9–1.1) |
| Health care coverage in past 12 months (ref: Full year coverage) | ||||
| No insurance | 0.9 (0.6–1.4) | 1.2 (0.8–1.9) | 1.0 (0.9–1.1) | 0.9 (0.8–1.0) |
| Partial year coverage | 0.8 (0.5–1.2) | 1.4 (0.8–2.4) | 0.9 (0.8–1.1) | 1.0 (0.9–1.2) |
| Cost as barrierd (ref: No) | 1.5 (1.1–1.9) | 1.1 (0.9–1.5) | 1.2 (1.1–1.4) | 1.5 (1.4–1.6) |
| Long-term control medications (ref: No) | 1.8 (1.5–2.1) | 2.3 (1.8–2.9) | 1.5 (1.4–1.6) | 1.7 (1.6–1.8) |
| Anyone Smoked inside child's homee (ref: No) | 1.0 (0.7–1.3) | 1.2 (0.8–1.7) | N/A | N/A |
| Smoking & SHS (ref: Non-smoker &No SHS) | ||||
| Current smoker &SHS | N/A | N/A | 1.1 (1.0–1.2) | 1.6 (1.4–1.8) |
| Current smoker & No SHS | N/A | N/A | 1.0 (0.9–1.2) | 1.5 (1.3–1.7) |
| Former smoker & SHS | N/A | N/A | 1.1 (0.8–1.3) | 1.4 (1.2–1.8) |
| Former smoker & No SHS | N/A | N/A | 1.0 (0.9–1.0) | 1.2 (1.1–1.3) |
| Non-smoker & SHS | N/A | N/A | 1.0 (0.8–1.2) | 1.1 (0.9–1.4) |
| Pets allowed in bedroom (ref: Does not have pets) | ||||
| Pets allowed | 1.1 (0.9–1.3) | 0.7 (0.6–0.9) | 1.1 (1.0–1.2) | 1.1 (1.0–1.1) |
| Pets not allowed | 1.2 (1.0–1.5) | 0.8 (0.7–1.1) | 1.1 (1.0–1.2) | 1.1 (1.0–1.2) |
| Saw cockroach inside home past 30 d (ref: No) | 1.1 (0.8–1.5) | 0.9 (0.6–1.4) | 1.1 (1.0–1.2) | 1.0 (0.9–1.1) |
| Saw or smelled mold past 30d (ref: No) | 1.4 (1.0–1.8) | 0.7 (0.5–1.0) | 1.2 (1.1–1.3) | 1.0 (0.9–1.1) |
| Obese (ref: Non-obese) | N/A | N/A | 1.0 (0.9–1.1) | 1.2 (1.1–1.3) |
| COPDf (ref: No) | N/A | N/A | 1.1 (1.0–1.2) | 1.5 (1.4–1.6) |
| Depression (ref: No) | N/A | N/A | 1.0 (0.9–1.1) | 1.2 (1.1–1.3) |
CI, confidence interval; N/A: not applicable; SHS, secondhand smoke exposure; COPD, chronic obstructive pulmonary disease.
Defined as the most impaired level from the one or more individual elements (i.e. daytime and night time symptoms in past 30 d and rescue medication use in past 3 months).
Adjusted prevalence ratio (predicted marginal risk ratio) and 95% confidence intervals. Adjusted for the variables listed in this table.
Race categories “white, non-Hispanic”, “black, non-Hispanic”, include persons who indicated only a single race group. “Other races, non-Hispanic” includes Asian, American Indian Alaskan Native, Native Hawaiian and Other Pacific Islander, persons reporting more than one race, and persons reporting their race as something other than those listed here.
Cost as a barrier to primary care doctor, specialist, and medicine.
Indicates secondhand smoke exposure (SHS).
Combined responses to questions for COPD, emphysema, and chronic bronchitis.
Adjusted results from the multivariate logistic regression analyses for children are presented in Table 3. Prevalence of not-well-controlled asthma was significantly higher among girls (unadjusted prevalence = 24.1%; adjusted prevalence rate ratios (aPR) = 1.2(1.1–1.5)) than boys (19.6%). It was also higher among children who reported cost barriers (31.9%; aPR = 1.5(1.1–1.9)) or were on long-term control medications (28.2%; aPR = 1.8(1.5–2.1)) compared with those not reporting cost barriers and not on control medications (20.3% and 15.7%, respectively).
In addition, more children with current asthma aged 0–4 years had very-poorly-controlled asthma (25.9%; aPR = 1.6(1.2–2.1)) than children aged 12–17 years (13.9%). Also, very-poorly-controlled asthma was more prevalent among children with annual household income of less than $15 000 (27.5%; aPR = 1.6(1.1–2.4)) and children who were on long-term control medications (24.2%; aPR = 2.3(1.8–2.9)) than children with household income of $75 000 or more (14.5%) and children not on long-term control medications (10.8%).
Whether adjusted or not, no associations were observed between asthma control and healthcare insurance status or any environmental factors (SHS, saw cockroach inside home in past 30 d, or saw or smelled mold in past 30 d) (Tables 2 and 3).
Adults with current asthma
Characteristics
In the combined 2006 through 2010 BRFSS ACBS sample, 52 210 adults had current asthma. Fifty percent of adults with current asthma had uncontrolled asthma (25.9% not well controlled; 24.1% very-poorly-controlled) and 41.5% were on long-term control medications (Table 4). Among adults with uncontrolled asthma, 53.4% were on long-term control medications and among adults who were taking long-term control medications, 64.4% had uncontrolled asthma (data are not shown).
Table 4.
Characteristics and level of asthma controla among adults (aged ≥18 years) with current asthmab: Behavioral Risk Factor Surveillance System Asthma Call-back Survey, 2006–2010.
| Level of asthma control among adults with current asthma | ||||||||
|---|---|---|---|---|---|---|---|---|
|
|
||||||||
| Controlled asthma | Uncontrolled asthma | |||||||
|
|
|
|||||||
| Survey respondents | Well-controlled asthma | Not-well-controlled | Very-poorly-controlled | |||||
|
|
|
|
|
|||||
| Characteristics | No.c | %d (95% CId) | No.c | %d (95% CId) | No.c | %d (95% CId) | No.c | %d (95% CId) |
| Total | 52 210 | 24 378 | 50.0 (48.9–51.0) | 13 395 | 25.9 (25.0–26.8) | 14 437 | 24.1 (23.3–25.0) | |
| Sexe | ||||||||
| Male | 13 760 | 36.9 (35.9–38.0) | 6665 | 53.1 (51.0–55.1) | 3286 | 24.3 (22.6–26.0) | 3809 | 22.7 (21.1–24.3) |
| Female | 38 450 | 63.1(62.0–64.1) | 17 713 | 48.2 (47.0–49.3) | 10 109 | 26.9 (25.9–27.9) | 10 628 | 25.0 (24.0–25.9) |
| Age, year rangee | ||||||||
| 18–34 | 5617 | 30.7 (29.5–31.9) | 3220 | 56.9 (54.3–59.4) | 1469 | 26.8 (24.6–29.2) | 928 | 16.4 (14.5–18.4) |
| 35–44 | 6821 | 18.5 (17.8–19.3) | 3682 | 52.7 (50.3–55.0) | 1748 | 26.8 (24.7–29.0) | 1391 | 20.5 (18.8–22.4) |
| 45–54 | 11 416 | 19.6 (18.9–20.3) | 5253 | 47.0 (45.3–48.8) | 2890 | 24.8 (23.4–26.4) | 3273 | 28.1 (26.6–29.8) |
| 55–64 | 13 509 | 15.9 (15.4–16.5) | 5982 | 44.5 (42.8–46.1) | 3439 | 25.1 (23.7–26.5) | 4088 | 30.5 (29.0–32.0) |
| 65+ | 14 643 | 15.3 (14.8–15.8) | 6148 | 42.5 (41.0–44.1) | 3792 | 25.4 (24.1–26.7) | 4703 | 32.1 (30.7–33.6) |
| Race/Ethnicitye,f | ||||||||
| White | 42 517 | 74.6 (73.6–75.6) | 20 052 | 50.5 (49.3–51.6) | 11 091 | 26.9 (25.9–27.9) | 11 374 | 22.7 (21.8–23.5) |
| Black | 3236 | 9.7 (9.0–10.3) | 1414 | 47.7 (44.1–51.4) | 774 | 24.5 (21.4–27.7) | 1048 | 27.8 (24.7–31.1) |
| Hispanic | 2342 | 9.0 (8.3–9.7) | 1094 | 49.8 (45.4–54.1) | 566 | 23.8 (20.3–27.8) | 682 | 26.4 (22.7–30.5) |
| Other race | 3656 | 6.7 (6.2–7.4) | 1606 | 48.7 (44.1–53.4) | 852 | 19.9 (16.9–23.3) | 1198 | 31.4 (27.6–35.5) |
| Education levele | ||||||||
| High School graduate or less | 19 215 | 36.4 (35.4–37.4) | 7583 | 41.6 (39.8–43.4) | 4692 | 24.8 (23.2–26.4) | 6940 | 33.7 (32.0–35.3) |
| Some college | 15 486 | 28.5 (27.6–29.4) | 6937 | 48.7 (46.8–50.5) | 4119 | 27.7 (26.0–29.4) | 4430 | 23.7 (22.3–25.1) |
| College 4 or more years | 17 452 | 35.2 (34.2–36.1) | 9843 | 59.8 (58.1–61.4) | 4570 | 25.7 (24.3–27.2) | 3039 | 14.5 (13.5–15.7) |
| Household incomee | ||||||||
| <$15 000 | 8146 | 14.7 (14.0–15.5) | 2509 | 33.8 (31.3–36.5) | 1885 | 24.5 (22.0–27.2) | 3752 | 41.6 (39.0–44.3) |
| $15 000–$24 999 | 8967 | 16.7 (15.8–17.5) | 3389 | 39.4 (36.6–42.3) | 2351 | 25.3 (23.1–27.7) | 3227 | 35.3 (32.7–38.0) |
| $25 000–$49 999 | 12 351 | 23.8 (23.0–24.7) | 5883 | 48.5 (46.5–50.5) | 3323 | 27.5 (25.7–29.4) | 3145 | 24.0 (22.4–25.7) |
| $50 000–$74 999 | 7143 | 15.1 (14.4–15.8) | 3879 | 55.3 (52.7–57.8) | 1915 | 27.7 (25.4–30.2) | 1349 | 17.0 (15.3–18.8) |
| ≥$75 000 | 10 428 | 29.7 (28.7–30.7) | 6332 | 61.9 (59.9–63.9) | 2644 | 25.2 (23.4–27.0) | 1452 | 12.9 (11.8–14.2) |
| Health care coverage in past 12 monthse | ||||||||
| No insurance | 4616 | 13.1 (12.3–14.0) | 1910 | 45.3 (41.8–48.9) | 1227 | 27.0 (24.0–30.2) | 1479 | 27.7 (24.8–30.7) |
| Partial year coverage | 2515 | 6.5 (6.0–7.1) | 1004 | 42.7 (38.4–47.2) | 607 | 26.0 (22.1–30.3) | 904 | 31.3 (27.3–35.7) |
| Full year coverage | 44 876 | 80.3 (79.4–81.3) | 21 324 | 51.2 (50.1–52.3) | 11 534 | 25.8 (24.8–26.8) | 12 018 | 23.0 (22.1–23.9) |
| Cost as barriere,g | ||||||||
| Yes | 9443 | 20.6 (19.7–21.4) | 2644 | 30.3 (28.1–32.6) | 2643 | 29.7 (27.6–32.0) | 4156 | 40.0 (37.7–42.3) |
| No | 42 572 | 79.4 (78.6–80.3) | 21 636 | 55.0 (53.9–56.1) | 10 701 | 25.0 (24.0–26.0) | 10 235 | 20.0 (19.2–20.9) |
| Long-term control medicationse | ||||||||
| Yes | 24 786 | 41.5 (40.5–42.5) | 8698 | 35.6 (34.3–37.0) | 7346 | 31.4 (30.0–32.8) | 8742 | 33.0 (31.7–34.4) |
| No | 27 424 | 58.5 (57.5–59.5) | 15680 | 60.2 (58.8–61.5) | 6049 | 22.1 (20.9–23.2) | 5695 | 17.8 (16.8–18.8) |
| Anyone Smoked inside homee,h | ||||||||
| Yes | 8866 | 18.2 (17.4–19.1) | 2714 | 35.3 (32.7–37.9) | 2233 | 26.5 (24.3–28.9) | 3919 | 38.2 (35.8–40.7) |
| No | 43 243 | 81.8 (81.0–82.6) | 21 603 | 53.2 (52.1–54.3) | 11 145 | 25.8 (24.8–26.8) | 10 495 | 21.0 (20.1–21.9) |
| Smoking statuse | ||||||||
| Current smoker | 9510 | 19.5 (18.7–20.3) | 2945 | 35.2 (33.0–37.6) | 2440 | 27.4 (25.2–29.6) | 4125 | 37.4 (35.2–39.6) |
| Former smoker | 17 622 | 27.1 (26.3–28.0) | 7690 | 46.6 (44.9–48.4) | 4613 | 25.2 (23.8–26.7) | 5319 | 28.2 (26.7–29.7) |
| Non-smoker | 24 858 | 53.4 (52.4–54.4) | 13 634 | 57.0 (55.5–58.5) | 6293 | 25.8 (24.5–27.1) | 4931 | 17.2 (16.2–18.3) |
| Smoking status & SHSe | ||||||||
| Current smoker &SHS | 6353 | 11.9 (11.2–12.6) | 1716 | 31.2 (28.2–34.3) | 1618 | 27.3 (24.5–30.2) | 3019 | 41.5 (38.7–44.5) |
| Current smoker &No SHS | 3138 | 7.6 (7.1–8.2) | 1219 | 41.4 (37.7–45.2) | 817 | 27.7 (24.3–31.3) | 1102 | 31.0 (27.5–34.7) |
| Former smoker &SHS | 1341 | 2.6 (2.3–2.9) | 461 | 34.6 (29.2–40.4) | 325 | 25.9 (20.1–32.7) | 555 | 39.5 (33.8–45.7) |
| Former smoker &No SHS | 16 247 | 24.6 (23.8–25.4) | 7210 | 47.9 (46.0–49.7) | 4285 | 25.2 (23.7–26.7) | 4752 | 27.0 (25.5–28.5) |
| Non-smoker & SHS | 1143 | 3.8 (3.3–4.3) | 527 | 48.7 (41.9–55.6) | 281 | 24.7 (20.0–30.1) | 335 | 26.6 (21.1–32.9) |
| Non-smoker &No SHS | 23 667 | 49.6 (48.6–50.7) | 13 075 | 57.6 (56.1–59.1) | 6003 | 25.9 (24.6–27.3) | 4589 | 16.5 (15.5–17.6) |
| If pets allowed in bedroome | ||||||||
| Pets allowed | 22 711 | 44.3 (43.3–45.3) | 10 565 | 49.2 (47.7–50.8) | 6069 | 27.1 (25.7–28.5) | 6077 | 23.7 (22.4–25.0) |
| Pets Not allowed | 6950 | 15.2 (14.4–16.0) | 3042 | 46.5 (43.7–49.4) | 1820 | 27.0 (24.5–29.6) | 2088 | 26.5 (24.2–29.0) |
| No pets | 22 542 | 40.5 (39.5–41.5) | 10 768 | 52.1 (50.5–53.6) | 5506 | 24.3 (22.9–25.7) | 6268 | 23.7 (22.5–24.9) |
| Saw cockroach inside home past 30 de | ||||||||
| Yes | 4098 | 9.7 (9.1–10.3) | 1800 | 43.7 (40.2–47.2) | 984 | 26.8 (23.6–30.2) | 1314 | 29.5 (26.7–32.5) |
| No | 47 972 | 90.3 (89.7–90.9) | 22 497 | 50.6 (49.5–51.7) | 12 383 | 25.8 (24.9–26.8) | 13 092 | 23.5 (22.7–24.4) |
| Saw or smelled mold past 30 de | ||||||||
| Yes | 6017 | 11.6 (11.0–12.2) | 2335 | 41.5 (38.7–44.5) | 1692 | 29.8 (27.2–32.6) | 1990 | 28.7 (26.3–31.1) |
| No | 45 844 | 88.4 (87.8–89.0) | 21 899 | 51.1 (50.0–52.2) | 11 640 | 25.4 (24.5–26.4) | 12 305 | 23.5 (22.6–24.4) |
| Body mass indexe | ||||||||
| Obese | 20 714 | 39.4 (38.4–40.4) | 8874 | 45.2 (43.5–46.9) | 5401 | 26.1 (24.7–27.5) | 6439 | 28.8 (27.3–30.2) |
| Non-obese | 29 539 | 60.6 (59.6–61.6) | 14 593 | 53.4 (52.0–54.7) | 7495 | 25.8 (24.6–27.0) | 7451 | 20.9 (19.9–21.9) |
| COPDe,i | ||||||||
| Yes | 21 539 | 34.6 (33.7–35.5) | 6868 | 33.7 (32.1–35.2) | 5619 | 27.2 (25.8–28.7) | 9052 | 39.2 (37.7–40.7) |
| No | 30 480 | 65.4 (64.5–66.4) | 17 417 | 58.6 (57.2–59.9) | 7724 | 25.3 (24.1–26.5) | 5339 | 16.2 (15.2–17.2) |
| Depressione | ||||||||
| Yes | 19 734 | 34.7 (33.7–35.6) | 7617 | 41.1 (39.4–42.7) | 5230 | 26.4 (25.0–27.9) | 6887 | 32.6 (31.0–34.1) |
| No | 32 133 | 65.3 (64.4–66.3) | 16 596 | 54.6 (53.3–55.9) | 8077 | 25.8 (24.6–26.9) | 7460 | 19.6 (18.7–20.6) |
CI, confidence interval; SHS, secondhand smoke exposure; COPD, chronic obstructive pulmonary disease.
Defined as the most impaired level from the one or more individual elements (i.e. daytime and night time symptoms in past 30 d and rescue medication use in past 3 months).
Persons who answered “yes” to the question “Have you ever been told by a doctor or other health professional that you had asthma?” or “Has a doctor.”
Unweighted pooled sample size, 2006–2010. Due to item non-response, individual characteristic categories may not sum to total.
Weighted prevalence and 95% confidence interval.
p values were <0.001 for the Chi-square test of association between asthma controlled status and all selected variables in the table.
Race categories “white, non-Hispanic”, “black, non-Hispanic”, include persons who indicated only a single race group. “Other races, non-Hispanic” insurance includes Asian, American Indian Alaskan Native, Native Hawaiian and Other Pacific Islander, persons reporting more than one race, and persons reporting their race as something other than those listed here.
Cost as barrier for primary care doctor, specialist, and medicine.
Indicates secondhand smoke exposure (SHS).
Combined responses to questions for COPD, emphysema, and chronic bronchitis.
The majority of adults with current asthma were non-Hispanic whites (74.6%) and female (63.1%). Among adults with current asthma, 30.7% were aged 18–34 years, 18.5% were aged 35–44 years, 19.6% were aged 45–54 years, 15.9% were aged 55–64, and 15.3% were aged 65+ years. Of adults with asthma, 14.7% had annual household incomes of less than $15 000; 80.3% had health insurance, 13.1% had no health insurance, and 6.5% had partial year insurance; 20.6% reported cost as a barrier to medical care, 18.2% reported exposure to SHS (someone other than the respondent smoked inside home), 19.5% were current smoker, and 11.9% were both current smoker and exposed to SHS. Thirty nine percent were obese (39.4%). One-third had COPD (34.6%) and 34.7% had depression. Sixty percent (59.5%) had pets, and 44.3% of adults with asthma allowed the pets in their bedroom. About 10% saw a cockroach inside the home in the past 30 d (9.7%) or saw/smelled mold in the past 30 d (11.6%) (Table 4).
Asthma control and associated risk factors among adults with asthma
All characteristics of adults with asthma listed in Table 4 were significantly associated with the level of asthma control (p values<0.05). However, after adjusting for other variables in the model, the associations for sex, healthcare and each of the environmental factors (pets allowed in bedroom, saw cockroach inside home in past 30 d, or saw or smelled mold in past 30 d) were no longer significant (Tables 3 and 4).
As seen in Table 3, prevalence of not-well-controlled asthma was significantly higher among adults who reported cost barriers (29.7%; aPR = 1.2(1.1–1.4)) and were on long-term control medications (31.4%; aPR = 1.5(1.4–1.6)), compared with those not reporting cost barriers and not on control medications (25.0% and 22.1%, respectively). Compared with adults aged 18–34 years, prevalence of not-well-controlled asthma was lower and prevalence of very-poorly-controlled asthma was higher among adults aged 45 years or older.
Very-poorly-controlled asthma was more prevalent among adults with current asthma who were 45 years or older (45–54: 28.1%; aPR = 1.4(1.2–1.5); 55–64: 30.5%; aPR =1.4(1.2–1.6); 65+: 32.1%; aPR = 1.5(1.3–1.7)) than among those aged 18–34 years (16.4%) and among adults with other race (31.4%; aPR = 1.3(1.1–1.4)) than among whites (22.7%). Not having 4-year college degree or higher was significantly associated with having very-poorly-controlled asthma (high school graduate or less: 33.7%; aPR = 1.4(1.3–1.5); some college: 23.7%; aPR = 1.2(1.1–1.3)). It was also more prevalent among adults with annual household income less than $15000 (41.6%; aPR=1.5(1.4–1.8)) and $15 000–$24 999 (35.3%; aPR = 1.4(1.3–1.6)) than among those with income of $75 000 or more (12.9%), and among those who reported cost barriers (40.0%; aPR = 1.5(1.4–1.6)) than those who did not (20.0%). Higher rates of very-poorly-controlled asthma were significantly associated with current or former smoking, regardless of SHS status (aPR ranges from 1.2 to 1.6), being obese (28.8%; aPR = 1.2(1.1–1.3)), having COPD (39.2%; aPR = 1.5(1.4–1.6)), and having depression (32.6%; aPR = 1.2(1.1–1.3)). The corresponding reference levels were 20.9%, 16.2%, and 19.6% respectively. In addition, being on long-term control medications was significantly associated with having very-poorly-controlled asthma (33.0%; aPR = 1.7(1.6–1.8)) than not being on long-term control medication (17.8%) (Tables 3 and 4).
Discussion
The purpose of this population-based study was to assess asthma control and identify related potential risk factors among children and adults with current asthma who participated in the ACBS in the years 2006 through 2010. Fifty percent of adults and 38% of children with current asthma had uncontrolled asthma. Among both children and adults, having uncontrolled asthma was significantly associated with age (aged 0–4 years or 45 years or older), having cost barriers for medical care, having low annual household income (less than $15 000 for children and less than $25 000 for adults), and taking long-term control medications. In addition, among adults, not having 4 year or more college education, being current or former smoker, and having COPD or depression were significantly associated with having uncontrolled asthma. These findings were similar to the findings of the state-specific reports [8,18,21].
The strong association between taking long-term control medication and having uncontrolled asthma among both children and adults is as predicted. According to the NAEPP 2007 guidelines, all persons with uncontrolled asthma should be on long-term control medications [2]. Therefore, we expected that all persons with uncontrolled asthma were on long-term control medications. However, our findings show that only 63% of children and 53% of adults with uncontrolled asthma were on long-term control medications and 52% of children and 64% of adults had uncontrolled asthma despite being on long-term control medications. Possible explanation for these findings is that providers are not implementing the NAEPP 2007 guidelines or improper use of controller medications [2,24]. Treating those with uncontrolled asthma with long-term control asthma medications appropriately, as defined in the NAEPP 2007 guidelines, is an important component of asthma management that needs further improvement.
Multiple extraneous factors (e.g. inadequate treatment, non-adherence to treatment regimens, reduced responsiveness to therapy, environmental triggers and irritants, and comorbid conditions) could contribute to uncontrolled asthma [3–8,22–25]. However, many of these factors were either not examined because of a lack of data or, if examined, did not show an association. We were able to identify that age (being aged 0–4 years or aged 45 years and older), low annual household income (less than $15 000 for children and less than $25 000 for adults), and cost as barriers to medical care were predictors of uncontrolled asthma, especially very-poorly-controlled asthma, among both children and adults. Among adults, other race, education of less than a 4-year college degree, smoking (current or former), and co-morbid conditions (obesity, COPD, or depression) were also predictors of uncontrolled asthma. Identifying modifiable predictors of uncontrolled asthma (low income or low educational attainment, unable to afford medical care, smoking, and co-morbid conditions) is an important step in developing targeted interventions, producing strategies that reduce the health risks and economic cost of asthma, and improving the health and well-being of persons with asthma and their families.
The strength of this study is the ability to assess the level of asthma control among the large sample of children and adults with current asthma from the states participating in the ACBS. The ACBS is the only survey providing indicators that allow a guideline-based classification of asthma control [2] to evaluate population-based asthma control status.
There are several limitations to our study. One limitation is that the indicators for asthma control classification that are available in the ACBS circumscribed our findings. Because of the nature of telephone surveys and the content of the ACBS questionnaire, we were unable to include all of the specified elements in the NAEPP guidelines (e.g. activity limitation, pulmonary function measures, asthma exacerbations that require oral corticosteroid and lung growth status in children) [2]. This limitation may alter asthma control prevalence estimates. However, the definition of asthma control used here is consistent with the definition in other reports of population-based estimates [8,15,18,21]. Another limitation is that the ACBS response rates for the participating states were around 50%. Low response rates may affect the results by introducing non-response bias, if survey respondents differed from non-responders on the characteristics studied. However, the BRFSS sampling and weighting procedures and varying response rates among states over the 5-year study period can minimize non-response effects on the results [16]. In addition, because of the cross-sectional nature of the survey data, we could not generally determine temporal sequence or causality. Finally, the findings cannot be generalized to the people with current asthma in states that did not participate in the ACBS.
In conclusion, despite national guidelines for managing asthma and available advanced medical treatments, 38% of children and 50% of adults with current asthma have uncontrolled asthma and not all those with uncontrolled asthma were on long-term control medications as recommended in the asthma treatment guidelines. Our findings indicate that multiple factors (low educational attainment, low income, tobacco smoking, and co-morbid conditions including obesity, COPD, and depression) were significantly associated with having uncontrolled asthma, especially very-poorly-controlled asthma. Development of targeted interventions or strategies aimed at reducing modifiable risk factors of poor asthma control can lead to better asthma control and improve the health and quality of life of people with asthma.
Acknowledgments
This research received no specific grant from any funding agency in the public, commercial, or non-profit sectors.
Footnotes
Declaration of interest: The authors declare no conflicts of interest. The authors alone are responsible for the content and writing of the article.
References
- 1.Centers for Disease Control and Prevention. National Center for Health Statistics. National Surveillance of Asthma: United States, 2001–2010. [last accessed 8 Apr 2013];National Center for Health Statistics Data brief No 94. 2012 May; Available from: http://www.cdc.gov/nchs/data/series/sr_03/sr03_035.pdf.
- 2.US Department of Health and Human Services, National Institutes of Health (NIH), National Heart, Lung, and Blood Institute, National Asthma Education and Prevention Program (NAEPP) [last accessed 23 Oct 2013];Expert panel report 3: guidelines for the diagnosis and management of asthma. 2007 Available from: http://www.nhlbi.nih.gov/guide-lines/asthma/asthgdln.pdf.
- 3.Hannaway PJ. Asthma—an emerging epidemic: a manual for patients with asthma, parents of children with asthma, asthma educators, health-care providers, school nurses and coaches. Marblehead (MA): Lighthouse Press; 2002. [Google Scholar]
- 4.Institute of Medicine Committee on the Assessment of Asthma and Indoor Air, Division of Health Promotion and Disease Prevention. “Front matter“ clearing the air: asthma and indoor air exposures. Washington (DC): The National Academies Press; 2000. [Google Scholar]
- 5.Bloomberg GR, Banister C, Sterkel R, Epstein J, Bruns J, Swerczek L, Wells S, et al. Socioeconomic, family, and pediatric practice factors that affect level of asthma control. Pediatrics. 2009;123:829–835. doi: 10.1542/peds.2008-0504. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Soriano JB, Rabe KF, Vermeire PA. Predictors of poor asthma control in European adults. J Asthma. 2003;40:803–813. doi: 10.1081/jas-120023572. [DOI] [PubMed] [Google Scholar]
- 7.Halterman JS, Auinger P, Conn KM, Lynch K, Yoos HL, Szilagyi PG. Inadequate therapy and poor symptom control among children with asthma: findings from a multistate sample. Ambul Pediatr. 2007;7:153–159. doi: 10.1016/j.ambp.2006.11.007. [DOI] [PubMed] [Google Scholar]
- 8.Nguyen K, Zahran HS, Iqbal S, Peng J, Boulay E. Factors associated with asthma control among adults in five New England states, 2006–2007. J Asthma. 2011;48:581–588. doi: 10.3109/02770903.2011.576744. [DOI] [PubMed] [Google Scholar]
- 9.Williams SA, Wagner S, Kannan H, Bolge SC. The association between asthma control and health care utilization, work productivity loss, and health-related quality of life. J Occup Environ Med. 2009;51:780–785. doi: 10.1097/JOM.0b013e3181abb019. [DOI] [PubMed] [Google Scholar]
- 10.Vollmer WM, Markson LE, O'Connor E, Sanocki LL, Fitterman L, Berger M, Buist AS. Association of asthma control with healthcare utilization and quality of life. Am J Respir Crit Care Med. 1999;160:1647–1652. doi: 10.1164/ajrccm.160.5.9902098. [DOI] [PubMed] [Google Scholar]
- 11.Barnett SBL, Nurmagambetov TA. Costs of asthma in the United States: 2002–2007. J Allergy Clin Immunol. 2011;127:145–152. doi: 10.1016/j.jaci.2010.10.020. [DOI] [PubMed] [Google Scholar]
- 12.Juniper EF, O'Byrne PM, Guyatt GH, Ferrie PJ, King DR. Development and validation of a questionnaire to measure asthma control. Eur Respir J. 1999;14:902–907. doi: 10.1034/j.1399-3003.1999.14d29.x. [DOI] [PubMed] [Google Scholar]
- 13.Nathan RA, Sorkness CA, Kosinski M, Schatz M, Li JT, Marcus P, Murray JJ, et al. Development of the asthma control test: a survey for assessing asthma control. J Allergy Clin Immunol. 2004;113:59–65. doi: 10.1016/j.jaci.2003.09.008. [DOI] [PubMed] [Google Scholar]
- 14.Skinner EA, Diette GB, Algatt-Bergstrom PJ, Nguyen TT, Clark RD, Markson LE, Wu AW. The Asthma Therapy Assessment Questionnaire (ATAQ) for children and adolescents. Dis Manag. 2004;7:305–313. doi: 10.1089/dis.2004.7.305. [DOI] [PubMed] [Google Scholar]
- 15.Zahran HS, Person CJ, Bailey CM, Moorman JE. Predictors of asthma self-management education among children and adults-2006-2007 behavioral risk factor surveillance system asthma callback survey. J Asthma. 2012;49:98–106. doi: 10.3109/02770903.2011.644012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Centers for Disease Control and Prevention. BRFSS call-back surveys, 2006–2010 Asthma call-back survey History and Analysis Guidance. [last accessed 6 May 2013]; Available from: http://www.cdc.gov/brfss/acbs.
- 17.Centers for Disease Control and Prevention. Asthma Control Analysis Workgroup. Measuring the impairment construct of asthma control from ERP-3 using the Asthma call-back Survey data. Unpublished data 2010 [Google Scholar]
- 18.Montana Asthma control Program. [last accessed 18 Nov 2013];The Health impact of uncontrolled asthma. Available from: http://www.dphhs.mt.gov/publi-chealth/asthma/documents/surveillancereportJan-March2010.pdf.
- 19.Allison PD. Logistic regression using SAS system: theory and application. Cary (NC): SAS Institute Inc; 1999. [Google Scholar]
- 20.Centers for Disease Control and Prevention. [last accessed 8 May 2013];Adult asthma data Technical information. 2007 Available from: http://www.cdc.gov/asthma/brfss/07/brfsstechinfo.htm#anal.
- 21.Utah Department of Health. [last accessed 25 Oct 2013];Asthma symptoms and management. 2013 Available from: http://health.utah.gov/asthma/pdf_files/Data/Burden%20Report/SymptomsManagement.pdf.
- 22.Barns PJ. Corticosteroid resistance in patients with asthma and chronic obstructive pulmonary disease. J Allergy Clin Immunol. 2013;131:636–645. doi: 10.1016/j.jaci.2012.12.1564. [DOI] [PubMed] [Google Scholar]
- 23.Dixon AE, Holguin F, Sood A, Salome CM, Pratley RE, Beuther DA, Juan C, et al. An official american thoracic society workshop report: obesity and asthma. Proc Am Thorac Soc. 2010;7:325–335. doi: 10.1513/pats.200903-013ST. [DOI] [PubMed] [Google Scholar]
- 24.Bender BG. Overcoming barriers to nonadherence in asthma treatment. J Allergy Clin Immunol. 2002;109:S554–S559. doi: 10.1067/mai.2002.124570. [DOI] [PubMed] [Google Scholar]
- 25.Pearlman AN, Chandra RK, Chang D, Conley DB, Tripathi-Peters A, Grammer LC, Schleimer RT, et al. Relationships between severity of chronic rhinosinusitis and nasal polyposis, asthma, and atopy. Am J Rhinol Allergy. 2009;23:145–148. doi: 10.2500/ajra.2009.23.3284. [DOI] [PMC free article] [PubMed] [Google Scholar]