Abstract
Objective
This study assessed Health-related Quality of Life (HRQoL) and related risk factors among adults with asthma in the United States. Using the 2015 Behavioral Risk Factor Surveillance System (BRFSS), we examined the association between four domains of impaired HRQoL and selected explanatory factors.
Methods
A BRFSS sample of 39 321 adults with asthma was used in this study. We examined the association between fair/poor health, ≥14 mentally unhealthy days, ≥14 physically unhealthy days, and ≥14 days of activity limitation and selected explanatory variables (sex, race/ethnicity, age, annual household income, healthcare coverage, physical activity, smoking status, Body Mass Index (BMI), having a coexisting disease, and being diagnosed with depression) using multivariable logistic regression models.
Results
Income, physical activity status, smoking status, coexisting diseases, and depression were strongly associated with all HRQoL domains. Blacks had significantly less ≥14 physically unhealthy days (23.4%; aPR = 0.82 [95% CI: 0.72, 0.92]) and ≥14 days of activity limitation (18.3%; aPR = 0.81 [0.70, 0.94]) and Hispanics had significantly more fair/poor health (38.4%; aPR= 1.31 [1.18, 1.45]) than whites. Underweight and obese had significantly more fair/poor health, and underweight significantly more ≥14 physically unhealthy days, compared with normal weight. Adults aged 55 years or older had significantly less ≥14 mentally unhealthy days than adults 18–24 years.
Conclusions
Multiple factors were associated with impaired HRQoL. Providing strategies to address potential risk factors such as low income, physically inactive, smoker, and obese or underweight should be considered to improve HRQoL among adults with asthma.
Introduction
In 2015, 7.6% (18.4 million) of the U.S. adult population had asthma [1]. Asthma prevalence has increased in the last decade, at an annual percentage rate of 1.5% from 2001 to 2010 [2]. Asthma, which costs the nation $56 billion annually [3], also takes a significant toll on the population, causing 439 000 hospital discharges [4], 1.6 million emergency department visits [5], and 3651 deaths annually [6].
Asthma is a disease characterized by inflammation of the airways, obstruction of airflow, and bronchial hyperresponsiveness and symptoms include wheezing, coughing, tightness of the chest, shortness of breath, and sleep awakenings. These symptoms can greatly affect persons with asthma in their daily activities and quality of life [7]. Therefore, understanding how quality of life is impaired among the increasing percentage of people living with asthma is especially important in understanding the current impact of asthma.
Numerous published studies have demonstrated impaired health-related quality of life (HRQoL) among adults with asthma. One study [8] determined that self-rated health, physical and mental domain impairments, and activity limitations as measured by the four Behavioral Risk Factor Surveillance System (BRFSS) HRQoL indicators, were significantly impaired among adults with asthma. Cui et al. [9] who measured these four HRQoL indicators on the National Health and Nutrition Examination Survey (NHANES), determined that only adolescents having asthma with symptoms had worse HRQoL. Adams et al. [10] found that Australian adults with asthma also reported more comorbid conditions, higher activity limitations measured by the Short Form-12 (SF-12), and a significantly decreased physical activity domain of the SF-12 compared with adults without asthma. Vietri et al. [11] found significantly impaired HRQoL among U.S. workers with poor asthma control, compared with well-controlled asthma, after adjusting for confounders. The study found impaired 12-item Short Form Survey Instrument (SF-12v2) physical health and mental health component scores and SF-6D health utility scores.
Decreased QoL has been associated with a number of risk factors among adults with asthma including being female [12, 13], older age [14, 15], smoking [16], lower income [15, 17], comorbidities [10, 15, 18], physical inactivity [16], obesity [16, 19, 20], poor mental health [13, 21, 22], poor asthma control [13, 18, 23, 24], and asthma severity [15,19, 25]. However, most studies are outdated and a limited number of studies [8, 9, 16, 26] evaluated/assessed HRQoL among adults with asthma using the Centers for Disease Control and Prevention (CDC) HRQoL measure. Only one of those studies [9] using the CDC HRQoL measure was published within the last 10 years and was conducted among adolescents.
The aim of this study was to examine the association between the four domains of HRQoL indicators (i.e., self-rated health, mentally unhealthy days, physically unhealthy days, and activity limitation days) and selected explanatory factors which included sociodemographic, behavioral, and health status indicators, among adults with asthma using the 2015 Behavioral Risk Factor Surveillance System (BRFSS). We hypothesized that demographic, socioeconomic, and behavioral factors, and comorbid conditions (e.g., sex, race/ethnicity, age, annual household income, healthcare coverage, physical activity, smoking status, Body Mass Index (BMI), having a coexisting disease, and being diagnosed with depression) will be associated with lower quality of life among adults with asthma.
Methods
This cross-sectional study includes a sample of 39 321 non-institutionalized adults aged 18 years and older with current asthma, in the United States including the District of Columbia, who completed the 2015 Behavioral Risk Factor Surveillance System (BRFSS) with and without asthma. BRFSS is a state-based, random-digit-dialed telephone survey of non-institutionalized U.S. adults. BRFSS monitors the prevalence of key health conditions, risk behaviors, and preventive health practices that can affect health status [27]. Data from all states and the District of Columbia were pooled to produce national estimates. The data include sample weights to adjust for nonresponse differences in the sample and unequal probability of sample selection [27]. The median survey response rate for all states, territories and Washington, DC, in 2015 was 47.2, and ranged from 33.9 to 61.1 (https://www.cdc.gov/brfss/annual_data/2015/pdf/2015-sdqr.pdf). Determination of asthma status was based on two core questions on the BRFSS: “Has a doctor, nurse, or other health professional EVER told you that you had any of the following? (Ever told) you had asthma?” and “Do you still have asthma?” A person was classified as current asthma if he or she responded yes to both questions.
Multiple associations between HRQoL indicators and explanatory factors (sociodemographic, behavioral, and health status indicators) were examined. Analysis variables included sex (male or female), race/ethnicity (non-Hispanic white, non-Hispanic black, non-Hispanic American Indian/Alaskan Native, non-Hispanic Asian, non-Hispanic other, or Hispanic), age group (18–24 years, 25–34 years, 35–44 years, 45–54 years, 55–64 years, or 65 years or older), annual household income (<$15 000, $15 000–$24 999, $25 000–$34 999, $35 000–$49 000, or ≥$50 000), healthcare coverage (yes or no), physical activity (physically active or physically inactive), smoking status (never smoker, past smoker, or current smoker), Body Mass Index (BMI) (underweight= 12 kg/m2 ≤ BMI < 18.5 kg/m2, normal weight= 18.5 kg/m2 ≤ BMI < 25 kg/m2, overweight= 25 kg/m2 ≤ BMI < 30 kg/m2, obese= 30 kg/m2 ≤ BMI), coexisting diseases (yes or no) and depression (yes or no). Coexisting diseases included heart disease, stroke, cancer, chronic obstructive pulmonary disease (COPD), arthritis, kidney disease, diabetes, and hypertension. The “other NH” category included non-Hispanic multiracial, Native Hawaiian or other Pacific Islander, and other races. For the remainder of the document, racial groups mentioned including white, black, American Indian/Alaskan Native, Asian, and other are assumed non-Hispanic.
We used the CDC Healthy Days (CDC HRQoL-4) scale to measure HRQoL. This scale includes the following standard 4-item set of healthy days questions that have been included on the BRFSS questionnaire since 1993 (https://www.cdc.gov/hrqol/hrqol14_measure.htm):
“Would you say that in general your health is—excellent, very good, good, fair, or poor?” (self-rated health)
“Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?” (mentally unhealthy days)
“Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?” (physically unhealthy days)
“During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation?” (activity limitation days)
In addition to “no activity limitation” responses, if the respondent reported “no” physically and mentally unhealthy days, and responses to activity limitation day question were missing, then the number of days of activity limitation was considered as “none.”
Following the CDC HRQoL program guidelines (https://www.cdc.gov/hrqol/index.htm), responses to the HRQoL measures were dichotomized as having 14 or more verses less than 14 mentally or physically unhealthy days, or days of activity limitation. Responses to the self-rated health question were dichotomized as “having fair or poor health” and “not having fair or poor health,” which includes responses “good, very good, and excellent health.”
Smoking status was determined according to two core questions on the BRFSS: “Have you smoked at least 100 cigarettes in your entire life?” and “Do you now smoke cigarettes every day, some days, or not at all?” Respondents were categorized as a current smoker if they answered “yes” on both questions and as a past smoker if they answered “yes” to the first question and “no” to the second question. Respondents who reported no participation in physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise in the past month were considered physically inactive. (2015 BRFSS Questionnaire: https://www.cdc.gov/brfss/questionnaires/pdf-ques/2015-brfss-questionnaire-12-29-14.pdf)
Statistical analysis
All statistical analyses were done in SAS 9.4-callable SUDAAN to account for the complex sampling design of the BRFSS. Sample weights were used during the analyses to produce estimates generalizable to a participating state’s population. Chi-square tests were conducted to determine statistical significance in the difference of current asthma prevalence by potential risk factor variables. For variables with more than two categories, we determined statistical significance difference between subpopulation groups if the 95% CI’s of the estimates did not overlap and confirmed with t-tests if p <0.05. Logistic regression models were used to test for association between the dependent variable (each HRQoL indicator) and potential risk factors as independent variables (sex, race/ethnicity, age, income, health care coverage, physical activity, smoking, BMI, coexisting diseases, and depression) among adults with asthma. For each HRQoL indicator, we calculated weighted percentages of each risk factor and constructed logistic regression models. Unadjusted logistic regression models contained a single risk factor to calculate the unadjusted prevalence ratios (PRs). The adjusted logistic regression models contained all risk factors to determine the adjusted prevalence ratios (aPRs) (also known as predicted marginal risk ratio). Corresponding 95% confidence intervals (CIs) were determined for each estimate. The PR and aPR values were considered statistically significant if the null value of 1 was not within the 95% CI of PRs.
Results
Characteristics
Overall 8.8% of survey respondents 18 years and older had current asthma (results not shown). All potential risk factors in Table 1 showed a statistically significant difference in current asthma prevalence (p<0.0000 for all variables). Significantly more females (weighted, %: 11.3%; 95% CI: 11.0, 11.5) had current asthma compared with males (6.2%; 6.0, 6.4). American Indian/Alaskan Native adults had the significantly highest (16.1%; 14.4, 18.0) and Asian adults had the significantly lowest asthma prevalence (5.2%; 4.4, 6.1), compared with all other race/ethnic groups. Prevalence was significantly higher among adults aged 18–24 years (10.3%; 9.7, 11.0) than adults of all other ages. Asthma prevalence was significantly higher among adults who had income less than $15 000 (14.1%; 13.5, 14.8), had health care coverage (9.1%; 8.9, 9.3), were physically inactive (10.8%; 10.4, 11.1), were a current smoker (11.5%; 11.0, 11.9), and were obese (12.5%; 12.1, 12.9) than adults who did not have these characteristics in the corresponding categories (Table 1). In addition, asthma was significantly more prevalent among adults reporting adverse health outcomes (i.e., one or more coexisting diseases and depression) than those who did not report them (Table 1).
Table 1.
Characteristics | Percent with current asthma | |
---|---|---|
Unweighted, n | Weighted, % (95% CI) | |
Total | 39 321 | |
Sex | *p<0.0000 | |
Male | 11 696 | 6.2 (6.0, 6.4) |
Female | 27 625 | 11.3 (11.0, 11.5) |
Race/ethnicity | *p<0.0000 | |
White NH | 29 528 | 9.0 (8.8, 9.2) |
Black NH | 3865 | 10.7 (10.1, 11.3) |
American Indian/Alaskan Native NH | 932 | 16.1 (14.4, 18.0) |
Asian NH | 503 | 5.2 (4.4, 6.1) |
Other NH | 1425 | 12.9 (11.8, 14.2) |
Hispanic | 2476 | 7.0 (6.6, 7.5) |
Age, year range | *p<0.0000 | |
18–24 | 2384 | 10.3 (9.7, 11.0) |
25–34 | 3675 | 8.4 (7.9, 8.8) |
35–44 | 4570 | 8.4 (8.0, 8.8) |
45–54 | 6789 | 8.9 (8.6, 9.3) |
55–64 | 9045 | 9.3 (8.9, 9.7) |
≥65 | 12 510 | 8.3 (8.0, 8.6) |
Annual household income | *p<0.0000 | |
<$15 000 | 5792 | 14.1 (13.5, 14.8) |
$15 000–$24 999 | 6467 | 10.6 (10.1, 11.2) |
$25 000–$34 999 | 3481 | 8.5 (7.9, 9.0) |
$35 000–$49 999 | 4176 | 8.2 (7.7, 8.7) |
≥$50 000 | 12 573 | 7.3 (7.0, 7.5) |
Healthcare coverage | *p<0.0000 | |
Yes | 36 690 | 9.1 (8.9, 9.3) |
No | 2482 | 6.9 (6.4, 7.4) |
Physical activity | *p<0.0000 | |
Physically active | 24 173 | 8.2 (8.0, 8.4) |
Physically inactive | 11 929 | 10.8 (10.4, 11.1) |
Smoking status | *p<0.0000 | |
Never smoker | 19 446 | 8.1 (7.9, 8.4) |
Past smoker | 11 338 | 9.1 (8.8, 9.5) |
Current smoker | 7138 | 11.5 (11.0, 11.9) |
BMI | *p<0.0000 | |
Underweight | 618 | 8.7 (7.4, 10.1) |
Normal weight | 9102 | 7.2 (6.9, 7.4) |
Overweight | 11 106 | 7.6 (7.3, 7.8) |
Obese | 14 999 | 12.5 (12.1, 12.9) |
Coexisting diseasesa | *p<0.0000 | |
Yes | 29 812 | 11.5 (11.2, 11.7) |
No | 9509 | 6.2 (6.0, 6.4) |
Depression | *p<0.0000 | |
Yes | 13 858 | 17.5 (16.9, 18.0) |
No | 25 214 | 7.0 (6.8, 7.1) |
Abbreviations: CI, confidence interval; NH, non-Hispanic
Coexisting diseases include heart disease, stroke, cancer, chronic obstructive pulmonary disease (COPD), arthritis, kidney disease, diabetes, and hypertension
p–value for the chi-square test of association between current asthma status and selected variables
HRQoL among adults with current asthma
Overall, 33.1% of adults with asthma reported having fair/poor health, 22.9% had ≥14 mentally unhealthy days, 25.2% had ≥14 physically unhealthy days, and 18.8% had ≥14 days of activity limitation (data not shown). Females were significantly less likely to report fair/poor health than males (unadjusted prevalence: 34.0%; adjusted prevalence ratio (aPR) = 0.92 [95% CI: 0.86, 0.98] versus 31.5% for males), after adjustment (Table 2). No other differences in HRQoL were observed between females and males after adjustment (Table 2 and Table 3).
Table 2.
Characteristics | Fair/poor self-rated health | ≥14 mentally unhealthy days | Fair/poor self-rated health | ≥14 mentally unhealthy days | |||
---|---|---|---|---|---|---|---|
Unadjusted prev, % (95% CI) |
Unadjusted prev, % (95% CI) |
Unadjusted PR (95% CI) |
Adjusted PR (95% CI) |
Unadjusted PR (95% CI) |
Adjusted PR (95% CI) |
||
Total | 33.1 (32.2, 34.0) | 22.9 (22.1, 23.8) | |||||
Sex | |||||||
Male | 31.5 (29.9, 33.2) | 20.3 (18.8, 21.9) | 1.00 | 1.00 | 1.00 | 1.00 | |
Female | 34.0 (32.9, 35.0) | 24.3 (23.3, 25.3) | 1.08 (1.01, 1.14) | 0.92 (0.86, 0.98) | 1.20 (1.10, 1.30) | 0.99 (0.91, 1.08) | |
Race/ethnicity | |||||||
White NH | 31.1 (30.2, 32.1) | 22.3 (21.3, 23.2) | 1.00 | 1.00 | 1.00 | 1.00 | |
Black NH | 36.0 (33.4, 38.7) | 23.0 (20.5, 25.7) | 1.16 (1.07, 1.25) | 1.06 (0.97, 1.16) | 1.03 (0.92, 1.16) | 1.00 (0.87, 1.14) | |
American Indian/Alaskan Native NH | 50.6 (44.4, 56.8) | 34.8 (28.9, 41.2) | 1.63 (1.43, 1.85) | 1.25 (1.06, 1.46) | 1.56 (1.30, 1.88) | 0.98 (0.78, 1.22) | |
Asian NH | 19.9 (14.0, 27.5) | 14.1 (9.4, 20.7) | 0.64 (0.46, 0.90) | 1.07 (0.76, 1.49) | 0.63 (0.43, 0.94) | 1.01 (0.70, 1.46) | |
Other NH | 41.7 (37.0, 46.6) | 32.5 (27.8, 37.6) | 1.34 (1.19, 1.51) | 1.34 (1.20, 1.49) | 1.46 (1.25, 1.71) | 1.21 (1.04, 1.42) | |
Hispanic | 38.4 (35.0, 42.0) | 24.5 (21.4, 28.0) | 1.23 (1.12, 1.36) | 1.31 (1.18, 1.45) | 1.10 (0.96, 1.27) | 1.06 (0.91, 1.22) | |
Age, year range | |||||||
18–24 | 16.5 (14.2, 19.2) | 22.9 (20.1, 25.9) | 1.00 | 1.00 | 1.00 | 1.00 | |
25–34 | 21.6 (19.3, 24.1) | 24.4 (21.9, 27.1) | 1.30 (1.08, 1.58) | 1.08 (0.88, 1.33) | 1.07 (0.91, 1.26) | 1.01 (0.85, 1.20) | |
35–44 | 27.9 (25.8, 30.2) | 24.3 (22.2, 26.5) | 1.69 (1.42, 2.01) | 1.16 (0.95, 1.42) | 1.06 (0.91, 1.24) | 0.96 (0.81, 1.13) | |
45–54 | 40.3 (38.3, 42.4) | 28.0 (26.1, 29.9) | 2.44 (2.07, 2.87) | 1.38 (1.13, 1.68) | 1.22 (1.06, 1.41) | 0.95 (0.81, 1.12) | |
55–64 | 46.3 (44.3, 48.3) | 24.3 (22.6, 26.1) | 2.80 (2.39, 3.28) | 1.47 (1.21, 1.79) | 1.06 (0.92, 1.23) | 0.77 (0.65, 0.92) | |
≥65 | 42.2 (40.5, 44.0) | 14.5 (13.2, 15.9) | 2.55 (2.18, 2.99) | 1.36 (1.11, 1.66) | 0.64 (0.54, 0.74) | 0.59 (0.49, 0.71) | |
Annual household income | |||||||
<$15 000 | 60.3 (57.7, 62.8) | 39.2 (36.8, 41.6) | 3.88 (3.54, 4.24) | 2.43 (2.21, 2.68) | 3.09 (2.77, 3.44) | 1.75 (1.54, 1.98) | |
$15 000–$24 999 | 44.4 (41.9, 46.9) | 31.4 (29.0, 33.9) | 2.85 (2.59, 3.15) | 1.95 (1.76, 2.15) | 2.48 (2.20, 2.79) | 1.67 (1.47, 1.89) | |
$25 000–$34 999 | 34.2 (31.2, 37.3) | 20.3 (17.7, 23.1) | 2.20 (1.95, 2.47) | 1.61 (1.43 1.82) | 1.60 (1.36, 1.87) | 1.21 (1.04, 1.41) | |
$35 000–$49 999 | 25.7 (23.2, 28.4) | 18.1 (15.7, 20.8) | 1.65 (1.45, 1.88) | 1.34 (1.18, 1.51) | 1.43 (1.21, 1.68) | 1.23 (1.05, 1.44) | |
≥$50 000 | 15.6 (14.4, 16.8) | 12.7 (11.6, 13.9) | 1.00 | 1.00 | 1.00 | 1.00 | |
Healthcare coverage | |||||||
Yes | 32.3 (31.4, 33.3) | 21.9 (21.1, 22.7) | 1.00 | 1.00 | 1.00 | 1.00 | |
No | 40.8 (37.1, 44.6) | 32.1 (28.4, 36.0) | 1.26 (1.15 1.39) | 1.09 (0.98, 1.22) | 1.46 (1.29, 1.66) | 1.07 (0.93, 1.23) | |
Physical activity | |||||||
Physically active | 24.8 (23.8, 25.8) | 19.2 (18.2, 20.2) | 1.00 | 1.00 | 1.00 | 1.00 | |
Physically inactive | 51.5 (49.6, 53.3) | 31.7 (29.9, 33.5) | 2.08 (1.97, 2.19) | 1.49 (1.40, 1.58) | 1.65 (1.53, 1.79) | 1.29 (1.19, 1.41) | |
Smoking status | |||||||
Never smoker | 23.8 (22.7, 25.1) | 16.6 (15.4, 17.7) | 1.00 | 1.00 | 1.00 | 1.00 | |
Past smoker | 39.6 (37.8, 41.3) | 21.9 (20.4, 23.4) | 1.66 (1.55, 1.78) | 1.15 (1.07, 1.23) | 1.32 (1.20, 1.46) | 1.13 (1.02, 1.25) | |
Current smoker | 48.6 (46.4, 50.7) | 40.0 (37.9, 42.2) | 2.04 (1.91, 2.18) | 1.21 (1.11, 1.31) | 2.42 (2.21, 2.64) | 1.34 (1.21, 1.48) | |
BMI | |||||||
Underweight | 44.1 (36.3, 52.2) | 31.0 (23.9, 39.1) | 1.82 (1.50, 2.21) | 1.45 (1.16, 1.80) | 1.49 (1.15, 1.93) | 1.26 (0.96, 1.67) | |
Normal weight | 24.2 (22.6, 25.8) | 20.8 (19.1, 22.6) | 1.00 | 1.00 | 1.00 | 1.00 | |
Overweight | 28.7 (27.1, 30.4) | 20.8 (19.3, 22.3) | 1.19 (1.09, 1.30) | 0.99 (0.91, 1.07) | 1.00 (0.89, 1.12) | 0.96 (0.86, 1.07) | |
Obese | 42.5 (40.9, 44.1) | 26.6 (25.1, 28.0) | 1.76 (1.63, 1.90) | 1.22 (1.13, 1.32) | 1.28 (1.15, 1.41) | 0.97 (0.88, 1.07) | |
Coexisting diseasesb | |||||||
Yes | 45.3 (44.2, 46.4) | 26.9 (25.9, 27.9) | 4.26 (3.76, 4.82) | 2.28 (1.99, 2.63) | 1.71 (1.54, 1.90) | 1.33 (1.18, 1.51) | |
No | 10.6 (9.4, 12.0) | 15.7 (14.2, 17.3) | 1.00 | 1.00 | 1.00 | 1.00 | |
Depression | |||||||
Yes | 48.6 (46.9, 50.3) | 47.1 (45.4, 48.8) | 1.97 (1.87, 2.08) | 1.34 (1.26, 1.43) | 4.63 (4.25, 5.05) | 3.39 (3.06, 3.76) | |
No | 24.7 (23.7, 25.7) | 10.2 (9.4, 11.0) | 1.00 | 1.00 | 1.00 | 1.00 |
Abbreviations: CI, confidence interval; Prev, prevalence; PR, prevalence ratio; NH, non-Hispanic
Adjusted logistic regression models constructed for each HRQoL indicator with all potential risk factors included in single model adjusting for all other risk factors in model (sex, race/ethnicity, age, income, health care coverage, physical activity, smoking, BMI, coexisting diseases, and depression). Unadjusted logistic regression models were constructed for each HRQoL indicator, each containing a single risk factor.
Coexisting diseases include heart disease, stroke, cancer, chronic obstructive pulmonary disease (COPD), arthritis, kidney disease, diabetes, and hypertension
Note: numbers in bold are statistically significantly associated with the reference category for each independent variable, determined by if the null value 1 is not within the 95% CI of PRs
Table 3.
Characteristics | ≥14 physically unhealthy days | ≥14 days of activity limitation | ≥14 physically unhealthy days | ≥14 days of activity limitation | |||
---|---|---|---|---|---|---|---|
Unadjusted prev, % (95% CI) |
Unadjusted prev, % (95% CI) |
Unadjusted PR (95% CI) |
Adjusted PR (95% CI) |
Unadjusted PR (95% CI) |
Adjusted PR (95% CI) |
||
Total | 25.2 (24.4, 26.0) | 18.8 (18.0, 19.5) | |||||
Sex | |||||||
Male | 22.7 (21.3, 24.1) | 17.4 (16.2, 18.7) | 1.00 | 1.00 | 1.00 | 1.00 | |
Female | 26.6 (25.6, 27.6) | 19.5 (18.6, 20.4) | 1.17 (1.09, 1.26) | 1.03 (0.96, 1.12) | 1.12 (1.03, 1.22) | 0.97 (0.89, 1.07) | |
Race/ethnicity | |||||||
White NH | 25.5 (24.6, 26.4) | 18.8 (17.9, 19.6) | 1.00 | 1.00 | 1.00 | 1.00 | |
Black NH | 23.4 (21.2, 25.7) | 18.3 (16.2, 20.6) | 0.92 (0.83, 1.02) | 0.82 (0.72, 0.92) | 0.98 (0.86, 1.11) | 0.81 (0.70, 0.94) | |
American Indian/Alaskan Native NH | 41.3 (35.3, 47.7) | 29.7 (24.2, 35.9) | 1.62 (1.39, 1.89) | 1.14 (0.90, 1.44) | 1.58 (1.29, 1.94) | 0.94 (0.69, 1.29) | |
Asian NH | 16.0 (10.2, 24.3) | 10.8 (6.6, 17.3) | 0.63 (0.41, 0.97) | 1.17 (0.81, 1.69) | 0.58 (0.35, 0.94) | 1.17 (0.74, 1.84) | |
Other NH | 32.5 (28.1, 37.1) | 28.3 (24.0, 32.9) | 1.27 (1.10, 1.47) | 1.29 (1.12, 1.50) | 1.51 (1.28, 1.77) | 1.41 (1.19, 1.67) | |
Hispanic | 23.7 (20.9, 26.6) | 17.2 (14.9, 19.8) | 0.93 (0.82, 1.05) | 1.02 (0.89, 1.17) | 0.92 (0.79, 1.07) | 0.99 (0.84, 1.16) | |
Age, year range | |||||||
18–24 | 10.7 (8.8, 12.9) | 8.1 (6.3, 10.2) | 1.00 | 1.00 | 1.00 | 1.00 | |
25–34 | 13.4 (11.9, 15.2) | 12.0 (10.4, 13.8) | 1.26 (1.00, 1.57) | 1.13 (0.86, 1.50) | 1.49 (1.12, 1.97) | 1.16 (0.85, 1.58) | |
35–44 | 22.9 (20.9, 25.0) | 18.2 (16.4, 20.2) | 2.14 (1.74, 2.64) | 1.57 (1.18, 2.07) | 2.26 (1.74, 2.95) | 1.45 (1.08, 1.93) | |
45–54 | 32.2 (30.2, 34.2) | 25.8 (24.0, 27.7) | 3.01 (2.47, 3.67) | 1.80 (1.37, 2.36) | 3.21 (2.49, 4.13) | 1.63 (1.22, 2.17) | |
55–64 | 36.1 (34.2, 38.1) | 26.7 (25.0, 28.6) | 3.38 (2.78, 4.11) | 1.77 (1.34, 2.32) | 3.32 (2.58, 4.27) | 1.51 (1.13, 2.01) | |
≥65 | 33.2 (31.4, 34.9) | 20.1 (18.7, 21.5) | 3.10 (2.55, 3.77) | 1.67 (1.27, 2.20) | 2.49 (1.93, 3.21) | 1.26 (0.94, 1.69) | |
Annual household income | |||||||
<$15 000 | 46.4 (43.9, 48.9) | 38.3 (35.9, 40.7) | 3.64 (3.30, 4.02) | 2.40 (2.13, 2.70) | 4.43 (3.91, 5.03) | 2.54 (2.21, 2.93) | |
$15 000–$24 999 | 32.9 (30.7, 35.2) | 26.7 (24.6, 28.9) | 2.59 (2.32, 2.88) | 1.99 (1.77, 2.23) | 3.09 (2.70, 3.54) | 2.18 (1.89, 2.52) | |
$25 000–$34 999 | 25.8 (23.2, 28.7) | 16.9 (14.8, 19.3) | 2.03 (1.77, 2.32) | 1.64 (1.43, 1.88) | 1.96 (1.65, 2.33) | 1.38 (1.16, 1.65) | |
$35 000–$49 999 | 20.1 (17.8, 22.7) | 13.0 (11.1, 15.1) | 1.58 (1.37, 1.83) | 1.39 (1.19, 1.61) | 1.51 (1.25, 1.81) | 1.30 (1.07, 1.57) | |
≥$50 000 | 12.1 (11.7, 13.8) | 8.6 (7.7, 9.6) | 1.00 | 1.00 | 1.00 | 1.00 | |
Healthcare coverage | |||||||
Yes | 25.4 (24.6, 26.3) | 18.7 (17.9, 19.4) | 1.00 | 1.00 | 1.00 | 1.00 | |
No | 23.2 (20.4, 26.2) | 19.5 (16.9, 22.5) | 0.91 (0.80, 1.04) | 0.81 (0.70, 0.93) | 1.05 (0.90, 1.21) | 0.89 (0.75, 1.05) | |
Physical activity | |||||||
Physically active | 18.5 (17.6, 19.5) | 13.0 (12.2, 13.8) | 1.00 | 1.00 | 1.00 | 1.00 | |
Physically inactive | 41.0 (39.2, 42.7) | 32.2 (30.5, 33.8) | 2.21 (2.07, 2.36) | 1.59 (1.48, 1.71) | 2.47 (2.28, 2.68) | 1.72 (1.58, 1.88) | |
Smoking status | |||||||
Never smoker | 17.7 (16.7, 18.7) | 12.5 (11.6, 13.4) | 1.00 | 1.00 | 1.00 | 1.00 | |
Past smoker | 31.3 (29.7, 33.0) | 22.2 (20.8, 23.7) | 1.77 (1.64, 1.91) | 1.19 (1.10, 1.29) | 1.78 (1.62, 1.97) | 1.18 (1.07, 1.30) | |
Current smoker | 37.4 (35.4, 39.4) | 30.6 (28.7, 32.6) | 2.11 (1.95, 2.29) | 1.22 (1.11, 1.34) | 2.46 (2.23, 2.71) | 1.27 (1.14, 1.42) | |
BMI | |||||||
Underweight | 35.9 (28.7, 43.8) | 26.4 (20.2, 33.8) | 1.82 (1.46, 2.28) | 1.43 (1.09, 1.88) | 1.80 (1.37, 2.38) | 1.36 (0.96, 1.93) | |
Normal weight | 19.7 (18.3, 21.3) | 14.6 (13.3, 16.1) | 1.00 | 1.00 | 1.00 | 1.00 | |
Overweight | 22.1 (20.7, 23.6) | 17.4 (16.0, 18.8) | 1.12 (1.01, 1.24) | 0.93 (0.84, 1.03) | 1.19 (1.05, 1.34) | 1.00 (0.88, 1.12) | |
Obese | 31.7 (30.3, 33.2) | 23.0 (21.7, 24.3) | 1.61 (1.47, 1.76) | 1.07 (0.98, 1.18) | 1.57 (1.40, 1.75) | 1.01 (0.91, 1.13) | |
Coexisting diseasesb | |||||||
Yes | 34.8 (33.7, 35.8) | 26.1 (25.1, 27.1) | 4.36 (3.84, 4.96) | 2.01 (1.72, 2.36) | 4.74 (4.00, 5.62) | 2.22 (1.84, 2.67) | |
No | 8.0 (7.0, 9.0) | 5.5 (4.7, 6.5) | 1.00 | 1.00 | 1.00 | 1.00 | |
Depression | |||||||
Yes | 39.1 (37.5, 40.7) | 34.5 (33.0, 36.1) | 2.20 (2.06, 2.35) | 1.47 (1.37, 1.58) | 3.30 (3.04, 3.58) | 2.01 (1.83, 2.21) | |
No | 17.8 (16.9, 18.7) | 10.4 (9.8, 11.2) | 1.00 | 1.00 | 1.00 | 1.00 |
Abbreviations: CI, confidence interval; Prev, prevalence; PR, prevalence ratio; NH, non-Hispanic
Adjusted logistic regression models constructed for each HRQoL indicator with all potential risk factors included in single model adjusting for all other risk factors in model (sex, race/ethnicity, age, income, health care coverage, physical activity, smoking, BMI, coexisting diseases, and depression). Unadjusted logistic regression models were constructed for each HRQoL indicator, each containing a single risk factor.
Coexisting diseases include heart disease, stroke, cancer, chronic obstructive pulmonary disease (COPD), arthritis, kidney disease, diabetes, and hypertension
Note: numbers in bold are statistically significantly associated with the reference category for each independent variable, determined by if the null value 1 is not within the 95% CI of PRs
Most of the observed significant associations between race/ethnicity and HRQoL measures were present before adjustment (Table 2 and Table 3). Blacks were significantly more likely to report fair/poor health compared with whites, but the differences became not statistically significant after adjustment. After adjustment, blacks were significantly less likely to report ≥14 physically unhealthy days (23.4%; aPR = 0.82 [0.72, 0.92]) and ≥14 days of activity limitation (18.3%; aPR = 0.81 [0.70, 0.94]), compared with whites (25.5% and 18.8%, respectively). Hispanics were significantly more likely to report fair/poor health, which remained significant even after adjusting for confounding factors (38.4%; aPR= 1.31 [1.18, 1.45]) versus 31.1% for whites. American Indian/Alaska Natives were significantly more likely to report statistically higher values in all four HRQoL domains compared with whites, but the differences were only statistically significant after adjustment for fair/poor health (50.6%; aPR= 1.25 [1.06, 1.46]) compared with whites (31.1%). Asians were significantly less likely to report all four HRQoL domains compared with whites, which became not statistically significant after adjustment. (Table 2 and Table 3)
Older adults were significantly more likely to report fair/poor health, ≥14 physically unhealthy days, and ≥14 days of activity limitation compared with adults aged 18–24 years. Adults aged 55–64 years (24.3%; aPR= 0.77 [0.65, 0.92]) and ≥65 years (14.5%; aPR= 0.59 [0.49, 0.71]) were significantly less likely to report ≥14 mentally unhealthy days compared with 18–24 year olds (22.9%). (Table 2 and Table 3)
Fair/poor health, ≥14 mentally unhealthy days, ≥14 physically unhealthy days, and ≥14 days of activity limitation were significantly more prevalent among adults with income less than $50 000 compared with adults with income of $50 000 or more, even after adjusting for confounding factors (Table 2 and Table 3). Compared with having income of ≥$50 000, having income of <$15 000 was strongly significantly associated with having fair/poor health (60.3%; aPR=2.43 [2.21, 2.68]), ≥14 mentally unhealthy days (39.2%; aPR=1.75 [1.54, 1.98]), ≥14 physically unhealthy days (46.4%; aPR=2.40 [2.13, 2.70]), and ≥14 days of activity limitation (38.3%; aPR=2.54 [2.21, 2.93]).
Significantly more adults without health care coverage reported fair/poor health and ≥14 mentally unhealthy days than adults with asthma who had healthcare coverage. The difference did not remain statistically significant after adjustment. Whereas, having ≥14 physically unhealthy days was significantly lower in adults without health care coverage (23.2%; aPR= 0.81 [0.70, 0.93]) than in adults with health care coverage (25.4%) (Table 2 and Table 3).
Significantly higher prevalence values of fair/poor health, ≥14 mentally unhealthy days, ≥14 physically unhealthy days, and ≥14 days of activity limitation were strongly significantly associated with physical inactivity, current or past smoking, one or more coexisting disease, and depression, regardless of adjusting for other confounding factors (Table 2 and Table 3).
Underweight adults were significantly more likely to report fair/poor health (44.1%; aPR= 1.45 [1.16, 1.80]) and ≥14 physically unhealthy days (35.9%; aPR= 1.43 [1.09, 1.88]) and obese adults were significantly more likely to report fair/poor health (42.5%; aPR= 1.22 [1.13, 1.32]) compared with normal weight adults (24.2% for fair/poor health and 19.7% for ≥14 physically unhealthy days). Obese adults were significantly more likely to have ≥14 mentally unhealthy days, ≥14 physically unhealthy days, and ≥14 days of activity limitation, but the differences were no longer significant after adjusting for confounders. (Table 2 and Table 3)
Discussion
This cross-sectional study was the first to examine HRQoL indicators among adults 18 years and older with asthma using the BRFSS, a large-scale survey, after its major survey methodology revision in 2011. This study determined that multiple sociodemographic, behavioral, and health status indicators were associated with impaired HRQoL among adults with asthma in the United States. Income, physical activity status, smoking status, coexisting diseases, and depression were associated with impaired HRQoL in all four dimensions: fair/poor self-rated health, frequent mentally unhealthy days, frequent physically unhealthy days, and frequent days of activity limitation. These results follow other published results that multiple risk factors are associated with impaired HRQoL, including physical inactivity [16], smoking [16], comorbidities [10, 18], and depression [12].
A previous study demonstrated that adults 65 years and older with asthma have better mental health than younger adults [26]. It has been suggested that older people are less likely to recognize and report psychological problems [28].
Our findings indicate that females had significantly higher fair/poor self-rated health than males before adjustment with confounding factors (sex, race/ethnicity, age, income, health care coverage, physical activity, smoking, BMI, coexisting diseases, and depression) included in this study, but was significantly lower after adjustment. Similar to other studies [12, 24], this suggests the importance of the effects of the confounding factors on the association between being a female and having fair/poor health. Böhmer, et al. found that the association between the mental health score of the Short Form 12 Health Survey Questionnaire and being a female adult with asthma was not significant, after adjusting for confounding factors [12]. Another study also found after adjustment with confounders, gender was not associated with asthma-related quality of life in the asthma symptoms, activity limitation, and emotional function domains [24]. Another study finding was that the prevalence of ≥14 physically unhealthy days and ≥14 days of activity limitations among blacks with asthma was similar to that of whites before adjustment but became lower among blacks after adjusting for confounding factors. This finding can be also explained by the effects of confounding factors on the association between black adults with asthma, and HRQoL.
The association between lower socioeconomic status and impaired HRQoL is a finding shared by Ford et al. [8]. The study also found that among adults with asthma, low annual household income was strongly associated with having fair poor health, ≥14 physically unhealthy days, or ≥14 days of activity limitations. Phillips et al. [29] showed that lower income was associated with fair/poor health. Ejebe et al. [30] determined that those of lower socioeconomic status have decreased asthma self-efficacy, which could contribute to the HRQoL outcomes observed. Archea et al. [17] found that negative life events significantly impaired asthma-specific HRQoL among adults with lower income. Adults with low income generally have fewer psychosocial and material resources to manage their disease [31].
Hispanics with asthma in our study had significantly higher fair/poor health compared to whites, agreeing with a previously published study [16]. Jylhä [32] determined that cultures rate their health differently based on differing frameworks in evaluating one’s own health within their own cultural contexts and environmental influences. Studies [31, 33] have determined that interview language for Hispanics was significantly associated with fair/poor health. Sanchez & Vargas found that when the commonly used Spanish translation of regular is given for the “fair” response, more respondents select this category [34]. Additionally, stressors and barriers to asthma management experienced by Hispanics [35] may also contribute to this impaired HRQoL.
This study determined that having depression was strongly associated with impaired HRQoL in all four domains. Another study demonstrated that reporting symptoms of depression in adults with asthma was significantly associated with impaired health-related quality of life on Short Form 12 Health Survey Questionnaire (SF-12) physical and mental health scores [12].
Similar to other studies, our findings indicate that underweight and obesity were associated with HRQoL measures. Other studies found that overweight and/or obese adults with asthma were significantly associated with having fair/poor health [19, 31], worse asthma control [13, 36], and impaired health-related quality of life in physical domains [37, 38, 39]. Obesity-related comorbidities have been shown to mediate the association between obesity and health-related quality of life, especially in physically oriented domains [38]. Adjusting for comorbidities in the model, could have potentially contributed to the lack of association with physically unhealthy days and days of activity limitation demonstrated in this study.
The finding that underweight adults with asthma had higher fair/poor health and ≥14 physically unhealthy days could be attributed partly to illnesses causing reduced body weight not included in the comorbidities (such as anorexia, bulimia, and autoimmune disease). Liu et al. [40] found that the underweight group had worse respiratory symptoms compared with normal weight, which could have contributed to increased ≥14 physically unhealthy days in our study. Although, we did not measure respiratory symptoms nor assess effects of comorbidities causing underweight BMI (i.e. anorexia, bulimia, autoimmune disease) to be certain of this.
Since BRFSS is a cross-sectional survey, only disease prevalence values and associations with risk factors could be measured and causal relationships could not be determined. Also, HRQoL measures are subject to recall bias since they are self-reported. Another limitation of this study is that we were unable to control the effects from all confounders. Accordingly, the BRFSS data do not contain questions on asthma control status, asthma severity status, symptoms, occupational exposure, or asthma treatment status, which may alter the results. Using the Asthma Quality of Life Questionnaire (AQLQ) and the generic health status measure EuroQol 5-D (EQ-5D), Chen et al. [41] demonstrated that asthma control at baseline was a significant independent predictor of asthma-specific HRQoL. Erickson et al. [15] found that asthma disease severity was an important predictor of HRQoL particularly in the physical domains, which could have affected the outcomes. Although, we could not measure disease severity from the BRFSS. Lastly, the HRQoL measures on the BRFSS were not asthma-specific. Further studies could use an asthma-specific questionnaire, such as the Asthma Quality of Life Questionnaire (AQLQ), to determine risk factors that impair quality of life among adults with asthma.
Conclusion
Multiple sociodemographic, behavioral, and health status indicators were associated with impaired HRQoL among adults with asthma in the United States. Providing strategies to address potential risk factors such as low income, physical inactivity, smoking, and obesity or underweight should be considered to improve HRQoL among adults with asthma.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
References
- 1.Centers for Disease Control and Prevention [Internet]. 2015. National Health Interview Survey (NHIS) Data. Available from: https://www.cdc.gov/asthma/nhis/2015/data.htm [last accessed 30 August 2017].
- 2.Moorman JE, Akinbami LJ, Bailey CM, Zahran HS, King ME, Johnson CA, et al. National Surveillance of Asthma: United States, 2001–2010. National Center for Health Statistics. Vital Health Stat 2012;3(35):1–67. [PubMed] [Google Scholar]
- 3.Barnett S and Nurmagambetov T. Costs of asthma in the United States: 2002–2007. J Allergy Clin Immuno 2011;127(1):145–152. [DOI] [PubMed] [Google Scholar]
- 4.Centers for Disease Control and Prevention [Internet]. National Hospital Discharge Survey (NHDS). Number of discharges from short-stay hospitals by first-listed diagnosis and age: United States, 2010. Available from: http://www.cdc.gov/nchs/data/nhds/3firstlisted/2010first3_numberage.pdf [last accessed 30 August 2017].
- 5.Rui P, Kang K, Albert M. Centers for Disease Control and Prevention [Internet]. National Hospital Ambulatory Medical Care Survey: 2013 Emergency Department Summary Tables. Available from: http://www.cdc.gov/nchs/data/ahcd/nhamcs_emergency/2013_ed_web_tables.pdf [last accessed 1 August 2017].
- 6.Kochanek KD, Murphy SL, Xu JQ, Tejada-Vera B. Deaths: Final data for 2014. Natl Vital Stat Rep 2016;65(4):43–45. [PubMed] [Google Scholar]
- 7.U.S. Department of Health and Human Services, National Institutes of Health (NIH), National Heart, Lung, and Blood Institute, National Asthma Education and Prevention Program. Expert Panel Report 3: Guidelines for the diagnosis and management of asthma. Full Report 2007. Available from: https://www.nhlbi.nih.gov/health-pro/guidelines/current/asthma-guidelines/full-report [last accessed 2 August 2017].
- 8.Ford E, Mannino D, Homa D, Gwynn C, Redd S, Moriarty D. Self-reported asthma and health-related quality of life: findings from the behavioral risk factor surveillance system. Chest 2003;123(1):119–127. [DOI] [PubMed] [Google Scholar]
- 9.Cui W, Zack MM, Zahran HS. Health-related quality of life and asthma among United States adolescents. J Pediatr 2015;166(2):358–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Adams R, Wilson D, Taylor A, Daly A, Tursan d’Espaignet E, Dal Grande E, et al. Coexistent chronic conditions and asthma quality of life. Chest 2006;129(2):285–291. [DOI] [PubMed] [Google Scholar]
- 11.Vietri J, Burslem K, Su J. Poor asthma control among US workers: health-related quality of life, work impairment, and health care use. J Occup Environ Med 2014;56(4):425–430. [DOI] [PubMed] [Google Scholar]
- 12.Böhmer M, Brandl M, Brandstetter S, Finger T, Fischer W, Pfeifer M, et al. Factors associated with generic health-related quality of life in adult asthma patients in Germany: Cross-sectional study. J Asthma 2017;54(3):325–334. [DOI] [PubMed] [Google Scholar]
- 13.Braido F, Brusselle G, Guastalla D, Ingrassia E, Nicolini G, Price D, et al. Determinants and impact of suboptimal asthma control in Europe: THE INTERNATIONAL CROSS-SECTIONAL AND LONGITUDINAL ASSESSMENT ON ASTHMA CONTROL (LIAISON) study. Respiratory Research 2016;17:51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Gonzalez-Barcala F, De la Fuente-Cid R, Tafalla M, Nuevo J, Caamaño-Isorna F. Factors associated with health-related quality of life in adults with asthma. A cross-sectional study. Multidiscip Respir Med 2012;7(32):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Erickson S, Christian R, Kirking D, Halman L. Relationship between patient and disease characteristics, and health-related quality of life in adults with asthma. Respir Med 2002;96(6):450–460. [DOI] [PubMed] [Google Scholar]
- 16.Ford E, Mannino D, Redd S, Moriarty D, Mokdad AH. Determinants of quality of life among people with asthma: findings from the Behavioral Risk Factor Surveillance System. J Asthma 2004;41(3):327–336. [DOI] [PubMed] [Google Scholar]
- 17.Archea C, Yen I, Chen H, Eisner M, Katz P, Masharani U, et al. Negative life events and quality of life in adults with asthma. Thorax 2007;62(2):139–146. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Upton J, Lewis C, Humphreys E, Price D, Walker S. Asthma-specific health-related quality of life of people in Great Britain: A national survey. J Asthma 2016;53(9):975–982. [DOI] [PubMed] [Google Scholar]
- 19.Sundh J, Wireklint P, Hasselgren M, Montgomery S, Ställberg B, Lisspers K, Janson C, et al. Health-related quality of life in asthma patients – A Comparison of two cohorts from 2005 and 2015. Respir Med 2017;132:154–160. [DOI] [PubMed] [Google Scholar]
- 20.Ali Z and Ulrik S. Obesity and asthma: A coincidence or a causal relationship? A systematic review. Respir Med 2013;107(9):1287–1300. [DOI] [PubMed] [Google Scholar]
- 21.Chun T, Weitzen S, Fritz G. The asthma/mental health nexus in a population-based sample of the United States. Chest 2008;134(6):1176–1182. [DOI] [PubMed] [Google Scholar]
- 22.Kullowatz A, Kanniess F, Dahme B, Magnussen H, Ritz T. Association of depression and anxiety with health care use and quality of life in asthma patients. Respir Med 2007;101(3):638–644. [DOI] [PubMed] [Google Scholar]
- 23.Sullivan P, Smith K, Ghushchyan V, Globe D, Lin S, Globe G. Asthma in USA: its impact on health-related quality of life. J Asthma 2015; 50(8):891–899. [DOI] [PubMed] [Google Scholar]
- 24.Sundbom F, Malinovschi A, Lindberg E, Alving K, Janson C. Effects of poor asthma control, insomnia, anxiety and depression on quality of life in young asthmatics. J Asthma 2016;53(4):398–403. [DOI] [PubMed] [Google Scholar]
- 25.Siroux V, Boudier A, Anto M, Cazzoletti L, Accordini S, Alonso J. Quality-of-life and asthma-severity in general population asthmatics: results of the ECRHS II study. Allergy 2008;63:547–554. [DOI] [PubMed] [Google Scholar]
- 26.Strine TW, Ford ES, Balluz L, Chapman DP, Mokdad AH. Risk behaviors and health-related quality of life among adults with asthma. Chest 2004;126(6):1849–1854. [DOI] [PubMed] [Google Scholar]
- 27.Centers for Disease Control and Prevention. The BRFSS Data User Guide. Available from: http://www.cdc.gov/brfss/data_documentation/pdf/userguidejune2013.pdf [last accessed 21 September 2015].
- 28.Jiang Y and Hesser J. Associations between health-related quality of life and demographics and health risks. Results from Rhode Island’s 2002 behavioral risk factor survey. Health Qual Life Outcomes 2006;4(14):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Phillips L, Hammock R, Blanton J. Predictors of self-rated health status among Texas residents. Prev Chronic Dis 2005;2(4):1–10. [PMC free article] [PubMed] [Google Scholar]
- 30.Ejebe I, Jacobs E, Wisk L. Persistent differences in asthma self-efficacy by race, ethnicity, and income in adults with asthma. J Asthma 2015;52(1):105–113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Beckles G and Truman B. Education and income – United States, 2009 and 2011. MMWR Suppl 2013;62(3):9–19. [PubMed] [Google Scholar]
- 32.Jylhä M What is self-rated health and why does it predict mortality? Towards a unified conceptual model. Soc Sci Med 2009;69(3):307–316. [DOI] [PubMed] [Google Scholar]
- 33.Brewer J, Miyasato G, Gates M, Curto M, Hall S, McKinlay J. Contributors to self-reported health in a racially and ethnically diverse population: focus on Hispanics. Ann Epidemiol 2013;23(1):19–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Sanchez G & Vargas E. Language Bias and Self-Rated Health Status among the Latino Population: Evidence of the Influence of Translation in a Wording Experiment. Qual Life Res 2016;25(5):1131–1136. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Rosser F, Forno E, Cooper P, Celedón J. Asthma in Hispanics An 8-Year Update. Am J Respir Crit Care Med 2014;189(11):1316–1327. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Pisi R, Aiello M, Tzani P, Marangio E, Olivieri D, Pisi G, et al. Overweight is associated with airflow obstruction and poor disease control but not with exhaled nitric oxide change in an asthmatic population. Respiration 2012;84(5):416–422. [DOI] [PubMed] [Google Scholar]
- 37.Müller-Nordhorn E, Muckelbauer R, Englert H, Grittner U, Berger H, Sonntag F, et al. Longitudinal Association between Body Mass Index and Health-Related Quality of Life. PLoS ONE 2014;9(3):e93071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Audureau E, Pouchot J, Coste J. Gender-Related Differential Effects of Obesity on Health-Related Quality of Life via Obesity-Related Comorbidities A Mediation Analysis of a French Nationwide Survey. Circ Cardiovasc Qual Outcomes 2016;9(3):246–256. [DOI] [PubMed] [Google Scholar]
- 39.Hinz A, Ernst J, Glaesmer H, Brähler E, Rauscher F, Petrowski K, et al. Frequency of somatic symptoms in the general population: Normative values for the Patient Health Questionnaire-15 (PHQ-15). J Psychosom Res 2017;96:27–31. [DOI] [PubMed] [Google Scholar]
- 40.Liu Y, Pleasants R, Croft J, Lugogo N, Ohar J, Heidari K, et al. Body mass index, respiratory conditions, asthma, and chronic obstructive pulmonary disease. Respir Med 2015;109(7):851–859. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Chen H, Gould M, Blanc P, Miller M, Kamath T, Lee J, et al. Asthma control, severity, and quality of life: Quantifying the effect of uncontrolled disease. J Allergy Clin Immunol 2007;120(2):396–402. [DOI] [PubMed] [Google Scholar]