Factors Associated with the Use of Complementary and Alternative Medicine/Therapy among United States Adults with Asthma

This article examined the sociodemographic and health-related factors associated with the use of complementary and alternative medicine/therapy (CAM) among adults with current asthma in the United States. We used data from 76,802 adults aged 18 years and above from the 2012–2019 Behavioral Risk Factor Surveillance System (BRFSS) Asthma Call-back Survey (ACBS) cycles. Weighted binary and multinomial logistic regression was used to examine the association of these factors with ever CAM use and the number of CAM use. We found that approximately 45.2% of US adults with asthma ever used CAM. Among adults with asthma, 25.3% and 19.9% endorsed using one CAM and ≥2 CAMs, respectively. CAM use was significantly associated with adults ≤ 35 years, female gender, multiple/other race/ethnicity, higher cost barriers, adults with two or more disease comorbidities, and those with poorly controlled asthma in both binary and multinomial models. CAM use was not associated with insurance and income status. Understanding factors associated with CAM use can provide asthma care professionals valuable insights into the underlying drivers of CAM use behavior in this population, enabling them to offer more informed and effective medical advice and guidance.


Introduction
Asthma is a chronic inflammatory lung condition affecting 21 million adults in the United States (US) [1]. Conventional asthma therapy includes the use of control agents, such as inhaled corticosteroids, bronchodilators, such as beta-agonists and anticholinergics, theophylline, leukotriene modifiers, and more recently, the use of anti-immunoglobulin E (IgE) antibodies, anti-IL5 antibodies, and anti-IL4/IL13 antibodies in selected patients [2]. These medications work by reducing the bronchoconstriction and airway inflammation associated with the disease and significantly reduce asthma disease burden. Even with the presence of these medications, there has been an increasing prevalence in the use of complementary and alternative medicine (CAM) [3]. The National Center for Complementary and Integrative Health defines complementary and alternative medicine (CAM) as "a group of diverse medical and health care systems, practices, and products that are not generally considered part of conventional medicine" [4]. In essence, if a person with asthma is treated outside of the earlier described conventional therapy, they are said to be using complementary or alternative medicine. When used together with conventional medicine, it is considered complementary, and when it is used instead of conventional medicine, it is regarded as an alternative [4]. A nationwide survey in 2012 estimated that more than 30% of people in the United States are using complementary or alternative medicine [4], and an estimated 47.6% are doing so without the knowledge of their physician [5].
Patients with asthma may opt to use complementary and alternative medicine (CAM) for a range of reasons, including a preference for natural remedies over synthetic drugs [6],

Study Population
The 2012 to 2019 Adult Behavioral Risk Factor Surveillance System (BRFSS) Asthma Call-back Survey (ACBS) was used for this study. The BRFSS is a state-based cross-sectional study of non-institutionalized adults, 18 years and above, residing in the United States. Funded by the Centers for Disease Control and Prevention (CDC), the BRFSS collects data on health risk behaviors, health care access, utilization and preventive health practices, as well as infectious disease and chronic diseases. The ACBS is a continuous survey conducted biweekly after the BRFSS and gathers annual data on demographics and illness histories of individuals with asthma. The survey is designed to address crucial questions concerning the health and experiences of people with asthma and offers state and local level data. Eligible participants for the ACBS are BRFSS adult respondents who report an asthma diagnosis. A positive response to the questionnaire items "Have you ever been told by a doctor, nurse, or other health professionals that you have asthma?" and "Do you still have asthma?" were employed to determine lifetime asthma and current asthma status, respectively. Response rates for ACBS were evaluated using the Council of American Survey and Research Organizations guidelines. The ACBS response rates ranged from 36.9% to 93.0% among participating states, colonies, and Washington DC in the 2012 to 2019 survey period [36].
A total of 76,802 adult respondents who reported a current diagnosis of asthma between the years 2012 and 2019 were included in this study. The ACBS obtained institutional review board approval from the relevant states, as well as the Asthma and Community Health Branch of the National Center for Environmental Health's Ethics Review Board, and informed consent was provided by all participants. Since the ACBS is a public use dataset, this study is exempt from full institutional review board review. Further details regarding the data, sampling method, and analytical guidelines can be found elsewhere and at https://www.cdc.gov/brfss/acbs/index.htm (accessed on 10 January 2023) [37].

Outcome Ascertainment: Complementary and Alternative Medicine/Therapy Use
During the ACBS survey, participants were initially asked if they utilized any complementary medicine or alternative therapies to manage their asthma. To assess the use of non-traditional, complementary, and alternative healthcare or asthma treatments, the survey item "In the past 12 months, have you used . . . to control asthma?" was employed. Eleven alternative treatments were evaluated in this manner, including herbs, vitamins, acupuncture, acupressure, aromatherapy, homeopathy, reflexology, yoga, breathing techniques, naturopathy, and other therapies. Responses were binary, recorded as either "Yes" or "No". We created two outcome variables. The first variable, "Ever use of CAM," was designed to indicate the use of at least one CAM method to control asthma. Participants who recorded a "Yes" response to any of the eleven therapy categories were reported as having ever used CAM to manage their asthma. The second variable, "Number of CAM use," was generated by summing up the number of "Yes" responses and categorizing the total as none, one CAM use, and two or more CAM use.

Covariates Ascertainment
Asthma control: Three measures of asthma impairment were utilized as proxies for asthma control: daytime symptoms, night-time symptoms, and use of short-acting β2-agonists (SABA) for symptom control (not for prevention of exercise-induced bronchospasm). Following the approach of Zahran et al. (2015), we constructed an asthma control variable comprising three distinct and non-overlapping categories: well-controlled asthma (daytime symptoms ≤2 days a week, night-time awakenings ≤2 times a month, and short-acting β2-agonists used for symptom control ≤2 days a week), not-well-controlled asthma (daytime symptoms >2 days a week, night-time awakenings 1-3 times a week, and short-acting β2-agonists used for symptom control >2 days a week), and very-poorlycontrolled asthma (daytime symptoms throughout the day, night-time awakenings ≥4 times a week, and short-acting β2-agonists used for symptom control several times a day) [38]. This adaptation of the 2007 National Asthma Education and Prevention Program Expert Panel Report 3 Guidelines was necessary due to the limited clinical measures available through the ACBS, which precluded the use of all the required clinical measures for current impairment (such as pulmonary function measures) and for future risk assessment (such as asthma exacerbations or progressive decline in lung function in adults) [39]. Other studies that have employed the ACBS to assess asthma control have also adopted the NAEPP guidelines [38,40].
Disease comorbidity status: Disease comorbidity status was evaluated by computing the sum of various diagnoses obtained from the BRFSS questionnaire, which included depressive disorder, heart attack, angina/coronary artery disease, stroke, skin cancer, any other cancer diagnosis, diabetes, COPD, arthritis, kidney disease, and morbid obesity. The comorbidity status was subsequently categorized into five levels, specifically, none, one comorbidity, two comorbidities, three comorbidities, and four or more comorbidities, to ascertain the burden of concurrent diseases in the participants. A prior systematic review has shown that the total number of diseases can predict health outcomes and perform comparably to more complex measures of disease morbidity [41].
Cost barriers: The evaluation of cost barriers to asthma care was performed across three domains in the ACBS survey, encompassing the cost of seeing a primary care physician, the cost of seeing a specialist for asthma care, and the cost of purchasing asthma medication within the past 12 months. By summing the affirmative responses across these domains, a composite categorical variable was derived. We categorized patients into distinct groups based on the number of cost barriers encountered, ranging from none to one, two, and three cost barriers.
Additional covariates in this study include demographic and socioeconomic factors, specifically age stratified as follows: 35 years or less, 36-50 years, 51-65 years, and greater than 65 years of age, gender (male or female), race (White non-Hispanic, Black, Hispanic, multiracial), health insurance coverage (yes or no), and income divided into four categories (greater than $75,000, between $50,000 and $75,000, between $25,000 and $49,999, and less than $25,000).

Statistical Analysis
The ACBS employs a stratified, multistage survey design to improve the representativeness of the US adult population with asthma. Complex analytical procedures recommended by the ACBS were followed, and appropriate weighting was applied to account for the sampling strata, cluster, and primary sampling unit (PSU).
Bivariate analysis between covariates and the dependent variables (ever use of CAM and number of CAM use) was performed using the Rao chi-square test. Covariates were initially identified based on a review of the literature on factors associated with CAM utilization. These explanatory variables were then selected using both forward and backward selection methods.
In the current study, we examined two dependent variables: the dichotomous measure of at least one CAM use (yes vs. no) and the multinomial measure of the number of CAM use, categorized into three levels: no CAM use, one CAM use, and two or more CAM use. We estimated unadjusted and adjusted weighted binary logistic regression models for the first dependent variable and unadjusted and adjusted weighted multinomial logistic regression models for the second dependent variable. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were reported for all independent variables in both models. Additionally, we adjusted for baseline smoking and survey year, in addition to all covariates in the model. Weighted percentages were used to report all percentages. We checked for multicollinearity by examining tolerance estimates and the Variance Inflation Factor (VIF) and found no issues as no tolerance estimate was below 0.1, and no VIF was above 10 [42]. The statistical significance of the hypothesis was assessed using a two-sided p-value less than 0.05 or non-overlapping 95% CIs. The data analysis was conducted using SAS 9.4 statistical software (SAS Institute, Cary, NC, USA).

Results
Among 76,802 adult respondents with current asthma in our study sample, 32,455 (45.2%) reported using at least one CAM (Table 1). The proportion of respondents who reported ever using CAM was significantly greater than the proportion who reported not using CAM among the following groups: adults ≤ 35 (50.9% vs. 49.1%), respondents of multiple/other race ethnicity (51.9% vs. 48.1%), respondents without a health insurance (53.3% vs. 46.7%), and adults who endorsed one cost barrier (57.2% vs. 42.8%), two cost barriers (65.5% vs. 34.5%), and three cost barriers (69.7% vs. 30.3%). The proportion of adults who endorsed ever using CAM compared to never CAM users was significantly greater among those with very poorly controlled asthma symptoms (57.5% vs. 42.5%) and not well controlled asthma symptoms (50.5% vs. 49.5%).
Respondents who ever used CAM had a lower proportion of no comorbidity ( In the binary regression model, males where 30% less likely than women to use CAM (adjusted odds ratio (aOR) = 0.70; 95% CI, 0.64-0.76) ( Table 3).

Discussion
Few recent studies have examined the factors associated with CAM use among current US adults with asthma despite the increasing trends in the use of CAM. In our study, more than 45% of adults with current asthma reported ever using CAM, while about 25% and 20% reported using one CAM and ≥2 CAM, respectively. Our finding is consistent with other studies that reported the prevalence of CAM use among the asthma population [18,28,30,33]. Consistent with Andersen's Behavioral Model of Healthcare Service Use, we showed that using CAM is associated with sociodemographic and health-related factors.
Males were less likely to ever use CAM, use one CAM, or use ≥2 CAM in our study compared to women. Our study agrees with other population-based studies that found women are more likely than men to use CAM [3,5,[25][26][27]43]. Shaw et al. (2008) reported a 61% increased odds of CAM use among females compared to males [3]. The reason for this consistent association remains to be determined. However, some theories to explain this association include women being more open to the utilization of health services and the increased health-seeking behavior of females compared to males [44]. Some researchers have suggested modern society's progressive and dynamic nature, which supports women's independence and personal transformation through self-reflection and self-discovery [45].
Odds of CAM use were higher among multi-/other race/ethnicity compared to white non-Hispanics and were not significant when other races were compared to white non-Hispanics. Huo et al. (2015) found 60% and 50% increased odds of using CAM among "multiple" and "other" race/ethnicity compared to whites in their analysis of the 2010 ACBS, respectively [43], while Kim et al. (2020) noted a decreased odds of CAM use among Blacks and Hispanics compared to Whites [28]. Bishop et al. (2010) reported 38 studies showing that adults from ethnic minorities use CAM less than Whites and 15 studies that show the opposite [25], highlighting the complexity of this association. The reason for these differences needs to be clarified and may include the differences in the sample design, survey year used in analysis, and definitions of CAM and racial classifications that are used in these studies.
Middle-aged and older adults were less likely to use CAM than younger adults in our study. Shaw et al. (2008) reported an inverted U-shaped relationship with age and a 128% increase in odds among asthma patients 45-54 years compared to children aged 9-14 years [3]. Jourbert et al. (2012) reported the age group 35-51 years as more likely to endorse CAM use [23]. Marino and Shen (2010) did not find an association between age of CAM use in their analysis of the 2006 ACBS after adjusting for demographics and region [30]. The difference in the observed association may be due to differences in age categories used in other studies. Moreover, the proportion of CAM users among those aged ≤35 years in our study is higher than in the other studies. In addition, given that asthma is more common among younger than older adults, it is likely that younger people are more likely to use CAM for asthma; however, more studies to understand the effect of age on CAM use behavior are needed.
Our results also show that there was a significant association between CAM use and one, two, and three cost barriers in a dose-response fashion. Furthermore, we found a dose-response association among users of single CAM and those using two or more CAM therapies and cost barriers. Interestingly, we observed a "lateral" dose-response relationship, where the odds ratio for individuals with ≥2 CAM use was higher than that for adults with single CAM use compared to non-users. Studies have suggested the high cost of healthcare and medications could be an incentive to use CAM instead of conventional treatment [13,18,30]. Pagan et al. (2005) found differences in CAM use across all CAM therapies when comparing the ability or inability of adults to obtain medical care because of cost after controlling for sociodemographic characteristics, income, and self-reported health status, implicating cost barrier as a factor for CAM use [13]. Among asthma patients, Bishop et al. (2010) showed that having a cost barrier in the last 12 months was associated with 180% increased odds of using CAM [25]. We also demonstrate a dose-response relationship between CAM use and multiple barriers, further providing evidence that as the number of cost barriers to asthma care increases, CAM use does not only increase but the number of CAM use increases as well. Our findings suggest that the use of CAM among asthma patients may reflect an increasing lack of access to asthma care for some patients and indicates that CAM may be used in an alternative rather than a complementary manner. CAM use in the US costs an estimated $34 billion per year in out-of-pocket expenditures [46]. Such out-of-pocket expenditures could be a testament to the belief that CAM/therapies have benefits that outweigh their costs and, as such, it is possible that asthma patients may try CAM first, thereby having less money available for conventional asthma treatment. Hence, CAM use could also be both an alternative and complementary therapy in that sense.
There was no significant association between income, insurance, and CAM use. Our finding on the relationship between income and complementary medicine use is consistent with one previous study that used a large population dataset [30] but differs from other studies that found CAM use to be linked to higher income [17,47,48] and lower income [41,49]. It is plausible that having insurance may increase CAM use, as insurance companies are increasingly covering CAM procedures, particularly office-based ones [48]. The National Center for Complementary and Integrative Health reports that insurance coverage for acupuncture (32% of large insurance firms), homeopathy (11% of insurance firms), and naturopathy (insurance more likely to cover a licensed naturopath) is increasing [50]. However, data also suggest that out-of-pocket costs for CAM are rising [46], which may indicate that other factors besides insurance are driving CAM use behavior. This discrepancy is noteworthy and warrants further investigation. The presence of two or more comorbidities was identified as a significant predictor of CAM use. Andersen and colleagues (1995) have previously posited that health service utilization can be explained by several key variables, including physical and psychological symptoms, disease burden, and overall health status [34]. In the context of asthma, adults experiencing increased chronic disease burden and asthma symptoms that are unresponsive to conventional therapies may be more likely to seek out alternative treatments in the hopes of finding a cure [25]. Furthermore, our findings indicate that CAM use is associated with poorly controlled asthma, consistent with prior studies that have reported an association between poor asthma control and CAM use [16,18,[29][30][31][32][33]. This association followed a doseresponse relationship and remained significant after adjusting for all covariates in both binary and multinomial models. Studies have suggested that those with poorly controlled asthma are more likely to be dissatisfied with the conventional treatments and are more likely to be motivated to use CAM [16,18].
The present study boasts notable strengths. Firstly, the use of data from the ACBS allowed for the collection of information on alternative therapies from a nationally representative sample of non-institutionalized US adults, affording a highly accurate estimation of CAM use across diverse population subgroups. Additionally, the large sample size enabled comprehensive investigation of the factors associated with CAM use and other self-reported health characteristics, including symptoms of asthma control and income. The utilization of ACBS data also afforded the opportunity to scrutinize individual CAM therapies, which is of paramount importance given that CAM use represents a highly heterogeneous behavior characterized by varied effectiveness, intensity, and side effects. Combining all CAM modalities into a single cluster would result in the loss of the direction of effect of specific therapies. Therefore, the ACBS, with its categorization of CAM modalities into eleven different kinds of CAMs, effectively accounts for this heterogeneity.
Several limitations should be acknowledged in this study. Firstly, the use of crosssectional data from the BRFSS precludes the determination of temporality between asthma severity and the use of alternative therapies. Additionally, selection bias may have been introduced as the BRFSS excludes institutionalized and hospitalized individuals, which could lead to an underestimation of the prevalence of active and severe asthma among adults. Such bias may also result in an overrepresentation of healthier individuals and those with greater access to alternative therapies, which could potentially bias the results towards the null. Furthermore, self-reported measures of asthma symptom status and CAM use may be susceptible to recall bias and misclassification. Lastly, the ACBS did not collect information on the dose and frequency of CAM use, limiting the ability to assess the intensity of alternative therapy behaviors.

Conclusions
Our study is the largest study to report the prevalence of CAM use among current asthma adults having used eight survey year datasets. The trend in CAM use among current asthma patients is increasing and our study supports other studies that found sociodemographic and health-related factors implicated in CAM use among adults with asthma. With the inclusion of breathing exercise as a useful supplement to asthma pharmacotherapy for symptoms and quality of life improvement in the GINA guidelines and ongoing clinical trial efforts in complementary/alternative therapies across the US, it is expected that CAM use would further increase among patients with asthma. Therefore, physicians and primary care providers should inquire about CAM use in a non-judgmental manner and to try to understand drivers of such perception and behavior. Our study also has policy implications, suggesting that cost barriers to asthma care may be a driving factor behind the use of CAM by patients. Therefore, policymakers should prioritize efforts to reduce these barriers to ensure that all asthma patients have access to affordable, evidence-based treatment options.