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Your Guide to Asthma Treatments

Inflamed airways that make it difficult to breathe, coughing, wheezing — these are a few of the symptoms that people with asthma experience. This chronic lung disease is controllable, but not curable according to Scientific American. Learn more about different options to discover the best asthma treatment for you.

No discussion of asthma symptoms and treatment would be complete without mentioning inhalers. When you picture an asthma patient in your mind, what do you see? Someone puffing on an inhaler, right? Inhaled corticosteroids, bronchodilators and combination medications like an inhaled corticosteroid and an inhaled long-acting beta-agonist (LABA) are among the most popular asthma treatments out there. As WebMD notes, these lifesaving treatments help reduce swelling and decrease sensitivity in the airways to manage asthma attacks and help prevent future attacks. They also quickly help manage symptoms like wheezing and shortness of breath.

Some patients have a hard time with small inhalers. In these cases, doctors often prescribe a nebulizer. This is a specialized machine that has a mask or mouthpiece through which you inhale your asthma medication. The machine transforms the medication from liquid into a mist, which makes it easier to breathe into the lungs. It also adds to the total treatment time. According to WebMD, using a nebulizer typically takes a few minutes longer than using an inhaler.

Leukotriene Modifiers

When your body’s immune system identifies an allergen, it releases leukotrienes, a type of inflammatory chemical that sparks the creation of excess mucus while leading to the tightening of the airways. Leukotriene modifiers like Pranlukast and Zafirlukast are taken by mouth to manage asthma by neutralizing those leukotrienes. Scientific American hails this advancement as “exciting,” and notes that it’s the first new class of asthma management medications to hit the market in several decades.

Oral Medications

Leukotriene modifiers aren’t the only treatments given by mouth. Other treatments are typically short-term options to manage severe symptoms or treatments designed to take only during an asthma attack. For example, some patients may need to take steroids like prednisone for a short time to help calm inflammation and relieve asthma symptoms. Beta-2 receptor stimulants relax the muscles in the airway, providing fast symptom relief, as noted by the American Academy of Allergy, Asthma & Immunology.

Preventive Measures

Stopping asthma symptoms before they start can go a long way toward controlling the disease. Doctors are increasingly adopting a preventive attitude toward treating asthma, as noted by Scientific American. In addition to other options for asthma treatment at home, these measures include:

• Avoiding environmental triggers like cigarette smoke, pet dander and dust mites
• Improved indoor air quality from newer, energy-efficient homes
• When your body’s immune system identifies an allergen, it releases leukotrienes, a type of inflammatory chemical that sparks the creation of excess mucus while leading to the tightening of the airways.

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Asthma Research

Why we need research.

Asthma research helps us understand how the disease is caused, how it develops and how it is best treated. Research can also help us understand who is at high risk for developing asthma, certain triggers, and ways to avoid getting asthma. 

Our Asthma Research Program

The American Lung Association is committed to funding asthma research. Our Awards and Grants Program funds top-notch researchers at important career crossroads to gain long-term commitment to lung health and disease research. Without the life-long dedication of lung researchers, important and much-needed discoveries would not be possible. In addition to the Awards and Grants Program, the Lung Association funds the Airways Clinical Research Centers (ACRC) Network , which implements patient-centered clinical trials, and has helped to change the nature of asthma patient care since its inception in 2000. 

What Research Is Being Done?

Some of the current topics American Lung Association funded researchers are investigating include understanding the immune system’s role in asthma, using mobile technology to reach young African Americans with asthma, and better defining subtypes of asthma. Together, studies like these lead to improved therapy, quality of life, and access to care for all people with asthma.

Thanks to the medical breakthroughs led by Lung Association researchers and their colleagues, we have made significant contributions to improve our understanding of asthma. 

Currently funded Lung Association researchers are:  

• Studying asthma that is resistant to steroid treatments 
• Boosting the immune system to reduce allergic inflammation in airways  
• Understanding which genes are responsible for severe asthma 
• Finding new genetic targets in lung tissue for new asthma therapy and prevention
• Improve our understanding of the challenges in access to asthma medication in schools 
• Evaluating asthma management policy in Chicago schools 
• Providing asthma education to children in the hospital and at home 

Asthma Researchers

Visit our Meet the Researchers section to view our asthma researchers and their studies.

Asthma Clinical Trials

• See our Lung Association  listing of current trials . 
• View ACRC clinical trials that are currently recruiting as well as outcomes from completed studies.

Airways Clinical Research Centers (ACRC) Network

The ACRC is the nation's largest not-for-profit network of clinical research centers dedicated to asthma and chronic obstructive pulmonary disease (COPD) treatment research, attracting some of the best investigators nationwide. The ACRC Network conducts large clinical trials that will directly impact patient care for COPD and asthma. 

Meet our Principal Investigators, see where our centers are located and learn more about some of the important research findings at Lung.org/acrc .

Page last updated: April 19, 2023

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An Assessment of Quality of Life in Patients With Asthma Through Physical, Emotional, Social, and Occupational Aspects. A Cross-Sectional Study

Zelal kharaba.

1 Department of Clinical Sciences, College of Pharmacy, Al-Ain University of Science and Technology, Abu Dhabi, United Arab Emirates

2 Al Ain University Health and Biomedical Research Center (HBRC), Al Ain University, Abu Dhabi, United Arab Emirates

Emilie Feghali

3 School of Pharmacy, Lebanese International University, Beirut, Lebanon

Farah El Husseini

4 INSPECT-LB: Institut National de Santé Publique, Epidemiologie Clinique et Toxicologie, Beirut, Lebanon

Carla Abou Selwan

Sylvia saadeh.

5 Life Sciences and Health Department, Paris-Est University, Paris, France

6 Health and Sciences Department, American University of Health and Sciences, Beirut, Lebanon

Souheil Hallit

7 School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon

8 Psychology Department, College of Humanities, Effat University, Jeddah, Saudi Arabia

9 Research Department, Psychiatric Hospital of the Cross, Jal El Dib, Lebanon

Feras Jirjees

10 College of Pharmacy, University of Sharjah, Sharjah, United Arab Emirates

Hala AlObaidi

11 College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates

Pascale Salameh

12 School of Medicine, Lebanese American University, Byblos, Lebanon

13 Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, Cyprus

14 Faculty of Pharmacy, Lebanese University, Hadat, Lebanon

Diana Malaeb

15 College of Pharmacy, Gulf Medical University, Ajman, United Arab Emirates

Associated Data

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Asthma is a prevalent hyperactive airway disease with physical and emotional impact. Severe asthma is associated with considerable health-related quality of life (HRQoL). The aim of this study is to assess the quality of life through physical, emotional, social and occupational aspects and evaluate the factors affecting HRQoL in patients with asthma.

This is a cross-sectional multicenter study conducted on adult asthmatic patients enrolled from community pharmacies across different Lebanese geographic areas.

Having wheezing sometimes and most of the time (Beta = −0.144 and −0.552), experiencing anxiety sometimes and most of the time (Beta = −0.205 and −0.573), encountering sleep problems sometimes and most of the time (Beta = −0.270 and −0.553), having previous chest discomfort sometimes and most of the time (Beta = −0.421 and −0.713), and having depression most of the times (Beta = −0.415) were associated with higher lower quality of life scores. On the other side, holding a secondary level of education was associated with a higher quality of life score (Beta = 0.192).

This study highlights that asthma affects adults' quality of life through social, emotional, physical, and occupational impacts. Improved follow-up and patient education may be essential in the future to stop disease progression and achieve ideal therapeutic outcomes.

Introduction

Asthma is a prevalent non-communicable disease identified by chronic airway inflammation affecting children and adults worldwide ( 1 ). Differential symptoms are wheezing, dyspnea, chest discomfort, and persistent cough in addition to airflow limitation, especially at night and in the early morning. The pattern and intensity of the symptoms and airflow limitation vary over time, with exercise, allergen, exposure to irritants, weather changes, and respiratory infections, leading to exacerbation of asthma ( 2 ). Although asthma cannot be cured, exacerbations can be prevented by adequate patient counseling and proper management ( 3 ). Since it is a chronic condition, patients must utilize medications, adhere to treatment recommendations, and follow the written action for the self-control of asthma.

Even though clinical and physiological variables are used to assess asthma, they may not be enough to assess the patient's interpretation of their state of health. Thus, quality of life (QoL) is a significant endpoint as it reflects the impact of the disease from the patient's perception. Improper asthma management can have a substantial effect on the QoL, including physical, emotional, occupational, and social impacts, where the symptoms differ from one patient to another ( 4 , 5 ). QoL is explained as the perception that patients have of their position in life in relation to their aims, expectations, concerns, and standards ( 6 ). The patient's wellbeing is the standard clinical outcome to assess QoL and prevent morbidity from uncontrolled disease ( 7 ).

In asthma, QoL is assessed by using the Mini asthma quality of life questionnaire (Mini AQLQ) ( 8 ); it is affected by the frequency of exacerbation, manifested through influence on daily work, deteriorated school performance, reduced social and other activities ( 9 , 10 ). Other asthma-related factors that negatively affect the patient's QoL are not fully understood, and must be identified and appropriately assessed to improve the QoL ( 11 – 15 ). Female gender, older age, obesity, comorbid diseases such as depression, are prognostic factors associated with poor QoL ( 11 , 16 , 17 ). Poor QoL in asthmatic patients is associated with detrimental consequences resulting in a high prevalence of behavioral and emotional difficulties, depression, and poor academic performance ( 18 ). Moreover, avoiding triggers of asthma, and enhancing patient QoL, are effective measures to reduce morbidity and mortality ( 1 ).

In Lebanon, asthma treatment in adults falls far short of the goals specified in the international asthma guidelines, similarly to many other countries around the world. This inadequate control of the illness is associated with disease progression and poor QoL ( 19 ). In Lebanon, several studies were conducted on asthmatic patients that tackled the preschool asthma risk factor scale, evaluated association between different factors and both wheezing and asthma development, assessed asthma control, and evaluated the influence of diet and obesity on asthma ( 20 – 25 ). However, data on patients' QoL is still scarce, particularly among adults ( 26 ). Therefore, the purpose of this study is to assess the effect of asthma on physical, emotional, social, and occupational aspects of QoL among Lebanese adult asthmatic patients.

Study Design

A cross-sectional multi-centered study was conducted between February and May 2019. Six Lebanese community pharmacies from all Lebanese districts gave consent to participate in the study from a total of 13 contacted pharmacies. An online software was used to randomly select different community pharmacies using the list of pharmacies provided by the Lebanese Order of Pharmacists (OPL).

Inclusion and Exclusion Criteria

Patients enrolled in the study were between 18 and 65 years, diagnosed by a physician to have asthma according to GINA criteria ( 27 ), and taking metered dose inhaler or dry powder inhaler for at least the past year. Patients excluded were those who could not fill the questionnaire appropriately because of decreased mental alertness or cognitive function (cognitive disorders, sedated patients, Alzheimer's disease, etc.).

Ethical Aspect

The Institutional Review Board of the Psychiatric Hospital of the Cross approved the study and written informed consent was acquired from all participants before study enrolment. In addition, the study followed the oxford equator guidelines for cross-sectional multi-centered studies.

Tools and Procedures

Data collection was carried out using a standardized structured questionnaire prepared in Arabic, the native language in Lebanon. A forward and backward translation was conducted for the Mini-AQLQ scale. One translator was in charge of translating the scales from English to Arabic, and another one was involved in the translation from Arabic back to English. Discrepancies between the original and translated English versions were resolved by consensus. It was administered via face-to-face interviews by trained researchers to ensure a higher quality of data collection. The first part of the questionnaire included patient demographics, such as gender, age, marital status, educational level (illiterate/primary, secondary, and higher education) and socioeconomic status. The second part assessed the QoL of asthmatic patients using the Mini AQLQ, a validated tool with good reliability since it is discriminates patients with different levels of impairment and validity as it measures asthma-specific quality of life ( 28 ).

This short version of the original Asthma Quality of Life Questionnaire requires a shorter time to complete ( 29 ) and includes 15 items distributed over four domains: symptoms (5 items), activity limitation (4 items), emotional function (3 items), and environmental stimuli (3 items). The symptoms field covered shortness of breath, cough, chest heaviness, sleep pattern, and wheezing. The activity limitation part assessed occupational, social, moderate, and strenuous activities. The three items evaluated by the emotional function were frustration from asthma, fear of not having the asthma medications, and concern about asthma. The fourth domain covered environmental stimuli, i.e., dust in the environment, cigarette smoking, and air pollution. The responses were scored on two 7-point Likert scales, from “all of the time” to “none of the time,” and from “severely limited” to “not limited at all.” For each item, a lower score indicated higher limitation. For each domain, the mean score was calculated by adding the answers and dividing the total by the number of questions, with scores below 6 indicating that asthma had an impact on the QoL ( 8 ). The total score was computed by summing all the answers and dividing it by the total number of questions. In our study, the reliability analysis showed a Cronbach's alpha of 0.904 for the total score, 0.717 for the symptoms' domain, 0.663 for the activity limitation domain, 0.751 for emotional function, and 0.815 for environmental stimuli.

Sample Size Calculation

A minimum sample of 74 participants was calculated by The Epi Info software version 7.2 (population survey) to ensure a confidence level of 95%, based on a 66.7% expected frequency of anxiety in asthmatic adults and an odds ratio of 9 in the lack of similar studies in Lebanon. This study accounted for a possible non-response rate for this reason, oversampling is needed ( 30 ).

Data Entry and Statistical Analysis

Using Statistical Package for Social Sciences version 21.0, statistical analysis was performed. Descriptive statistics were used for patients' characteristics, with frequencies and percentages for categorical variables and means ± standard deviations for continuous variables. Bivariate associations assessed through the Pearson correlation analysis for the continuous variables along with the HRQol score, and the Student t -test and ANOVA F tests were used for categorical variables with two or more levels, respectively. Multivariable linear regression using the Forward method was done for patient's QoL score assessment as the dependent variable, using variables that showed a p < 0.2 in the bivariate analysis. All reported p -values are two-sided, with alpha set at a significance level of 0.05.

Socio-Demographic Characteristics

Data was collected from 200 participants out of whom 172 were enrolled in the study as the others had mental disorders and thus were excluded. The mean (±SD) age was 31.79 ± 20.92 years, with the majority being females 96/172 (55.8%), single 117/172 (68.0%), and with secondary level of education 86/172 (50.0%). 69/172 (46.3%) of families had a medium monthly income.

Patients reported frequently a discomfort affecting performance of difficult tasks (49.4%), discomfort due to coughing (48.3%) and the need to avoid going out due to weather changes (47.1%). Other asthma related symptoms reported less frequently are summarized in Table 1 .

Demographic characteristics of study participants.

Quality of Life Domain Assessment

Based upon the Mini AQLQ, the majority of patients (90.1%) had poor QoL. The mean QoL scores (±standard deviation) for the four domains of the Mini AQLQ ( Figure 1 ) were 3.53 ± 1.45 for the environmental stimuli, 4.53 ± 1.27 for the symptoms, 5.09 ± 1.41 for the emotional function, and 5.45 ± 1.27 for the activity limitation. The mean total score of the mini-AQLQ was 4.65 ± 1.22.

Means and standard deviation of the Quality of life scale and subscales scores.

Bivariate Analysis

A meaningfully higher mean QoL score was found in males vs. females (4.88 vs. 4.50; p = 0.020), non-married vs. married (4.89 vs. 4.20; p < 0.001), high income vs. low income (4.89 vs. 4.16; p = 0.032) and secondary and University vs. lower levels of education (4.87 and 4.76 vs. 4.35; p = 0.02), and in those who had never have difficulty sleeping due to asthma (5.39 vs. 3.56; p < 0.001) or never had depression (5.11 vs. 2.63; p < 0.001) vs. most of the time. Similar results were found for feeling anxious, wheezing experience, discomfort due to coughing, discomfort related to difficult tasks and going out during weather changes. Bivariate analysis for factors associated with QoL score is summarized in Table 2 . However, the results highlight that higher age was significantly associated with minor QoL score (r = −0.354, n = 172, p < 0.001) which is not illustrated in the table.

Bivariate analysis for factors associated with quality of life score.

Multivariable Analysis

The results of a first linear regression, taking the quality of life score as the dependent variable and several other factors as independent variables, showed that holding a secondary level of education was associated with higher QoL scores. However, participants who had wheezing, anxiety, sleeping problems, chest discomfort, and discomfort to avoid going out, discomfort due to coughing, and avoidance of tasks performance on a frequency of “sometimes” and “most of the times” compared to “never” had lower QoL scores shown in Table 3 . Furthermore, participants who had depression “most of the time” compared to “never,” and aging were significantly associated with lower QoL scores.

Linear regression taking quality of life score as the dependent variable and wheezing, anxiety, insomnia, chest discomfort, depression, asthma knowledge score, and monthly income as the independent variables.

The study findings support the negative effect of asthma on quality of life and show the need for continuous patient monitoring and QoL evaluation during the course of the disease. Holding a secondary educational level was associated with higher QoL score, whereas aging, depression, wheezing, chest discomfort, anxiety, sleeping problems, and avoiding discomfort related to going out, discomfort due to coughing, and avoidance of tasks performance were significantly associated with lower QoL.

Wheezing and Chest Discomfort

Wheezing and chest discomfort are critical prognostic factors in asthma ( 31 ). Indeed, wheezing is a frequent and recurrent symptom of asthma that often results in disease exacerbations and limits asthma patients' normal life. Moreover, disease severity is an essential clinical factor affecting the QoL of asthmatic patients and is associated with frequent hospitalizations and unscheduled clinic visits due to asthma exacerbation which has been documented in a previous study conducted in Germany that poorly controlled asthma is associated with a lower HRQOL in adult asthma patients ( 32 ). Consequently, wheezing and chest discomfort are associated with bad quality of life as demonstrated in our study. These findings are similar to those described in a study done in Poland on 100 asthmatic patients where high QoL scores were associated with better disease control and minimal exacerbations ( 11 ). The results emphasize on the importance of early diagnosis and management of asthma related symptoms to stop the disease progression. Our findings may be explained by the fact that patients with respiratory symptoms manifested by wheezing and chest discomfort have difficulty engaging in physical activities and daily life tasks that impair the quality of life.

Anxiety, Depression, Sleeping Problems

Our study findings demonstrate that insomnia is highly linked to poor QoL, consistent with the results of the study conducted by Luyster et al. ( 33 ). Sleep difficulties encountered in asthmatic patients are often the outcome of nocturnal awakenings, resulting from nighttime asthma symptoms, poor control of the trigger factors, and the regular need for rescue inhaler medication; all being symptoms of uncontrolled asthma ( 33 ). Poor QoL is also related to the daytime consequence of insomnia, namely fatigue, irritability, and impaired concentration ( 34 ).

Our results also support the previous findings in which greater depression and anxiety are associated with lower QoL among asthmatic patients ( 35 , 36 ). Moreover, insomnia was associated with higher levels of depression and anxiety symptoms, poor asthma control, low QoL, and more frequent asthma-related health care utilization ( 37 ). The possible hypothesis is that asthmatic patients may face physical limitation due to uncontrolled disease that enhances depression, and thus, decreases the QoL. A study conducted in Pennsylvania highlighted this finding and showed that severe asthma with increased symptom burden is positively highly associated with risk for co-morbid depression ( 33 ). Leander et al. also demonstrated a statistical correlation between anxiety, depression, and the asthma symptoms, including attacks of shortness of breath after activity, that all compromise the QoL ( 38 ).

Aging Process

Our findings show that older age was associated with low QoL scores, in line with previous literature ( 32 , 39 , 40 ). The underlying evidence that explains this effect is that elderly people have higher disease exacerbation mainly due to the poor adherence to treatment, greater physical activity limitation, and end-stage disease that leads to development of irreversible asthma ( 40 ).

Quality of Life

Quality of life is influenced by many factors, related to both the sociodemographic characteristics of the patients and comorbid disease conditions ( 9 ). This study highlights that asthma significantly affects patient's QoL as the majority of patients included in the study reported poor QoL since severe asthma has a tremendous effect on the QoL, which is an essential tool for characterizing patient populations and assessing therapeutic interventions ( 34 ). It guides healthcare professionals especially when taking care of chronic and critically ill patients. Our findings can be explained by the fact that patients with severe asthma describe poor quality of life due to excessive symptoms, frequent and life-threatening attacks, increased comorbidity burden, and high pharmacological treatment requirements ( 34 ).

The majority of the individuals endorse a poor quality of life which can be explained by the fact that many participants had secondary level of education and were of the medium socioeconomic status. According to previous literature, patients of lower socioeconomic status often report poor health behaviors that may exacerbate asthma, including higher rates of current smoking, reduced consumption of fruits and vegetables, and obesity. Also, patients with lower educational level have lower socioeconomic status and may have higher exposures to indoor and outdoor allergens, and tend to be less complaint with medication, thus increasing risk for acute asthma exacerbations which impair the quality of life ( 41 , 42 ).

Limitations and Strengths

The study has some limitations. This is a cross-sectional study where the findings cannot establish a causal relationship. Moreover, this study might be also subject to selection bias as the study enrolled more females, young age participants, and single status which can influence the quality of life. The utilization of questionnaires in the general population, especially in elderly, may not always be precise and is limited by the difficulty in questions clear comprehension and influenced by recall bias which underestimates the association between different factors and quality of life. In this study, we accounted for several variables that could affect QoL; however, not all factors were accounted for as diseases, medications, and asthma severity. To remove the confounding effect of several variables, we performed a multivariate analysis; however, we could not ignore the possibility of residual confounding of the variables that we did not evaluate as cardiovascular disease, cancer, and hypertension. Accordingly, prospective studies, taking into account these limitations, are needed. The strengths of this study include the geographical distribution of participants recruited from different pharmacies across Lebanese and the use of the mini AQLQ questionnaire in the methodology, which is a validated tool utilized to assess the QoL.

The study demonstrated that asthma affects patient's QoL that was assessed through physical, emotional, occupational and social negative impacts. Patient education is an important part of treatment however, to be successful, it should not only be limited to providing knowledge, but should impact behavior and lead to a consistent change in patient's behaviors. Pharmacists and patients need to cooperate together to improve an asthma care program that targets for optimizing asthma management for a symptom-free asthma disease. During counseling, pharmacists could provide patients with adequate information about the correct use of medications and associated risks of misuse, in addition to general awareness about avoiding asthma triggers to prevent exacerbations and thus improving the QoL. In conclusion, sustained efforts are needed to optimize patients' awareness on asthma disease and its management and to dispel their myths and misconception for an improved QoL.

Implications for Practice

Health-care professionals can utilize the findings of this study to make suitable treatment strategies for asthma for the future and develop healthcare measures based on fundamental evidence. In addition to continuous medical therapy, asthmatic patients should be provided with healthcare education and psychological consultancy services. Therefore, training and psychological support should be provided to patients to ensure optimal therapeutic outcome and disease management. It is recommended that future studies assess other factors affecting QoL among asthmatic patients and methods for improving it.

Data Availability Statement

Ethics statement, author contributions.

DM, EF, and FE analyzed and interpreted the collected data from study participants. ZK, EF, DM, HS, PS, FJ, HA, and SH contributed in idea conceptualization and study design and interpretation of data. ZK, EF, DM, FE, HS, CA, SS, SH, PS, FJ, and HA were major contributors in writing the manuscript. All authors read and approved the final manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors would like to thank all patients who participated in this study.

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Asthma Research

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Over the years, and as part of our broader commitment to research on lung diseases, the NHLBI has led and supported asthma research to discover better prevention and treatment options. Research supported by the NHLBI has also helped us understand what leads to and affects asthma, and it has provided doctors with information about what treatments work best for people who have asthma. 

NHLBI research that really made a difference

For nearly 20 years, the NHLBI  Severe Asthma Research Program (SARP)  has transformed our knowledge of severe asthma. Research supported through the program has identified  secondhand smoke, pneumonia, and obesity as key risk factors for asthma . Studies have also found genetic variations linked with severe asthma and biomarkers for asthma severity. Researchers can  request access to the data on dbGaP .

Research funded by the NHLBI

Our  Division of Lung Diseases  and its  Airway Biology and Disease Branch  oversee much of the research on asthma we fund. The Asthma Program supports research related to asthma, including the role of inflammation in the development of asthma, genetics and asthma, and clinical management of asthma in adults and children.

Find  funding opportunities  and  program contacts  for asthma research.

Current research on asthma treatment

• How ventilators may lead to asthma: The NHLBI-funded Post-Vent study will use data collected from the Prematurity-Related Ventilatory Control (Pre-Vent): Role in Respiratory Outcomes NHLBI Collaborative Program to study long-term health outcomes of premature birth and intermittent low oxygen levels shortly after babies are born prematurely. These babies often develop asthma. This study will try to predict which premature babies are most likely to develop asthma. 
• Why medicines work: An NHLBI-funded study is assessing how an antibiotic called azithromycin (AZ) reduces severe wheezing in preschool children seen in the emergency room. While prior studies have shown that AZ benefits these children, it is unclear if the beneficial effects are because of the antibacterial activity of AZ or because of the anti-inflammatory activity of AZ. To help answer this question, this study will compare whether children with bacteria growing in their throats get more benefit from AZ treatment than children who do not have bacteria growing in their throats at the time they go to the emergency room with severe wheezing.
• Personalized medicine: The  Precision Interventions for Severe and/or Exacerbation Prone Asthma Network (PrecISE) is conducting clinical trials to identify personalized medicine approaches that treat severe asthma more effectively. It has established 30 locations nationwide that will test new and approved treatments based on each patient’s specific biology and biomarkers.

Find more NHLBI-funded studies on asthma treatment at NIH RePORTER. 

Learn about one PrecISE study that is looking at treatments that may help support people with severe asthma or asthma that hasn’t responded to traditional treatments: Personalizing treatment for severe asthma .

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The different bacteria in a person’s body can affect the immune system. We support studies to figure out whether different bacteria play a role in developing certain types of asthma.

• Airway cells and asthma: NHLBI-funded research will look at how genes are regulated in airway epithelial cells to better understand how they affect the development of asthma. Epithelial cells line the lung’s airways. As researchers learn more about how changes in the cells lead to asthma, they hope to develop treatments to reprogram the epithelium and prevent or cure asthma and other lung diseases.
• Bacteria in the airways: An AsthmaNet  study found  different bacteria in the airways of people with asthma compared to those without asthma. Some of the observed differences could help predict the response to inhaled steroids. Researchers can  request the data  through our Biologic Specimen and Data Repository Information Coordinating Center.
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Find more NHLBI-funded studies on asthma biology at NIH RePORTER.

Researchers have identified a key role for the circadian system — the biological clock that controls your sleep/wake cycle: Study of biological clock may explain why asthma worsens at night .

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• Genetic factors: The  Consortium on Asthma among African-Ancestry Populations in the Americas (CAAPA)  aims to discover genes that confer asthma risk among individuals of African ancestry and to study genetic diversity in populations of African descent. Read  some of the results here  or  request access to the data on dbGaP .
• Comprehensive care for at-risk children: We also fund the  Asthma Empowerment Collaborations to Reduce Childhood Asthma Disparities . We support clinical trials to evaluate programs that provide comprehensive care for children at high risk of poor asthma outcomes, such as low-income minority children. 
• Race, sex, and socioeconomic factors: The NHLBI recently launched the DECIPHeR program to study differences in heart and lung diseases among groups defined by race and ethnicity, sex and/or gender, and socioeconomic status. The first projects began in September 2020, with one project focused on asthma in children in Colorado.  Working with communities across the state, from rural to urban areas in Colorado, researchers will work with school-based asthma navigators and nurses to test a team approach to asthma control in school children in low-income areas.

Find more NHLBI-funded studies on  asthma and health disparities .

An NHLBI-funded study found that African-American boys, but not girls, with higher levels of BPA (Bisphenol A) tended to have more asthma symptoms: Study links exposure to higher levels of BPA plasticizer to more asthma symptoms in black boys .

Asthma research labs at the NHLBI

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“By studying the pathways of the disease, we identified a new biological mechanism that leads to asthma,” explained Stewart J. Levine, M.D. Read the research feature: Disease pathways lead to possible new treatment for severe asthma .

Learn about research opportunities in the lab:

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• Graduate Medical Education (GME): NHLBI/UMD Pulmonary-Critical Care Fellowship  

Related asthma programs and guidelines

• The NAEPP’s  Expert Panel Report 4 (EPR-4) Working Group  was established in 2018 to update the 2007  Guidelines for the Diagnosis and Management of Asthma (EPR-3) in focused topic areas. The working group members reviewed the latest research to update the guidelines on treatments and management of asthma, including the role of immunotherapy, the effectiveness of indoor allergen reduction, and the use of fractional exhaled nitric oxide (FeNO). Read  Asthma Management Guidelines: Focused Updates 2020 .
• Learn More Breathe Better®  is a national health education program for asthma, COPD, and other lung and respiratory diseases. The program raises awareness about asthma and other lung conditions and supports the promotion, implementation, and adoption of evidence-based care.  Learn More Breathe Better® Asthma offers a  series of asthma handouts  to patients and caregivers, including tips for talking to your doctor. 
• Since 1989, the  National Asthma Education and Prevention Program (NAEPP)  has worked with medical associations, voluntary health organizations, and community programs to educate patients, healthcare professionals, and the public about asthma.
• The Lung Tissue Research Consortium (LTRC)  provides human lung tissues to qualified investigators for use in their research. The program enrolls patients who are planning to have lung surgery, collects blood and other clinical data from these donors, and stores donated tissue that otherwise would be discarded after the lung surgery. The LTRC provides tissue samples and data at no cost to approved investigators.

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• Published: 27 April 2023

Asthma control among treated US asthma patients in Practice Fusion’s electronic medical record research database

• Jonathan Davitte 1 ,
• Bailey DeBarmore   ORCID: orcid.org/0000-0002-9911-0917 2 ,
• David Hinds 2 ,
• Shiyuan Zhang   ORCID: orcid.org/0000-0001-8523-0419 3 ,
• Jessica Chao 1 &
• Leah Sansbury 2  

npj Primary Care Respiratory Medicine volume  33 , Article number:  17 ( 2023 ) Cite this article

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This study investigated burden of ‘not well-controlled’ asthma, overall and by Global Initiative for Asthma (GINA) Step, among treated asthma patients in Practice Fusion’s research database. Asthma control (Asthma Control Test [ACT]) was stratified by GINA Step; prevalence ratios were estimated using Poisson regression with robust variance controlled for confounders. ACT scores ≤19 reflect not well-controlled; >19 reflect ‘well-controlled’ asthma. Of 15,579 patients, 30% had not well-controlled asthma at index date. The proportion of patients with not well-controlled asthma increased from GINA Step 1 (29%) to Step 5 (45%). Compared with Step 1, the proportion of patients with not well-controlled asthma was 0.87-times lower in Step 2, 1.10-times greater in Step 4, and 1.37-times greater in Step 5. Results suggest that despite available treatments, patients remain symptomatic across GINA Steps in real-world primary care and specialist outpatient practices, with incremental disease burden and unmet medical need in these populations.

Introduction

Asthma is a chronic, heterogenous disease, usually characterized by chronic airway inflammation and defined by a history of respiratory symptoms including wheeze, shortness of breath, chest tightness and cough that varies both over time and in intensity together with variable expiratory airflow limitation 1 . Asthma affects 1–18% of the population across different countries 1 . In the United States (US) alone, there were an estimated 25.1 million individuals living with asthma in 2019 2 . Asthma control is defined as the degree to which asthma manifestations, such as symptoms, reliever use, lung function and exacerbations, are reduced or removed by treatment 3 , and has a major impact on patient outcomes; poor control of asthma symptoms substantially impairs health-related quality of life and is strongly associated with an increased risk of future asthma exacerbations 4 , 5 , 6 , 7 , 8 . The contribution of asthma severity to patient outcomes is also important to consider, as more severe forms of the disease are associated with greater symptom burden and higher asthma-related healthcare costs 9 , 10 , 11 . Asthma severity is determined by the intensity of treatment required to maintain good control, with more severe and difficult-to-treat asthma requiring higher dosages or supplemental treatments 3 .

The Global Initiative for Asthma (GINA) report recommends that asthma symptom control should be assessed at every opportunity, including during routine prescribing or dispensing, via direct questioning regarding symptoms and instruments designed to assess asthma control 1 . While several patient-reported instruments are available to assess asthma control, recording and integration into electronic health records (EHR) and other real-world data sources as part of routine clinical practice is limited. Consequently, while these real-world data sources contain rich data for recording asthma diagnoses and describing asthma treatment (e.g., prescriptions, claims), they have limited ability to describe asthma symptom control based on validated instruments.

In 2015, the Practice Fusion Electronic Medical Record (EMR) database integrated the Asthma Control Test (ACT) into their platform. The ACT is a patient-reported measure commonly used to distinguish different levels of symptom control by evaluating the frequency of shortness of breath and general asthma symptoms, use of rescue medications, the effect of asthma on daily functioning, and overall self-assessment of asthma control 12 . Practice Fusion, a free EMR platform, generates a notification for clinicians to consider administering the ACT or the childhood ACT (for children aged 4–11 years), whenever a patient with asthma visits the office.

The GINA report recommends that once asthma treatment has been started, ongoing decisions should be based on regular patient assessments and adjustment of treatment 1 . The asthma treatment paradigm involves five ‘treatment steps’, where asthma treatment is adjusted based on changes in asthma control status. The Steps outlined in the GINA 2019 report correspond to asthma severity: mild asthma is controlled with Step 1 or 2 treatment (as-needed controller medication alone or with low-intensity maintenance controller treatment); moderate asthma is controlled with Step 3 treatment (e.g., low-dose inhaled corticosteroids/long-acting b2 agonist [ICS/LABA]); and severe asthma requires Step 4 or 5 treatment (high-dose ICS/LABA or add-on treatments) to prevent it from becoming uncontrolled or remains uncontrolled despite this treatment 13 , 14 . Clinicians may recommend stepping up or stepping down asthma treatment to improve asthma control.

This study investigated the burden of not well-controlled asthma both overall, and by GINA treatment step (GINA Step), among the treated asthma patient population in Practice Fusion’s research database. While many real-world data sources allow for the investigation of asthma treatment status and patterns, along with indicators of asthma control, the absence of data from patient-reported tools in secondary sources inhibits the ability of researchers to understand the burden of not well-controlled asthma outside of clinical trials or other research settings. The Practice Fusion research database, with the integrated ACT tool, is uniquely positioned to describe asthma control among the treated asthma population as it exists in real-world primary care and specialist outpatient practices.

Study population

A retrospective cohort was established which included patients with asthma and a valid ACT measurement in Practice Fusion’s EMR database between January 1, 2015 and December 31, 2018, and with at least 1 prescription for any asthma treatment in the 6 months prior to the 4-week recall period of their first valid ACT measurement. The date of a patient’s first valid ACT measurement was defined as their index date. Patients were required to have activity in the database, defined as an encounter in the database for any reason, at least 6 months (182 days) prior to their index date. In addition, patients were excluded from our sample if they had ≥1 chronic obstructive pulmonary disease diagnosis code(s) reported at any time on or before their index date or had a missing value for their calendar year of birth (Fig. 1 ).

ACT asthma control test; Chronic obstructive pulmonary disease.

Data source

Practice Fusion is a cloud-based connected health platform used in 30,000 healthcare practices with 8% market share among small practices (1–3 physicians) in the US, is linked with 90% of US pharmacies, and 600 laboratory and imaging entities. Practices were included in Practice Fusion’s research database if they met any of the following criteria: over 13,000 chart pulls; 1 or more providers with a verified National Provider Identifier and over 500 chart pulls; sent 500 or more electronic prescriptions; sent 500 or more laboratory orders. Practices were excluded if they were used by Practice Fusion for testing and production purposes, did not have at least one Doctor of Medicine, or were located outside the US. Practice Fusion’s research database contained patient-level data on demographics, office visits, insurance, allergies, vitals, medications, laboratory tests, diagnoses, prescriptions, and immunizations. As of December 2018, the cut of Practice Fusion’s research database contained data for 1.9 million asthma patients with ≥1 ICD-9 493.xx, ICD-10 J45.xx, or SNOMED-CT CTV3 H33xx diagnosis codes between 2007 and 2018 with patients across all 50 US states.

The database is certified as statistically de-identified through the removal of all personally identifiable indicators, transformation of dates, generalization of certain demographic and geographic information, standardization of free text and other sensitive fields, and substitution of patient- and provider-related unique identifiers with random values.

Asthma control

The ACT is comprised of five questions, each item response is captured on a 5-point scale (where 1 is the worst scenario and 5 is the best) utilizing a 4-week recall period. ACT scores range from 5 (poor control of asthma) to 25 (complete control of asthma) with higher scores reflecting greater asthma control. ACT scores ≤19 reflect not well-controlled asthma while ACT scores >19 reflect well-controlled asthma 15 .

Beginning in 2015, Practice Fusion implemented a clinical decision support program that notified providers that an ACT should be conducted when a patient with asthma missing symptom assessments visited them. While the notification indicated that an ACT should be completed, the system did not require clinicians to complete and/or record the ACT results.

We defined a valid ACT as: (1) having complete responses for all 5 questions; (2) not occurring on the same date as another ACT measurement for the same patient; and (3) not occurring within 28 days of another ACT measurement for the same patient. Scores that reflect asthma control as measured by the ACT cannot be calculated if any of the 5 questions are missing responses. The rationale behind this 28-day time gap is that the ACT reflects a 4-week recall period; if two ACT scores are measured on the same day or within 28 days of each other, it is impossible to determine which of these indicate the correct measurement of asthma control.

GINA Step was assessed based on the medications prescribed during the 6-month period prior to the 4-week recall period of patients’ ACT record at index date. Asthma treatment was defined as one of the following medications: short-acting β 2 -agonists (SABA), short-acting muscarinic antagonist (SAMA), inhaled corticosteroids (ICS), ICS and long-acting β 2 -agonist (ICS/LABA) combination products, leukotriene receptor antagonist, cromolyn or nedocromil (mast cell stabilizers), methylxanthines, biologics (e.g., mepolizumab) or long-acting muscarinic antagonist. Further details on GINA Step definition and asthma treatments are in Supplementary Table 1 .

Determination of a patient’s GINA Step required calculation of ICS and ICS/LABA daily doses. The Practice Fusion prescription data includes fields that were generated using MedEx, a natural language processing system which extracts medication information from clinical notes 16 . There are three MedEx-derived fields that were used for calculation of ICS daily dose: (1) frequency (e.g., once per day ); (2) dose amount ( e.g., ‘2’ in ‘2 puffs’ ); (3) dose unit ( ‘puff’ in ‘2 puffs’ ). For missing values of frequency, dose, or dose amount, we imputed values from the mode across each National Drug Code. We converted all ICS strength to micrograms (mcg) prior to calculating ICS daily dose 17 . ICS daily dose was calculated as: (Frequency)*(Dose amount)*(Strength) . Finally, the ICS and ICS/LABA dosage levels required for GINA Step calculation were defined for each medication based on generic names or ingredients (Supplementary Table 2 ).

ICS/LABA includes fixed-dose ICS/LABA combination medications and ‘open’ ICS/LABA combinations. For patients that had individual ICS and LABA prescriptions, we considered them as ‘open’ ICS/LABA combinations only if the ICS medication and LABA medication were prescribed within 30 days of each other. Patients with separate ICS and LABA prescriptions more than 30 days apart were considered as ‘ICS only’ in the GINA Step calculation. For patients that had multiple ICS or ICS/LABA prescriptions in the eligible period, we used only the prescription(s) that were closest to the patient’s index date for calculation of the ICS daily dose.

GINA steps were defined according to GINA asthma treatment guidelines in 2018 17 . The GINA 2019 treatment guidelines include the addition of as-needed low-dose ICS-formoterol for Step 1 13 . Given that this additional criteria for Step 1 did not align with our observation period, we chose to define GINA Step according to the guidelines clinicians would have followed at the time they prescribed asthma medications in our study. We assumed that any oral corticosteroid (OCS) use was not used continuously (e.g., supply ≤28 days) by the patient and thus had no impact on GINA Steps. This decision was made given the difficulty in calculating a consistent day supply for OCS from the Practice Fusion prescription data. Treatment with SABA, SABA-SAMA, or SAMA was classified simply as SABA. Treatment with SABA only was defined as GINA Step 1. However, SABA use was allowed in all other steps. All individuals that were missing key information required for the GINA Step determination or had combinations of prescriptions that did not clearly meet definitions for a GINA Step were classified as ‘Undefined’.

Age in years was calculated as the difference between the calendar year of a patient’s index date and their birth year. Ethnicity was defined as ‘Hispanic’, ‘Non-Hispanic’ or ‘Missing’. Race was defined as ‘White’, ‘Black/African American’, ‘Other’ and ‘Unknown’. ‘Unknown’ race was assigned to individuals that had conflicting responses for race at any time in the database (e.g., patients may have multiple race information) or did not have any documentation of race in the Practice Fusion database. ‘Current’ smoking status was assigned to patients with a status of ‘current smoker’ on the smoking status record closest to their index date. ‘Former’ was assigned to patients with smoking status of ‘former smoker’ at any time on or before their index date. ‘Non-smoker’ was assigned to patients with only records of ‘non-smoker’ at any time in the database on or prior to their index date. Finally, we used the value for body mass index (BMI) in kg/m 2 that was recorded on the individual’s index date or a prior record closest (e.g., least number of days) to the index date. Visit type was categorized according to the specialty of the provider with whom the patient had an appointment for the encounter on their index date: ‘Primary Care’ includes ‘Internal Medicine’, ‘General Medicine’, and ‘Family Medicine’; ‘Specialist’ includes ‘Allergy and Immunology’, ‘Pulmonary Disease’, and ‘Emergency Medicine’; ‘Other’ includes all other specialties.

Statistical analysis

Descriptive frequencies both overall and by patient asthma control status at index date were calculated for each GINA Step. We used Poisson regression with robust variance to directly estimate the prevalence ratio (PR) and 95% confidence intervals (95% CI) of not well-controlled asthma by patient GINA Step at index date, adjusting for age, race, Hispanic ethnicity, smoking status, BMI, and the visit type at index date. Given that not well-controlled asthma was quite common in our study population (e.g., >10%), odds ratios derived from logistic regression would violate the rare disease assumption and consequently would overestimate the strength of associations and not approximate the relative risk 18 . However, Poisson regression models with robust variance can directly estimate the PR and are a suitable alternative to logistic regression modeling in cross-sectional studies with a dichotomous outcome 19 . The primary exposure of interest was GINA Step at index date with Step 1 as the reference group. The dependent variable (outcome) was not well-controlled asthma at index date, defined as an ACT score ≤19. We used a Directed Acyclic Graph to identify covariates for confounding control in the regression model. The final model included age (in years), BMI, race, Hispanic ethnicity, smoking status, and type of visit at index date.

The data used in this study are data collected from routine activity as part of patients’ interactions with the healthcare system through their provider’s medical records software. The original data collection is for administration and healthcare delivery purposes but is aggregated and deanonymized for research purposes. The analysis used fully deidentified retrospective data, and as such, this is not classified as research involving human participants as defined by 45 CFR 46.102(f) under the US Department of Health and Human Services Policy for Protection of Human Subjects ( https://www.hhs.gov/ohrp/regulations-and-policy/regulations/2018-req-preamble/index.html ). Therefore, institutional review board approval and informed consent were not required.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Overall baseline characteristics

Overall baseline characteristics are shown in Table 1 . We identified 15,579 treated patients with asthma for our study sample after applying all inclusion and exclusion criteria (Fig. 2 ). Overall, the sample had a mean age of 44 years (standard deviation = 22) and was predominantly female (64%, n  = 9995), non-Hispanic (80%, n  = 12,489), and white (46%, n  = 7153) (Table 1 ). The majority of the sample received their index ACT record at a primary care visit (55%, n  = 8527) with 22% ( n  = 3352) receiving their index ACT at a specialist visit and 22% ( n  = 3485) at a non-primary care/non-specialist visit.

Inclusion and exclusion criteria applied to construct the final study sample. ACT asthma control test, COPD chronic obstructive pulmonary disease, GINA Global Initiative for Asthma.

Baseline demographics and characteristics by GINA step

Individuals in GINA Step 1 were younger than the other GINA Step groups: mean 39.4 years for Step 1 compared with 44–56 years for Steps 2–5 and ‘Undefined’. Patients in the Step 5 GINA group were the oldest (mean 56 years). The sex composition was similar across all GINA Steps with males comprising 35 to 38% of each GINA Step group (Table 1 ).

Approximately 20% of patients in GINA Steps 1–4 and Undefined groups were of Hispanic Ethnicity. By comparison, GINA Step 5 had substantially fewer individuals of Hispanic Ethnicity (9.1%, n  = 15). The racial composition for white and African Americans across GINA Step groups (1–5) was relatively similar with 39–50% and 15–18%, respectively. While proportion of patients self-identifying as ‘Other’ and ‘Unknown’ race were similar for GINA Steps 1–4 and Undefined groups, GINA Step 5 had a much smaller proportion of patients identifying as ‘Other’ race and a greater proportion of those with ‘Unknown’ race.

While GINA Steps 1–4 groups had similar proportions of non-smokers (65–71%) and former smokers (12–15%), Step 5 and ‘Undefined’ groups had fewer non-smokers (55%, n  = 90 and 59%, n  = 114, respectively) and more former smokers (21%, n  = 34 and 20%, n  = 39, respectively). The greatest proportion of current smokers was observed in the ‘Undefined’ GINA Step group (14%, n  = 26) followed by Step 1 (12%, n  = 660), Step 5 (12%, n  = 19), Step 4 (10%, n  = 373), Step 3 (8%, n  = 172), and Step 2 (7%, n  = 244).

The percentage of individuals with ‘Obese’ BMI increased from GINA Step 1–5 with 39% ( n  = 2116) in Step 1, 40% ( n  = 1511) in Step 2, 41% ( n  = 894) in Step 3, 49% ( n  = 1921) in Step 4, and 55% ( n  = 90) in Step 5.

Similar proportions of ACT records at index date were recorded in primary care visits for GINA Steps 1–4 (48%–57.0%). However, relatively few individuals in GINA Step 1 received their index date ACT at a specialist visit (1%, n  = 570) compared with 56% ( n  = 92) for Step 5, 32% ( n  = 1234) for Step 4, 27% ( n  = 585) for Step 3, and 22% ( n  = 820) for Step 2.

Asthma control overall and by GINA step

At index date, 30% ( n  = 4597) of individuals had not well-controlled asthma compared with 71% ( n  = 10,982) with well-controlled asthma (Table 1 ). With respect to GINA Step, 35% ( n  = 5374) of the overall population were classified in Step 1; 24% ( n  = 3751) in Step 2; 14% ( n  = 2187) in Step 3; 25% ( n  = 3909) in Step 4; 1% ( n  = 165) in Step 5; and 1% ( n  = 193) were ‘Undefined’ GINA Step (Table 2 ). Distribution by GINA Step was similar for individuals with not well-controlled asthma compared with individuals with well-controlled asthma for Step 1 (34%, n  = 1572 vs. 35%, n  = 3802), Step 3 (13%, n  = 617 vs. 14%, n  = 1570), and ‘Undefined’ (1%, n  = 63 vs. 1%, n  = 130). However, fewer individuals with not well-controlled asthma were classified in GINA Step 2 compared with individuals with well-controlled asthma: 20% ( n  = 940) and 26% ( n  = 2811), respectively. By comparison, a larger proportion of individuals with not well-controlled asthma were classified as GINA Step 4 and Step 5 compared with individuals with well-controlled asthma; 29% ( n  = 1331) of individuals with not well-controlled asthma were in Step 4 compared with 23% ( n  = 2578) with well-controlled asthma, and in Step 5 the proportions were 2% ( n  = 74) and 1% ( n  = 91), respectively (Table 2 ).

Across all GINA Steps more than a quarter of individuals were classified as having not well-controlled asthma (Table 1 ). Figure 3 illustrates the absolute numbers and percentages of individuals with well-controlled and not well-controlled asthma at their index date by GINA Step. The largest proportion of individuals with not well-controlled asthma was within GINA Step 5 (45%, n  = 74); although it should be noted that relatively few individuals were classified in GINA Step 5 ( n  = 165) compared with the other steps. Among GINA Steps 1–4 (which had similarly large numbers of individuals), the largest burden of not well-controlled asthma was present in GINA Step 4 (34%, n  = 1331) followed by Step 1 (29%, n  = 1572), Step 3 (28%, n  = 617), and Step 2 (25%, n  = 940). Additional information on asthma control distribution by patient and provider characteristics can be found in Table 2 .

Percentage and number of individuals with ‘Not well-controlled’ and ‘Well-controlled’ asthma. GINA Global Initiative for Asthma.

Association between GINA step and asthma control at index date

Compared with patients in Step 1, the proportion of patients with not well-controlled asthma was 0.87 times lower among patients in Step 2 (PR: 0.87, 95% CI: 0.79–0.94), 1.10 times greater among patients in Step 4 (PR: 1.10, 95% CI: 1.03–1.16), and 1.37 times greater among patients in Step 5 (PR: 1.37, 95% CI: 1.19–1.55), after adjusting for age, race, Hispanic ethnicity, smoking status, BMI, and visit type at index date (Fig. 4 ). We did not observe significant differences in not well-controlled asthma among patients in Step 3 compared with Step 1 in our fully adjusted model.

Adjusted prevalence ratio of ‘Not well-controlled’ asthma by GINA Step among the treated asthma population. CI confidence interval, GINA Global Initiative for asthma, PR prevalence ratio.

Results from this sample of 15,579 treated patients with asthma in the US showed that despite a variety of available treatments, patients with asthma remain symptomatic across GINA Steps in real-world primary care and specialist outpatient practices. At index date, nearly one-third of our sample had not well-controlled asthma despite receiving prescriptions for asthma treatment in the 6 months prior to the 4-week recall period of their ACT record. The proportion of patients with not well-controlled asthma in our sample was lower than in previous studies under more controlled study design 20 , 21 . This may indicate a potential bias in patients that were administered the ACT. We observed an increasing proportion of individuals with not well-controlled asthma from GINA Step 1 to GINA Step 5 in our descriptive analysis. However, when comparing GINA Steps, our modeling results only showed significant differences in the proportion of patients with not well-controlled asthma between Steps 2, 4, and 5 compared with Step 1 after full adjustment (not Step 3). We observed significantly better asthma control in GINA Step 2 compared with GINA Step 1; while we observed significantly worse asthma symptom control in GINA Steps 4 and 5 compared with GINA Step 1. The finding that asthma control was significantly better in GINA Step 2 compared with Step 1 may also suggest that patients in the Step 1 group are misclassified and undertreated, and therefore should be assigned to a higher GINA Step.

The significantly better asthma control among patients in GINA Step 2 compared with GINA Step 1 may be attributed to the addition of a regular controller medication in addition to a reliever medication in Step 2 compared with only a reliever for Step 1. As-needed SABA with no controller was the recommendation for Step 1 in the GINA 2018 guidelines which were used during the observation period for this study 17 . However, in a major change from the 2018 recommendations, the GINA 2019 guidelines no longer recommend SABA-only treatment (without ICS) and instead recommend as-needed low-dose combination ICS-formoterol as a controller for Step 1 in addition to the reliever 13 .

We did not see a significant difference in asthma control when comparing patients in Step 3 with the Step 1 group. Steps 4 and 5 represent severe and difficult-to-treat asthma 13 , 14 , which likely account for the significantly higher proportions of not well-controlled asthma in these steps compared with Step 1.

There are several strengths to our study that warrant mention. Our study was able to link systematically measured asthma symptom control via the ACT and prescription information in a real-world setting. We also had sufficient prescription information and history within Practice Fusion’s EMR to calculate GINA Steps for our patient population, enabling us to describe burden of not well-controlled asthma among patients across all GINA Steps. Additionally, the use of this real-world data source allowed our study to identify a large number of patients with asthma, across the entire asthma continuum, with a mixture of asthma symptom control. Finally, we were able to demonstrate that the significant differences in asthma control at higher GINA Steps were maintained after adjusting for confounding.

Despite the variety and availability of asthma treatment, there are large numbers of patients with asthma that remain symptomatic across the treatment continuum in real-world primary care and specialist outpatient practices. Consequently, our study highlights the importance of assessing asthma symptom control at every opportunity; and evaluating treatment response, inhaler technique, adherence, and environmental exposures for patients with symptomatic asthma. There are several limitations to our study. The EHR data is intended for clinical decision making, not research. Thus, in using routinely collected healthcare data for research we must recognize the limitations in data quality. In addition, the Practice Fusion asthma patient population is an open cohort, in which patients may enter or leave care at any time. As such, while investigating changes in GINA Step over time may provide added insight into patients’ asthma control status, the current study was limited by the data available in the Practice Fusion database.

Moreover, given that Practice Fusion is used primarily by smaller outpatient primary and specialist care offices in the US, it is likely that the asthma patient population in the Practice Fusion research database will be systematically different from the overall US asthma population which includes patients seen in large practices, inpatient facilities, or other healthcare settings. Indeed, patients in this study population were primarily of White or Unknown race, making it challenging to assess the impact of variables such as socioeconomic status and race/ethnicity on asthma control, which have previously been linked to asthma-related health outcomes 22 , 23 , 24 , 25 . Additionally, as the dataset does not include all levels of care (e.g., inpatient care) or data from large healthcare systems, the study population may represent patients with better control/less severe disease compared with a sample that included ACT measures from across the healthcare continuum. Further, the ACT was administered by clinicians via a clinical decision support prompt in the EHR rather than self-administered by the patient. This process leads to the possibility of reporting bias as patient answers may reflect how they would like to be perceived by the clinician rather than their actual experience.

There are also limitations in using GINA Step categorization for research purposes. Prescriptions represent the intent of the prescriber not actual medication use or adherence. We assumed that patients took their prescription medications as prescribed. We were reliant on natural language processing of prescription signature information captured in the Practice Fusion database for the calculation of ICS daily dose; which may have incorrectly specified actual frequency and/or dosage. Due to the limitations with the prescription data in the Practice Fusion EHR, we cannot account for daily OCS that is typically used in calculating GINA Step 5. We had to assume that all OCS use was less than or equal to 28 days; and, consequently had no impact on GINA Step. Furthermore, it is worth noting that the current study did not differentiate between not well-controlled and poorly controlled asthma.

Lastly, our analysis did not assess the duration at which a patient was in a GINA Step. Given the nature of the EHR data, our patient population contained a mixture of patients that: (1) recently initiated asthma treatment (new users); (2) recently modified their previous asthma treatment; and 3() had used a specific asthma treatment for an extended period and were renewing the prescription that had been working for them (prevalent users). Our analysis was not able to distinguish between these three distinct groups of patients.

Data availability

Anonymized individual participant data and study documents can be requested for further research from www.clinicalstudydatarequest.com .

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Acknowledgements

This study was funded by GSK (GSK study number: 206913). The funders of the study had a role in study design, data analysis, data interpretation, and writing of the report. The corresponding author had full access to all the data and the final responsibility to submit for publication. Editorial support in the form of collation and incorporation of author feedback to develop draft 2 and the final draft of the paper, was provided by Joanne Ashworth and Evelin O. Szalai, and was funded by GSK. This work was previously presented at American Thoracic Society (ATS) 2020 (Davitte J, DeBarmore B, Hinds D, Zhang W, Sansbury L. Asthma control among the treated U.S. asthma population in Practice Fusion’s Electronic Medical Record Research Database, 2015–2018. American Journal of Respiratory and Critical Care Medicine 2020;201:A1814).

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Davitte, J., DeBarmore, B., Hinds, D. et al. Asthma control among treated US asthma patients in Practice Fusion’s electronic medical record research database. npj Prim. Care Respir. Med. 33 , 17 (2023). https://doi.org/10.1038/s41533-023-00338-7

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DOI : https://doi.org/10.1038/s41533-023-00338-7

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Clinical Trials

Displaying 35 studies

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• Work-Related Asthma

Allergies are one of the most common chronic diseases. An allergy occurs when the body’s immune system sees a substance as harmful and overreacts to it. The substances that cause allergic reactions are  allergens .

• Drug Allergies
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• Nasal Allergies (Rhinitis)
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• The Allergic March

AAFA offers a variety of educational programs, resources and tools for patients, caregivers, and health professionals. AAFA launches educational awareness campaigns throughout the year. We teach the general public about asthma and allergic diseases.

• ACT for Asthma & Allergy
• No Appetite for Bullying
• Type 2 Inflammation
• Health Equity
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• Managing Food Allergies
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Research & Reports

Research is an important part of our pursuit of better health. Through research, we gain better understanding of illnesses and diseases, new medicines, ways to improve quality of life and cures. The Asthma and Allergy Foundation of America (AAFA) conducts and promotes research for asthma and allergic diseases.

• Asthma Capitals

Allergy Capitals

• Asthma Disparities in America
• AFFORD Asthma Study
• Anaphylaxis in America
• Atopic Dermatitis in America
• Life with Eosinophilic Esophagitis (EoE)
• My Life With Asthma
• My Life With Food Allergy
• Climate Change & Health Report
• Patient Focused Drug Development
• Access to Pseudoephedrine

Clinical Trials

Patient engagement in asthma research.

• For Researchers
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AAFA works to support public policies that will benefit people with asthma and allergies. Advocacy and public policy work are important for protecting the health and safety of those with asthma and allergies. We advocate for federal and state legislation as well as regulatory actions that will help you.

• Become an Advocate
• AAFA’s Positions & Statements
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• Accessing Your Medical Records
• Cost of Asthma on Society
• Patient and Family Engagement
• Asthma in Schools
• Access to Health Care
• Albuterol in Schools
• Epinephrine in Schools
• National Asthma Control Program
• Food Allergies in Child Care Settings
• Food Allergen Labeling
• Health Disparities
• Healthy Settings

Get Involved

There are several ways you can support AAFA in its mission to provide education and support to patients and families living with asthma and allergies. You can make a donation, fundraise for AAFA, take action in May for Asthma and Allergy Awareness Month, and join a community to get the help and support you need.

• Get Support
• Planned Giving
• Fundraise for AAFA
• Take Action
• Social Media Tools
• Eczema Awareness Month

AAFA can connect you to all of the information and resources you need to help you learn more about asthma and allergic diseases.

• AAFA’s e-Newsletters
• Press Releases
• FreshAAIR Magazine – Previous Issues
• Join the Community

Research & Reports

This collection of 12 videos are part of AAFA’s project: Promoting Asthma Patient Engagement in Research (PAPER). We believe that patients have a valuable voice and an important role in advancing research.

Full Program Video: Patient Engagement in Asthma Research

This compilation is 37:20 minutes long and includes all of our project’s episodes in one video. If you would rather watch shorter episodes, you can find them below.

Asthma Basics

Different Levels of Asthma

Warning Signs of an Asthma Attack

Asthma Triggers

Asthma Treatment

Why We Need Asthma Research

Finding Accurate Research Information

Outcome Measures

Clinical Research

Acknowledgements

AAFA's Commitment to Racial Justice

Over the past 15 years, there have been moderate advances in U.S. public policy, health care and research, but racial gaps in asthma outcomes have not changed. Minority groups continue to bear disproportionate hardship in managing asthma.

Your location can have an impact on your seasonal allergies. AAFA’s Allergy Capitals™ report looks at the top 100 most challenging cities in the continental United States to live with seasonal pollen allergies.

Read the Report>

Support Our Work

Donate Today >

Related Content

Asthma Disparities in America: It’s Time for Real and Lasting Change

2022 Asthma Capitals™ Report: Where Does Your City Rank?

2023 Allergy Capitals™ Report: Where Does Your City Rank?

View more blog articles >

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Sample Collections From the Airways of Asthmatic Patients

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Fiberoptic bronchoscopy is a procedure which involves passing a pencil-thin tube into the lung in order to collect fluid and cells from the airways. Fiberoptic bronchoscopy can collect cells from the walls of airways by gently brushing them (bronchial brushing). In addition, squirting small amounts of sterile water in to the airway and gently suctioning it back into the bronchoscope (bronchoalveolar lavage) collects cells.

In this study, researchers plan to perform these tests on patients with asthma and normal volunteers. This research may help to improve the understanding of the processes involved in airway inflammation and asthma....

• INCLUSION CRITERIA:

ASTHMATICS:

• Patients undergoing a research bronchoscopy will be between 18 and 75 years of age, male or female. The diagnosis of asthma requires a history of intermittent, reversible expiratory flow limitation. In addition, patients will have demonstrated evidence of either an abnormal methacholine challenge or reversible airway obstruction. An abnormal methacholine challenge will be defined as a decrease in FEV1 of at least 20% at a PD20 dose < 200 microgram. Reversible airway obstruction will be defined as an improvement of at least 10% in either the FEV1 or FVC following bronchodilator treatment. Methacholine challenge testing will not be performed if the subject has a history of allergy to methacholine. Results of testing performed by the subject s primary care provider may be accepted as evidence of reversible airflow obstruction.

For women of childbearing potential, negative pregnancy test and willingness to adhere to reliable birth control methods prior to

bronchoscopy or sputum induction.

• Asthmatic research subjects who will only be providing research blood specimens, nasal epithelial lining fluid, spontaneously expectorated sputum, or exhaled breath condensate, and will not be undergoing a research bronchoscopy or sputum induction, may participate in the protocol by providing a clinical history that they have asthma and are not pregnant. Documentation of an abnormal methacholine challenge or reversible airflow obstruction or a negative pregnancy test, for women of childbearing age, will not be required for donation of research blood specimens, or other non-invasive samples, such as nasal epithelial lining fluid, sputum, or exhaled breath condensate.

EXCLUSION CRITERIA:

Diagnosis of a pulmonary disorder other than asthma (e.g., chronic bronchitis, cystic fibrosis, HIV-related lymphocytic airway inflammation).

History of drug or alcohol abuse within the past year.

Positive test for human immunodeficiency virus (to exclude patients with HIV-related lymphocytic airway inflammation) or hepatitis virus.

INCLUSION CRITERIA - RESEARCH VOLUNTEERS:

• Research volunteers undergoing a research bronchoscopy will be between 18 and 75 years of age, male or female.
• A negative inhalational methacholine challenge as defined by the absence of a 20% decrease in FEV1 at a PD20 dose of > 400 microgram (normal bronchial hyperresponsiveness). Methacholine challenge testing will not be performed if the subject has a history of allergy to methacholine. Results of testing performed by the subject s primary care provider may be accepted as evidence of a negative methacholine challenge.
• For women of childbearing potential, negative pregnancy test within 2 weeks prior to bronchoscopy and willingness to adhere to reliable birth control methods prior to bronchoscopy or sputum induction.
• Research volunteers who will only be providing research blood specimens, nasal epithelial lining fluid, sputum, spontaneously expectorated sputum, or exhaled breath condensate, and will not be undergoing a research bronchoscopy or sputum induction, may be included in the protocol without documentation of a negative inhalational methacholine challenge or a negative pregnancy test if they provide a history that they do not have asthma and that they are not currently pregnant, for women of childbearing

EXCLUSION CRITERIA - RESEARCH VOLUNTEERS:

Same as the asthmatic exclusion criteria plus a history of asthma.

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