Asthma is a common chronic inflammatory disease of the airways characterised by variable respiratory symptoms, including wheezing, breathlessness, chest tightness, and cough, together with fluctuating expiratory airflow limitation. It affects individuals across all age groups and remains an important cause of morbidity, impaired quality of life, healthcare utilisation, and preventable mortality. Despite considerable advances in diagnostic approaches and pharmacological treatment, achieving sustained asthma control remains challenging in routine clinical practice. According to the Global Burden of Disease Study 2021, approximately 260 million individuals worldwide were living with asthma, with considerable differences in disease burden across geographical regions and socioeconomic groups. The persistence of asthma-related disability and mortality highlights the importance of identifying factors that interfere with effective disease management, particularly among adults requiring regular respiratory care [1]. The primary objective of asthma management is to achieve adequate symptom control while minimising the risk of exacerbations, persistent airflow limitation, and treatment-related adverse effects. The Global Initiative for Asthma (GINA) emphasises that asthma control should be evaluated through two complementary domains: current symptom control and future risk of adverse outcomes. Symptom control is assessed by considering the frequency of daytime respiratory symptoms, nocturnal awakening, reliever medication use, and limitations in daily activities. Poor asthma control may present as recurrent respiratory complaints, disturbed sleep, reduced physical activity, or frequent reliance on rescue medication. Importantly, patients with relatively few symptoms may still be at increased risk of severe exacerbations because of underlying clinical characteristics or inadequate preventive treatment. A comprehensive assessment is therefore necessary to identify both symptomatic disease and factors associated with future deterioration [2]. Evidence from observational studies indicates that inadequate asthma control remains common even among patients receiving prescribed treatment. The REcognise Asthma and LInk to Symptoms and Experience (REALISE) survey, involving 8,000 adults with asthma across 11 European countries, reported that 45% had uncontrolled asthma, although many participants perceived their condition as adequately controlled. This difference between perceived and objectively assessed control demonstrates the limitations of relying exclusively on patients' subjective impressions during follow-up consultations [3]. Similar concerns have been reported in India. Shrinath and colleagues conducted a cross-sectional study among 80 patients attending a tertiary respiratory care centre in Western Maharashtra and observed uncontrolled asthma in 41.25% of participants. Poor inhaler technique was significantly associated with inadequate asthma control, suggesting that treatment-related factors contribute substantially to persistent symptoms in outpatient settings [4]. Among the factors contributing to inadequate asthma control, poor adherence to prescribed controller medication is particularly important because it is common and potentially modifiable. Inhaled corticosteroids (ICS) remain central to the long-term management of asthma because they suppress airway inflammation and reduce the risk of exacerbations. However, some patients discontinue treatment when symptoms improve, forget scheduled doses, or avoid regular medication because of concerns about adverse effects, treatment costs, or limited understanding of the disease. Engelkes et al., in a systematic review examining medication adherence and severe asthma exacerbations, found that better adherence generally corresponded with a lower risk of severe exacerbations. Their findings support the importance of reviewing medication-taking behaviour before escalating treatment in patients with persistent symptoms [5]. Incorrect inhaler technique represents another major obstacle to effective asthma management. Even when appropriate medications are prescribed, inadequate coordination between inhaler activation and inspiration, insufficient inspiratory effort, and failure to follow device-specific instructions may reduce drug delivery to the airways. Sanchis et al., in a systematic review involving 144 studies and more than 54,000 participants, reported that correct inhaler technique was observed in only approximately one-third of assessments. These findings demonstrate that inhaler errors remain widespread despite the availability of educational interventions [6]. Furthermore, Sulaiman et al. demonstrated that structured education supported by feedback on inhaler use improved adherence among patients with severe uncontrolled asthma. These observations indicate that assessment of inhaler technique and medication adherence should form an essential component of routine respiratory clinic evaluations [7]. Excessive reliance on short-acting beta-2 agonists (SABAs) is another important indicator of potentially inadequate asthma management. Frequent use of reliever medication may temporarily improve respiratory symptoms without adequately addressing underlying airway inflammation. In a nationwide cohort study conducted as part of the SABINA programme, Nwaru et al. found that increasing SABA overuse was associated with a higher risk of asthma exacerbations and mortality. These findings highlight the clinical importance of reviewing reliever use alongside prescribed anti-inflammatory treatment [8]. Patient-related characteristics and coexisting medical conditions may also influence asthma control. Obesity has been associated with greater asthma severity, altered respiratory mechanics, systemic inflammation, and reduced functional capacity. Scott et al., in a systematic review and meta-analysis, demonstrated differences in airway and systemic inflammatory characteristics between obese and non-obese adults with asthma, suggesting that obesity-related physiological changes may contribute to poorer respiratory outcomes [9]. Other conditions, including allergic rhinitis, chronic rhinosinusitis, gastroesophageal reflux disease, obstructive sleep apnoea, anxiety, and depression, may increase respiratory symptom burden or complicate disease management. Porsbjerg and Menzies-Gow emphasised the importance of recognising and managing comorbidities during the assessment of difficult-to-treat asthma [10]. Environmental and behavioural exposures further complicate asthma management. Tobacco smoking, second-hand smoke exposure, household allergens, occupational dust, chemical fumes, and ambient air pollution may trigger respiratory symptoms or contribute to exacerbation risk. Socioeconomic difficulties, limited healthcare access, inadequate knowledge of asthma, and poor continuity of follow-up may also interfere with effective disease management. These factors are especially relevant in settings where patients experience financial constraints or difficulties obtaining regular controller medication. Current international guidelines recommend systematically assessing environmental exposures, psychosocial problems, treatment barriers, and relevant comorbidities when evaluating patients with persistent symptoms [2]. Accurate identification of poor asthma control requires standardised clinical assessment rather than symptom history alone. The Asthma Control Test (ACT) is a validated, patient-reported questionnaire that evaluates asthma-related symptoms and functional limitations over the preceding four weeks. Nathan et al. developed the ACT as a practical instrument for assessing asthma control in routine clinical settings [11]. Incorporating such tools into respiratory outpatient services may facilitate early recognition of inadequately controlled disease. When clinically appropriate, spirometry provides additional information about airflow limitation and future risk, while a detailed review of treatment practices and potential triggers helps identify modifiable contributors to poor outcomes. Although several factors associated with inadequate asthma control have been documented, their relative importance may vary according to patient characteristics, healthcare accessibility, environmental conditions, and local treatment practices. Findings from community-based surveys or highly specialised severe-asthma centres may not fully represent the clinical characteristics of adults attending general respiratory medicine outpatient clinics. Identifying locally relevant associations is therefore important for developing practical strategies to improve disease management. The present study aims to assess the level of asthma control and identify demographic, clinical, environmental, and treatment-related factors associated with poor asthma control among adult patients attending a respiratory medicine clinic. The findings may assist clinicians in recognising patients at increased risk of inadequate control and implementing targeted interventions involving patient education, improved inhaler technique, medication adherence, management of comorbidities, and appropriate follow-up care.

A hospital-based, analytical cross-sectional study was designed to evaluate factors associated with poor asthma control among adult patients attending the Respiratory Medicine outpatient clinic at Siddhi Medical Hospital and Respiratory Care Centre, Patan, Gujarat, India. The study period extended over 24 months, from July 2024 to June 2026. The study framework included an assessment of demographic characteristics, clinical history, asthma-related symptoms, environmental exposures, medication adherence, inhaler technique, pulmonary function, and associated comorbidities. The primary objective was to determine the proportion of patients with poorly controlled asthma and identify independent factors associated with inadequate disease control. Each participant was assessed at a single index outpatient visit, thereby avoiding repeated inclusion of the same individual. The study design and reporting framework were based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations for cross-sectional studies. Study Population and Sample Size The study population comprised 750 adult patients aged 18 years or older with an established diagnosis of bronchial asthma who attended the Respiratory Medicine outpatient clinic during the study period. Asthma diagnosis was established through clinical history, previous medical documentation, and available objective evidence of variable expiratory airflow limitation, consistent with the Global Initiative for Asthma (GINA) recommendations. A non-probability consecutive sampling strategy was selected to recruit eligible patients presenting during the study period. Consecutive recruitment was intended to reduce discretionary patient selection and improve representation of the outpatient population. Each patient was included only once, irrespective of the number of subsequent follow-up visits. The sample size was fixed at 750 participants. This sample was considered appropriate for estimating the frequency of poor asthma control with reasonable precision and examining multiple clinically relevant associations. All eligible participants were assessed using a standardised data collection framework. Inclusion criteria 1. Adult patients aged 18 years or older, irrespective of sex. 2. Patients with a physician-established diagnosis of bronchial asthma supported by clinical documentation. 3. Patients attending the Respiratory Medicine outpatient clinic for evaluation, treatment, or routine follow-up. 4. Patients able to provide information concerning respiratory symptoms, medication use, and relevant clinical history. 5. Patients who provided informed consent, where required by the approved study protocol. Exclusion criteria 1. Patients without sufficient clinical evidence to support a diagnosis of asthma. 2. Patients with another predominant respiratory disorder, such as established chronic obstructive pulmonary disease, bronchiectasis, or interstitial lung disease, where attribution of symptoms to asthma could not be made reliably. 3. Patients with severe cognitive impairment or communication difficulties preventing reliable completion of the asthma control assessment. 4. Patients requiring immediate emergency stabilisation who could not safely undergo the study assessment. 5. Patients with incomplete primary outcome information that prevented valid calculation of the ACT score. Patients with common asthma-related comorbidities, including allergic rhinitis, obesity, gastroesophageal reflux disease, and obstructive sleep apnoea, were retained because these conditions represented potential explanatory variables. Data Collection Procedure Clinical and demographic information was obtained through a structured patient interview, clinical examination, and review of available medical records. A standardised case record form was used to collect information under the following domains. Sociodemographic characteristics: Age, sex, place of residence, educational status, occupation, and socioeconomic characteristics were recorded to examine potential differences in asthma control across patient groups. Clinical characteristics: Information included age at asthma diagnosis, duration of disease, family history of asthma, previous hospitalisation, history of asthma-related emergency visits, exacerbations during the preceding 12 months, and the presence of physician-diagnosed allergic disorders. Current smoking status, previous tobacco exposure, and household exposure to second-hand smoke were also documented. Environmental and occupational exposures: Information was collected regarding domestic dust, biomass fuel smoke, household pets, dampness or mould, workplace dust, chemical fumes, and other patient-reported respiratory triggers. Exposure history was based on patient interviews and clinical documentation rather than direct environmental monitoring. Treatment characteristics: Details of prescribed asthma medications, inhaler devices, inhaled corticosteroid-containing treatment, reliever medication use, adherence to prescribed therapy, and previous treatment modifications were recorded. Medication details were cross-checked against available prescriptions and treatment records. Assessment of Asthma Control Asthma control was assessed using the Asthma Control Test (ACT), a validated five-item questionnaire designed to evaluate asthma-related impairment during the preceding four weeks. The ACT evaluates limitations in daily activities, frequency of shortness of breath, nocturnal or early-morning symptoms, use of rescue medication, and the patient's overall perception of asthma control. Each item is scored from 1 to 5, generating a cumulative score ranging from 5 to 25, with higher scores indicating better control. Participants were classified according to their total ACT scores as follows: ACT score Classification 20–25 Well-controlled asthma 16–19 Not well-controlled asthma 5–15 Very poorly controlled asthma For the primary analysis, participants with ACT scores of 19 or less were classified as having poor asthma control, whereas those scoring 20–25 were classified as having well-controlled asthma. The ACT total score was also retained as a continuous measure for supplementary analyses. Symptom control assessed using the ACT was distinguished from future exacerbation risk, which was evaluated separately through clinical history and relevant risk factors. Where questionnaires were administered in a regional language, the version used and any translation or validation procedures were to be documented. Assessment of Potential Factors Associated With Poor Asthma Control Potential explanatory variables were selected based on clinical relevance and established determinants of asthma management. Medication adherence Adherence to prescribed controller medication was evaluated through a structured interview focusing on missed doses, treatment interruptions, and regularity of medication use during the preceding four weeks. Where the information permitted quantification, adherence was expressed as the proportion of prescribed doses reportedly taken. Participants taking at least 80% of prescribed controller doses were categorised as having adequate self-reported adherence, while those taking less than 80% were considered to have suboptimal adherence. This assessment was recognised as a self-reported estimate rather than an objective electronic measure of medication use. Patients without prescribed controller treatment were identified separately. Inhaler technique Inhaler technique was evaluated by asking patients to demonstrate their usual technique using the prescribed inhaler device or an appropriate demonstration device. Assessment was based on a device-specific checklist covering preparation, inhalation manoeuvre, coordination where applicable, breath-holding, and completion of the procedure. Patients were categorised according to the presence or absence of critical technique errors likely to substantially compromise medication delivery. Device type was documented because inhalation requirements differ between pressurised metered-dose inhalers and dry-powder inhalers. Smoking and environmental exposures Participants were classified as never-smokers, former smokers, or current smokers. Where available, cumulative cigarette exposure was expressed in pack-years. Passive smoking, biomass fuel exposure, household allergens, and occupational irritants were documented through patient history. Environmental exposures were analysed as potential correlates of asthma control rather than direct measures of pollutant concentration. Comorbid conditions Associated medical conditions were identified through clinical history and available medical documentation. These included allergic rhinitis, chronic rhinosinusitis, gastroesophageal reflux disease, obesity, obstructive sleep apnoea, diabetes mellitus, and other clinically relevant disorders. Height and weight were measured or extracted from documented clinical observations, and body mass index (BMI) was calculated as weight in kilograms divided by the square of height in metres. Comorbidities were treated as separate explanatory variables where sufficient data were available. Asthma exacerbation history An asthma exacerbation was defined as an acute or subacute worsening of respiratory symptoms and lung function requiring an unplanned escalation of treatment. Information on exacerbations during the preceding 12 months was obtained from patient history and available medical records. Events requiring systemic corticosteroids, emergency department attendance, or hospital admission were documented separately to characterise exacerbation burden. Previous exacerbations were evaluated as markers of disease burden associated with current asthma control; their inclusion did not imply a causal relationship with the ACT outcome. Pulmonary Function Assessment Pulmonary function was assessed using available spirometry performed during routine clinical evaluation. Measurements included forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), and the FEV₁/FVC ratio. When clinically indicated and available, bronchodilator responsiveness was evaluated following administration of an appropriate inhaled bronchodilator. Spirometry procedures and interpretation were based on American Thoracic Society/European Respiratory Society technical recommendations. FEV₁ expressed as a percentage of the predicted value was considered an additional indicator of airflow limitation. Pulmonary function findings were interpreted alongside clinical information and were not used as substitutes for symptom-based asthma control assessment. Participants without usable spirometry results were retained in the primary analysis if valid ACT assessments and other essential information were available. The number of missing pulmonary function observations was to be reported explicitly. Outcome Measures • The primary outcome was the presence of poor asthma control, defined as an ACT score of 19 or less. • Secondary outcomes included the distribution of ACT scores, differences in patient characteristics between well-controlled and poorly controlled groups, and the associations between asthma control and treatment-related, clinical, demographic, and environmental variables. • An additional objective was to identify factors independently associated with poor asthma control after adjustment for clinically relevant confounding variables. Statistical Analysis Statistical analysis was specified using IBM SPSS Statistics or equivalent validated statistical software. Categorical variables were to be summarised as frequencies and percentages. Continuous variables were to be expressed as mean ± standard deviation for approximately normally distributed data or median and interquartile range for skewed distributions. The normality of continuous variables was assessed through graphical methods and appropriate distributional tests. Differences between patients with well-controlled and poorly controlled asthma were evaluated using the independent-samples Student's t-test or Mann–Whitney U test, as appropriate. Associations between categorical variables were assessed using Pearson's chi-square test or Fisher's exact test when expected cell frequencies were insufficient. Univariable binary logistic regression was used to examine the relationship between individual explanatory variables and poor asthma control. Subsequently, multivariable logistic regression was specified to identify independent associations while adjusting for potential confounding. Candidate predictors were selected based on clinical relevance, previous literature, and the number of outcome events available for reliable estimation. These included age, sex, BMI, smoking status, duration of asthma, medication adherence, inhaler technique, environmental exposures, treatment characteristics, and major comorbidities. Adjusted odds ratios (aORs) with 95% confidence intervals were to be reported. Continuous predictors were retained in their original form where appropriate, and potential non-linear relationships were assessed before imposing categorical thresholds. Multicollinearity was examined using variance inflation factors and related diagnostic measures. Given that poor asthma control may represent a relatively common outcome, a supplementary modified Poisson regression with robust standard errors was considered for estimating adjusted prevalence ratios. Model discrimination was evaluated using the area under the receiver operating characteristic curve, while calibration was assessed through comparison of predicted and observed outcome probabilities. Missing data were quantified for each variable. Complete-case analysis was appropriate where missingness was minimal; multiple imputation was considered for explanatory variables with substantial missing data when the missing-at-random assumption was reasonably defensible. All statistical tests were two-sided, and a p-value below 0.05 was considered statistically significant. Interpretation emphasised effect sizes, confidence intervals, and clinical relevance rather than statistical significance alone

In the illustrative dataset, 750 adult patients with bronchial asthma were included, of whom 428 (57.1%) had well-controlled asthma and 322 (42.9%) had poor asthma control based on their Asthma Control Test (ACT) scores. The mean age of participants was 47.9 ± 14.4 years, and 418 (55.7%) were female. The mean age was comparable between the well-controlled and poorly controlled groups (47.8 ± 14.0 versus 47.9 ± 14.8 years; p=0.905). No statistically significant differences were observed with respect to sex, residence, educational status, BMI, or asthma duration. However, current smoking was significantly more common among patients with poor asthma control (20.5%) than among those with well-controlled asthma (12.9%; p=0.007). Table 1. Baseline demographic and anthropometric characteristics of adult patients according to asthma control status (n=750) Characteristics Total (n=750) Well-controlled (n=428) Poorly controlled (n=322) p-value Age (years), mean ± SD 47.9 ± 14.4 47.8 ± 14.0 47.9 ± 14.8 0.905 Age ≥60 years 150 (20.0%) 76 (17.8%) 74 (23.0%) 0.077 Female sex 418 (55.7%) 238 (55.6%) 180 (55.9%) 0.995 Rural residence 304 (40.5%) 172 (40.2%) 132 (41.0%) 0.883 Education up to secondary level 351 (46.8%) 197 (46.0%) 154 (47.8%) 0.679 BMI (kg/m²), mean ± SD 26.1 ± 4.2 26.0 ± 4.2 26.2 ± 4.3 0.483 Obesity (BMI ≥30 kg/m²) 124 (16.5%) 62 (14.5%) 62 (19.3%) 0.101 Current smoker 121 (16.1%) 55 (12.9%) 66 (20.5%) 0.007 Asthma duration (years), mean ± SD 8.5 ± 5.7 8.6 ± 5.5 8.3 ± 5.9 0.466 Asthma duration ≥10 years 236 (31.5%) 141 (32.9%) 95 (29.5%) 0.355 Data are presented as n (%) unless otherwise specified. Percentages are calculated using the corresponding group denominators. Student's t-test and Pearson's chi-square test were applied as appropriate. SD: standard deviation; BMI: body mass index. Assessment using the ACT indicated that 428 patients (57.1%) had well-controlled asthma, while 196 (26.1%) had not well-controlled asthma and 126 (16.8%) had very poorly controlled asthma. The overall mean ACT score was 19.5 ± 4.0. Patients with well-controlled asthma had a mean score of 22.3 ± 1.6, compared with 17.7 ± 1.0 among those with not well-controlled asthma and 12.4 ± 2.0 among those with very poorly controlled asthma. The combined prevalence of poor asthma control, defined by an ACT score ≤19, was 42.9% (322/750). Table 2. Distribution of participants according to Asthma Control Test classification (n=750) Asthma control category ACT score Number of patients Percentage ACT score, mean ± SD Well-controlled asthma 20–25 428 57.1% 22.3 ± 1.6 Not well-controlled asthma 16–19 196 26.1% 17.7 ± 1.0 Very poorly controlled asthma 5–15 126 16.8% 12.4 ± 2.0 Total 5–25 750 100.0% 19.5 ± 4.0 Clinical assessment demonstrated that a history of asthma exacerbation during the preceding 12 months was more frequent among patients with poor asthma control than among those with well-controlled asthma (41.0% versus 27.1%; p<0.001). Allergic rhinitis was present in 38.1% of participants and was significantly more common in the poorly controlled group (44.1% versus 33.6%; p=0.005). Gastroesophageal reflux disease was also associated with poor asthma control (28.3% versus 19.9%; p=0.009). No statistically significant association was observed between obstructive sleep apnoea and asthma control status. Usable spirometry results were available for 621 patients (82.8%). Mean FEV₁ was significantly lower in patients with poor asthma control (72.4 ± 16.4% predicted) than in those with well-controlled asthma (85.0 ± 13.7% predicted; p<0.001). Among participants with available spirometry, FEV₁ below 80% of predicted was observed in 67.5% of patients with poor asthma control compared with 35.5% of patients with well-controlled asthma. Table 3. Clinical characteristics, associated comorbidities, and pulmonary function according to asthma control status Clinical variables Total (n=750) Well-controlled (n=428) Poorly controlled (n=322) p-value Exacerbation in previous 12 months 248 (33.1%) 116 (27.1%) 132 (41.0%) <0.001 Allergic rhinitis 286 (38.1%) 144 (33.6%) 142 (44.1%) 0.005 Gastroesophageal reflux disease 176 (23.5%) 85 (19.9%) 91 (28.3%) 0.009 Obstructive sleep apnoea 83 (11.1%) 50 (11.7%) 33 (10.2%) 0.616 FEV₁ (% predicted), mean ± SD 79.4 ± 16.2 85.0 ± 13.7 72.4 ± 16.4 <0.001 FEV₁ <80% predicted* 309 (49.8%) 122 (35.5%) 187 (67.5%) <0.001 Reatment-related characteristics differed significantly between the two asthma control groups. Among the 750 participants, 405 (54.0%) reported adequate adherence to prescribed controller medication, 254 (33.9%) reported suboptimal adherence, and 91 (12.1%) were not prescribed controller therapy. Suboptimal adherence was reported by 42.2% of patients with poor asthma control compared with 27.6% of those with well-controlled asthma. Similarly, critical inhaler technique errors were more common among poorly controlled patients (42.9% versus 30.1%; p<0.001). Frequent reliever medication use was observed in 58.7% of the poorly controlled group compared with 13.1% of the well-controlled group (p<0.001). Household exposure to smoke or respiratory irritants was also more prevalent among patients with poor asthma control (42.9% versus 33.4%; p=0.010). Occupational dust or chemical exposure was not significantly associated with asthma control status. Table 4. Comparison of treatment-related characteristics and environmental exposures between asthma control groups Variables Total (n=750) Well-controlled (n=428) Poorly controlled (n=322) p-value Controller medication status <0.001 Adequate adherence 405 (54.0%) 269 (62.9%) 136 (42.2%) — Suboptimal adherence 254 (33.9%) 118 (27.6%) 136 (42.2%) — No controller prescribed 91 (12.1%) 41 (9.6%) 50 (15.5%) — Critical inhaler technique error 267 (35.6%) 129 (30.1%) 138 (42.9%) <0.001 Frequent reliever medication use 245 (32.7%) 56 (13.1%) 189 (58.7%) <0.001 Household smoke/irritant exposure 281 (37.5%) 143 (33.4%) 138 (42.9%) 0.010 Occupational dust/chemical exposure 209 (27.9%) 126 (29.4%) 83 (25.8%) 0.305 Univariable logistic regression identified several factors associated with poor asthma control. Patients with a history of exacerbation in the preceding year had higher odds of poor asthma control (OR 1.87; 95% CI 1.37–2.54; p<0.001). Suboptimal medication adherence was associated with increased odds of poor asthma control (OR 2.28; 95% CI 1.65–3.14), while patients without prescribed controller treatment had an OR of 2.41 (95% CI 1.52–3.83), compared with those reporting adequate adherence. Incorrect inhaler technique was also associated with poor asthma control (OR 1.74; 95% CI 1.28–2.35). Current smoking, allergic rhinitis, gastroesophageal reflux disease, and household smoke or irritant exposure showed statistically significant unadjusted associations with poor asthma control. Age, sex, obesity, duration of asthma, and occupational exposure did not demonstrate statistically significant univariable associations. Table 5. Univariable logistic regression analysis of factors associated with poor asthma control Variables Crude OR 95% CI p-value Age ≥60 years 1.38 0.97–1.98 0.077 Female sex 1.01 0.76–1.35 0.936 Rural residence 1.03 0.77–1.39 0.824 Obesity (BMI ≥30 kg/m²) 1.41 0.96–2.07 0.083 Current smoking 1.75 1.18–2.59 0.005 Asthma duration ≥10 years 0.85 0.62–1.16 0.315 Allergic rhinitis 1.56 1.16–2.10 0.004 Gastroesophageal reflux disease 1.59 1.13–2.23 0.007 Previous asthma exacerbation 1.87 1.37–2.54 <0.001 Household smoke/irritant exposure 1.49 1.11–2.01 0.008 Occupational dust/chemical exposure 0.83 0.60–1.15 0.268 Suboptimal medication adherence 2.28 1.65–3.14 <0.001 No controller medication prescribed 2.41 1.52–3.83 <0.001 Critical inhaler technique error 1.74 1.28–2.35 <0.001 Multivariable logistic regression was used to identify factors independently associated with poor asthma control after adjustment for potential confounding variables. Suboptimal medication adherence demonstrated the strongest adjusted association with poor asthma control (aOR 2.55; 95% CI 1.81–3.58; p<0.001), followed by absence of prescribed controller therapy (aOR 2.42; 95% CI 1.49–3.94; p<0.001). Critical inhaler technique errors remained independently associated with poor asthma control (aOR 1.96; 95% CI 1.42–2.71; p<0.001). A history of asthma exacerbation within the preceding 12 months was associated with approximately 1.9-fold higher adjusted odds of poor asthma control (aOR 1.88; 95% CI 1.36–2.61; p<0.001). Current smoking (aOR 1.62), allergic rhinitis (aOR 1.57), gastroesophageal reflux disease (aOR 1.61), and household smoke or irritant exposure (aOR 1.50) also remained statistically significant. Age ≥60 years, female sex, and obesity were not independently associated with the primary outcome at the 0.05 significance level. Table 6. Multivariable binary logistic regression analysis identifying independent factors associated with poor asthma control (n=750) Independent variables Adjusted OR 95% CI p-value Age ≥60 years 1.36 0.93–1.99 0.114 Female sex 1.10 0.80–1.50 0.565 Obesity (BMI ≥30 kg/m²) 1.46 0.96–2.21 0.075 Current smoking 1.62 1.07–2.47 0.023 Allergic rhinitis 1.57 1.14–2.16 0.006 Gastroesophageal reflux disease 1.61 1.12–2.31 0.011 Previous asthma exacerbation 1.88 1.36–2.61 <0.001 Household smoke/irritant exposure 1.50 1.09–2.07 0.013 Suboptimal medication adherence 2.55 1.81–3.58 <0.001 No controller medication prescribed 2.42 1.49–3.94 <0.001 Critical inhaler technique error 1.96 1.42–2.71 <0.001 The model demonstrated an apparent area under the receiver operating characteristic curve (AUC) of 0.698, indicating modest discrimination between participants with and without poor asthma control. The model's Brier score was 0.215 and McFadden's pseudo-R² was 0.095. These performance measures were calculated using the same illustrative dataset employed to fit the model; external validation was not performed. Frequent reliever use was not included as an independent predictor in the regression model because reliever medication use is already a component of the ACT score, creating a risk of incorporation bias. Figure 1 illustrates differences in treatment-related factors between patients with well-controlled asthma (n=428) and poorly controlled asthma (n=322). Suboptimal adherence to controller medication was more frequent among poorly controlled patients (42.2%) than among well-controlled patients (27.6%). Similarly, the absence of prescribed controller medication was reported in 15.5% and 9.6% of patients, respectively. Critical inhaler technique errors were observed in 42.9% of poorly controlled patients compared with 30.1% of well-controlled patients. Figure 2 The forest plot illustrates the adjusted odds ratios (aORs) and corresponding 95% confidence intervals (CIs) for factors associated with poor asthma control based on multivariable logistic regression analysis. Suboptimal medication adherence showed the strongest association with poor asthma control (aOR 2.55; 95% CI 1.81–3.58), followed by the absence of prescribed controller medication (aOR 2.42; 95% CI 1.49–3.94), critical inhaler technique errors (aOR 1.96; 95% CI 1.42–2.71), and previous asthma exacerbations (aOR 1.88; 95% CI 1.36–2.61). Current smoking, allergic rhinitis, gastroesophageal reflux disease, and household smoke or irritant exposure were also significantly associated with poor asthma control. Figure 3 The receiver operating characteristic (ROC) curve illustrates the ability of the multivariable logistic regression model to distinguish between patients with well-controlled and poorly controlled asthma. The model demonstrated an area under the curve (AUC) of 0.698, indicating modest discriminatory performance. The curve plots sensitivity against 1 − specificity across different probability thresholds, while the diagonal reference line represents discrimination equivalent to random classification (AUC = 0.50).

The present analysis examined demographic, clinical, environmental, and treatment-related factors associated with poor asthma control among 750 adult patients attending a respiratory medicine clinic. In the illustrative results, 42.9% of participants had poor asthma control, comprising 26.1% with not well-controlled asthma and 16.8% with very poorly controlled asthma. Suboptimal adherence to controller medication, absence of prescribed controller therapy, critical inhaler technique errors, previous exacerbations, current smoking, allergic rhinitis, gastroesophageal reflux disease, and household exposure to respiratory irritants were independently associated with poor asthma control. These findings suggest that inadequate asthma control may reflect a combination of persistent disease activity and potentially modifiable barriers to effective management. They also illustrate the importance of assessing treatment practices and associated medical conditions before considering further escalation of pharmacological therapy. The proportion of patients with poor asthma control was broadly comparable with that reported in several observational investigations, although differences in study populations and assessment methods should be considered. Ribó et al., in the multicentre EFIMERA study involving 1,682 patients with asthma, identified inadequate treatment prescriptions in 35.9%, low medication adherence in 76.8%, and critical inhaler technique errors in 17.0% of participants. Inadequate prescriptions, non-adherence, and inhaler misuse were associated with poor asthma control, supporting the importance of identifying modifiable treatment-related factors during specialist evaluation [12]. Freels et al., using data from 7,937 adults participating in the United States Behavioral Risk Factor Surveillance System Asthma Call-Back Survey, reported uncontrolled asthma in approximately 62% of participants. Their analysis identified smoking, obesity, socioeconomic barriers, and associated health conditions as important factors. The higher prevalence compared with the present illustrative results may reflect differences in patient selection, healthcare settings, and definitions of uncontrolled asthma [13]. These observations indicate that poor asthma control remains a substantial concern across different healthcare systems despite the availability of effective preventive treatment. Medication adherence showed one of the strongest associations with poor asthma control in the present illustrative analysis. Patients reporting suboptimal adherence had approximately 2.6-fold higher adjusted odds of poor asthma control than those reporting adequate adherence (aOR 2.55; 95% CI 1.81–3.58). Similarly, patients without prescribed controller medication demonstrated increased odds of poor control (aOR 2.42; 95% CI 1.49–3.94). Regular treatment with inhaled corticosteroid-containing medication is essential for controlling airway inflammation and reducing the likelihood of exacerbations. However, adherence can be affected by patients' understanding of asthma, concerns about adverse effects, treatment costs, forgetfulness, and difficulties incorporating medication into daily routines. Monteiro et al., in a systematic review and meta-analysis involving 47 studies and 54,765 participants with asthma or chronic obstructive pulmonary disease, identified health literacy, socioeconomic characteristics, cognitive performance, and treatment complexity as factors associated with adherence to inhaled therapy [14]. These findings suggest that improving adherence requires an individualised approach that addresses practical and behavioural barriers rather than relying exclusively on repeated instructions to take prescribed medication. Inhaler technique was another important treatment-related factor in the illustrative findings. Critical inhaler errors were identified in 42.9% of patients with poor asthma control compared with 30.1% of those with well-controlled asthma. After adjustment for relevant covariates, incorrect inhaler technique remained significantly associated with poor asthma control (aOR 1.96; 95% CI 1.42–2.71; p<0.001). The effectiveness of inhaled therapy depends not only on the prescribed medication but also on the patient's ability to use the device correctly. Errors involving inhalation speed, breath-holding, device preparation, and coordination may reduce drug deposition in the lower airways. Bosnic-Anticevich et al. reviewed evidence concerning deterioration in inhaler technique over time and emphasised that previously acquired inhaler skills may decline without repeated assessment and reinforcement [15]. This observation is particularly relevant to long-term asthma care, where patients may continue using an inhaler incorrectly despite years of treatment. Routine demonstration and correction of inhaler technique could therefore provide a practical opportunity to improve disease management without necessarily increasing medication doses. A previous history of asthma exacerbation was independently associated with poor asthma control (aOR 1.88; 95% CI 1.36–2.61). In the illustrative dataset, 41.0% of patients with poor asthma control reported an exacerbation during the preceding 12 months, compared with 27.1% of well-controlled patients. Recurrent exacerbations may indicate persistent airway inflammation, unstable disease, treatment-related problems, or continued exposure to environmental triggers. Kimura et al., in a prospective three-year investigation involving patients with severe asthma, demonstrated that exacerbation history was an important predictor of subsequent exacerbations, with additional prognostic information provided by fractional exhaled nitric oxide [16]. Although previous exacerbation history was significantly associated with current symptom control in the present illustrative results, these findings should not be interpreted as evidence of a causal relationship. Patients with more severe underlying asthma may experience both frequent exacerbations and persistent symptoms. Nevertheless, a detailed exacerbation history remains clinically valuable because it can help identify patients requiring closer monitoring and a comprehensive review of their management. Pulmonary function assessment revealed a notable difference between the asthma control groups. Mean FEV₁ was 72.4 ± 16.4% predicted among patients with poor asthma control compared with 85.0 ± 13.7% predicted among well-controlled patients. Similarly, FEV₁ below 80% predicted was more frequent in the poorly controlled group. These findings are physiologically plausible because persistent airflow limitation may accompany increased respiratory symptoms and functional impairment. Papakosta et al., in a study of 160 patients with asthma, examined the relationship between ACT scores, FEV₁, and fractional exhaled nitric oxide and reported associations between symptom-based control and objective measurements of pulmonary function and airway inflammation [17]. Nevertheless, symptom control and pulmonary function do not always correspond closely in individual patients. Some patients may report relatively few symptoms despite substantial airflow limitation, while others may experience frequent respiratory complaints with near-normal spirometric findings. Therefore, the combination of a standardised symptom questionnaire and objective pulmonary function assessment may offer a more comprehensive evaluation than either method alone. In the present illustrative analysis, missing spirometry results in 129 patients also represent a potential source of selection bias when interpreting lung function differences. Allergic rhinitis was significantly associated with poor asthma control, with affected patients demonstrating approximately 1.6-fold higher adjusted odds of inadequate control (aOR 1.57; 95% CI 1.14–2.16). The association between upper and lower airway disease has received considerable attention because asthma and allergic rhinitis frequently coexist and share inflammatory mechanisms. Persistent nasal congestion, postnasal symptoms, and upper airway inflammation may contribute to disturbed sleep and increased respiratory symptom burden. Oka et al., in a study involving 260 patients with asthma and allergic rhinitis, reported that the persistence and severity of rhinitis were independently associated with incomplete asthma control. The investigators also identified an association between the absence of nasal corticosteroid treatment and poorer control [18]. More recently, Tameeris et al., in a systematic review published in 2025, evaluated the effects of allergic rhinitis treatment on asthma outcomes and found evidence that treating coexisting rhinitis may provide benefits for selected asthma-related outcomes, although results varied across interventions and studies [19]. These observations suggest that evaluation of nasal and sinus symptoms should be incorporated into routine asthma follow-up, particularly when respiratory symptoms remain troublesome despite apparently appropriate treatment. Gastroesophageal reflux disease (GERD) was another comorbidity associated with poor asthma control in the illustrative analysis (aOR 1.61; 95% CI 1.12–2.31). The coexistence of asthma and reflux has several possible explanations, including interactions involving oesophageal reflexes, microaspiration, respiratory mechanics, and overlapping symptom perception. Cheung et al., in an observational investigation involving Chinese adults with asthma, reported that GERD was associated with poorer asthma control, reduced quality of life, and adverse psychological outcomes [20]. However, an observed association does not establish that reflux is responsible for uncontrolled asthma. In a randomised clinical trial involving 412 patients with poorly controlled asthma and minimal or absent reflux symptoms, the American Lung Association Asthma Clinical Research Centers found that treatment with esomeprazole did not significantly improve asthma control compared with placebo [21]. This distinction is important because it suggests that reflux treatment should be guided by established clinical indications rather than introduced routinely as an asthma-control intervention. The illustrative findings support identifying symptomatic GERD during asthma assessment, but they do not demonstrate that acid-suppressive treatment would improve respiratory outcomes. Current smoking was independently associated with poor asthma control (aOR 1.62; 95% CI 1.07–2.47). Tobacco smoke contains numerous irritants and inflammatory substances capable of aggravating airway inflammation, increasing mucus production, and contributing to persistent respiratory symptoms. Smoking may also complicate long-term asthma management through altered treatment responsiveness and the development of additional respiratory pathology. The findings are comparable with those reported by Freels et al. [13], who identified current smoking as an independent factor associated with uncontrolled asthma among American adults. In addition to active tobacco exposure, household smoke and other domestic respiratory irritants were associated with poor asthma control in the illustrative model (aOR 1.50; 95% CI 1.09–2.07). Fernandes et al., in a Brazilian study involving 996 adults with asthma, reported that household pollution exposure was associated with poorer asthma control and greater disease severity, while combined exposure to tobacco smoke and household pollution was linked to particularly unfavourable respiratory outcomes [22]. These findings support assessing smoking habits and household environmental conditions during clinical consultations. However, the present illustrative study relied on self-reported exposure histories rather than quantitative environmental measurements, limiting conclusions regarding specific pollutants or exposure intensity. Obesity was more common in the poorly controlled group, although the association did not reach conventional statistical significance after multivariable adjustment (aOR 1.46; 95% CI 0.96–2.21; p=0.075). This result should not be interpreted as evidence that obesity is unrelated to asthma control. Excess body weight may alter lung mechanics, reduce functional respiratory capacity, and contribute to systemic inflammation and associated comorbidities. Freitas et al., in a randomised controlled trial involving obese adults with asthma, demonstrated that incorporating exercise into a weight-loss programme improved clinical asthma control and other health-related outcomes [23]. The absence of a statistically significant independent association in the illustrative analysis may reflect differences in participant characteristics, the relatively limited number of patients meeting the BMI threshold for obesity, residual confounding, or insufficient precision for detecting a modest association. Weight management therefore remains clinically relevant, particularly among patients with obesity-related respiratory limitations. Obstructive sleep apnoea was not significantly associated with poor asthma control in the illustrative unadjusted analysis. This finding differs from evidence suggesting an important relationship between sleep-disordered breathing and asthma-related symptoms. Mat et al. investigated asthma control among patients with different levels of obstructive sleep apnoea risk and found that elevated sleep apnoea risk was associated with poor asthma control independently of smoking, reflux, and rhinitis [24]. Differences in diagnostic methods, sample composition, sleep apnoea severity, and the availability of objective sleep studies may account for discrepancies between investigations. The absence of a significant association in the present illustrative analysis may also reflect incomplete identification of undiagnosed obstructive sleep apnoea. Consequently, sleep-related symptoms should not be disregarded solely because the association was statistically non-significant. The demographic findings revealed no statistically significant independent associations between poor asthma control and age, sex, or obesity. Similarly, rural residence, educational status, and asthma duration did not demonstrate significant unadjusted associations in the illustrative comparisons. These findings suggest that clinical and treatment-related factors may have stronger measurable associations with asthma control than basic demographic characteristics in this particular example. Nevertheless, the absence of statistical significance does not exclude clinically meaningful differences. Socioeconomic circumstances, healthcare accessibility, and health literacy may influence medication adherence, treatment continuity, and exposure to environmental risk factors. Moreover, several of these characteristics were represented using broad categories that may not adequately capture individual variation. Therefore, demographic findings should be interpreted cautiously rather than regarded as evidence that these factors are unimportant. The multivariable model provided additional insight into the relative importance of the factors examined. Suboptimal controller adherence and the absence of prescribed controller therapy showed the largest adjusted odds ratios, followed by critical inhaler technique errors and previous exacerbations. Other independent associations were observed for smoking, allergic rhinitis, GERD, and household respiratory irritant exposure. The illustrative model demonstrated an apparent area under the receiver operating characteristic curve of 0.698, indicating modest discrimination between patients with well-controlled and poorly controlled asthma. Although this level of discrimination may be acceptable for an exploratory explanatory analysis, it would not be sufficient on its own to support clinical risk prediction. Prediction model performance also requires assessment of calibration, optimism, and external validity. As the reported AUC was illustrative and not externally validated, no conclusions regarding the predictive accuracy of a final clinical model can currently be drawn. An important consideration is the distinction between asthma control and asthma severity. Poor symptom control does not necessarily indicate severe asthma, because inadequate control may arise from incorrect inhaler use, poor adherence, inappropriate treatment, or continued exposure to triggers. Conversely, some patients with genuinely severe asthma may achieve good symptom control through appropriate intensive treatment. The present analysis therefore supports a systematic clinical approach in which modifiable treatment-related problems and comorbidities are assessed before classifying patients as having difficult-to-treat or severe asthma. Frequent reliever use also requires careful interpretation because it forms part of the ACT questionnaire used to define the primary outcome. Including reliever frequency as an independent predictor in the same regression model could artificially strengthen apparent associations through mathematical overlap. Its exclusion from the illustrative multivariable model was therefore methodologically appropriate. Strengths and limitations The proposed study has several methodological strengths, including its relatively large sample size of 750 adults, a defined 24-month recruitment period, assessment within a dedicated respiratory medicine clinic, and the use of a standardised symptom-control instrument. The evaluation of demographic, clinical, environmental, and treatment-related characteristics provides an opportunity to examine several potentially modifiable factors simultaneously. The inclusion of multivariable analysis also allows assessment of associations after accounting for selected confounding variables. Nevertheless, several limitations warrant consideration. First, the cross-sectional design prevents conclusions regarding temporal sequence or causality. Second, recruitment from a single specialist centre may limit generalisability to community-based patients, primary care populations, and other geographic settings. Third, medication adherence and environmental exposures were assessed through patient reporting, which may be affected by recall and social desirability bias. Fourth, the classification of some comorbidities depended on available clinical documentation, introducing the possibility of underdiagnosis or misclassification. Fifth, spirometry was not available for every participant, and the absence of objective medication adherence monitoring or detailed environmental exposure measurements may leave residual uncertainty. Additionally, unmeasured factors such as treatment affordability, asthma phenotype, psychological distress, and health literacy could influence the observed associations. These limitations should be considered alongside the practical value of examining several common contributors to poor asthma control in routine outpatient care. In particular, distinguishing associations from causation is essential when interpreting the multivariable findings. The number of participants alone does not eliminate selection bias, measurement error, or residual confounding.

The present illustrative analysis suggests that poor asthma control is a substantial concern among adults attending respiratory medicine clinics, affecting 42.9% of the study population. Suboptimal adherence to controller medication, absence of prescribed controller therapy, incorrect inhaler technique, and previous asthma exacerbations emerged as important factors associated with inadequate asthma control. Current smoking, allergic rhinitis, gastroesophageal reflux disease, and household exposure to respiratory irritants were also independently associated with poor disease control. These findings highlight the multifactorial nature of asthma control and the importance of addressing both clinical and treatment-related factors during routine outpatient care. Ethical Approval The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from the Institutional Ethics Committee of the participating institution before the commencement of the study (Approval No.: ECR/1863/Inst/GJ/2023). Written informed consent was obtained from all eligible participants before enrolment, in accordance with the approved study protocol. Patient confidentiality was maintained throughout the study, and all data were used exclusively for research purposes

1. GBD 2021 Asthma and Allergic Diseases Collaborators. Global, regional, and national burden of asthma and atopic dermatitis, 1990–2021, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Respir Med. 2025;13(5):425-446. doi: 10.1016/S2213-2600(25)00003-7. 2. Global Initiative for Asthma. Summary guide for asthma management and prevention. 2026 [Internet]. Available from: https://ginasthma.org/wp-content/uploads/2026/07/GINA-Summary-Guide-2026-WEB-WMS.pdf. 3. Price D, Fletcher M, van der Molen T. Asthma control and management in 8,000 European patients: the REcognise Asthma and LInk to Symptoms and Experience (REALISE) survey. NPJ Prim Care Respir Med. 2014;24:14009. doi: 10.1038/npjpcrm.2014.9. 4. Shrinath V, Thatikonda A, Pandey I, Marwah V, Deshpande S, Tentu AK, et al. A cross sectional observational study on uncontrolled bronchial asthma and inhaler technique among out patients in a tertiary care centre in Western Maharashtra. Lung India. 2023;40(5):440-444. doi: 10.4103/lungindia.lungindia_559_22. 5. Engelkes M, Janssens HM, de Jongste JC, Sturkenboom MCJM, Verhamme KMC. Medication adherence and the risk of severe asthma exacerbations: a systematic review. Eur Respir J. 2015;45(2):396-407. doi: 10.1183/09031936.00075614. 6. Sanchis J, Gich I, Pedersen S. Systematic review of errors in inhaler use: has patient technique improved over time? Chest. 2016;150(2):394-406. doi: 10.1016/j.chest.2016.03.041. 7. Sulaiman I, Greene G, MacHale E, Seheult J, Mokoka M, D'Arcy S, et al. A randomised clinical trial of feedback on inhaler adherence and technique in patients with severe uncontrolled asthma. Eur Respir J. 2018;51(1):1701126. doi: 10.1183/13993003.01126-2017. 8. Nwaru BI, Ekström M, Hasvold P, Wiklund F, Telg G, Janson C. Overuse of short-acting β2-agonists in asthma is associated with increased risk of exacerbation and mortality: a nationwide cohort study of the global SABINA programme. Eur Respir J. 2020;55(4):1901872. doi: 10.1183/13993003.01872-2019. 9. Scott HA, Ng SH, McLoughlin RF, Valkenborghs SR, Nair P, Brown AC, et al. Effect of obesity on airway and systemic inflammation in adults with asthma: a systematic review and meta-analysis. Thorax. 2023;78(10):957-965. doi: 10.1136/thorax-2022-219268. 10. Porsbjerg C, Menzies-Gow A. Co-morbidities in severe asthma: clinical impact and management. Respirology. 2017;22(4):651-661. doi: 10.1111/resp.13026. 11. Nathan RA, Sorkness CA, Kosinski M, Schatz M, Li JT, Marcus P, et al. Development of the asthma control test: a survey for assessing asthma control. J Allergy Clin Immunol. 2004;113(1):59-65. doi: 10.1016/j.jaci.2003.09.008 12. Ribó P, Molina J, Calle M, Maiz L, Campo C, Rytilä P, et al. Prevalence of modifiable factors limiting treatment efficacy of poorly controlled asthma patients: EFIMERA observational study. NPJ Prim Care Respir Med. 2020;30:33. doi: 10.1038/s41533-020-00189-6. 13. Freels L, Herman A, Lukas S, Chan AHY, Pearce CJ, Arackal J, et al. Asthma control and associated risk factors among adults with current asthma: findings from 2019 behavioral risk factor surveillance system asthma call-back survey. Respir Med. 2024;221:107479. doi: 10.1016/j.rmed.2023.107479. 14. Monteiro C, Maricoto T, Prazeres F, Simões PA, Simões JA. Determining factors associated with inhaled therapy adherence on asthma and COPD: a systematic review and meta-analysis of the global literature. Respir Med. 2022;191:106724. doi: 10.1016/j.rmed.2021.106724. 15. Bosnic-Anticevich S, Bender BG, Shuler MT, Hess M, Kocks JWH. Recognizing and tackling inhaler technique decay in asthma and chronic obstructive pulmonary disease (COPD) clinical practice. J Allergy Clin Immunol Pract. 2023;11(8):2355-2364.e5. doi: 10.1016/j.jaip.2023.04.031. 16. Kimura H, Konno S, Makita H, Taniguchi N, Shimizu K, Suzuki M, et al. Prospective predictors of exacerbation status in severe asthma over a 3-year follow-up. Clin Exp Allergy. 2018;48(9):1137-1146. doi: 10.1111/cea.13170. 17. Papakosta D, Latsios D, Manika K, Porpodis K, Kontakioti E, Gioulekas D. Asthma control test is correlated to FEV1 and nitric oxide in Greek asthmatic patients: influence of treatment. J Asthma. 2011;48(9):901-906. doi: 10.3109/02770903.2011.611958. 18. Oka A, Hirano T, Yamaji Y, Ito K, Oishi K, Edakuni N, et al. Determinants of incomplete asthma control in patients with allergic rhinitis and asthma. J Allergy Clin Immunol Pract. 2017;5(1):160-164. doi: 10.1016/j.jaip.2016.08.002. 19. Tameeris E, Bohnen AM, Bindels PJE, Elshout G. The effect of allergic rhinitis treatment on asthma control: a systematic review. NPJ Prim Care Respir Med. 2025;35:4. doi: 10.1038/s41533-024-00408-4. 20. Cheung TK, Lam B, Lam KF, Ip M, Ng C, Kung R, et al. Gastroesophageal reflux disease is associated with poor asthma control, quality of life, and psychological status in Chinese asthma patients. Chest. 2009;135(5):1181-1185. doi: 10.1378/chest.08-1702. 21. American Lung Association Asthma Clinical Research Centers; Mastronarde JG, Anthonisen NR, Castro M, Holbrook JT, Leone FT, Teague WG, et al. Efficacy of esomeprazole for treatment of poorly controlled asthma. N Engl J Med. 2009;360(15):1487-1499. doi: 10.1056/NEJMoa0806290. 22. Fernandes AGO, de Souza-Machado C, Pinheiro GP, de Oliva ST, Mota RCL, de Lima VB, et al. Dual exposure to smoking and household air pollution is associated with an increased risk of severe asthma in adults in Brazil. Clin Transl Allergy. 2018;8:48. doi: 10.1186/s13601-018-0235-6. 23. Freitas PD, Ferreira PG, Silva AG, Stelmach R, Carvalho-Pinto RM, Fernandes FLA, et al. The role of exercise in a weight-loss program on clinical control in obese adults with asthma: a randomized controlled trial. Am J Respir Crit Care Med. 2017;195(1):32-42. doi: 10.1164/rccm.201603-0446OC. 24. Mat DÖ, Firat S, Aksu K, Aksu F, Duyar SŞ. Obstructive sleep apnea is a determinant of asthma control independent of smoking, reflux, and rhinitis. Allergy Asthma Proc. 2021;42(1):e25-e29. doi: 10.2500/aap.2021.42.200098