Epidemiology of comorbidities and their association with asthma control
Bibliographic record
Abstract
BACKGROUND: The prevalence of comorbidities and their relation to asthma control and treatment is a topic of increasing interest, however comprehensive studies are scarce. We aimed to determine the prevalence of the most common comorbidities in asthma in relation to patient characteristics (age, gender and body mass index [BMI]) and their association with asthma control in a large, specialist-managed representative patient population. METHODS: A secondary, exploratory analysis of the Asthma Reality (ARL), across-sectional, non-interventional real-life study was conducted. Basic patient characteristics, the prevalence of comorbidities and data on asthma control and risk factors had been collected and their interactions examined. Descriptive statistics and binomial regression were used to assess the distribution of the prevalence of comorbidities and propensity matching was applied to assess their effect on asthma control. RESULTS: Overall, 12,743 patients were enrolled in our study in 187 treatment centres covering all regions of Hungary. Most comorbidities showed significantly different distribution for all basic patient characteristics. Gender, age group, smoking status, BMI and the duration of asthma had a significant impact on asthma control. The frequency of uncontrolled asthma was higher in females (37.1%), in the age group of 46-65 years (39.6%), in severely obese patients (43.2%), in patients who had been diagnosed with asthma for more than 20 years (40.4%), and in active heavy smokers (55%), compared with respective groups in the same category. Based on the binomial regression with propensity score matching, concomitant chronic obstructive pulmonary disease (COPD) (odds ratio [OR] = 2.06, 95% confidence interval [CI] 1.80-2.36), ischaemic heart disease (OR = 1.86, 95% CI 1.64-2.10) and cerebrovascular events (OR = 1.85, 95% CI 1.47-2.32) had the strongest negative effect on asthma control, with the presence of all of these conditions increasing the risk of uncontrolled asthma. CONCLUSIONS: This evaluation of comorbidity data of more than 12,000, adult asthmatic patients has provided a clearer picture of diseases that can frequently co-exist with asthma, and their influence on asthma control, assessed by the prevalence of symptoms. Our study suggests that most asthmatic patients have at least one comorbidity, and the presence of comorbidities may have a high impact on asthma control measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".