Prevalence of Asthma Triggers and Control Status Among Pediatric Asthmatic Patients in Saudi Arabia.
Mohammed M AlyamiAhmed H AlasimiAbdullah A AlqarniFahad H BalharithAbdulelah Mastour AldhahirPublished in: The Journal of asthma : official journal of the Association for the Care of Asthma (2024)
Aim: This study aims to assess the prevalence of asthma triggers and control status among pediatric asthmatic patients in Saudi Arabia. Methods: From October 2015 to March 2016, an Arabic version of the Asthma Trigger Inventory (ATI) questionnaire and Asthma Control Test (ACT) were distributed to 200 parents of children diagnosed with asthma at the pulmonary clinic of King Fahad Medical City (KFMC) in Riyadh, Saudi Arabia. Data were collected and analyzed using the Statistical Package for Social Sciences (SPSS) software version 29. Descriptive statistics of the participants were presented in frequencies, percentages, means, and standard deviations for categorical variables. Results: The survey data revealed that the most prevalent asthma triggers among pediatric asthmatic children in Saudi Arabia were Arabic incense (Bakhour) with a mean score of 3.76 (± 1.3), followed by being excited 3.70 (± 1.5), and stress at home 3.58 (± 1.4). Furthermore, the degree of asthma control among children with asthma in Saudi Arabia was 72.0% with a mean score of 17.7 (± 4.7) for the Asthma Control Test (ACT), indicating partial degree of asthma. Conclusions: Arabic incense (Bakhour) and psychological stimuli emerged as significant determinants of asthma triggers in Saudi Arabian children diagnosed with asthma. Further studies are warranted to elucidate the physiological mechanisms underpinning the response to Arabic incense (Bakhour).
Keyphrases
- lung function
- chronic obstructive pulmonary disease
- allergic rhinitis
- end stage renal disease
- saudi arabia
- psychometric properties
- young adults
- chronic kidney disease
- primary care
- air pollution
- ejection fraction
- mental health
- depressive symptoms
- cross sectional
- peritoneal dialysis
- single cell
- deep learning
- machine learning
- case control