Association of asthma exacerbations with paper mulberry (Broussenetia papyrifera) pollen in Islamabad: An observational study.

Background: Although the role of airborne plant pollen in causing allergic rhinitis has been established, the association of concentrations of paper mulberry (Broussenetia papyrifera) pollens in the air and incidence of asthma exacerbations has not, despite an observed increase in the number of asthma patients attending physician clinics and hospital Accident and Emergency (A&E) Departments during the paper mulberry pollen season. We aimed to assess the association between paper mulberry pollen concentrations (typically peaking in March each year) and asthma exacerbations in the city of Islamabad. Methods: We used three approaches to investigate the correlation of paper mulberry pollen concentration with asthma exacerbations: A retrospective analysis of historical records (2000-2019) of asthma exacerbations of patients from the Allergy and Asthma Institute, Pakistan (n = 284), an analysis of daily nebulisations in patients attending the A&E Department of the Pakistan Institute of Medical Sciences (March 2020 to July 2021), a prospective peak expiratory flow rate (PEFR) diary from participants (n = 40) with or without asthma and with or without paper mulberry sensitisation. We examined associations between pollen data and asthma exacerbations using Pearson correlation. Results: We found a strong positive correlation between mean paper mulberry pollen counts and clinical records of asthma exacerbations in patients sensitised to paper mulberry (Pearson correlation coefficient (r) = 0.86; P < 0.001), but not in non-sensitised patients (r = 0.32; P = 0.3). There was a moderate positive correlation between monthly nebulisation counts and pollen counts (r = 0.56; P = 0.03), and a strong negative correlation between percent predicted PEFR and pollen counts in sensitised asthma patients (r = -0.72, P < 0.001). However, these correlations were of low magnitude in the non-sensitised asthma (r = -0.16; P < 0.001) and sensitised non-asthma (r = -0.28; P < 0.001) groups. Conclusions: Our three approaches to analysis all showed an association between high paper mulberry pollen concentration in Islamabad and asthma exacerbations. Predicting pollen peaks could enable alerts and mobilise strategies to proactively manage these peaks of asthma exacerbations.

Predictors for detecting chronic respiratory diseases in community surveys: A pilot cross-sectional survey in four South and South East Asian low- and middle-income countries.

BACKGROUND: Our previous scoping review revealed limitations and inconsistencies in population surveys of chronic respiratory disease. Informed by this review, we piloted a cross-sectional survey of adults in four South/South-East Asian low-and middle-income countries (LMICs) to assess survey feasibility and identify variables that predicted asthma or chronic obstructive pulmonary disease (COPD). METHODS: We administered relevant translations of the BOLD-1 questionnaire with additional questions from ECRHS-II, performed spirometry and arranged specialist clinical review for a sub-group to confirm the diagnosis. Using random sampling, we piloted a community-based survey at five sites in four LMICs and noted any practical barriers to conducting the survey. Three clinicians independently used information from questionnaires, spirometry and specialist reviews, and reached consensus on a clinical diagnosis. We used lasso regression to identify variables that predicted the clinical diagnoses and attempted to develop an algorithm for detecting asthma and COPD. RESULTS: Of 508 participants, 55.9% reported one or more chronic respiratory symptoms. The prevalence of asthma was 16.3%; COPD 4.5%; and 'other chronic respiratory disease' 3.0%. Based on consensus categorisation (n = 483 complete records), "Wheezing in last 12 months" and "Waking up with a feeling of tightness" were the strongest predictors for asthma. For COPD, age and spirometry results were the strongest predictors. Practical challenges included logistics (participant recruitment; researcher safety); misinterpretation of questions due to local dialects; and assuring quality spirometry in the field. CONCLUSION: Detecting asthma in population surveys relies on symptoms and history. In contrast, spirometry and age were the best predictors of COPD. Logistical, language and spirometry-related challenges need to be addressed.