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

• Main Authors: Agarwal, Dhiraj, Hanafi, Nik Sherina, Khoo, Ee Ming, Parker, Richard A., Ghorpade, Deesha, Salvi, Sundeep, Abu Bakar, Ahmad Ihsan, Chinna, Karuthan, Das, Deepa, Habib, Monsur, Hussein, Norita, Isaac, Rita, Islam, Mohammad Shahidul, Khan, Mohsin Saeed, Liew, Su May, Pang, Yong Kek, Paul, Biswajit, Saha, Samir K., Wong, Li Ping, Yusuf, Osman M., Yusuf, Shahida O., Juvekar, Sanjay, Pinnock, Hilary, Collaboration, RESPIRE
• Format: Article
• Published: University of Edinburgh 2021
• Subjects: R Medicine; RA Public aspects of medicine; RA0421 Public health. Hygiene. Preventive Medicine
• Online Access: http://eprints.um.edu.my/35378/

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.