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Review
. 2021 Oct 30:11:04065.
doi: 10.7189/jogh.11.04065. eCollection 2021.

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

Dhiraj Agarwal  1 , Nik Sherina Hanafi  2 , Ee Ming Khoo  2 , Richard A Parker  3 , Deesha Ghorpade  4 , Sundeep Salvi  4 , Ahmad Ihsan Abu Bakar  5 , Karuthan Chinna  6 , Deepa Das  7 , Monsur Habib  8 , Norita Hussein  2 , Rita Isaac  7 , Mohammad Shahidul Islam  9 , Mohsin Saeed Khan  10 , Su May Liew  2 , Yong Kek Pang  2 , Biswajit Paul  7 , Samir K Saha  9 , Li Ping Wong  2 , Osman M Yusuf  10 , Shahida O Yusuf  10 , Sanjay Juvekar  1 , Hilary Pinnock  11 ; RESPIRE Collaboration
Affiliations
Review

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

Dhiraj Agarwal et al. J Glob Health. .

Abstract

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.

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Conflict of interest statement

Competing interests: EMK reports grants from the National Institute for Health Research Global Health Research Unit on Respiratory Health (RESPIRE) and Seqirus UK; personal fees from AstraZeneca and GlaxoSmithKline; and is board director of the International Primary Care Respiratory Group. All authors have completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author), and declare no further conflicts of interest.

Figures

Figure 1
Figure 1
Clinical diagnostic algorhitm.
Figure 2
Figure 2
ROC curve showing sensitivity against specificity for model predicted values compared with the gold-standard asthma diagnosis.
See this image and copyright information in PMC

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