Central Data Catalog

Background Our previous scoping review revealed limitations and incon-
sistencies 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 fea-
sibility and identify variables that predicted asthma or chronic obstructive

pulmonary disease (COPD).

Methods We administered relevant translations of the BOLD-1 question-
naire 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 diagno-
ses and attempted to develop an algorithm for detecting asthma and COPD.

Results Of 508 participants, 55.9% reported one or more chronic respirato-
ry 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 in-
cluded 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.