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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 feasib...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Society of Global Health
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8561335/ https://www.ncbi.nlm.nih.gov/pubmed/34737865 http://dx.doi.org/10.7189/jogh.11.04065 |
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author | 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 |
author_facet | 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 |
author_sort | Agarwal, Dhiraj |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8561335 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | International Society of Global Health |
record_format | MEDLINE/PubMed |
spelling | pubmed-85613352021-11-03 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 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 J Glob Health Articles 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. International Society of Global Health 2021-10-30 /pmc/articles/PMC8561335/ /pubmed/34737865 http://dx.doi.org/10.7189/jogh.11.04065 Text en Copyright © 2021 by the Journal of Global Health. All rights reserved. https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License. |
spellingShingle | Articles 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 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8561335/ https://www.ncbi.nlm.nih.gov/pubmed/34737865 http://dx.doi.org/10.7189/jogh.11.04065 |
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