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Discriminative Accuracy of Chronic Obstructive Pulmonary Disease Screening Instruments in 3 Low- and Middle-Income Country Settings

Siddharthan, T; Pollard, SL; Quaderi, SA; Rykiel, NA; Wosu, AC; Alupo, P; Barber, JA; ... GECo Study Investigators; + view all (2022) Discriminative Accuracy of Chronic Obstructive Pulmonary Disease Screening Instruments in 3 Low- and Middle-Income Country Settings. Journal of the American Medical Association , 327 (2) pp. 151-160. 10.1001/jama.2021.23065.

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Abstract

Importance: Most of the global morbidity and mortality in chronic obstructive pulmonary disease (COPD) occurs in low- and middle-income countries (LMICs), with significant economic effects. Objective: To assess the discriminative accuracy of 3 instruments using questionnaires and peak expiratory flow (PEF) to screen for COPD in 3 LMIC settings. Design, Setting, and Participants: A cross-sectional analysis of discriminative accuracy, conducted between January 2018 and March 2020 in semiurban Bhaktapur, Nepal; urban Lima, Peru; and rural Nakaseke, Uganda, using a random age- and sex-stratified sample of the population 40 years or older. Exposures: Three screening tools, the COPD Assessment in Primary Care to Identify Undiagnosed Respiratory Disease and Exacerbation Risk (CAPTURE; range, 0-6; high risk indicated by a score of 5 or more or score 2-5 with low PEF [<250 L/min for females and <350 L/min for males]), the COPD in LMICs Assessment questionnaire (COLA-6; range, 0-5; high risk indicated by a score of 4 or more), and the Lung Function Questionnaire (LFQ; range, 0-25; high risk indicated by a score of 18 or less) were assessed against a reference standard diagnosis of COPD using quality-assured postbronchodilator spirometry. CAPTURE and COLA-6 include a measure of PEF. Main Outcomes and Measures: The primary outcome was discriminative accuracy of the tools in identifying COPD as measured by area under receiver operating characteristic curves (AUCs) with 95% CIs. Secondary outcomes included sensitivity, specificity, positive predictive value, and negative predictive value. Results: Among 10 709 adults who consented to participate in the study (mean age, 56.3 years (SD, 11.7); 50% female), 35% had ever smoked, and 30% were currently exposed to biomass smoke. The unweighted prevalence of COPD at the 3 sites was 18.2% (642/3534 participants) in Nepal, 2.7% (97/3550) in Peru, and 7.4% (264/3580) in Uganda. Among 1000 COPD cases, 49.3% had clinically important disease (Global Initiative for Chronic Obstructive Lung Disease classification B-D), 16.4% had severe or very severe airflow obstruction (forced expiratory volume in 1 second <50% predicted), and 95.3% of cases were previously undiagnosed. The AUC for the screening instruments ranged from 0.717 (95% CI, 0.677-0.774) for LFQ in Peru to 0.791 (95% CI, 0.770-0.809) for COLA-6 in Nepal. The sensitivity ranged from 34.8% (95% CI, 25.3%-45.2%) for COLA-6 in Nepal to 64.2% (95% CI, 60.3%-67.9%) for CAPTURE in Nepal. The mean time to administer the instruments was 7.6 minutes (SD 1.11), and data completeness was 99.5%. Conclusions and Relevance: This study demonstrated that screening instruments for COPD were feasible to administer in 3 low- and middle-income settings. Further research is needed to assess instrument performance in other low- and middle-income settings and to determine whether implementation is associated with improved clinical outcomes.

Type: Article
Title: Discriminative Accuracy of Chronic Obstructive Pulmonary Disease Screening Instruments in 3 Low- and Middle-Income Country Settings
Location: United States
DOI: 10.1001/jama.2021.23065
Publisher version: https://doi.org/10.1001/jama.2021.23065
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Respiratory Medicine
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10142019
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