Predictive performance of the PUMA questionnaire as an opportunistic COPD case-finding tool in Singapore primary care

Predictive performance of the PUMA questionnaire as an opportunistic COPD case-finding tool in Singapore primary care | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predictive performance of the PUMA questionnaire as an opportunistic COPD case-finding tool in Singapore primary care Vicky Mengqi Qin, Kenneth Tan, Geak Poh Tan, Valery Ho, Zeyuan Yin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8394069/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2026 Read the published version in npj Primary Care Respiratory Medicine → Version 1 posted 11 You are reading this latest preprint version Abstract Background The PUMA scale has shown good discrimination in identifying people with COPD in primary care. We evaluated the predictive performance of PUMA for opportunistic case-finding and assessed the prevalence of COPD in at-risk primary care patients in Singapore. Methods This is a multicentre cross-sectional study of participants aged ≥40 years and current/former smokers. Participants completed the PUMA scale and spirometry. Predictive performance of PUMA was assessed using AUC-ROC; optimal cutoff was determined by Youden’s index. Results 359 participants were included in final analysis; 12.5% had COPD confirmed on spirometry. PUMA showed acceptable discrimination with AUC-ROC of 0.75 (95% CI:0.67–0.83). Optimal cutoff maximising sensitivity and specificity was ≥5 (Se 62.2%, Sp 79.3%; PPV 30.1%, NPV 93.6%); cutoff of ≥4 increased sensitivity to 80.0% (Sp of 56.7%; PPV 20.9%, NPV 95.2%.) Conclusion The PUMA scale demonstrated acceptable predictive performance for opportunistic COPD case-finding in Singapore's primary care setting. A cutoff of ≥4 enhanced case identification. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors COPD predictive performance external validation PUMA questionnaire Singapore multiethnic Figures Figure 1 Figure 2 Introduction Chronic obstructive pulmonary disease (COPD) is a progressive, largely preventable lung disease with symptoms such as dyspnoea, chronic cough, and sputum production. 1 , 2 COPD has affected more than 300 million people worldwide, making it one of the leading causes of death globally and causing tremendous economic burden. 3 – 5 People with COPD frequently present with comorbid chronic diseases that share underlying mechanisms and risk factors, including cardiovascular, respiratory, and renal conditions. 6 In Singapore, COPD was ranked the 10th leading cause of death in 2023. 7 Early diagnosis and treatment of COPD are essential in slowing disease progression, improving patients’ quality of life and reducing healthcare costs and the burden to healthcare systems. Despite evidence-based diagnostic and treatment guidelines, COPD remains substantially underdiagnosed in many countries, with spirometry underutilised particularly in primary care. 8 The prevalence of self-reported doctor diagnosed COPD among Singaporeans in the community is much lower than the prevalence of spirometry-confirmed COPD. 9 , 10 This reflects a high rate of underdiagnosis, with many patients first diagnosed at an advanced stage of disease. 11 One barrier to optimizing COPD detection in primary care is the lack of diagnostic tool such as spirometers. 8 , 12 Questionnaires have been shown to be valuable tools for improving COPD case-finding when spirometry is unavailable. 1 , 13 – 15 The Study of Prevalence and Regular Practice, Diagnosis and Treatment, among General Practitioners in Populations at Risk of COPD in Latin America (PUMA) questionnaire has been validated in some Latin American countries and in mono-ethnic Asian populations including Hong Kong, Taiwan, India and Indonesia. 16 – 22 However, its predictive performance for case finding in multi-ethnic Asian populations remains unknown. Although PUMA has demonstrated comparable predictive performance across countries, the cutoff values differ, underscoring the need to establish population-specific thresholds. This study aimed to first estimate the prevalence and stage of COPD among the at-risk multi-ethnic primary care population in Singapore. Second, we evaluated the predictive performance of the PUMA questionnaire and determined the optimal cutoff for opportunistic case-finding in the same population. Finally, we compared the predictive performance observed in our study with findings from other published studies. Methods Study design and subject recruitment This multicentre cross-sectional study employed convenience sampling between 1 July 2024 and 30 September 2025 in Singapore. Singapore is a multi-ethnic country, with majority of the population being Chinese, followed by Malay and Indian. Participants were recruited from both private and public primary care practices (during consultations and via poster advertisements in clinics) and community events (via poster advertisements in community centres and social media platforms). Eligibility was assessed through telephone or face-to-face interviews. A study team member then explained the study purpose and procedures to eligible participants and obtained written informed consent prior to enrolment. Ethics approval was obtained from the Institutional Research Board of Nanyang Technological University (IRB-2024-235). We offered participants S $ 20 cash as a token of appreciation. Inclusion and exclusion criteria Participants aged 40 years old and above, who were current or former smokers (defined as having smoked at least 100 cigarettes), attended primary care within the past 12 months, and were able to read and write in English or Chinese were recruited. Exclusion criteria included allergy to bronchodilators or medical conditions contraindicating spirometry (e.g., myocardial infarction or stroke, aortic or cerebral aneurysm, or detached retina in the last three months). PUMA questionnaire The PUMA questionnaire is a seven-item scale used to identify participants at risk of COPD (Supplementary file S1). Its development has been described in detail elsewhere. 16 The questionnaire consists of four objective items on risk factors (age, sex, smoking history in pack-years, prior use of spirometry) and three subjective symptom-based items (dyspnoea, chronic cough, regular sputum production). Objective items are scored from 0 to 2 points and subjective symptom-based items from 0 to 1 point, yielding a total score ranging from 0 to 9. The questionnaire was translated into Mandarin Chinese and presented in a bilingual format. COPD definition Clinical COPD diagnosis requires compatible symptoms and risk factors in addition to post-bronchodilator airflow obstruction. In this study, we defined COPD based on post-bronchodilator spirometric airflow obstruction (FEV 1 /FVC ratio < 0.70). 23 We used the Global Initiative for Obstructive Lung Disease (GOLD) grades (Grade 1 ( FEV 1 ≥ 80%), Grade 2 (50%-80%), Grade 3 (30%-50%), and Grade 4 (< 30%)) for severity classification of persistent airflow obstruction. 23 Study procedure Enrolled participants completed a self-administered questionnaire capturing sociodemographic information (e.g., age, gender, self-identified ethnicity, education, household income, history of COPD) and the PUMA scale. Spirometry was performed by trained technicians in accordance with the latest ATS/ERS 2019 standards using two calibrated spirometer models (EasyOne Air, ndd Medical Technologies, Switzerland; Datospir touch, Sibelmed, Barcelona, Spain). 24 Pre-bronchodilator spirometry was performed until at least three acceptable FEV 1 and FVC measurements were obtained. Following bronchodilator administration (400 µg of salbutamol delivered via a spacer), another minimum three acceptable FEV 1 and FVC measurements were obtained. Acceptability and repeatability were assessed separately for the pre- and post-bronchodilator sets. The Global Lung Initiative (GLI) Quanjer 2012 reference values were selected based on age, height, gender and self-reported ethnicity mapped to GLI ethnic groups. 25 All spirometry results were independently reviewed and interpreted by study team (at least one family physician and one respiratory physician). Discrepancies were resolved by discussion to reach consensus. Participants were included in the study if they completed the PUMA scale and sociodemographic questionnaire, and had usable post-bronchodilator spirometry based on ATS/ERS 2019 quality grading (i.e., tests met acceptability criteria and permitted calculation of post-bronchodilator FEV 1 , FVC and FEV 1 /FVC, quality grade C and above). Sample size The sample size for assessing the predictive performance of the PUMA scale was estimated based on pre-determined sensitivity, specificity, precision and prevalence. A group of local respiratory physicians on the study advisory committee estimated the prevalence of COPD in Singapore to be 10–15%, based on their experience with community spirometry programmes. Another local study reported that the prevalence of COPD was 25.1% based on the pre-bronchodilator FEV 1 /FVC ratio < 0.7 among community dwellers aged 55 years old and above. 9 As applying pre-bronchodilator FEV 1 /FVC < 0.7 on older subjects would likely overestimate the diagnosis of COPD, we estimate a prevalence of 15% among participants aged 40 years old and above who had ever smoked. A sample size of 410 was calculated to provide sensitivity and specificity of at least 0.8 with 95% confidence level and 10% precision. 26,27 This sample size also allows estimation of COPD prevalence with 95% confidence level, within ± 4% of the measured value. Statistical analysis Descriptive analyses were conducted to summarise participants’ sociodemographic and clinical characteristics. Differences between participants with and without COPD were assessed using chi-squared tests for categorical variables and t-tests for continuous variables. PUMA scores were compared by COPD status using t-test, and by COPD stage among diagnosed participants using ANOVA test. Statistical significance was set at p < 0.05. The predictive performance of the PUMA scale was evaluated using AUC-ROC analysis. The area under the ROC curve reflects the ability of the PUMA score to discriminate between COPD and non-COPD cases. The AUC-ROC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the cutoff points using non-parametric method. AUC values of 0.5–0.6, 0.6–0.7, 0.7–0.8, 0.8–0.9 and 0.9-1.0 were interpreted as weak, median, good, very good and excellent classification, respectively. 28 The optimal cutoff was determined using Youden’s index in conjunction with consideration of sensitivity, specificity and clinical applicability. All analyses were conducted with Stata version 16.0. Results Participant characteristics 714 potentially eligible participants were screened for eligibility, with 422 meeting the selection criteria and completing both spirometry and questionnaire. Among them, 359 spirometry tests (85.1%) met the acceptability criteria and were included in the analysis (see Figure 1). Figure 1. Flowchart of participant recruitment Note: Successful participants were included in the study if they completed the PUMA scale and sociodemographic questionnaire, and had usable post-bronchodilator spirometry based on ATS/ERS 2019 quality grading (i.e., tests met acceptability criteria and permitted calculation of post-bronchodilator FEV1, FVC and FEV1/FVC, quality grade C and above) . Table 1 presents participant characteristics in total and by COPD status. Most of the participants were male (85.78), of Chinese ethnicity (71.7%), married (71.0%) and employed (69.1%). Compared with non-COPD participants, those with spirometry-defined COPD were older in age, less educated, more single/divorced/widowed status, more phlegm symptoms and higher smoking pack-years. Table 1. Characteristics of study population (n=359) Total COPD (n=45) Non-COPD (n=314) P value ⸸ n % n % n % Age 40-49 138 38.44 7 15.56 131 41.72 <0.01 50-59 95 26.46 9 20.00 86 27.39 60+ 126 35.10 29 64.44 97 30.89 Gender Male 308 85.79 40 88.89 268 85.35 0.53 Female 51 14.21 5 11.11 46 14.65 Ethnicity Chinese 258 71.67 33 73.33 225 71.66 0.76 Malay 53 14.72 8 17.78 45 14.33 Indian 27 7.50 2 4.44 25 7.96 Others 22 6.11 2 4.44 19 6.05 Education Primary or below 55 15.32 16 35.56 39 12.42 <0.01 Secondary/post-secondary 198 55.15 26 57.78 172 54.78 Diploma and above 103 28.69 3 6.67 100 31.85 Prefer not to say 3 0.84 0 0 3 0.96 Marital status Married 255 71.03 26 57.78 229 72.93 0.04 Single/divorced/widowed 104 28.97 19 42.22 85 27.07 Employment status Unemployed 111 30.92 15 33.33 96 30.57 0.71 Employed 248 69.08 30 66.67 218 69.43 BMI (kg/m 2 ), mean (SD) 25.71 4.62 25.12 5.01 25.79 4.57 0.36 ⸸⸸ Phlegm No 254 70.75 24 53.33 230 73.25 <0.01 Yes 105 29.25 21 46.67 84 26.75 Cough No 249 69.36 26 57.78 223 71.02 0.07 Yes 110 30.64 19 42.22 91 28.98 Breathless No 218 60.72 26 57.78 192 61.15 0.67 Yes 141 39.28 19 42.22 122 38.85 Pack-year Less than 20 236 65.74 17 37.78 219 69.75 <0.01 20-30 65 18.11 9 20.00 56 17.83 More than 30 58 16.16 19 42.22 39 12.42 Prior doctor diagnosis of COPD No 353 98.33 43 95.56 310 98.73 0.12 Yes 6 1.67 2 4.44 4 1.27 Note: 1 missing questionnaire, hence total sample size for analysis is 366. ⸸ Chi squared test was conducted unless otherwise stated. ⸸⸸ T-test was conducted. Pulmonary function and PUMA score The percentage of participants with spirometry-defined COPD was 12.5% (45 out of 359). Majority of those with spirometry-defined COPD had airflow obstruction at moderate severity (51.1%), followed by mild (42.2%), severe (4.4%) and very severe (2.2%). (Table 2) The mean of the ratio of post-bronchodilator FEV 1 /FVC was 0.65 (SD=0.03) for participants with COPD compared to the mean of 0.82 (SD=0.05) for those without COPD. The mean PUMA score for participants with COPD (5, SD=1.79) was significantly higher than those without COPD (3.37, SD=1.66). Table 2. COPD diagnosis and PUMA score n % Post-BD FEV 1 /FVC P Value PUMA Score P Value Mean (SD) Median Mean (SD) Median Non-COPD 314 87.47 0.82 (0.05) 0.81 <0.01 ⸷ 3.37 (1.66) 3 <0.01 ⸷ COPD 45 12.53 0.62 (0.08) 0.65 5 (1.79) 5 Mild 19 42.22 0.65 (0.03) 0.65 <0.01 ⸷⸷ 4.37 (1.71) 4 0.04 ⸷⸷ Moderate 23 51.11 0.60 (0.08) 0.61 5.57 (1.70) 6 Severe 2 4.44 0.43 (0.16) 0.43 6 (0) 6 Very Severe 1 2.22 0.56 (0) 0.56 2 (0) 2 ⸷ two-sample t-test was conducted. ⸷⸷ one-way ANOVA was conducted. PUMA predictive performance for COPD case-finding PUMA demonstrated an acceptable AUC-ROC of 0.75 (95% CI: 0.67, 0.83) (see Figure 2). Table 3 shows that the PUMA score with a cutoff point of ≥ 5 had the highest Youden index, with sensitivity of 62.22%, specificity of 79.30%, PPV of 30.1%, and NPV of 93.6%. A cutoff of ≥4 had an increased sensitivity of 80% and NPV of 95.2%, but reduced specificity of 56.69% and PPV of 20.9%. Table 3. Predictive performance of each cut-off points of the PUMA questionnaire in screening for COPD Cut point Sensitivity Specificity PPV NPV Youden index ≥ 1 100.00% 1.91% 12.75% 100% 0.019 ≥ 2 97.78% 11.46% 13.66% 97.30% 0.092 ≥ 3 86.67% 31.53% 15.35% 94.29% 0.182 ≥ 4 80.00% 56.69% 20.93% 95.19% 0.367 ≥ 5 62.22% 79.30% 30.11% 93.61% 0.415 ≥ 6 46.67% 88.54% 36.84% 92.05% 0.352 ≥ 7 22.22% 94.90% 38.46% 89.49% 0.171 ≥ 8 4.44% 98.73% 33.33% 87.82% 0.032 PPV = positive predictive value NPV = negative predictive value Bold for best cut-off point according to Youden’s index (sensitivity + specificity). Discussion Summary of findings This study estimated spirometry-defined COPD prevalence, and validated the PUMA questionnaire in a real-world, multi-ethic, primary-care, ever-smoker population in Singapore. The prevalence of spirometry-defined COPD among the studied participants was 12.5%, with majority in mild to moderate severity stage, and more than 6% at the severe or very severe stage. PUMA demonstrated good discrimination (AUC 0.75). At a cut-off ≥ 5, sensitivity and specificity were 62.22% and 79.30% respectively; PPV and NPV were 30.11% and 93.61% respectively, indicating usefulness as a rule-out. At a cut-off ≥ 4, the sensitivity was higher (80%) but specificity was lower (56.69%), indicating usefulness as a case-finding tool. The prevalence of spirometry-defined COPD among participants aged 40 years old and above in Singapore primary care was comparable with the same age group in other Asian countries (17.4% in China, 10.6% in Malaysia, 10.3% in Japan, 8.1% in Vietnam, 6.3% in Indonesia). 29 – 32 The lower percentage in Vietnam and Indonesia could be due to the focus on non-smokers who were exposed to biomass pollutants. 31 The strong association of smoking with COPD highlights the importance of reducing the smoking rate on the burden of COPD. 33 Despite numerous efforts made on tobacco control by Singapore government, primary care could take a more active role in encouraging patients to quit smoking with its advantages of accessibility, continuity and long-term relationships with patients. 34 , 35 The cutoff of PUMA that maximised sensitivity and specificity in Singapore (≥ 5) was one point lower than the other Asian populations but similar to two Latin American studies. (Table 4 ) At the cutoff of ≥ 5, 62.2% of participants with airflow limitation were correctly identified by the questionnaire (true positive) while 37.8% with the diagnosis would be missed. The sensitivity was higher (80%) but specificity was lower (56.69%) at the cutoff of ≥ 4 in our study, which means that it could identify more participants who have COPD but would be less accurate at identifying participants without COPD. This is consistent across all studies validating PUMA questionnaire regardless of healthcare settings. The discriminatory performance of our primary care-based study was similar to most of the previous primary care- or hospital-based studies. (Table 4 ) As to predictive performance, there were variations in sensitivity, specificity, PPV and NPV, which could be due to the difference in clinical setting, the prevalence of COPD and smoking status of the studied population in respective study. 14 , 21 Table 4 Comparison of the predictive performance of PUMA across countries and regions Study Setting Sample size COPD diagnosis (%) Cutoff ≥ 4 Cutoff ≥ 5 Cutoff ≥ 6 AUC-ROC Se Sp PPV NPV Se Sp PPV NPV Se Sp PPV NPV Latin America Lopez Varela 2016 16 Argentina, Colombia, Venezuela, Uruguay, primary care 1,540 17.7% 87.1 45.5 28.7 93.3 74.2 64.8 34.7 90.9 55.2 81.9 43.5 87.9 0.76 Lopez Varela 2019 17 Mexico, primary care 974 45.1% 95.2 18.9 49.1 82.8 85.4 37.6 52.9 75.8 69.0 62.1 59.9 59.9 0.70 Bastidas 2023 18 Columbia, hospital 681 27.5 - - - - 58.8 64.2 38.3 80.5 - - - - 0.67 Asia Au-Doung 2022 19 Hong Kong, primary care 377 27.1 96.1 21.1 31.5 93.4 91.2 42.6 37.5 92.7 76.5 63.3 44.1 63.3 0.76 Sebayang 2024 20 Indonesia, hospital 76 65.8 - - - - - - - - 72.55 84 60 90.24 0.78 Gadam 2024 22 India, hospital 50 54 - - - - - - - - 45.6 42.2 58.7 30.43 0.44 Su 2024 21 Taiwan, hospital 240 32 97 17 36 93 88 38 41 87 77 64 51 85 0.75 Current study Singapore, primary care 359 12.5 80.00 56.69 20.9 95.2 62.22 79.30 30.1 93.6 47.92 88.68 39.0 91.9 0.76 Se = sensitivity Sp = specificity PPV = positive predictive value NPV = negative predictive value Bold numbers indicate the best cutoff determined by Youden index. Implication for clinical practice and policy PUMA could be used as an opportunistic case-finding tool for ever-smokers ≥ 40 years, presenting for any reason, completed by patients in the waiting room to help clinicians identify patients at risk of COPD and prompt spirometric assessment in Singapore primary care setting. A low PUMA score would indicate a low likelihood of clinically significant COPD, and avoid unnecessary resource utilisation. When spirometry access is limited, and false-positives are costly, a higher cut-off (≥ 5) could be used to maximise specificity. When the priority is to minimize missed cases, and spirometry capacity is higher, a more sensitive threshold (≥ 4) could be used to improve case-finding. Many newly identified cases were mild or moderate, which represented an opportunity for primary care interventions: smoking cessation, vaccination, optimization of inhaled therapy and co-morbidity management. The PUMA questionnaire is particularly useful for primary care with limited healthcare resources and may be suitable for COPD case-finding in countries with similar multi-ethnic population profile, although the PUMA questionnaire may need to be re-evaluated to identify appropriate cutoff value for different populations. Strength and limitations This is the first study to examine the proportion of current or former smokers aged over 40 years with COPD based on post-bronchodilator FEV 1 /FVC, and to assess the predictive performance of PUMA questionnaire for COPD case finding in Singapore primary care setting. The strengths of this study include the use of a multi-centre, real-world primary care recruitment sample, the use of post-bronchodilator spirometry based on ATS/ERS standards and local guidelines as the primary outcome measure, the inclusion of a multi-ethnic Asian population, and direct comparison of different cut-offs with full operating characteristics (sensitivity, specificity, PPV, NPV), to help translation into practice. This study has some limitations. First, findings generated from a convenience sample may not be representative of the whole primary care population due to potential selection bias. Our sample included only ever-smokers aged ≥ 40 years who had attended primary care within the past year, therefore our findings may not be generalizable to never-smokers or the broader community. Patients who have attended primary care in the last 12 months may also have a higher symptom burden than the general population, and inflate the prevalence estimate and possibly performance statistics due to a different spectrum of disease from a true screening population. The findings may not apply directly to never-smokers with other exposures such as biomass or occupational dusts. As this is a cross-sectional study design, we cannot evaluate the progression of COPD, frequency of exacerbations and the long-term outcomes of those identified via case-finding. Secondly, we did not achieve the predefined sample size partially due to the lower-than-expected success rate of spirometry tests that met ATS/ERS quality grade C and above (85.1%, 359 out of 422). This reflects our stringent quality criteria, and highlights that there is room for improvement in coaching and manoeuvre performance for acceptable and repeatable spirometry. Access to quality spirometry testing including trained personnel to conduct spirometry is an important consideration. Conclusion External validation of the PUMA questionnaire in Singapore primary care population suggests fair accuracy, similar to the original study where the questionnaire was developed. PUMA questionnaire is a feasible opportunistic COPD case-finding tool for high-risk individuals in Singapore primary care setting. A PUMA score of ≥ 4 may serve as a practical threshold to guide spirometry in at-risk ever-smokers aged 40 years and above, prioritising the detection of COPD cases for subsequent intervention. Declarations Data availability The data used to support the findings of this study are available from the corresponding author upon request. Acknowledgement We thank the medical students and primary care researchers who volunteered to onboard participants for the study: Mr Ady Riandy, Mr Andric Alfonsus, Dr Ariffin Kawaja, Dr Chole Cheung, Dr Ashley Hsu, Ms Cassie Chua, Ms Citrine Ong, Ms Dharana Muthu, Ms Eleanor Chua, Mr Ethan Tan, Mr Eugene Chua, Ms Grace Chung, Mr Hanxin Liu, Mr Jason Zhang, Mr Jeremy Ling, Ms Jie Lee, Ms Jie Ying Khok, Mr Jonas Cham, Ms Lionel Hoe, Ms Li Zi Leong, Ms Sharleen Goh, Ms Weidi Sun and Ms Xin Hui Sam. We especially acknowledge Ms Andrea Rudd who helped with data extraction from the spirometry reports. We are grateful to the comments on study design from the study advisory committee: Ms Ai Ling Sim-Devadas, Dr Akshar Saxena, Prof Carmen Wong, Dr Choon Kit Leong, Prof Christian Apfelbacher, Prof Fernando Martinez, Prof Gerlad Koh, Prof Jansen Koh, Prof Sanjay Chotirmall. We appreciate the support from the following Singapore based organisations: Smartfuture Pte Ltd, all the GPs and clinic assistants in private GP clinics that were involved in patient recruitment, nurses and manager in Frontier Medical Associates, nurses and manager in Raffles Medical Group, OneCare Medical Clinic, National University Polyclinics, Changi General Hospital, Heartbeat@Bedok, One Punggol, Hong Lim Residents’ Network, Mawar Community Services, NUS Public Health Screening Committee. This study would not be possible without all the support. We would also like to acknowledge all the participants. Conflict of interest None declared. Author’s contribution LES and JMN obtained funding to the study. LES, KT and VMQ conceived the study. VMQ, KT, VH, YZY, SW DW, and LES participated in data collection. KT, GPT, VH and LES were responsible for spirometry test result interpretation. VMQ conducted data analysis and wrote the first draft of the manuscript, with inputs from KT and LES. All authors commented critically to the intellectual content of the article and gave approval for publication of the final version. Funding This study was supported by AstraZeneca Singapore. The funder had no role in study design, data collection, analysis and preparation of the manuscript. References Schnieders, E. et al. Performance of alternative COPD case-finding tools: a systematic review and meta-analysis. Eur. Respir. Rev. Off. J. Eur. Respir. Soc. 30 , (2021). Agustí, A. et al. Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary. Am. J. Respir. Crit. Care Med. 207 , 819–837 (2023). Pham, H. Q. et al. Economic Burden of Chronic Obstructive Pulmonary Disease: A Systematic Review. Tuberc. Respir. Dis. 87 , 234–251 (2024). Chen, S. et al. The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020–50: a health-augmented macroeconomic modelling study. Lancet Glob. Health 11 , e1183–e1193 (2023). Boers, E. et al. Global Burden of Chronic Obstructive Pulmonary Disease Through 2050. JAMA Netw. Open 6 , e2346598 (2023). Fabbri, L. M. et al. COPD and multimorbidity: recognising and addressing a syndemic occurrence. Lancet Respir. Med. 11 , 1020–1034 (2023). Principal Causes of Death. https://www.moh.gov.sg/others/resources-and-statistics/principal-causes-of-death/ (2024). Roberts, N. J., Smith, S. F. & Partridge, M. R. Why is spirometry underused in the diagnosis of the breathless patient: a qualitative study. BMC Pulm. Med. 11 , 37 (2011). Lee, S. Y. et al. Co-occurrence of Physical Frailty and COPD and Association With Disability and Mortality. Chest 161 , 1225–1238 (2022). Lim, S. et al. Impact of chronic obstructive pulmonary disease (COPD) in the Asia-Pacific region: the EPIC Asia population-based survey. Asia Pac. Fam. Med. 14 , 4 (2015). Khakban, A. et al. Ten-Year Trends in Direct Costs of COPD: A Population-Based Study. Chest 148 , 640–646 (2015). Derom, E. et al. Primary care spirometry. Eur. Respir. J. 31 , 197–203 (2008). Aaron, S. D. et al. Early Diagnosis and Treatment of Chronic Obstructive Pulmonary Disease: The Costs and Benefits of Case Finding. Am. J. Respir. Crit. Care Med. 209 , 928–937 (2024). Kotz, D., Nelemans, P., van Schayck, C. P. & Wesseling, G. J. External validation of a COPD diagnostic questionnaire. Eur. Respir. J. 31 , 298–303 (2008). Martinez, F. J. et al. Development and initial validation of a self-scored COPD Population Screener Questionnaire (COPD-PS). COPD 5 , 85–95 (2008). López Varela, M. V. et al. Development of a simple screening tool for opportunistic COPD case finding in primary care in Latin America: The PUMA study. Respirol. Carlton Vic 21 , 1227–1234 (2016). Lopez Varela, M. V. et al. External validation of the PUMA COPD diagnostic questionnaire in a general practice sample and the PLATINO study population. Int. J. Chron. Obstruct. Pulmon. Dis. 14 , 1901–1911 (2019). Bastidas, A. R. et al. Comparison of the Diagnostic Performance of Five Clinical Questionnaires for Chronic Obstructive Pulmonary Disease. Can. Respir. J. 2023 , 2821056 (2023). Au-Doung, P. L. W. et al. PUMA screening tool to detect COPD in high-risk patients in Chinese primary care–A validation study. PLOS ONE 17 , e0274106 (2022). Sebayang, R. R., Pandia, P., Pradana, A., Tarigan, A. P. & Wahyuni, A. S. Comparative analysis between PUMA and CAPTURE questionnaires for chronic obstructive pulmonary disease (COPD) screening in smokers. Narra J 4 , e654 (2024). Su, K.-C. et al. The Accuracy of PUMA Questionnaire in Combination With Peak Expiratory Flow Rate to Identify At-risk, Undiagnosed COPD Patients. Arch. Bronconeumol. S0300289624002345 (2024) doi:10.1016/j.arbres.2024.06.013. Gadam, R., Suryakanth, A., Rani, M. Y., Dattu, Guttula. N. R. S. & Kumar, V. S. Predictive capability of PUMA score in detection of chronic obstructive pulmonary disease. Heart Vessels Transpl. 8 , (2024). Venkatesan, P. GOLD COPD report: 2025 update. Lancet Respir. Med. 13 , e7–e8 (2025). The Standards for the Performance and Interpretation of Office Spirometry in Singapore . https://www.ams.edu.sg/view-pdf.aspx?file=media%5C7407_fi_938.pdf&ofile=Standards+and+Performance+of+Office+Spirometry+in+Singapore+(Final)+Jan+2025.pdf (2025). Quanjer, P. H. et al. Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations. Eur. Respir. J. 40 , 1324–1343 (2012). Akoglu, H. User’s guide to sample size estimation in diagnostic accuracy studies. Turk. J. Emerg. Med. 22 , 177–185 (2022). Hajian-Tilaki, K. Sample size estimation in diagnostic test studies of biomedical informatics. J. Biomed. Inform. 48 , 193–204 (2014). Santini, A., Man, A. & Voidăzan, S. Accuracy of Diagnostic Tests. J. Crit. Care Med. Univ. Med. Si Farm. Din Targu-Mures 7 , 241–248 (2021). Pan, Z. et al. Accuracy and cost-effectiveness of different screening strategies for identifying undiagnosed COPD among primary care patients (≥40 years) in China: a cross-sectional screening test accuracy study: findings from the Breathe Well group. BMJ Open 11 , e051811 (2021). Ching, S.-M. et al. Detection of airflow limitation using a handheld spirometer in a primary care setting. Respirol. Carlton Vic 19 , 689–693 (2014). Nguyen Viet, N. et al. The prevalence and patient characteristics of chronic obstructive pulmonary disease in non-smokers in Vietnam and Indonesia: An observational survey. Respirol. Carlton Vic 20 , 602–611 (2015). Minakata, Y. et al. Prevalence of COPD in primary care clinics: correlation with non-respiratory diseases. Intern. Med. Tokyo Jpn. 47 , 77–82 (2008). Current smoking prevalence. The Tobacco Atla https://tobaccoatlas.org/challenges/prevalence/ (2023). Lua, Y. H. A., How, C. H. & Ng, C. W. M. Smoking cessation in primary care. Singapore Med. J. 65 , (2024). Smoking prevalence in S’pore population dropped from 13.9% in 2010 to 10.1% in 2020. Sngapore Ministry of Health https://www.moh.gov.sg/newsroom/smoking-prevalence-in-s'pore-population-dropped-from-139-in-2010-to-101-in-2020/ (2022). Additional Declarations No competing interests reported. Supplementary Files PUMASupplfinal.docx Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2026 Read the published version in npj Primary Care Respiratory Medicine → Version 1 posted Editorial decision: Revision requested 06 Feb, 2026 Reviews received at journal 18 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviews received at journal 08 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviewers invited by journal 29 Dec, 2025 Editor assigned by journal 22 Dec, 2025 Submission checks completed at journal 22 Dec, 2025 First submitted to journal 18 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8394069","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":567119106,"identity":"a6d79656-7e7a-48d0-af26-cb521071a11f","order_by":0,"name":"Vicky Mengqi Qin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYNACAwYGNgbmAxDOAeK1sCVAVBOnBQx4DIjTotvee/jFmwIG2T7pnm+PP7YxyPHdSGD++AWPFrMz59Is5xgwGLfJnN1ucLCNwVjyRgKbtAw+LTdyzIyBTkpsk8jdJgHUkrgBqIVZAp+W+29gWnKegbTUA7Uwf8ar5QaP8WOoFjaQlgSDGwkMkh/w+iXHjHGOgYRxm0SamcSZcxKGM888bJPGo4PB7PgZ4w9v/tjIzp+R/EyiosxGnu948uGPP/DpAUaiBA+DBGMDhAPyBGMDMw9+LcwfeEDKkIUYCdgyCkbBKBgFIwsAAPkSTiSiydc1AAAAAElFTkSuQmCC","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":true,"prefix":"","firstName":"Vicky","middleName":"Mengqi","lastName":"Qin","suffix":""},{"id":567119107,"identity":"5361b9d0-6652-4897-95e3-b4a18221dfdd","order_by":1,"name":"Kenneth Tan","email":"","orcid":"","institution":"Kenneth Tan Medical Clinic","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"","lastName":"Tan","suffix":""},{"id":567119111,"identity":"ddfdd35e-527f-42b8-baab-fdaaee05c587","order_by":2,"name":"Geak Poh Tan","email":"","orcid":"","institution":"Tan Tock Seng Hospital","correspondingAuthor":false,"prefix":"","firstName":"Geak","middleName":"Poh","lastName":"Tan","suffix":""},{"id":567119115,"identity":"d195d824-1368-4c4e-8fa7-a046c514a0df","order_by":3,"name":"Valery Ho","email":"","orcid":"","institution":"Tan Tock Seng Hospital","correspondingAuthor":false,"prefix":"","firstName":"Valery","middleName":"","lastName":"Ho","suffix":""},{"id":567119118,"identity":"9342d863-68f5-4f0e-b781-31be6c48b2bf","order_by":4,"name":"Zeyuan Yin","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Zeyuan","middleName":"","lastName":"Yin","suffix":""},{"id":567119119,"identity":"cf632c26-6c16-4d0e-9a34-6a99c69ade58","order_by":5,"name":"Susie Wong","email":"","orcid":"","institution":"Frontier Healthcare Group","correspondingAuthor":false,"prefix":"","firstName":"Susie","middleName":"","lastName":"Wong","suffix":""},{"id":567119120,"identity":"20e29797-2815-45c5-b984-3b0805718171","order_by":6,"name":"Dingding Wang","email":"","orcid":"","institution":"Frontier Healthcare Group","correspondingAuthor":false,"prefix":"","firstName":"Dingding","middleName":"","lastName":"Wang","suffix":""},{"id":567119121,"identity":"0e62e602-4c7d-47ef-84c7-359e45d01936","order_by":7,"name":"Jo-Anne Manski-Nankervis","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Jo-Anne","middleName":"","lastName":"Manski-Nankervis","suffix":""},{"id":567119122,"identity":"8d46d1d8-7162-4fc2-a937-114c34ec5e41","order_by":8,"name":"Eng Sing Lee","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Eng","middleName":"Sing","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2025-12-18 10:08:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8394069/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8394069/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41533-026-00510-9","type":"published","date":"2026-04-14T15:59:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99274014,"identity":"00ed086e-c975-4204-bbca-aad94c311a49","added_by":"auto","created_at":"2025-12-31 06:39:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174643,"visible":true,"origin":"","legend":"","description":"","filename":"PUMAstudyfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/cb073bb50a89131b400ab6ac.docx"},{"id":99274021,"identity":"3b85b7e7-1346-4a4f-a910-266e924affc9","added_by":"auto","created_at":"2025-12-31 06:39:28","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10958,"visible":true,"origin":"","legend":"","description":"","filename":"34bca9eb7ca7400a95c0b6a2d8683380.json","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/dc713aeb55bc6c2a332ae116.json"},{"id":99273999,"identity":"d747620f-7868-468f-92a8-86c37356fa5c","added_by":"auto","created_at":"2025-12-31 06:39:24","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":523333,"visible":true,"origin":"","legend":"","description":"","filename":"PUMASupplfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/6f02ceeb106468ec399bd463.docx"},{"id":99274026,"identity":"7078f3af-a6dd-4874-920c-92c71eb07d74","added_by":"auto","created_at":"2025-12-31 06:39:29","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122815,"visible":true,"origin":"","legend":"","description":"","filename":"34bca9eb7ca7400a95c0b6a2d86833801enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/d523a59e28c06a2221f28aef.xml"},{"id":99274013,"identity":"72bf4825-27d4-4269-884a-12d3b8364966","added_by":"auto","created_at":"2025-12-31 06:39:27","extension":"emf","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26124,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/fe92a5255f070481abdd3227.emf"},{"id":99274008,"identity":"28d353f3-5550-4105-a200-8bad709954e1","added_by":"auto","created_at":"2025-12-31 06:39:26","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43896,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/51e677d9d83029f7a2353cf4.jpeg"},{"id":99274019,"identity":"5bb94aa2-5fb6-4e74-a79c-f8bbe99ea5e8","added_by":"auto","created_at":"2025-12-31 06:39:28","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92233,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/8b9bb46d1bf81c6c9be86959.png"},{"id":99274012,"identity":"8a270961-32eb-4da4-bf31-1dca52f21c56","added_by":"auto","created_at":"2025-12-31 06:39:27","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24279,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/1bd7c623e0380dcc8d193efe.png"},{"id":99274011,"identity":"23db9622-d77a-4126-98ad-6b0cd0fd248c","added_by":"auto","created_at":"2025-12-31 06:39:26","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119594,"visible":true,"origin":"","legend":"","description":"","filename":"34bca9eb7ca7400a95c0b6a2d86833801structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/82774b7be0b73ad1cf401b1a.xml"},{"id":99274007,"identity":"a2751879-6064-4fd7-bf43-eb6b65d9cd56","added_by":"auto","created_at":"2025-12-31 06:39:25","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134224,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/1878dedf01ea4457b353cced.html"},{"id":99274010,"identity":"5772ec26-43de-43a8-8c9e-42d9bccef1f8","added_by":"auto","created_at":"2025-12-31 06:39:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27744,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant recruitment\u003c/p\u003e\n\u003cp\u003eNote: Successful participants were included in the study if they completed the PUMA scale and sociodemographic questionnaire, and had usable post-bronchodilator spirometry based on ATS/ERS 2019 quality grading (i.e., tests met acceptability criteria and permitted calculation of post-bronchodilator FEV1, FVC and FEV1/FVC, quality grade C and above) .\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/ef174944ccaaefff3ef48fda.png"},{"id":99274009,"identity":"77734ae0-02f5-4a10-9e97-00e685b29661","added_by":"auto","created_at":"2025-12-31 06:39:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35995,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve: PUMA score and COPD diagnosis among current- and ex-smokers (n=359)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/2cb7828c0a055390e77716f7.png"},{"id":107350912,"identity":"202e00ca-4309-406a-abd0-bdee4df29446","added_by":"auto","created_at":"2026-04-20 16:06:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":827955,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/d2fd27fb-bffb-45c0-8d56-cde89d9d7098.pdf"},{"id":99274015,"identity":"a60c5dea-ce46-491b-9814-7a1341837c25","added_by":"auto","created_at":"2025-12-31 06:39:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":523333,"visible":true,"origin":"","legend":"","description":"","filename":"PUMASupplfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8394069/v1/c5c063ab31a3f07e37174698.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive performance of the PUMA questionnaire as an opportunistic COPD case-finding tool in Singapore primary care","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is a progressive, largely preventable lung disease with symptoms such as dyspnoea, chronic cough, and sputum production.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e COPD has affected more than 300\u0026nbsp;million people worldwide, making it one of the leading causes of death globally and causing tremendous economic burden.\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e People with COPD frequently present with comorbid chronic diseases that share underlying mechanisms and risk factors, including cardiovascular, respiratory, and renal conditions.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn Singapore, COPD was ranked the 10th leading cause of death in 2023.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Early diagnosis and treatment of COPD are essential in slowing disease progression, improving patients\u0026rsquo; quality of life and reducing healthcare costs and the burden to healthcare systems. Despite evidence-based diagnostic and treatment guidelines, COPD remains substantially underdiagnosed in many countries, with spirometry underutilised particularly in primary care.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e The prevalence of self-reported doctor diagnosed COPD among Singaporeans in the community is much lower than the prevalence of spirometry-confirmed COPD.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e This reflects a high rate of underdiagnosis, with many patients first diagnosed at an advanced stage of disease.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e One barrier to optimizing COPD detection in primary care is the lack of diagnostic tool such as spirometers.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eQuestionnaires have been shown to be valuable tools for improving COPD case-finding when spirometry is unavailable.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e The Study of Prevalence and Regular Practice, Diagnosis and Treatment, among General Practitioners in Populations at Risk of COPD in Latin America (PUMA) questionnaire has been validated in some Latin American countries and in mono-ethnic Asian populations including Hong Kong, Taiwan, India and Indonesia.\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e However, its predictive performance for case finding in multi-ethnic Asian populations remains unknown. Although PUMA has demonstrated comparable predictive performance across countries, the cutoff values differ, underscoring the need to establish population-specific thresholds.\u003c/p\u003e \u003cp\u003eThis study aimed to first estimate the prevalence and stage of COPD among the at-risk multi-ethnic primary care population in Singapore. Second, we evaluated the predictive performance of the PUMA questionnaire and determined the optimal cutoff for opportunistic case-finding in the same population. Finally, we compared the predictive performance observed in our study with findings from other published studies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and subject recruitment\u003c/h2\u003e \u003cp\u003eThis multicentre cross-sectional study employed convenience sampling between 1 July 2024 and 30 September 2025 in Singapore. Singapore is a multi-ethnic country, with majority of the population being Chinese, followed by Malay and Indian. Participants were recruited from both private and public primary care practices (during consultations and via poster advertisements in clinics) and community events (via poster advertisements in community centres and social media platforms). Eligibility was assessed through telephone or face-to-face interviews. A study team member then explained the study purpose and procedures to eligible participants and obtained written informed consent prior to enrolment. Ethics approval was obtained from the Institutional Research Board of Nanyang Technological University (IRB-2024-235). We offered participants S\u003cspan\u003e$\u003c/span\u003e20 cash as a token of appreciation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eParticipants aged 40 years old and above, who were current or former smokers (defined as having smoked at least 100 cigarettes), attended primary care within the past 12 months, and were able to read and write in English or Chinese were recruited. Exclusion criteria included allergy to bronchodilators or medical conditions contraindicating spirometry (e.g., myocardial infarction or stroke, aortic or cerebral aneurysm, or detached retina in the last three months).\u003c/p\u003e\n\u003ch3\u003ePUMA questionnaire\u003c/h3\u003e\n\u003cp\u003eThe PUMA questionnaire is a seven-item scale used to identify participants at risk of COPD (Supplementary file S1). Its development has been described in detail elsewhere.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e The questionnaire consists of four objective items on risk factors (age, sex, smoking history in pack-years, prior use of spirometry) and three subjective symptom-based items (dyspnoea, chronic cough, regular sputum production). Objective items are scored from 0 to 2 points and subjective symptom-based items from 0 to 1 point, yielding a total score ranging from 0 to 9. The questionnaire was translated into Mandarin Chinese and presented in a bilingual format.\u003c/p\u003e\n\u003ch3\u003eCOPD definition\u003c/h3\u003e\n\u003cp\u003eClinical COPD diagnosis requires compatible symptoms and risk factors in addition to post-bronchodilator airflow obstruction. In this study, we defined COPD based on post-bronchodilator spirometric airflow obstruction (FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.70).\u003csup\u003e23\u003c/sup\u003e We used the Global Initiative for Obstructive Lung Disease (GOLD) grades (Grade 1 ( FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026ge;\u0026thinsp;80%), Grade 2 (50%-80%), Grade 3 (30%-50%), and Grade 4 (\u0026lt;\u0026thinsp;30%)) for severity classification of persistent airflow obstruction.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eStudy procedure\u003c/h3\u003e\n\u003cp\u003eEnrolled participants completed a self-administered questionnaire capturing sociodemographic information (e.g., age, gender, self-identified ethnicity, education, household income, history of COPD) and the PUMA scale.\u003c/p\u003e \u003cp\u003eSpirometry was performed by trained technicians in accordance with the latest ATS/ERS 2019 standards using two calibrated spirometer models (EasyOne Air, ndd Medical Technologies, Switzerland; Datospir touch, Sibelmed, Barcelona, Spain).\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Pre-bronchodilator spirometry was performed until at least three acceptable FEV\u003csub\u003e1\u003c/sub\u003e and FVC measurements were obtained. Following bronchodilator administration (400 \u0026micro;g of salbutamol delivered via a spacer), another minimum three acceptable FEV\u003csub\u003e1\u003c/sub\u003e and FVC measurements were obtained. Acceptability and repeatability were assessed separately for the pre- and post-bronchodilator sets.\u003c/p\u003e \u003cp\u003eThe Global Lung Initiative (GLI) Quanjer 2012 reference values were selected based on age, height, gender and self-reported ethnicity mapped to GLI ethnic groups.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e All spirometry results were independently reviewed and interpreted by study team (at least one family physician and one respiratory physician). Discrepancies were resolved by discussion to reach consensus. Participants were included in the study if they completed the PUMA scale and sociodemographic questionnaire, and had usable post-bronchodilator spirometry based on ATS/ERS 2019 quality grading (i.e., tests met acceptability criteria and permitted calculation of post-bronchodilator FEV\u003csub\u003e1\u003c/sub\u003e, FVC and FEV\u003csub\u003e1\u003c/sub\u003e/FVC, quality grade C and above).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample size\u003c/h2\u003e \u003cp\u003eThe sample size for assessing the predictive performance of the PUMA scale was estimated based on pre-determined sensitivity, specificity, precision and prevalence. A group of local respiratory physicians on the study advisory committee estimated the prevalence of COPD in Singapore to be 10\u0026ndash;15%, based on their experience with community spirometry programmes. Another local study reported that the prevalence of COPD was 25.1% based on the pre-bronchodilator FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.7 among community dwellers aged 55 years old and above.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e As applying pre-bronchodilator FEV\u003csub\u003e1\u003c/sub\u003e/FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.7 on older subjects would likely overestimate the diagnosis of COPD, we estimate a prevalence of 15% among participants aged 40 years old and above who had ever smoked. A sample size of 410 was calculated to provide sensitivity and specificity of at least 0.8 with 95% confidence level and 10% precision.\u003csup\u003e26,27\u003c/sup\u003e This sample size also allows estimation of COPD prevalence with 95% confidence level, within \u0026plusmn;\u0026thinsp;4% of the measured value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive analyses were conducted to summarise participants\u0026rsquo; sociodemographic and clinical characteristics. Differences between participants with and without COPD were assessed using chi-squared tests for categorical variables and t-tests for continuous variables. PUMA scores were compared by COPD status using t-test, and by COPD stage among diagnosed participants using ANOVA test. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eThe predictive performance of the PUMA scale was evaluated using AUC-ROC analysis. The area under the ROC curve reflects the ability of the PUMA score to discriminate between COPD and non-COPD cases. The AUC-ROC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the cutoff points using non-parametric method. AUC values of 0.5\u0026ndash;0.6, 0.6\u0026ndash;0.7, 0.7\u0026ndash;0.8, 0.8\u0026ndash;0.9 and 0.9-1.0 were interpreted as weak, median, good, very good and excellent classification, respectively.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e The optimal cutoff was determined using Youden\u0026rsquo;s index in conjunction with consideration of sensitivity, specificity and clinical applicability. All analyses were conducted with Stata version 16.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eParticipant characteristics\u003c/p\u003e\n\u003cp\u003e714 potentially eligible participants were screened for eligibility, with 422 meeting the selection criteria and completing both spirometry and questionnaire. Among them, 359 spirometry tests (85.1%) met the acceptability criteria and were included in the analysis (see Figure 1).\u003c/p\u003e\n\u003cp\u003eFigure 1. Flowchart of participant recruitment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNote: Successful participants were included in the study if they completed the PUMA scale and sociodemographic questionnaire, and had usable post-bronchodilator spirometry based on ATS/ERS 2019 quality grading (i.e., tests met acceptability criteria and permitted calculation of post-bronchodilator FEV1, FVC and FEV1/FVC, quality grade C and above) .\u003c/p\u003e\n\u003cp\u003eTable 1 presents participant characteristics in total and by COPD status. Most of the participants were male (85.78), of Chinese ethnicity (71.7%), married (71.0%) and employed (69.1%). Compared with non-COPD participants, those with spirometry-defined COPD were older in age, less educated, more single/divorced/widowed status, more phlegm symptoms and higher smoking pack-years.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Characteristics of study population (n=359)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD (n=45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-COPD (n=314)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003csup\u003e⸸\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e38.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e15.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e41.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e26.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e27.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e60+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e35.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e64.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e30.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e85.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e88.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e85.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e14.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e14.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eChinese\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e71.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e73.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e71.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eMalay\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e14.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e17.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e14.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eIndian\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e7.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003ePrimary or below\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e15.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e35.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eSecondary/post-secondary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e55.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e57.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e54.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eDiploma and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e28.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e31.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003ePrefer not to say\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e71.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e57.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e72.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eSingle/divorced/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e28.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e42.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e27.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e30.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eEmployed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e69.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e66.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e69.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e25.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e25.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e25.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.36\u003csup\u003e⸸⸸\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhlegm\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e70.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e53.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e73.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e29.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e46.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e26.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCough\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e69.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e57.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e71.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e42.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e28.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreathless\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e57.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e61.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e39.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e42.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e38.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePack-year\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eLess than 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e65.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e37.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e69.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e20-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e18.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e17.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eMore than 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e16.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e42.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrior doctor diagnosis of COPD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e98.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e95.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e98.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: 1 missing questionnaire, hence total sample size for analysis is 366.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e⸸\u0026nbsp;\u003c/sup\u003eChi squared test was conducted unless otherwise stated.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e⸸⸸\u0026nbsp;\u003c/sup\u003eT-test was conducted.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePulmonary function and PUMA score\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe percentage of participants with spirometry-defined COPD was 12.5% (45 out of 359). Majority of those with spirometry-defined COPD had airflow obstruction at moderate severity (51.1%), followed by mild (42.2%), severe (4.4%) and very severe (2.2%).\u0026nbsp;(Table 2) The mean of the ratio of post-bronchodilator\u0026nbsp;FEV\u003csub\u003e1\u003c/sub\u003e/FVC was 0.65 (SD=0.03) for participants with COPD compared to the mean of 0.82 (SD=0.05) for those without COPD. The mean PUMA score for participants with COPD (5, SD=1.79) was significantly higher than those without COPD (3.37, SD=1.66).\u003c/p\u003e\n\u003cp\u003eTable 2. COPD diagnosis and PUMA score\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-BD FEV\u003csub\u003e1\u003c/sub\u003e/FVC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePUMA\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNon-COPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e87.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.82 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003csup\u003e⸷\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3.37 (1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003csup\u003e⸷\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.62 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e5 (1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e42.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.65 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003csup\u003e⸷⸷\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e4.37 (1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.04\u003csup\u003e⸷⸷\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e51.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.60 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e5.57 (1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.43 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e6 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eVery Severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.56 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e⸷\u0026nbsp;\u003c/sup\u003etwo-sample t-test was conducted.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e⸷⸷\u003c/sup\u003eone-way ANOVA was conducted.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePUMA predictive performance for COPD case-finding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePUMA demonstrated an acceptable AUC-ROC of 0.75 (95% CI: 0.67, 0.83) (see Figure 2). Table 3 shows that the PUMA score with a cutoff point of \u0026ge; 5 had the highest Youden index, with sensitivity of 62.22%, specificity of 79.30%, PPV of 30.1%, and NPV of 93.6%. A cutoff of \u0026ge;4 had an increased sensitivity of 80% and NPV of 95.2%, but reduced specificity of 56.69% and PPV of 20.9%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Predictive performance of each cut-off points of the PUMA questionnaire in screening for COPD\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut point\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYouden index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e12.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026ge; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e97.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e11.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e13.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e97.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026ge; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e86.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e31.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e15.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e94.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026ge; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e80.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e56.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e20.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e95.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge; 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e62.22%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e79.30%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.11%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.61%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.415\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026ge; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e46.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e88.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e36.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e92.05%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026ge; 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e22.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e94.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e38.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e89.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026ge; 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e98.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e33.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e87.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePPV = positive predictive value\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNPV = negative predictive value\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBold for best cut-off point according to Youden\u0026rsquo;s index (sensitivity + specificity).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSummary of findings\u003c/h2\u003e \u003cp\u003eThis study estimated spirometry-defined COPD prevalence, and validated the PUMA questionnaire in a real-world, multi-ethic, primary-care, ever-smoker population in Singapore. The prevalence of spirometry-defined COPD among the studied participants was 12.5%, with majority in mild to moderate severity stage, and more than 6% at the severe or very severe stage. PUMA demonstrated good discrimination (AUC 0.75). At a cut-off \u0026ge;\u0026thinsp;5, sensitivity and specificity were 62.22% and 79.30% respectively; PPV and NPV were 30.11% and 93.61% respectively, indicating usefulness as a rule-out. At a cut-off \u0026ge;\u0026thinsp;4, the sensitivity was higher (80%) but specificity was lower (56.69%), indicating usefulness as a case-finding tool.\u003c/p\u003e \u003cp\u003eThe prevalence of spirometry-defined COPD among participants aged 40 years old and above in Singapore primary care was comparable with the same age group in other Asian countries (17.4% in China, 10.6% in Malaysia, 10.3% in Japan, 8.1% in Vietnam, 6.3% in Indonesia).\u003csup\u003e\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e The lower percentage in Vietnam and Indonesia could be due to the focus on non-smokers who were exposed to biomass pollutants.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e The strong association of smoking with COPD highlights the importance of reducing the smoking rate on the burden of COPD.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Despite numerous efforts made on tobacco control by Singapore government, primary care could take a more active role in encouraging patients to quit smoking with its advantages of accessibility, continuity and long-term relationships with patients.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe cutoff of PUMA that maximised sensitivity and specificity in Singapore (\u0026ge;\u0026thinsp;5) was one point lower than the other Asian populations but similar to two Latin American studies. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) At the cutoff of \u0026ge;\u0026thinsp;5, 62.2% of participants with airflow limitation were correctly identified by the questionnaire (true positive) while 37.8% with the diagnosis would be missed. The sensitivity was higher (80%) but specificity was lower (56.69%) at the cutoff of \u0026ge;\u0026thinsp;4 in our study, which means that it could identify more participants who have COPD but would be less accurate at identifying participants without COPD. This is consistent across all studies validating PUMA questionnaire regardless of healthcare settings. The discriminatory performance of our primary care-based study was similar to most of the previous primary care- or hospital-based studies. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) As to predictive performance, there were variations in sensitivity, specificity, PPV and NPV, which could be due to the difference in clinical setting, the prevalence of COPD and smoking status of the studied population in respective study.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the predictive performance of PUMA across countries and regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSetting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCOPD diagnosis (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eCutoff\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003eCutoff\u0026thinsp;\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c16\" namest=\"c13\"\u003e \u003cp\u003eCutoff\u0026thinsp;\u0026ge;\u0026thinsp;6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAUC-ROC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eSp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"17\" nameend=\"c17\" namest=\"c1\"\u003e \u003cp\u003eLatin America\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLopez Varela 2016\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArgentina,\u003c/p\u003e \u003cp\u003eColombia,\u003c/p\u003e \u003cp\u003eVenezuela,\u003c/p\u003e \u003cp\u003eUruguay, primary care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e74.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e64.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e34.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e90.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e55.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e81.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e87.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLopez Varela 2019\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMexico, primary care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e85.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e69.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e62.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e59.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e59.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBastidas 2023\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColumbia, hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e58.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e64.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e38.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e80.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"17\" nameend=\"c17\" namest=\"c1\"\u003e \u003cp\u003eAsia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAu-Doung 2022\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHong Kong, primary care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e92.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e76.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e63.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e44.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e63.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSebayang 2024\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndonesia, hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e72.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e90.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGadam 2024\u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndia, hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e45.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e42.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e58.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e30.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSu 2024\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaiwan, hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingapore, primary care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e62.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e79.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e30.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e93.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e47.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e88.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e91.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003eSe\u0026thinsp;=\u0026thinsp;sensitivity\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003eSp\u0026thinsp;=\u0026thinsp;specificity\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003ePPV\u0026thinsp;=\u0026thinsp;positive predictive value\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003eNPV\u0026thinsp;=\u0026thinsp;negative predictive value\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBold numbers indicate the best cutoff determined by Youden index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplication for clinical practice and policy\u003c/h2\u003e \u003cp\u003e PUMA could be used as an opportunistic case-finding tool for ever-smokers\u0026thinsp;\u0026ge;\u0026thinsp;40 years, presenting for any reason, completed by patients in the waiting room to help clinicians identify patients at risk of COPD and prompt spirometric assessment in Singapore primary care setting. A low PUMA score would indicate a low likelihood of clinically significant COPD, and avoid unnecessary resource utilisation. When spirometry access is limited, and false-positives are costly, a higher cut-off (\u0026ge;\u0026thinsp;5) could be used to maximise specificity. When the priority is to minimize missed cases, and spirometry capacity is higher, a more sensitive threshold (\u0026ge;\u0026thinsp;4) could be used to improve case-finding. Many newly identified cases were mild or moderate, which represented an opportunity for primary care interventions: smoking cessation, vaccination, optimization of inhaled therapy and co-morbidity management. The PUMA questionnaire is particularly useful for primary care with limited healthcare resources and may be suitable for COPD case-finding in countries with similar multi-ethnic population profile, although the PUMA questionnaire may need to be re-evaluated to identify appropriate cutoff value for different populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrength and limitations\u003c/h2\u003e \u003cp\u003eThis is the first study to examine the proportion of current or former smokers aged over 40 years with COPD based on post-bronchodilator FEV\u003csub\u003e1\u003c/sub\u003e/FVC, and to assess the predictive performance of PUMA questionnaire for COPD case finding in Singapore primary care setting. The strengths of this study include the use of a multi-centre, real-world primary care recruitment sample, the use of post-bronchodilator spirometry based on ATS/ERS standards and local guidelines as the primary outcome measure, the inclusion of a multi-ethnic Asian population, and direct comparison of different cut-offs with full operating characteristics (sensitivity, specificity, PPV, NPV), to help translation into practice.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, findings generated from a convenience sample may not be representative of the whole primary care population due to potential selection bias. Our sample included only ever-smokers aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years who had attended primary care within the past year, therefore our findings may not be generalizable to never-smokers or the broader community. Patients who have attended primary care in the last 12 months may also have a higher symptom burden than the general population, and inflate the prevalence estimate and possibly performance statistics due to a different spectrum of disease from a true screening population. The findings may not apply directly to never-smokers with other exposures such as biomass or occupational dusts. As this is a cross-sectional study design, we cannot evaluate the progression of COPD, frequency of exacerbations and the long-term outcomes of those identified via case-finding. Secondly, we did not achieve the predefined sample size partially due to the lower-than-expected success rate of spirometry tests that met ATS/ERS quality grade C and above (85.1%, 359 out of 422). This reflects our stringent quality criteria, and highlights that there is room for improvement in coaching and manoeuvre performance for acceptable and repeatable spirometry. Access to quality spirometry testing including trained personnel to conduct spirometry is an important consideration.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eExternal validation of the PUMA questionnaire in Singapore primary care population suggests fair accuracy, similar to the original study where the questionnaire was developed. PUMA questionnaire is a feasible opportunistic COPD case-finding tool for high-risk individuals in Singapore primary care setting. A PUMA score of ≥ 4 may serve as a practical threshold to guide spirometry in at-risk ever-smokers aged 40 years and above, prioritising the detection of COPD cases for subsequent intervention.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the medical students and primary care researchers who volunteered to onboard participants for the study: Mr Ady Riandy, Mr Andric Alfonsus, Dr Ariffin Kawaja, Dr Chole Cheung, Dr Ashley Hsu, Ms Cassie Chua, Ms Citrine Ong, Ms Dharana Muthu, Ms Eleanor Chua, Mr Ethan Tan, Mr Eugene Chua, Ms Grace Chung, Mr Hanxin Liu, Mr Jason Zhang, Mr Jeremy Ling, Ms Jie Lee, Ms Jie Ying Khok, Mr Jonas Cham, Ms Lionel Hoe, Ms Li Zi Leong, Ms Sharleen Goh, Ms Weidi Sun and Ms Xin Hui Sam. We especially acknowledge Ms Andrea Rudd who helped with data extraction from the spirometry reports.\u003c/p\u003e\n\u003cp\u003eWe are grateful to the comments on study design from the study advisory committee:\u0026nbsp;Ms Ai Ling Sim-Devadas, Dr Akshar Saxena, Prof Carmen Wong, Dr Choon Kit Leong, Prof Christian Apfelbacher, Prof Fernando Martinez, Prof Gerlad Koh, Prof Jansen Koh, Prof Sanjay Chotirmall.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe appreciate the support from the following Singapore based organisations: Smartfuture Pte Ltd, all the GPs and clinic assistants in private GP clinics that were involved in patient recruitment, nurses and manager in Frontier Medical Associates, nurses and manager in Raffles Medical Group, OneCare Medical Clinic, National University Polyclinics, Changi General Hospital, Heartbeat@Bedok, One Punggol, Hong Lim Residents’ Network, Mawar Community Services, NUS Public Health Screening Committee. This study would not be possible without all the support. We would also like to acknowledge all the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLES and JMN obtained funding to the study. LES, KT and VMQ conceived the study. VMQ, KT, VH, YZY, SW DW, and LES participated in data collection. KT, GPT, VH and LES were responsible for spirometry test result interpretation. VMQ conducted data analysis and wrote the first draft of the manuscript, with inputs from KT and LES. All authors commented critically\u0026nbsp;to the intellectual content of the article and gave approval for publication of the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by AstraZeneca Singapore. The funder had no role in study design, data collection, analysis and preparation of the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSchnieders, E. \u003cem\u003eet al.\u003c/em\u003e Performance of alternative COPD case-finding tools: a systematic review and meta-analysis. \u003cem\u003eEur. Respir. Rev. Off. J. Eur. Respir. Soc.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, (2021).\u003c/li\u003e\n\u003cli\u003eAgust\u0026iacute;, A. \u003cem\u003eet al.\u003c/em\u003e Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary. \u003cem\u003eAm. J. Respir. Crit. Care Med.\u003c/em\u003e \u003cstrong\u003e207\u003c/strong\u003e, 819\u0026ndash;837 (2023).\u003c/li\u003e\n\u003cli\u003ePham, H. Q. \u003cem\u003eet al.\u003c/em\u003e Economic Burden of Chronic Obstructive Pulmonary Disease: A Systematic Review. \u003cem\u003eTuberc. Respir. Dis.\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 234\u0026ndash;251 (2024).\u003c/li\u003e\n\u003cli\u003eChen, S. \u003cem\u003eet al.\u003c/em\u003e The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020\u0026ndash;50: a health-augmented macroeconomic modelling study. \u003cem\u003eLancet Glob. Health\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, e1183\u0026ndash;e1193 (2023).\u003c/li\u003e\n\u003cli\u003eBoers, E. \u003cem\u003eet al.\u003c/em\u003e Global Burden of Chronic Obstructive Pulmonary Disease Through 2050. \u003cem\u003eJAMA Netw. Open\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e2346598 (2023).\u003c/li\u003e\n\u003cli\u003eFabbri, L. M. \u003cem\u003eet al.\u003c/em\u003e COPD and multimorbidity: recognising and addressing a syndemic occurrence. \u003cem\u003eLancet Respir. Med.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1020\u0026ndash;1034 (2023).\u003c/li\u003e\n\u003cli\u003ePrincipal Causes of Death. https://www.moh.gov.sg/others/resources-and-statistics/principal-causes-of-death/ (2024).\u003c/li\u003e\n\u003cli\u003eRoberts, N. J., Smith, S. F. \u0026amp; Partridge, M. R. Why is spirometry underused in the diagnosis of the breathless patient: a qualitative study. \u003cem\u003eBMC Pulm. Med.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 37 (2011).\u003c/li\u003e\n\u003cli\u003eLee, S. Y. \u003cem\u003eet al.\u003c/em\u003e Co-occurrence of Physical Frailty and COPD and Association With Disability and Mortality. \u003cem\u003eChest\u003c/em\u003e \u003cstrong\u003e161\u003c/strong\u003e, 1225\u0026ndash;1238 (2022).\u003c/li\u003e\n\u003cli\u003eLim, S. \u003cem\u003eet al.\u003c/em\u003e Impact of chronic obstructive pulmonary disease (COPD) in the Asia-Pacific region: the EPIC Asia population-based survey. \u003cem\u003eAsia Pac. Fam. Med.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 4 (2015).\u003c/li\u003e\n\u003cli\u003eKhakban, A. \u003cem\u003eet al.\u003c/em\u003e Ten-Year Trends in Direct Costs of COPD: A Population-Based Study. \u003cem\u003eChest\u003c/em\u003e \u003cstrong\u003e148\u003c/strong\u003e, 640\u0026ndash;646 (2015).\u003c/li\u003e\n\u003cli\u003eDerom, E. \u003cem\u003eet al.\u003c/em\u003e Primary care spirometry. \u003cem\u003eEur. Respir. J.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 197\u0026ndash;203 (2008).\u003c/li\u003e\n\u003cli\u003eAaron, S. D. \u003cem\u003eet al.\u003c/em\u003e Early Diagnosis and Treatment of Chronic Obstructive Pulmonary Disease: The Costs and Benefits of Case Finding. \u003cem\u003eAm. J. Respir. Crit. Care Med.\u003c/em\u003e \u003cstrong\u003e209\u003c/strong\u003e, 928\u0026ndash;937 (2024).\u003c/li\u003e\n\u003cli\u003eKotz, D., Nelemans, P., van Schayck, C. P. \u0026amp; Wesseling, G. J. External validation of a COPD diagnostic questionnaire. \u003cem\u003eEur. Respir. J.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 298\u0026ndash;303 (2008).\u003c/li\u003e\n\u003cli\u003eMartinez, F. J. \u003cem\u003eet al.\u003c/em\u003e Development and initial validation of a self-scored COPD Population Screener Questionnaire (COPD-PS). \u003cem\u003eCOPD\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 85\u0026ndash;95 (2008).\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez Varela, M. V. \u003cem\u003eet al.\u003c/em\u003e Development of a simple screening tool for opportunistic COPD case finding in primary care in Latin America: The PUMA study. \u003cem\u003eRespirol. Carlton Vic\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 1227\u0026ndash;1234 (2016).\u003c/li\u003e\n\u003cli\u003eLopez Varela, M. V. \u003cem\u003eet al.\u003c/em\u003e External validation of the PUMA COPD diagnostic questionnaire in a general practice sample and the PLATINO study population. \u003cem\u003eInt. J. Chron. Obstruct. Pulmon. Dis.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1901\u0026ndash;1911 (2019).\u003c/li\u003e\n\u003cli\u003eBastidas, A. R. \u003cem\u003eet al.\u003c/em\u003e Comparison of the Diagnostic Performance of Five Clinical Questionnaires for Chronic Obstructive Pulmonary Disease. \u003cem\u003eCan. Respir. J.\u003c/em\u003e \u003cstrong\u003e2023\u003c/strong\u003e, 2821056 (2023).\u003c/li\u003e\n\u003cli\u003eAu-Doung, P. L. W. \u003cem\u003eet al.\u003c/em\u003e PUMA screening tool to detect COPD in high-risk patients in Chinese primary care\u0026ndash;A validation study. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, e0274106 (2022).\u003c/li\u003e\n\u003cli\u003eSebayang, R. R., Pandia, P., Pradana, A., Tarigan, A. P. \u0026amp; Wahyuni, A. S. Comparative analysis between PUMA and CAPTURE questionnaires for chronic obstructive pulmonary disease (COPD) screening in smokers. \u003cem\u003eNarra J\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, e654 (2024).\u003c/li\u003e\n\u003cli\u003eSu, K.-C. \u003cem\u003eet al.\u003c/em\u003e The Accuracy of PUMA Questionnaire in Combination With Peak Expiratory Flow Rate to Identify At-risk, Undiagnosed COPD Patients. \u003cem\u003eArch. Bronconeumol.\u003c/em\u003e S0300289624002345 (2024) doi:10.1016/j.arbres.2024.06.013.\u003c/li\u003e\n\u003cli\u003eGadam, R., Suryakanth, A., Rani, M. Y., Dattu, Guttula. N. R. S. \u0026amp; Kumar, V. S. Predictive capability of PUMA score in detection of chronic obstructive pulmonary disease. \u003cem\u003eHeart Vessels Transpl.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, (2024).\u003c/li\u003e\n\u003cli\u003eVenkatesan, P. GOLD COPD report: 2025 update. \u003cem\u003eLancet Respir. Med.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e7\u0026ndash;e8 (2025).\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eThe Standards for the Performance and Interpretation of Office Spirometry in Singapore\u003c/em\u003e. https://www.ams.edu.sg/view-pdf.aspx?file=media%5C7407_fi_938.pdf\u0026amp;ofile=Standards+and+Performance+of+Office+Spirometry+in+Singapore+(Final)+Jan+2025.pdf (2025).\u003c/li\u003e\n\u003cli\u003eQuanjer, P. H. \u003cem\u003eet al.\u003c/em\u003e Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations. \u003cem\u003eEur. Respir. J.\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 1324\u0026ndash;1343 (2012).\u003c/li\u003e\n\u003cli\u003eAkoglu, H. User\u0026rsquo;s guide to sample size estimation in diagnostic accuracy studies. \u003cem\u003eTurk. J. Emerg. Med.\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 177\u0026ndash;185 (2022).\u003c/li\u003e\n\u003cli\u003eHajian-Tilaki, K. Sample size estimation in diagnostic test studies of biomedical informatics. \u003cem\u003eJ. Biomed. Inform.\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 193\u0026ndash;204 (2014).\u003c/li\u003e\n\u003cli\u003eSantini, A., Man, A. \u0026amp; Voidăzan, S. Accuracy of Diagnostic Tests. \u003cem\u003eJ. Crit. Care Med. Univ. Med. Si Farm. Din Targu-Mures\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 241\u0026ndash;248 (2021).\u003c/li\u003e\n\u003cli\u003ePan, Z. \u003cem\u003eet al.\u003c/em\u003e Accuracy and cost-effectiveness of different screening strategies for identifying undiagnosed COPD among primary care patients (\u0026ge;40 years) in China: a cross-sectional screening test accuracy study: findings from the Breathe Well group. \u003cem\u003eBMJ Open\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, e051811 (2021).\u003c/li\u003e\n\u003cli\u003eChing, S.-M. \u003cem\u003eet al.\u003c/em\u003e Detection of airflow limitation using a handheld spirometer in a primary care setting. \u003cem\u003eRespirol. Carlton Vic\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 689\u0026ndash;693 (2014).\u003c/li\u003e\n\u003cli\u003eNguyen Viet, N. \u003cem\u003eet al.\u003c/em\u003e The prevalence and patient characteristics of chronic obstructive pulmonary disease in non-smokers in Vietnam and Indonesia: An observational survey. \u003cem\u003eRespirol. Carlton Vic\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 602\u0026ndash;611 (2015).\u003c/li\u003e\n\u003cli\u003eMinakata, Y. \u003cem\u003eet al.\u003c/em\u003e Prevalence of COPD in primary care clinics: correlation with non-respiratory diseases. \u003cem\u003eIntern. Med. Tokyo Jpn.\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 77\u0026ndash;82 (2008).\u003c/li\u003e\n\u003cli\u003eCurrent smoking prevalence. \u003cem\u003eThe Tobacco Atla\u003c/em\u003e https://tobaccoatlas.org/challenges/prevalence/ (2023).\u003c/li\u003e\n\u003cli\u003eLua, Y. H. A., How, C. H. \u0026amp; Ng, C. W. M. Smoking cessation in primary care. \u003cem\u003eSingapore Med. J.\u003c/em\u003e \u003cstrong\u003e65\u003c/strong\u003e, (2024).\u003c/li\u003e\n\u003cli\u003eSmoking prevalence in S\u0026rsquo;pore population dropped from 13.9% in 2010 to 10.1% in 2020. \u003cem\u003eSngapore Ministry of Health\u003c/em\u003e https://www.moh.gov.sg/newsroom/smoking-prevalence-in-s\u0026apos;pore-population-dropped-from-139-in-2010-to-101-in-2020/ (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-primary-care-respiratory-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjpcrm","sideBox":"Learn more about [npj Primary Care Respiratory Medicine](https://www.nature.com/npjpcrm/)","snPcode":"41533","submissionUrl":"https://submission.springernature.com/new-submission/41533/3","title":"npj Primary Care Respiratory Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COPD, predictive performance, external validation, PUMA questionnaire, Singapore, multiethnic","lastPublishedDoi":"10.21203/rs.3.rs-8394069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8394069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eThe PUMA scale has shown good discrimination in identifying people with COPD in primary care. We evaluated the predictive performance of PUMA for opportunistic case-finding and assessed the prevalence of COPD in at-risk primary care patients in Singapore.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e This is a multicentre cross-sectional study of participants aged ≥40 years and current/former smokers. Participants completed the PUMA scale and spirometry. Predictive performance of PUMA was assessed using AUC-ROC; optimal cutoff was determined by Youden’s index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e 359 participants were included in final analysis; 12.5% had COPD confirmed on spirometry. PUMA showed acceptable discrimination with AUC-ROC of 0.75 (95% CI:0.67–0.83). Optimal cutoff maximising sensitivity and specificity was ≥5 (Se 62.2%, Sp 79.3%; PPV 30.1%, NPV 93.6%); cutoff of ≥4 increased sensitivity to 80.0% (Sp of 56.7%; PPV 20.9%, NPV 95.2%.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e The PUMA scale demonstrated acceptable predictive performance for opportunistic COPD case-finding in Singapore's primary care setting. A cutoff of ≥4 enhanced case identification.\u003c/p\u003e","manuscriptTitle":"Predictive performance of the PUMA questionnaire as an opportunistic COPD case-finding tool in Singapore primary care","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 06:38:38","doi":"10.21203/rs.3.rs-8394069/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-06T18:55:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-18T18:24:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173620995694808948997747738612164445306","date":"2026-01-18T07:54:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265042800122500029241795116381866226801","date":"2026-01-16T08:03:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142235144608244758497224659665194238316","date":"2026-01-16T07:58:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-08T16:08:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37743832622783570448346250113091510181","date":"2025-12-29T14:19:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-29T09:59:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-22T05:59:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-22T05:55:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Primary Care Respiratory Medicine","date":"2025-12-18T09:51:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-primary-care-respiratory-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjpcrm","sideBox":"Learn more about [npj Primary Care Respiratory Medicine](https://www.nature.com/npjpcrm/)","snPcode":"41533","submissionUrl":"https://submission.springernature.com/new-submission/41533/3","title":"npj Primary Care Respiratory Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"de128ec2-6004-42c7-aeed-31c2fd2de416","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":60359515,"name":"Health sciences/Diseases"},{"id":60359516,"name":"Health sciences/Health care"},{"id":60359517,"name":"Health sciences/Medical research"},{"id":60359518,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-20T16:04:08+00:00","versionOfRecord":{"articleIdentity":"rs-8394069","link":"https://doi.org/10.1038/s41533-026-00510-9","journal":{"identity":"npj-primary-care-respiratory-medicine","isVorOnly":false,"title":"npj Primary Care Respiratory Medicine"},"publishedOn":"2026-04-14 15:59:35","publishedOnDateReadable":"April 14th, 2026"},"versionCreatedAt":"2025-12-31 06:38:38","video":"","vorDoi":"10.1038/s41533-026-00510-9","vorDoiUrl":"https://doi.org/10.1038/s41533-026-00510-9","workflowStages":[]},"version":"v1","identity":"rs-8394069","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8394069","identity":"rs-8394069","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}