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      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

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          Abstract

          Objectives

          To examine the accuracy and cost-effectiveness of various chronic obstructive pulmonary disease (COPD) screening tests and combinations within a Chinese primary care population.

          Design

          Screening test accuracy study.

          Setting

          Urban and rural community health centres in four municipalities of China: Beijing (north), Chengdu (southwest), Guangzhou (south) and Shenyang (northeast).

          Participants

          Community residents aged 40 years and above who attended community health centres for any reason were invited to participate. 2445 participants (mean age 59.8 (SD 9.6) years, 39.1% (n=956) male) completed the study (February–December 2019), 68.9% (n=1684) were never-smokers and 3.6% (n=88) had an existing COPD diagnosis. 13.7% (n=333) of participants had spirometry-confirmed airflow obstruction.

          Interventions

          Participants completed six index tests (screening questionnaires (COPD Diagnostic Questionnaire, COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk (CAPTURE), Chinese Symptom-Based Questionnaire (C-SBQ), COPD-SQ), microspirometry (COPD-6), peak flow (model of peak flow meters used in the study (USPE)) and the reference test (ndd Easy On-PC).

          Primary and secondary outcomes

          Cases were defined as those with forced expiratory volume in one second (FEV 1)/forced vital capacity (FVC) below the lower limit of normal (LLN-GLI) on the reference test. Performance of individual screening tests and their combinations was evaluated, with cost-effectiveness analyses providing cost per additional true case detected.

          Results

          Airflow measurement devices (sensitivities 64.9% (95% CI 59.5% to 70.0%) and 67.3% (95% CI 61.9% to 72.3%), specificities 89.7% (95% CI 88.4% to 91.0%) and 82.6% (95% CI 80.9% to 84.2%) for microspirometry and peak flow, respectively) generally performed better than questionnaires, the most accurate of which was C-SBQ (sensitivity 63.1% (95% CI 57.6% to 68.3%) specificity 74.2% (95% CI 72.3% to 76.1%)). The combination of C-SBQ and microspirometry used in parallel maximised sensitivity (81.4%) (95% CI 76.8% to 85.4%) and had specificity of 68.0% (95% CI 66.0% to 70.0%), with an incremental cost-effectiveness ratio of £64.20 (CNY385) per additional case detected compared with peak flow.

          Conclusions

          Simple screening tests to identify undiagnosed COPD within the primary care setting in China is possible, and a combination of C-SBQ and microspirometry is the most sensitive and cost-effective. Further work is required to explore optimal cut-points and effectiveness of programme implementation.

          Trial registration number

          ISRCTN13357135.

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          Most cited references33

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            The REDCap consortium: Building an international community of software platform partners

            The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.
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              Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

              Summary Background Chronic obstructive pulmonary disease (COPD) and asthma are common diseases with a heterogeneous distribution worldwide. Here, we present methods and disease and risk estimates for COPD and asthma from the Global Burden of Diseases, Injuries, and Risk Factors (GBD) 2015 study. The GBD study provides annual updates on estimates of deaths, prevalence, and disability-adjusted life years (DALYs), a summary measure of fatal and non-fatal disease outcomes, for over 300 diseases and injuries, for 188 countries from 1990 to the most recent year. Methods We estimated numbers of deaths due to COPD and asthma using the GBD Cause of Death Ensemble modelling (CODEm) tool. First, we analysed data from vital registration and verbal autopsy for the aggregate category of all chronic respiratory diseases. Subsequently, models were run for asthma and COPD relying on covariates to predict rates in countries that have incomplete or no vital registration data. Disease estimates for COPD and asthma were based on systematic reviews of published papers, unpublished reports, surveys, and health service encounter data from the USA. We used the Global Initiative of Chronic Obstructive Lung Disease spirometry-based definition as the reference for COPD and a reported diagnosis of asthma with current wheeze as the definition of asthma. We used a Bayesian meta-regression tool, DisMod-MR 2.1, to derive estimates of prevalence and incidence. We estimated population-attributable fractions for risk factors for COPD and asthma from exposure data, relative risks, and a theoretical minimum exposure level. Results were stratified by Socio-demographic Index (SDI), a composite measure of income per capita, mean years of education over the age of 15 years, and total fertility rate. Findings In 2015, 3·2 million people (95% uncertainty interval [UI] 3·1 million to 3·3 million) died from COPD worldwide, an increase of 11·6% (95% UI 5·3 to 19·8) compared with 1990. There was a decrease in age-standardised death rate of 41·9% (37·7 to 45·1) but this was counteracted by population growth and ageing of the global population. From 1990 to 2015, the prevalence of COPD increased by 44·2% (41·7 to 46·6), whereas age-standardised prevalence decreased by 14·7% (13·5 to 15·9). In 2015, 0·40 million people (0·36 million to 0·44 million) died from asthma, a decrease of 26·7% (−7·2 to 43·7) from 1990, and the age-standardised death rate decreased by 58·8% (39·0 to 69·0). The prevalence of asthma increased by 12·6% (9·0 to 16·4), whereas the age-standardised prevalence decreased by 17·7% (15·1 to 19·9). Age-standardised DALY rates due to COPD increased until the middle range of the SDI before reducing sharply. Age-standardised DALY rates due to asthma in both sexes decreased monotonically with rising SDI. The relation between with SDI and DALY rates due to asthma was attributed to variation in years of life lost (YLLs), whereas DALY rates due to COPD varied similarly for YLLs and years lived with disability across the SDI continuum. Smoking and ambient particulate matter were the main risk factors for COPD followed by household air pollution, occupational particulates, ozone, and secondhand smoke. Together, these risks explained 73·3% (95% UI 65·8 to 80·1) of DALYs due to COPD. Smoking and occupational asthmagens were the only risks quantified for asthma in GBD, accounting for 16·5% (14·6 to 18·7) of DALYs due to asthma. Interpretation Asthma was the most prevalent chronic respiratory disease worldwide in 2015, with twice the number of cases of COPD. Deaths from COPD were eight times more common than deaths from asthma. In 2015, COPD caused 2·6% of global DALYs and asthma 1·1% of global DALYs. Although there are laudable international collaborative efforts to make surveys of asthma and COPD more comparable, no consensus exists on case definitions and how to measure disease severity for population health measurements like GBD. Comparisons between countries and over time are important, as much of the chronic respiratory burden is either preventable or treatable with affordable interventions. Funding Bill & Melinda Gates Foundation.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2021
                23 September 2021
                : 11
                : 9
                : e051811
                Affiliations
                [1 ]departmentGeneral Practice Department , Peking University First Hospital , Beijing, People's Republic of China
                [2 ]departmentPulmonary and Critical Care Medicine , Peking University Third Hospital , Beijing, People's Republic of China
                [3 ]departmentInstitute of Applied Health Research , University of Birmingham , Birmingham, West Midlands, UK
                [4 ]Lung Investigation Unit, University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham , Birmingham, UK
                [5 ]departmentGeneral Practice Development and Research Centre , Peking University Health Science Centre , Beijing, People's Republic of China
                [6 ]departmentNIHR Birmingham Biomedical Research Centre , University Hospitals Birmingham NHS Foundation Trust and University of Birmingham , Birmingham, UK
                [7 ]departmentLife and Health Sciences Research Institute (ICVS), School of Medicine , University of Minho , Braga, Portugal
                [8 ]International Primary Care Respiratory Group , London, UK
                [9 ]departmentICVS/3B's , PT Government Associate Laboratory , Braga/Guimarães, Portugal
                [10 ]departmentHealth Services Management Centre, School of Social Policy, College of Social Sciences , University of Birmingham , Birmingham, UK
                [11 ]departmentGeorgian Respiratory Association , Georgia
                [12 ]departmentPetre Shotadze Tbilisi Medical Academy , Georgia
                [13 ]departmentFamily Medicine , ABC Medical School , Sao Paolo, Brazil
                [14 ]departmentCenter for Family Medicine, Faculty of Medicine , Ss.Cyril and Methodius University in Skopje , North Macedonia
                [15 ]departmentPulmonary Division, Heart Institute (InCor), Hospital das Clinicas da Faculdade de Medicina da , Uinversidade de Sao Paulo , Sao Paulo, Brazil
                Author notes
                [Correspondence to ] Dr Chunhua Chi; chichunhua2012@ 123456qq.com ; Dr Peymané Adab; p.adab@ 123456bham.ac.uk

                ZP and APD are joint first authors.

                Author information
                http://orcid.org/0000-0003-4502-1107
                http://orcid.org/0000-0002-7591-8129
                Article
                bmjopen-2021-051811
                10.1136/bmjopen-2021-051811
                8461701
                34556515
                ac86bf76-13c5-458c-8fad-84a03ca3314a
                © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 02 April 2021
                : 04 August 2021
                Funding
                Funded by: the National Institute for Health Research (NIHR);
                Award ID: 16/137/95
                Categories
                Respiratory Medicine
                1506
                1731
                Original research
                Custom metadata
                unlocked

                Medicine
                general medicine (see internal medicine),respiratory medicine (see thoracic medicine),chronic airways disease

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