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      Geographical Disparity and Associated Factors of COPD Prevalence in China: A Spatial Analysis of National Cross-Sectional Study

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          Abstract

          Purpose

          COPD prevalence has rapidly increased in China, but the geographical disparities in COPD prevalence remain largely unknown. This study aimed to assess city-level disparities in COPD prevalence and identify the relative importance of COPD related risk factors in mainland China.

          Patients and Methods

          A nationwide cross-sectional study of COPD recruited 66,752 adults across the mainland China between 2014 and 2015. Patients with COPD were ascertained by a post-bronchodilator pulmonary function test. We estimated the city-specific prevalence of COPD by spatial kriging interpolation method. We detected spatial clusters with a significantly higher prevalence of COPD by spatial scan statistics. We determined the relative importance of COPD associated risk factors by a nonparametric and nonlinear classification and regression tree (CART) model.

          Results

          The three spatial clusters with the highest prevalence of COPD were located in parts of Sichuan, Gansu, and Shaanxi, etc. (relative risks (RRs)) ranging from 1.55 (95% CI 1.55–1.56) to 1.33 (95% CI 1.33–1.33)). CART showed that advanced age (≥60 years) was the most important factor associated with COPD in the overall population, followed by smoking. We estimated that there were about 28.5 million potentially avoidable cases of COPD among people aged 40 or older if they never smoked. PM 2.5 was an important associated risk factor for COPD in the north, northeast, and southwest of China. After adjusting for age and smoking, the spatial cluster with the highest prevalence shifted to most of Sichuan, Gansu, Qinghai, and Ningxia, etc. (RR 1.65 (95% CI 1.63–1.67)).

          Conclusion

          The spatial clusters of COPD at the city level and regionally varied important risk factors for COPD would help develop tailored interventions for COPD in China. After adjusting for the main risk factors, the spatial clusters of COPD shifted, indicating that there would be other potential risk factors for the remaining clusters which call for further studies.

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          Most cited references 16

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          Kriging: a method of interpolation for geographical information systems

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            An elliptic spatial scan statistic.

            The spatial scan statistic is commonly used for geographical disease cluster detection, cluster evaluation and disease surveillance. The most commonly used shape of the scanning window is circular. In this paper we explore an elliptic version of the spatial scan statistic, using a scanning window of variable location, shape (eccentricity), angle and size, and with and without an eccentricity penalty. The method is applied to breast cancer mortality data from Northeastern United States and female oral cancer mortality in the United States. Power comparisons are made with the circular scan statistic. Copyright (c) 2006 John Wiley & Sons, Ltd.
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              Classification and regression tree analysis in public health: methodological review and comparison with logistic regression.

              Audience segmentation strategies are of increasing interest to public health professionals who wish to identify easily defined, mutually exclusive population subgroups whose members share similar characteristics that help determine participation in a health-related behavior as a basis for targeted interventions. Classification and regression tree (C&RT) analysis is a nonparametric decision tree methodology that has the ability to efficiently segment populations into meaningful subgroups. However, it is not commonly used in public health. This study provides a methodological overview of C&RT analysis for persons unfamiliar with the procedure. An example of a C&RT analysis is provided and interpretation of results is discussed. Results are validated with those obtained from a logistic regression model that was created to replicate the C&RT findings. Results obtained from the example C&RT analysis are also compared to those obtained from a common approach to logistic regression, the stepwise selection procedure. Issues to consider when deciding whether to use C&RT are discussed, and situations in which C&RT may and may not be beneficial are described. C&RT is a promising research tool for the identification of at-risk populations in public health research and outreach.
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                Author and article information

                Journal
                Int J Chron Obstruct Pulmon Dis
                Int J Chron Obstruct Pulmon Dis
                COPD
                copd
                International Journal of Chronic Obstructive Pulmonary Disease
                Dove
                1176-9106
                1178-2005
                13 February 2020
                2020
                : 15
                : 367-377
                Affiliations
                [1 ]National Center for Chronic Non-Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention , Beijing 100050, People’s Republic of China
                [2 ]School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology , Brisbane, QLD 4059, Australia
                [3 ]Center of Respiratory Medicine, China–Japan Friendship Hospital , Beijing, People’s Republic of China
                [4 ]Department of Environmental Health, Rollins School of Public Health, Emory University , Atlanta, GA, USA
                [5 ]Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing, People’s Republic of China
                Author notes
                Correspondence: Liwen Fang National Center for Chronic Non-Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention , 27 Nanwei Road, Xicheng District, Beijing100050, People’s Republic of ChinaTel +86 135 5239 3376Fax +86 010 6304 2350 Email fangliwen@ncncd.chinacdc.cn
                Wenbiao Hu School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology , Brisbane, Queensland 4059, AustraliaTel/Fax +61 7 3138 5724 Email w2.hu@qut.edu.au
                Article
                234042
                10.2147/COPD.S234042
                7025678
                © 2020 Wang et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                Page count
                Figures: 4, Tables: 2, References: 26, Pages: 11
                Funding
                The study was funded by the Ministry of Science and Technology of People’s Republic of China (National Key R&D Program of China: 2016YFC1303905, 2016YFC1303900), The Chinese Central Government (Key Project of Public Health Program, Grant No. 2014814).
                Categories
                Original Research

                Respiratory medicine

                classification and regression tree, copd, spatial clusters, kriging

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