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Geographical Distribution of COPD Prevalence in Europe, Estimated by an Inverse Distance Weighting Interpolation Technique

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Journal name: International Journal of COPD Article Designation: Original Research Year: 2018 Volume: 13 International Journal of COPD Dovepress Running head verso: Blanco et al Running head recto: Geographical distribution of COPD prevalence open access to scientific and medical research DOI: http://dx.doi.org/10.2147/COPD.S150853 Open Access Full Text Article ORIGINAL RESEARCH Geographical distribution of COPD prevalence in Europe, estimated by an inverse distance weighting interpolation technique Ignacio Blanco1 Abstract: Existing data on COPD prevalence are limited or totally lacking in many regions Isidro Diego2 of Europe. The geographic information system inverse distance weighted (IDW) interpolation Patricia Bueno3 technique has proved to be an effective tool in spatial distribution estimation of epidemiological Eloy Fernández4 variables, when real data are few and widely separated. Therefore, in order to represent carto- Francisco Casas- graphically the prevalence of COPD in Europe, an IDW interpolation mapping was performed. Maldonado5 The point prevalence data provided by 62 studies from 19 countries (21 from 5 Northern European countries, 11 from 3 Western European countries, 14 from 5 Central European countries, and Cristina Esquinas6 16 from 6 Southern European countries) were identified using validated spirometric criteria. Joan B Soriano7 Despite the lack of data in many areas (including all regions of the eastern part of the continent), Marc Miravitlles6 For personal use only. the IDW mapping predicted the COPD prevalence in the whole territory, even in extensive 1Alpha1-Antitrypsin Deficiency areas lacking real data. Although the quality of the data obtained from some studies may have Spanish Registry, Lung Foundation some limitations related to different confounding factors, this methodology may be a suitable Breathe, Spanish Society of Pneumology, Barcelona, 2Materials tool for obtaining epidemiological estimates that can enable us to better address this major and Energy Department, School of public health problem. Mining Engineering, Oviedo University, 3Internal Medicine Department, Keywords: epidemiology, geographic information system, GIS, Geographic Resources Analysis County Hospital of Jarrio, 4Clinical Support System Analysis Laboratory, University Hospital of Cabueñes, Principality of Asturias, 5Pneumology Department, Introduction University Hospital San Cecilio, Granada, 6Pneumology Department, COPD is a large, growing public health problem. According to the World Health Hospital Universitari Vall d’Hebron, Organization, its expected burden will increase in the coming decades, mostly due to CIBER de Enfermedades Respiratorias (CIBERES), Barcelona, 7Instituto de continued exposure to risk factors, population growth and aging, to become the third Investigación Hospital Universitario leading cause of death by 2030.1 de la Princesa, Universidad Autónoma Despite being a major health problem, existing data on COPD prevalence are lim- de Madrid, Madrid, Spain ited in many countries, and universally high COPD underdiagnosis2 or misdiagnosis3 International Journal of Chronic Obstructive Pulmonary Disease downloaded from https://www.dovepress.com/ by 156.35.62.29 on 03-Oct-2018 deprives patients and health authorities of the implementation of adequate preventive and therapeutic measures, in order to avoid its potential serious effects and high costs.4 In fact, of the 50 sovereign European countries, only 19 (38%) of them have available reliable data on COPD prevalence.5 The geographic information system (GIS) is considered a useful tool for reporting Correspondence: Marc Miravitlles the distribution of health-related states, in particular diseases.6,7 Specifically, the Pneumology Department, Hospital GIS inverse distance weighting (IDW) interpolation (or spatial analysis) technique Universitari Vall d’Hebron, Pg. Vall d’Hebron 119-129, 08035, Barcelona, (an informatics mathematical approach of manipulating spatial information to extract Spain new information and meaning from the original data, using points with known values Tel +34 93 274 6107 Email [email protected] to estimate values at other unknown points) has proved to be an effective tool in spatial submit your manuscript | www.dovepress.com International Journal of COPD 2018:13 57–67 57 Dovepress © 2018 Blanco 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 http://dx.doi.org/10.2147/COPD.S150853 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). Powered by TCPDF (www.tcpdf.org) 1 / 1 Blanco et al Dovepress distribution estimation of epidemiological variables, when samples included. The translation was made through the real data are few and widely separated, as might be the case Google Maps application programming interface using the with the COPD prevalence in Europe.8–10 web page of GPS Visualizer (Carleton University Library, The objective of the present study was to apply the IDW Ottawa, ON, Canada). mapping to integrate the existing data from different European Then, these geographical coordinates were imported into areas, in order to represent cartographically the mean percent- the database and then exported to a “csv” (comma separated age of the population affected by COPD in each geographi- value) text file, which was opened and visualized in QGIS. cal region of the entire continent, both in studied areas with A shape file containing the contours of all world countries known data and in many other areas in which no studies have was also loaded. been conducted and consequently with blank data. Finally, the interpolation process was carried out by applying the “v.surf.idw” library included in the GRASS GIS Methods version 7, another free and open source GIS software suite Source of epidemiological studies for used for geospatial data management and analysis, spatial COPD prevalence modeling, image processing, graphics and maps production, The vast majority of the data entered in the database used and visualization. in the IDW interpolation software were taken from the The v.surf.idw library provided surface interpolation from systematic review and meta-analysis recently published by vector point data by filling out a raster matrix with interpo- Adeloye et al on the prevalence of COPD across the world lated values generated from a set of irregularly spaced data from January 1990 to December 2014, based on a confirmed points, using numerical approximation (weighted averaging) diagnosis of COPD with different formally acknowledged techniques. The interpolated value of a cell was determined spirometric criteria.5 For the current study, only the variables by the values of the nearby data points and the distance of referring to the place of origin of the samples and the mean the cell from those input points. An implementation of an IDW technique, as defined by For personal use only. COPD prevalence (in percent) of the samples composed of subjects aged $40 years were used.11–63 To the initially Shepard,7 is as follows: the way to find an interpolated value u selected 61 studies, a later one on the prevalence of COPD, at a given (arbitrary) point x based on N known samples as defined by the Global Initiative for Obstructive Lung ui=u(xi) for i=1, 2, …, N is: Disease (GOLD), in a representative sample of northeastern 64 N Italy general population was added. wx()u ∑ i=1 ii The application of an age selection criterion of $40 years N if d (x, x)i ≠ 0 forall i u (x) = wx() ∑ i=1 i excluded, from the present study, an international study that uif d (x, x)= 0 forsomei included young subjects from several cities in Europe aged i i between 20 and 44 years,65 2 studies performed in 12 Russian 1 regions,66,67 and another study of the Turkish region of Elazig where wx ( )= i d (x,x)p all of them carried out in subjects from the age of 18.68 i In addition, to facilitate visual comparisons between Europe and the neighboring regions of Asia and Africa, the The “N known samples” is the N closest ones to the available COPD prevalence data from several Middle East interpolation point. In our study, the usual N value of 4 International Journal of Chronic Obstructive Pulmonary Disease downloaded from https://www.dovepress.com/ by 156.35.62.29 on 03-Oct-2018 and North Africa regions were also included.69–77 (N=4) was chosen. This means that a sample point takes the 4 closest pixel centers and linearly interpolates their color IDW multivariate interpolation method values according to their distance from the sample point; p is To elaborate colored geographical maps of COPD preva- called the power parameter, which has a default value of 2, lence, an IDW interpolation process was started through the which was the value used in this study. freely available software QGIS 18.9 in order to link it with To express the range of values in the maps, a progres- the 7.3 64-bit wxPython 3 Geographic Resources Analysis sive gradient (low–high) range scale of 10 colors, creating a Support System (GRASS). diverging low–mid–high color gradient, with
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