Praveen Kumar Rai Mahendra Singh Nathawat Geoinformatics in Health Facility Analysis Geoinformatics in Health Facility Analysis Praveen Kumar Rai • Mahendra Singh Nathawat Geoinformatics in Health Facility Analysis Praveen Kumar Rai Mahendra Singh Nathawat Department of Geography, Professor of Geography & Director, Institute of Science School of Sciences (SoS) Banaras Hindu University Indira Gandhi National Open University Varanasi , Uttar Pradesh , India New Delhi , India ISBN 978-3-319-44623-3 ISBN 978-3-319-44624-0 (eBook) DOI 10.1007/978-3-319-44624-0 Library of Congress Control Number: 2016950526 © Springer International Publishing Switzerland 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifi cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfi lms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specifi c statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Dedicated to my parents, wife, family members, relatives and, teachers Introduction Health GIS is an important subdiscipline of health science and medical geography which is traditionally focused on the spatial aspects of disease ecology and health care facility analysis. With the advent of Geographic Information Systems (GIS) and remote sensing technologies (computers and sophisticated spatial analytic software programs), medical geography has been dramatically transformed like many other spatial sciences Study Area and Methodology. Keeping this in view, an attempt has to be made for the Varanasi district, India, to analyze the characteristics of population and its related variables, vis-a-vis health care facilities and the status of vector-borne diseases and malaria modeling using GIS techniques and statistical methods. The variables, i.e., literacy, sex ratio, occupational structure, etc., are selected, calculated, processed, mapped, and analyzed to fi nd out the spatiotemporal distribution of the characteristics of population. All the data in Excel format are integrated with develop- ment block boundaries on the ARC GIS version-9.3 platform, and different chorop- leth outputs are produced. The existing location and number of government hospitals, i.e., PHCs, CHCs, subcenters, etc., and location of existing hospitals including private ones are shown during fi eld survey with the help of a Global Positioning System (GPS). Result and Discussion the district as a whole, the number of medical institu- tions per 100,000 population has been worked out as 16.25 for the year 2009. The number of doctors/100,000 population was 5.8 in 1995–1996, which marginally increased to 5.66 in 2001, and then this fi gure decreased drastically and reached 3.54 in 2009. A GIS-based buffer analysis is also performed, and it is calculated that maximum villages, i.e., 73.51 % (136 villages out of 185), in the Baragaon develop- ment block fall under safe villages, whereas minimum villages, i.e., 30.82 % (only 45 villages out of 146), in the Kashi Vidyapith development block fall under the safe zone. The main aim of this study is also to determine and map the density areas of vector- borne diseases using GIS techniques. The important types of vector-borne dis- eases (VBD) highlighted in this study are malaria, fi lariasis, kala-azar, and dengue. Remote sensing data is used to identify the malaria mosquito breeding sites, such as ponds, streams, tanks, etc. The locations of kala-azar patients in different development blocks and in Varanasi are shown with the help of GPS. Through this study, the maximum cases (188) of malaria were found in Varanasi and the maximum cases of vii viii Introduction dengue (90) were recorded in city government hospitals. The Malaria Susceptibility Index (MSI) and Malaria Susceptibility Zone (MSZ) are calculated by using three statistical methods, i.e., multiple linear regression, information value, and heuristic approach. A number of thematic maps (referred to as data layers in GIS) on specifi c parameters which are related to the occurrence of malaria, i.e., land use, NDVI, dis- tance to water ponds/tanks, distance to river, distance to road, distance to hospital, rainfall, temperature, and projected population density, for the year 2009 have been generated. The cumulative frequency curve of MSI values has been segmented into fi ve classes representing near equal distribution to yield fi ve malaria susceptibility zones (MSZ), i.e., very low, low, moderate, high, and very high, using all the three statistical methods. In the multiple linear regression method, 36.06 % of the pixel area falls under the ˗4000–5000 model index class and susceptibility index <˗15000 con- tains 2.57 % of the pixel area, whereas 13.43 % of the pixel area falls under the >7000 susceptibility index class. The information value (InfoVal) analysis includes two spe- cifi c steps, i.e., bivariate analysis and multivariate analysis. The area of distribution of malaria based on the information value analysis, i.e., 0.1–0.6, looks sensitive, and 29.73 % of the pixel area includes the quantities more than this amount, so this value can be defi ned as the crucial value for malaria, and 39.86 % and 26.29 % of the areas of malaria fall under high and very high susceptibility classes. The MSI values from the heuristic approach (weighting method) are found to lie in the range of 21–37. To compare each model for MSZ, the Qs (malaria density ratio) method is used, and it is found that the information value method having Qs=3.96 has been selected as the optimum model for malaria susceptibility zonation in the study area, whereas the Qs values for the qualitative map combination (heuristic method) and multiple linear regression method are 1.67 and 1.43, respectively. The area under a curve is used to assess the prediction accuracy qualitatively, and verifi cation results show that in the information value case, the area under curve (AUC) is 0.696 and the prediction accu- racy is 69.60 %. In the heuristic case, the AUC is 0.603 and the prediction accuracy is 60.30 %. In the multiple linear regression case, the AUC is 0.484 and the prediction accuracy is 48.40 %. Primary data is also collected from 800 respondents of 16 selected villages (two villages from each development block) in the rural part of Varanasi district to know about the utilization of health care facilities, and their results are analyzed with the help of the Statistical Package for the Social Sciences (SPSS) software. An attempt has also been made here to calculate the Hospital Requirement Index (HRI) and Hospital Requirement Zone (HRZ) through the weighting method by which it can be known as to which area needs more development in health facilities. The Hospital Requirement Index (HSI) values calculated using the weighting method are found to lie in the range of 11–23. The cumulative frequency curve of HRI values has been segmented into four classes to yield four hospital requirement zones, i.e., low, moderate, high, and very high. It is found that the areas coming under very high and high requirement classes are 46.62 % and 7.55 %, respectively, whereas 3.39 % and 42.63 % of the total areas fall under low and moderate requirement classes in Varanasi district. Acknowledgements It gives me immense pleasure to acknowledge the help, support, encouragement, and guidance that I have received in the course of completing this book. I would like to remember some of the important personalities for their invaluable support. I express my deep sense of gratitude and profound indebtedness to my teacher and coauthor of this book Dr. M.S. Nathawat, Former Professor and Head, Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi, and Professor of Geography, School of Sciences, Indira Gandhi National Open University, New Delhi, for his valuable guidance, continuous support, inspiration, and encouragement throughout my study. I am also grateful to my co-guide and teacher Dr. S.B. Singh, Professor and Former Head, Department of Geography, Banaras Hindu University (BHU), Varanasi, for valuable suggestions whenever needed. I express my sincere thanks to Prof. K.N.P. Raju, Course Coordinator, PGDRS and GIS, and Coordinator, UGS SAP, DRS-I, and Prof. V.K. Kumra, Ex-Head, Department of Geography, and Former Registrar, Banaras Hindu University, Varanasi, for constant support and encouragement upheld my enthusiasm. I would like to express my heartfelt thanks to Mr. Mohhamad Onagh, Former Research Scholar, Department of Geography, BHU, and Mr. M.A. Khan, District Malaria Offi cer, Varanasi, for their suggestions and cooperation. I am also grateful to Dr. Kshitij Mohan, Technical Assistant, Department of Geography, Banaras Hindu University, for his friendly guidance during the preparation of this book. I also express my sincere thanks to Mr. Amit Srivastava, S.T.A., Department of Geography, Banaras Hindu University, for giving me moral support.