2011- HealthGIS Conference E - Proceedings

healthGIS Managing Health Geospatially ISBN 978-616-90698-1-2 Editors: Nitin Kumar Tripathi Pawan Kumar Joshi Hamid Mehmood 4th International Conferenece on healthGIS 2011, 5-6 August 2011, New Delhi, India

Reviewers Ms. Chitrini Mozumder, Asian Institute of Technology Mr. Ahsan ul Hoque, Asian Institute of Technology Mr. Khagendra Pralhad Bharambe, Asian Institute of Technology Mr. Keneizhatuo Kuotsu, Asian Institute of Technology Managing Health Geospatially (Proceedings of Fourth International Conference on Health GIS) is edited by Dr. Nitin Kumar Tripathi, Asian Institute of Technology, Thailand, Dr. P. K. Joshi, TERI University, India and Hamid Mehmood, Asian Institute of Technology, Thailand It is a collection of papers from healthGIS 2011. Publication Year: 2011 Disclaimer: Authors are responsible for the views and contents expressed in their papers. For any general queries, please contact publisher at: TERI University Plot No. 10 Institutional Area Vasant Kunj New Delhi - 110 070 / India Tel. +91 11 26122222, 26139110, 26139011 Fax +91 11 26122874 Web site: http://www.teriuniversity.ac.in/ Managing Health Geospatially a

Dr Nitin K Tripathi Preface Organising Secretary, HealthGIS 2011 Health and Education are the foundation of social growth and development. We President, Association for have to manage the health geospatially to ensure better health for our people. GeoInformation Technology Location of new hospitals should be carefully planned keeping multiple factors in (AgIT) consideration. Geographic Information Systems (GIS) and Global Positioning Editor-in-Chief, International Systems (GPS) have the potential to integrate data of various kinds and from various Journal of Geoinformatics sources such as existing maps, tables, locations representing various factors such as Director, UNIGIS Centre, AIT physical, geographic, economic, environmental, social, Government guidelines etc. Coordinator, Remote Sensing There is a growing adaptation of the geoinformation technologies in health sector in and GIS few countries but there exists wide gap in developing countries due to lack of Asian Institute of Technology awareness and expertise. I am sure healthGIS conference is able to bring the health P.O. Box:44, Klong Luang, and geoinformatics community together and encourage use of these technologies. Pathumthani 12120, Thailand Phone: HealthGIS 2011 brings you many new innovative applications and approaches of +66-81751 8384 (Mobile), using GIS, remote sensing and GPS into healthcare management, disease +66-2-524 6392 (Office), Fax: surveillance, medicine distribution, telemedicine, and ambulance management for +66-2-524 5597 trauma assistance. Esri, MNNIT, and UNIGIS are conducting workshops to train the E-mail: [email protected] health sector people and showcase potential of Geoinformation Technologies. International Journal of Geoinformatics will publish a special issue on selected papers from this conference after peer review. There are five awards in various categories of presentations. Key note presentations from experts from allover the world will surely exciteand motivate delegates to collaborate and use this technology for reaching out to all those who need healthcare services and make a healthy world and ready to face the challenges of existing and emerging diseases. I wish a fruitful conference to all the delegates and hope they will be the messengers to their colleagues who could not attend it due to various reasons. Managing Health Geospatially b

Dr P K Joshi Preface Organising Secretary, HealthGIS 2011 “It is health that is real wealth and not pieces of gold and silver” said Mahatma Head Department of Gandhi, an Indian Philosopher, internationally esteemed for his doctrine of Natural Resources nonviolent protest. And health is not merely the absence of disease or infirmity; it is TERIUniversity, a state of complete physical, mental and social well-being. 10 InsitutitonalArea, VasantKunj In the present context of global environmental changes and climate debates, health is New Delhi 110070 India one of the prime issues. The human health services organization shares a mission Phone: +91-9818025993 with other worldwide to help achieve the highest levels of physical and social well- Office: +91-11-26122222 being. Worldwide people are looking for the best treatment services and more Fax: +91-11-2612 2874 importantly facilities which make such services more effective and affordable while Email: optimizing resource use and deliverance. To this, Geoinformatics help leveraging [email protected], limited resources and multiply the positive impact of benefits to individuals, families [email protected] and society. Geoinformatics - an umbrella term covering a wide gamut of GIS and related technologies (remote sensing, global positioning system [GPS], image processing, high bandwidth communication) - is widely believed to play an increasingly important role in our day-today life. Health problems have special associations to local geography, especially vector-borne diseases can be effectively analyzed using mapping and modeling techniques. This involves mapping current boundaries of a health problem, followed by identifying and plotting of determinant factors and developing models to project distribution. The changes in the boundary of the health problem are noted with respect to demographic distribution and socio-cultural and economic centers of interest to identify potential changes. These can be modeled effectively using remotely sensed climate data sets with GIS and GPS inputs. Typical examples of such applications from the developing world include malaria, dengue, lymphatic filariasis, schistosomiasis, and others. Geoinformatics improves understanding of the situation, what is needed and how to intervene with prevention, mitigation and adaptation strategies when necessary. It offers organization and analytical set of tools that expand the effectiveness of response to growing demand and limited budgets. This is what 4th International Conference on HealthGIS 2011 is proposed at New Delhi, India. This conference after three grand successful conferences looks at “Geoinformation technology in healthcare and epidemic surveillance and management”. It attracts scholars, philosophers and practitioners to discuss geoinformatics in health and health using geoinformatics. It is indeed a great pleasure to invite all the delegates to this conference. These two days will be about significant and innovative developments. You will, I am sure, go away inspired and buzzing with ideas. So enjoy the conference. Managing Health Geospatially c

Prof. Said Irandoust Foreword President Asian Institute of Since the last decade the world has witnessed tremendous changes in social and Technology economic order. These changes still continue and countries such as China, India, and Vietnam are going through rapid economic growth. Many Asian countries are experiencing population growth, which drives demand factors of various facilities. Another change, which is of grave concern, is climate change and global warming. All these have varying impacts on human beings and especially on their health. We witness frequent outbreaks of viral diseases, increasing trends in lifestyle-related diseases such as diabetes, heart diseases, respiratory illness, water borne diseases, e- coli scare etc. We experience that healthcare centers and services are inadequate to fulfill the demand. There is a need to know “where” these diseases are occurring frequently. People in most of the fast developing world face such challenges and the current health infrastructure needs to be enhanced and modernized to ensure health for all. Geoinformation Technologies provide powerful tools of remote sensing, global positioning systems and robust decision support systems in the form of geographic information systems. These are being increasingly adopted in disease surveillance, hotspotting and provide valuable input for disease control. I am very pleased that the Asian Institute of Technology (AIT) has joined hands with TERI University and several other partner institutions to organize the 4th International Conference in New Delhi to ponder over these issues. Delegates coming from various locations of the globe will surely benefit from sharing experiences with each other and I hope innovative ideas and approaches will emerge for collaborative work to ensure global health. Managing Health Geospatially d

Prof. Bhavik R Bakshi Foreword Vice Chancellor TERI University, New Delhi TERI University with Association for Geoinformation Technology (AgIT) and Asian Institute of Technology (AIT) is pleased to present this specialty HealthGIS 2011 conference during August 5-6, 2011 and a pre-conference workshop on August 4, 2011 to explore and strengthen the uses of Geoinformatics for public health access and decision making. The theme is very relevant and timely considering the real challenge to save lives from dreaded epidemics. The concept that location influences health is a very old one in medicine. As far back as the time of Hippocrates (c. 3rd century BC), physicians observed that certain diseases tend to occur in some places and not others. In the present era of globalization these may emerge in one country and quickly spread to many others. Status of health and health care can be association with different profiles of the Earth: physical, biological, environment, economic, social, cultural and even spiritual. These are parameters governing distribution and status of disease can be captured and modeled for decision using Geoinformatics. I am pleased that both the organization institutions are pioneers in introducing Masters‟ level programs in Geoinformatics in their respective locations. Education, training, research and outreach in the fields of Geographical Information System (GIS), Remote Sensing System and Application allows various professions to use this technology for human, social, economic and physical development of a country at micro and macro levels. I convey my best wishes for the success of all the events encourage meaningful deliberation, useful recommendations and precise identification of issues that need to be addressed jointly by different governments, educational institutions and industrial sectors in the near future. I greet the scientists, scholars, academicians and industry representatives to the TERI University and congratulate the organizers for their meticulous planning of the event and wish the conference a grand success. Managing Health Geospatially e

Gp Capt. Rajiv Seth Foreword (Retd.), Ph.D. Registrar As the world develops keeping sustainability in mind, various diseases do tend to TERI University, New Delhi crop-up, not only in the developing world but also in developed countries. Often these diseases originate at a particular location and then spread over the planet without following any administrative or political boundaries. A healthy planet is only possible if we monitor it regularly using satellite remote sensing data and Global Positioning System (GPS), analyse it in Geographical Information System (GIS) and disseminate the information using Information Communication Technology (ICT). And no one can deny that for sustainable development, we need a healthy planet. Although using maps to identify and study locations of diseases was done even in the 19th century, there is a need now to look at integrating available technologies (remote sensing, GPS, GIS, ICT and others) into identifying sources of diseases, potential areas of spread and the hotspots, for better governance. The scope of using geoinformatics for real time surveillance for disease control is beyond doubt. Various applications of geoinformatics are being used for allocation of health services and their delivery, but the potential for improving operational applications is tremendous. I am pleased that the Asian Institute of Technology (AIT) and the TERI University are jointly organising the 4th International Conference on Health GIS on 5-6 August 2011. This would be an opportune occasion for experts from the geoinformatic arena to get together with those from the health area, to strategise on synergistic planning of health care systems and surveillance and management of epidemics. I welcome all delegates to the conference, and wish them a fruitful two days. Managing Health Geospatially f

Geomedicine: A Patient’s Perspective Dr. Bill Davenhall Geomedicine is the practical application of public health knowledge to personal Global Manager health intervention, either by physicians or the health seeking consumer. The Health and Human Services presentation will describe how the inclusion of a geographically accurate place Esri, Redlands, California history within the context of a medical encounter can provide a richer source of E-mail: [email protected] clinically relevant information to accelerate diagnosis, increase the likelihood of medical compliance, and engage the patient in a productive partnership with medicine and health. Medical research published over the past half-century describes that as much of 30% of all known morbidity and mortality is a direct result of living or working in and around unhealthy physical and social environments. Linking generalized medical and public health knowledge to geographically specific environmental and social conditions at the personal (household, neighborhood, community, etc.) level has great potential for increasing a physician‟s contextual understanding of the patient‟s work and social environments that may have contributed to their current health status. Geomedicine ultimately changes the way our physicians will come to understand the impact of our personal environments while at the same time making the health seeking consumer an informed partner in their own health journey. Managing Health Geospatially g

GIS –based Information System for the Management of Water Quality Data Geographic Information System (GIS) is used to display and facilitate interpretation of water-quality data of a River and the Groundwater System of a district in Kerala. Here we report the fluoride problem of Alappuzha district and water quality status of Periyar River using GIS based information system. Prof. K.V. Jayakumar The fluoride problems of Alappuzha was studied by collecting water samples Executive Director, Centre for Water from 67 locations which comprised of 25 open well samples, 8 filter point well Resources Development and samples,20 shallow tube well samples, 22 deeper tube well samples and 2 bore Management well samples. The effects of fluorides on the health of man are generally from Kunnamangalam, Kozhikode dissolved fluoride present in drinking water sources. Particulate fluorides can be 673571 suspended and dispersed as a contaminant in drinking water. Invariably all the E mail: [email protected] open wells and filter point wells of the study area tap the recent alluvium. Majority of the shallow tube wells of the area tap the recent alluvium with the Dr. P.S. Harikumar exception of certain tube wells which tap both the Warkali and recent alluvium Scientist, CWRDM Kozhikode together or the Warkali aquifer alone which in turn forms the topmost aquifer 673571 layer of the tertiary aquifer system. The spatial representation of the chemical parameters namely calcium, bicarbonate, chloride, fluoride, pH and EC were Mr. George Abe deciphered using GIS separately for the various vertical layers of the aquifer viz, Scientist, CWRDM Kozhikode shallow, intermediate and deep. Zonation map was prepared using GIS for 673571 various water quality parameters for shallow and intermediate aquifers of the study area for various seasons. It has been observed that almost all the open well samples and the filter point well samples had fluoride concentration much below the desired limit. Shallow tube well and deeper tube well samples recorded higher content, almost irrespective of the sampling season. There is a marked spatial variation (both in the horizontal and vertical aspects) with regard to the distribution of fluoride in the ground waters of the study area. The fluoride content being higher in water samples collected from water abstraction structures tapping deeper aquifers. A significant positive association was noted between the source of drinking water and prevalence of dental fluorosis in the study area. The prevalence is high among children who use pipe water for drinking purposes. This can be associated with the increased fluoride content in the deeper tube wells which form the source of water supply through pipes in urban and rural areas GIS platform was used to study the spatial and temporal variation of water quality of Periyar, a major river in Kerala. The River with an aerial extent of 5398 sq km is one of the largest river basins in the State. Periyar River has the multiple uses including catering to need of drinking water requirement of Kochi Corporation, Aluva Municipality and Paravoor Municipality and number of panchayaths like Kadamakudi and Puthencruz. It serves as sources for many irrigation and power requirements. Water quality analysis indicates that iron, alkalinity and phosphate were on a higher side in the downstream of the river. The Dissolved Oxygen (DO) values varied between 6.27mg/l to 8.47 mg/l. Biochemical Oxygen Demand (BOD) varied between 0.34mg/l & 2.07 mg/l. Bacteriological analysis confirmed the presence of total coliform and E.Coli in almost all the samples Biological analysis showed the high nutrient enrichment in the downstream towards the Manjummal region (according to the chlorophyll value) and comparatively high pollution index value (Palmers pollution index) was recorded towards the downstream. Canadian Council of the Ministers of the Environment (CCME) Water Quality Index of Periyar river was calculated for 34 stations during the four seasons (post-monsoon-2005, pre-monsoon-2006, pre-monsoon-2007 and post- monsoon - 2007). Fifteen parameters, which include pH, turbidity, colour, total dissolved solids, alkalinity, total hardness, calcium, magnesium, chloride, nitrate-N, sulphate, iron, total coliforms and faecal coliforms and dissolved oxygen, were used for the calculation. The index values of most of the stations are in the range of 65-79, which indicates that the upstream part of the River is stations have fair water quality, which means that they are occasionally threatened. However, six stations in the downstream showed marginal water quality, which indicate that they are frequently threatened. Classification of different stations in Periyar River reveals that most of the stations fall under the class C that implies that the water in these stations can be used as drinking water with conventional treatment and disinfection. About 13 stations are in the class E, and water can only be used for irrigation, industrial cooling and waste disposal. The water quality data was correlated with stream flow which clearly indicates the deterioration of quality with reduced flow. Rapid urban growth of Greater Kochi Region during the last 100 years supports the high pollution levels in the downstream of the River Managing Health Geospatially h

Prof. Ranjith P. DeSilva Chronic Kidney Disease in Sri Lanka - Geo-informatics at University of Peradeniya Potential for Geo-informatics Applications Founder President of the Geo- Informatics Society of Sri Chronic Kidney Disease (CKD) has been prevalent in several districts of Sri Lanka Lanka during the last decade although the exact history of occurrence is largely Author of the Text book on unknown. The etiology of the local disease has not been established and unscientific Spatial Statistics: Theory and speculations often aggravate the problems and bring more suffering to the Applications people. Looking at the number of research studies conducted on this problem at the expense of public funds and the uncertainties in the research outputs which are delivered to as solutions to the same aggrieved people who funded (public funds) these research programmes undeniably demands for a careful revisit to our research agenda, methodologies, and approaches. It is disheartening to note that other than few scientific facts derived from the past patient statistics or background studies, no proper scientifically accepted outcome has resulted from these studies. Being a country with a predominant agriculture sector (>13% of GDP), it is distressing to note that farming communities seem to be the most vulnerable or affected group of this unfortunate calamity. Quoting the social value attached to the affected group as an agricultural community, large amount of national wealth has been exhausted on research to discover the causative factors for CKD. It is apperent that an approach utlizing geo-informatics tools have the potential to reveal some of the underlying causative factors and assist us in finding solutions to save the lives of the rural peasantry in Sri Lanka. Managing Health Geospatially i

Dr. Shahnawaz Problems and Prospects of GIScience Director (South & Southeast Asia) Education UNIGIS International Centre for Geoinformatics GIScience education has been well established in a number of institutions but a (Z_GIS) majority of institutions offering structured study programmes are situated only in a University of Salzburg, few countries. Admitting that such examples are only sporadic in some parts of the Austria world, it is fortunate that many countries have initiated GIScience education in one form or the other. Most of such courses only offer „Basics of GIScience‟ because of lack of „know-how‟ among instructors and a scarcity of GIScience-focussed curricula. On the other side, number of employers requiring „GIS professionals‟ is growing rapidly due of expanding „GIS application-fields‟ as well as increasing „GIS organisations‟. Unfortunately, the available number of properly educated and well trained professionals can fill the huge „GIS job-pool‟ only partially and most jobs are occupied by the „Basic GIS specialists‟. The juxtaposition of demand and supply in „GIS Job-market‟ puts responsibility of growth on the supply side. Recognising the emerging needs, this discussion forum invites you to present your views and share your experiences and contribute in streamlining the plight of GIScience education in several countries! Managing Health Geospatially j

Dr. R. D. GUPTA Open Source GIS for Health Spatial Data Professor (Geoinformatics) & Coordinator, GIS Cell Infrastructure Department of Civil Engineering The developing countries, in general, lack a well organised GIS based public health Motilal Nehru National information infrastructure and management system. Health SDI can facilitate Institute of Technology decision makers to plan the strategy from preparedness to combat the emergence of diseases to ensure safer living of the people. The geospatial user community could (MNNIT) benefit a lot by using Open Source GIS resources for web-enabled geospatial applications in comparison to proprietary solutions. Allahabad- 211004, U.P., (India) The major emphasis of workshop has been on various aspects related to the Ph: +91-532-2271308(O), development of an open source GIS based web enabled technological framework +91-532-2271708/ for Health SDI implementation. The open source resources used for development of 2541505(R), +91-9838346268 prototype Health SDI include Quantum GIS, MYSQL, ALOV, Apache Tomcat and (M) JSP. The SDI technological framework developed is interoperable with thin client architecture to provide better geospatial web services for different health sector applications. It is high time that a structured National Health SDI with its nodes at State and District levels is designed, developed and put in place in developing countries on the similar lines of NSDI (National Spatial Data Infrastructure) of that particular country using OGC standards. The proposed workshop will help the researcher, health officials and decision maker for realization of Health SDI framework for its wider applicability and penetration to the ground level. The workshop is intended for general audience with basic SDI knowledge, Health officials, Technical experts, geospatial web application developers. Managing Health Geospatially k

Dr. Bill Davenhall Shaping Global Health with GIS Global Manager, Health and Human Services, ESRI, The ESRI Workshop USA This workshop is designed to help attendees learn how GIS is helping shape the Dr. David Kwan health of nations. In-depth presentations will describe the various ways that GIS is MPH, Public Health being used across the world to solve complex health and social problems. Workshop Specialist, ESRI, USA presenters will discuss and highlight specific aspects of Esri technology that help guide the use of GIS in several key areas such as defining health risks, identifying Dr. Deb Jyoti Pal health disparities, tracking and monitoring disease, and improving public health and VP & Head of Technology health care service delivery in general. Workshop participants will also have the and Services, ESRI India (NIIT GIS) opportunity to interact with the presenters to gain additional insights and information on topics such as spatial analysis in public health. The workshop will Ms. Zigisha Mhaskar begin at 10am and conclude at 4pm. Program Manager, CHF International, Pune, India About ESRI Esri is the world leader in GIS technology used in health. Esri‟s innovative software plays a major role in helping health professionals understand the dynamics of disease, identify gaps in service, and evaluate the effectiveness of governmental policy and expenditures. Esri software is used by hundreds of health organizations throughout the world, including the World Health Organization (WHO), 121 national health ministries, and many national medical centers. To learn more, visit us at http://www.esri.com/health Managing Health Geospatially l

Content b d Preface g Foreword Keynote Sessions j Workshop Sessions 1 29 Technical Sessions 55 93 IT and Health 123 Viral Diseases 155 Healthcare Planning and Management 187 Emerging Diseases Health GIS Databases 224 Advances in Environmental Monitoring and Health Realtime and Early Warning Systems Author Index Managing Health Geospatially m

Technical Session-IT and Health Information Technology for Better Health Care 2 Khandoker Tamirul Islam, Azharul Islam Khan, Lutfe Ara and Mark Pietroni 5 8 Preliminary Geospatial Analysis of Cell Tower Location for Vaccine Cold Chain Optimization in Developing Countries 12 Alice B. Conant, Adonis Kontos, David Taverner, Kent Smetters, Harvey Rubin 17 21 Use of Geo-Informatics System ( GIS ) in Disease Assessment and Management: by using Single 25 Nucleotide Polymorphism (SNP) 25 Gaurav Sharma, Shivam Gupta, Amit K. Awasthi, Garima Sharma 26 26 Use of Mobile GIS for Qualitative and Quantitative Data Collection for Public Health Purposes in 27 Vietnam 28 Manoj Pant, Bryan K Kapella, James C Kile, Alistair Briscombe, Kapil Chaudhery, Ramesh C Dhiman, Kuldeep Pareta Development of Health Information System using Spatial Technology for Sitheri Hills – An Integrated Approach M. Govindaraju, I.P. Sunish, B. K. Tyagi, C. Rajina, P. Suganthi The Application of GIS to Identify the Risk Area of Pulmonary Tuberculosis Amphoe Selaphum, ROI- ET, Thailand Wijitra Buttama, Wutjanun Muttuitanon Development of Map-Based Generic and Multipurpose GIS Platform for Health GIS Bageshree Vinodray Parmar Delivering Opportunity for Managing Primary Health Care with Inception of HealthGIS in Nepal Paban Kumar Ghimire The Spatial Temporal Analysis to Identify Risk Area Epidermic of Influenza Disease in Nonthaburi Province, Thailand Supath Triyawong,,Wutjanun Munttitanon Geographic Information System of Opisthorchis Viverrini in Northeast Thailand Somchai Nichpanit, Choosak Nithikethkul, Thitima Wongsaroj, Louis Royal and Pipat Reungsang Geographic Information and Serological Surveillance of the Prevalence of Schistosoma Mekongi in A Population around the High Risk Area of Pakmoon Dam, Ubon Ratchathani Province, Thailand Thitima Wongsaroj, Choosak Nithikethkul, Vladimir Buntilov, Viroj Kitikoon, Yuwaporn Sakolvaree, Wanpen Chaichumpa Effective Monitoring and Surveillance through GIS at a Peri Urban Site of Pakistan Momin Kazi, Murtaza Ali, Ayub Khan and DAE Anita Zaidi Managing Health Geospatially 1

INFORMATION TECHNOLOGY FOR BETTER HEALTH CARE Khandoker Tamirul Islam, Dr. Azharul Islam Khan, Lutfe Ara & Dr. Mark Pietroni E-mail: [email protected], [email protected], [email protected], [email protected] ABSTRACT The ability for providers to obtain accurate information to better manage their patients and to communicate with the hospital administration in formulating innovative applications is likely to improve quality of care. Paper based medical record systems require huge storage space and data extraction is time consuming. There is often less accountability, including missing files and records. On the contrary, a computerized Hospital Management System stores data electronically and provides accurate data to all users for treatment, research, and continuous monitoring of quality of care. The Dhaka Hospital of International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR, B) introduced a computerized and paperless Hospital Management System, called SHEBA, in 2009. Quantitative and qualitative indicators were defined by all stake-holders and set for observation, analysis and documentation over a period of time. Policy for continuous monitoring & evaluation and sustainable strategies have been formulated and initiated. A team approach was adopted to extract data from SHEBA. Quality indicators agreed by all stakeholders were documented as base line at the start of the monitoring process. Providers are responsible for prompt treatment ensuring patient care soon after arrival. Delays indicate problem in patient care management and raise ethical concerns. SHEBA showed a baseline time gap of 1 hr+/-13 min. A continuous monitoring process through Sheba was started to assist management practices. Strategies were developed to reduce time gap to < 30 minutes. Consumption of Intravenous (IV) fluid and Oral Rehydration Saline (ORS) are the two key indicators to directly monitor and evaluate status of dehydration. Initiatives were taken to improve accountability. Sheba guided the management team to formulate appropriate strategies for accuracy and over a period of five months the recording shows a consistent increase from 75% to 98%. Length of Stay (LOS) is very important for our hospital which sees around 250-300 patients per day. Over 50% of patients going home within 24 hours reflects the quick recovery of patients at ICDDR,B hospital. Only 1%-2% of total patients have been found to stay over 48 hours. Rate of Movement of Personal Digital Assistance (PDA) due to mal-functioning is an important issue to judge the efficiency of data acquiring equipment. SHEBA service integrated with patient management in a Hospital setting like ours is an evolving process of improving Quality Indicators. This process is participatory and accountability of all stakeholders has increased. Target outcomes of process indicators are being achieved; however sustainability demands continuous monitoring and evaluation. KEY WORDS: Service Indicator, SHEBA, Quality Care, Paperless, Participatory and Accountability 1. INTRODUCTION The Dhaka Hospital of The International Center for Quality health care relies on physicians, nurses, health Diarrhoeal Disease Research, Bangladesh (icddr, b) attendants, patients and their families, and others having introduced a computerized and paperless Hospital the right information at the right time and using it to Management System, called SHEBA, on 15th February, make the right decisions1. The ability for providers to 2009. With the participation of all stakeholders through obtain information to better manage their patients and to meetings, quantitative and qualitative indicators were set communicate with the hospital administration and policy for observation, analysis and documentation over a makers in formulating better health system applications period of time. Policy has been taken for continuous could also improve the efficiency and quality of care. monitoring & evaluation and sustainable strategies have been formulated and initiated. 1.1 Objective Paper based medical record systems require a huge space The objective of this paper is to document the processes to preserve records and data extraction is also time involved in improving patient care with the help of consuming. There is often less accountability, missing Information Technology. files and records and more man power is required to maintain the system. On the contrary, a computerized 2. METHOD Hospital Management System stores data electronically A team approach was adopted by the medical director and provides accurate data to the users, stakeholders and and other stakeholders to define indicators to monitor the providers to use for treatment, research, monitoring the quality of patient care. Data from SHEBA was also used quality of care and also for better administration. Health to inform decision making about which indicators to IT also offers the promise of more structured, accessible, choose. secure, and clinically rich information on populations of Data, extracted from SHEBA, were documented at a patients that can collectively provide evidence on a specific point for the agreed indicators. These data were variety of strategies for improving care2. set as the base line at the start of the monitoring process. 2 Managing Health Geospatially

2.1 Data Used and Findings Figure 2: Length of Stay (LOS) of different periods Necessity was felt by the authority to judge the quality of aggregated by dehydration status patient care of the hospital. Accordingly a discussion was called with the process owner and stake holders. To see Above data shows only 3%-4% on average patients are the status of patient care, data have been extracted from staying more than 48 hours in the hospital. Strategies SHEBA. Considering the data as baseline, indicators have been initiated to decrease the percentage of LOS have been set, as below – more than 48 hours to agreed and approved accepted range of 1%-2%.. Since LOS indicates discharge rate of 1. Time delay to see a patient the patients, to maintain quality treatment of the hospital, 2. Intravenous (IV) fluid and Oral Rehydration patients‟ re-visit data have been started to observe. A graph for this has been plotted in figure 3. Saline (ORS) dispensing to patients Graph shows an increase of re-visit in 2nd week of As a provider, this is our duty to keep time delay as starting the monitoring, which may reflect aggressive minimum as possible and realistic. Increase in time delay discharge. Different initiatives are now being taken to indicates a problem either in management or in the decrease the percentage value with the help of opinions process. Initially, SHEBA data shows the time delay of from process owners and stake holders. SHEBA data is above 1 hour with 13 minute standard deviation (SD). giving feedback for their opinions and initiatives. Management and process owners started monitoring data regularly and initiatives have been taken to reduce the gap and also set the accepted time delay of less than 30 minutes. Data, extracted from SHEBA, have been used to observe the status every week. Management found the gap in the process and to mitigate those, number of physicians at emergency, which is the entrance of the hospital, has been increased and strategy has been developed not to bypass any patient without seeing a doctor at emergency. These initiatives have reflected in SHEBA data and showed the decrease of time delay. .After a couple of meeting with stake holders when time delay has been found within acceptable range, need was felt to observe patients‟ length of stay as an indicator. Approximately 50% patients discharged within 24 hours indicate treatment management efficiency of our hospital. The following graph shows length of stay statistics of different periods in percentage aggregated by dehydration status. Figure 1: Average Time Gap of Patient Arrival and First Figure 3: Percentage of Re-Visit Seen by a Doctor Prescribing and dispensing of Intravenous (IV) fluids and Oral Rehydration Saline (ORS) are two main indicators for management of dehydration. Lack of accurate data recording was found initially in SHEBA. Through group discussions with the users, management took several initiatives to increase accuracy. They made users understand the importance of proper data capturing. Their initiated strategies reflected in SHEBA data showing consistent improvement. Continuous monitoring Managing Health Geospatially 3

over data and process has let the indictor reach to target encompass a wide variety of clinical and public health value of 98% over a period of 5 months, January to May activities that are critical for improving patient care. 2011. These applications include quality measurement and reporting and benefit design based on quality rather than simply the volume or intensity of services provided, and public health surveillance. All of these activities ultimately feed back to better decisions in patient care. Figure 4: IV Fluid Dispense Status Figure 6: PDA & Grabba Movement Status Since all the indicators data are based on SHEBA, data capturing equipment efficiency also needs to be checked At ICDDR, B Dhaka Hospital, SHEBA has provided the and monitored. This realization added one more indicator prime support to introduce the participatory processes to in the set in early April, 2011. In SHEBA, data are lead an enormous improvement in patient care by mainly captured in Personal Digital Assistant (PDA) and increasing the accountability of all users, stake holders a bar code scanner called Grabba is attached with the and process owners and also by giving important PDA to scan the bar coded patient ID. In the case of mal feedbacks to the management for making appropriate functioning of either PDA or Grabba, instant replacement decisions and initiatives. policy is followed. This is maintained by SHEBA Once target is achieved for any quality indicator, Technical Office. Movement of PDA and Grabba due to different strategies are initiated to sustain this. mal-functioning indicates equipments efficiency and system‟s smoothness and hence has been defined an ACKNOWLEDGEMENT indicator. Management of SHEBA developed strategies This activity study was funded by ICDDR,B and its to monitor the equipments and increased the frequency of donors which provide unrestricted support to the Centre ward round to check the same. Gradually, SHEBA data for its operations and research. Current donors providing showed an improvement for both the equipment. unrestricted support include: Australian Agency for International Development (AusAID), Government of the Figure 5: ORS Dispense Status People‟s Republic of Bangladesh, Canadian International 3. CONCLUSION Development Agency (CIDA), Embassy of the Kingdom This is important to realize the potential to use electronic of the Netherlands (EKN), SDC, (in full), Swedish health information collected in the course of care International Development Cooperation Agency (Sida), delivery – such as health information stored in EHRs, and the Department for International Development, UK claims data, registries – to promote a learning health care (DFID). We gratefully acknowledge these donors for system2. Such “enhanced uses” of health information their support and commitment to the Centre's research efforts. REFERENCECS 1. Report to the Congress: New Approaches in Medicare – http://www.medpac.gov/documents/june04_enti re_report.pdf 2. Final Issue – May 2010 – Engelberg Center for Health Care Reform – The Brookings Institution www.brookings.edu/healthreform 4 Managing Health Geospatially

PRELIMINARY GEOSPATIAL ANALYSIS OF CELL TOWER LOCATION FOR VACCINE COLD CHAIN OPTIMIZATION IN DEVELOPING COUNTRIES Alice B. Conant1, Adonis Kontos2, David Taverner3, Kent Smetters PhD4, Harvey Rubin MD PhD5 1University of Pennsylvania Email: [email protected], 2Marathon Data Systems, Greece, 3Global Systems for Mobile Communication Association (GSMA), 4University of Pennsylvania Wharton School of Business, 5University of Pennsylvania School of Medicine ABSTRACT In order to eradicate vaccine-preventable deaths worldwide, the vaccination \"cold chain\" or refrigeration, must be optimized to ensure effective delivery. A recently proposed solution to this global health challenge is to use cell phone towers as the energy source to power vaccine refrigerators in remote off-grid locations that currently lack the energy infrastructure necessary to preserve the cold chain. Using ArcGIS software, maps of Kenya, Zimbabwe and India have been created, overlaying cell tower location, cold chain facility coordinates, population distribution and demographics, and transportation routes. Once all data and maps have been finalized, decisions will be made on where pilot sites will be located. This global health project represents the future of the vaccine cold chain and has the potential to impact millions of lives. Geospatial analysis has been essential in determining the optimal location of pilot sites for this project. KEY WORDS: Health Care Delivery, Vaccine Cold Chain 1. INTRODUCTION power installations at cell towers as the energy source to 1.1 Background power vaccine refrigeration units in remote locations that There are over two million vaccine preventable deaths currently lack the energy infrastructure needed to each year in developing countries. Although a shortage preserve the cold chain. of vaccines may be one key reason, another is that many available vaccines lose their potency due to a lack of Access to refrigeration at these remote destination points adequate refrigeration. Typical vaccine distribution would enable vaccines to be effectively stored. This models have relied on a \"cold chain\", a series of storage would allow for a critical mass of vaccines to be units and insulated cold-boxes to keep vaccines delivered at one time, warranting the use of a refrigerated until received at their remote destinations. transportation vehicle (e.g. a refrigerated truck). This An efficient cold chain normally ensures that vehicle would provide more stable temperature temperature-sensitive vaccines remain effective and any conditions than a cold-box, thus preserving the integrity disruption of the cold chain severely impairs these of the vaccines and eliminating the pressure to prevention efforts. The current approach requires that immediately administer them. All of this would not only vaccines be administered almost immediately upon improve the integrity of the vaccines, but also reduce arrival, as the cold-boxes are limited in their ability to costs. maintain the necessary temperature conditions (between 2ºC and 8ºC). Due to these limitations, vaccines often Approximately 75 percent of the world is covered by a either freeze or exceed their upper temperature range and mobile cellular signal, and that percentage is expected to are rendered virtually useless. There is no noticeable reach nearly 100 percent by 2015 (World change in the optical appearance of a degraded vaccine, Telecommunication, 2010). This expansion of mobile and thus millions of individuals receive denatured coverage transports the presence of energy by necessity vaccines and assume they are immunized when in fact to remote locations, many of which are otherwise without they are still vulnerable to the disease. Additionally, in access to centrally provisioned power. In off-grid remote areas families may have to travel great distances regions, cell towers offer a constant supply of energy, to access vaccine clinics for their children. The longer the sourced from any combination of diesel generators, trip, the more days of valued work wage each family battery backup, gas turbine, renewable energy, and other member, usually the mother, misses. Due to this costly options. process, many children do not receive their completed immunizations. In the absence of thermo-stable vaccines A typical vaccine-storage refrigeration unit requires at – an exciting, but distant possibility – preserving and least eight hours of daily power supply (UNICEF, 2010). expanding the vaccination cold chain requires immediate Harnessing the energy potential of cell tower facilities focus. provides the means to power these refrigeration units. Base stations often have a surplus of power capacity of Energize the Chain (EtC), a recently formed, not-for- about 5kw for a diesel generator powered BTS and under profit organization, aspires to eradicate vaccine- 5kw for a BTS powered by alternative energy sources preventable deaths worldwide by preserving the (Community Power, 2010). Considering that a vaccination cold chain to ensure delivery of active refrigerator unit consumes between 0.5 – 1.9kWh/24h vaccines. At the simplest level, EtC proposes to use (WHO, 2000), there is ample power at most cell tower Managing Health Geospatially 5

sites to supply vaccine refrigeration units. Many tower 3. Cell tower sites in close proximity to villages sites also have spare land available to support an covered by a primary health center additional shelter for these units. The cell phone industry‟s strong incentive to maintain operations 4. Supportive local government and/or health conveniently results in a positive synergy with the need organizations for a reliable energy source. 5. Collaborative local telecom partner 1.2 The Potential Socioeconomic Impact The numbers of lives impacted by increasing the delivery 2.2 Methods and access to effective vaccines may extend well beyond All GSMA data was first digitized into raster form, the two million lives lost to vaccine-preventable illnesses georeferenced for location, and then vectorized to each year. It is estimated that under the current coverage represent the numerical coverage data. Basemap data was of vaccine delivery and utilization there are almost 400 loaded from the ArcMap 10 database for transportation million life years saved and 97 million disability-adjusted and topographical information. The CCEM database, life years saved annually by vaccines. The same study containing data for over 4000 cold chain facilities, was showed that there are almost six million deaths prevented then loaded and the cold chain facility X,Y coordinates annually by vaccination (Ehreth, 2003). The World were pulled from each facility's longitude and latitude Health Organization has declared that “… in sub-Saharan coordinate. The CCEM database was then joined with the Africa only half of the children have access to basic basemap's district dataset for spatial significance. Once immunization against common diseases such as this union was established all aspects of the CCEM tuberculosis, measles, tetanus and whooping cough. In nonspatial database could be analyzed geospatially. poor and isolated areas of developing countries, vaccines reach fewer than one in twenty children.” Such statistics Several layers were created representing the size, electric demonstrate that Energize the Chain‟s efforts to ensure capabilities, and functionality of each facility, as shown an effective and expanded cold chain could positively in Table 1. A first round of geospatial analysis was impact hundreds of millions of people. completed and graphs were generated from the electrical data representing each district and the number of cold In addition, there are positive economic impacts of chain facilities with degrees of electrical grid availability. vaccination extending beyond just life years saved. In a Using the facility type, vaccine action, resupply and widely influential paper, Bloom, Canning and Weston distance data, cold chain networks were created argue that immunizations not only prevent illness but representing a model flow of vaccines within each region also provide long-term benefits in cognitive development, of high priority as determined from the first analysis. physical strength and emotional stability (Bloom, Trend plots were also created from the population and Canning, Weston, 2005). These factors, they argue, have vaccine storage net volume data to visualize where significant positive downstream effects on the workforce patient populations are concentrated in comparison to size and productivity, educational accomplishments, vaccine availability. savings and investments as well as economic growth of communities. We believe that an increase in the delivery Table 1: CCEM Data used for layer generation and and utilization of effective vaccines will have a scalable analysis impact on millions of lives, both life years saved and improved economic wellbeing. Data Title Description 2. GEOSPATIAL ANALYSIS FOR PILOT Ft_facility_type Facility type, ie. Store, Dispensary, PROJECT Hospital, Clinic (Government, NGO, 2.1 Aims Fi_tot_pop Private, Mission) The next step for Energize the Chain is to implement pilot projects. The regions where the implementation of Fi_cc_storage, Recorded population of region served cell tower powered vaccine refrigerators could have the fi_cc_delivery, by facility greatest impact can be measured using geospatial fi_cc_outreach Vaccine action at facility – storage, on analysis of the cell tower locations in respect to the Fi_electric site vaccine delivery, or outreach current cold chain facilities. Using ArcGIS software we immunization prioritized locations for pilot sites using Cold Chain Fi_resupp_inter Equipment Manager (CCEM) data provided by PATH val Electrical grid availability at site per (http://www.path.org/index.php), and cell tower coverage Fn_distance day: none, <8 hours, 8-16 hours, >16 and location data provided by Bharti Airtel, Econet and hours GSMA. The CCEM is a Microsoft Access-based Fn_net_volume software tool developed by PATH to monitor and _4deg Number of weeks between vaccine manage cold chain performance data for continuous resupply quality improvements in the cold chain performance. At Distance vaccines travel before the end of analysis the aim is to have a prioritized list of arriving at facility districts according to the following parameters: Volume of vaccines that can be stored in cold chain unit at 4°C according to 1. A lack of reliable centrally provisioned power WHO regulations 2. High prevalence of vaccine-preventable diseases 3. CONCLUSION 3.1 Results All data analyzed is confidential to the national governments and telecommunication industry. In order to 6 Managing Health Geospatially

show the power of the ArcGIS software we have chain facilities that currently have minimal grid-tied scrambled the data to generate a mock data set for electricity available (white dots) that are also covered by presentation. The aim of the analysis was to determine the districts of Kenya, Zimbabwe and India that would be cell tower signal (pink polygon). optimally impacted by implementing cold chain facilities at present cell tower sites. Figure 1 displays the live data, 3.2 Future Analysis basic overlay map of cold chain facilities with cell tower Given the dependence on multiple stakeholders to coverage in Kenya. This confirmed our assumption of implement this concept and ensure its operational success, current overlap, but also the existence of cell towers in cross-sector collaboration is essential and new data is areas without cold chain facilities. constantly arriving. Future work will include geospatial comparison of district specific data on vaccine Figure 1: Overlay of GSM Kenya Cell Coverage preventable disease cases with electrical grid availability. represented by pink polygons with CCEM Cold Chain Additionally, a distance correlation between cell tower sites and cold chain facilities will be completed. Facility Coordinates represented by black points. Districts were prioritized for pilot site impact potential ACKNOWLEDGEMENTS according to the number of cold chain facilities with less I would like to acknowledge the support of Sophie than 8 hours of grid-tied electricity available. In the Newland of PATH, Amalia Apanagiotopoulou of analysis of the mock data, Nandi, Kenya was selected as Marathon Data Systems and the Energize the Chain the district with the largest number of off-grid cold chain Team for their support of this project. facilities that were also in a region of cell tower coverage (Figure 2). Further mapping and geospatial analysis will REFERENCES be presented as decisions are made and clearance is World Telecommunication/ICT Development Report granted by Bharti Airtel, Econet and PATH. 2010. Monitoring The WSIS Targets: A mid-term review. International Telecommunication Union. UNICEF, 2010. Handbook for Vaccine & Cold Chain Handlers 2010. Department of Health and Family Welfare. Ministry of Health and Family Welfare, Government of India. Community Power, January, 2010. Using Mobile to Extend the Grid. The GSM Association Green Power for Mobile. World Health Organization, 2000. Product Information Sheets. UNICEF Catalog. Ehreth, J., 2003. The Global Value of Vaccination. Vaccine, 21, 596-600. Bloom, DE., Canning, D., and Weston, M., 2005. The Value of Vaccination. World Economics, 16, 15-39. Figure 2: Mock selection demonstrating Nandi, Kenya as 7 the nation's district with the greatest number of cold Managing Health Geospatially

Use of Single Nucleotide Polymorphism in disease assessment and management by using Geo-Informatics System Gaurav Sharma*1, Shivam Gupta1, Amit K. Awasthi2, Garima Sharma3 1 Institute of Pharmacy, 2 Dept. of Mathematics, Pranveer Singh Institute of Technology, Kanpur, India, 3 Dept. of Zoology, Dayalbagh Educational Institute, Agra, India, Email: [email protected], [email protected], [email protected][email protected] ABSTRACT 99.9% of human DNA is similar & 0.1% dissimilar this is because of Single Nucleotide Polymorphism (SNP). SNPs are the efficient way of identifying the genes implicated to common complex diseases. In this paper it is hypothesized that particular SNPs relevant to particular disease obtained from F-SNP database can be helpful in making SNP profile of patients, having coded information (different types of SNP) of each individual. SNP information available on the GIS can be used by physicians, health care providers and government to find out genetic origin of disease, susceptibility of patient towards them and drugs to treat them. Physicians can use the SNP medical card as a powerful tool in designing the individual drug and dosage regimen, prediction of drug ADR related to particular patient. Finally, SNP medical card is also helpful in revealing information about the inheritance of disease and finding and providing proper medication for nuclear disaster victims. Use of SNP information available on the GIS can provide better medication to the patient and also act as a substantial tool in patient care. KEYWORDS: Single Nucleotide Polymorphism (SNP), F-SNP database, SNP profile, GIS (Geo-Informatics System), SNP medical card. 1. INTRODUCTION epidemiology and public health (Barnes, 1994) (Clarke, Recent advancement in pharmacogenomics provides 1996). It has being used in mapping disease spread, various promising approaches in medical fields. determining areas of prevalence and identifying the Nowadays genetic basis of treating diseases is widely in population at risk. This science and technology has use and have a great future. Discovery of drugs and drugs abilities such as receiving and transferring the data to be prescribed to the patient is done recently by reading between sources, organizing data, timely receiving and patient DNA sequence (Haga, 2002). For example: showing, processing and merging various data and their polymorphisms in the cytochrome P450 CYP2C9 with ability of offering multipurpose services. warfarin dose requirement and risk of bleeding complications (Aithal, 1999). After the completion of 1.2 GIS in public health management Human Genome Project (HGP) mapping of DNA GIS is now been applicable in health field also like sequence helped in deciphering the various enigmas of determination of the number of private health disease origin and their treatment. One of its fruit is SNP practitioners in India (Mallick, 2001). Many diseases which is a point variation in the base pair sequence in occur in human society but policies and regulations will DNA. It has been found that 99.9% of human DNA is help in preventing the lessening of disease occurrence. similar & 0.1% dissimilar this is because of Single To map and monitor the time and location of disease Nucleotide Polymorphism (SNP) (Shastry, 2002). In the happening, the special data is collected which are in the HGP more than 1.4 million of SNPs in the human form of point representing regions along with disease genome have being identified (Altshuler, 2000). The statistics (Mesgari, 2008). The relationships between the SNPs are the efficient way of identifying the genes intensity of the disease and causing factors will be implicated common complex diseases like CNS modeled using geo-statistical tools in GIS. Such models disorders, cancer, cardiovascular diseases, Type I will be used to prioritize not only the affecting factors of diabetes, multiple sclerosis (Kwok, 2003). Pluralities of diseases but also the action and regulation required for database are been available on the internet which controlling the disease. Furthermore, appropriate disease contains SNP information relevant to particular disease. history is available which provides a proper medication This will give a new dimension in the health system. But to the patient. still this technique has not reached to the masses becoming one of the major follies in the health system. 1.3 Objective The present paper attempts to provide better curing of the 1.1 Geo Informatics Systems (GIS) patient in the two distinctive areas of health i.e. It is a system of hardware, software and procedures to epidemiology and health care. It provides patient oriented facilitate the management, manipulation, analysis, medication as the user will view and control the map modeling, representation and display of geo-referenced features directly through the internet. By providing SNP data to solve complex problems regarding planning and information available on the individual medical card as management of resource (Rahman, 2006). Nowadays well as on the GIS system will help us getting relevant GIS has been used in health area especially in medical information of a wide range of patients resulting 8 Managing Health Geospatially

in proper health facilities. Moreover, also assist health 3.2 Programming Language care providers and physicians for quick and easy Many programming language (Application Programming medication of the patient. Interface) have been developed for smart cards. Some of them are: GlobalPlatform/OpenCard/SC-PC/CT-API. 1.4 Problems in health GIS Many reasons are offered for the lack of incorporation of 3.3 Medical card security GIS technologies. The majority of these are concerned All data and passwords stored on the card can be erased with the end-users of GIS and a lack of consideration of or modified by an unusual voltage supply, heating, UV their needs (Nemeth, 2005) (Magruder, 2005). Another light supply and physical attacks. These threats can reason for the less popularity of GIS than other remove the security code or erase the data stored. So the application software is the time required to learn and card will be secured by providing Operating System understand its functionality is very much (Rob, 2003) security, PIN number and Software security. (Magruder, 2005). Furthermore, GIS may be viewed as an undemocratic technology (Obermeyer, 1998), as it 3.4 Preparation of a Card may broaden the gaps between the powerful and less SNP discovered in the HGP and the newly discovered empowered people because of difficulty to access the SNPs are stored in the SNPs like Functional Single data and technology by the poor. Those in power will use Nucleotide Polymorphism (F-SNP) and will act as the surveillance capabilities to which other sectors of the markers in the search for the origin of diseases (Queens population do not have access (Craig, 2002). University, 2010). F-SNPs database integrates Participatory GIS is a field of study and application that information obtained from 16 bioinformatics tools and has emerged from the criticism of GIS and power databases about the functional effects of SNPs (Lee, structures. Participatory GIS or community integrated 2008). Another distinguishing feature of the F-SNP GIS refers to a methodology that allows systems (GIS) to database is its integration of human-disease databases to address the needs of people who are concerned with facilitate identification of potential disease-causing SNPs participation in decision making (Nyerges, 2002). as genetic markers in association studies (Johnson, 2001). The current version of the F-SNP database 2. MATERIAL AND METHODS contains the functional information for 559,322 SNPs in 2.1 Medical card 18,282 genes relevant to 85 major human diseases Medical or smart card is a plastic card in the size of a (Queens University, 2010). So F-SNP provides us a good credit card that one or more chips are used in it as database where we will extract good information about aggregated services. There can be also be one or more the particular SNP. technologies such as magnetic strips, bar codes, biometric information and image recognition, which will In this process SNP of individual person is identified. be done by machines, used in it. The combination of Particular SNPs relevant to particular disease will be common plastic cards and a microprocessors, allows obtained from F-SNP database which will be helpful in large information to be stored, processed and accessible making SNP profile of each individual. SNP profile has online and offline. the coded information (different types of SNP) of a particular patient. According to the profile SNP medical 2.2 Types of smart cards card of each individual is produced which comprises of There are two of smart cards 1- Memory cards that only coded information as a unique identity of the patient. It include nonoverflowable memory and some processes in will be helpful in revealing medical history from order to provide security and 2- CPU cards which include centralized database, genetic origin of disease, a CPU and overflowable too (Haghiri, 2002). susceptibility of patient towards the disease and drugs to treat them. SNP profile on the basis of SNPs will be 2.3 Amount of data stored in a Medical Card made. Groups may be formed on the basis of different Smart card will store more data than the normal cards SNP profile. According to these SNPs profiles, we will (Magnetic strips) and by storing encoding algorithms, produce identification cards/medical cards of each group they will also improve the security of transfers. The data uniquely. These medical cards are having some coded stored on an IC chip is transferred by means of an information of SNP and unique identity of patient. This electronic mojule that is connected to a terminal or a card identity may be used to track the medical history of the reader device or by a magnetic field (Haghiri, 2002). patient from centralized database. The SNP information is made available on the internet or GIS database where 3. Functionality of Cards the data is stored and can also be manipulated. 3.1 Operating System According to the number of people it uses has been The new pattern and programming in the operating divided into two categories i.e. on community basis and system of medical card is CardJava (Oracle, 2010). This on individual basis (Figure 1). operating system is made by Sun Microsystem Company. Other operating systems that will be named as developed for smart cards are MULTOS (Multi-Application Operating System) and SmartCard for Windows (Smartcard for Windows, 2010). Managing Health Geospatially 9

Figure 1: Division of people according to their SNPs expenses and human force, they provide better managing profiles of distribution, control and guidance in health and medical services weather in the medical section or in 3.4.1 On Community Basis facilities, equipment and services offering centers. If we It involves a large scale use of GIS where the evaluation have a vigil on the present health care management some of spreading and detecting of epidemic disease will be of the well developed countries have contributed a lot in done. SNP information are made available on the GIS this field. GIS has been applicable in many European will be used by physicians, health care providers and countries and have improved a lot. But in developing government to find out various drug related aspects like country like India, where only 2% of its budget is spent genetic origin of disease, susceptibility of patient towards on the health system, it is a very difficult to successfully that disease, drugs to treat them and vaccination in the start and run this system on a grand scale. Finding epidemics (Wang, 2004). Moreover, the above individual SNP, formation of medical card, data storage information provides data and so helps health policy and data management on GIS requires a substantial makers and policy implementers to provide physical and amount of financial aid, sophisticated and technical financial help to the particular region relevant to workers, and efficient management system. So it‟s the particular SNP groups (Figure 2). responsibility of the government to have a propensity towards this system. Apart from medical and clinical 3.4.2 On individual basis tools, national programs for such a huge country as India SNP medical card will also act as a powerful tool for the also need state of the art management and planning tool. physician so that it‟s easy for them to design the Furthermore, it provides the ability of recognition, individual drug and dosage regimen, predicting ADR of definition and good performance in short span of time. drug related to particular patient (Guzey, 2002), helpful in conducting individual patient vaccination and REFERENCE immunization program especially for those person on Aithal, G.P., Day C.P., et al, 1999, Association of which normal vaccine is ineffective (Sauerbrei, 2004) (Dhiman, 2010). Allergies related to particular drug polymorphisms in cytochrome p450 CYP2C9 with shown only in some patients will also be predicted. For warfarin dose requirement and risk of bleeding instance SNP will be used in understanding the complications. Lancet, 353, 717-719. pathophysiology of asthma (Palmer, 2001). Moreover, Alonso, A., and Martín, P., 2005, Challenges of DNA SNP medical card is also helpful in getting information Profiling in Mass Disaster Investigations. Croat Med about the inheritance of disease (Altshuler, 2008). J, 46, 540-548. Furthermore, SNP data is also helpful in finding and Altshuler, D., Daly, M.J., et al, 2008, Genetic Mapping providing proper medication for nuclear disaster victims in Human Disease. Science, 322, 881-888. (Alonso, 2005) (Figure 2). Altshuler, D., and Pollara, V.J., 2000, An SNP map of the human genome generated by reduced Figure 2: Use of SNPs on Individual and Community representation shotgun sequencing. Nature, 407, 513- Basis. 516. Barnes, S., Peck, A., et al, 1994, Mapping the future of 4. CONCLUSION health care: GIS applications in health care analysis. According to the mentioned issues, it can be concluded Geographic Information Systems, 4, 31-33. that GIS based medical card will be suitable equipment Clarke, K.C., McLafferty, S.L., et al, 1996, On for patient medical record. In addition to saving on epidemiology and geographic information systems: A review and discussion of future directions. Emerging Infectious Diseases, 2, 85-92. Craig, W.J., Harris, T.M., and Weiner, D., 2002, Community participation and geographic information systems, (London: Taylor and Francis). Dhiman, N., and Haralambieva, I.H., 2010, SNP/haplotype associations in cytokine and cytokine receptor genes and immunity to rubella vaccine. Springer link, 62, 197-210. Guzey, C., Spigset, O., et al, 2002, Genotyping of drug targets: a method to predict adverse drug reactions? Drug Safety, 25, 553-560. Haga, H., and Yamada, R., 2002, Gene-basared SNP discovery as part of the Japanese Millennium Genome Project: identification of 190562 genetic variations in the human genome. J Hum Genet, 27, 605-610. Haghiri, Y., Tarantino, T., et al, 2002, Smart card manufacturing: a practical guide, (New York. John Wiley & Sons). 10 Managing Health Geospatially

Johnson, G.C.L., and Esposito, L., 2001, Haplotype Obermeyer, N.J., 1998, The Evolution of Public tagging for the identification of common disease Participation GIS. Cartography and Geographic genes. Nature geneticist, 29, 233-237. Information Systems, 25, 65. Kwok, P.Y., 2003, Single Nucleotide Polymorphisms: Oracle. Java Card Technology Overview methods and protocols, (New Jersey: Humana Press [http://www.oracle.com/technetwork/java/javacard/ov Inc). erview/overview-jsp-135353.html] Lee, P.H., and Shatkay, H., 2008, F-SNP: Palmer, L.J., and Cookson, W., 2001, Using single computationally predicted functional SNPs for nucleotide polymorphisms as a means to disease association studies. Nucleic Acid Research, understanding the pathophysiology of asthma. 36, 820-824. Respiratory Research, 2, 102-112. Magruder, C., Burke, C., Hann, N.E., and Ludovic, J.A., Queens University [http://compbio.cs.queensu.ca/F- 2005, Using information technology to improve the SNP/] Public Health System. J Public Health Manag Pract, 11, 123-130. Rahman, A.A., Zlatanova, S., and Coors, V., 2006, Innovations in 3D Geo Information Systems, (New Mallick, R.K., and Routray, J.K., 2001, Identification York: Springer). and accessibility analysis of rural service centers in Kendrapara District, Orissa, India; a GIS-based Rob, M.A., 2003, Some challenges of integrating spatial application. JAG, 3, 99-105. and non-spatial datasets using geographical information system. Information Technology for Mesgari, M.S., and Masoomi, Z., 2008, GIS Applications Development, 10,171-178. in Public Health as a Decision Making Support System and It‟s Limitation in Iran. World Applied Sauerbrei, A., and Rubtcova, E., 2004, Genetic Profile of Sciences Journal, 3, 73-77. an Oka Varicella Vaccine Virus Variant Isolated from an Infant with Zoster. J Clin Microbiol, 42, 5604– Nemeth, C., Nunnally, M., O'Connor, M., Klock, P.A., 5608. and Cook. R., 2005, Getting to the point: developing IT for the sharp end of healthcare. J Biomed Inform, Shastry, B.S., 2002, SNP alleles in human disease and 38, 18-25. evolution. Journal of Human Genetics, 47, 561-566. Nyerges, T., Jankowski, P., and Drew, C., 2002, Data- Smartcard for Windows [http://msdn.microsoft.com/en- gathering strategies for social behavioral research us/windows/hardware/gg487492] about participatory geographical information system use. International Journal of Geographical Wang, C., and Tang, J., 2004, HLA and cytokine gene Information Science, 16, 1-22. polymorphisms are independently associated with responses to hepatitis B vaccination. Hepatology, 39, 978–988 Managing Health Geospatially 11

USE OF MOBILE GIS FOR QUALITATIVE AND QUANTITATIVE DATA COLLECTION FOR PUBLIC HEALTH PURPOSES IN VIETNAM Manoj Pant1, Bryan K Kapella2, James C Kile2, Alistair Briscombe3, Kapil Chaudhery1, Ramesh C Dhiman4, Kuldeep Pareta1 Spatial Decisions 68C Tran Quang Dieu, Dong Da, Hanoi, Vietnam U.S. Centers for Disease Control and Prevention, Hanoi, Vietnam CARE International in Viet Nam National Institute of Malaria Research, Delhi, India E-mail: [email protected] ABSTRACT Geographic Information System (GIS) and Remote Sensing (RS) are a set of technologic and scientific tools that increasingly play important roles in public health surveillance and research. These tools enable data with spatial dimensions to provide a better understanding of the vulnerabilities, hazards, and risks to zoonotic disease outbreak investigations. In order to perform such analyses, data collection by surveys is a major component of these investigations in terms of resources for any health research. This article summarizes the methods used to rapidly and effectively collect and integrate health data into a GIS in order to facilitate further epidemiological analyses. In order to better understand human-animal and animal-animal interface issues in cross-species transmission of disease, a GIS/RS based approach was used to collect high resolution household level point data in Hau My Phu and Nam Cao communes of Tien Giang and Thai Binh provinces of Vietnam, respectively. In the absence of high resolution household level qualitative and quantitative data on animals and husbandry practices, additional information was collected verbally from study participants. To add spatial dimensions and to make the surveying and data integration time shorter, mobile GIS systems were used. Remote Sensing data was used to facilitate the process of data collection and analysis. Survey forms were designed to collect information related to human, pig, chicken, and duck populations, plus various animal husbandry practices. Data collected are both qualitative and quantitative. Based on the survey forms and expert knowledge solicitation, database schema was created using Unified Modelling Language (UML). The schema in UML was transferred to ESRI geodatabases. Further, the database schema in desktop GIS environment was transferred to mobile GIS environment of ESRI ArcPad. Customizations were introduced in the mobile GIS environment to suit the needs of surveyor‟s, including language, default value, and validation support. Land use maps were created from high resolution satellite images and other ancillary datasets. Emphasis was given to extract information about transportation features, agricultural land, water bodies, and household objects. This generalized information is transferred to mobile GIS to be used as a cue in data collection. GIS and RS were rapid and effective tools for collecting, reviewing, and collating household level data from both hand- held devices and paper surveys. KEY WORDS: GIS, Mobile GIS, RS, Satellite Image, Survey, Public Health 1. INTRODUCTION details of causation which might not be apparent from Vietnam is an agrarian country, with agriculture as major purely quantitative perspective (Rothman, Kenneth J., land use (FAOSTAT, 2011). As common practise in 2002). south-east Asia livestock animals are reared in backyard and commercial farms (Tiensien etal. 2005, 2007). In order to ascertain information which can be used for an in-depth analysis of epidemiology of zoonotic With recent increase in influenza like disease outbreaks diseases outbreak it is important to collect information at in various parts of world and especially in Vietnam household level. In the absence of availability of such (WHO, 2011, Phan etal. 2009), it is important to keep data a mobile GIS based household survey is planned. surveillance and monitoring for evidence based The planned survey form intends to collect information mitigation. regarding poultry/livestock (chicken, ducks and pig), animal husbandry practises (John, 2005) and Epidemiology has relied on surveying to collect demography. This will help to better understand practises information to understand cause-effect relationship. It in light of animal-animal and animal-human interface of takes into consideration both quantitative and qualitative disease transmission (Graham etal. , 2008). information. Quantitative information gives clues of presence or absence of an effect with statistical Mobile GIS is integration of traditional GIS with GPS significance, while qualitative information provides on a mobile platform (Heywood et al. 2006, Burrough & 12 Managing Health Geospatially

McDonnell 1998). The GIS/GPS component helps in schema for survey form along with secondary capturing spatial information while mobile computation information extracted from satellite image. device provide a movable platform with easy integration and visualization of spatial data (base map, satellite FGDB provide large storage capacity, support to images and surveyed point etc.). topology, relationship classes and incorporation of image data inside the geodatabase making them suitable for 2. MATERIAL AND METHODS high resolution data survey. 2.1 Study Area The household survey was carried out in two communes While using Databases for data collection and of Thai Binh and Tien Giang province namely Nam Cao integration, it is important to create structure (schema) and Hau My Phu commune. Thai Binh is situated in (Burrough & McDonnell 1998). It works as a template North-East Vietnam. It is a coastal province situated in for data collection and assist in its use for analysis. A the Red river delta (Figure 1). database schema also helps in sharing the method of data collection without sharing data. ¯ ¯ Nam Cao Commune Tien Giang Province Thai Binh Province Hau M y Phu Commune 0 0.5 1 0 0.5 1 Km Km Figure 3: Nam Cao Commune Thai Binh province Figure 4: Hau My Phu Commune Tien Giang province Surrounded by Red, Tra Ly, Luoc and Hoa River, Thai In order to create schema, Unified Modelling Language Binh faces Gulf of Tonkin in East China Sea. Tien Giang (UML) was used. UML helped in documenting the province on the other hand is situated in South Vietnam, database elements, their structure and relationship. along Tien River north to Mekong River (Figure 2). Designing databases through UML has certain advantages e.g. ease in corrective measures, sharing the The landscape in both communes is predominantly flat, schema, serve as memory documentation which can be deltaic, and agricultural with wet rice as major crop. further used and modified. However one difference in landscape identified using satellite imagery was pattern of built-up area. In Nam 2.4 Satellite Image Data Cao, built-up area was found to be aggregated in clusters. Panchromatic images from CARTOSAT-1 (2.5m In Hau My Phu built-up areas were dispersed with few resolution) and multi-spectral images from IKONOS (1m aggregations. This information was useful in planning resolution) covering Nam Cao and Hau My Phu and carrying out survey, as travel distance per household communes respectively were procured. Satellite data thus sampled in Hau My Phu was more than in Nam Cao. acquired was processed for geometric and radiometric errors. The image data was mosaicked to get seamless 2.2 Survey Design image base map before being imported into the It was planned to survey each and every household in geodatabase. both communes. Survey form consists of 60 questionnaires. It is further divided into four sections Processed satellite images were used to prepare land use based on information collected i.e. pigs, chicken, ducks map with aquaculture, Plantation, River, Road, and human. Settlement and Wet rice cultivation as main classes. Extraction of land use classes like settlement, rivers and Survey forms consist of both quantitative and qualitative roads was directly helpful in estimating and planning questionnaires. Information regarding current and past field work. The base map was also used for navigational population sizes, animal husbandry practices, proximity purposes. between human-animal and animal-animal instances and observation of influenza like symptoms was prominent. 2.5 Desktop to Mobile ArcPadTM is a GIS software providing flexibility to 2.3 Database Design collect Geo-Information through mobile platform. It also It was planned to use a database for collection and storage of all spatial and attribute data. ESRI File Geodatabase (FGDB) was chosen to implement database Managing Health Geospatially 13

help in collection of attribute information via digital 2.7 Data Assimilation forms, geocoded photos etc. Data collected in the field using mobile GIS and paper based survey forms were imported into geodatabase. The database schema inside FGDB was replicated for the Importing data from mobile GIS system was straight mobile GIS environment. Subsequently a project in forward however data from paper based form was first ArcPadTM was created. Data entry forms were imported in a MS-Access database using a custom build customized to facilitate user interaction in the field using form (Figure 4) and subsequently imported to the ArcPad StudioTM. Satellite image and base map were also geodatabase. integrated into the mobile GIS software (Figure 3). Further in order to facilitate data entry from paper based Figure 6: Data entry form in MS-Access survey, database schema was exported to MS-Access. 3. RESULTS (Figure 4). GIS deployed in mobile phone with integrated GPS chip helped in rapid collection and update of the data to 2.6 Survey desktop GIS. It was also easy to incorporate qualitative Household level Survey was conducted in both the data in survey, which can be typed in during the communes with objective to collect information from interview. each household. Each commune consists of a thousand to fifteen hundred households. Updated base map created from high resolution satellite images were used to capture information like location of In order to complete the task with limited number of household, roads/streets, canals etc. The detailed land use mobile GIS devices and handheld GPS two methods assisted in planning and conducting survey. Pre-field were adopted. digitization of households resulted in saving valuable time finding survey locations. 2.6.1 Method-1 Customized electronic survey forms integrated with Survey data collected is stored in ESRI File Geodatabase mobile GIS device were used to collect data from Nam in defined schema. The geodatabase created is structured Cao commune. to provide a 3 tiered information system. The bottom The ArcPadTM based custom forms were used to collect level contains satellite images of the study area. Above it data from the field. Each household was mapped as a is the land use and house hold survey information. Thus, point object and attribute information collected using the one file geodatabase forms the repository of all data, form interface (Figure 3). stored in vector / raster and tables format. Figure 5: Survey form and base map in mobile GIS The information stored in the file geodatabase can be 2.6.2 Method-2 readily used for various purposes including creation of In Hau My Phu commune, data collection was carried out thematic maps, reports, queries, visualization (Figure 5), using paper based survey forms. Along with hardcopy identify spatial and non-spatial relationship between forms handheld GPS devices were used. These handheld variables further it can be used for GIS based modelling. GPS devices were standalone GPS‟s with very limited user interface to collect attribute information. The location of each household was captured using the GPS device which is cross-referenced with the paper based survey form. 14 Managing Health Geospatially

Managing Health Geospatially Nam Cao Commune Hau My Phu Commune \" \"\"\"\" \" \"\"\"\"\"\"\"\"\"\" \"\"\"\"\"\"\" \"\" \"\" \" \"\"\" \"\"\"\"\"\"\"\"\"\"\"\" \" \" \" \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\" \"\" \"\" \"\"\"\"\"\"\" \" \" 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\" \"\" \"\"\"\" \"\" \"\"\"\"\"\"\"\" \" \"\" \" \" \" \"\"\" \" \"\" \"\" \"\" \"\"\" \" \" \" \" \"\" \" \" \" \"\"\"\"\"\" \" \" \"\" \" \"\"\" \" \" \" \"\" \" \"\" \" \"\" \"\"\"\"\"\"\" \" \" \"\" \" \" \"\" \" \" \" \" \"\"\"\"\" \" \" \" \" \"\"\"\" \"\" \" \" \" \"\"\"\"\" \"\" \"\" \" \" \" \"\"\"\" \" \" \"\" \" \" \" \"\" \" \"\" \" \" \" \" \"\"\" \" \"\"\"\"\" \" \" \" \"\" \" \"\"\" \"\" \"\" \" \" \" \" \"\"\"\" \" \" \"\"\" \"\"\" \"\" \" \" \"\"\"\"\"\" \" \"\" \"\"\"\"\"\"\" \" \" \" \"\"\"\" \" \"\" \" \" \"\" \" \" \"\"\"\" \" \" \"\"\"\"\"\"\"\"\" \" \"\" \" \"\" \"\"\" \" \"\"\" \" \" \" \" \" \"\" \" \" \" \"\"\" \" \"\"\" \"\"\"\" \" \" \"\"\"\" \" \" \" \" \" \"\" \" \" \" \"\" \"\"\"\"\" \" \" \"\" \"\" \" \"\"\" \" \" \"\" \" \" \" \" \"\" \"\" \"\"\"\" \"\" \" \" \"\" \" \" \"\"\"\" \" \" \" \"\" \" \" \" \" \"\" \"\"\" \" \"\"\" \"\"\"\"\"\" \"\" \" \" \"\"\"\"\"\"\" \" \"\" \" \"\" \" \" \"\" \" \"\" \"\"\"\"\"\"\"\"\"\"\" \" \" \"\"\"\" 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4. CONCLUSION ACKNOWLEDGEMENT A traditional paper based survey is one of the key We would like thank CARE partner network in Nam Cao methods to collect information in a public health survey; and Hau My Phu Commune who were instrumental in however it was observed that it is more time consuming data collection. We would also like to thank CDC for and require far more steps to collect and assimilate the providing financial support to this project. data for processing. REFERENCES Advances in mobile phone technology are paving ways in which this platform can be used in conjunction with Journal GIS to rapidly collect spatial data (Richards etal.1999). Especially mobile phones with embedded GPS chips and Jay P. Graham, Jessica H. Leibler, Lance B. Price, touch screen display are becoming important tools for collecting geocoded data. A touch screen based interface Joachim M. Otte, Dirk U. Pfeiffer, T. 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John Halpin MD (2005): Avian Flu from an Because there are fewer steps in data capture and Occupational Health Perspective, Archives of assimilation using a mobile GIS system, it has lower probability in terms of human errors as compared to Environmental &Occupational Health, 60:2, 59-69 paper based data collection. Some of the limitation of mobile GIS technology is Phan Q. Minh, Mark A. Stevenson, Chris Jewell, Nigel spatial accuracy. Spatial accuracy to such devices ranges from 5-10m. Although low, this accuracy is acceptable French, Birgit Schauer (2009): Spatio–temporal for collecting point data for household in a rural setting. analyses of highly pathogenic avian influenza The method for data collection in this study is offline data collection i.e. data collection and data integration to H5N1outbreaks in the Mekong River Delta, Vietnam, the geodatabase are two separate steps. There are approaches with near real time or real time data 2009, Spatial and Spatio-temporal Epidemiology 2 collection and integration e.g. using mobile GIS with Server GIS where mobile functionality is part of a mobile (2011) 49–57 service rendered to the client in the field from a remote server connected through internet. This will further Prasert Auewarakul, Wanna Hanchaoworakul & reduce the time of data collection and visualization of results. It will also give more control over data collection Kumnuan Ungchusak (2008): Institutional responses strategies and will allow interventions to surveys from a central location in a timely manner. Further with recent to avian influenza in Thailand: Control of outbreaks development in cloud computing such processes will be more cost effective as users don‟t have to own server side in poultry and preparedness in the case of human-to- infrastructure and will only pay for a mobile service hosted on cloud. human transmission, Anthropology & Medicine, 15:1, In the domain of epidemiology where we are 61-67 continuously facing challenges in terms of unavailability of high resolution geocoded information of public health Richards TB, Croner CM, Rushton G, Brown CK, statistics, mobile GIS is showing promising role data collection and dissemination for preparedness and Fowler L. Geographic information systems and public mitigation (Prasert Auewarakul, Wanna Hanchaoworakul & Kumnuan Ungchusak, 2008). Not only these methods health: mapping the future. Public Health Reports present simple and effective data collection but they are fast enough to provide access to data for further analysis 1999; 114:359-373 in a timely manner. Tiensin T, Chaitaweesub P, Songserm T, Chaisingh A, Hoonsuwan W, Buranathai C, et al. Highly pathogenic avian influenza H5N1, Thailand, 2004. Emerg Infect Dis 2005; 11:1664-72. Tiensin T, Nielen M, Songserm T, Kalpravidh W, Chaitaweesub P,Amonsin A, et al. Geographic and temporal distribution of highly pathogenic avian influenza A virus (H5N1) in Thailand, 2004–2005: an overview. Avian Dis 2007; 51(Suppl 1):182-8. Books Burrough, P.A. & McDonnell, R.A., 1998. Principles of Geographical Information Systems 2nd ed., Oxford University Press. Heywood, D.I., Cornelius, M.S. & Carver, D.S., 2006. An Introduction to Geographical Information Systems 3rd ed., Prentice Hall. Rothman, Kenneth J., ,2002. Epidemiology: An Introduction Oxford University Press, USA References from websites: Food and Agriculture Organization of the United Nations. FAOSTAT [Accessed Feb. 30 2011]. Available from: http://faostat.fao.org WHO, WHO | Viet Nam. Global Alert and Response. Available at: http://www.who.int/csr/don/archive/country/vn m/en/ [Accessed July 4, 2011]. 16 Managing Health Geospatially

DEVELOPMENT OF HEALTH INFORMATION SYSTEM USING SPATIAL TECHNOLOGY FOR SITHERI HILLS – AN INTEGRATED APPROACH M. Govindaraju, I.P.Sunish*, B.K. Tyagi*, C. Rajina, P. Suganthi Department of Environmental Biotechnology, Bharathidasan University, Tiruchirappalli-620024. *Centre for Research in Medical Entomology (ICMR), Madurai-625002. E mail: [email protected] ABSTRACT In recent years, vector-borne diseases (VBD) have emerged as a serious public health problem in countries of the South- East Asia Region, including India. The study area covers the geographical area of 654.52 Sq.Km, one of the segments of Eastern Ghats of Dharmapuri district in Tamil Nadu, within the geographical limit of 78o15‟00‟ – 78o 45‟00‟E longitude and 11o44‟00‟ - 2o 08‟00‟ N latitude. Remote sensing covers vast area, including un-surveyed and inaccessible areas and thus helps in understanding the breeding and spreading patterns of vectors spatially. In the present study totally 14 panchayat villages have been surveyed for vector habits. By using Survey of India toposheets thematic maps were prepared such as base map, road network and water logged areas. Also the epidemiological survey has been carried out to estimate the mosquito diversity. January 2011 - IRSP6 satellite imagery was used to identify mosquito larval habitat.NDVI was performed to identify vector habits in inaccessible area of forest ecosystem using ERDAS 2011 software. The study has developed a health information system by studying landscape ecology and vector biology. GIS and remote sensing technology is used for the identification of high risk areas for the control of vector borne diseases which provide excellent means for visualizing and analyzing epidemiological data, revealing trends and inter-relationship for better tribal health management. KEY WORDS: Geographic Information System (GIS), ERDAS software, Vector Borne Diseases (VBD), Normalized Difference Vegetation Index (NDVI). 1. INTRODUCTION on 2009 Census. The altitude of Sitheri hill is about Vector borne diseases have been major public health 3600 feet. problem in globally. In any disease control programme, there are several factors involved such as estimation of Figure 1: Study Area Location Map disease burden, monitoring of disease trend, 3. METHODOLOGY identification of risk factors, planning, allocation of 3.1 Epidemiological Survey resources, implementation etc. Remote sensing data Epidemiological data was collected based on the becomes an important input for GIS in mapping the occurrence of disease symptoms in the nearby localities vector habitats. It has been used for surveillance and and the available information also collected from monitoring of vector-borne diseases, in environmental Primary Health Centre‟s (PHCs) and other Medical health, analysis of disease policy and planning. In the Centers of Sitheri hills. This data would serves as input present study, these remotely sensed data have been used for further analysis. to identify the vector habitats and landscape ecological 3.2 Attribute Data Analysis classes as input for ArcGIS 10 software. Attribute data such as climatology, demography surface water quality and quantities were collected and 2. STUDY AREA incorporated in the present study to reveal the Sitheri Hills are one of the segments of Eastern Ghats of Tamil Nadu, within the geographical distribution of the study area is 654.52 km2 limit with 78°45‟00‟E longitude and 12°08‟00‟ N latitude shown in the figure.1. The study area comprises of various vegetation types such as the evergreen, semi-evergreen, riparian, dry deciduous scrub and southern thorn scrub forests. The minimum and maximum temperature is 19°C in winter and 40°C in summer respectively. The average annual rainfall is 900 mm attained from both northeast and southwest monsoons. Topographically, the area is undulating with an altitude varying from 240 to 1266 m. Soil is generally shallow and reddish-loam, varying in fertility and often mixed with gravel and boulders. The total area of Sitheri village is found to be 400 Sq.km with a total population of 9045 (Male 4656 & female 4389) as Managing Health Geospatially 17

environmental conditions. Basic water quality parameters Figure 3: Road Network Map were obtained by laboratory analysis. 3.3 Identification of Vector Habitat The required maps can be prepared in the scale of 1:50,000. IRS P6 LISS III MX was used for delineating vector habitats. Creation of geopersonnal database such as base map, water bodies, watershed area, vegetation cover, slope, population density, diversity, paddy fields. NDVI was performed to assess the landscape conditions. Vector breeding habitats in the targeted area is identified using suitable interpretation and image processing techniques. Validation is also done by ground truth verification. 4. RESULTS AND DISCUSSION 4.1 Base Map Preparation Base map of the study area is prepared from the survey of India toposheets representing Sitheri hills including 58I/5, 58I/6, 58I/9, and 57L/12 in GIS plot form. The hill boundary, village area, and road network are clearly, noticeable from the base map. Also village wise field survey was carried out for detailed analysis and accuracy of the relevant data. It is noted that there is a lack of proper road network to connect PHCs with in the villages. The Base map and road network of the study area presented in the figures 2&3. Figure 2: Base Map of Study Area. Figure 4: Satellite Image of IRS P6 – LISS III (2011) 4.2 Digital Image Interpretations Result of this analysis identifies that the declining sate of Satellite image of IRS P6 - LISS III January 2011 (figure vegetatal cover and the increase of settlements. Also 4) was analysed by digital image processing methods. forest degradation, shifting cultivation / grazing practices Land use and land cover changes were detected and are the most influencing landscape ecological factors calculated with NDVI analysis by using ERDAS may lead a considerable increase of vector habitats. IMAGINE software. 4.3 Socio Economic Status Most of the houses have low mud walls with hipped roofs thatched with hay or sugarcane leaves in this area. Water closets have been fitted near the houses, with no walls around, no proper septic tank and no water supply. The predominant occupation of the people is agriculture and which is mainly rain-fed. Most of the adults both men and women are illiterate. Quality of education imparted in this area is generally very poor. 18 Managing Health Geospatially

4.4 Health Information’s this area, all the collected immature mosquito were brought to the laboratory for emergence. The entire Sitheri Hills contains 59 hamlets, 1 Primary Health emerged adult mosquitoes were identified using standard Centre (PHC) & 4 Health Sub Centre (HSC) and having keys. The study reveals a total of 2533 female population of 9045 (Male 4656 & female 4389) as on mosquito specimen contributing of 19 species belonging 2009 census residing in 1908 houses. It is covered by to 7 genera were collected utilizing a total of 44 man four forest ranges viz., Harur, Morappur, Theerthamalai hours. The abundance map prepared from the collected & Kottapatti under Harur Forest Division (Tamil Nadu data indicates the highest and lowest abundance of Government). All the 59 hamlets are under the control of vectors such as S.Ammapalayam and Suriyakadai one panchayat president. The details of the human census respectively represented spatially in Figure 6. and fauna census are given in Table 1 & 2. Table 3: Water Quality Parameters (2009, 2010) Table 1: Population Details (2009) S. Name of the Male Female Total No. of S.No Sample Physico-Chemical No. HSC Houses Location Parameters 881 1823 1. Kalasapadi 942 1439 2956 514 DO FreeCO2 HSC 1129 2281 pH (mg/ l) (mg/l) 940 1985 645 2. Nochikuttai 1517 4389 9045 HSC 364 3. Ammapalay 1152 385 1. Nochikuttai 7.18 2.23 6.6 am HSC 1908 2. Suriyakadi 6.60 2.23 15.4 Suriyakadai 19.8 4. HSC 1045 3 Sulakurichi 7.55 4.47 19.8 Total 4656 4 Kattukottai 7.96 4.47 8.8 * HSC- Health Sub Centre 8.8 5. Pudhur 7.38 6.70 22.0 70.4 Table 2: Fauna Details (2009) 15.4 6. Pereri 7.49 7.82 28.6 39.6 Sl. No. Animals Nos. 7. Sakkalithapu 7.41 6.9 41.8 123.2 1 Cow 3321 8. Kalnad 8.07 5.0 2 Sheep 962 37.4 3 Hen & Cock 1352 9. Chitteri 7.55 6.9 4 Goat 1981 5 Dog 898 10. Mannur 7.44 8.0 6 Turkey 7 Duck 2 11. Govindankadai 7.31 6.8 8 Pig 4 Nil 12. Mambara 7.40 7.5 Total 13. Oomath 7.31 10.5 8520 14. Nitamangadai 8.11 11.2 4.5 Water Quality Analysis 4.7 Vector Surveillance Surface water quality parameters ware analysed. pH was In the present study, vector surveillance is used to varies in all the villages. In which the lowest pH is 6.60 determine the geographical distribution, changes in (Suriyakadi) and the highest is 8.07 (kalanad). From the density of the vector. The species which were collected results of water quality analysis, it is noted that there was from Indoor resting were identified as Culex no considerable changes in parameters when compared to quinquefasciatus was said to be the dominant species standards refer table.3. followed by Anopheles subpictus Anopheles vagus, etc., and 5 species identified with outdoor landing collection 4.6 Entomological Survey are St. w-albus, St. albopictus, Cx.tritaeniorhynchus etc.,. Entomological and parasitological surveys were carried Several indexes have been calculated from the obtained out in 14 hamlets under the 3 HSCs. In order to carryout results and are currently used in monitoring vector entomological investigation adult and immature population of dengue and chikunguya fever. With the mosquito collection were made by two methods such as data collected from survey, the House index (the indoor resting and outdoor landing collection. Dusk percentage of houses infested with larvae or pupae) were collection was also carried out in 10 hamlets around the also calculated, and which is ranged from 3.82 - 23.08. In cattle shed and bushes. Immature mosquito survey was which the lowest in pereri village and the surrounding also carried out in all the selected hamlets in and around area ranged from 3.82 - 5.95. The highest in sellur area houses in various habitats viz. tyre, mud pot, plastic pot, ranged from 20.94 - 23.08. The high human population plastic drum, metal drum, cement cistern, cement tank, density leads to the exposure of people which results in grinding stone, drain, pond, rocky hole, irrigation low mosquito house index. Distances between residences channel, paddy fields, stream slow flowing and bamboo may also be considerable epidemiological significance, stumps etc. To identify the prevalence of especially in areas with single – storey dwellings. The Dengue/Chikungunya, filarial and other vector species in House index of the study area is given in Table. 4. Managing Health Geospatially 19

Figure 6: Vectors Abundance Map life style especially land use / land cover practices will create more vector habitats and receives variety of vector Table 4: House Index for Vectors Surveillance born diseases in future. The available health facility in the study area is insufficient compare to the population. No.of houses House Also local the existing poor local transportation, sanitation, facilities are the major reason for increasing Hamlets Examined Positive index diseases. Further remote sensing provides exact information‟s about vector habitats with in the forest Azhagur 8 1 12.50 area. GIS helps us to process the data to prepare disease Kullampatti 10 2 20.00 control and management strategies. Mullai Nagar 5 0 0.00 Nochikuttai 66 7 10.61 REFERENCE Selur 20 5 25.00 C. Jeganathan I, S A Khan Z, Ramesh Chandra 3, H Pereri 48 1 2.08 Pudhur 52 3 5.77 Singh 4 , V Srivastava I AndP L N Raju I 2001. S.Ammapalayam 15 2 13.33 Characterization of malaria Vector habitats Using Sakkampatti 6 0 0.00 Remote Sensing and GIS. Journal of the Indian Shanmugapuram 16 2 12.50 Society of Remote Sensing, Vol.29, No.1&2. Sitheri 64 8 12.50 S.J. Connor, M.C. Thomson, S. Flasse, and J.B. Williams Sulakurichi 20 2 10.00 The use of Low-Cost Remote Sensing and GIS for Suriyakadai 22 3 13.64 Identifying and Monitoring the Environmental Thadhukkana Halli 18 2 11.11 Factors Associated with Vector–Borne Diseases Thekkalpatti 10 0 0.00 Transmission. V.Naduvalavu 5 0 0.00 Srivastava A., Nagpal, B.N., Saxena R. and Subbarao, Velampalli 5 1 20.00 S.K. (2001). Predictive habitat modelling for forest 390 39 10.00 malaria vector species An.dirus in India - A GIS Total based approach, Current science, 80(9): 1129-1134. Connor, S.J., Thomson, M.C. and Molyneux, D.H. (1999). Forecasting and prevention of epidemic malaria - new perspectives on an old problem. Parasitilogia, 41:439 – 448. Hay, S.I., Packer, M.J., and Rogers, D.J. (1997). The impact of remote sensing on the study and control of invertebrate intermediate hosts and vectors for disease. Int. J. Remote Sensing, 18(14): 2899-2930. www.tribalhealth.org. www.tn.gov.in www.icmr.nic.in www.censusindia.net 4. 8 Parasitological Survey Night human blood survey was carried out in all the 9 hamlets, for filariasis. A total of 420 blood smears were collected within population of 3191 in different age groups and both sexes. None of the smear was positive for the filarial infections in 2010. But in upcoming years, the increase of vector habitats results from the increase of settlements may leads to considerable positive results. 5. CONCLUSION The present study reveals that the tribal people have no awareness about the health and sanitation process. Their 20 Managing Health Geospatially

THE APPLICATION OF GIS TO IDENTIFY THE RISK AREA OF PULMONRRY TUBERCULOSIS AMPHOE SELAPHUM, ROI-ET, THAILAND Wijitra Buttama1, Wutjanun Muttitanon2 1Technology of Information System Management, Faculty of Engineering Mahidol University 25/25 Phuttamonthon 4 Road, Salaya, Nakornpathom 73170, THAILAND, 2Department of Civil Engineering, Faculty of Engineering Mahidol University 25/25 Phuttamonthon 4 Road, Salaya, Nakornpathom 73170, THAILAND E-mail: [email protected], [email protected] ABSTRACT Pulmonary Tuberculosis (TB) is a contagious disease and is a major public health problem. The Tuberculosis was the cause of the problem in many countries around the world due to spread of AIDS. Thailand is one of the 22 countries that WHO determine whether a country has the highest tuberculosis outbreak and Thailand ranked 17th out of 22 countries. In 2010 have patients all of 120,000 and had a death rate 13,000 /year and 44,000 of all patients were spread period. The Ministry of Health announced that the tuberculosis situation is urgent need to resolve. The Northeast of Thailand has spread of tuberculosis is ranked 2nd from the North and Roi- ET province largest number of patients at 3 of the Northeast. Amphoe Selaphum in Roi Et, in past 10 years, found that the number of TB patients is increasing, because of the staff can‟t analyze the height risk areas of disease and to find people at risk of disease is necessary. The study was application of geographical information system and analysis of height risk areas of tuberculosis. Using the Delphi technique to identify risk factors and incidence rate of tuberculosis as measure the spread of the disease by using technique Getis-Ord Gi *. KEY WORDS: Pulmonary Tuberculosis, Getis-Ord Gi *, GIS, Spread area, Risk area. 1. INTRODUCTION 140 -160 cases/year, and 227 cases in 2007. There were 1.1 General Introduction 87 sputum positive patients and 153 sputum positive TB is a chronic disease that a major public health patients in 200. In 2009 there were two Multi Drug problem. AIDS, poverty and migration are difficult of Resistant patients (MDR) and 13.33%. TB sputum growth technology. Cause epidemic of Tuberculosis on positive missing patient (Selaphum hospital; 2009) the situation will be even more intense and more. World Health Organization (WHO) was declared that „„TB is a By modelling the spatial nature of epidemiological data, global emergency in the world ‟‟ and the strategies it has been found that cases of disease tend to congregate required DOTS (Directly Observed Treatment Short at particular locations (Z. Munch et al; 2003). The Course) to control. WHO reported 1 in 3 people in the implementing Geographic Information System (GIS) is to worlds were infectious and prevalence rate was 14.6 support decision making and planning problems million. The incident rate was 8.8 million in 2005, half of correctly, to aware of the factors affecting the spread of patient have spreading were in the developing country. disease, can anticipate transmission and outbreaks (hot spots) of disease and to identity the risk areas. WHO estimated that there are 40,000 positive sputum (equivalent to 63 per 100,000 populations). Department 1.2 Objective of disease control, Ministry of Public Health report that The objective was to use exploratory disease mapping to patient with TB 30 - 40% in 2005 were TB patient in determine whether distinguishable spatial patterns could Thailand total 58,691. The new sputum positive patients be found in the distribution of tuberculosis patients in a were 30,109, recurrence patients 1,784, TB sputum high incidence rate area and using Geographic negative patients 19,167, and TB outside the lung patients Information System (GIS) to identify risk area of 7,631 (WHO; 2007). tuberculosis. Specific aims were to investigate whether risk factors such as by Delphi technique. The analysis of The death rate was 1.7 million/year in the developing the spread of the disease (Hot Spot Analysis) and spatial country. The World Health Organization (WHO) has patterns in specific areas. Illustrate the distribution of ranked the first 22 countries with serious TB problem spatial data in formats specific to penetrate the area. used 80% of patient live in country. Thailand is ranking in 18th among 22 countries of the world. 2. DATA 2.1 Population The situation TB in Amphoe Selaphum Roi – Et in past The population studied included all residents of Amphoe six year (2001- 2006), TB patient were registered about Selaphum, Roi- Et. These adjacent suburbs covering a Managing Health Geospatially 21

total surface area 792.338 km2, density of populations Gi* = Z-score at position i 152.58 km2, have a population of 120,896 (2010 census), n = Weighted point of area associate 18 tambol , 187 villages and are served by two primary health care clinics. All the houses have running water and wij = Spatial weight matrix of position at j electricity. In the households is there a person with an d = distance of position i income, mostly famer workers. The average household size is 3–5 people, and the average household income xj = relationship with the surrounding areas was 35,000 baths per year. Wi* = total of weight S = Standard deviation at position of data S1i* = summary of Squared weight Property boundary data stored in a computer based Calculate the incidence rate of tuberculosis cases per geographical information system (GIS) were integrated 100,000 populations in 2006 - 2010, then linked with the with clinical data of tuberculosis patients. ArcGIS9.3 was map data in Amphoe Selaphum. used to spatially join properties to their appropriate enumerator areas within census districts and saved in Incidence rate of TB = Number of TB patients100000 shape files. The enumerator area boundaries used for the Population 2010 population census were superimposed on the property boundary coverage To identify and calculate the distance (d), using Getis- Ord Gi * tools, and Calculate Distance Band from 2.2 Cases Neighbor count in Arc Toolbox (ArcGIS), to determine As part of an ongoing study, information is being the distance of the area around each district. collected on all cases treated for tuberculosis in this area Amphoe Selaphum. Patients included in the study were Analysis Hot Spot, using Hot Spot Analysis tool (Getis- tuberculosis cases, confirmed by positive sputum smear Ord Gi *) in Arc Toolbox, to find the Hot Spot or Cold and/or chest x-ray of the study area and started on Spot of the area, to show the areas with high morbidity treatment between January 2006 and December 2010. For rate or low morbidity rate of tuberculosis patients form each patient number and clinical data were entered into z-score. Group z-score value uses the Z-score Rendering Microsoft Access 2007 and linked to the property in Arc Toolbox. location in the GIS. Analysis and Interpretation show the spatial data group of 2.3 Rates morbidity rate tuberculosis in 2006- 2010, if have spatial The case tuberculosis for each enumerator area was autocorrelation, result is the z-score is positive or calculated by mapping the address of every selected negative. patient (bacteriological positive) in a specific year to the exact address within the enumerator area and by 4. RESULT calculating the case (Incidence rate) per 100,000 The analysis of tuberculosis that occurred between 2006- population per enumerator area (Incidence rate is the 2010 Amphoe Selaphum using Getis-Ord Gi *. To study number of cases per 100 000) distribution pattern of tuberculosis form of disease with high or low average. The distance d is equal to 27,339 m 3. METHODS covering an area of TB disease in amphoe. The results tambol in form z-score if there is spatial autocorrelation 3.1 Spatial Statistics will be positive or negative. The results were analyzed in 2006 found Z-score that is Spatial statistic has been used extensively to study the worth more than 2 standard diviations printhead includes 2 tambol Wang Luang, Khwan Muean ,show in figure 1. correlation of the rate and geographical location. It can The results were analyzed in 2007 found tambols and show a group of diseases and health development of more than 2 standard diviations have 3 tamblos, Khwan Muean, Klang, Mueang Phrai, show in Figure 2. spatial relationships. The analysis of the spread of the The results were analyzed in 2008 found tambols and disease (Hot Spot Analysis) and spatial patterns in more than 2 standard diviations have 2 tamblos, Khwan Muean, Mueang Phrai, show in Figure 3. specific areas. The determine relationship of morbidity The results were analyzed in 2009 found tambols and rate and location or situation analysis of TB disease in the more than 2 standard diviations have 2 tamblos, Wang Luang, Lao Noi, show in Figure 4. community and the distribution pattern of tuberculosis, The results were analyzed in 2010 found tambols and consists of 5 sections. The hot spot analysis and statistical more than 1 to 2 standard diviations have 3 tamblos, Si Wilai ,Wang Luang, Lao Noi, show in Figure 5. techniques Getis-Ord Gi *, it development of cooperation Getis and Ord Gi* (d) = n  wij (d )x j  Wi* x j    S nS1*i  Wi*2 / n  1 1/ 2 n n xj and x j2 x = j1 j 1  S=  x2 n n where 22 Managing Health Geospatially

Figure: 1 hot spot (Getis- OrdGi*) 2006. Figure 5: hot spot (Getis- OrdGi*) 2010. Figure 2: hot spot (Getis- OrdGi*) 2007. Figure 3: hot spot (Getis- OrdGi*) 2008. 5. Conclusion Figure 4: hot spot (Getis- OrdGi*) 2009. When analyzing the distribution pattern of tuberculosis and spatial statistics. The hot spot based on the distance of each amphoe there incidence rate, shows spatial group, central point of TB disease each year. Found that each year there are spatial differences and relationship of the area around it. The representation of the spread of diseases that affect it. This shows that the patterns of point that represent the area consist incidence rate of TB disease. The incidence rate of TB disease found every year or every two years. The results indicate of hot spot is higher than the average statistical significance of each year in amphoe. The result analysis makes it possible to control the area in a timely and constraints of accordance with the personnel and budget. This corresponds to Tobler‟ s first law of geography “ everything is related to near something more than a relationship that is far ”. REFERENCES WHO, 2007a. Management of Tuberculosis for the inevitable in Global defense against the infectious disease, 2009. Tuberculosis Control Office. Public Health Office, A guide for health workers, Thailand; 2007. .Patterson K. and Pyle G., 1991. The geography and mortality of the 1918 influenza pandemic. Bull Hist Med, 65: 4–21. Johnson NPAS and Mueller J., 2002. Global mortality of the 1918-1920 “Spanish influenza pandemic”. Bull Hist Med, 76:105–15. Chan-yeung M, et al; 2005, Socio-demographic and geographic indicators and distribution of tuberculosis in Hong Kong: a spatial analysis. Int J Tuberc Lung Dis, 9(12):1320-6. Lara M. Jacobson, et al ; 2005, Changes in the geographical distribution of tuberculosis patients in Veracruz, Mexico, after reinforcement of a tuberculosis control programmes. Tropical Medicine and International Health, Apr;10(4): 305-311. Sato M., 2009. Pandemics of influenza, Center for Influenza Virus Research, National Institute of Infectious Diseases (NIID), Japan. Managing Health Geospatially 23

Bureau of Epidemiology, 2007. Annual Epidemiological Johnson A.I., Pettersson C.B., Fulton J.L., 1992. surveillance Report. Department of Disease Control, Geographic Information Systems (GIS) and mapping- Ministry of Public Health, 1:80-82. practices and standards. American society for testing and materials, Philadelphia (PA): n.p. Department of Epidermiology, 2010a. Epidemiological surveillance report, Roi- ET Provincial Public Health Melnick AL., Fleming DW., 1999. Modern geographic Office. information system promise and pitfalls. Journal of Public Health Management Practice. 5(2):13-10. Thai Junior Encyclopedia Project (TJE), Vol.4, NO. 6. http://kanchanapisek.or.th/kp6/New/sub/book/book.ph Anselin L., 1995. Local indicators of spatial association p?book=4&chap=6&page=chap6.htm (LISA). Geographical Analysis, Vol. 27, 2. 24 Managing Health Geospatially

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EFFECTIVE MONITORING AND SURVEILLANCE THROUGH GIS AT A PERI URBAN SITE OF PAKISTAN Momin Kazi, Murtaza Ali, Ayub Khan and DAE Anita Zaidi Aga Khan University, Karachi, Pakistan E-mail; [email protected] ABSTRACT Department of Paediatrics and Child Health, Aga Khan University, Pakistan has a community based study site, located at peri-urban area of Karachi. The total study catchment area is around 8.1 sq miles and a population of around 270,000. Annually around 10,000 pregnant women (PW) and 8,000 newborns (NB) are followed through active surveillance. As part of e-mapping the study catchment area which consists of around 50,000 structures has been digitized. The study area is divided into clusters/blocks of about 200 structures. Each structure has a unique ID correlated with its GPS coordinate. Information of each household is taken through baseline surveillance and updated on quarterly bases. Important landmarks and relevant study variables for example health facilities, TBAs, and diseases under surveillance are digitized. Detailed GIS maps are prepared (1) To facilitate the study team and CHWs in surveillance and patient care follow ups (2) Ongoing epidemiological studies data are mapped for monitoring and insuring that protocols are being implemented & (3) Different relationships are correlated to analyze epidemiological patterns. GIS generated map may facilitate from both epidemiological and monitoring aspects. Geospatial visualization helps in tracking ongoing surveillance and epidemiological studies and exploring different geospatial relationships. 28 Managing Health Geospatially

Technical Session-Viral Diseases Tuberculosis Prevention among Household Contacts in Surin, Thailand 30 Sumattana Glangkarn, Vorapoj Promasatayaprot, Jaruwat Sreekaew 33 37 Need for Intervention by using GIS as a tool for HIV/Aids Infection in the State of Rajasthan, India 43 Rashi Dadhich, Priyanka Jariwala, Sahil Chopra 49 52 Spatiotemporal Correlates of the Variation in Health Outcomes in the US during Infectious Disease 52 Outbreaks: A County-Level Risk Mapping for Optimal Resource Management 53 Loganathan Ponnambalam, Hao Ran Lee, Lakshminarayanan Samavedham Spatial Characteristics of the HIV Epidemic in Odessa Region, Ukraine Sergiy Pozdnyakov, Oleksandr Postnov, Nina Slavina, Tatyana Gerasimenko, Oleksandr Neduzhko Khana Coverage of HIV/Aids Response 2008-2010: Spatial Analysis Mengieng Ung,Heng Sopheab, Sovannary Tuot, Soksan Moeun Spatial Patterns of Tuberculosis in Sisaket Province, Thailand Siriwan Hassarangsee, Nitin K Tripathi Examining Spatial Patterns in the Distribution of Low Birth Weight in Southern India: the Role of Maternal, Socio-Economic and Environmental Factors Mark Rohit Francis, Rakesh P.S., Venkata Raghava Mohan, Vinohar Balraj, Kuryan George Risk Assessment of Measles Based on Bayesian Network Liao Yilan, Wu Jilei, Zheng Xiaoying Managing Health Geospatially 29

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