Health GIS - Enabling Health Spatially

Preface Dr. Nitin Kumar Tripathi Organising Secretary, HealthGIS 2009 Humans have evolved from the earth and are interwoven Director, Academic Quality Assurance and with its’ environment. If the earth and its’ environment are Accreditation (AQAA), AIT not healthy then naturally the human beings are endangered. Editor-in-Chief, International Journal of Scientists have confirmed the global warming due to green Geoinformatics house gases produced by rapid development and say it is Director, UNIGIS@AIT Centre irreversible even if best attempts are made for zero carbon Asian Institute of Technology society in next 100 year. In other words the generations to P.O. Box: 4, Klong Luang, Pathumthani come will always accuse us for the irreparable mistakes 12120. Thailand committed if we do not learn from it and do enough to Phone: +66-81751 8384 (Mobile) reduce the global warming and its’ impact. The reason, I (Office): +66-2-524 6392 emphasize the global warming is that it is causing changes Fax: +66-2-524 5597 in the climate and result in many emerging diseases. e-mail: [email protected], [email protected] Healthy earth is possible only if we monitor it regularly using satellite remote sensing data and analyzes it in GIS for sustainable development. Geo-information technologies have powerful tools for preventive and controlling measures towards immediate and long-term strategies for emerging as well as existing diseases. Using satellite data, maps, patients data and other ancillary data in GIS can provide us useful knowledge about hotspots of diseases, trends of diffusion, transmission and relationship with various causative factors. These can be very useful for public health department to control the disease and provide better healthcare. This year, the 3rd International Conference on healthGIS is being held in Hyderabad, India after the grand success of first two conferences in Bangkok, Thailand in 2005 and 2007. The theme “enabling health geospatially” has many promises if we can establish a dialog between geoinformation scientists and health sector experts. HealthGIS Conference was started in 2005 after the globe was shaken by SARS. This year it is being held amidst the pandemic of H1N1 (Swine Flu). Can we work together to have a fearless society from disease in future? People especially in the rural areas of the world have many wounds from shock of diseases and losing their loved ones. Let us do our duty to heal these wounds. Together we can… surely In the words of Michael Jackson, a legendary singer, who is

Foreword The Asian region has witnessed tremendous industrial development in the Professor Dr. Said Irandoust last decade and is contributing a major share in the global economy. This President has resulted in the betterment in the quality of life in many countries in this Asian Institute of Technology region. But, emerging diseases such as SARS, Avian Influenza (H5N1), and now Swine Flu (H1N1) have been posing significant challenges. Not only are they causing fear but also slowing down the economic growth due to constraints on travel. These diseases are serious and become uncontrollable if they escalate into a pandemic level. These diseases may emerge in one country and quickly spread to many others and take on a global dimension. Therefore, it is a challenge to find the causative factors and also its trend of diffusion to develop a strategy to control and prevent larger populations from being infected. It is a real challenge to save millions of lives from these dreaded epidemics. I am sure that the august participants in this very important meeting from public health departments and geoinformation technology will work together in developing solutions. This is the 3rd International conference on health GIS which is being held in India. The two prior to this were held in Bangkok, Thailand in 2005 and 2007 and have stimulated great interest and brought the medical and geoinformation community together. It is that interest and promise which has brought this conference to India where many challenges occur in the form of population, size of the country and remoteness of many areas from the big cities. I am sure that the tools of information technology, telemedicine and GIS will be very useful for policy makers and managers of health missions. We have to use these tools and for that preparing human resources will be a mega-task. AIT is involved in educating and training high-level professionals for the last 50 years. We would like to also extend our expertise to the health sector for accepting this challenge in the coming years. I welcome all the Indian and foreign delegates and wish a grand success to this conference and look forward to fruitful deliberations and outcome. Thank you

Preface Dr. Iyyanki V Murali Krishna PhD [IISc] FIE FIS FAPASc MIEEE Welcome to Health GIS 2009 delegates. How GIS helps in changing the Chairman Health GIS organizing Committee 2009 world. Today we are concerned and trying to address various issues related E-mail: [email protected] advances in technology and their relevance to the developmental activities being taken up various Governments or simply in Governance. Governance has many facets, many dimensions, many issues and many many things. It requires information from a variety of sources at different times with varied amounts of breadth and depth of information and linkages. Health sector is no exception and GIS is a very effective integrating tool suitable for such purposes. Health care planning, Urban transportation, electric power, telecommunications, water supply, land records, real estate, insurance, banking, police etc are the major themes that have relevance to good governance and shown great potential for geospatial modeling application. GIS use in public health and health care is constantly increasing. GIS has given new meaning to public health professionals who need to utilize the technology as part of their daily research activities. One of the significant steps take in this direction as found in literature is that during 2001-2002 a pilot project on the application of Geographic Information System (GIS) for analyzing and planning of Reproductive Health (RH) services was launched in Pattani, Thailand. With many available functions to view, understand, question, interpret, and visualize data researchers can utilize for disease surveillance, data collection, and data visualization. From the analysis, it showed that distances from TBAs and health centre had some influence on pregnant women. Because of the success of the pilot program, the GIS technology was implemented on large scale to develop health plans and help to manage other health concerns such as STD/HIV/AIDS protection, family planning, adolescent reproductive health, health education and epidemiology for common and rare disease control. Major companies like ESRI, MAPINFO, Bentely etc are constantly improving the Geospatial technology soft ware capabilities commensurate with the developments being taken place particularly with reference to large scale mapping and high resolution satellite data availability and applications in utilities mapping. The boundary between GIS technologies and main stream computing is completely blurred with all tools of open source and interoperability becoming semi operational . These technological developments are helping to go for high-performance dynamic map publishing and better sharing of Geographic Information. GIS is continually used in health research and promotion such as disease surveillance, community health projects, It is in this context the theme of 3 rd International Conference HEALTH GIS 2009 becomes very much significant in addressing all health related issues within geospatial environment. The Conference is supplemented by Technogy driven workshops on health GIS, UNIGIS, Spatial data infrastructure, digital governance and Gerontology. ALL these events have received continuous encouragement by all scientists, engineers, vendors and Governments involved in utilities mapping and infrastructure development. We look forward to a technical stimulating Health GIS 2009 event. It is indeed with great sense of privilege and pleasure I invite all the delegates to this conference.

Content c d Foreword e Organizing Secretary f Organizing Chairman President Message 1 23 Key Note Sessions 51 Technical Sessions 82 110 Technical Session 1 136 Technical Session 2 149 Technical Session 3 170 Technical Session 4 185 Technical Session 5 211 Technical Session 6 237 Technical Session 7 272 Technical Session 8 Technical Session 9 Poster Session Abstracts Author Index

Health Implication of Global Climate Change: A Still Debatable Subject? Global climate change has been made known to be a reality by Dr. Kaew Nualchawee scientists around the world. The global climate warming has Chairman been known to have been caused by human activity, especially Former Coordinator the burning of fossil fuels. The recent scientific evidence Geographic Information Systems indicated that as the earth gets warmer, the delicate balance of Asian Institute of Technology climate, weather events and human life is interrupted which Faculty of Geography would threaten human health and, ultimately human survival. Burapha University Thailand Another source of information has been quoted as saying it was E-mail: [email protected] evidence that inter-annual and inter-decadal climate variability have a direct influence on the epidemiology of vector-borne diseases. This evidence has been assessed at the continental level in order to determine the possible consequences of the expected future climate change. It was estimated for the next century (by 2100) that average global temperatures will have risen by 1.0-3.5 °C, increasing the likelihood of many vector- borne diseases in new areas. Certain vector-borne diseases were quoted for certain region of the world. However, health risks due to climatic changes will differ between countries that have developed health infrastructures and those that do not. Climatic anomalies associated with the El Nino-Southern Oscillation phenomena and resulting in drought and floods are expected to increase in frequency and intensity. Climate change has far-reaching consequences and touches on all life- supporting systems. It is therefore a factor that should be placed high among those that affect human health and survival. Another study of similar subject indicated that the subject is of most important societal concern and is extremely complex. The study finally went on to say that in light of the many complex phenomena that concurrently impact the spread of vector-borne diseases, it is clearly unjustified to claim that any future warming of the globe will necessarily lead to a net increase in the global incidence of vector-borne human diseases, for just the opposite could be true, depending on the type and degree of a number of current and potential societal impacts on the world of future, as well as the diverse nature of the evolving states of the planet’s multiple human societies.

The Role of GIS in an Integrated Approach to the Understanding of Infectious Diseases The fight against infectious diseases can not be Dr. Marc Souris reduced to the pathogen or the therapeutic Directeur de Recherche responses. The health risk is always IRD-UAM2, UMR 190, site de Thailande multifactorial, and some factors depend on the e-mail: [email protected] individual, much depends on the context in website: www.rsgis.ait.ac.th/~souris which he lives, both directly (through contact tel. (+66) 87 900 00 84 with other individuals) or indirectly (through its relationship with its natural environment or social, which influence its behavior). An integrated approach aims to assess the many factors to model the whole system of the disease, at different scale (from the pathogen to the environment) and to assess the health risk by computer simulation. Geographic information systems offer a number of geostatistical tools for analyzing observed endemic or epidemic situations. They must now develop modeling tools to assess the health risk, integrating analytical and geographical approach.

Geospatial Technologies for Disease Mapping and Epidemiology Epidemiologists, public health administrators, certified Dr Iyyanki V Murali Krishna environmental hygienists and other public health professionals are M Tech (IIT-Chennai), PhD(IISc), FIE, FIS, exploring the potential of Geospatial technology to map out the FAPASc, FICDM, MIEEE spatial distribution of various diseases and its variation over space Saraswati Samrajyam, Plot 8, Lakshmi nagar and in time. Epidemiology is concerned with describing and Mehdipatnam Ring Road, Near PV Express explaining the incidence of disease. It follows that geospatial Way to Airport Flyover Pillar No 67 epidemiology requires methods that will provide good descriptions of HYDERABAD 500028, AP, India, the spatial incidence of disease, together with methods that offer the Ph: 91- (040) 2352 0000 / 6458 9624 prospect of modeling such incidence. The tools for visualizing, Mobile +91- 984 804 9624 exploring and modeling the geographical incidence of disease need to E -mail: [email protected] , [email protected] be identified and customized for operational implementation. Relatively few applications focus specifically on the linking of health databases to those on environment. Geospatial technology and information system have emerged as a major tool for public health professionals to track the status and distribution of health indicators. Disease mapping helps to delineate diseases in the form of clusters which may be suspected when people report that several family members, friends, neighbors, coworkers or community members have been diagnosed with the same illness. Some significant disease clusters include the initial cases of a rare pneumonia among homosexual men in the early 1980s that led to the identification of the human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS). There are several challenges in investigating disease clusters. Epidemiologists should carry out geospatial analysis to make sure that the suspected cluster they are investigating involves one disease. The paper addresses the issues mentioned above through analyses of Indian case studies. The geospatial analysis studies helped to map the spatial variation in the occurrence of the disease and also carry out factor analysis with the help of multivariate statistical techniques. These are socio-economic, epidemiological, socio-cultural factors to identify the major dimensions. The factor scores derived from these dimensions drawn are expected to explain the significance of spatial pattern of variation in the occurrence. In a study related to malaria, GIS tool is used to analyze on priority the far edge residents for presumptive treatment to control the spread of malaria. The edge definition technique identified here helped identification of far edges of various orders.. The polygon regions represent houses with people affected by Plasmodium Vivax and houses with people affected by Plasmodium Falciparum. First, GIS tools used for the generation of the adjacency matrix that is key to doing any spatial statistical analysis. The matrix provided scope for querying the neighbors of an area or selected areas. The GIS tool is used to select one or more areas in a layer to determine the neighbors of area of interest so as to query the neighbors of areas in an interactive and visual environment to determine patterns and topological adjacency. The analyses showed that GIS has proven “value added” for targeting public health interventions, identifying study cohorts and mapping disease patterns. Of course there are few basic issues on understanding the patterns clusters generated for diseases to formulate and test epidemiological hypotheses using GIS? This study attempts to place GIS and disease clustering techniques within the context of a systematic approach for formulating and testing epidemiological hypotheses and to advance epidemiological science by increasing our understanding of disease etiology.

Sustainable Health and Well-Being: Persistent GIS GIS continues to make major contributions Bill Davenhall around the globe in efforts to improve disease Health & Human Services Solutions Manager surveillance, reduce health disparities, ESRI Inc. understand the links between human health CA, USA and the environment, and improve access to Tel: +1 (909)793-2853, x2878 health and human services. While every nation Fax: +1 (909)307-3072 is faced with unprecedented challenges to Website: www.esri.com ensure affordable health care for everyone, E-mail: [email protected] you will learn how GIS is contributing to reduce health care costs, improve quality of care, and assure accessibility to services. Learn how health and human service organizations are addressing their greatest challenges with GIS technology. The presentation will conclude with several forward-thinking ideas that can accelerate the wider use of GIS in the health and human services community.

GeoMedStat: A GIS-based Integrated Surveillance System to Track Pollution and Pollution-Related Diseases Environmental air quality has a major impact on Professor Fazlay S. Faruque human health. One example is the effect of air Director of GIS and Remote Sensing and pollutants on asthma and other respiratory Professor of Health Systems diseases. An integrated surveillance system, that The University of Mississippi Medical Center tracks and correlates air pollution with pollution- USA related disease incidence, can assist in risk E-mail: [email protected] assessment, public awareness and healthcare preparedness. The University of Mississippi Medical Center (UMMC) has developed GeoMedStat, an evolving surveillance system capable of tracking, mapping and analyzing both real-time and historical patient encounters along with environmental pollution data. GeoMedStat utilizes links with hospital information systems to allow access to both chief complaints and International Classification of Diseases (ICD9) codes of patient encounters. GeoMedStat utilizes NASA satellite data and EPA ground-monitor data as inputs for surface modeling for air pollution. The two types of air pollutants included in this project are: particulates with a diameter less than or equal to 2.5 microns (PM2.5) and Ozone (O3). This presentation will delineate the different components and functionality of GeoMedStat.

Fractal Analysis of Temporal Concentrations of PM1, PM2.5 and PM10 and Meteorological Condition at a Mountain Site, Korea for Springtime Yellow Sand Event, 2004 Hourly concentrations of PM10, PM2.5 and PM1 particulate matter Professor Hyo Choi were investigated by measuring mass concentrations per particle Dept. of Atmospheric Environmental Sciences size of 0.3 μm to 20 μm using a GRIMM aerosol sampler at College of Natural Sciences mountain site (Mt. Taegualyang; 896m) in the eastern coast of Kangnung-Wonju National University Korean peninsula from March 26 to April 4, 2004. Before the Gangneung, Gangwondo 210-702, Korea influence of dust transported from China to the mountain site until Tel: +82-10-7240-0357, +82-33-652-0356 March 29, PM10, PM2.5 and PM1 concentrations near the ground of Fax: +82-33-652-0356 the city were very low and their maximum values of each PM E-mail: [email protected] concentrations were 72.33 μg m-3, 41.00 μg m-3, 35.33 μg m-3, Web: http://atmos.kangnung.ac.kr/ch respectively. The mimimum values of PM concentrations 12.53 μg m-3, 6.75 μg m-3, 5.82 μg m-3. On the other hand, from on March 30 http://www.nukw.ac.kr through April 2, when the large amount of dust from China passed over the mountain of Korea under a westerly wind influence, PM10, PM2.5 and PM1 concentrations reached 238.87 μg m-3, 46.50 μg m-3, 30.25 μg m-3, while their minima were 41.68 μg m-3, 7.17 μg m-3, 2.77 μg m-3, indicating their maxima being at least five times larger than their minima. PM 2.5 and PM1 concentrations were not much changed regardless before or during the period of dust. The fractions of PM10-PM2.5/PM2.5 and PM2.5-PM1/PM1 before, during and after dust storm, showing 5 ~ 6 times higher of coarse particulate and 3 times higher of fine particulate. Correlation coefficients of PM2.5 to PM10, PM1 to PM2.5 and PM1 to PM10 before the dust storm period were 0.76, 1.00 and 0.71. The coefficients during the dust storm period were 0.64, 0.82 and 0.14. The coefficients after the dust storm period were 0.73, 0.99 and 0.69. For whole period, correlation coefficients of PM10, PM2.5 and PM1 were 0.45, 0.86 and -0.03, respectively. During the Yellow Sand period, abrupt high concentrations of PM were detected under a special meteorological conditions such as the shrunken of daytime convective atmospheric boundary layer with upper inversion layer and also the more shrunken of nighttime surface inversion layer. Meteorological situation influenced upon the high concentration of PM was investigated by a three- numerical modeling simulation using WRF-2.2 model. Further consideration was given to the analysis of the transportation of dust to Korean peninsula during the Yellow Sand Event using by GOES-9 DCD satellite images and NOAA back trajectories. This work was funded by Korea Research Foundation under Grant –“Comparison of temporal high concentrations of particulate matters (PM) to the concentrations of gases (CO, NOx) during duststorm period in 2007-2008 year-KRF-2007-313-C00777”.

Technical Session -1 IT and Health Web Based GIS for Visualizing and Analyzing Epidemical Data of Tumkur District Hospital B. G. Premasudha, Patil S. D. S and B. Suryanarayana Adiga ………………..……………………………2 Integrated Spatio-Temporal Framework for Realization of an Mobile Health Management System Arindam Dasgupta, Indira Mukherjee and S K Ghosh……………. ……………………………………….6 Health Service Offering System using GIS Based Smart Cards Ehsan Khayambashi, Hamidreza Rezaei and Hajar Namaz………………..………………………………12 Mobile Data Management for Integrated Medical Care Services S. R. Balasundaram and Saravanan Appu……………………………………………..…………………....16 Mapping the Poor and Technology Enabled Micro Planning For Improved Health and Basic Services Yogesh Kale, Anuprit Minhas and Tamara Failor………………………………………………………….20 Geospatial Kerala Health Information System (GKHIS) Suresh Francis and Bindu. P Ramankutty………………………………………………………….………21 GIS: A Tool in Road Traffic Injury Research Mohan Venkata Raghav, Rajiv Sarkar, Vinod Joseph Abraham and Vinohar Balraj……….……………22

WEB BASED GIS FOR VISUALIZING AND ANALYZING EPIDEMICAL DATA OF TUMKUR DISTRICT HOSPITAL B.G. Premasudha1, Patil S.D.S2 and B.Suryanarayana Adiga 3 1Research Scholar, Dr.MGR University, Chennai. [email protected] 26th SEM, MCA, Siddaganga Institute of Technology, Tumkur. E-mail: [email protected] 3Consultant supervisor,T CS, Bangalore, India ABSTRACT: Disease data analysis and sharing is important for the collaborative preparation, response, and recovery stages of disease control. Disease phenomena are strongly associated with spatial factors. Web-based Geographical Information Systems (WGIS) provide a real-time and dynamic way to represent disease information on maps. WGIS provides excellent means for visualizing and analyzing epidemiological data, revealing trends, dependencies and inter- relationships. GIS serves as a common platform for convergence of multi-disease surveillance activities. This paper focuses on presenting a WGIS application created for studying distribution of malaria patients in Tumkur city, India. The application covers two main epidemiological issues: (i) defining the spatial distribution of malaria patients; and (ii) modelling spatial variation of malaria in the city. This study is useful to find out patient distribution, patient data classifications and accessibility to hospital. The spatial data infrastructure can enhance the efficiency and effectiveness of public health surveillance for India in district levels. KEY WORDS: Web-GIS, Thematic mapping, surveillance, epidemics. 1. INTRODUCTION GIS functions and tools are identified and divided into data-exploratory and data-modelling functions. This Currently, the factors such as increasing population, paper covers two main epidemiological issues: (i) environmental pollution, rapid Urbanization and global defining the spatial distribution of malaria patients and warming all influence the conditions for disease (ii) modelling spatial variation of malaria in the city. In outbreaks in India. Disease studies have revealed strong addition, a WGIS geostatistical analysis technique is spatial aspects, including disease case location and used to model the spread of malaria patients at Tumkur disease diffusion. Thus, mapping spatial aspects of city. WGIS is an application for map representation of diseases could help people understand some puzzles of the spatial data via the Internet (Benneyan JC, et.al, disease outbreak (Gupta R, Shriram R, 2004). Unlike the 2000). The WGIS provides us the interactive mapping raw disease data, disease maps offer a visual means of analysis which enables the user to visualize predefined identifying cause and effect relationships existing maps of selected indicators in order to study the spatial between humans and their environment. Disease maps distribution of a phenomenon for information purposes. can enable decision makers to make their work easier. This helps the user to view and control the map features Geographical Information Systems (GIS) provide an directly through the internet. The Figure 1 shows the effective way of managing, storing, analyzing, and Web representation of Tumkur city map. mapping disease information. GIS has strong capabilities in mapping and analyzing spatial and non- Figure 1: Web Display of Tumkur City Map. spatial data, GIS could represent disease information rapidly and analyze the disease's spread dynamically. Meanwhile, the rapid development of the Internet influences the popularity of Web based GIS, which itself shows great potential for the sharing of disease information through distributed networks. Distributing and sharing disease maps via the Web could help decision makers across health jurisdictions and authorities collaborate in preventing, controlling and responding to a specific disease outbreak. Spatial epidemiology is considered one of the main GIS uses in healthcare research (Lang, Laura, 1999). Several worldwide cases are presented for the purpose of identifying the potentials and benefits of using GIS in epidemiology (Boulos MN, 2004). The relevant

2. METHODS pitals and patients. All these data are linked to the de- mand coverage and used for the second issue of this ap- 2.1 Need of study plication which is related to classification of spatial vari- ations of malaria locations by knowing distribution of Tumkur is one of 57 cities in Karnataka. Tumkur is the patients in city by using overlay analysis. Overlay analy- headquarters of the district of the same name. It is sis manipulates the spatial data organized in different located to the North West of Bangalore at a distance of layers to create combined spatial features according to 70km.City is in close proximity to Bangalore and has a logical condition specified in Boolean Algebra.(Roovali, decadal growth of about 40% over the last few decades. R. Kiivet,1997).The logical conditions are specified More than 15 Districts should pass through Tumkur to with operands (data elements) and operators (relation- reach Capital city Bangalore. The presented study has ships among data elements). The most well used overall selected government District hospital. This hospital has functions are called union, intersect and identity. This a capacity of 300 beds and 70 doctors working at overlay function creates a new output coverage with the different departments including family medicine, display of thematic maps, that has city wards that falls gynecology and pediatric department. It is located inside hospital service area and patient data. The pa- almost at the centre of the city but expected to serve tient’s data are classified into different groups according most parts of city districts. to their age and sex to present it on thematic mapping District hospital collects data of patients from all the and graphs to find out disease outbreak of malaria in PHC’s once per week, organizes them and prepares the Tumkur city and this data is visualized on internet web final patient’s data for analysis. In this study, we are pages by using MapXtreme java(6). representing the outbreak of Malaria at this hospital. We are concentrating only on malaria patients data of Six 3. RESULTS AND DISCUSSIONS months from January/2008 to June/2008. The data for infectious disease mapping used in this study includes 3.1 Using GIS on Tumkur District Hospital: the above disease data and the geometric boundary data of Tumkur city. The infectious disease data for Tumkur Health facilities in general and hospitals in particular are represented by the hospital discharge data records at are faced with different challenges related to their Tumkur District Hospital. locations, their market service areas and their demand In present scenario finding out the outbreaks of disease status (L. Roovali, R. Kiivet. 1997). This part of the in hospitals needs more manpower and consumes more paper presents a GIS application that is created for time. For decision makers, they need to collect all District Hospital of Tumkur. The application is designed patients data from all the hospitals, sort them out to find to be as a spatial decision support system for defining out factors such as, which disease is out breaking the spatial distribution of malaria patients. Using GIS in currently?, what may be the reason for the outbreak?, health care planning studies is well acknowledged by the what action should be taken to overcome that problem?. western European researchers and it is used for various This process is very much time consuming. Hence the health care issues at the developed countries. However, WGIS helps us to predict the factors such as catchments in India this technology is still not very well explored by of patient’s location towards the infectious diseases. We health authorities and researchers. Therefore, the created can also categorize patients according to their area, sex, application provides a good example for explaining how age etc; it also provides thematic shading of maps which to use GIS by health planners and officers in Tumkur helps us in analysis. Web-GIS make the online analysis and/or in any other developing cities. more interactive in right time. 3.2 Spatial distribution of malaria Patients 2.2 Research issues and analysis techniques The Tumkur district hospital has a database about its GIS has several techniques and functions that can be existing patient and saves such data in different Manage- used for health service planning. Each one of these func- ment Information Systems (MIS). These systems are tions can be applied on different health related issues. used for finding needed information about patient num- This study has selected two major hospital planning ber or recording file and for reviewing the medical his- issues and uses GIS for analyzing these issues. The first tory of every patient. One of the main issues related to issue is related to defining the spatial distribution of spatial distribution is regarding defining patients loca- malaria patients by using on-screen digitization. This is tion within the city. A GIS function called Geocoding used by the presented study to capture and define health can be used to create points features on a map from a ta- demand location at Tumkur city. The geometric bound- ble having x, y coordinates of any addresses. The pre- ary data of Tumkur city has been collected from the Mu- sented study has used on-Screen digitizing method for nicipal Corporation of Tumkur city. The collected data the purpose of identifying hospital and patient location. included the ward wise population data, road maps and Based on the collected data, hospital and patient data the hospital information. The road map and the ward GIS coverage is created with their geographical loca- map was geo-referenced using the survey of India tions and then the attributes data of health details are en- Toposheet. The maps were then digitized using Map- tered as records in the coverage table. After building the info. Then a point feature layer was created for the hos-

database of health details, the next step was to use GIS for identifying spatial distribution of malaria patients. This step is achieved by using the graduated color func- tion that subdivides numerical data into a set of classes. The presented study has used the data classification in natural breaks method that minimizes the variance within class and maximizes the variance between classes (Roovali, R. Kiivet,1997). Figure 1 shows the resulted patient distribution for Tumkur District hospital. Figure 4: Ward wise Thematic map representing male patients of age greater than 5 years. Figure 2: Spatial distribution of the patients from Gov- Figure 5: Ward wise Thematic map representing female ernment District hospital of Tumkur city. patients of age less than 5 years. One of the main results of this distribution is most of the Figure 6: Ward wise Thematic map representing female malaria patients come from the ward number 12, where patients of age greater than 5 years. the city slum is situated. The less number of malaria patients are seen from high income residential areas and rural areas of the city. 3.3 Spatial variation of malaria in the city All malaria patients data is divided into four groups. Figure 3 shows the thematic representation of male patients of age less than 5 years in which we can find that, there are more patients of this category in ward number 12 which is shaded in blue colour. Figure 4 shows the thematic representation of male patients of age greater than 5 years in which we can find that, there are more patients of this category in same ward. Figure 5 shows the thematic representation of female patients of age less than 5 years in which we can find that the outbreak of this category is also occurred from the same ward. Figure 6 shows the thematic representation of female patients of age greater than 5 years which also represents the outbreak in same ward. Figure 3: Ward wise Thematic map representing male Figure 7: Graph representing ward wise patients data. patients of age less than 5 years. Figure 7 is representing the graph of malaria patients of all the above four groups. From this graph we can pre- dict that there are 6 male patients of age less than 5 years, 11 male patients of age greater than 5 years, 4 fe- male patients of age less than 5 years and 10 female pa- tients of age greater than 5 years in ward number 12

which is considered as an outbreak in their particular categories respectively. Combining all the groups 31 pa- tients were belong to ward number 12 which is consid- ered as an outbreak area. The cause for the disease out break in that area may be evaluated further as future en- hancement. 4. CONCLUSION This paper discusses a GIS application for hospital facility planning in Tumkur city. The application covers two main hospital issues that are distribution of patients in city and spatial variation of malaria in the city. Each one of these issues has a direct spatial dimension. Therefore, the use of WGIS for analyzing and manipulating them was of greater value and benefit. WGIS is used to define all patients’ location and produces an output showing Malaria outbreak with spatial variation around the city. This output can be used by health planners to define the real catchments of health facilities. By studying the Figure 6 and 7 we can conclude that, the blue coloured area which is located at the centre of the city is the area where the malaria outbreak has occurred. This output is used for further studies to define the causes of the occurrences of malarial. REFERENCES 1. Benneyan JC, Satz D, Flowers, SH: Development of a Web-based multifacility healthcare surveillance information system. J.Healthc.Inf.Manag. 2000. 2. Boulos MN: Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in United Kingdom. International Journal of Health Geographics 2004. 3. Gupta R, Shriram R: Disease surveillance and monitoring using GIS. 7th Annual international conference Map India 2004. 4. H. Jordan, P. Roderick, D. Martin, S. Barnett, Dis- tance, rurality, and the need for care: access to health services in South West England, Int. J. Health Geography. 2004. 5. http://reference.mapinfo.com/common/docs/ mapxtreme-java_edition-3.1-readme-pdf-none-eng/ readme.pdf 6. Lang, Laura, 1999, “GIS for health organization”, Environmental System Research Institute, Inc. 7. L. Roovali, R. Kiivet, Geographical variations in hospital use in Estonia, Health & Place 2006. Y. Chou, Exploring Spatial Analysis in Geographic In- formation Systems, On Word Press, Santa Fe, NM, 1997.

INTEGRATED SPATIO-TEMPORAL FRAMEWORK FOR REALIZATION OF AN MOBILE HEALTH MANAGEMENT SYSTEM Arindam Dasgupta, Indira Mukherjee, S. K. Ghosh* School of Information Technology Indian Institute of Technology, Kharagpur 721302, India Email : [email protected] ; [email protected] ; [email protected] ABSTRACT: The need for timely monitoring and management of public health is increasing with easy availability of computational facilities and network connectivity. Further, with advancement of mobile communication technologies, it is possible to collect data from remote locations and access backend infrastructure. This has lead to the possibility of realization of a mobile public health management and monitoring system. Incidentally, the spread of epidemic diseases, a major concern of public health, are often related to geospatial conditional of the region and may also have some temporal dimension. Hence, there is a need to integrate health data with spatio-temporal geo-information for proper monitoring and decision making. The problem becomes complex as the health data has to be collected from the epidemic location and need to be integrated with other geospatial information which are often maintained by diverse organizations in a distributed manner. In this work, a mobile device driven spatio-temporal framework for public health monitoring and management has been proposed. The proposed system is based on service-oriented architecture and will help in visualization, analysis of epidemiological information and appropriate decision making. A three-tier architecture has been adopted. The top tier provides application interface to both mobile and desktop devices; where the health workers can upload information from various regions and health managers can access and visualize the integrated information for monitoring and decision making. The bottom layer is an Enterprise-GIS infrastructure which integrates various heterogeneous spatio- temporal geo-databases. The middle layer provides various spatial web services to connect the mobile devices/ desktop systems with the backend spatio-temporal database. The proposed framework will facilitate effective monitoring and management of public health. KEY WORDS: Mobile GIS, Health Management, Geo-Spatial Data, Spatial Web Service 1. INTRODUCTION correct form. Further, the health information may vary with time and space. Hence, there is a need With advancement of technology there is a of a spatio-temporal framework which will increasing need for timely monitoring and facilitate in timely availability of health data with management of public health. At present, most other co-related geospatial information. The of current public health information systems do integration of mobile technology, mobile not provide support for dynamic data uploading communication network, web services, at source, visualization, analyzing and decision geospatial data repositories and public health making. Moreover, geospatial information along disease information is expected to augment the with health related data are often required for analytical capabilities, decision making, health proper management. These data are usually huge monitoring and visualization process. Mobile in volume and dynamic in nature and usually device driven public health management system maintained by diverse organizations in a not only significantly supports the on time distributed manner. Due to the lack of decision making process but also enhances the coordination between the capturing and communication among the concerned decision- uploading the information, it may happen that makers. The objective is to provide strong health information collected at field is not support in logistics of public health management available for decision making in proper time or for the epidemic prevention planners, health workers, and other concerned supporting

organization for public health to improve the GML (Geographic Markup Language) is used as decision-making process and also the modeling language for describing geospatial communication among them. information. To visualize the GML a style sheet file, namely, Style Layer Descriptor (SLD) is GIS plays a critical role in public health care for used. epidemiologic studies, as in tracking the sources of diseases and its spread in the communities, so In this paper, a mobile device driven spatio- that the public health and associated department temporal framework for health management has can respond more effectively to outbreaks of been proposed. The proposed system is based on diseases with appropriate intervention measures. service-oriented architecture and will help in The map is the most famous and classical visualization, analysis of epidemiological visualization example in the field of medical information and appropriate decision making. A cartography (Anderson, 2003). The continuous three-tier architecture has been adopted. The top advancement of GIS technology and mobile tier provides application interface to both mobile computing with use of spatio-temporal analysis and desktop devices; where the user can upload leads to enhance the on time public health information from various regions and health monitoring process in context of epidemic managers can access and visualize the integrated disease control. A significant portion of the information for monitoring and decision making. research (Chittaro, 2006) is done for various The bottom layer is an Enterprise-GIS applications on mobile devices. Some of them infrastructure which integrates various have been focused on the development of heterogeneous spatio-temporal geo-databases. different spatial related applications such as The middle layer provides various spatial web location based service, context based map services to connect the mobile devices/ desktop visualization, tourist guide information system. systems with the backend spatio-temporal CyberGuide (Abowd, 2000), and CRUMPET database. (Poslad, 2001) are examples of this type of application. Kaplan et al. (Kaplan, 2005) The paper is organized in five sections. Section 2 integrate the GIS and GPS technology to solve describes the design methodology of the the location based GIS problem. Derekenaris et framework. In section 3, the overall architecture al. (Derekenaris, 2001) combined GIS, GPS and of system and the functionalities of the each GSM technologies to manage the routing subsystem are described. Section 4 demonstrates problem of ambulances. a case study to evaluate the process detecting and predicting the spreading of the epidemic disease The most important challenge for is stored various heterogeneous databases. development of mobile GIS application is limited processing power and hardware 2. DESIGN METHODOLOGY resources. Web service is the appropriate technology to compute the geospatial functions In order to access public health information on the remote servers. Hence, spatial web stored in different geographical locations and services may help in integration of spatial integrate/analyze/visualize it, an integrated repositories and health information. Such type of spatio-temporal framework has been attempted. information exchange between different supporting organizations with the public health The public health information, namely, the department may facilitate epidemic disease epidemic disease information, is maintained by control initiatives. the public health department. Typically, the Open Geospatial Consortium (OGC, 2003) has information may comprise of the disease type, its introduced specific kind of web services, to spatial location, severity, number of sufferers in overcome spatial heterogeneity and a location and other relevant disease information. interoperability problems. Web Feature Service The databases are often heterogeneous in nature (WFS) and Web Map Service (WMS) are the and may be distributed in different geographical most fundamental geospatial Web services. In area. A disease feature service for epidemic this context OGC web services framework disease has been developed based web feature allows distributed spatial processing systems to service of OGC to deliver the different types of interact with the Hypertext Transfer Protocol services like spatial queries, buffering, data (HTTP) and acts as a standard for development insertion, and data modification. This server is of mobile based enterprise geographical connected with the epidemic disease database to information system.

access the scattered information of different 3. FRAMEWORK & IMPLEMENTATION epidemic diseases. The epidemic disease information is considered as a GIS layer, so that The architecture for different component is each layer can be visualized separately in the depicted in Figure 1. It is composed of four resource constraint device like mobile, PDA, service agents: Mobile agent for visualize and Smart phone. update information, Disease Information Service In a typical scenario, during visiting a interior (DIS) which offers epidemic feature related place when a field worker found some patients service, Alert Service Agent (ASA) which is suffering from a kind of epidemic disease, then used to generate automatic alert message for the field worker upload the information through taking immediate preventive actions, Map their mobile device which contains a mobile Display Agent (MDA) which offers the agent with an input interface for this purpose. visualization different affected area by the The mobile device is connected with DIS server epidemic disease with the color intensity effect. through the internet by using GPRS technology Each epidemic disease and its severity is to get the upload service. represented in appropriate colors The architecture of proposed frame work is The mobile agent at first calculates the distributed in nature. location with the help of inbuilt GPS device. The other related information along with position details are sent to the DIS server. The database also maintains a disease impact index according to severity of the disease. GPS Enabled Update Disease Info Disease info and Map Internet Mobile Network Get Get AlertGet population disease administrative Serverinfo Layer Map Census Feature Map Server Display Server Disease Administrative Information Feature Server Server Spatial Administrative Census Contact Epidemic Map Database information Database Database Database EGIS Organization Figure 1 The overall architecture of mobile based public health monitoring system

The functionalities of each component are Symbol Description described below. <d> Thematic, i.e., alphanumeric, 3.1 Mobile agent: The mobile agent is a J2ME domain (e.g., integer, string). enabled user interface installed into the mobile devices. It will display a user interface with <t> Temporal symbol, graphically relevant input fields. As the mobile devices are GPS enabled, at the time of uploading information illustrated by a triangle. the current location is also captured. After gathering the information about epidemic disease <d<t>> Temporal symbol inside a thematic the mobile agent connects to the Disease Information Service (DIS) of via a GPRS enabled symbol. mobile network for database update purpose. DIS server is maintained by the heath department and Table 1: Primitives used to denote elements of temporal data may be located any location of within the state. model 3.2 Disease Information Service (DIS): The 3.4 Alert Service Agent: The alert service agent working principle of this component is based connects with the database which maintains OGC’s Web Feature Service. This component collection the contact information including spatial integrates different disease information databases location of different supporting department. This which are diverse in nature, maintained by agent will access the service of DIS within a different sectors of health department. According specific period of time interval. The agent will to this service each disease type is considered as a access the each epidemic disease layer and create a geo-spatial feature. Each epidemic disease layers’ buffer zone according to severity of the disease. is maintained by spatial data repositories scattered The agent will access the population information in different locations. Any update information of from the web feature server of the census disease will be stored into these data repositories department according to bounding box of buffered through this disease information service. It offers region. If the probable population exceeds the limit services like layer filtering service, query service. value of the disease layer then the agent will automatically generate alert message to the field 3.3 Heterogeneous Temporal Data Model: Data workers within that buffered region, nearest health that are uploaded through mobile device from a centers and hospitals. It also alerts the district level remote location into various heterogeneous or state level health officers and other supporting databases are time dependent. For example, at time departments. t1 a mobile agent uploads data into a database; again at time t2 another user updates the database 3.5 Map Display Agent: The map display server with a new set of data. Therefore, in order to is a web map service which access the feature preserve the information of different time services form the disease information server and instances, a temporal data model is required. The government’s administrative feature map server. temporal data model will preserve the time Each layer of a particular epidemic disease is dependent properties of the data. A generic denoted by a particular color. With the increase of temporal data model has been developed by number of sufferers in a particular area, the extending UML by adding some new feature using intensity of that color will be increased. The layers some small set of new construct (Nectaria, 2003), like administrative boundary, drainage, road shown in table 1. network, census, health centers and hospitals may also be displayed along with different epidemic The temporal symbols (<t>) are used to denote the disease layers. Data visualization techniques such temporal extents like valid time, transaction time. as zooming, panning, focusing, or sequencing can Thematic symbols (<d>) represent thematic be specified in order to display the desired (attributes) data. presentation. By visualizing the layers of the map various disease analysis such as the source of the disease like water body or drainage, probable spreading trend of the disease, route map for relief team.

Time T1 Time T2 Time T3 Above 90 % affeceted Road Network Above 50 % affeceted Not % affeceted 4. CASE STUDY information about the epidemic diseases. In order to integrate these one need to address the A case study has been presented where the heterogeneity. In the proposed framework the evaluation of spreading and detecting of epidemic heterogeneity between different data sources are disease has been shown. The data related to addressed through geospatial data model. Along evaluate the process detecting and predicting the with heterogeneity the data also varies with respect spreading of the epidemic disease is stored various to time. The geospatial model addresses both heterogeneous databases. All these data are needed heterogeneity and the temporal aspect. to be integrated in order to compute the required Administrative Boundary Name <d> : String State District Bolck Village Shape <s> : Polygon Shape <s> : Polygon Shape <s> : Polygon Shape<s> : Polygon Epidemic Disease Disease_Name <d> : String +OccuredIn <t> W ard Disease_Type <d> : String Shape <s> : Point Spreading_intensity <d> : Number +has +Severe <t> Severity Rate Census Total_Population<d> : Number Total_Population <d> : Number Attacked <d<t>> : Number Male_Population <d> : Number Severity <d> : Number Female_Population <d> : Number Population_Below_Poverty <d> : Number Figure 3: Temporal Geospatial Model for Capturing Epidemic Disease Effect

In figure 3 a UML class diagram has been shown G. Abowd, C. Atkeson, J. Hong, S. Long, R. where the heterogeneous temporal data has been Kooper and M. Pinkerton, 2000, Cyberguide: a mapped into the model. The administrative Mobile Context-Aware Tour Guide, Wireless boundary class represents different administrative Networks, 3(5), pp. 421-433. area. S. Poslad, H. Laamanen, R. Malaka, A. Nick, P. State District Block Village Ward Buckle and A. Zipf., 2001, CRUMPET: Creation of User-Friendly Mobile Services Personalized for Figure 4: Concept hierarchy of Administrative Tourism. Proceedings of 3G Mobile Boundary Communication Technnologies, pp. 26-29. The State is the topmost element and Ward is the Kaplan, Elliott D, 2005, Understanding GPS: lowermost element in the concept hierarchy of the principles and applications. Boston: Artech House administrative boundary (refer Figure 4). The Publishers. epidemic disease class is associated with the Ward class. The association is temporal since it may G. Derekenaris , J. Garofalakis , C. Makris , J. occur in a ward at a time t1 and may not be present Prentzas, S. Sioutas , A. Tsakalidis, Integrating at time t2. Therefore, the temporal aspect has been GIS, GPS and GSM technologies for the effective mapped in proposed framework. Similarly, the management of ambulances, Computers, severity of the epidemic disease is also temporal. Environment and Urban Systems 25 (2001) 267- 278 5. CONCLUTION OGC, 2003. The OpenGIS Reference Model, This proposed mobile-based health management http://portal.opengeospatial.org/files framework may provide accurate, timely and relevant information for decision-making and Nectaria Tryfona, Rosanne Price, and Christian S. disease monitoring. It will help in prediction of the Jensen. Conceptual Models for Spatio-temporal spreading trend of epidemic disease, severity of the Applications. Spatio-Temporal Databases. pp. 79- disease, severity intensity over time, and allocating 116. 2003. resources to control the disease. It further provides a decision support platform for epidemic control through the seamless integration of various data sources. Mobile GIS is a powerful tool that can be used to assist in the control of environmental diseases and the spatial analysis is essential to improve the efficacy of control and decrease the burden of disease. The proposed framework will facilitate effective decision-making process and maximizing the use of health data and spatio- temporal information to improve the public health system. REFERENCES K. Anderson, 2003, Spatial Analysis Trends in Health and Safety, Directions Media, Glencoe, Illinois, USA. L. Chittaro, 2006, Visualizing Information on Mobile Devices. IEEE Computer,39(3):40–45.

Third International Conference on HealthGIS 2009 July 24-26, 2009, Hyderabad, India HEALTH SERVICE OFFERING SYSTEM USING GIS BASED SMART CARDS Ehsan Khayambashi, Hamidreza Rezaei, Hajar Namazi Faculty of Khomeini-Shahr Islamic Azad University, Expert of GIS Office, GIS Managing and Mechanized Systems of Esfahan Council Affiliation, Bachelor of Science of Nourishment and Diet Treating [email protected], [email protected] ABSTRACT: The point of this paper is presenting a comprehensive scheme for substituting health service notebooks, prescriptions written by doctors and people’s numerous medical records in different health centers using GIS BASE smart cards. Although smart health cards are occasionally used in developed countries but here, the emphasis is on using the advantages of GIS in such cards. In this scheme a smart card is intended for each person and after loading information on it, is used instead of health service notebooks and medical records. In addition, identification cards, used for accessing the patient’s medical record, inspection and prescription cards designed for doctors and service offering identification cards are designed for health centres and pharmacies. As well as using all the common usages of smart cards in health services, this system provides different ways of getting reports and observing researching and managing factors such as methods of distributing health and healing services, number of visits, observing geographical distribution and concentration of different diseases and identifying their cause, examining nourishment and diet treatment, creating regional health maps, identifying and resisting crisis on time etc. All this is done according to placement and transfer of data while bearing in mind privacy policies, amount of legal access to insurance offices’ data, medical science universities or in general, managing the country’s health and hygiene network by defining layers of information and using the 1.2000 digital map and satellite pictures and on the whole by optimum managing in the health section, whether in individual aspects or collective aspects. KEY WORDS: Smart cards, GIS, health maps, Smart GIS BASE health card, Smart health service card, Electronic town. 1. INTRODUCTION 3) Doctors’ smart card that when ran, connects the doctor to the program in the patient’s card and Nowadays societies are progressing towards making and he/she can write the patient’s prescription. In developing electronic towns. In addition presenting health this condition, using a computer and one of the services to citizens is essential and everyone is somehow statuses of ONLINE or OFFLINE appropriately involved with it and therefore needs correct planning. As will be necessary. a result and bearing in mind the complications of today’s societies, using modern technology for ease and speed in 4) Health centers’ service offering card that are this field, is unavoidable. given to centers such as pharmacies, laboratories Smart cards can be used for providing health services for etc. and when ran, also connects to the program all citizens. If this system is based on a suitable GIS in the patient’s card and service is given. basis, it will provide different ways of getting reports and observing, using data systems of the area. The possibility 2. HISTORY OF SMART CARDS of planning and better guidance in the sectors of health, treatment and medical education is also provided. In this system, four major sectors are predicted: 1) Setup of a suitable program and the GIS soft- ware and dedicating required hardware by the insurance and managing office with the two ma- jor purposes of issuing and extending smart cards for citizens, doctors and health centers ex. pharmacies, laboratories, etc. and also the ability of receiving and analyzing various data. 2) Smart health service cards and medical records that are given to all citizens and are used when receiving health services.

Smart cards where first invented in 1968 by German In conflict, because of their security systems, smart cards missile science scientist “Helmut Grotrop” and his store data in themselves to be used when required without colleague “Burgan Detholf” and finally registered in the need of connecting to a network. 1982. The first public and widespread usage of smart cards was in 1983 for electronically paying the French The state of being ONLINE or OFFLINE in systems credit telephones. A smart card is a plastic card in the size designed for smart and magnetic cards is demonstrated of a credit card that one or more chips are used in it as here. Therefore the smart cards’ system is designed so aggregated circuits. There can also be one or more that data is stored on it and because the CPU in it has technologies such as magnetic strips, barcodes, biometric control over access to the card’s data, it has the essential information and image recognisation, which can be done security. However in common magnetic cards, owing to by a machine, used in it. The combination of a common the lack of information security, data is stored on the plastic card and a microprocessor, allows a large amount network and the card only includes an identification code. of information to be stored, processed and accessible, Consequently access to the network is needed for using online or offline. magnetic cards and therefore cannot be used if the data network is off at the time. On the contrary, using smart 3. TYPES OF SMART CARDS cards, needs no network and can therefore be used even if the network is not accessible at the time. There are two types of smart cards: 1- memory cards that only include nonoverflowable memory and some 6. FUNCTIONALITY OF CARDS processes in order to provide security and 2- CPU cards which include a CPU and overflowable too. The structure 6.1 Operating System of smart cards is shown in the figure below The new pattern and programming in the operating system of smart cards is CardJava. This operating system 4. Amount of Data Stored on Smart Cards: was developed by the Sun Microsystem Company and Smart cards can store more data than normal cards afterwards extended in the form of JavaCard. This operating system is very popular because it provides Figure 1 Structure of card independence and freedom in architecting for designers and programmers. Furthermore practical programs based 4. AMOUNT OF DATA STORED ON SMART on the Java operating system can also be used for any CARDS smart card that supports JavaCard. Nowadays most smart cards use their own specific operating system for Smart cards can store more data than normal cards communicating and performing planned actions. (magnetic strips) and by storing encoding algorithms, However for real support of practical programs, the they can also improve the security of transfers. The data operating system of smart cards, are based on the stored on an IC chip is transferred by means of an operations provided by international standard ISO7816. electronic mojule that is connected to a terminal or a card reader device or by a magnetic field. Smart cards can Other operating systems that can be named as have up to 8 kilo bytes RAM (Random Access Memory), developed for smart cards are MULTIOS (Multi- 364 kilo bytes ROM (Read Only Memory), 256 PROM Application Operating System) and SmartCard for (Programmable Read Only Memory) and a 16 bit CPU. Windows. 5. INFORMATION SECURITY IN SMART 6.2 Programming Language CARDS Many practical programming languages (Application Programming Interface) have been developed for smart The information available on common magnetic cards cards. Some of them are: can be easily read, rewritten on, deleted or edited. GlobalPlatform/ OpenCard/ SC-PC/ CT-API Therefore use of such cards requires complicated computer networking systems to receive and process data 7. ADVANTAGES AND CAPABILITIES OF and to also insure the data being correct. This is why SMART CARDS magnetic cards are not a suitable location for storing data. Smart cards have many advantages and capabilities and this fact has caused their usages to extend in many ways. Of the most important advantages of their usage the following can be mentioned: 1) Size: This small card eliminates the need of carrying around large amounts of documents. 2) Security: Because of security systems on these cards, such as encoding abilities, the data on them is kept safe.

3) Data Capacity: Smart cards are capable of patient’s personal code for communicating between the storing bigger amounts of information compared to card and card reader devices. magnetic cards. Therefore using this card, the patient will be carrying the main part of his/her medical information and can use 8. USAGES OF SMART CARDS them when visiting a doctor. Also the doctor can use the patient’s latest information for better diagnosing and Today, in many countries, smart cards are used in various prescription. tasks. These usages are generally put into three groups: Using smart cards in the doctors’ office is made possible 1) Identification Usages: These cards are used for by card reader devices. Doctors can use a card reader identifying people and their owners, such as parking device to issue prescriptions electronically and benefit cards, traffic cards, etc. from advantages such as information security, being sure of accuracy in the performance of prescriptions, access to 2) Financial Usages: Banks offer smart cards to professional data sources in medical care, access to the their customers as their electronic identity in the bank patient’s medical records, etc. Doctors also have a special they were issued in. By presenting this card that is inserted into the card reader along with the patient’s. 9. USAGES OF SMART CARDS 11. GIS TECHNOLOGY AND ITS USAGE IN Today, in many countries, smart cards are used in THE HEALTH SECTION various tasks. These usages are generally put into three groups: Geographical Information System is a tool for storing, combining, analyzing and demonstrating geographical 1) Identification Usages: These cards are used for data. GIS is a combination of a computer, a database, identifying people and their owners, such as software and a professional user who can process and parking cards, traffic cards, etc. illustrate categorized data in a geographical frame. In the past years GIS has made undeniable evolution in 2) Financial Usages: Banks offer smart cards to geographical sciences. It has also developed organisation their customers as their electronic identity in the and management in locational data. This science and bank they were issued in. By presenting this card technology has abilities such as receiving and transferring to ATM machines, the customer can benefit data between different sources, organising data, timely from banking services. receiving and showing, processing and merging various data and the ability of offering multipurpose services. 3) Storing Data Usages: An identification code and These abilities have opened new doors to researchers in some personal information are stored on smart the last decade. It has been used for many purposes and cards. This data can be accessed using a card its usage are extending. With the improvement in reader device. Cards such as smart license cards, technology, users discover new abilities of GIS everyday smart health cards, national identification cards and are drawn towards it. and student cards are in this group. Using GIS, firstly we can discover what the health 10. SMART HEALTH CARDS problems are and where they occur and secondly what qualities the locations have. By answering these These days, in most developed countries, particularly questions, we can help prevent and solve the problems. In European countries, smart cards have a special position in the health section, the data gained from observing the health systems. source location is merged with illustrated data and maps that include different layers of health and hygiene data Performing projects related to health cards in the world are made and given to professionals for use. started about two decades ago and has been extending ever since. Countries such as America, France and Germany have been pioneers in this area. Activities that can be mentioned in this topic are SesamVitale in France, QUEENS Health Network in America and Electronische and Gesundheistkarte in Germany. The Social Security Organisation and the Health Service Organisation in Iran have also been designing smart health cards for special diseases. The use of these cards eases managing services and accessing patients’ medical records at any time. Two types of data are stored on smart cards, personal data and medical data. This data includes the patient’s personal information, complete up-to-date medical records, insurance data, emergency data, prescriptions, the

Figure 2 Sample of the progress of accessing people’s 4) Analysing the patient’s health data according to locational information using smart cards. time and place. Scientifically and with concern for public health, Health- 5) Using Mobile GIS and the technology of Geo- GIS has scientific and practical usages. These usages are graphical Positioning System for collecting data based on the two bases of presenting and surveying fields from health data and sending them to GIS public health managing services and also on database. acknowledging the scattering of effective factors in improving health, such as diet treating, amplitude of 6) Transferring GIS technology to health managing diseases and dying etc. Primary and fundamental and servicing systems nationwide. indicators of improving the level of health and resisting diseases in society and also improving public hygiene, are 7) Monitoring unusual aggregation of diseases in measuring the amount of accession and effective factors specific locations and their breadth. on health or illness. Not only is accuracy in measuring these indicators, accuracy in assessment on presenting 8) Offering help and instructions for correct regis- health and medical services but also it is a way to identify tration of health information nationwide with the and evaluate effective factors on decreasing the level of aim of giving them placement in future. health and increase in number of diseases, and the norms that are effective on health and profusion of illnesses. 9) Optimum managing of the distribution of health Health-GIS is undoubtedly a multi-dimensioned string and medical services appropriate with the needs that is generally made of three groups: 1) GIS and of the citizens. evaluation from a distant, 2) epidemiology and statistics, 3) computer and database. 10) Ease of planning and offering services to partic- ular patients. 12. SMART HEALTH CARD BASED ON GEO- 11) An effective managing tool in crisis handling GRAPHICAL INFORMATION SYSTEM (GIS) bearing in mind the ability of recognition and correct, speedy and real understanding of the ex- As mentioned in the smart health card section, the basis tents of crisis. of smart cards is collecting data and keeping this data up- to-date so it can be used efficiently anytime and 13. CONCLUSION anywhere. GIS’s usage is analysing events based on geographical data. According to the definition of GIS, a According to the mentioned issues, it can be concluded database can be achieved by combining and connecting that GIS based smart cards can be a suitable replacement people’s locational information (address, postal code, for health service notebooks and patients’ medical geographical longitude and attitude) and the data stored records. In addition to saving on expenses and human on each person’s smart health card and also the locational force, they provide better managing of distribution, information of health centers (address, postal code, control and guidance in health and medical services geographical longitude and attitude) with the following whether in the medical section or in facilities, equipment goals: and service offering centers. Furthermore it provides the ability of recognition, definition and good performance in 1) Designing GeodataBase for different subgroups the health section ex. fatness, undernourishment, congenital and identifying their cause. It also makes on of the health section such as hospitals and health time predicting and better resistance in accession of diseases and handling unpredicted crisis possible. centers’ databases, doctors’ databases, different Generally it is considered as a managing tool in keeping the desirable conditions for society. diseases database, insured members’ database REFERENCES along with the patients’ medical records Wolfgang Wolfgang, Rankl Effing, Smart Card 2) Developing epidemiology maps of disease distri- Handbook, Wiley Publishing, 2003. Deitel Deitel, Harevy Paul, How to program C++, bution in various comparisons and measures. Second Edition, 1998. Mazidi Mazidi, Mohammad Ali Janice Gillispie, The 3) Creating maps of distribution of the locations of 8051 Microcontroller and Embedded System, Stew- art Chales E. Publishing, 2000. medical and health services like hospitals, emer- Finkenzeller, Kalaus, RFID-Handbook, Wiley Pub- lishing 1999. gency centers, pharmacies etc. or even creating a Lahiri, Sendip, RFID Sourcebook, IBM Press Pub- lishing, 2005. map of distribution of doctors and specialists.

Hendry, Mike, Smart Card Security and Applica- tions, Artech House Publishing, 2001. http://www.elektor.com http://www2.sso.ir/web/sso/home http://www.acs.com.hk/ http://msio.org.ir/ http://www.smartcardalliance.org http://www.smartcardalliance.org/pages/activiti- escouncils-Healthcare http://www.ESRI.com

MOBILE DATA MANAGEMENT FOR INTEGRATED MEDICAL CARE SERVICES S. R. Balasundaram1 and A. Saravanan2 Department of Computer Applications, National Institute of Technology Tiruchirappalli – 620 015, Tamil Nadu, INDIA. [email protected], [email protected] ABSTRACT: Mobile technologies are extensively involved in various real life activities such as Mobile-banking, Mobile- commerce, Mobile-web, Mobile-sports etc. Mobile technology provides better flexibility in communication, collaboration and information sharing through the wireless medium using portable mobile devices. It extends computing and allows users to have anytime, anywhere access to information and applications. Specifically, location- aware mobile service has become the most essential component in dealing with mobile based applications. Mobile health care is one such promising area where mobile technologies are highly useful to provide services to the affected people. Assume some problem happens at location i.e. a patient has to be immediately attended. There may not be immediate possibilities to admit the patient to the hospital. In this case, a message is given to the dedicated server to call the ‘mobile doctor’ based on his/her status and current location. The server will acquire the logical lock on the record of a doctor till his/her status returns to idle condition. Before the doctor arrives to the concerned place, the details related to the patient, past history, medical advices given to the patient, medicines prescribed for him are all informed to the doctor to take necessary steps. It is also possible to provide immediate facilities such as calling an ambulance, making arrangements related to medical equipments etc. To define multilevel facilities, an integrated system is initiated by the mobile devices. KEY WORDS: Medical Care, Mobile technology, Location-aware, Mobile device, Mobile Data Management. 1. INTRODUCTION One scenario where mobile technologies will be highly suitable, is providing timely services for the needy Recent technologies have revolutionized the ways of people who are in risk during their mobility. Problems performing traditional things in the society in better and that may happen during mobility are occurrence of more convenient forms. The growth of technologies both natural calamities, accidents or sudden changes in health wired and wireless, aim at providing timely services to conditions. If facilities exist to take care of the people all sectors of people in varieties of ways. Especially, the who are at risk, then it is possible to recover the affected advancement of wireless technology plays a vital role in people in time. several walks of life. People in some way or other enjoy In case of road fatalities, if the affected person is the benefits of wireless technologies through PDAs, attended immediately, the death rates may be reduced. Mobiles, Bluetooth etc. Broadly speaking wireless Due to poor traffic controls, non-availability of technologies can be divided into fixed wireless, mobile ambulance facilities and lack of medical care services wireless and portable wireless, employed in various higher rates of deaths occur. The major constraint in communications and control systems. most of the situations is the inability to link the service The latest-generation architectures employed in mobile providers to take appropriate actions. This paper focuses devices, with the help of GSM and GPRS, make it on linking various medical care services to the affected possible for defining ubiquitous communications and person(s), with the help of mobile technologies. computing across the globe. A new class of GPS-enabled, Assume some problem happens at location i.e. a patient wireless-networked management supports the applications to has to be immediately attended. There may not be track the locations with respect to the application possibilities to take him to hospital at once. In this case, requirements. a ‘mobile doctor’ is informed about the problem to take These technologies provide features to acquire, process necessary action. and manage data on mobility. This area of data Before the doctor arrives to the concerned place, the management called mobile data management is finding details related to the patient (if he/she is a registered its importance in almost all applications. The major goal one), past history, medical advices given to the patient, of mobile data management is to ensure the availability medicines prescribed are all informed to the doctor to of data anywhere and anytime on both mobile and fixed take necessary steps. It is also possible to provide hosts in seamless way. The importance of mobile data immediate facilities such as calling an ambulance, management can be seen in numerous applications such making arrangements related to medical equipments etc. as banking, education, health sectors etc.

2. MOBILE DATA MANAGEMENT - The traveler may belong to either registered category or general category. Mobile data management handles both local and global data in turn dealing with various activities such as data - The details related to the registered persons are access, caching, query processing, location, addressing, stored in the networked medical care system. replication etc (Vijaykumar 2006). Various aspects of mobile data management have been dealt by numerous - Communication to the LAMECS system, may researchers in different situations. Especially, defining be done by the individual or any informer location aware services in applications has become the (traffic police or general public). current area of research. Based on the location based information, the users can get services involving data 3.1 LAMECS Architecture management features. Many researchers have dealt with In general, when the informer sends message to the location aware mechanisms for exploiting data from the LAMECS system, the location details are used to assign fixed locations to manage data for convenience. (Uwe the nearest idle mobile doctor. The selection of mobile Kubach 2002). doctor is determined by LAMECS based on the status The location-dependency of the information available in (free) of a doctor as well as the distance criterion. The mobile applications depends on the amount of data that architecture involves three components such as server, can be cached, disseminated or prefetched for a certain mobile clients and mobile doctors as depicted in Figure reasons (Ye.T 1998). Efficient ambulance dispatching 1. and real-time multimedia symptom delivery for emergency rescues has been proposed in Context- Figure 1 Architectural Diagram of LAMECS Aware emergeNcy rEmedy system (CANE) (Hsu-Yang Kung 2005). Server (S) : Server will receive the request from the Effective coordination of mobile data has been strongly accident location along with the patient ID information emphasized for handling the patient related data in (for registered patient). Then server can send the patient system such as Continuous Care Model for home based details such as location and patient ID to the mobile assistance (Federica Paganelli 2007). Filippos et.al doctor through GIS/GPS mechanism. Before sending the (2005) have proposed the importance of timeliness message to the mobile doctor, the server will check the services that can be provided in pervasive environment. status (‘idle’ or ‘busy’) of the nearest doctor. A doctor Neha Padmanabhan et.al (2006) have described the can be assigned only when the status is ‘idle’. If the availability of the personal device to support triage doctor is assigned to a location, the status becomes nurses in making decisions in the emergency ‘busy’. At the time of calling a doctor, the logical lock departments. will be made for the doctor’s record, until his /her status returns to ‘idle’ condition. 3. LOCATION AWARE MEDICAL CARE SERVICES (LAMECS) Mobile Doctor (MD) : Server will send the patient information to the mobile doctor. In case of registered In the occurrence of calamities, either natural or patients, before the doctor arrives to the concerned accidental, if proper measures exist to attend the place, the details related to the patient, past history, affected people in time, then fatalities can be reduced to medical advices given to the patient, medicines a greater extent. When a traveller meets with a problem prescribed for him are all informed to the doctor to take (accident or physical ailment) during mobility, he/she necessary steps. It is also possible to provide immediate may be linked with the nearby networked hospital for obtaining medical care services in time effective manner. The Location Aware MEdical Care Services (LAMECS) system focuses on providing services to the risky people in the required place at right time. LAMECS uses GPS technology, to achieve location-based services, which could help the patients in their locations to obtain the rescue treatment from the mobile doctors. The request received from a location in the form of mobile data to the LAMECS, will enable the system to identify the available mobile doctor to be assigned to the affected person. The aim of sending mobile doctors is to provide first hand treatment to the patients and rescue them from critical conditions. In this regard, certain assumptions made as well as prerequisites expected are listed below :

facilities such as calling an ambulance, making arrangements related to medical equipments etc. Mobile Client (MC) : Normally, the requests will be raised by the patient’s attender or patients themselves (clients) through mobile devices such as cell phones or smart phones or PDAs. 3.2 LAMECS Data Model The various entities involved in the LAMECS system are explained in Table 1. Table 1 Entities of LAMECS Entity Details Stored Attributes Treatment_ Request Treatment request {Patient_ID, Figure 2 Working Principles of LAMECS with respect to time Location, Patient initiated by patient RequestTime } When there are more than one doctor available, attender/patient choosing the nearest doctor is one of the major features of LAMECS. The algorithm related to the allocation of Patient details {Patient_ID, mobile doctors to the concerned locations, is given along with Past Name, below. history for Address, registered patients Contact, Algorithm : Doctor_ Allocation PastHistory} Patient/Attender sends request to Server. Server checks the availability of MD(s). Mobile_Do Mobile doctor {Doctor_ID, If more than one MD is ‘free’ then ctor details Name, LockStatus } Begin Allocation Allocation details L = min { distance(cur_loc, LMD) } of mobile doctor {Doctor_ID, // cur-loc = Current Location of Patient Location, //LMD = Locations of ‘idle’ Mobile Start_time, End_time } Doctors Select the doctor based on distance L; 4. LAMECS - WORKING PRINCIPLES End; In the context of a patient informed about his/her status If MD requires ambulance or any equipment then to the server related to the ailment/problem, the system involves certain essential steps to link the mobile doctor ‘message’ sent to Server. to the patient. The overall scenario is shown in Figure 2. If treatment/ advice completed then The working principles of LAMECS are listed below: ‘message’ sent to Server. Step 1 : Patient/Attender sends request to server. Step 2 : Server checks the availability of mobile doctor 5. CONCLUSION Step 3 : Patient details collected Step 4 : Patient details linked with doctor details Attending the persons who are affected due to accidents, Step 5 : Combined details passed to server during travel or persons whose health conditions Step 6 : Server communicates the details to the doctor suddenly worse during mobility is the primary concern Step 7 : Doctor moves to the patients’ place of this work. The significance of mobile technologies Step 8 : Information updated in the server with the help of devices and the management of mobile data are discussed in this paper. The mobile data enables the nearest doctor to be assigned to the patient to recover him/her from risk. The location aware model helps in selecting the nearest doctor and in arranging the medical facilities to the needy people.

REFERENCES Journal Federica Paganelli and Dino Giuli. A Context-Aware Service Platform to Support Continuous Care Networks for Home-Based Assistance. HCII 2007, LNCS 4555, pp.168–177, 2007. Springer-Verlag Berlin Heidelberg. Filippos Papadopoulos, Apostolos Zarras, Evaggelia Pitoura, and Panos Vassiliadis. Timely Provisioning of Mobile Services in Critical Pervasive Environments. CoopIS/DOA/ODBASE ‘05, LNCS 3760, pp. 864–881, 2005. Springer-Verlag Berlin Heidelberg. Hsu-Yang Kung, Mei-Hsien Lin, Chi-Yu Hsu, and Chia- Ni Liu, Context-Aware Emergency Remedy System Based on Pervasive Computing, EUC 2005, LNCS 3824, pp.775 – 784, 2005. © IFIP International Federation for Information Processing 2005. Neha Padmanabhan, Frada Burstein, Leonid Churilov, Jeff Wassertheil,Bernard Hornblower, Nyree Parker, A Mobile Emergency Triage Decision Support System Evaluation, Proceedings of the 39th Hawaii International Conference on System Sciences – 2006. Nirvana Meratnia. Two Approaches for Successful Mapping GPS Data to Underlying Road Network in Location-based Services, Proceedings of the first workshop on positioning ,navigation and communication,WPNC’04. Uwe Kubach and Kurt Rothermel. Estimating the Benefit of Location-Awareness for Mobile Data Management Mechanisms. Pervasive 2002, LNCS 2414, pp. 225–238, 2002. Springer-Verlag Berlin Heidelberg. Ye.T, H.A. Jacobsen, and Katz.R. Mobile awareness in a wide area wireless network of info-stations. In Proceedings of the Fourth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom’98), pages 109–120, Dallas, TX, USA, 1998. Books Vijay Kumar. Mobile Database Systems, John Wiley & Sons,Inc. Publication , 2006.

Mapping the Poor and Technology Enabled Micro Planning For Improved Health and Basic Services Abstract Yogesh Kale Although the 2001 census reported that India was only 28% urbanized, there is every reason Anuprit Minhas to believe that the nation will pass the 50% urbanized threshold sometime between 2030 and and Tamara Failor 2050, by which time an additional 300 million Indians will have entered urban areas. If India’s economy continues to grow, it may even reach the 70% levels of urbanization now observed in Eastern Europe and North America. That would mean about 800 million new residents in Indian cities over the next century. The consequences are alarming:shortages of housing, water, sanitation and solid waste disposal services, transportation, rapid environmental degradation, increase in diseases, and decline in law enforcement are creating deplorable living conditions for millions. Delivery of essential services will fall further behind due to rapid growth resulting in sub-standard health care infrastructure, lack of information and increasing risks of spread of diseases. CHF International, in partnership with the Pune Municipal Corporation (PMC), conceived an information based system enhancement program called “Utthan” (from the bottom – in Hindi) wherein the aim is to enable PMC provide better services to its poorer citizens. The initiative is based on two objectives:a) delivery of services can only be enhanced with the availability of accurate and updated information on the end receiver; and b) delivery mechanisms must be based on actual demand and there must be systems wherein poorer communities can be empowered using inclusive and participatory planning. First objective is met by assisting PMC develop a system to map (and concurrently update) its entire urban poor population living in informal settlements. This includes over 524+ slum communities, home to over 260,000 households. While the mapping and data analysis is enabled using GIS, GPS and other technology tools, the collection and updating of demographic and socio-economic data is aggregated using community based representatives and corresponding hierarchical structures. The second objective of creating demand is the greatest challenge as it requires sustained involvement of the people themselves. The aim is to engage maximum households in the participatory micro-planning exercises wherein needs and concerns are translated to projects which culminate into becoming part of the annual budget. The success of this effort will be ensured when these systems become mainstreamed into the routine governance of the city. CHF’s efforts will conclude with the transfer of the systems, methodologies and technologies to PMC such that the tools are utilized each year. Health Services:The systems created, governance structures established and the methodologies used can be adopted for any sector including health. CHF’s current focus is on developing the framework applicable to the services rendered by PMC’s Urban Community Development wing where the focus is on issues related to livelihoods, education, training, financial savings/credit, etc. Collecting health specific information on households, infrastructure (hospitals, clinics and pharmacies) and service providers is just a build on to the current information infrastructure. Currently the newly developed tools enable the following analyses: • slum dwellers access to basic services water and sanitation • slum dwellers access to hospitals, private clinics (including frequency of visits) • expenditures on health and utilization of health insurance • information on disability, major illnesses or addiction • Indebtedness, which is often incurred by a medical emergency The paper will demonstrate that the GIS based system created by CHF proves a valuable tool in formulating and implementing various government interventions related to improving the health of slum residents. It builds the capacity, accountability and efficacy of UCD to institute policies and programs for improved health. This adaptable, replicable and multifaceted system places it on the cutting edge of governance applications, especially in dense and incongruous areas, which have historically been a challenge to survey and document.

Geospatial Kerala Health Information System (GKHIS) Abstract Suresh Francis and Kerala State, with its high level of literacy, awareness of the people and by the Bindu. P Ramankutty activities of Government, is keeping high standards in the health domain. Health department of Kerala is always in the forefront, to take upkeep actions, reaching the grass root level. With its wide spread infrastructural facilities and reach to the rural population, the department is combating the health related issues of the State with people participation. Thus, for maneuvering the complex machinery and infrastructural facilities, the Health department, with its NRHM segment, envisaged to build up a Geospatial Information System. The Geospatial Kerala Health Information System (GKHIS) is a Web GIS application, envisaged to comprehensively visualize the infrastructural facilities, monitor the activities of the Health department, to collect systematic data on all major aspect of health especially on disease surveillance. The information system has got powerful functional abilities, including a) displaying spatial data on Govt. Health Institution of Kerala, administrative boundary upto panchayath level, road network of Kerala, drainage network of Kerala, etc., b) Querying spatial data on all the layers and the database attached and the query can be simple as well as complex, c) retrieving information both from spatial and non spatial content, d) updating the non spatial data from the client end with the administrator privilege. The GKHIS has got two major segments; one is with administrative privilege and the other for the general users. The general user can view, query and analyze the spatial and non spatial data. The user with administrator privilege can update the nonspatial information. The updated information can be brought to the spatial platform as all the nonspatial information is spatially linked.The Web GIS application is developed in the three tier architecture with the front end or the user interface developed with ASP.NET, the database end in the SQL Server 2005 and Arc IMS as the third party tool.

GIS: A Tool in Road Traffic Injury Research Abstract Mohan Venkata Raghav Road traffic injuries (RTI) are a major public health problem worldwide killing Rajiv Sarkar almost 1.2 million people every year and injuring or disabling between 20 and 50 million more, 90% of which occurs in low and middle income countries. Vinod Joseph Abraham Developing an effective injury prevention strategy requires data on the and Vinohar Balraj incidence and types of injuries, and knowledge about their causation. In injury research, GIS can help identify the determinants of the risk of injury to guide in prevention efforts, and evaluate the spatial organization and accessibility of acute trauma care systems. Information on all RTIs in Vellore district was collected by evaluating the FIR data from all police stations, between January 2005 and May 2007. Geo-coordinates of all the police stations were collected and all major highways mapped using Garmin GPS V. RTI spots were identified using the FIR data and linked to the corresponding police station. Vehicular type, density and traffic movement patterns were monitored in two areas on Chennai-Bangalore national highway for all days spanning a week. Rates of RTI were calculated using this as the denominator. Of all RTI’s occurring on the stretch of highway in the study region, 13.5% were fatal. Using ArcGIS 9.1 hotspots of RTI in the study area were identified and mapped with respect to the nearest town, and segments of the highway with a higher risk identified. Application of GIS to map hotspots of RTI could aid local authorities and policy makers in identifying vulnerable areas, and in planning and implementing an effective traffic management system thereby making the roads safer for the general user.

Technical Session - 2 Viral Diseases Application of a Remoteness Index: Funding Malaria Programs C Beaver, S Pontifex, Y Zhao, L Marston, A Bobogar and, G Taleo……………….……………………….24 A Study of HIV/AIDS Related Knowledge and Attitudes among the Engineering College students of Lucknow, India Arjit Kumar, P. Bhardwaj, P. Gupta, J. P. Srivastava and K. P. Mathur……………. ……………………28 Socio-Environmental Variability and Barmah Forest Virus Disease Transmission: A Review of Epidemiological Evidence Suchithra Naish and Shilu Tong……………………………..………………………………………………32 Dengue Fever and Dengue Haemorrhagic Fever Risk Zonation in Chachoengsao Province, Thailand using Analytical Hierarchy Process (AHP) Phaisarn Jeefoo, Nitin K. Tripathi, Marc Souris, Seishiro Kibe and Vivarad Phonekeo…..………………38 Geographical Information System: Understanding the Epidemiology of Japanese Encephalitis Amarjeet Singh and Dinesh Kumar……………………………………………………………….………….45 Spatial Analysis of 5-Year Typhoid Incidence in Kelantan, Malaysia Shamsul Azhar Shah, Mohd Rohaizat Hassan, Shaharudin Idrus and Abdul Hadi Harman Shah.…..…49 The Knowledge and Attitude of Students of the Azad University of Saveh about AIDS and Challenges in Iran Esmael Shariat and Ali Akbar Nazary……………………………..…………………………………………50

APPLICATION OF A REMOTENESS INDEX: FUNDING MALARIA PROGRAMS C Beaver, S Pontifex, Y Zhao, L Marston, A Bobogare, G Taleo Carol Beaver, Scott Pontifex , Alby Bobogare and Luke Marston, National Vector Borne Disease Control Program Ministry of Health and Medical Services Solomon Islands , George Taleo, Manager - Malaria and Vector Borne Dis- ease Control Program Ministry of Health Vanuatu ,Yuejen Zhao Charles Dariwn N.T. University Australia , Email: [email protected]; [email protected] ; [email protected] [email protected] Yue- [email protected], [email protected] ABSTRACT: The national malaria control programs in Vanuatu and Solomon Islands are characterised by a combination of intensified control and progressive malaria elimination that will be funded through a consolidated budget. Funding sources are the country budget, the Global Fund, AusAID, the World Health Organisation and in the case of the Solomon Islands, Rotary Against Malaria. Per capita funding for the malaria program is high compared to other countries. To understand some of the key reasons underlying the significant differences in per capita costs observed, current funding for Lao PDR has been compared to funding for Vanuatu and Solomon Islands. Economies of scale arise when there are a large number of people to be serviced and the share of fixed costs or, fixed costs per capita, is low. This applies to Lao PDR where the target population is 3,600,000. Whereas, in Vanuatu (238,000) and Solomon Islands (521,129) the reverse applies as the population level is low (very low compared to Lao PDR) and the per capita costs are higher (relative to Lao PDR). The impacts of greater distance and a scattered population on service delivery need to be carefully considered. Greater distance and a scattered population means longer time and higher costs for travelling, diseconomies of scale and resulting inefficiencies, and higher personnel costs. A review of funding for each country taking account of the impact of remoteness was undertaken. A remoteness and incapacity index (RII) was has been developed and applied to funding for all three countries to allow for inter country comparisons of per capita share. A remoteness index is a relative concept which assigns the degree of remoteness or isolation to a particular area in the country. Its definition centres on population of different sizes (number of persons) applied to distance in space (area). Such a model of remoteness that combines the elements of travel distance, time and cost has been suggested to quantify rural accessibility/remoteness in both the Solomon Islands and Vanuatu using latest available population data or estimates and GIS based technology. Such an index could also be used to show comparison with other countries which also are funding active anti-malaria programmes (such as Lao PDR). The ratio of Vanuatu and Solomon Islands RIIs to Lao PDR RII has been used to deflate estimated per capita costs. The RII adjusted per capita revised budget estimates for Vanuatu and Solomon Islands are less than the actual per capita estimates for Lao PDR. Whereas the actual per capita estimates for the two countries were considerably more than Lao PDR. The significant differential per capita budget estimates between Lao PDR and the other two countries because of economies of scale for Lao PDR and the reverse for the other two countries (diseconomies of scale) as well as remoteness, travel by air and sea and the impact of international prices. The above analysis has provided an understanding of expected expenditure (budget estimates) for the implementation of current malaria plans in Vanuatu and Solomon Islands compared to Lao PDR in terms of level of funding by objectives and by inputs, source of funds, and funding level of malaria compared to other health services. KEY WORDS: Malaria, Budget estimates, Geographic diversity, Remoteness index

1. INTRODUCTION 2. METHODOLOGY The national malaria control programs in Vanuatu and A remoteness index is a relative concept which assigns Solomon Islands are characterised by a combination of the degree of remoteness or isolation to a particular area intensified control and progressive malaria elimination in the country. Its definition centres on population of that will be funded through a consolidated budget. different sizes (number of persons) applied to distance in Funding sources are the country budget, the Global space (area). Such a model of remoteness that combines Fund, AusAID, the World Health Organisation and in the elements of travel distance, time and cost has been the case of the Solomon Islands, Rotary Against suggested to quantify rural accessibility/remoteness in Malaria. both the Solomon Islands and Vanuatu using latest Per capita funding for the malaria program is high available population data or estimates and GIS based compared to other countries. technology. Such an index could also be used to show comparison with other countries which also are funding A review of selected countries found that reported active anti-malaria programmes (such as Lao PDR). malaria, budget estimates for Vanuatu and Solomon Islands grants are higher per capita than the other 5 The impacts of greater distance and a scattered countries as shown below. Budget estimates for Vanuatu population on service delivery need to be carefully and Solomon Islands are from multiple funding sources considered. Greater distance and a scattered population (consolidated work program). Estimates for other means longer time and higher costs for travelling, countries are Global Fund budget estimates. i diseconomies of scale and resulting inefficiencies, and higher personnel costs. Papua New Guinea $ 3.22 Mali $ 0.70 RII is a continuous geographical measure of remoteness. Central African Region $13.20 ii The RII values for Vanuatu, Solomon Islands, Papua Lao PDR $ 8.91 New Guinea, Australia and Lao PDR were developed Timor Leste using existing data sets (population numbers and area Solomon Islands $ 8.92 measured in terms square kilometres) for each country. Vanuatu $ 56 $ 76 RII is a function of remoteness times incapacity such that RII = a1/2p-3/2. Where a is the square root of area To understand some of the key reasons underlying the divided by population. Incapacity is measured by the significant differences in per capita costs current budget reciprocal of the population measured in thousands. The esitamtes for Lao PDR has been compared to funding RII represents the average distance people travel over for Vanuatu and Solomon Islands. Funding estimates at the capacity for interaction for example when a the present time are for 3.5 years for the Pacific Island service/activity is provided. countries and for 5 years for Lao PDR. Budget estimates for Vanuatu and Solomon Islands include funding for an The RII has been applied to per capita funding (revised intensified elimination program (Objective 4) in one budget estimates). province in each country. Lao PDR estimates do not include a similar objective. The remaining 3 objectives 3. RESULTS from all three country proposals are similar. For comparison purposes budget estimates for the two The value of the remoteness and incapacity index (RII) Pacific Island countries have been adjusted. Funding for index for Vanuatu was estimated to be 31.038 for the elimination objective (AusAID funded), domestic Vanuatu, 14.4 for Solomon Islands and 0.98 and for Lao government contributions and external support services PDR. RIIs were also developed for Papua New Guinea have been deleted from the total budget to produce a (1.3) and Australia (0.87) for comparative purposes revised budget. (Table 1). The most remote and incapacitated country (in terms of a measure remoteness and capacity of its It is suggested that the significant differential per capita population) is Vanuatu followed by Solomon Islands, budget estimates between Lao PDR and the other two then Papua New Guinea, Lao PDR and finally Australia countries is because of a combination of economies of (Figure 1). scale for Lao PDR and the reverse for the other two countries (diseconomies of scale) as well as In simple terms a comparison on RIIs means that geographical diversity and the impact of international Vanuatu is 31.67 times more remote than Lao and prices (due to small population numbers there are less Solomon Islands 14.4 times more remote than Lao PDR. experts in the Pacific Island countries than Lao PDR who can carry out required activities and international The RII can be used as to adjust cost estimates. The experts are more likely to be utilised in the Pacific Australian Grants’ Commission uses a similar index, the Island Countries). Accessibility and Remoteness Index of Australia (ARIA), to inform the distribution of Commonwealth

Author’s Guidelines – HealthGIS 2009 Grant Funds between states/territories. The cost The adjusted per capita estimates (adjusted budget) for adjustment index utilised in this study is the ratio of the Vanuatu ($1.48) and Solomon Islands ($ 3.09) are less each country’s index to the base country index: in this than the actual per capita estimates for Lao PDR ($ case Lao PDR. 6.80) whereas the actual per capita estimates for Vanuatu ($46.73) and Solomon Islands ($44.56) are The ratio of Vanuatu and Solomon Islands RIIs to Lao olver seven times that estimates for Lao PDR. PDR RII has been used to deflate estimated per capita estimates (revised budget). Per capita estimates for It is suggested that the significant differences between country are shown in Table 2. the actual and the deflated per capita estimates are due to the impact of remoteness and incapacity on cost of service delivery. Table 1 Remoteness and Incapacity Index (RII) for Selected Countries Country Population Area (Sq a 1/2 p -3/2 Remoteness Index (100000) Km) Vanuatu 2.3302 12190 110.408333 0.281121091 31.03811102 Solomon Islands 5.2112 28248 168.071413 0.084060775 14.12821322 PNG 64.7391 462840 680.323452 0.001919774 1.306066999 Lao 62.1714 238000 480.416486 0.002039924 0.980012978 Australia 215.386 7617930 2760.05978 0.000316355 0.873157614 Figure 1 Remoteness and Incapacity Index (RII) for Selected Countries Table 2 RII Adjusted Per Capita Estimates Malaria Program: Vanuatu, Solomon Islands and Lao PDR Country Total Budget Population Per Capita Per Capita Estimates Estimates Budget deflated by RII Vanuatu $11,122,015 $46.73 Solomon Islands $23,220,721 238,000 $1.48 Lao PDR $24,628,020 $44.56 $3.09 521,120 $6.84   3,600,000 Adjusting Budget Estimates for 1 Key Activity: The cost RII for the Solomon Islands then the adjusted cost would of the Temotu Province, Solomon Islands, baseline be $20,619 (compared to budget estimate for Lao PDR survey was in excess of $292,792. Lao PDR budget of $11,600). estimates for a baseline survey for 5 provinces including 13 districts and 49 villages is $58,000. This equates to This finding suggests that adjusting cost estimates by the $11,600 for 1 province. The cost of the Temotu survey RII can help explain cost differentials between Vanuatu, is 25 times that of the Lao PDR survey for 1 province Solomon Islands and Lao PDR. ($292,792). If the Temotu survey cost is deflated by the

Author’s Guidelines – HealthGIS 2009 4. DISCUSSION: WHY DO WE NEED A Islands compared to Lao PDR in terms of level of fund- REMOTENESS AND INCAPACITY INDEX? ing by objectives and by inputs, source of funds, and funding level of malaria compared to other health ser- A remoteness index can assist in understanding the cost vices. differentials between different target populations. The review has provided a beginning understanding of The average distance between people measured by the potential impact of remoteness and incapacity on the remoteness (RII) reflects: cost service delivery and the importance of taking this into account when comparing per capita expenditure • the comparative ease with which people can interact across different settings. - fewer opportunities (and ease of) communication both for program management and community engagement; The significant differential per capita budget estimates between Lao PDR and the other two countries arises pri- • the availability of goods, services and skills base marily because of economies of scale for Lao PDR and required to implement the malaria program; and the reverse for the other two countries (diseconomies of scale) as well as geographical diversity and related ser- • the ability and ease of travel and associated costs vice incapacity and the impact of international prices. (roads, public transport and communication are themselves a direct consequence of population numbers Further work is needed to provide a more robust mea- and spread). iii sure of the differences in costs for within and between countries. It is important to take account of the country Even with the effects of recent strong population specific service delivery models, the different needs of growth and urbanization the vast majority of both the population groups at any given time within and between Vanuatu and Solomon Islands population remains rural. countries as well as different cost structures between The use of urban versus rural definitions in Melanesia countries. Information is available the will allow for fur- does not adequately clearly distinguish between ther analysis of the impact to remoteness and incapacity characteristics of urban and rural populations. For at the village level and the impact on service delivery example the peri-urban village communities of Ifira, models and, hence, costs of servicing a population Pango, Mele and Erakor on the outskirts of Port Vila are spread over numerous islands versus a land mass and situated in close proximity to comprehensive urban living in different terrain. Further, work can also be un- infrastructure and services, including more ready access dertaken to explore the impact of distance and price paid to formal sector employment than rural people for goods and services. elsewhere in Vanuatu. While other village settlements are in areas that clearly do not contain a major Endnotes population centre. The degree of social, economic, and cultural isolation varies widely. In both cases these communities would be classified as rural by the commonly employed definitions without regard to their degree of isolation or remoteness. The factors of geographic diversity, accessibility or isolation such as a lack of transportation, infrastructure and communication and slower spread of technology can be inhibiting factors in the provision of services and development progress. Distance and the ability to travel are reliant on infrastructure such as roads, transport and communication which are, themselves, a direct consequence of population size. For the most part, persons living in rural areas are very aware of impact of long distances as travelling is an integral part of their way of life. However remote rural areas in Melanesia present many challenges particularly in access to health services. For example limited, irregular and expensive shipping and few roads have done little for rural populations in the Solomon Islands and large rural populations in Sanma and Tafea provinces in Vanuatu. 5.CONCLUSION The above analysis has provided an understanding of ex- pected expenditure (budget estimates) for the implemen- tation of current malaria plans in Vanuatu and Solomon

i http://www.theglobalfund.org/en/portfolio/ accessed June 2009 ii Zhao Y Gutheridge S 2008 Rethinking Remoteness: A simple and Objective Approach Geographical Research December p415 iii IBID

A STUDY OF HIV/AIDS RELATED KNOWLEDGE AND ATTITUDES AMONGST THE ENGINEERING COLLEGE STUDENTS Arjit Kumar, P. Bhardwaj, P. Gupta, J. P. Srivastava, K. P. Mathur Department of Community Medicine, Era,s Lucknow Medical College, Lucknow [email protected] ABSTRACT: One hundred seventy four randomly selected students studying in the various engineering colleges, studying in Uttar Pradesh were surveyed to assess their knowledge on HIV/AIDS. Pre tested, pre designed and preformed questionnaire was used to collect data. Response rate of 87% was obtained (174 out of 200). Overall, females showed less knowledge pertaining to issues related to human sexuality and HIV transmission, As compared to their male peers. Anal intercourse was observed as a risk for HIV transmission by 3 % of females as compared to 20% of males. In general, there were considerable misconceptions regarding the spread and risk of HIV transmission among all engineering students. Attitudes of most of the students toward HIV-infected individuals could be best described as ambivalent. Interesting to note that female students showed more positive attitude towards HIV infected people than their male peers. Findings suggest the need of integrating IEC activities and BCC activities promotion in the community starting from the initial stages mainly concentrating on teenagers and youngsters. KEY WORDS: HIV, AIDS, Engineering Students, Knowledge. 1. INTRODUCTION found to be most cost effective in bringing about desired behavioural change(Sood and Nambiyaar, 2006)The In April 2005, experts from Global Fund to Fight AIDS Government needs to respond to the desire for formal sex stated that India had overtaken South Africa with regard education, which has been expressed by the majority of to the number of AIDS cases(pandey.g,2005) The joint students and teachers (H.K.Agarwal ,1999)Teachers, who United Nations program for HIV/AIDS (UNAIDS) are crucial for the success of any sex education reports that India harbors between 7-9 million HIV cases programme, need to be adequately trained to handle compared to 4-6 million cases of HIV found in South delicate and sensitive queries from students Africa (UNAIDS,2005) The official Indian figures (S.D.Dhoundiyal,1996)The present study designs to find (111,000 HIV infected cases) do not reveal the scale of out the knowledge and attitudes of the students of the HIV infection because of the weaknesses in engineering colleges. The information obtained in this surveillance systems, bias against targeting groups such study will be used to demonstrate the need for as commercial sex workers for testing, and the lack of development and integration of an HIV IEC training testing services in many parts of the country (Avert,2005) module. Although the prevalence of HIV in India is 0.9%, the total number of people living with active HIV infection in 2. MATERIAL AND METHODS the country is estimated to be 10% of all global cases(World Bank ,2004a)A fraction of a percent increase A cross sectional study was done amongst students in the prevalence of HIV in India will increase the studying in different departments of various engineering number of adults living with HIV by approximately colleges under UPTECH. A pre tested pre designed million people(World Bank ,2004b) Even though an questionnaire with questions pertaining to the knowledge appreciable number of intensive HIV/AIDS-related and awareness of risk of HIV infection was given to the programmes and policies are in existence, the students which was approved by the ethical committee of effectiveness of these still needs to be evaluated. Youth College. The purpose of the study was explained to the stand at the centre of the HIV/AIDS pandemic in India students and they were asked to answer question regarding transmission, impact, vulnerability and sincerely. The questionnaire was filled in the class in the potential for change – they also represent the window of presence of doctors and investigators of Era’s Lucknow hope and opportunity. Since most of the new infections Medical College. They were given half an hour to occur in youth, any intervention in this age group is likely complete the form without mutual consultation. to have an impact on the disease trend (Anita nath,2002)The key to HIV/AIDS control among youth Results lies in health education, behavioural change Consent forms were obtained from 192 out of 200 communication (BCC), and ensuring safe sex practices. students. Eighteen students did not return surveys after Mass media, especially imparting HIV/AIDS education consenting to participate in the study. Around 174 (87%) through TV spots, reality shows and drama, have been students returned the completed surveys.

General characteristics of respondents Table-2 Knowledge regarding agent and routes of transmission of HIV Out of 174 respondents, 110 (63%) were boys and 64 Boys N=110 Girls N=64 Total (37%) were girls. The mean age of the study population No (Percent) No (Percent) N=174 was 21.74 years. No Sources of HIV/AIDS information (Percent ) As shown in Table-1, there was a significant difference Will be friends with HIV infected people found between male and female students with regard to 97 mass media and friends as sources of HIV related Yes 73(66.3) 24(37.5) (55.7) information. Ninety-percent (90%) of males compared to 77 78% of females indicated mass media as source of No 37 (33.6) 40 (62.5) (44.3) information. Similarly, of those who reported friends as a source of information, 40 (37%) were males and 12 Will care for people infected with HIV 153 (19%) were females (p<0.05). (87.9) Yes 95(86.63) 58 (90.6) 21 (12.4) Table-1 information sources regarding HIV/AIDS. No 15 (13.63) 6 (9.3) 116 Boys N=110 Girls Total Abstinence message for HIV transmission (66.6) N=64 N=174 58 No(Percent ) No No Yes 64 (58.i8) 52 (81.2) (33.3) (Percent ) (Percent ) 99 (90) 50 (78) 149 (85.6) No 46(41.81) 12 (18.75) 7 (4.02) 6 (6.3) 3 (9.3) 9 (7.3) 167 Mass media 40 (36.5) 7 (18.7) 47 (29.8) Is HIV/AIDS curable 2 (3.1) (95.9) Family doctors 55 (50) 29 (46.6) 84 (48.8) Yes 5 (4.5) 62 (96.8) Friends 59 (53.7) 27 (43.7) 86 (50.0) No 105(95.5) 162 Radio 15(14.5) 12 (20.3) 27(17.2) (93.1) Advertisements Awareness regarding condoms 12(6.8) News paper Yes 106 (96.3) 56 (87.5) 75 (43.1) 3. KNOWLEDGE ON CAUSATIVE AGENT FOR No 4 (3.7) 8 (12.5) 45(25.8 AIDS, AND DETECTION AND TRANSMISSION 49 (76.5) ) Concept of safe sex 1(1.5) 54 OF HIV Condo 26 (26.63) 14 (21.8) (31.03) InfectionWhen asked about the difference between AIDS m only and HIV, out of 174, 24 (14%) engineering college Monog 44(40) students replied that HIV and AIDS are the same amy condition. Of these, eight (8%) were boys and 16 (25%) Both 40(38.2) were girls. Table 2 showed that there was a difference regarding knowledge about routes of HIV transmission 4. DISCUSSIONS amongst boys and girls. In particular, twenty-eight (44%) female students, as compared to 31 (28%) male students, IEC materials and media have the potential to facilitate believed that saliva was a potent route of transmission. the development of positive behaviors and attitudes Sharing of razors was reported as a route of HIV among engineering students as they relate to HIV. transmission by 97 (88%) males compared to 50 (78%) Studies over the past decade among health professionals females. Sixty-three students (36%) reported both female in India identify the gaps in their knowledge concerning commercial sex workers and multiple sex partners as risks and transmission of HIV individuals at maximum risk of contracting HIV. These (Banerjee.P,Mattle,2005)These studies also document the two categories were followed by homosexuals (11%), negative attitudes students toward HIV infected people. intravenous drug users (5%), commercial blood donors The data from our study shed light on the critical gaps (4%), illiterate people (3%) and male commercial sex concerning knowledge regarding the spread and risk of workers (1%). Twenty (12%) engineering students HIV transmission among the medical students. For reported having no knowledge of the relationship example, saliva was reported as a transmission route by between the type of sexual intercourse and the risk of 34% of students, which is in contrast to scientific HIV infection. Only 26 (14 %) students in our sample evidence that considers saliva as a weak vehicle of associated anal sex with maximum risk of HIV transmission because of low viral loads. (R.Farid, 2003a) transmission. Of these 22 (20%) were males and four (3%) were females (p<0.05).

The low level of knowledge among medical students who are crucial for the success of any sex education concerning the relationship between HIV transmission programme, need to be adequately trained to handle and type of sexual intercourse also suggest a lack of delicate and sensitive queries from students. awareness concerning human sexuality. In our study only 14 % of the engineering students reported anal AKNOWLEDGEMENTS intercourse as a risk of contracting HIV. Even lesser number (10%) of students reported homosexuality as a Authors higly oblidged to Dr Farzana Mahdi( Director risk factor. Our findings contrast sharply with prevalence Academics) And Mr Mohsin Ali Khan (secretary) Era studies conducted in Mumbai, which estimate that 15% of Educational Trustfor their support financial as well as homosexual men tested in sexually transmitted disease administrative for the study. clinics are HIV positive. Our findings concerning level of knowledge pertaining to anal intercourse, homosexuality REFERENCES and HIV transmission, although much lower are comparable to the work reported by (Farid & Agrawal, H.K., Rao, R.S,Chandrashekar, S. & Choudhary,2003b) among Pakistani medical students Coulter, J.B. (1999). Knowledge of and where only 40% of medical students reported attitudes to HIV/AIDS of senior homosexuality as possible route of transmission(Anita secondary school pupils and trainee Nath,2009)Overall, mass media was found to be a leading teachers in Udupi District, Karnataka, source of HIV-related information followed by news India. Annals of Tropical papers and television Lal et al., reported comparable Pediatrics,19(2), 143-149. findings in a study among college students in southern India where the majority of students suggested mass Anita Nath HIV/AIDS and Indian youth – a media as source of HIV related information review of the literature (1980 - 2008) (SSLal,2000a)The high response to mass media as a Journal of Social Aspects of HIV/AIDS source of information is better understood considering the VOL. 6 NO. 1 MARCH 2009,4-6. impact that Internet and satellite television has had on India in the past decade. As compared to their male peers, Avert. HIV and AIDS in India, 2005. Available female students in our study showed positive attitudes at:http://www.avert.org/aidsindia.htm. toward HIV infected individuals. As it was shown in the Accessed MAY 24 2009. study of Rao et al (SS.Lal 2000b). Banerji, P. & Mattle, C. (2005). Knowledge, 5. CONCLUSIONS perceptions and attitudes of youths in. India regarding HIV/AIDS: A review of Although awareness level about HIV/AIDS among Indian current literature. youth is fairly high (although individual studies show varying results depending upon study setting), high-risk Bhatt, S.D. & Dhoundiyal, N.C., eds. sexual behavior without condom use and the presence of (1996).Sexual behaviour and safer sex certain misconceptions constitute a major area of practices in adolescents and youths with concern. A small proportion of youth appear still to hold reference to HIV/AIDS: implications for negative attitudes toward HIV voluntary testing and HIV- further research in Indian positive people (Anita Nath, 2009c) Even though an settings ,Journal The International appreciable number of intensive HIV/AIDS-related El8, 48-56. ectronic Journal of Health programmes and policies are in existence, the Education. effectiveness of these still needs to be evaluated. Youth stand at the centre of the HIV/AIDS pandemic in India Cohen MS, Sugars DC & Fiscus SA. Limits on regarding transmission, impact, vulnerability and potential for change – they also represent the window of oral transmission of HIV-1. Lancet.2000; hope and opportunity. Since most of the new infections 356(9226): 272. occur in youth, any intervention in this age group is likely to have an impact on the disease trend (The key to Farid R a AIDS/HIV infection among female HIV/AIDS control among youth lies in health education, college students. J Coll Physicians Surg behavioural change communication (BCC), and ensuring Pak. 2003; 13(3):135-7. nd Choudhary safe sex practices. Mass media, especially imparting HIV/ AJ.Knowledge about . AIDS education through TV spots, reality shows and drama, have been found to be most cost effective in In: Horns of a dilemma: AIDS. Volume- bringing about desired behavioural change (Sood & II.Almora: Shree Almora Book Depot, Nambiar, 2006). The Government needs to respond to the 39-77. desire for formal sex education, which has been expressed by the majority of students and teachers Khan S. MSM (Men having Sex with Men) and (Agrawal et al., 1999; Indian Express, 1996). Teachers,

HIV/AIDS in India, 2004.Available at: http:// www.nfi.net/NFI%20Publications/Essays/2004/MSM, %20HIV%20and%20India.pdf. Accessed November14, 2009. Lal SS, Vasan RS, Sankara Sarma P, et al. Knowledge and Attitudes of College Students in Kerala towards HIV/AIDS, S2000; TDS and Sexuality. Natl Med J Ind. Pandey G. India rejects HIV infection claim.BBC News Service, New Delhi, 2005.231-361.Available at:http://news.bbc.co.uk/2/hi/south_asia/ 4463899.stm. Accessed May 14, 2009. Richman KM & Rickman LS. The potential for transmission of human immunodeficiency virus through human bites. Journal Acquired Immune Deficiency Syndr.1993; 6(4): 402-6. Sood, S. cost-effectiveness of the components of a behaviour change communication campaign on HIV/AIDSin North India. Journal of Health Communication, 11(2), 143-162& Nambiar, D. (2006). Comparative. UNAIDS Global Summary of HIV/AIDS Epidemic, 2005. Available at:http://www.unaids.org/en/Resources/ . faq/faqtransmission.asp#8. Accessed November 23, 2005 World Bank, 2004. South Asia Region (SAR)- India-Regional Report . Availableat:http://web.worldbank.org. Accessed June 1, 2009.

SOCIO-ENVIRONMENTAL VARIABILITY AND BARMAH FOREST VIRUS DISEASE TRANSMISSION: RESEARCH DEVELOPMENT AND FUTURE PROSPECTS Suchithra Naish1* and Shilu Tong1 1School of Public Health & Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia 1*Suchithra Naish, School of Public Health & Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Brisbane, Queensland 4059, Australia Telephone: +61-7-3138 8296; Fax: +61-7-3138 3369; Email: [email protected] ABSTRACT: Arboviral diseases have emerged as a global public health problem. However, the impact of socio-environmental variability on the transmission of arboviral diseases remains to be determined. This paper provided an overview of current research and discusses the future research directions about the interrelationship between climatic, social and environmental factors and the transmission of Barmah Forest virus (BFV), one of the most common arboviral diseases in Australia. We conducted a systematic literature search on climatic, social and environmental factors, and BFV disease. The relevant studies were identified from a series of electronic databases. Additionally, we mapped the distribution of BFV disease in Queensland between 1992 and 2001. The epidemiological evidence revealed that the transmission cycles of BFV disease appear to be influenced by many factors. Temperature, high tide and socio- economic factors were among the major determinants of the transmission of BFV disease. However, the interrelationships between climate variability and the transmission of BFV disease varied with geographic area and socio-environmental condition. Preliminary results obtained from mapping on BFV distribution indicated that there may be certain high-risk BFV infection areas in Queensland. Projected anthropogenic global climatic change may result in an increase in BFV infections, and the key determinants of BFV transmission we have identified here may be useful in the development of disease control and risk management programs. It is important to determine the interrelationships between climatic, social and environmental factors and BFV disease transmission using advanced epidemiological methods. These research findings could be regarded as an impetus for applying epidemiological evidence in future disease control/surveillance decision making. KEY WORDS: Climate variability, Early warning system, Barmah Forest virus, Social and environmental factors, GIS. 1. INTRODUCTION and all are transmitted by mosquitoes (Mackenzie et al 1994; Arboviruses are an increasing threat to population health globally (Liu et al 2008). Arboviruses are viruses that Russell 1995). Of the arboviruses important in human are ‘‘maintained in nature by a biological transmission infection, Barmah Forest virus (BFV) is the second cycle between susceptible vertebrate hosts and commonest MBD (after Ross River virus), causing BFV haematophagous arthropods’’ (World Health disease in Australia (Russell & Kay 2004). Over the last Organization Scientific Group 1967). Climate change is fourteen years (ie, 1995-2008), 15, 592 laboratory expected to increase the activity of climate-sensitive confirmed BFV cases (1114 cases/year) have been arboviruses and their vectors by gradually raising the reported in Australia (Queensland Department of Health temperature and sea levels, and the changing pattern of 2009). Of these, over half (n=8050) were reported in rainfall globally (Russell 2009) . Australia is not Queensland, a sub-tropical and tropical state immune to these climatic changes and therefore, (Queensland Department of Health 2009). BFV is a mosquito-borne disease (MBD) has become a significant seasonal disease in Queensland with peak occurrence in concern for Australians (Jacups et al 2008; Russell February (summer) and March (autumn) (Kelly-Hope et 2009). The Department of Health and Aging report al 2002). There is a trend of increasing BFV states that as the climate warms up, the tropical weather transmission in Australia over recent years (Figure 1) zone in Australia will spread south, bringing with it (Queensland Department of Health 2009). disease vectors found prevalent in tropical weather The reasons for this increased BFV may include urban zones (Department of Health and Aging 2007). developments in or near wetland and salt-marsh habitats, and socio-ecologic changes (Russell 2002; Tong 2004). In Australia more than 75 arboviruses have been The major risk factors for BFV disease transmission documented, but only 12 are related to human disease

Suchithra and Shilu– HealthGIS 2009 include behavioural, environmental and ecological and The ecology of BFV is complex. For the transmission climatic changes (e.g., increasing temperature and rainfall), people’s movement, and deteriorating vector of BFV, the virus and its reservoir hosts (Russell 1995), control programs. vector and the human population and climatic (Reeves 70 ACT NSW NT Qld SA Tas Vic WA et al 1994) conditions are key factors (Mackenzie et al 60 1998). BFV has been isolated from over 73 species of 50 mosquitoes belonging to four genera including Aedes, 40 Culex (some Aedes species were renamed BFV rate of notifications (per 100 000 population)30 Ochlerotatus). Marsupials such as kangaroos, wallabies 20 are the possible reservoir hosts for BFV disease. The 10 distribution and abundance of the reservoir population 0 will affect the availability of viraemic individuals to 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Year (1995-2008) mosquitoes and a non-immune reservoir population Figure 1: BFV rate of notifications during the period leads to increased virus activity (Boughton et al 1984). from 1995 to 2008 in Australia Weather conditions such as temperature and rainfall The reasons for this increased BFV may include urban developments in or near wetland and salt-marsh habitats, directly affect mosquitoes breeding, survival, and and socio-ecologic changes (Russell 2002; Tong 2004). The major risk factors for BFV disease transmission abundance and the extrinsic incubation period and adult include behavioural, environmental and ecological and climatic changes (e.g., increasing temperature and longevity (Russell 1998b; Russell & Dwyer 2000; Turell rainfall), people’s movement, and deteriorating vector control programs. & Lundstrom 1990). Humidity and tides are additional BFV is an emerging disease and there has been an increasing interest on the determination of major risk important considerations that can also influence the factors for the BFV transmission. Therefore, to identify current knowledge gaps and future research needs, this transmission of the disease (Naish et al 2009; Naish et al paper critically reviewed the impact of climatic, social and environmental variability on the transmission of 2006; Tong et al 2005b). However, other socio- BFV disease and provided an overview of research development and future research directions in this ecological factors such as human behaviour, life style emerging field. and immunity also are involved as determinants in the Characteristics and Ecology of Barmah Forest Virus Disease transmission of BFV disease. Barmah Forest virus (BFV) (Alphavirus, Togaviridae) is Climate variability, social and environmental factors a mosquito-borne virus which causes Barmah Forest Virus disease, unique to Australia (Mackenzie et al In addition to our research, systematic literature search 1998; Russell & Kay 2004). The disease is usually non- was conducted to identify all relevant studies on the fatal. The symptoms include arthralgia, myalgia, fever, climatic, social and environmental factors and BFV rash and polyarthritis. Other uncommon symptoms are disease was conducted. A series of electronic databases glomerulonephritis and Guillain - Barre syndrome which such as MEDLINE (via EBSCOhost), ScienceDitrect, includes kidney inflammation. Sometimes the Web of Science and Pub Med were searched. symptoms may persist for up to several months. The Literature searches were conducted in May 2009, with intrinsic incubation period of the disease is about 7 - 9 no date or language restriction. The databases were days. searched with the following key words or MeSH terms: \"Barmah Forest Virus\" AND \"climate\" OR \"social\" OR BFV was named after it was first isolated from Culex \"environmental\" OR \"climatic\" OR \"rainfall\" OR annulirostris mosquitoes trapped in the Barmah Forest \"temperature\" OR \"ecological\" OR \"hunidity\". of the Murray River in northern Victoria, Australia in 1974, but it was only shown to be pathogenic to humans Key predictors of BFV transmission since 1988 (Merianos et al 1992). Since 1988, BFV disease has been reported in every state in Australia We have assessed all the publications obtained through (Queensland Department of Health 2009). the literature search based on the titles and abstracts and selected only those suitable for inclusion. Full copies of all relevant papers were then obtained, and critically reviewed. All studies were conducted in Australia; 6 papers were published during 2000 to 2009 (Table 1). The primary objectives of our own research were to assess the impact of climate variability, social and environmental factors on the transmission of BFV disease, and the importance of developing forecasting systems for the control and prevention of this emerging vector-borne disease in Australia. We collected data for the period 1992 to 2001 from relevant government agencies on climate variables, social and environmental variables and notified cases of BFV disease for six coastal cities in Queensland,

Suchithra and Shilu– HealthGIS 2009 Study Study Major Comments The computerised data set of notified BFV cases was details design findings obtained from Queensland Health, climate and Doggett et An outbreak Climatic al, 1999, investi- Rainfall and factors are Table 1: Studies retrieved from literature search on NSW gation high tide involved in climate variability, social and environmental factors and Australia were key the the BFV disease in Australia (Abbreviations: QLD, Correlation factors in mosquitoes Queensland, NSW, New South Wales, ARIMA, auto- Bi et al, analysis determining distribution regressive integrated moving average; SARIMA, QLD, 2000, BFV seasonal auto-regressive integrated moving average; Australia transmissio Spatiotempor SOI, Southern oscillation index). n al variations of BFV Tidal data were obtained form Australian Bureau of SOI is disease Meteorology and Queensland Transport, respectively associated remain to be Population data and SEIFA index including information with BFV assessed on socioeconomic status of the local government areas transmissio was supplied by Australian Bureau of Statistics. For the n six coastal cities in Queensland, the climate-BFV correlation coefficients were in the range of 0.16-0.24 Passmore et An outbreak The Mosquito for maximum and minimum temperatures, 0.03-0.09 for rainfall, 0.15-0.14 for humidity, 0.32-0.31 for high tide al, 2002, investi- relationship distribution and 0.29-0.25 for low tide. As maximum and minimum temperature (rs = 0.81), and high and low tide (rs = Victoria gation between is related to 0.80) were highly correlated with each other, separate models were developed to assess the impact of climate rainfall and rainfall variability on the transmission of BFV disease (Naish et al 2009). mosquito Time-series regression models were performed to assess density the impact of climate variability on BFV transmission in coastal Queensland cities (Naish et al 2009). We found Tong et al, Disease An increase Key that temperature, high tide and socio-economic conditions were key risk factors in the transmission of 2005, QLD mapping in the determinants BFV disease. We run several models and obtained the best-fit model for the BFV dataset with different sets of using BFV geographic of BFV predictor variables. The models included the number of BFV cases as the response variable and the climate and cases in distribution transmission tidal variables in the current month and with a moving average of lags 1–2 months, and SEIFA index, as QLD for the of BFV need to be explanatory variables. The best-fit model was selected based on the deviance reduction as measured with Chi- period cases in examined squared test statistic and the AIC value. The model with smallest AIC value and deviance showed that BFV 1993-2001 Queensland counts were positively and statistically significantly associated with maximum temperature (b = 0.139, P = over recent 0.000) and high tide (b = 0.005, P = 0.000) and negatively and statistically significantly associated with years SEIFA index (b = 0.010, P = 0.000). The models were adjusted for confounding effects of seasonality and Naish et al, Ecological Found Climatic population size. The goodness-of-fit test indicates that the model developed was the most parsimonious for this 2006, time series minimum factors dataset with AIC = 3532.97, Pearson χ2 value of 1.05, deviance value = 0.95 and Omnibus likelihood ratio of Gladstone, analysis temperature impact the 444.3 (Naish et al 2009). Australia, and high BFV We performed an ecologic time-series analysis to examine the association between climate variability and tide as key disease the transmission of BFV disease between 1992 to 2001 in Gladstone region, Queensland, Australia (Naish et al determinant 2006). Information on notified cases of BFV was obtained from the Queensland Health, and the climate s for BFV Naish et al, Ecological Found Both Coastal time series minimum climatic and QLD, analysis temperature social factors Australia, , social are involved 2009 factors and in BFV high tide as key predictors Australia, where most cases are usually reported (Figure 2). 70.00 Brisbane 60.00 Bundaberg 50.00 Cairns Mackay Townsville Gladstone BFV cases 40.00 30.00 20.00 10.00 - Jan-1993 Jan-1994 Jan-1995 Jan-1996 Jan-1997 Jan-1998 Jan-1999 Jan-2000 Jan-2001 Jan-1992 Year Figure 2: Patterns of BFV distribution in coastal Queensland during 1992- 2001

Suchithra and Shilu– HealthGIS 2009 and population data were supplied by the Australian vegetation - have created ideal breeding habitats for bureau of Meteorology and the Australian bureau of saltmarsh and freshwater mosquito species. statistics, respectively. The autoregressive integrated Deforestation due to cultivation and urban development model was used to examine the relation between the due to increase in human movements could increase the climate variability, tides, and the monthly incidence of potential for BFV transmission (Lindsay & Mackenzie notified BFV infections. As maximum and minimum 1998; Mackenzie et al 2000; Russell 2009). Tourism and temperature were highly correlated with each other (rs = travel have also become important mechanisms for 0.95), two separate models were developed. The facilitating the BFV and its vectors. Climatic changes correlation coefficients were in the range of 0.037 to periodically may also influence the local weather 0.385 for actual maximum and minimum temperatures, conditions and the life cycle of the disease reservoirs 0.020 to 0.129 for rainfall and 0.022 to 0.152 for relative and cyclic changes in human outdoor activities. humidity (Naish et al 2006). Monthly minimum However, few data were available on many of these temperature and high tide at the current month played a factors (eg: human movements and immunity), and significant role in the transmission of BFV disease in therefore, it might be difficult to explain a broad Gladstone with a log-likelihood of –79.33 and AIC = spectrum of reasons for the increase in the transmission 168.66. of BFV disease. In a previous study, Tong et al (Tong et al 2005a) used Forecasting GIS tools to assess the distribution of BFV in Queensland, Australia. For that study, they used the The development of disease outbreak forecasting computerized data set on the notified BFV cases in systems is important in the control and prevention of Queensland for the period of 1993 to 2001. They found mosquito-borne diseases. A large-scale public health that there has been an increase in the geographic intervention is usually required during an outbreak. distribution of the notified BFV cases in Queensland Early warning systems based on weather forecasts can over the last decade (Tong et al 2005a). assist in improving vector control in high-risk areas. Therefore, analysis based on GIS may have an Passmore er al (Passmore et al 2002) obtained the opportunity to develop an early warning system to climate data between 2001 and 2002 from Victoria, predict the outbreaks in high-risk zones and improve Australia and compared the relationships between vector control and public health intervention. rainfall from each mosquito trapping site and mosquito counts. They found that mosquito counts were increased Mapping the BFV outbreaks when rainfall was increased. We also mapped the distribution of BFV cases using Bi and his associates (Bi et al 2000) used BFV cases Geographic Information Systems (GIS) to identify high- from 1992 to 1996 in Queensland, Australia-and found risk areas and predict outbreaks of BFV in Queensland, that there was an association between positive southern Australia (Figure 3). BFV is found in tropical and oscillation index (SOI) values and sea level, which had subtropical Queensland, Australia, where the salt marsh an impact on the breeding sites of some Aedes mosquito, Aedes vigilax, and the fresh water species, mosquitoes. The reported place of onset for each case Culex annulirostris, are vectors of BFV disease. was used to characterise the geographic distribution of Mapping the high risk areas or hot spots facilitates an the BFV infection within Queensland. assessment of the breeding haibitats of the mosquito species of BFV disease. First, we considered a simple Doggett et al (Doggett et al 1999) obtained data on risk map that was applied to BFV data for the climate, BFV and mosquitoes in New South Wales, Queensland. It was identified that several hotspots for Australia from 1994 to 1995 and studied the BFV disease were found across and around the wet areas relationships between climate and mosquitoes. They in coastal and as well as inland areas: mosquitoes of found that mosquito populations had increased due to freshwater species (eg, Culex) were found in inland the increased levels of rainfall and high tides which then areas and saltmarsh species (eg, Aedes) were found in caused increased BFV cases. coastal areas. Other risk factors Future Prospects Many other risk factors such as virus, vector, host, and Our future research aims at applying GIS-based environment are involved in the BFV transmission statistical tools for prediction of BFV disease outbreaks cycles. Changes in weather; virus strain; mosquito in Queensland. The main focus will be on identifying populations, survival, and breeding; human activities, the correlation of key environmental factors associated movement and immunity, and socioeconomic status may with BFV incidence. This relationship will be used in all contribute to the transmission cycles of BFV disease. conjunction with GIS to map BFV distribution vector Changes in farming practices-such as building dams and breeding sites. The aim is to show how GIS combined irrigation canals, and identification of new wetlands and with the predictors of BFV disease may help to control

Suchithra and Shilu– HealthGIS 2009 and prevent BFV transmission and to reduce the disease providing the BFV data, climate, tides and population burden. As such information when mapped together data, respectively. SN is supported by both the QUT creates a powerful tool to analyse distribution of micro- Scholarship and ARC-Linkage project grant organisms and their relationship to different ecological (#LP776918). ST is supported by an NHMRC Research niches, and may dramatically improve our ability to Fellowship. quantify the impacts of climatic and ecological changes on BFV and other mosquito-borne diseases. The ability REFERENCES to predict outbreaks of BFV disease will greatly enhance the efficacy of prevention efforts and will substantially reduce costs of prevention with efficient targeting of high-risk areas.  Suburb with BFV   20 to Max (20)   15 to <20 (20)  10 to <15 (37)   6 to <10 (68)   1 to <6 (672)                  0 250 500    kilometers                                                                                                                                                                         Figure 3: GIS based distribution of notified BFV cases in Queensland, Australia, 1992-2001 (Numbers in parentheses indicate the number of localities). 2. CONCLUSIONS We found that socio-environmental variables such as temperature, SEIFA index and tides have played a key role in the transmission of BFV disease in Queensland. Baseline mapping of the BFV data indicated that there are hot spots or high-risk areas for the transmission of BFV disease. These research findings may be useful for planning BFV disease control and risk management programs. ACKNOWLEDGEMENTS The authors would like to thank the Queensland Health, Australian Bureau of Meteorology, Queensland Transport and the Australian Bureau of Statistics for

DENGUE FEVER AND DENGUE HAEMORRHAGIC FEVER RISK ZONATION IN CHACHOENGSAO PROVINCE, THAILAND USING ANALYTICAL HIERARCHY PROCESS (AHP) Phaisarn Jeefoo1, Nitin Kumar Tripathi1, Marc Souris2, Seishiro Kibe1 and Vivarad Phonekeo1 1 Remote Sensing and GIS Field of Study, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand. 2 Research Center for Emerging Viral Diseases IRD UR 178, Center for Vaccine Development, Institute of Science and Technology for Research and Development, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom 73170, Thailand. E-Mails: [email protected]; [email protected]; [email protected] ABSTRACT: The goal of this study is to use the Geographic Information System (GIS) techniques to develop risk zonation map of Dengue Fever (DF) and Dengue Haemorrhagic Fever (DHF) in Chachoengsao province, Thailand. The risk zonation is derived by Analytical Hierarchy Process (AHP). Medical database was referenced to the geographical and meteorological data layers. Based on the analysis, potential areas were categorized as very high, high, moderate high, moderate low, low, and very low risk categories.The model used in this study provided valuable information to prepare DF/DHF risk zones for decision support to mitigate the epidemic. Analytical Hierarchy Process (AHP) was used. Factors weights obtained were acceptable as the consistency ratio (CR) was 0.031, which was < 0.1. Spatial modelling included huge database about physical-environment, climatic, and demographic factors. Approximately, 74.96% people lived in very high, high, and moderate high risk zones. The most of risk zone was shown in district Mueang Chachoengsao including 142,485 or 22.34% of total population. The results from AHP based DF/DHF risk zonation yielded areas and useful information on different level of disease risk. The methodology is general and can be applied in other applications field such as disease outbreak during natural disasters. KEY WORDS: Geographic Information System (GIS), Dengue Fever (DF), Dengue Haemorrhagic Fever (DHF), Analytical Hierarchy Process (AHP) 1. INTRODUCTION mosquitoes had a worldwide distribution in the tropics. Over 2.5 billion people now live on areas where dengue epidemic. 1.1 General Instructions Currently, dengue fever causes more illness and death than any other arbovirus disease of human (Rosen, 1982). DF/DHF Dengue is an arboviral disease including four serotypes of poses a constant serious risk and continues to be a major Flavivirus, Dengue-1, Dengue-2, Dengue-3, and Dengue-4 public health threat in Thailand (Nakhapakorn and (DEN-1, DEN-2, DEN-3, and DEN-4) transmitted by Jirakajohnkool, 2006). mosquitoes, mainly Aedes Aegypti (Gubler and Kuno, 1999; Rotela et al., 2007; Thammapalo et al., 2008). Dengue Fever Thailand is located in the heart of the endemic areas for DF (DF) is primarily a disease of older children and adults. It is and DHF (Kittayapong et al., 2008). The Bureau of characterized by the sudden onset of fever and a variety of Epidemiology has reported that there have been regular nonspecific signs and symptoms, including frontal headache, outbreaks in past in Thailand. The highest number of cases retro-orbital pain, body aches, nausea and vomiting, joint was reported in 1987 when the incidence rate was as high as pains, weakness, and rash. Dengue Haemorrhagic Fever 325 cases per 100,000 population based on the number of (DHF) is primarily a disease of children under the age of 15 cases reported. The latest epidemic was in 1998 when the years, although it may also occur in adults. It is characterized incidence rate was as high as 211 cases per 100,000 by sudden onset of fever, which usually lasts for 2 to 7 days, populations. This was the second highest incidence rate in the and a variety of nonspecific signs and symptoms (Gubler, 40 years’ history of DF/DHF outbreaks (MOPH, 2002; 1998). Promprou et al., 2006). Cases of DF/DHF in Thailand have risen 36% since last year as an epidemic of the mosquito- The first known epidemic of DHF occurred in Manila, borne disease swept the region. The outbreak has killed 17 Philippines, in 1953 to 1954, but within 20 years the disease people in Thailand and affected more than 21,000 since the in epidemic form had spread throughout Southeast Asia; by beginning of the year. The number infected by the virus, the mid-1970s, DHF had become a leading cause of which is especially dangerous in children and the elderly, has hospitalization and death among children in the region (WHO, risen by 36% from the same period in year 2006. The DF/DHF 1997). In Asia, DHF epidemic has geographically expanded incidence situation in 2007 is more serious than last year from Southeast Asian countries towards west i.e. India, Sri because of the earlier arrival of the rainy season, which Lanka, the Maldives, and Pakistan and also in east to China brought forward the hatching period of the dengue mosquito. (Gubler, 1998). In 1997, dengue viruses and Aedes Aegypti In 2008, rising temperatures, longer rainy seasons and