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Über dieses Buch

This book demonstrates how GIS techniques and statistical methods can be used to emphasise the characteristics of population and its related variables, vis-à-vis care facilities and the status of vector borne diseases, as well as for malaria modeling. Concentrating on the Varanasi district of India, the main aim of the book is to determine and map the density areas of vector borne diseases using GIS techniques.

The book explores how health GIS is an important sub-discipline of health science and medical geography, which is traditionally focused on the spatial aspects of disease ecology and health care facility analysis.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Health Care System and Geospatial Technology: A Conceptual Framework of the Study

In this chapter an introduction to the main themes of the book, and the role health geography and geospatial technologies like geographic information system techniques, remote sensing etc. in the health sector and different aspects related with health GIS, medical geography, spatial epidemiology, concept of primary health care are very clearly discussed. The present study has been designed to analyses the pattern of health care delivery system in rural areas of Varanasi district and status of vector born disease (VBD) especially of malaria along with effects of important factors responsible for spreading the malaria area including population density of each development blocks.
Praveen Kumar Rai, Mahendra Singh Nathawat

Chapter 2. Applying Remote Sensing and GIS in Study of Physical and Cultural Aspects of Varanasi District

The city of Varanasi (Benaras) lies on a bend of Ganga river (Ganges), one of the largest rivers of the World. In this chapter, various physical i.e. geological, physiographic, drainage, climatic characteristics, condition of ground water, land use etc, as well as cultural aspect like transportation and communication facilities, solid waste generation etc. through remote sensing and GIS are very well presented. On the basis of relief variation, geology and drainage characteristics the area has been divided into three main physiographic divisions i.e. Upper Ganga-Varuna Plain,Varuna-Gomati Interfluence and Ganga-Varuna Interfluence. In this study it is found that the current water supply system is not adequate to supply to around 1.6 million people in the city. There is a substantial gap between the recommended norm and the actual water supply. Land use land cover (LULC) map is prepared using remote sensing data (IRS-1C/1D-LISS-III) of 2008, in which five important classes are delineated i.e. agricultural field, fallow land, vegetation, built-up area, and water bodies. In Varanasi district there are 85 Ganga ghats with typical structure on the river Ganga which are frequented by a large number of pilgrim’s everyday for taking a holy dip in river Ganga. The city along the Ghats has facing the severe serious problems of solid waste management due to congested lanes. These physical and cultural aspects are directly and indirectly used in the main themes of the study.
Praveen Kumar Rai, Mahendra Singh Nathawat

Chapter 3. Integration of Census Data with GIS for Analysis of Population Characteristics

GIS is very well used to integrate the census data for population characteristics. Analysis of population characteristics plays an important role in population geography. In this chapter various aspect of population characteristics like population growth, distribution, density, sex ratio, literacy, occupational structure of peoples in Varanasi are discussed. Population characteristics are relatively vital because it defines the economic and cultural condition as well as characteristics of the area, concerned. The decennial growth rate of Varanasi district had been very high during the last three decades. This has resulted in the pressure of population on land. Growth rate of male-female population during last 3–4 decades (from 1971 to 2001) in Varanasi district has been increased more than double. As per the census 2001, the current sex ratio (female population per 1000 male) in Varanasi city is 876, which is lower than the state urban average of 885 and national urban average of 901. Such rural-urban differentials in sex-ratio are the product of sex selective migration from rural areas to urban areas. Occupational composition is an important index to through light upon the economic dynamic of health and vigor of a region. The growth in population is also likely to pressure already stressed public transport and will have effect on health services, hence planned efforts are required to direct the growth of the district both rural and urban area in right direction.
Praveen Kumar Rai, Mahendra Singh Nathawat

Chapter 4. Analysis of Health Care Facility Using GIS and GPS

Analyzing distribution of hospitals through GIS and GPS is a significant measure in health care facility because every category of population should get access to the hospital facility optimally. Spatial analysis of health care facilities using GIS is analysed in this chapter. Health care service in Varanasi district go from bad to worse, all it shortage of man power, especially of doctors, or negligence in providing health services, many government hospitals, primary health centres (PHCs), community health centres (CHCs) seem to be witnessing health care delivery services at their worst in the region. Buffer (Proximity) analysis is used widely for many situations – e.g. to understand the association between transportation facilities in the study area to existing health care facilities. Buffer technique also play a vital role in the health GIS application through which we can easily calculate the number of persons live within a 10 km. radius from a particular primary health centre’s (PHCs) or community health centres (CHCs) or from the other governmental hospitals etc. of the district. By applying the proximity analysis of health centres, it is found that maximum rural population are totally depended on the existing government health centres. Shortest route estimation through network analysis is used for identifying the most efficient routes or paths for allocation of services.
Praveen Kumar Rai, Mahendra Singh Nathawat

Chapter 5. GIS in Vector Born Disease Mapping

The representation and analysis of maps of vector born disease (VBD) and other related data is an important tool in the analysis and representation of local and regional variation in public health care system. GIS plays a variety of roles in the planning and management of the dynamic and complex healthcare system and disease mapping. Important vector born diseases like malaria, dengue fever, kala-azar etc. are discussed in this chapter. Spatial disease models study and predict the movements of people, information, and goods from one area to the other area. By accurately modeling these movements through GIS techniques, it is effortlessly to identify areas most at risk for disease transmission and thus target intervention efforts. Development block-wise report of VBD cases are mapped to recognize clusters necessitating intense attention for the control of disease. Location of dengue and kala-azar cases are identified through GPS. Important favorable indicators i.e. stream, ponds/water tanks, nalas, sewage zone, overhead tanks and slum areas in the Varanasi city also are very helpful malaria breeding sources and these indicators are extracted from remote sensing satellite data for the analysis. Outcomes of the present study recognized target variables that potentially favor mosquito breeding locations in the survey areas.
Praveen Kumar Rai, Mahendra Singh Nathawat

Chapter 6. A Study of Malaria Susceptibility Mapping Using Statistical Methods with GIS

Developments in the area of geographical information systems (GISs) can offer new ways to symbolize epidemiological data spatially. In this chapter, three statistical methods i.e. Multiple Linear Regression, Information Value (Infoval) and Heuristic Method are used to develop malaria susceptibility index (MSI) and malaria susceptibility zonation (MSZ) through GIS and remote sensing. Village-wise malaria location data were collected from each primary health centre (PHC) and then the locations were identified using GPS. Malaria influencing data layers like rainfall, temperature, population density, distance to river, distance to roads, distance to health facilities, distance to ponds, NDVI, land use. are very well described in this study though GIs and remote sensing. These layers are used to produce the malaria-susceptibility model map. Comparison of statistical methods to select optimum model for MSZ by malaria density method (Qs) is also calculated. The malaria investigation is also completed using the information value, multiple linear regressions, and heuristic models, and the analysis outcomes are tested using the malaria locations for the study area. The verification method is achieved by Area under Curve (AUC).
Praveen Kumar Rai, Mahendra Singh Nathawat

Chapter 7. An Analysis of Geographical Survey for Utilization of Health Care Facilities

Utilization of health services is a complex phenomenon which, on the hand, is influenced by the awareness by an individual of the need for services thereby endorsing him to take a choice to use them and, on the other hand, by the availability, accessibility and organizational characteristics of health care services itself. The main objective of this chapter is to estimate the utilization pattern of health care services in the Varanasi district of India. Primary data pertaining to the utilization of health care facilities are collected from 800 respondents of 16 selected villages of rural Varanasi and analyzed with the SPSS statistical software. Varanasi City proper was not considered for this purpose because the presence and functioning of many private and government hospitals here meant that people were able to avail themselves of a fairly good range of healthcare facilities in comparison to people residing in the rural areas. Results of the findings revealed a high level of awareness among the local public of both the existence of the health care centres (78 %) and the type of health services they provided (75 % for vaccination; 70 % mother-child health (MCH) services; 62 % family planning; and 52 % general treatment). Despite such high levels of awareness only 25 % of them are satisfied with all the health care services provided by the primary health centres (PHC), 60 % are only partially satisfied and the remaining 14 % were not satisfied at all. These findings thus underline the geographical disparities in health facilities between urban and rural area of Varanasi.
Praveen Kumar Rai, Mahendra Singh Nathawat

Chapter 8. GIS Initiatives in Health Care Planning

The geographical information system (GIS) is very helpful in a variety of application areas points to an increasing interest in the spatial aspects of health policies and planning. The main aim of this chapter is to examine the relevance of Geographical information system (GIS) supporting health planners for a district level healthcare planning. For this purpose, an effort has been made here to calculate the hospital requirement area to know the specific sector that needs to better develop health facilities. The weightage is assigned to the class of thematic layers respectively to produce weighted thematic maps, which have been overlaid and numerically added in order to produce a Hospital requirement index (HRI) and hospital requirement zone (HRZ) map. These maps are very useful to calculate the exact area having good health facilities and also those wherein healthcare facilities need to be improved in Varanasi district. When the outcomes are compared with Multiple Linear Regressions and Information Value method then it is found that the malaria model developed with Information Value method is an optimum model which is selected for the measurement of hospital requirement index using the related parameters. The Hospital requirement index (HRI) values according to the weighting method are found to lie in the range from 11 to 23. After calculation by weighting method using selected indicators, it is found that the areas coming under very high and high requirement class is 46.62 % and 7.55 %, respectively, whereas 3.39 % and 42.63 % of the total areas comes under low and moderate requirement classes in Varanasi district.
Praveen Kumar Rai, Mahendra Singh Nathawat
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