Browsing by Author "M, Prashanthi Devi"

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SPACE TIME MODEL FOR CANCER INCIDENCES IN TAMIL NADU: MAPPING HEALTH STATISTICS FOR POLICY PROGRAMMING AND DECISION MAKING
(International Journal of Advanced Research in Computer Science and Software Engineering, 2015-04) P B, Harathi; Janani Selvaraj; M, Prashanthi Devi
The burden of cancer is growing globally and is one of the top leading causes of death. Information on cancer patterns is essential for effective planning of cancer control interventions. In specific the geographical study of cancer will help in identifying the high risk communities for further etiological studies. The objective of the present study is to analyze the time based geographical expansion of cancer incidences in the study region. The spatialtemporal model using Knox and Mantel statistic was applied to identify if additional cases are added in subsequent time period from high incidence areas or from moderate areas or from low incidence areas. This study will provide an indication to any association between time trend and cancer incidences. Through the spatial temporal model, the high risk areas have been identified and the temporal variations in the risky zones were assessed
SPATIAL ANALYSIS OF CANCER INCIDENCES TO IDENTIFY RISK AREAS AND HOT SPOTS: A CASE STUDY IN THE WESTERN REGIONS OF TAMIL NADU, INDIA
(International Journal of Scientific Research, 2014-07) P B, Harathi; Janani Selvaraj; M, Prashanthi Devi; S, Valarmathi; S, Balasubramanian
The burden of cancer is growing globally and is one of the top leading causes of death. Information on cancer patterns is essential for effective planning of cancer control interventions. In specific the geographical study of cancer will help in identifying the high risk communities for further etiological studies. Objective: The present study aims to investigate the application of various spatial statistical tools to identify the high cancer risk zones in the western regions of Tamil Nadu, India. Methodology: Spatial point pattern analysis was performed to assess the area based risk factor for cancer in the study area. The cancer incidences recorded in each address were geo-coded to build point features. Dual kernel estimation method was used to simplify the complex point patterns without diminishing the significance of the incidence level data. The incident hot spots were verified and tested for their statistical significance against a random distribution by means of Nearest Neighborhood Index, Ripley’s K, Geary’s C and Moran’s I test. CrimeStat software (CrimeStat III, 2004) and ArcGIS 9.1 were used to obtain these results. Results and Conclusion: The smoothed map produced through the Kernel estimation method showed high clustering in the Coimbatore North, Coimbatore South and Erode taluks and was confirmed statistically by the Nearest Neighbouhood Index and Ripley’s K test. Further, from the values obtained by the Moran’s I and Geary’s C test it is observed that there exists positive partial autocorrelation in the point data. Hence the spatial analytical methods will be useful tools in conducting further etiological studies in the high risk regions. In addition, it will be also helpful for the health professionals to organize early cancer screening programs and better prevention strategies for the society
TRACING THE SPATIO TEMPORAL PATH OF PEAK MALARIA INCIDENCES USING WALK ANALYSIS AND GIS
(2008) S, Valarmathi; M, Prashanthi Devi; P B, Harathi; S, Balasubramanian
Epidemic risk is a dynamic phenomenon with changing geographic pattern based on the temporal variations, in determinant factors including weather and other eco epidemiological characteristics of area at high risk. Epidemic early warning systems should take account of non uniform effects of these factors by space and time and hence temporal dimensions could be considered in spatial models of epidemic risks (Abeku, 2004).Based on this concept, the present study is aimed to analyse the geographical based time expansion of malarial transmission. Monthly malaria incidences data for a period of 101 months (Jan 1996- May 2004) recorded from Salem distrct, India were used for the study To estimate the spatial effects based on two components i.e., the overall difference among the regions and the rate of change over time for these regions, a spatio-temporal analysis for fixed and random effects are performed. The model was used to identify if additional cases are coming from malarial predominant areas (High Incidence areas), from moderate areas, or from low incidence areas. The conditional auto regressive model is used to model the random effects. Correlated Walk and Random Walk analysis is used to show the movement of the disease over time. Markov Chain Monte Carlo simulation is used to obtain estimates of the posterior and predictive quantities of interest. CrimeStat is used to analyze statistically and Arcview 3.2 is used to map the results at different time periods and maps of smoothed time incidence. The results have significant implication over space and time and can be used for malaria control activities in the study area and also other infected areas. Based on the time and space aspect, the regional malarial control authorities have an opportunity to assess the risk of encountering the disease infection and to plan prevention measures accordingly. This study also provides an indication to any association between time trend and basic malarial incidence.