Spatio-Temporal risk-analysis of cancer endemicity in Sulthan Bathery Taluk of Wayanad District of Kerala-A geo-informatics approach
Context: Asian countries have to confront with the global burden of cancer and various environmental factors predisposing the incidence. Geoinformatics can assist in spatial autocorrelation and statistical analysis in determining environmental and demographic correspondence to endemicity. What is of...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer Medknow Publications
2018-01-01
|
| Series: | Indian Journal of Community Medicine |
| Subjects: | |
| Online Access: | http://www.ijcm.org.in/article.asp?issn=0970-0218;year=2018;volume=43;issue=3;spage=199;epage=203;aulast=Gory |
| Summary: | Context: Asian countries have to confront with the global burden of cancer and various environmental factors predisposing the incidence. Geoinformatics can assist in spatial autocorrelation and statistical analysis in determining environmental and demographic correspondence to endemicity. What is of prime importance is the availability of the spatial datasets of cancer cases. Aims: The aim of this study was to reveal the distribution pattern of cancer and its magnitude in the eight panchayats of Sulthan Bathery Taluk of Wayanad district. The present study also attempted to develop and implement a data frame facilitating better data collection. Settings and Design: This was a taluk-level cross-sectional retrospective analysis and interventional study. Subjects and Methods: A retrospective survey created a geodatabase with 547 cancer cases registered along the timeline of 2015–2016. Input datasets were geocoded using Google Earth Pro software. Statistical Analysis Used: The analysis was performed using ArcMap-10.2 version. Results: Registration revealed the high breast cancer incidences and temporal increment mainly in town areas. The incidence depicted male predominance and prevalence along the age group of 30–69 years. The pattern showed cancer incidence at a proximity to state borders and forest regions (Noolpuzha) which are high population density regions, instantiated relation of geographic variables, and cancer incidences. The implementation of data frame ensured structured data collection. Conclusions: This study concluded the spatial association of cancer incidence demonstrating the high-risk regions with male predominance and role spatial risk analysis in cancer database management. |
|---|---|
| ISSN: | 0970-0218 1998-3581 |