Journal of Ecophysiology and Occupational Health

Umesh Prasad ¹, Gurmit Singh ²

Affiliations
1 Isabella Thoburn College, Lucknow, U.P., IN
2 Department of Computer Science and Information Technology, Sam Higginbottom Institute of Agriculture, Technology and Sciences, Allahabad, U.P., IN

Abstract

The new paradigm envisioned for epidemiological studies in 21^st century advocates shifting from the current survey based protocols to combination of evidence based laboratory/ clinical studies coupled with in silico approaches. One among the key strategies is to adopt an integrated approach, which can acquire, analyze, and interpret the data both qualitative and quantitatively in one go. But, this challenging task has highly hampered due to complexity of human physiological systems, ethical dubious, etc. Thus, the present investigations were aimed to develop an interactive online tool for prediction and management of human health for Indian population engaged in different occupation. At first step, a comprehensive questionnaire on human health risk assessment and management was developed and used for offline data collection. Simultaneously, a database management system (DBMS) was developed using software and programming languages mainly including MySQL, MS Office 2007, Hypertext Preprocessor (PHP), Cascading Style Sheets (CSS), Macromedia Dreamweaver, SPSS, java script, C++ language, Microsoft DOT NET, etc. For the purpose, the DBMS was developed and is being used through website "www.healthriskindia.in" for online survey. Upon the analysis of data collected from 2000 individuals of Jhansi and Lucknow districts of Uttar Pradesh, the socioeconomic status, education, hygiene status, occupation type, etc. were found to be associated with proneness to the diseases. Further, the relation between health status and day to day activities of the individuals engaged in different occupations of different groups in the society was analyzed successfully using DBMS. The analysis made in different combination of permutation using the data of 2000 volunteers show significant simulation with epidemiological data generated through conventional means for the mimicking population residing in the study area.

Keywords

In silico, DBMS, Health, Risk.

Full Text