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Title: | An Application of Demographic Data as a Surrogate for Epidemiological Profiling in India |
Authors: | Kumar, K Vinay Kumar, K Vijay |
Keywords: | Public Health;Epidemiological Profiling;Patient Health Prediction |
Issue Date: | 2015 |
Publisher: | Indian Institute of Management, Ahmedabad |
Citation: | Kumar, K. V., & Kumar, K. V.. (2015). An Application of Demographic Data as a Surrogate for Epidemiological Profiling in India. 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. Indian Institute of Management, Ahmedabad |
Series/Report no.: | IC 15;088 |
Abstract: | The absence of nation-wide profiling of the health condition of people in India has lead to the Government of India tending to take a broad brush approach with very little reference to the specific health condition of people in a region. This causes inefficient health management mechanism with the government erring on the side of caution in order to ensure coverage. The key challenges arising out of inadequate and poor quality of data are uneven health care spending, and mismatch between disease profiles and provision of care. As a result, a significant gap exists in the ability of policy makers to understand the implications for public health in those geographies which are not extensively covered. A resultant approach is a broad-brush health intervention which is inefficient while achieving its objective of universal health coverage. Many of the data collected through the Census have a strong correlation to the health status of the individual. A question therefore arises as to whether it is possible to use demographic data available through the Census, for the purpose of deriving healthcare indicators, and thus using some of the data available through census as a means of deriving epidemiological data. The results of the mapping of each of the demographic data collected from Census, initially within the NFHS data set, and subsequently, mapped to the Census dataset provides an insight into the potential disease that individuals in various geographies are likely to suffer. This data, once extrapolated to the ward level in a city provides a level of granularity which helps in a better understanding of the health parameters that are actionable. |
URI: | http://hdl.handle.net/11718/14057 |
Appears in Collections: | 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence |
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