Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/66869
Title: Development and application of Semiparametric Regression Models for estimation of short term health effects related to Air Pollution in Chennai
Researcher: SANTU GHOSH
Guide(s): BHASWATI GANGULI DR
Keywords: Air Pollution
health effects
Semiparametric
University: Sri Ramachandra University
Completed Date: 19/09/2015
Abstract: Air pollution in the outdoor ambient environment ranks among the leading risk factors contributing to both the global and regional burden of disease Time series studies of the effects of short term exposure on morbidity and mortality from cardiovascular or respiratory diseases have provided some of the most consistent evidence of serious adverse health effects of air pollution for regulatory policies in North America Europe and Asia More recently results from a coordinated set of time series studies examining the association of natural all cause mortality with PM10 exposures in the cities of Chennai Delhi and Ludhiana have been published These initial studies in India pointed out to the need for additional validation of the methods using data over extended time periods as well as the need to develop model refinements to address unique features of exposure and health datasets available through relevant Governmental agencies in India The study is aimed at developing semiparametric methods to describe exposure response relationships between daily average ambient PM10 concentrations and short term health effects in Chennai city through time series analyses The excess risk estimates obtained across models in the study are very similar to the summary estimates obtained from the meta analyses of all Asian studies as well as European and North American Studies This study explored several approaches to improve outputs of models for estimating the exposure response relationship between PM10 and mortality or morbidity through time series analyses These were able to use data over an extended period of time compared to earlier study in Chennai to provide more refined and robust effects estimates for all cause mortality Finally the development of an autoregressive Poisson model to address serial correlation in outcomes in the face of missing exposure data together with simulation studies to validate the model has significantly enhanced the ability to address model uncertainties. Until such time when infrastructural inve
Pagination: 1-157
URI: http://hdl.handle.net/10603/66869
Appears in Departments:College of Allied Health & Sciences

Files in This Item:
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01 title page.pdfAttached File123.42 kBAdobe PDFView/Open
02 declaration by the candidate.pdf3.04 MBAdobe PDFView/Open
03 certificates.pdf3.78 MBAdobe PDFView/Open
04 acknowledgement.pdf52.39 kBAdobe PDFView/Open
05 table of contents.pdf73.17 kBAdobe PDFView/Open
06 acronyms.pdf89.56 kBAdobe PDFView/Open
07 list of figures.pdf71.69 kBAdobe PDFView/Open
08 list of table.pdf92.87 kBAdobe PDFView/Open
09 abstract.pdf102.69 kBAdobe PDFView/Open
10 introduction.pdf121.85 kBAdobe PDFView/Open
11 detailed literature review.pdf855.89 kBAdobe PDFView/Open
12 aims and objectives.pdf83.03 kBAdobe PDFView/Open
13 methods.pdf943.27 kBAdobe PDFView/Open
14 results.pdf1.46 MBAdobe PDFView/Open
15 discussion and conclusion.pdf188.6 kBAdobe PDFView/Open
16 references.pdf153.47 kBAdobe PDFView/Open
17 annexures.pdf763.29 kBAdobe PDFView/Open


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