Association of pollution with quantiles and expectations of the hospitalization rate of elderly people by respiratory diseases in the city of São Paulo, Brazil
Associations of pollution with expected rates of morbidity and mortality are discussed extensively in the literature, but associations between pollution and quantiles of these rates may change substantially. In this study, we compare the association of pollution and climate variables controlling for...
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Veröffentlicht in: | Environmetrics (London, Ont.) Ont.), 2014-05, Vol.25 (3), p.165-171 |
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description | Associations of pollution with expected rates of morbidity and mortality are discussed extensively in the literature, but associations between pollution and quantiles of these rates may change substantially. In this study, we compare the association of pollution and climate variables controlling for seasonality with the expectation and quantile of the hospitalization rate. The generalized linear model with the binomial negative distribution and the quantile regression are fitted to the daily number of hospitalizations of resident people older than 65 years in São Paulo City from 2006 to 2011. The daily average nitrogen oxide concentration presented the most significant association with the expected hospitalization rate and with the 90th percentile of this rate but no significant association with the median rate, controlling for seasonality and climate variables. The minimum temperature and relative humidity presented significant association with the expected hospitalization rate but no significant association with the 90th percentile. The effects may be very distinct for the average rate or high quantiles, which may affect planning the number of hospital beds mainly during the winter. Copyright © 2014 John Wiley & Sons, Ltd. |
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P. ; Santos, B. R.</creator><creatorcontrib>Alencar, A. P. ; Santos, B. R.</creatorcontrib><description>Associations of pollution with expected rates of morbidity and mortality are discussed extensively in the literature, but associations between pollution and quantiles of these rates may change substantially. In this study, we compare the association of pollution and climate variables controlling for seasonality with the expectation and quantile of the hospitalization rate. The generalized linear model with the binomial negative distribution and the quantile regression are fitted to the daily number of hospitalizations of resident people older than 65 years in São Paulo City from 2006 to 2011. The daily average nitrogen oxide concentration presented the most significant association with the expected hospitalization rate and with the 90th percentile of this rate but no significant association with the median rate, controlling for seasonality and climate variables. The minimum temperature and relative humidity presented significant association with the expected hospitalization rate but no significant association with the 90th percentile. The effects may be very distinct for the average rate or high quantiles, which may affect planning the number of hospital beds mainly during the winter. 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P.</creatorcontrib><creatorcontrib>Santos, B. R.</creatorcontrib><title>Association of pollution with quantiles and expectations of the hospitalization rate of elderly people by respiratory diseases in the city of São Paulo, Brazil</title><title>Environmetrics (London, Ont.)</title><addtitle>Environmetrics</addtitle><description>Associations of pollution with expected rates of morbidity and mortality are discussed extensively in the literature, but associations between pollution and quantiles of these rates may change substantially. In this study, we compare the association of pollution and climate variables controlling for seasonality with the expectation and quantile of the hospitalization rate. The generalized linear model with the binomial negative distribution and the quantile regression are fitted to the daily number of hospitalizations of resident people older than 65 years in São Paulo City from 2006 to 2011. The daily average nitrogen oxide concentration presented the most significant association with the expected hospitalization rate and with the 90th percentile of this rate but no significant association with the median rate, controlling for seasonality and climate variables. The minimum temperature and relative humidity presented significant association with the expected hospitalization rate but no significant association with the 90th percentile. The effects may be very distinct for the average rate or high quantiles, which may affect planning the number of hospital beds mainly during the winter. Copyright © 2014 John Wiley & Sons, Ltd.</description><subject>Binomials</subject><subject>Climate</subject><subject>generalized linear model</subject><subject>Hospitals</subject><subject>Mathematical models</subject><subject>negative binomial</subject><subject>Pollution abatement</subject><subject>quantile regression</subject><subject>Quantiles</subject><subject>Respiratory diseases</subject><subject>Winter</subject><issn>1180-4009</issn><issn>1099-095X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqNkc1u1TAQhSMEEqVU6iN4yYIU_8WOl-WqFERVkEqhO8vxnega3Di1Hdr0aZB4FF4M516EYIHEamZ0vjmLc6rqkOAjgjF9AcPXI0olf1DtEaxUjVVz9bDspMU1x1g9rp6k9BmXTTRyr_p2nFKwzmQXBhR6NAbvp-1x6_IG3UxmyM5DQmZYI7gbweYtmxY4bwBtQhpdNt7d7zyiybBo4NcQ_YxGCKMH1M0oQiGLHOKM1i6BScXWDVsX6_K8fF38-B7QezP58By9jObe-afVo974BAe_5n51-erkw-p1ffbu9M3q-Ky2TCheixYwYEK5sH3HmVW463rGYC1YKwXDjFiJO0W56YAq1dJWkAZLwRtO-0ZStl892_mOMdxMkLK-dsmC92aAMCVNBKesBPpfKCGClsT_QG0MKUXo9RjdtYmzJlgvfenSl176Kmi9Q29L3vM_OX1y_vFv3qUMd795E79oIZls9KfzUy3pRcvU25Vu2E-GmagI</recordid><startdate>201405</startdate><enddate>201405</enddate><creator>Alencar, A. 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R.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Pollution Abstracts</collection><collection>Safety Science and Risk</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Environmetrics (London, Ont.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alencar, A. P.</au><au>Santos, B. R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Association of pollution with quantiles and expectations of the hospitalization rate of elderly people by respiratory diseases in the city of São Paulo, Brazil</atitle><jtitle>Environmetrics (London, Ont.)</jtitle><addtitle>Environmetrics</addtitle><date>2014-05</date><risdate>2014</risdate><volume>25</volume><issue>3</issue><spage>165</spage><epage>171</epage><pages>165-171</pages><issn>1180-4009</issn><eissn>1099-095X</eissn><abstract>Associations of pollution with expected rates of morbidity and mortality are discussed extensively in the literature, but associations between pollution and quantiles of these rates may change substantially. In this study, we compare the association of pollution and climate variables controlling for seasonality with the expectation and quantile of the hospitalization rate. The generalized linear model with the binomial negative distribution and the quantile regression are fitted to the daily number of hospitalizations of resident people older than 65 years in São Paulo City from 2006 to 2011. The daily average nitrogen oxide concentration presented the most significant association with the expected hospitalization rate and with the 90th percentile of this rate but no significant association with the median rate, controlling for seasonality and climate variables. The minimum temperature and relative humidity presented significant association with the expected hospitalization rate but no significant association with the 90th percentile. The effects may be very distinct for the average rate or high quantiles, which may affect planning the number of hospital beds mainly during the winter. Copyright © 2014 John Wiley & Sons, Ltd.</abstract><pub>Blackwell Publishing Ltd</pub><doi>10.1002/env.2274</doi><tpages>7</tpages></addata></record> |
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subjects | Binomials Climate generalized linear model Hospitals Mathematical models negative binomial Pollution abatement quantile regression Quantiles Respiratory diseases Winter |
title | Association of pollution with quantiles and expectations of the hospitalization rate of elderly people by respiratory diseases in the city of São Paulo, Brazil |
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