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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1016/j.wasman.2018.06.053
|2 doi
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|a pubmed24n1037.xml
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|a DE-627
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|e rakwb
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| 041 |
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|a eng
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1 |
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|a Minoglou, Minas
|e verfasserin
|4 aut
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|a Describing health care waste generation rates using regression modeling and principal component analysis
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|c 2018
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 22.06.2020
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Copyright © 2018 Elsevier Ltd. All rights reserved.
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|a This work examined the dependence of the health care waste generation rates (HCWGR) from economic factors (gross domestic product per capita, health expenditure per capita), social and health-related factors (human development index, life expectancy at birth, mean years of schooling, deaths due to tuberculosis, under-five mortality rate, hospital beds, improved sanitation facilities, physicians, nurses and midwives, diabetes prevalence, deaths due to cancer, deaths due to asthma, deaths due to influenza and pneumonia), and one environmental sustainability factor (carbon dioxide emissions) from 41 countries using multiple linear regression modeling and principal component analysis (PCA). In addition, the Pearson correlation coefficients were calculated for all pairwise comparisons and a geographical grouping of the HCWGR was performed. The examined HCWGR included both the hazardous and the municipal fraction of health care waste (HCW). Results showed that the CO2 emissions and the life expectancy at birth positively correlated to the HCWGR (kg/bed/d) and can be used as adequate statistical predictors. The resulting best reduced model explained 84.7% of the variability. The hospital beds and the deaths due to cancer were not correlated to any principal component due to their low loadings. Only the diabetes prevalence was correlated to the F2 principal component. The other fourteen variables were correlated to the F1, which was the most significant principal component. Thus, the HCWGR and the other thirteen variables that were grouped to the F1 component have strong autocorrelation and can be treated as one variable
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|a Journal Article
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|a Hazardous waste
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| 650 |
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|a Hospital waste
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| 650 |
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|a Medical waste
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| 650 |
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|a Solid waste
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| 650 |
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|a Statistical analysis
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1 |
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|a Komilis, Dimitrios
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Waste management (New York, N.Y.)
|d 1999
|g 78(2018) vom: 15. Aug., Seite 811-818
|w (DE-627)NLM098197061
|x 1879-2456
|7 nnns
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| 773 |
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|g volume:78
|g year:2018
|g day:15
|g month:08
|g pages:811-818
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|u http://dx.doi.org/10.1016/j.wasman.2018.06.053
|3 Volltext
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