A research study of the infectious waste output of general hospitals in Taoyuan

• Main Authors: Su-chu Chang, 張素珠
• Other Authors: Kuen-Sheng Wang
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/96645916169970406387

碩士 === 國立中央大學 === 環境工程研究所碩士在職專班 === 99 === With the increasing environmental concerns on infectious wastes from medical systems, the economical collection, proper treatment, and efficient regulation of such infectious wastes have become an important issue. These tasks, however, all rely upon an accurate prediction for the infectious wastes (IWs) generation. This study tried to develop an predic-tion model for IWs generated from hospitals by applying principal com-ponent analysis and multivariate analysis. The former identified the im-portant factors that contribute to the generation of the IWs; and latter formulate a predict model from the related factors. Infectious wastes may be generated from medical research laboratory, general hospital, and small clinic. The prediction model may be various with the type of medical systems. Therefore, only the IWs generated from typical general hospitals was considered in this study. Principal compo-nent analysis and multivariate analysis were applied on the amounts of IWs generation from five regional general hospitals over a span of 5 years, regarding to six initially selected economical factors. The results indi-cated only four of them, including bed numbers(BN), physician num-bers(PN), nursing numbers(NN), and inpatient numbers(IN) were found significant contributing to the generation of IWs. The amount of informa-tion as shown by the principal component is 3.001, 1.654, and 1.015 for BN, PN, and NN, respectively; and the cumulative information amounts to 94%, a value more than a 0.8 benchmarks. The prediction model for IWs generation was obtained as follows: IWs=-70216.891+164.120 BN -1101.206 PN +483.213 NN +0.003 ON -1.470 IN +2.384 EN (1) Where, IWs=infectious generation (ton/yr) BN=bed number (bed/hospital) PN=physician number (cap/hospital) NN=nursing number (cap/hospital) ON=outpatient number (cap/hospital) IN=inpatient number (cap/hospital) EN=emergency number (cap/hospital) The model was further verified with generation data from a general hospital of of Taoyuan district and showed good prediction capacity. This study developed a prediction model by principal component analysis and multivariate analysis. The results may contribute to the de-sign and operation of the treatment facility and the regulatory manage-ment of the infectious wastes.