Literature DB >> 26437681

Developing models for the prediction of hospital healthcare waste generation rate.

Esubalew Tesfahun1, Abera Kumie2, Abebe Beyene3.   

Abstract

An increase in the number of health institutions, along with frequent use of disposable medical products, has contributed to the increase of healthcare waste generation rate. For proper handling of healthcare waste, it is crucial to predict the amount of waste generation beforehand. Predictive models can help to optimise healthcare waste management systems, set guidelines and evaluate the prevailing strategies for healthcare waste handling and disposal. However, there is no mathematical model developed for Ethiopian hospitals to predict healthcare waste generation rate. Therefore, the objective of this research was to develop models for the prediction of a healthcare waste generation rate. A longitudinal study design was used to generate long-term data on solid healthcare waste composition, generation rate and develop predictive models. The results revealed that the healthcare waste generation rate has a strong linear correlation with the number of inpatients (R(2) = 0.965), and a weak one with the number of outpatients (R(2) = 0.424). Statistical analysis was carried out to develop models for the prediction of the quantity of waste generated at each hospital (public, teaching and private). In these models, the number of inpatients and outpatients were revealed to be significant factors on the quantity of waste generated. The influence of the number of inpatients and outpatients treated varies at different hospitals. Therefore, different models were developed based on the types of hospitals.
© The Author(s) 2015.

Keywords:  Ethiopia; Healthcare waste; generation rate; hospitals; models; prediction

Mesh:

Substances:

Year:  2015        PMID: 26437681     DOI: 10.1177/0734242X15607422

Source DB:  PubMed          Journal:  Waste Manag Res


  9 in total

1.  Comparative study of predicting hospital solid waste generation using multiple linear regression and artificial intelligence.

Authors:  Somayeh Golbaz; Ramin Nabizadeh; Haniye Sadat Sajadi
Journal:  J Environ Health Sci Eng       Date:  2019-02-26

2.  Prediction of medical waste generation using SVR, GM (1,1) and ARIMA models: a case study for megacity Istanbul.

Authors:  Zeynep Ceylan; Serol Bulkan; Sermin Elevli
Journal:  J Environ Health Sci Eng       Date:  2020-06-19

3.  Healthcare Waste Management Practices and Associated Factors in Private Clinics in Addis Ababa, Ethiopia.

Authors:  Berhanu Wassie; Binyam Gintamo; Zelalem Negash Mekuria; Zemichael Gizaw
Journal:  Environ Health Insights       Date:  2022-01-17

4.  Assessment of medical waste generation, associated environmental impact, and management issues after the outbreak of COVID-19: A case study of the Hubei Province in China.

Authors:  Jinquan Ye; Yifan Song; Yurong Liu; Yun Zhong
Journal:  PLoS One       Date:  2022-01-24       Impact factor: 3.240

5.  Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture.

Authors:  Carlo Ricciardi; Alfonso Maria Ponsiglione; Arianna Scala; Anna Borrelli; Mario Misasi; Gaetano Romano; Giuseppe Russo; Maria Triassi; Giovanni Improta
Journal:  Bioengineering (Basel)       Date:  2022-04-14

6.  Analysis of healthcare waste management in hospitals of Belo Horizonte, Brazil.

Authors:  Arthur Couto Neves; Camila Costa Maia; Maria Esther de Castro E Silva; Gisele Vidal Vimieiro; Marcos Paulo Gomes Mol
Journal:  Environ Sci Pollut Res Int       Date:  2022-07-23       Impact factor: 5.190

7.  Identifying and Predicting Healthcare Waste Management Costs for an Optimal Sustainable Management System: Evidence from the Greek Public Sector.

Authors:  Anastasios Sepetis; Paraskevi N Zaza; Fotios Rizos; Pantelis G Bagos
Journal:  Int J Environ Res Public Health       Date:  2022-08-09       Impact factor: 4.614

Review 8.  Review of Current Healthcare Waste Management Methods and Their Effect on Global Health.

Authors:  Christina Kenny; Anushree Priyadarshini
Journal:  Healthcare (Basel)       Date:  2021-03-05

9.  Regression Models to Study the Total LOS Related to Valvuloplasty.

Authors:  Arianna Scala; Teresa Angela Trunfio; Lucia De Coppi; Giovanni Rossi; Anna Borrelli; Maria Triassi; Giovanni Improta
Journal:  Int J Environ Res Public Health       Date:  2022-03-07       Impact factor: 3.390

  9 in total

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