Medical waste management in Iraq: a case study of Baghdad

Mensoor, Montazer K.

doi:10.1007/s42768-020-00055-8

Cited by 8 publications

(9 citation statements)

References 18 publications

(14 reference statements)

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“…However, little commitment and will is shown in adhering to guidelines and health standards rules. Decision-makers disregard apathy toward practices for waste management exacerbate climate change and environmental pollution significantly (Mensoor, 2020 ). Hospital waste is thrown directly into the Tigris River, contaminating it with harmful chemical and biological toxins.…”

Section: Resultsmentioning

confidence: 99%

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach

Chew

Khaw

Alnoor

et al. 2023

Environ Sci Pollut Res

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Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.

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Section: Resultsmentioning

confidence: 99%

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach

Chew

Khaw

Alnoor

et al. 2023

Environ Sci Pollut Res

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“…Qualitative approaches have also been used exclusively in countries like Nigeria [37] and Iraq [38]. Qualitative approaches include on-site investigations and personal and group interviews.…”

Section: Assessment Of Hcwm Practicesmentioning

confidence: 99%

Healthcare Waste Management: An Overview

Raji,

Adeogun

2024

AJERD

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Healthcare waste (HCW) is a vital global issue that cannot be overlooked due to its threat to humans and the environment stemming from its infectious and hazardous nature. This study examines previous works undertaken on healthcare waste management (HCWM) practices around the world, notably the developing countries with a particular interest in segregation, collection, transportation, treatment, and disposal of HCW. This study draws attention to the environmental hazards arising from each stage of HCWM. Factors affecting HCWM practices have also been discussed. This study revealed evidence of poor HCWM practices in many developing countries. It also showed the impacts of human and non-human factors on HCWM practices. Proper documentation, sufficient budget, adequate supply of HCWM materials, frequent training of healthcare workers, and development of local manuals and guides are essential if a country is determined to achieve an efficient and sustainable HCWM system. Liquid HCW needs to be investigated as much as the solid HCW. Exploration of HCW minimization, reuse, and recycling opportunities is recommended for future research. The use of Modern-day technology such as Artificial Intelligence and geographic information system (GIS) has provided good results so far. However, they can be explored further for prediction, real-time monitoring, and reporting of HCW. The present study can be adopted as a guide in discussing issues about HCWM.

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“…However, little commitment and indifference to adhering to health standards and rules are observed. Signi cantly, decision-makers disregarding best practices for waste management exacerbate climate change and environmental pollution (Mensoor, 2020). Hospital waste was thrown directly into the Tigris River, contaminating it with harmful chemical and biological toxins.…”

Section: Practical Implicationsmentioning

confidence: 99%

Circular Economy of Medical Waste: Novel Intelligent Medical Waste Management Framework Based on Extension Linear Diophantine Fuzzy FDOSM and Neural Network Approach

Chew

Khaw

Alnoor

et al. 2022

Preprint

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The COVID-19 pandemic has caused overwhelming levels of medical waste, resulting in constant threats to environmental pollution. Furthermore, many environmental issues related to medical waste have emerged. This study aims to propose an application that allows the identification and classification of hospitals that generate overwhelming levels of medical waste aftermath of the COVID-19 pandemic by using Multi-Criteria Decision-Making methods (MCDM). MCDM was designed on the integration of the Analytic Hierarchy Process (AHP), linear diophantine fuzzy set-fuzzy decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis. Ten hospital managers were interviewed to determine the volume of medical waste generated by the hospitals they manage. Five types of medical waste were identified: general waste, sharps waste, pharmaceutical waste, infectious waste, and pathological waste. Among these five types, pharmaceutical waste is appointed as one that most impacts the environment. After that 313 experts in the health sector with experience in sustainability techniques were targeted to determine the best and worst technique for the Circular Economy to manage medical waste using the neural network approach. Findings also revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporised hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most vital and effective methods to dispose of medical waste during the pandemic. Additionally, ozone was ranked first as the most Circular Economy-related method for medical waste disposal. Among the implications of this study for governments, policymakers, and practitioners identify actions that hospitals may consider regarding the Circular Economy concept. Another implication is the supportive role of policymakers in transitioning most pollutant hospitals to becoming more sustainable.

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Medical waste management in Iraq: a case study of Baghdad

Cited by 8 publications

References 18 publications

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach

Healthcare Waste Management: An Overview

Circular Economy of Medical Waste: Novel Intelligent Medical Waste Management Framework Based on Extension Linear Diophantine Fuzzy FDOSM and Neural Network Approach

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