Developing models for the prediction of hospital healthcare waste generation rate

Predicting medical waste generation and associated factors using machine learning in the Kingdom of Bahrain

Effective planning and managing medical waste necessitate a crucial focus on both the public and private healthcare sectors. This study uses machine learning techniques to estimate medical waste generation and identify associated factors in a representative private and a governmental hospital in Bahrain. Monthly data spanning from 2018 to 2022 for the private hospital and from 2019 to February 2023 for the governmental hospital was utilized. The ensemble voting regressor was determined as the best model for both datasets. The model of the governmental hospital is robust and successful in explaining 90.4% of the total variance.Similarly, for the private hospital, the model variables are able to explain 91.7% of the total variance. For the governmental hospital, the significant features in predicting medical waste generation were found to be the number of inpatients, population, surgeries, and outpatients, in descending order of importance. In the case of the private hospital, the order of feature importance was the number of inpatients, deliveries, personal income, surgeries, and outpatients. These findings provide insights into the factors influencing medical waste generation in the studied hospitals and highlight the effectiveness of the ensemble voting regressor model in predicting medical waste quantities.

A review on medical waste treatment in COVID-19 pandemics: Technologies, managements and future strategies

Since the outbreak of COVID-19 few years ago, the increasing of the number of medical waste has become a huge issue because of their harmful impact to environment. A major concern associated to the limitation of technologies for dealing with medical waste, especially conventional technologies, are overcapacities since pandemic occurs. Moreover, the outbreak of new viruses from post COVID-19 should become a serious attention to be prevented not only environmental issues but also the spreading of viruses to new pandemic near the future. The high possibility of an outbreak of new viruses and mutation near the future should be prevented based on the experience associated with the SARS-CoV-2 virus in the last 3 yr. This review presented information and strategies for handling medical waste during the outbreak of COVID-19 and post-COVID-19, and also information on the current issues related to technologies, such as incineration, pyrolysis/gasification, autoclaves and microwave treatment for the dealing with high numbers of medical waste in COVID-19 to prevent the transmission of SARS-CoV-2 virus, their advantages and disadvantages. Plasma technology can be considered to be implemented as an alternative technology to deal with medical waste since incinerator is usually over capacities during the pandemic situation. Proper treatment of specific medical waste in pandemics, namely face masks, vaccine vials, syringes, and dead bodies, are necessary because those medical wastes are mediums for transmission of the SARS-CoV-2 virus. Furthermore, emission controls from incinerator and plasma are necessary to be implemented to reduce the high concentration of CO2, NOx, and VOCs during the treatment. Finally, future strategies of medical waste treatment in the perspective of potential outbreak pandemic from new mutation viruses are discussed in this review paper.Implications: Journal of the air and waste management association may consider our review paper to be published. In this review, we give important information related to the technologies, managements and strategies for handling the medical waste and control the transmission of SARS-CoV-2 virus, starting from proper technology to control the high number of medical waste, their pollutants and many strategies for controlling the spreading of SARS-CoV-2 virus. Moreover, this review also describes some strategies associated with control the transmission not only the SARS-CoV-2 virus but also the outbreak of new viruses near the future.

Is there any difference in organizational commitment between general hospitals and specialized hospitals? Empirical evidence from public hospitals in Beijing, China

OBJECTIVE

The purpose of the study on the one hand is to see different hospital organization commitment have difference, including the overall score and various dimensions, on the other hand, due to the different hospital type, its function orientation is different, the factors of the doctor organization commitment may also exist differences, so the study of another purpose is to determine for different types of hospital doctor organization commitment the focus and key groups, provide reference for the doctor incentive strategy.

METHODS

A total of 292 doctors in four large public hospitals in Beijing were investigated. Physicians' perceived organizational commitment was investigated using self-made electronic questionnaires. Data were analyzed by factor analysis, descriptive statistics, t-test, ANOVA, and multiple linear regression.

RESULTS

In the large public hospital doctor perception of the hospital commitment status, Specialized hospitals had higher overall commitment behavior scores, it is 3.47 ± 0.86; General hospital commitment behavior scored low at 3.39 ± 0.91. In the regression results, department category, working years, administrative position, and entry mode are the influencing factors of the organizational commitment of doctors in general hospitals, while in specialized hospitals, in addition to whether to hold an administrative position, entry mode, and working hours, the influencing factors also include gender, professional title and overseas learning background.

CONCLUSION

There are differences in the perceived organizational commitment by doctors in different types of public hospitals, and different factors influencing their organizational commitment.Hospital type directly influences physicians' organizational commitment and plays a moderating role in influencing other factors. A possible solution is general hospital specialization, encouraging general hospitals to develop the dominant discipline. These findings can help healthcare service hospital executives or government policymakers understand the impact of hospital specialization strategies and develop more efficient medical staff incentive systems.

Multi-criteria evaluation of medical waste management process under intuitionistic fuzzy environment: A case study on hospitals in Turkey

Waste management has come to the fore in the whole world with the increasing impact of the Covid-19 pandemic along with concerns about human health, environmental threats, and socio-economic factors, etc. Medical waste is one of the waste types that need special management processes including particularly collection, storage, separation, and disposal. Healthcare activities create a great amount of medical waste deriving from the hospitals. This study aims to determine the hospital that carries out medical waste management in the most effective way in Erzurum, Turkey. To handle intense uncertainty in the evaluation process, the case is analyzed by Intuitionistic Fuzzy Multi-Criteria Decision-Making (IFMCDM) methods. The present study contributes to the literature by focusing on a real case problem under IF environment in a Group Decision-Making (GDM) framework. Additionally, based on the literature review and expert judgments, the evaluation criteria relevant to the case are defined in this paper. To this end, a four-phased integrated methodology that involves Intuitionistic Fuzzy Weighted Averaging (IFWA), IF Analytical Hierarchy Process (IFAHP), IF Technique for Order Preference by Similarity to Ideal Solution (IFTOPSIS) and One-Dimensional Sensitivity Analysis, is conducted. Firstly, IFWA is aimed to express the significance levels of decision makers (DMs) based on their knowledge, qualifications and experiences. Secondly, IFAHP is used to calculate the importance weights of the decision criteria and IFTOPSIS is preferred to rank the available hospitals. Then, sensitivity analysis is employed to display robustness. According to the results, the most important criteria are Qualified personnel, Health institution infrastructure, and Control of waste, respectively and the most efficient hospital is determined.

Healthcare Waste-A Serious Problem for Global Health

Healthcare waste (HCW) is generated in different healthcare facilities (HCFs), such as hospitals, laboratories, veterinary clinics, research centres and nursing homes. It has been assessed that the majority of medical waste does not pose a risk to humans. It is estimated that 15% of the total amount of produced HCW is hazardous and can be infectious, toxic or radioactive. Hazardous waste is a special type of waste which, if not properly treated, can pose a risk to human health and to the environment. HCW contains potentially harmful microorganisms that can be spread among healthcare personnel, hospital patients and the general public, causing serious illnesses. Healthcare personnel are the specialists especially exposed to this risk. The most common medical procedure, which pose the highest risk, is injection (i.e, intramuscular, subcutaneous, intravenous, taking blood samples). The World Health Organization (WHO) estimates that around 16 billion injections are administered worldwide each year. However, if safety precautions are not followed, and needles and syringes are not properly disposed of, the risk of sharps injuries increases among medical staff, waste handlers and waste collectors. What is more, sharps injuries increase the risk of human immunodeficiency virus (HIV), hepatitis B and C viruses (HBV/HCV), tuberculosis (TB), diphtheria, malaria, syphilis, brucellosis and other transmissions. Disposing of medical waste in a landfill without segregation and processing will result in the entry of harmful microorganisms, chemicals or pharmaceuticals into soil and groundwater, causing their contamination. Open burning or incinerator malfunctioning will result in the emission of toxic substances, such as dioxins and furans, into the air. In order to reduce the negative impact of medical waste, waste management principles should be formulated. To minimize health risks, it is also important to build awareness among health professionals and the general public through various communication and educational methods. The aim of this paper is to present a general overwiev of medical waste, its categories, the principles of its management and the risks to human health and the environment resulting from inappropriate waste management.

Predictive analysis of lower limb fractures in the orthopedic complex operative unit using artificial intelligence: the case study of AOU Ruggi

The length of stay (LOS) in hospital is one of the main parameters for evaluating the management of a health facility, of its departments in relation to the different specializations. Healthcare costs are in fact closely linked to this parameter as well as the profit margin. In the orthopedic field, the provision of this parameter is increasingly complex and of fundamental importance in order to be able to evaluate the planning of resources, the waiting times for any scheduled interventions and the management of the department and related surgical interventions. The purpose of this work is to predict and evaluate the LOS value using machine learning methods and applying multiple linear regression, starting from clinical data of patients hospitalized with lower limb fractures. The data were collected at the "San Giovanni di Dio e Ruggi d'Aragona" hospital in Salerno (Italy).

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

Healthcare waste (HCW) management is a challenge for establishments that generate this type of waste, especially hospitals, as they are one of the largest generators. A determining factor in waste management is the amount of waste generation, which must be used for management planning. This study aims to compile and evaluate information on the management of HCW generated in Belo Horizonte's (located in Brazil) hospitals declared in their respective Healthcare Waste Management Plans (HCWMP) sent for approval by the municipality's Superintendency of Urban Cleaning. Therefore, a comparative analysis of the hospitals' generations in relation to their characteristics (nature, specialty, and size) was carried out, using the Kruskal-Wallis statistical test with post hoc in Nemenyi. For the study hospitals, a generation rate of 7.18 (6.17-8.23) kg·bed^-1·day^-1 was estimated, a generation rate close to that of developed countries. When comparing the generation according to the specialty of the hospitals, it was identified that the maternity hospitals (9.00 (7.05-10.90)) kg·bed^-1·day^-1 had a significantly higher generation rate than the low-complexity hospitals (4.75 (3.28-6.18)) kg·bed^-1·day^-1. It was also possible to demonstrate that the specialty and size of hospitals influence the structure available for waste storage. Finally, it can be observed that there are few treatment alternatives, with incineration and autoclaving being the technologies most commonly used by hospitals. It is expected that the results presented can serve as a reference for waste managers, in a context where there is little shared information on the subject.

Quantifying the spatiotemporal evolution characteristics of medical waste generation during the outbreak of public health emergencies

Based on the medical waste quantity and patient data during the corona virus disease 2019 (COVID-19) outbreak in China, this study used scenario analysis to quantitatively analyze the temporal and spatial evolution of medical waste generation during the pandemics. First, the results show that the estimated medical waste per capita reached 15.4 kg/day if only patients were considered in Scenario 1, while the figures were reduced to 3.2 kg/day in Scenario 2 and 2.5 kg/day in Scenario 3 when the effects of both the patient type and the number of medical staffs were considered. The estimated results also demonstrated that the per capita medical waste related to the epidemic showed the characteristics of a U-shaped and trailing phenomenon over time. Then, the amount of medical waste related to the COVID-19 generated that generated due to COVID-19 was estimated in Hubei, Heilongjiang, Zhejiang, Henan and Hunan provinces under Scenario 2 and Scenario 3. The results indicated that the spatiotemporal evolution characteristics of five provinces show the significant differences, and the patient type has a remarkable influence on the generation of medical waste. Finally, a novel decomposition-ensemble approach was designed to make a better short-term forecasting effect for future medical waste generation in different provinces.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10163-022-01523-5.

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

The healthcare sector is an ever-growing industry which produces a vast amount of waste each year, and it is crucial for healthcare systems to have an effective and sustainable medical waste management system in order to protect public health. Greek public hospitals in 2018 produced 9500 tons of hazardous healthcare wastes, and it is expected to reach 18,200 tons in 2025 and exceed 18,800 tons in 2030. In this paper, we investigated the factors that affect healthcare wastes. We obtained data from all Greek public hospitals and conducted a regression analysis, with the management cost of waste and the kilos of waste as the dependent variables, and a number of variables reflecting the characteristics of each hospital and its output as the independent variables. We applied and compared several models. Our study shows that healthcare wastes are affected by several individual-hospital characteristics, such as the number of beds, the type of the hospital, the services the hospital provides, the number of annual inpatients, the days of stay, the total number of surgeries, the existence of special units, and the total number of employees. Finally, our study presents two prediction models concerning the management costs and quantities of infectious waste for Greece's public hospitals and proposes specific actions to reduce healthcare wastes and the respective costs, as well as to implement and adopt certain tools, in terms of sustainability.

Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey

Predicting medical waste (MW) properly is vital for an effective waste management system (WMS), but it is difficult because of inadequate data and various factors that impact MW. This study’s primary objective was to develop an ensemble voting regression algorithm based on machine learning (ML) algorithms such as random forests (RFs), gradient boosting machines (GBMs), and adaptive boosting (AdaBoost) to predict the MW for Istanbul, the largest city in Turkey. This was the first study to use ML algorithms to predict MW, to our knowledge. First, three ML algorithms were developed based on official data. To compare their performances, performance measures such as mean absolute deviation (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were calculated. Among the standalone ML models, RF achieved the best performance. Then, these base models were used to construct the proposed ensemble voting regression (VR) model utilizing weighted averages according to the base models’ performances. The proposed model outperformed three baseline models, with the lowest RMSE (843.70). This study gives an effective tool to practitioners and decision-makers for planning and constructing medical waste management systems by predicting the MW quantity.

Healthcare waste generation in hospitals per continent: a systematic review

There are increasing worldwide concerns about the negative impacts of healthcare waste generated in hospitals, especially in low- and middle-income countries. Hazardous type of waste can contribute to adverse effects both in human populations and the environment because of its physical, chemical, and biological characteristics. A comprehensive view on increasing waste in the world has not been conducted to understand the breadth of the issue; thus, this paper sought to provide an analysis of hospitals' healthcare waste generation rate. Comparisons were made with Wilcoxon and Kruskal-Wallis tests for simple and multiple comparisons, to analyze nonparametric data, with post hoc by Nemenyi test. Median values indicated that hospital waste was the highest in North and South America (4.42, 1.64 kg/bed/day, respectively) and was almost nonexistent in Oceania (0.19 kg/bed/day), while the median rates for hazardous waste were the highest in Oceania (0.77 kg/bed/day). Africa was almost the lowest producer of waste in each category (0.19 and 0.39 kg/bed/day for hospital and hazardous waste, respectively). Over time, linear regression indicated that hazardous waste in Asia and Europe has increased, while in Oceania, the total waste also increased. Interestingly, in North America, it was observed a reduction in the generation for both total and hazardous waste. This information highlights the importance of understanding continent-specific characteristics and rates, which can be used to create a more individualized approach to addressing healthcare waste in the world.

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

Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R²) was achieved by the support vector machine (R² = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare.

Regression Models to Study the Total LOS Related to Valvuloplasty

Background: Valvular heart diseases are diseases that affect the valves by altering the normal circulation of blood within the heart. In recent years, the use of valvuloplasty has become recurrent due to the increase in calcific valve disease, which usually occurs in the elderly, and mitral valve regurgitation. For this reason, it is critical to be able to best manage the patient undergoing this surgery. To accomplish this, the length of stay (LOS) is used as a quality indicator. Methods: A multiple linear regression model and four other regression algorithms were used to study the total LOS function of a set of independent variables related to the clinical and demographic characteristics of patients. The study was conducted at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno (Italy) in the years 2010–2020. Results: Overall, the MLR model proved to be the best, with an R² value of 0.720. Among the independent variables, age, pre-operative LOS, congestive heart failure, and peripheral vascular disease were those that mainly influenced the output value. Conclusions: LOS proves, once again, to be a strategic indicator for hospital resource management, and simple linear regression models have shown excellent results to analyze it.

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

COVID-19 greatly challenges the human health sector, and has resulted in a large amount of medical waste that poses various potential threats to the environment. In this study, we compiled relevant data released by official agencies and the media, and conducted data supplementation based on earlier studies to calculate the net value of medical waste produced in the Hubei Province due to COVID-19 with the help of a neural network model. Next, we reviewed the data related to the environmental impact of medical waste per unit and designed four scenarios to estimate the environmental impact of new medical waste generated during the pandemic. The results showed that a medical waste generation rate of 0.5 kg/bed/day due to COVID-19 resulted in a net increase of medical waste volume by about 3366.99 tons in the Hubei Province. In the four scenario assumptions, i.e., if the medical waste resulting from COVID-19 is completely incinerated, it will have a large impact on the air quality. If it is disposed by distillation sterilization, it will produce a large amount of wastewater and waste residue. Based on the results of the study, we propose three policy recommendations: strict control of medical wastewater discharge, reduction and transformation of the emitted acidic gases, and attention to the emission of metallic nickel in exhaust gas and chloride in soil. These policy recommendations provide a scientific basis for controlling medical waste pollution.

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

BACKGROUND

Healthcare waste management requires special attention and every healthcare teams should be involved in handling of wastes at point of generation. However, less attention is given to healthcare waste management in Ethiopia and there is no evidence about healthcare waste management practices in private clinics in Addis Ababa. Accordingly, this study was conducted to assess healthcare waste management practices and associated factors in private clinics in Addis Ababa, Ethiopia.

METHODS

A health facility-based cross-sectional study was conducted in 278 randomly selected private clinics in Addis Ababa. Data were collected using questionnaire and observational checklists. Multivariable binary logistic regression analysis was used to identify factors associated with healthcare waste management practices on the basis of adjusted odds ratio (AOR) with 95% confidence interval (CI) and P-values <.05.

RESULT

Results showed that 61.2% of the surveyed clinics had poor healthcare waste management practices, out of which, 56.8% had poor waste segregation practice, 55.0% had poor waste collection practice, 85.6% had poor waste transportation practice, 63.3% had poor waste storage practice, 61.9% had poor waste treatment, and 57.9% had poor disposal system. Healthcare waste management practice in the surveyed clinics was significantly associated with presence of guidelines (AOR: 1.98, 95% CI: 1.06, 3.69), budget allocation (AOR: 2.05, 95%, CI: 1.20, 3.49), and inspection by the regulatory bodies (AOR: 2.47, 95% CI: 1.26, 4.84).

CONCLUSION

Healthcare waste management practice was poor in the surveyed clinics. This suggests that the healthcare industries in the studied region may create health treats to healthcare workers, waste handlers, patients, the community, and the environment at large. The following key elements are needed to improve healthcare waste management practices in private clinics: promoting practices that reduce the volume of waste generated and ensure proper waste segregation; developing strategies and systems, as well as strong oversight and regulation, to incrementally improve waste segregation, destruction, and disposal practices with the ultimate goal of meeting national and international standards; and selecting safe and environmentally-friendly management options, to protect people from hazards when collecting, handling, storing, transporting, treating or disposing of waste.

Medical Waste Treatment Technologies for Energy, Fuels, and Materials Production: A Review

The importance of medical waste management has grown during the COVID-19 pandemic because of the increase in medical waste quantity and the significant dangers of these highly infected wastes for human health and the environment. This innovative review focuses on the possibility of materials, gas/liquid/solid fuels, thermal energy, and electric power production from medical waste fractions. Appropriate and promising treatment/disposal technologies, such as (i) acid hydrolysis, (ii) acid/enzymatic hydrolysis, (iii) anaerobic digestion, (vi) autoclaving, (v) enzymatic oxidation, (vi) hydrothermal carbonization/treatment, (vii) incineration/steam heat recovery system, (viii) pyrolysis/Rankine cycle, (ix) rotary kiln treatment, (x) microwave/steam sterilization, (xi) plasma gasification/melting, (xii) sulfonation, (xiii) batch reactor thermal cracking, and (xiv) torrefaction, were investigated. The medical waste generation data were collected according to numerous researchers from various countries, and divided into gross medical waste and hazardous medical waste. Moreover, the medical wastes were separated into categories and types according to the international literature and the medical waste fractions’ percentages were estimated. The capability of the examined medical waste treatment technologies to produce energy, fuels, and materials, and eliminate the medical waste management problem, was very promising with regard to the near future.

Developing an effectiveness index for biomedical waste management in Indian states using a composite indicators approach

The waste from healthcare facilities (HCFs) is most devastating as they induce health hazards and pollute the environment. The effective management of biomedical waste is an essential function of the state governments, depending on state policies and facilities in HCFs. The performance assessment at the state level provides explanatory information for the decision-makers to dispose of biomedical waste. Therefore, this paper aims to establish an effectiveness index for assessing the performance of biomedical waste management for the Indian states. The designed conceptual framework, which acts as the building block for the index, interlinks the technical, managerial, and sustainability dimensions. To assess the existing waste management practices, significant sub-indicators are analyzed for India's northern and southern states. The indicators are transformed into comparable units using the proportionate normalization technique. The weight to the respective indicators follows the entropy method and additive aggregation to form the indices for various states. The developed index allows comparing management practices among the states and highlights the alarming situation. Based on the magnitude of indices values, states are categorized as red, yellow, and green zones. The robustness of the model is validated by performing sensitivity analysis and the cluster analysis tests the reliability of indicators and categorization of states with the existing methodology. The analysis will be useful to the decision-makers of state pollution boards by providing special attention to capacity building and waste prevention technologies.

Analysis of medical waste management and impact analysis of COVID-19 on its generation in Taiwan

Medical waste management in Taiwan is a crucial issue due to its highly environmental and human health risks. The main objectives of this paper were to analyse the status of medical waste generation and treatment in recent years, and also address the discussions on the impacts of coronavirus disease 2019 (COVID-19) on its generation in the first half of 2020. It showed that the reported quantities have slightly increased from 35,747 metric tonnes (Mt) in 2016 to 40,407 Mt in 2019, showing an average increase by 4.17%. This rate of increase was consistent with the hospital services. When classified by the reported codes, the C-type waste (infectious waste) accounted for about 89% of the reported quantities, which indicated an annual increase by 4.14% during the same period. In addition, the medical waste treatment in 2019 was mainly dependent on the commissioned treatment (80.18%), followed by the recycling (18.53%) and the self-treatment (1.29%). Furthermore, the impact of COVID-19 on the medical waste generation in Taiwan was not significant in the first half of 2020 compared to the data during the years of 2016-2019. It was indicated that the consistent trend was observed at the daily confirmed COVID-19 cases in Taiwan during this period. Obviously, the reduction in the hospital medical services during the COVID-19 outbreak should be offset by the increase in medical waste generation from the medical services. In order to try to ensure safe and complete destruction of the COVID-19 virus, all the waste generated from the healthcare facilities should be treated in the incineration plants.

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

Healthcare is a rapidly growing industry as medical treatments become more sophisticated, more in demand due to increasing incidence of chronic disease and more widely available worldwide. This booming industry is also creating more waste than ever before and, as such, there is a growing need to treat and dispose of this waste. Healthcare waste (HCW) disposal includes a multitude of disposal methods, including incineration, landfilling and chemical treatments. These rudimentary methods and their growing use present their own problems that negatively impact both the environment and, in turn, damage public health, thus contributing to a global healthcare crisis. The aim of this review was to examine the current HCW disposal methods in place and the harmful effects they have on the environment and on public health. The findings accumulated in this review demonstrate a heavy reliance on basic, low tech HCW disposal techniques and uncovered the negative impacts of these methods. There is a notable lack of employment of "greener" HCW disposal methods on a largescale due to cost, access and feasibility. Despite innovations in HCW disposal, there is no scalable, global green solution at present. Further, the review highlights that global health consequences of HCW disposal methods often differ depending on how developed the country is.

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

PURPOSE

Estimation of the amount of waste to be generated in the coming years is critical for the evaluation of existing waste treatment service capacities. This study was conducted to evaluate the performance of various mathematical modeling methods to forecast medical waste generation of Istanbul, the largest city in Turkey.

METHODS

Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), Grey Modeling (1,1) and Linear Regression (LR) analysis were used to estimate annual medical waste generation from 2018 to 2023. A 23-year data from 1995 to 2017 provided from the Istanbul Metropolitan Municipality's affiliated environmental company ISTAC Company were utilized to examine the forecasting accuracy of methods. Different performance measures such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R²) were used to evaluate the performance of these models.

RESULTS

ARIMA (0,1,2) model with the lowest RMSE (763.6852), MAD (588.4712), and MAPE (11.7595) values and the highest R² (0.9888) value showed a superior prediction performance compared to SVR, Grey Modeling (1,1), and LR analysis. The results obtained from the models indicated that the total amount of annual medical waste to be generated will increase from about 26,400 tons in 2017 to 35,600 tons in 2023.

CONCLUSIONS

ARIMA (0,1,2) model developed in this study can help decision-makers to take better measures and develop policies regarding waste management practices in the future.

Application of healthcare waste indicators for assessing infectious waste management in Bolivia

In the developing world, healthcare waste management is a human health and environmental burden that should be solved for improving sustainability. Solutions should be introduced in the short term, concerning management, planning, financial assistance and expertise. The paper introduces an indicator set for assessing healthcare waste management in developing cities, implemented in La Paz (Bolivia) as a case study. The objective is to suggest an integrated management tool as a first assessment technique to identify the prevailing problems with a healthcare waste management system. Results suggest that, in La Paz, the application of such indicators is useful for evaluating which priorities should be addressed for improving the healthcare waste management system. The tool was applied for introducing a study necessary for the application of new management plans, especially concerning healthcare waste treatment. The method can be replicated in other contexts worldwide, with a focus on the developing world, for comparing cities, management solutions and improvements carried out along the years. The approach is of interest for boosting sustainability and human health, improving the awareness of the actors and policy-makers involved in waste management.

Dynamic assessment of economic and environmental performance index and generation, composition, environmental and human health risks of hospital solid waste in developing countries; A state of the art of review

Many studies have been conducted on hospital solid waste management (HSWM) throughout the world, especially developing countries. This interdisciplinary study aims to summarize the available knowledge on the health and environmental risks of hospital solid waste (HSW) and also, develop a dynamic associational assessment among hospital solid waste generation rate (HSWGR), hospital solid waste composition (HSWC), gross domestic product (GDP) per capita, and environmental performance index (EPI) in some developing countries for the first time. The results of this study showed that researchers from India, China, Pakistan, Brazil, and Iran had found more evidence about the health, economic, and environmental issues in HSW than the other developing countries. The literature showed that the highest and lowest reported HSWGR (in national average level) belonged to Ethiopia (6.03) and India (0.24) kg bed ^-1 day^-1, respectively. It has also been shown that all studied countries except Serbia, have higher levels of hazardous waste in their HSWC, based on the WHO's standard. Furthermore, the quantity and quality of HSW in developing countries depend on the service provided by the hospital, type of hospital, HSWM system, and the level of regional economic and culture. The association analysis showed that the EPI and GDP per capita of developing countries were significantly (p-value <0.05) associated with HSWGR, non-hazardous HSW, and hazardous HSW by the Spearman coefficients equal to 0.389, 0.118, -0.118, and 0.122, 0.216, and -0.346, respectively. However, it can be concluded that GDP per capita and EPI have a weak correlation with hazardous HSW and non-hazardous HSW. Moreover, HSW has many hazardous health and environmental risks such as dioxin and furan, that must be controlled and managed through implementing programs and policies based on sustainable development. As a final point, we believed that the present study can be considered to be a guide for future studies on HSWM in developing countries.

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

PURPOSE

A successful hospital solid waste (HSW) management needs an accurate estimation of waste generation rates. The conventional regression methods upon increasing the number of input variables hardly can predict the HSW generation rate and require more complex modeling. In return, application of machine learning methods seems to be able to increase the power of predicting the produced wastes.

METHODS

To predict the HSW, Multiple Linear Regression(MLR) and several Neuron- and Kernel-based machine learning methods were employed to analyze data from hospitals of Karaj metropolis. The number of wards, active and occupied beds, staffs and inpatients, and ownership type and activity years of hospital were defined as the model inputs. In addition, proposed models performance was evaluated based on coefficient of determination (R²) and Mean-Square Error (MSE).

RESULTS

The performance of Neuron- and Kernel-based machine learning methods indicated that both models were satisfactory in predicting HSW. However, the better results of 0.82-0.86 for average R² value and 0.003-0.008 for average MSE value, indicated relative superiority of Kernel-based models compared to Neuron based (average R² = 0.68-0.74, average MSE = 0.009-0.023) and MLR models. Number of staffs and hospital ownership type were the most influential model variables in predicting the HSW generation rate.

CONCLUSIONS

The machine learning methods could interpret the relationship between waste generation rate and model inputs, appropriately. Thus, they may play an effective role in developing cost-effective methods for suitable HSW management.

Healthcare waste management current status and potential challenges in Ethiopia: a systematic review

Objective

During the healthcare delivery process, hazardous wastes can be generated from the health facilities. Improper healthcare waste management is responsible for the transmission of more than 30 dangerous bloodborne pathogens. The aim of this systematic review was to evaluate the healthcare waste management practice and potential challenges in Ethiopia.

Results

Electronic databases and direct Google search yielded 1742 articles from which 17 studies met the inclusion criteria. The proportion of hazardous waste generated in Ethiopian healthcare facilities was unacceptably high which ranged from 21 to 70%. Most studies indicated the absence of proper waste segregation practice at the source of generation. Treatment of the healthcare waste using low combustion incinerator and/or open burning and open disposal of the incinerator ash were very common. Lack of awareness from the healthcare staff, appropriate waste management utilities and enforcement from the regulatory bodies were mainly identified as a common factor shared by most of the studies. The healthcare waste management practice in Ethiopian healthcare facilities was unsatisfactory. There should be close supervision of the waste disposal process by the regulatory bodies or other stakeholders.

Operating room greening initiatives - the old, the new, and the way forward: A narrative review

Healthcare waste is a rampant issue in Australian hospitals. The operating room (OR) contributes disproportionately to total hospital waste. There has been considerable research in the literature concentrating on strategies to improve OR and hospital waste accumulation, in an attempt to provide guidance and direction on how to reduce the healthcare ecological footprint. We reviewed the literature for leading greening initiatives currently utilised in the OR in Australia and internationally. This narrative literature review focuses on the trend of OR greening initiatives over the last 25 years, comparing different innovative approaches, the successes and setbacks, and the financial implications of initiatives. A variety of measures that hospital management, surgeons, anaesthetists, nurses and other healthcare personnel can take to reduce the ecological footprint of their healthcare facility are outlined. Greening initiatives include reducing, recycling, reusing, rethinking and researching, as well as novel technology and smarter architectural design. We also evaluated the barriers to improving waste management, which include lack of leadership, misconceptions among staff, and an overall resistance to change. In conclusion, in a world where greenhouse gas emissions cause unprecedented climate change and landfill space is finite, it is incumbent upon hospitals to help reduce the environmental impact of their facility. Reducing pollution and greenhouse gas emissions would moderate the incidence of human disease, save money for the healthcare system and society as a whole, and contribute to a safer and healthier world we all would like to live in.