Evaluation of healthcare waste treatment/disposal alternatives by using multi-criteria decision-making techniques

Abstract

Healthcare waste should be managed carefully because of infected, pathological, etc. content especially in developing countries. Applied management systems must be the most appropriate solution from a technical, environmental, economic and social point of view. The main objective of this study was to analyse the current status of healthcare waste management in Turkey, and to investigate the most appropriate treatment/disposal option by using different decision-making techniques. For this purpose, five different healthcare waste treatment/disposal alternatives including incineration, microwaving, on-site sterilization, off-site sterilization and landfill were evaluated according to two multi-criteria decision-making techniques: analytic network process (ANP) and ELECTRE. In this context, benefits, costs and risks for the alternatives were taken into consideration. Furthermore, the prioritization and ranking of the alternatives were determined and compared for both methods. According to the comparisons, the off-site sterilization technique was found to be the most appropriate solution in both cases.

Keywords

Healthcare waste management off-site sterilization multi-criteria decision-making analytic network process (ANP)ELECTRE III

Introduction

Healthcare waste materials include a larger portion of infectious wastes, which are potentially dangerous since they may contain pathogenic agents. The production of these waste materials will continue to be an on-going phenomenon as long as there are human activities (Abdulla et al., 2008; Birpınar et al., 2009). Safe management of healthcare waste is necessary, to avoid environmental and public health problems, especially related to transmission of infectious diseases, such as HIV infection and hepatitis. In this respect, healthcare waste producers should develop waste management plans to minimize the risks and overall management cost (Graikos et al., 2010).

Recently, a number of studies have focused on healthcare waste management practices in Turkey. Birpınar et al. (2009) carried out a survey about the current status of the generation, collection, on-site handling, storage, processing, recycling, transportation and safe disposal of healthcare waste through interviews made with healthcare services managers. Alagöz and Kocasoy performed a survey study of healthcare waste in Istanbul Metropolitan Region and discussed the optimum healthcare waste management system for Istanbul (Alagöz and Kocasoy, 2007, 2008).

There have been many studies about healthcare waste generation and composition in different countries or cities (Cheng et al., 2010; Graikos et al., 2010) and healthcare waste management (El-Salam, 2010; Jang et al., 2006; Mato and Kassenga, 1997; Taghipour and Mosaferi, 2009). In addition, there are several studies that have used decision-making techniques for healthcare waste management. Brent et al. (2007) used the life cycle management and analytical hierarchy process (AHP) for choosing a healthcare waste management system, Karagiannidis et al. (2010) used the AHP and Dursun et al. (2011) used a fuzzy approach. For the selection of healthcare waste disposal firms, Hsu et al. (2008) and Ho (2011) used AHP and fuzzy AHP, respectively. Karamouz et al., (2007) used an AHP in order to rank the hospitals and determine the share of each hospital in the total hospital solid waste pollution load.

In the present study, healthcare waste treatment/disposal alternatives were evaluated by two different multi-criteria decision-making (MCDM) techniques. Unlike other studies in the literature, the analytic network process (ANP) and ELECTRE, which are techniques that have different approaches, were used for healthcare waste management systems. The paper is organized as follows. In the next section, the current status of healthcare waste management in Turkey is briefly introduced. The problem is defined and solved using a spreadsheet evaluation tool for ELECTRE III and Super Decisions software prepared by Creative Decisions Foundation for ANP in the following section. The results for both methods are presemted in the next section and concluding remarks and recommendations are given in the final section.

Present situation of healthcare waste management in Turkey

The Healthcare Waste Control Regulation, which is the regulation that is related to healthcare waste management in Turkey, was published in May 1993 and this regulation was changed in July 2005 in accordance with the EU Directives. It clarifies the principles of healthcare waste management which include the collection, transportation and temporary storage in healthcare facilities, and the treatment and final disposal of healthcare waste (Turkish Ministry of Environment and Forestry, 2005).

In 2010, a status report related to healthcare waste (its new name is Ministry of Environment and Urbanism) was published (Turkish Ministry of Environment and Forestry, 2010a). According to this report, the total number of hospitals in Turkey is 1328 and the number of beds in use is 175 141. As a result of calculations made according to provinces considering the bed occupancy ratio, inpatient and outpatient healthcare establishments generate a daily amount of 273 tons and an annual amount of 99 645 tons of healthcare waste. The daily total is shown in Table 1 .

Table 1.

Amount of healthcare wastes in Turkey (source: Turkey Ministry of Environment and Forestry, 2010).

	Amount of healthcare wastes
	(ton day⁻¹)	(ton year⁻¹)
In-patient hospitals	237.6	86 724
Out-patient hospitals	35.4	12 921
Total	273	99 645

To ensure the safe management of healthcare waste, a circular was published in 2006, which supported the use of healthcare waste sterilization systems. Furthermore, according to the Regulation on the Storage of Wastes (Turkish Ministry of Environment and Forestry, 2010b) landfilling of healthcare waste materials that have not been subjected to any pre-processing is prohibited. At present, there are 17 sterilization facilities and 33 648 tons of healthcare waste representing 34% of the total was sterilized. Furthermore, there is one incineration facility for healthcare waste in İstanbul. The current status of healthcare waste in Turkey is given in Figure 1.

Figure 1.

The current status of healthcare waste treatment/disposal in Turkey.

In 2008, a study was performed by Alagöz and Kocasoy for İstanbul Metropolitan Area. According to the results of this study, the percentage distribution of the composition of healthcare waste generated in the institutions of the Istanbul Metropolitan Area was determined to be 46% domestic, 17% infectious, 16% recyclable, 8% pathological, 5% sharps, 4% pharmaceuticals, 3% pressurized and 1% radioactive (radioactive wastes are handled by the Atomic Energy Commission according to the related regulations) (Alagöz and Kocasoy, 2008). Another study related to healthcare waste composition in Turkey was carried out by Altın et al. (2003). The results of the study are given in Table 2.

Table 2.

Physical composition of healthcare waste for Turkey.

Kind of waste	Weight in the waste (%)	Average moisture content of kind of waste (%)	Moisture content in the unit waste (%)
Paper	16.1	4.5	0.72
Food	17.1	63	10.77
Textile	10.2	8.6	0.87
Cartoon	4.6	5	0.23
Plastic	41	2.8	1.15
Other	3	8	0.24
Combustible	92
Metal	0.8	2.25	0.02
Glass	7.2	2.05	0.15
Non-combustible	8.0
		Average	14.15

Methodology

In the present study, two MCDM methods were employed: ANP and ELECTRE III. The alternatives and criteria – which were the same in both cases – were set out initially, followed by the MCDM methods. A flow diagram of the study is given in Figure 2.

Figure 2.

The flow diagram of the study.

Alternatives

The alternatives were selected for evaluation within the frame work of the present study, after a survey of the relevant literature on contemporary technologies which is summarized in Table 3. In this context, the following five alternatives, namely incineration, microwaving, on-site steam sterilization, off-site steam sterilization and landfilling were determined. Explanations of these alternatives are also given in Table 3.

Table 3.

The alternatives and their explanations.

Alternative no.	Alternative name	Explanations
1	Incineration	The numerous advantages of incineration have led to its worldwide use as the preferred means of treating and disposing healthcare solid waste (Hossain et al., 2011). In this study, incineration is defined as the combustion of healthcare waste under controlled conditions, using equipment that operates at temperatures on the order of 900–1000 °C, and that includes secondary combustion chamber and air pollution control equipment (Diaz et al., 2005).
2	Microwaving	In this treatment method, microwaves which are electromagnetic waves with frequencies between radio and infrared waves are used. The high frequency makes the molecules in the receiving body (liquid or solid) vibrate very rapidly as they attempt to align to the changing electromagnetic field. It has been demonstrated that disinfection in microwave units is not a result of exposure of material to the microwaves. The steam produced from the moisture in the waste by the microwave energy brings about the destruction of the pathogenic organisms in the waste (Diaz et al., 2005). Microwaving healthcare waste might be economically competitive compared to the incinerator. Nevertheless microwave technology is not suitable for large-scale treatment. (Hossain et al., 2011).
3	On site steam sterilization (autoclaving)	Steam autoclave treatment combines moisture, heat and pressure to inactive micro-organisms. The factors that affect the efficiency of steam autoclave treatment of healthcare waste are those affecting internal waste load temperature, steam penetration of the waste and the duration of treatment (http-1, 2012) The advantages of on-site healthcare waste treatment are situated at the level of convenience and minimization of risks to public health and the environment by confinement of hazardous healthcare waste to the healthcare premises. Decentralized, on-site treatment is often the only feasible solution for rural/remote healthcare facilities and is also advisable when healthcare facilities are situated far from each other and the road system is poor. Disadvantages are that extra technical staff may be required to operate and maintain the systems. It may also be difficult for authorities to monitor the performance of many small facilities: this may result in poor compliance with operating standards, depending on the type of systems, and subsequent increased environmental pollution (http-2, 2012).
4	Off-site steam sterilization (autoclaving)	In off-site centralized healthcare waste treatment, greater cost-effectiveness can be achieved in larger units unless the running costs for waste collection and transportation remain too expensive. Furthermore, efficient operation and maintenance of units can be easier and conformance to environmental norms can be easier to achieve thanks to the use of more sophisticated/ expensive technology (http-2, 2012).
5	Landfilling	Landfilling is the common method because of low cost, easy operation in waste management. However, landfilling of healthcare wastes can be a potential threat to human health and the quality of the environment because of infectious content.

Criteria

The criteria used in this study are given in Table 4, in which the number and name of these criteria are specified for further MCDM applications. The same criteria were determined for ANP and ELECTRE III. In addition, for the ‘benefit opportunity cost risk’ analysis in ANP, the criteria were classified as benefit, cost and risk. For ELECTRE III studies, the units of the quantitative criteria and the scores for the qualitative criteria are given.

Table 4.

The criteria for ANP* and ELECTRE III applications.

Criteria no	Name of criteria	Unit ascending order (for ELECTRE)	Explanations
Benefit cluster (for ANP)
g1	Volume reduction	% increasing	Less residual volume because of increasing of landfill useful life.
g2	Microbial inactivation efficiency	% increasing	Less bacteriologic content after treatment.
g3	Energy recovery	kW ton⁻¹ increasing	Revenues of electrical energy.
Cost cluster (for ANP)
g4	Capital costs	$ decreasing	Capital costs for processes including land, construction, and equipment.
g5	Operating costs	$ year⁻¹ decreasing	Operating costs for processes including personnel, energy, etc.
Risk cluster (for ANP)
g6	Greenhouse gases	Score (1–9) decreasing	Gas formation that cause to greenhouse effect such as carbon dioxide and methane.
g7	Emissions that have an impact on health	Score (1–9) decreasing	Gas emissions that cause to health effects such as PCDD/PCDF.
g8	Solid residuals	Ton decreasing	Less solid residuals to increase landfill capacity
g9	Hygiene	Score (1–9) increasing	Hygienic conditions for human and environmental health.

Opportunity cluster has not been considered.

Multi-criteria decision-making studies

Choosing the best alternative among a finite number of alternatives according to the given criteria is the focus of MCDM, which is a developing research area (Vincke, 1992). There are several methods used to solve MCDM problems, such as goal programming, AHP, ANP, PROMETHEE, TOPSIS, and ELECTRE (I, II, III, IV,TRI) (Figueira et al., 2005). Although these methods have been used in several disciplines, as well as in environmental engineering, ELECTRE and ANP were chosen for use in this study. ELECTRE III is one of the multi-objective ranking methods based on outranking relations. Indifference, weak preference, strong preference, and incomparability are used for the extended model of the decision maker’s local preferences in ELECTRE III (Zak, 2005). ANP, which is another MCDM method, is based on the utility function that aggregates different criteria (points of view) into one global criterion. The difference between ANP and ELECTRE is incomparability among the alternatives; specifically, ANP eliminates incomparability between alternatives, whereas ELECTRE III takes it into account. Hence, ELECTRE III and ANP were utilized in this study because of their different viewpoints.

ANP and ELECTRE III were used to choose the most suitable healthcare waste treatment/disposal system with particular regard to developing countries. In the present study, five alternatives and nine criteria were developed and evaluated by means of ANP and ELECTRE III. Super Decision software was used for ANP and MS Excel and VBA were used for ELECTRE III. Finally, the results obtained were compared and interpreted.

In MCDM studies, it is very important to define the decision maker. This may be one person or a group of people (e.g. a committee), who make the final choice among the alternatives. The decision maker(s) should have sufficient knowledge and experience to apply the decisions. In the determination of a healthcare waste treatment/disposal system, municipal authorities and academic staff were considered to be the decision makers in this study.

ELECTRE III studies

ELECTRE III, which was developed by Bernard Roy in 1968, was built based on the outranking relation for modelling the decision maker’s preferences. The method is based on pair-wise comparison. Comparison between alternatives proceeds on a pair-wise basis with respect to each decision criterion and establishes the degree of dominance or outranking of one option over another (Rogers and Bruen, 2000). The outranking relation in ELECTRE III is a fuzzy (imprecise and uncertain) binary relation (Roy, 1991).

Most complex decision problems include conflicting criteria that need to be accommodated. In most complex decision problems it is generally found that the numerical values of alternatives of some criteria are subject to imprecision, uncertainty and indetermination (Takeda, 2001). According to Takeda, Roy explained these three phenomena in the following manner.

Imprecision: because of the difficulty of determining a value, even in the absence of random fluctuation.

Uncertainty: because the value involved varies with time.

Indetermination: because evaluation results from a relatively arbitrary choice among several possible definitions.

ELECTRE III allows all three phenomena to be taken into account.

For more details on the calculation of the discordance matrix and distillation procedure the studies of Tam et al. (2003), Hokkanen and Salminen (1997) and Rogers and Bruen (2000) can be examined.

In the ELECTRE III studies of the present work, a performance matrix was first prepared. It is given in Table 5. The ascending orders were considered for the evaluation of the criteria with non-numerical values and the decision makers were asked to assign first place to the least important criterion. Then the other importance values were assigned based on how many times more important they appeared than the least important criterion. Thus, if a criterion was considered to be five times more important than the least important one, five was the value to be assigned to that criterion. The decision makers were asked to assign the weighting of criteria such that the sum of these values was 100.

Table 5.

Performance values of criteria.

Criteria no	A1	A2	A3	A4	A5
g1	95	80	75	75	0
g2	100	99.99	99.9999	99.9999	0
g3	428	0	0	0	0
g4	250 000	450 000	300 000	135 000	136 875
g5	0.25	0.25	1.3	0.15	0.0325
g6	5	1	1	1	3
g7	7	1	3	2	1
g8	0.05	0.2	0.25	0.25	1
g9	8	6	3	6	4

Non numerical values were scaled from 1 to 9 where excellent = 9; very good = 8; good = 7; more or less good = 6; indifferent = 5; somewhat bad = 4; bad = 3; very bad = 2; awful = 1 for increasing ascending order and excellent = 1; very good = 2; good = 3; more or less good = 4; indifferent = 5; somewhat bad = 6; bad = 7; very bad = 8; awful = 9 for decreasing ascending order.

The factors for score/values of the criteria are now explained and relevant references are included.

g1 (volume reduction). This criterion was based on the reduction in volume of healthcare wastes after the treatment/disposal. The values used were 95% for A1 (Şamat, 2006), 80% for A2 (Malkoç, 2004), 75% for A3 and A4 (Malkoç, 2004), and 0% was used for A5 because there is no treatment process.

g2 (microbial efficiency). The values used were 100% for A1 (Malkoç, 2004); 99.99% for A2 (DHV Consultant, 2010) and 99.9999% for A3 and A4 (Malkoç, 2004). Blenkharn (2006) reported that Staphylococcus aureus, Enterococcus spp., Salmonella spp. and other bacteria are found in healthcare waste landfill leachate. Therefore, it was deduced that there is no microbial removal in landfill.

g3 (energy recovery). In a healthcare waste incineration plant in Turkey, 5.22 ton h⁻¹ steam and 428 kW h⁻¹ electric power are produced for 1 ton of healthcare waste (Şamat, 2006). Large-scale microwave systems can have from one to six microwave generators (magnetrons). Generally, each magnetron has a power output of the order of 1.2 kW (Diaz et al., 2005). Similarly, steam is used for the sterilization process. Therefore, it was considered that there was no energy recovery for microwave and sterilization systems. It was also considered that there was no energy recovery in landfill due to low degradable organic carbon (DOC) content in healthcare waste (Karagiannidis et al., 2010).

g4 (capital cost) and g5 (operating cost). The values used for these costs are given in Table 6 with references. The capacity for incineration, microwave and off-site sterilization was estimated at 250 kg h⁻¹. The capacity for on-site sterilization was taken as 25 kg h⁻¹ and the values were multiplied by 10 for correction of capacity difference. Healthcare waste density (100 kg m⁻³), landfill height (20 m), landfill useful life (10 years), amount of waste (250 kg h⁻¹) were taken into account in the calculation of the capital cost of landfill and the calculated values are given in Table 6.

g6 (greenhouse gas emission) and g7 (emissions that have an impact on health). Incineration emits many harmful pollutants and those of particular concern include: carbon monoxide (as a result of incomplete combustion), hydrogen chloride, metals (e.g. mercury lead, arsenic, cadmium), dioxins and furans (Hossain, 2011). Hadar et al. (1997) reported that a number of volatile organic compounds are released at low levels during sterilization. For most volatile organic compounds, these concentrations do not constitute an acute health hazard. However, they may be higher than acceptable indoor air quality (IAQ) limits, and may cause discomfort, headaches or other symptoms when workers are exposed to autoclave emissions. Edlich et al. (2006) reported that air tests performed on the microwaving system confirm that no harmful air emissions are released during waste processing because the system includes a high efficiency particulate air (HEPA) filter. Furthermore, Karagiannidis et al. (2010) noted that greenhouse gas emissions from the final disposal healthcare waste were estimated in small amounts, as they were expected to be insignificant, due to low DOC content in healthcare waste. These criteria were graded accordingly.

g8 (solid residuals). The solid residuals of the process are related to the volume reduction. Therefore, the values of this criterion were given on the basis of the g1 criterion, which is based on 1 to n.

g9 (hygiene). For this criterion, human and environmental health was taken into consideration and numerical values of 1–9 were allocated. It was considered that as on-site sterilization should be accorded a lower score because it is carried out in plant and landfill sites and involves no microbial removal. Therefore, the values of this criterion were given as 8, 6, 3, 6 and 4, respectively.

Table 6.

Capital and operating costs.

Alternatives	Capital cost (g4) (US$)	Operating cost (g5) (US$ kg⁻¹)	References
A1	250 000	0.25	Karagiannidis et al. (2010)
A2	450 000	0.25	Diaz and Savage (2003)
A3	300 000	1.3	Karagiannidis et al. (2010)
A4	135 000	0.15	Diaz and Savage (2003)
A5	125 000 (US$ ha⁻¹)	0.0325	Diaz and Savage (2003)

With regard to the thresholds, all of the decision makers might assume different thresholds according to the criteria values. In this case, the average of these thresholds was used. The veto thresholds for all criteria were omitted. Using veto thresholds affects the final ranking. However, in this study, decision makers stated that there was no alternative that could be vetoed. Hence, the veto threshold was not used, and the discordance matrix was not considered. However, if needed, the spreadsheet tool developed for this study had the capability to incorporate the veto threshold. Parameters α_q and α_p were given values of zero, the values assigned to β_q and β_p for indifference and preference thresholds, respectively are given in Table 6. Similarly for ELECTRE III, the weights that were determined by the decision makers for each criterion are given in Table 7.

Table 7.

Threshold and weights of criteria for ELECTRE III.

Criteria no	β _q	β _p	W
g1	70	85	10
g2	99	99.99	15
g3	100	400	6
g4	200 000	100 000	12
g5	0.2	0.1	12
g6	3	2	15
g7	4	2	15
g8	0.3	0.2	10
g9	4	6	5

β_q, coefficient for indifference threshold; β_p, coefficient for preference threshold; W, weights of criteria.

Analytic network process studies

The ANP was developed by Thomas L. Saaty and provides a way to input judgments and measurements to derive ratio scale priorities for the distribution of influence among the factors and groups of factors in the decision. The ANP is a coupling of two parts. The first consists of a control hierarchy or network of criteria and sub-criteria that control the interactions in the system under study. The second is a network of influences among the elements and clusters. The network varies from criterion to criterion and a super matrix of limiting influence is computed for each control criterion. The super matrix is a two-dimensional matrix of elements by elements. The priority vectors from the paired comparisons are placed in the appropriate column of the super matrix. As the super matrix is built in this way, the sum of each column corresponds to the number of comparison sets. Finally, each of these super matrices is weighted by the priority of its control criterion and the results are synthesized through the addition of all the control criteria. In addition, a problem is often studied initially through a control hierarchy or system of benefits, a second for costs, a third for opportunities, and a fourth for risks. The synthesized results of the four control systems are combined by taking the quotient of the benefits multiplied by the opportunities to the costs multiplied by the risks to determine the best outcome (Ulutas, 2005).

The applications of ANP have been noticeably limited when compared with AHP, due to its complexity and time-consuming nature. So far, the ANP approach has proven itself to be successful when expert knowledge is used within environmental applications (Kone and Buke, 2007; Promentilla et al., 2006; Tran et al., 2004).

The decision makers are asked to make pairwise comparisons of the criteria of the network using a nine point scale suggested by Saaty. Whereas the basic ANP structure consists of only one network, the most complex one can analyse the benefit, opportunity, cost and risk that each alternative can cause together. The important issue is that benefit, opportunity, cost and risk may have different significance degrees according to the problem. This weighting procedure is called ‘benefit opportunity cost risk’ (BOCR) analysis. It should be noted that for costs and risks one must ask which is more costly and more risky (not which is less costly and which is less risky) because in paired comparisons it is only possible to estimate how much more the dominant member of a pair has a property as a multiple of how much the less dominant one has it and not the other way around (Saaty, 2001).

Super Decision software was used and BOCR analysis was conducted to apply ANP to the evaluation of a healthcare waste management system. In BOCR analysis, each alternative was evaluated in terms of its benefits, costs and risks; the opportunity cluster was not considered. The ‘benefits costs risks’ model was used to determine the values presented in Tables 5 and 6. For the criteria g1 to g5 and g8, the direct values of the criteria were entered into the program and for the other criteria, pairwise comparison was performed. In addition, a committee that included individuals from healthcare waste authorities and Anadolu University was set up to act as decision maker. It was pointed out that the consistency ratios were less than 10% due to the nature of the method; a self-evident fact.

In ANP, significance and impact weighting between each criterion may be determined by the decision maker. In this study, the significance of the weighting of the chosen criteria was been formulated in the program as additive (reciprocal)

\begin{array}{l} Formula : b B + o O + c (1 / C) + r (1 / R) \\ with r = 1 / 2; c = 1 / 3; b = 1 / 6; and o = 0 . \end{array}

(1)

In this context, first of all each cluster is rated separately. Then, these ratings are combined using the cluster weighting and the formulae including that to multiply the benefit ratios, reciprocals of cost and risk ratios. Finally these raw results are normalized and the values can be used as percentages for the evaluation of the alternatives (Saaty, 2001).

Results and discussion

The alternatives for choosing the appropriate healthcare waste treatment/disposal system for developing countries were evaluated using the ANP and ELECTRE III techniques. For ELECTRE III, the concordance index and outranking degree means that the credibility matrix was obtained through the use of an Excel worksheet, which was developed by the researchers for similar MCDM problems. The distillation procedure that determines ranking orders in the present study is illustrated in Table 8. As the veto threshold was not used in this study, the discordance matrix was not calculated. The credibility matrix that gave the outranking degree was equal to the concordance matrix if the discordance matrix was not used. A value most approximate to 1 gave the most preferable alternative. For ANP, according to the criteria and the formula above, the appropriate order of the alternatives was evaluated and it is illustrated in Figure 3. The results of each cluster (benefit, cost and risk) and overall results as a percentile distribution can be seen in this figure.

Table 8.

Credibility and concordance matrix for ELECTRE III.

	A1	A2	A3	A4	A5
A1	1	0.7	0.9	0.7	0.7
A2	0.9	1	1	0.9	0.8
A3	0.8	0.9	1	0.9	0.9
A4	0.9	1	1	1	1
A5	0.6	0.7	0.7	0.7	1

Figure 3.

ANP results for healthcare waste treatment/disposal system (A1, incineration; A2, microwaving; A3,: on site steam sterilization; A4, off-site steam sterilization; A5, landfilling).

The results of both techniques are compared in Table 9. According to the overall results for both techniques, A4 (off-site sterilization) was the most appropriate alternative for healthcare waste treatment/disposal system on account of especially low costs and risks. A2 (microwaving) was in second place, because of low risks. Although A1 had the highest benefits in terms of energy recovery, it was ranked last overall because of the high risks due to hazardous constituents. A4 and A2 took first place in both techniques indicating the clear benefits of these alternatives. There were changes in the ranking of other alternatives. In the ANP technique, relations and directions between components represent a network. Therefore, it was considered that indirect interactions and feedbacks between components were not directly associated. On the other hand, although the ELECTRE III did not cover the above-mentioned network, there were preference and indifference thresholds for all criteria. Thus, it can be claimed that the most fundamental difference between these techniques is the cause of disparity in ranking. In this manner, the ranking of A3 and A5 changed.

Table 9.

Comparison of the results of ANP and ELECTRE III for healthcare waste treatment/disposal system.

Alternatives	ANP (ranking)	ELECTRE III (ranking)
A1 (incineration)	5	5
A2 (microwaving)	2	2
A3 (on site steam sterilization)	4	3
A4 (off-site steam sterilization)	1	1
A5 (landfilling)	3	4

Conclusion

Healthcare waste management has a different significance in an integrated solid waste management system especially in developing countries. In the planning of a healthcare waste management system in a given country, the decision makers of that country should consider various parameters including technical, economic, social and political factors. At this point, the MCDM procedure would be very helpful to decision makers in solving this complex problem. In the present paper, ANP and ELECTRE III, which are two well-known MCDM techniques, were used to decide between healthcare waste treatment/disposal alternatives. According to the evaluations, similar results were obtained using both techniques and off-site steam sterilization was found to be the most appropriate treatment option. The main factor influencing this result was low cost. The overall conclusion was that all of the healthcare waste generated in a city should be sterilized under the supervision of the local authorities. This is a process that has already been initiated in some cities in Turkey.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

Abdulla

Abu Qdais

Rabi

(2008) Site investigation on medical waste management practices in northern Jordan. Waste Management 28: 450–458.

Alagöz

Kocasoy

(2007) Treatment and disposal alternatives for health-care waste in developing countries – a case study in Istanbul, Turkey. Waste Management and Research 25: 83–89.

Alagöz

Kocasoy

(2008) Determination of the best appropriate management methods for the health-care wastes in Istanbul. Waste Management 28: 1227–1235.

Altın

Altin

Elevli

. (2003) Determination of hospital waste composition and disposal methods: a case study. Polish Journal of Environmental Studies 12(2): 251–255.

Blenkharn

(2006) A backward step: landfill disposal of clinical waste. Journal of Hospital Infection 63: 105–112.

Birpınar

Bilgili

Erdogan

(2009) Medical waste management in Turkey: A case study of İstanbul. Waste Management 29: 445–448.

Brent

Rogers

DEC

Ramabitsa-Siimane

TSM

. (2007) Application of the analytical hierarchy process to establish health care waste management systems that minimize infection risks in developing countries. European Journal of Operational Research 181: 403–424.

Cheng

Sung

(2010) Medical waste generation in selected clinical facilities in Taiwan. Waste Management 30: 1690–1695.

DHV Consultant (2010) Appropriate Technologies for Healthcare Waste Management. Amersfoort, the Netherlands: DHN Consultant, Technical Report, 50 p.

10.

Diaz

Savage

(2003) Risks and Costs Associated with the Management of Infectious Wastes. Study Prepared for WHO/WPRO, Manila, Philippines.

11.

Diaz

Savage

Eggerth

(2005) Alternatives for the treatment and disposal of healthcare wastes in developing countries. Waste Management 25: 626–637.

12.

Dursun

Karsak

Karadayi

(2011) A fuzzy multi-criteria group decision making framework for evaluating health-care waste disposal alternatives. Expert Systems with Applications 38: 11453–11462.

13.

Edlich

Borel

Jensen

. (2006) Revolutionary advances in medical waste management: the Sanitec® system. Journal of Long-Term Effects of Medical Implants 16(1): 1–10.

14.

El-Salam

MMA

(2010) Hospital waste management in El-Beheira Governorate, Egypt. Journal of Environmental Management 91: 618–629.

15.

Figueira

Greco

Ehrgott

(2005) Multiple Criteria Decision Analysis: State of the Art Surveys. Boston, MA, USA: Springer, 1045 pp.

16.

Graikos

Voudrias

Papazachariou

. (2010) Composition and production rate of medical waste from a small producer in Greece. Waste Management 30: 1683–1689.

17.

Hadar

Tirosh

Grafstein

. (1997) Autoclave emissions-hazardous or not. Journal of American Biological Safety Association 2(3): 44–51.

18.

(2011) Optimal evaluation of infectious medical waste disposal companies using the fuzzy analytic hierarchy process. Waste Management 31: 1553–1559.

19.

Hokkanen

Salminen

(1997) Choosing a solid waste management system using multicriteria decision analysis. European Journal of Operational Research 98: 19–36.

20.

Hossain

Santhanam

Norulaini

. (2011) Clinical solid waste management practices and its impact on human health and environment – A review. Waste Management 31: 754–766.

21.

Hsu

(2008) Selection of infectious medical waste disposal firms by using the analytic hierarchy process and sensitivity analysis. Waste Management 28: 1386–1394.

22.

http-1 (2012) http://www.epa.gov/osw/nonhaz/industrial/medical/publications.htm (accessed February 2012).

23.

http-2 (2012) www.healthcarewaste.org (accessed February 2012).

24.

Jang

Lee

Yoon

. (2006) Medical waste management in Korea. Journal of Environmental Management 80: 107–115.

25.

Karagiannidis

Papageorgiou

Perkoulidis

. (2010) A multi- criteria assessment of scenarios on thermal processing of infectious hospital wastes: A case study for Central Macedonia. Waste Management 30: 251–262.

26.

Karamouz

Zahraie

Kerachian

. (2007) Developing a master plan for hospital solid waste management: A case study. Waste Management 27: 626–638.

27.

Kone

Buke

(2007) An analytical network process (ANP) evaluation of alternative fuels for electricity generation in Turkey. Energy Policy 35: 5220–5228.

28.

Malkoç

(2004) Chemical and Microbiological Investigation of Gas Emissions and Fly/Bottom Ashes from Medical Waste Incinerator, Performing Toxicity and Mutagenicity Tests. PhD Thesis, Anadolu University Graduate School of Natural and Applied Sciences, 146 pp (original in Turkish).

29.

Mato

RRAM

Kassenga

(1997) A study on problems of management of medical solid wastes in Dar es Salaam and their remedial measures. Resources, Conservation and Recycling 21: 1–16.

30.

Promentilla

Furuichi

Ishii

. (2006) Evaluation of remedial countermeasures using the analytic network process. Waste Management 26(12): 1410–1421

31.

Rogers

Bruen

(2000) Using ELECTRE III to choose route for Dublin Port Motorway. Journal of Transportation Engineering 126(4): 313–323.

32.

Roy

(1991) The outranking approach and the foundations of ELECTRE methods. Theory and Decisions 31(1): 49–73.

33.

Saaty

(2001) The Analytic Network Process: Decision Making with Dependence and Feedback. Pittsburgh, PA, USA: RWS Publications.

34.

Şamat

(2006) Incineration of healthcare wastes. Program on Medical Waste Management and Training of Trainers, 1–2 November, Ankara/Turkey (original in Turkish). Ankara, Turkey: Turkish Ministry of Environment and Forestry.

35.

Taghipour

Mosaferi

(2009) Characterization of medical waste from hospitals in Tabriz, Iran. Science of the Total Environment 407: 1527–1535.

36.

Takeda

(2001) A method for multiple pseudo-criteria decision problems. Computer Operational Research 28: 1427–1439.

37.

Tam

Tong

TKL

Lau

(2003) ELECTRE III in evaluating performance of construction plants: case study on concrete vibrators. Construction Innovation 3: 45–61.

38.

Tran

Knight

O’Neill

. (2004) Integrated environmental assessment of the Mid-Atlantic region with analytical network process. Environmental Monitoring and Assessment 94: 263–277.

39.

Turkish Ministry of Environment and Forestry (2005) Turkish Medical Waste Control Regulation, Official Gazette No: 25883. Ankara, Turkey: Turkish Ministry of Environment and Forestry.

40.

Turkish Ministry of Environment and Forestry (2010a) Status Report related with Healthcare Wastes. Ankara, Turkey: Turkish Ministry of Environment and Forestry.

41.

Turkish Ministry of Environment and Forestry (2010b) Regulation on the Storage of Wastes, Official Gazette No. 27533. Ankara, Turkey: Turkish Ministry of Environment and Forestry.

42.

Ulutaş

(2005) Determination of the appropriate energy policy for Turkey. Energy 30(7): 1146–1161.

43.

Vincke

(1992) Multicriteria Decision Aid. NewYork, NY, USA: Wiley.

44.

Zak

(2005) The comparison of multiobjective ranking methods applied to solve the mass transit systems’ decision problems. e-Proceedings of the 16th Mini - EURO Conference and 10th Meeting of EWGT, Poznan, 13–16 September, pp. 184–193.

	A1	A2	A3	A4	A5
A1	1	0.7	0.9	0.7	0.7
A2	0.9	1	1	0.9	0.8
A3	0.8	0.9	1	0.9	0.9
A4	0.9	1	1	1	1
A5	0.6	0.7	0.7	0.7	1

	A1	A2	A3	A4	A5
A1	1	0.7	0.9	0.7	0.7
A2	0.9	1	1	0.9	0.8
A3	0.8	0.9	1	0.9	0.9
A4	0.9	1	1	1	1
A5	0.6	0.7	0.7	0.7	1

	A1	A2	A3	A4	A5
A1	1	0.7	0.9	0.7	0.7
A2	0.9	1	1	0.9	0.8
A3	0.8	0.9	1	0.9	0.9
A4	0.9	1	1	1	1
A5	0.6	0.7	0.7	0.7	1