The Impact of Multi-Criteria Decision Analysis Parameters on an Urban Deprivation Index

<p>Multi-criteria decision analysis (MCDA) is a family of decision support methods that allow analysts to structure a decision problem through the selection and evaluation of multiple and often conflicting criteria, using established techniques to standardize, weight, and combine these criteria. Through a case study of an area-based deprivation index for the city of Toronto’s 140 neighbourhoods, we examine the variability of MCDA results under different decision models. We use interactive cartographic visualization to explore the impact of criterion weighting and three decision rules: weighted linear combination, locally weighted linear combination, and ordered weighted averaging. The modelling of socioeconomic deprivation using these different decision rules and their parameters yielded different spatial patterns of deprivation for the same set of variables and weights. The results highlight the importance of examining multiple decision models before making policy recommendations.</p> <p><br></p> <p> </p> <p>Keywords: analytic hierarchy process, area-based composite index, locally weighted linear combination, multi-criteria decision analysis, ordered weighted averaging, socio-economic deprivation</p>

Multi-criteria decision analysis (MCDA) is a family of decision support methods that allow analysts to structure a decision problem through the selection and evaluation of multiple and often conflicting criteria, using established techniques to standardize, weight, and combine these criteria. Through a case study of an area-based deprivation index for the city of Toronto's 140 neighbourhoods, we examine the variability of MCDA results under different decision models. We use interactive cartographic visualization to explore the impact of criterion weighting and three decision rules: weighted linear combination, locally weighted linear combination, and ordered weighted averaging. The modelling of socio-economic deprivation using these different decision rules and their parameters yielded different spatial patterns of deprivation for the same set of variables and weights. The results highlight the importance of examining multiple decision models before making policy recommendations.

Purpose of ReviewSince the coronavirus disease-19 (COVID-19) pandemic started, there has been a rise in published studies using area-based deprivation indices to explore the link between neighborhood-level social determinants of health (SDoH) and susceptibility to infectious diseases. However, questions remain about how these deprivation indices were developed and how effective they are at identifying and addressing healthcare-associated infection (HAI) disparities. This review aims to clarify the origins of the most commonly used deprivation indices in HAI epidemiology research and to offer key considerations and recommendations for their use to enhance prevention strategies and advocacy efforts.Recent FindingsThe two most frequently used area-based deprivation indices in HAI epidemiology research are the area deprivation index and the social vulnerability index. Of interest, both indices use data from the American Community Survey disseminated by the US Census Bureau to describe area-level socioeconomic and material deprivation across various geographic areas nationwide. Researchers have combined these area-based indices with clinical and individual-level sociodemographic variables and found that higher levels of disadvantage correlate with an increased occurrence of HAIs. Despite similarities in findings when using these indices, they have distinct differences that should be considered.SummaryArea-level deprivation can increase an individual’s risk of HAIs, and deprivation indices are tools that can quantify this relationship. Despite the availability of relevant data, there is a need to expand the existing literature using deprivation indices in HAI research. Ultimately, this exploratory research has the potential to inform prevention strategies and policy reforms aimed at reducing disparities in HAIs.

BACKGROUND: In the context of the progressive movement towards patientcentred care, patient-specific decision support is an important focus of interest. Many diagnostic and treatment patient decision aids (PDAs) are now available to help patients make informed choice decisions. An increasing number of these are software-based, with some available online. Multi-Criteria Decision Analysis (MCDA) is a potentially useful technique on which to base a software-assisted PDA, especially when the decision is complex - as is the case in choosing the best treatment for non-small cell lung cancer – but it has so far been relatively little exploited in this area. The use of any from a number of existing MCDA-based software applications in the development and delivery of a MCDA-based interactive PDA can be an effective way of achieving “best-practice” or normative standards of decision making, such as 1) a well-constructed set of decision criteria or 2) logically consistent patient preferences. However, it also involves the use of resources such as the time and cognitive effort involved in decision-making. The comparative evaluation of alternative MCDA-based software applications in developing and delivering a PDA therefore involves trade-offs between decision effectiveness and decision resource criteria moving from the normative to the prescriptive. MCDA is an ideal tool for this meta-evaluation task as well as for the adoption decision itself. AIM: To analyse, as proof of concept, the use of MCDA for the development, implementation and evaluation of interactive PDAs in routine clinical practice. OBJECTIVES: 1. To assess the use with clinicians in the Spanish NHS of two alternative MCDA software applications which implement dissimilar MCDA techniques in the development of a PDA in routine clinical practice; 2. To assess the use with clinicians in the Spanish NHS of the same two alternative MCDA software applications in the implementation of a PDA in an environment replicating actual clinical consultations; 3. To build a meta-multi-criteria decision model based on the Decision Resources Decision Effectiveness Analysis (DRDEA) framework and assess the use of this model by clinicians in the Spanish NHS to make the choice between the two MCDA applications as the basis for a PDA. METHODS: 1) Two dissimilar MCDA software applications served as a basis for the development of a lung cancer clinical management PDA in close collaboration with two different groups of three clinicians from two different Spanish NHS hospitals (H1 and H2): 1) Expert Choice, which implements the Analytic Hierarchy Process (AHP) MCDA approach; 2) Annalisa in Elicia (ALEL), which implements the Simple Attribute Weighting (SAW) MCDA approach. The process of codevelopment of the PDA in hospitals H1 and H2 was documented; 2) Expert Choice was used to implement (i.e. deliver) the lung cancer clinical management PDA in three hypothetical consultations in hospital H1. In each consultation, one of the three clinicians involved in the development of the tool, with support by this researcher, guided a proxy patient (a non-clinical member of hospital staff) through the PDA. The same process was repeated with the MCDA software ALEL in hospital H2. The process of delivery of the PDA in hospitals H1 and H2 was documented; 3) This researcher built a meta-multi-criteria decision model based on the DRDEA framework to help clinicians choose between different MCDA software applications as the basis of a PDA. The MCDA approach used for this meta-model was Multi- Attribute Value Theory (MAVT). The model was implemented, using the software HiView 3, with three clinicians from hospital H3 for the choice between Expert Choice and ALEL as the basis of a lung cancer clinical management PDA. RESULTS: The thesis makes a three-fold contribution to research in patient-centred decision support. First, it presents two new MCDA software-based approaches to clinical decision support, based on joint work with clinicians in the Spanish NHS, for developing an interactive PDA for the clinical management of non-small cell lung cancer. Second, it describes the use of these decision support tools in the delivery of 5 an interactive PDA for the clinical management of non-small cell lung cancer in a hospital environment via simulated consultations between actual clinicians, with support from this researcher, and proxy lung cancer patients. Third, it presents and applies a new MCDA-based methodology for evaluating the use of alternative MCDA software applications in the development and delivery of interactive PDAs.

n modern aircraft design, increased attention is being paid to the conceptual and preliminary design phases so as to increase the odds of creating a design that will ultimately be successful at the completion of the design process. Since aerospace systems are complex systems with interacting disciplines and technologies, the decision makers dealing with such design problems are involved in balancing multiple, potentially conflicting attributes/criteria, transforming a large amount of customer supplied guidelines into a solidly defined set of requirement definitions. As a result, the criteria have to be all simultaneously taken into account and a compromise essentially becomes part of the decision making process. Various methods and techniques are available to deal with such sort of multi-criteria decision making (MCDM) problems. In the 1970’s, Saaty proposed the Analytic Hierarchy Process (AHP), which facilitates the MCDM problems that have a hierarchical structure of attributes by reducing complex decisions to a series of pair-wise comparisons. In this method, the preference information is elicited as the pair-wise comparisons between attributes or alternatives and treated using the eigenvector method. The other straightforward method to handle the MCDM problem is the Overall Evaluation Criterion (OEC) technique, presented in Ref 3. The OEC is a single metric and is obtained by summing multiple non-dimensional attribute metrics normalized by the metric values of a relevant baseline. Another commonly used MCDM technique is the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). The “best” solution chosen by TOPSIS is the alternative that is the closest to the positive ideal solution and the furthest from the negative ideal solution. The separation between each alternative solution and the ideal solution, which is determined by the weighted criteria, is rather sensitive to criterion weights, so typically several weighting scenarios are investigated to determine the final solution. Among these developed MCDM methods, different methods have different underlying assumptions, information requirements, analysis models, and decision rules that are designed for solving a certain class of decision making problems. This implies that it is critical to use the most appropriate method to solve the problem under consideration since the use of unsuitable method always leads to misleading design decisions. Consequently, bad design decisions will result in big loss to the society, such as property damage or personal injury. Thus, it is necessary to review the existing MCDM methods, discuss in depth their advantages, disadvantages, applicability, computational complexity, etc. in order to make right decision when choosing the right method for the given problem. In this paper a hybrid MCDM method is developed to deal with the problem under consideration. Relative weights of the evaluation criteria are elicited by using the eigenvector method to describe the decision maker’s preference information. The TOPSIS method is used to analyze the qualitative and quantitative data of input parameters and find the solution to the given problem. An aircraft technology selection problem is conducted as a proof of implementation to demonstrate the functionality and effectiveness of the proposed methodology.

Several individual-based social deprivation and vulnerability indices have been developed to measure the negative impact of low socioeconomic status on health outcomes. However, their variables and measurable characteristics have not been unequivocally assessed. A comprehensive database literature scoping review was performed to identify all individual-based social deprivation and vulnerability indices. Area-based indices and those developed for pediatric populations were excluded. Data were extracted from all eligible studies and their methodology was assessed with quality criteria. A total of 14 indices were identified, of which 64% (9/14) measured social deprivation and 36% (5/14) measured socioeconomic vulnerability. Sum of weights was the most common scoring system, present in 43% (6/14) of all indices, with no exclusive domains to either vulnerability or deprivation indices. A total of 83 different variables were identified; a very frequent variable (29%; 5/14) related to an individual's social relationships was "seen any family or friends or neighbors." Only five deprivation indices reported a specific internal consistency measure, while no indices reported data on reproducibility. This is the first scoping review of individual-based deprivation and vulnerability indices, which may be used interchangeably when measuring the impact of SES on health outcomes.

<p>Multi-criteria decision making (MCDM) has been introduced to GIS about 15 years ago. Decision rules that have been implemented in the GIS environment include weighted linear combination, analytical hierarchy process, ideal point analysis, concordance-discordance analysis, and ordered weighted averaging. The spatial dimensions of MCDM include spatially distributed decision-makers and decision alternatives, decision objectives relating to geographical objects, and a non-uniform weighting across space. However, few (if any) MCDM methods incorporate spatial relationships in the decision rule itself. This presentation suggests using geographical weighting to influence the calculation of aggregated suitability scores. Inverse distance-based weights are used to adapt the suitability of locations to their neighbours’ scores. This method was implemented in the thematic mapping package CommonGIS, and applied to a site selection problem to demonstrate the usefulness of geographically weighted MCDM. Through its interactive cartography, CommonGIS supports the application of geographical weighting in conjunction with other spatial dimensions of multi-criteria analysis.</p>

<p>Multi-criteria decision making (MCDM) has been introduced to GIS about 15 years ago. Decision rules that have been implemented in the GIS environment include weighted linear combination, analytical hierarchy process, ideal point analysis, concordance-discordance analysis, and ordered weighted averaging. The spatial dimensions of MCDM include spatially distributed decision-makers and decision alternatives, decision objectives relating to geographical objects, and a non-uniform weighting across space. However, few (if any) MCDM methods incorporate spatial relationships in the decision rule itself. This presentation suggests using geographical weighting to influence the calculation of aggregated suitability scores. Inverse distance-based weights are used to adapt the suitability of locations to their neighbours’ scores. This method was implemented in the thematic mapping package CommonGIS, and applied to a site selection problem to demonstrate the usefulness of geographically weighted MCDM. Through its interactive cartography, CommonGIS supports the application of geographical weighting in conjunction with other spatial dimensions of multi-criteria analysis. </p>

<p>Multi-criteria decision making (MCDM) has been introduced to GIS about 15 years ago. Decision rules that have been implemented in the GIS environment include weighted linear combination, analytical hierarchy process, ideal point analysis, concordance-discordance analysis, and ordered weighted averaging. The spatial dimensions of MCDM include spatially distributed decision-makers and decision alternatives, decision objectives relating to geographical objects, and a non-uniform weighting across space. However, few (if any) MCDM methods incorporate spatial relationships in the decision rule itself. This presentation suggests using geographical weighting to influence the calculation of aggregated suitability scores. Inverse distance-based weights are used to adapt the suitability of locations to their neighbours’ scores. This method was implemented in the thematic mapping package CommonGIS, and applied to a site selection problem to demonstrate the usefulness of geographically weighted MCDM. Through its interactive cartography, CommonGIS supports the application of geographical weighting in conjunction with other spatial dimensions of multi-criteria analysis. </p>

<p>Multi-criteria decision making (MCDM) has been introduced to GIS about 15 years ago. Decision rules that have been implemented in the GIS environment include weighted linear combination, analytical hierarchy process, ideal point analysis, concordance-discordance analysis, and ordered weighted averaging. The spatial dimensions of MCDM include spatially distributed decision-makers and decision alternatives, decision objectives relating to geographical objects, and a non-uniform weighting across space. However, few (if any) MCDM methods incorporate spatial relationships in the decision rule itself. This presentation suggests using geographical weighting to influence the calculation of aggregated suitability scores. Inverse distance-based weights are used to adapt the suitability of locations to their neighbours’ scores. This method was implemented in the thematic mapping package CommonGIS, and applied to a site selection problem to demonstrate the usefulness of geographically weighted MCDM. Through its interactive cartography, CommonGIS supports the application of geographical weighting in conjunction with other spatial dimensions of multi-criteria analysis.</p>

Introduction:The use of multi-criteria decision analysis (MCDA) in health technology assessment (HTA) studies has become more common due to the fact that MCDA offers a comprehensive technique for decisions that involve multiple criteria and stakeholders. How MCDA contributes to the HTA decision making process is an issue to be investigated. A systematic review was carried out in order to provide an overview of the benefits identified in MCDA applications for the strategic HTA decision making process.Methods:A systematic review developed by Philip Wahlster et al. (2014) was updated. The papers were analyzed in order to determine how MCDA is connected with traditional HTA, and to identify opportunities through the application of MCDA. In total 965 papers were found, and 43 articles were included in the review. The included articles detailed MCDA applications oriented to tactical and strategic decision making processes. The review was conducted by two researchers.Results:Of the available studies published on MCDA, 76 percent were published between 2014 and 2017, and 24 percent were published prior to 2014. Regarding the MCDA methodology defined in the included studies, 10 used the analytical hierarchy process, four used multi-attribute theory, and others refer the methodology only as “MCDA”. Seventeen studies also included health technology economic analysis, in special cost-effectiveness, safety and technological innovation. The studies suggest MCDA adds value since it allows different stakeholders to be engaged in the decision making process.Conclusions:The increase in studies on MCDA and healthcare point to the possibility to add different criteria, engage people with different knowledge levels, and make the decision-making process more transparent. In comparison with other technical areas, the use of MCDA in healthcare is more focused on achieving the decision about adding the new technology, and to show how to engage stakeholders than to explain how to develop the algorithms and methodologies.

Qualitative methods tend to be used to incorporate patient preferences into healthcare decision making. However, for patient preferences to be given adequate consideration by decision makers they need to be quantified. Multi-criteria decision analysis (MCDA) is one way to quantify and capture the patient voice. The objective of this review was to report on existing MCDAs involving patients to support the future use of MCDA to capture the patient voice. MEDLINE and EMBASE were searched in June 2014 for English-language papers with no date restriction. The following search terms were used: 'multi-criteria decision*', 'multiple criteria decision*', 'MCDA', 'benefit risk assessment*', 'risk benefit assessment*', 'multicriteri* decision*', 'MCDM', 'multi-criteri* decision*'. Abstracts were included if they reported the application of MCDA to assess healthcare interventions where patients were the source of weights. Abstracts were excluded if they did not apply MCDA, such as discussions of how MCDA could be used; or did not evaluate healthcare interventions, such as MCDAs to assess the level of health need in a locality. Data were extracted on weighting method, variation in patient and expert preferences, and discussion on different weighting techniques. The review identified ten English-language studies that reported an MCDA to assess healthcare interventions and involved patients as a source of weights. These studies reported 12 applications of MCDA. Different methods of preference elicitation were employed: direct weighting in workshops; discrete choice experiment surveys; and the analytical hierarchy process using both workshops and surveys. There was significant heterogeneity in patient responses and differences between patients, who put greater weight on disease characteristics and treatment convenience, and experts, who put more weight on efficacy. The studies highlighted cognitive challenges associated with some weighting methods, though patients' views on their ability to undertake weighting tasks was positive. This review identified several recent examples of MCDA used to elicit patient preferences, which support the feasibility of using MCDA to capture the patient voice. Challenges identified included, how best to reflect the heterogeneity of patient preferences in decision making and how to manage the cognitive burden associated with some MCDA tasks.

Effects of increasing fuzziness on analytic hierarchy process for spatial multicriteria decision analysis

Entscheiden bei unschärfe — Fuzzy decision support systeme: Springer-Verlag, Berlin, 1988, ix + 304 pages, DM45.00

The Tri-State Mining District was formed to encompass areas of Oklahoma, Kansas, and Missouri where lead, zinc, and other metals were mined from the 1900s until the 1960s. Tar Creek in Ottowa County, Oklahoma was the recipient of much of the mining waste generated during this period. The Tar Creek watershed is an approximately 53.3-square-mile area, where 19,566 people reside. It is characterized by high heavy metal soil concentrations, contaminated surface and ground waters, air transport of contaminants, and exposed mining wastes. There are human health and ecological exposure hazards from these media. A need for evaluations of long-term solutions that could be constructed or implemented to improve the ecosystems is apparent. There has been a movement toward a more ‘holistic’ response to human health and wildlife risks at and adjacent to Tar Creek, including determining problems affecting residents and identifying appropriate remedial actions. In 1983, the area along Tar Creek was listed on the National Priority List (NPL) as a Superfund Site. The Environmental Protection Agency signed a Memorandum of Understanding with United States Army Corps of Engineers and the Department of Interior in 2003 to collaborate on assessment and remediation efforts with multiple stakeholders, which include tribal authorities, local interest parties, and other entities. Multi-Criteria Decision Analysis (MCDA) is a systematic and structured process beneficial to users during the pre- and postphase of decision making. MCDA could prove an asset to the Tar Creek project, particularly when dealing with multiple stakeholders coupled with numerous remediation objectives and risk remedies, by applying decision processes such as Analytical Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT). Commercial software packages use decision processes as engines; for example, Expert Choice® utilizes AHP while Criterium DecisionPlus® exercises MAUT. MCDA, paired with decision-making tools, provides the results of modeling/-monitoring studies, risk analysis, cost, and stakeholder preferences so that risk managers are able to systematically evaluate and compare alternatives and actions supporting risk management and thus credibly prioritize resources. The following sections will discuss the background and history of the Tar Creek Superfund Site, the MCDA framework/structure, commonly used MCDA tools in conjunction with theories, and a methodology for how MCDA can be effectively used at the site.