Assessing the Health Vulnerability Caused by Climate and Air Pollution in Korea Using the Fuzzy TOPSIS

Bae, Hyun-Joo; Kang, Jung Eun; Lim, Yu-Ra

doi:10.3390/su11102894

Cited by 17 publications

(13 citation statements)

References 31 publications

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“…This paper collected hospital data, but did not include sanitation stations, epidemic prevention and control centers, or community health care centers. In addition, we also selected economy, road density, land use, and night-time light except for hospital data, as these indicators can reflect the coping capacity to deal with the risks caused by air pollutants [32], and the original data sources are shown in Table 2. The data of pertussis vaccination rate was collected from Sichuan Health Statistics Yearbook (2017) [33].…”

Section: Methodsmentioning

confidence: 99%

Health Risk and Resilience Assessment with Respect to the Main Air Pollutants in Sichuan

Xiong

Zhou

et al. 2019

IJERPH

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Rapid urbanization and industrialization in developing countries have caused an increase in air pollutant concentrations, and this has attracted public concern due to the resulting harmful effects to health. Here we present, through the spatial-temporal characteristics of six criteria air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) in Sichuan, a human health risk assessment framework conducted to evaluate the health risk of different age groups caused by ambient air pollutants. Public health resilience was evaluated with respect to the risk resulting from ambient air pollutants, and a spatial inequality analysis between the risk caused by ambient air pollutants and hospital density in Sichuan was performed based on the Lorenz curve and Gini coefficient. The results indicated that high concentrations of PM2.5 (47.7 μg m−3) and PM10 (75.9 μg m−3) were observed in the Sichuan Basin; these two air pollutants posed a high risk to infants. The high risk caused by PM2.5 was mainly distributed in Sichuan Basin (1.14) and that caused by PM10 was principally distributed in Zigong (1.01). Additionally, the infants in Aba and Ganzi had high health resilience to the risk caused by PM2.5 (3.89 and 4.79, respectively) and PM10 (3.28 and 2.77, respectively), which was explained by the low risk in these two regions. These regions and Sichuan had severe spatial inequality between the infant hazard quotient caused by PM2.5 (G = 0.518, G = 0.493, and G = 0.456, respectively) and hospital density. This spatial inequality was also caused by PM10 (G = 0.525, G = 0.526, and G = 0.466, respectively), which is mainly attributed to the imbalance between hospital distribution and risk caused by PM2.5 (PM10) in these two areas. Such research could provide a basis for the formulation of medical construction and future air pollution control measures in Sichuan.

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Section: Methodsmentioning

confidence: 99%

Health Risk and Resilience Assessment with Respect to the Main Air Pollutants in Sichuan

Xiong

Zhou

et al. 2019

IJERPH

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“…The outcome supply managerial insights concerning the performance of public hospitals and chances for superior resource apportionment in the healthcare field. The research in [ 40 ] assessed healthcare vulnerability utilising subjective and ambiguous environmental data for the health sector using the fuzzy TOPSIS for the impact of climate and air pollution in Korea. The work in [ 41 ] developed a classification arrangement that concentrated on practical concerns about TOPSIS that categorized into application areas, publication year, journal name, authors’ nationality, and integrating other MADM/MCDM methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…of physicians, Total No. of professionals Public hospitals from 92 municipalities of Rio de Janeiro, for a period from 2008–2013 [ 40 ] -Vulnerability assessment of the health effects caused by climate variation -Validity of the vulnerability assessment results and the occurrence rate of cardiovascular patients Fuzzy TOPSIS Meteorological, air pollution, vulnerable group, vulnerable environment, disease distribution, socioeconomic-capacity, health/medical-capacity, air-pollution control Climate and air pollution data from South Korea in 2010 [ 41 ] Ranking variances between the TOPSIS and other MCDM approaches TOPSIS Combined-other-methods, year of publication, publication of journal, nationality of author 266 papers about TOPSIS applications published since 2000 in 103 journals of scholarly [ 45 ] Research priorities ranking for organization of military health AHP, TOPSIS Time priority, accordance with organizational goals, applicability and usefulness Cross-sectional study done in 2013 (clinics of a military health organization in Iran) [ 42 ] efficiency estimation of the health information systems in health care services and delivery AHP-TOPSIS Number of users of information systems Data redundancy Monthly growth of interoperability rates Rate of return on investment Monthly growing of the employment of data (big data) Valuation of compliance with HIPPA principles top 20 of the most used software solutions at the beginning of 2016 (from Electronic Health Record) [ 43 ] Selection of Power Plant Site of Inland Nuclear under Z‐numbers Z -number ‐BWM, Z -number ‐DEMATEL, and Z -number ‐TOPSIS Eco‐environment, physical geography, socio‐economics Hunan Province of China [ 44 ] Rank the hotel BEER projects ...…”

Section: Literature Reviewmentioning

confidence: 99%

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

Albahri

Hamid

Albahri

et al. 2021

Artificial Intelligence in Medicine

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Highlights Develop a new framework that can handle the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers. The most important laboratory criteria were selected and implemented based on two feature selection approaches (i.e. data-driven and knowledge-driven). A new decision matrix was developed based on a crossover of (i) multi-laboratory characteristics criteria and (ii) lists of infected patients for patient prioritisation using integration Entropy-TOPSIS methods. Advantages of new framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.

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“…The results showed that hybrid MADM and fuzzy MADM approaches (27.92%) were used more often than other methods. The most commonly used methods are AHP and fuzzy AHP [59] (24.87%), ELECTRE, fuzzy ELECTRE [60], MCDA and MCA (12.69%), and TOPSIS, fuzzy TOPSIS [61], PROMETHEE and fuzzy PROMETHEE [62] (5.08%) [1].…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Attribute Decision-Making Methods as a Part of Mathematical Optimization

Vinogradova

2019

Mathematics

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Optimization problems are relevant to various areas of human activity. In different cases, the problems are solved by applying appropriate optimization methods. A range of optimization problems has resulted in a number of different methods and algorithms for reaching solutions. One of the problems deals with the decision-making area, which is an optimal option selected from several options of comparison. Multi-Attribute Decision-Making (MADM) methods are widely applied for making the optimal solution, selecting a single option or ranking choices from the most to the least appropriate. This paper is aimed at providing MADM methods as a component of mathematics-based optimization. The theoretical part of the paper presents evaluation criteria of methods as the objective functions. To illustrate the idea, some of the most frequently used methods in practice—Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Complex Proportional Assessment Method (COPRAS), Multi-Objective Optimization by Ratio Analysis (MOORA) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)—were chosen. These methods use a finite number of explicitly given alternatives. The research literature does not propose the best or most appropriate MADM method for dealing with a specific task. Thus, several techniques are frequently applied in parallel to make the right decision. Each method differs in the data processing, and therefore the results of MADM methods are obtained on different scales. The practical part of this paper demonstrates how to combine the results of several applied methods into a single value. This paper proposes a new approach for evaluating that involves merging the results of all applied MADM methods into a single value, taking into account the suitability of the methods for the task to be solved. Taken as a basis is the fact that if a method is more stable to a minor data change, the greater importance (weight) it has for the merged result. This paper proposes an algorithm for determining the stability of MADM methods by applying the statistical simulation method using a sequence of random numbers from the given distribution. This paper shows the different approaches to normalizing the results of MADM methods. For arranging negative values and making the scales of the results of the methods equal, Weitendorf’s linear normalization and classical and author-proposed transformation techniques have been illustrated in this paper.

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Assessing the Health Vulnerability Caused by Climate and Air Pollution in Korea Using the Fuzzy TOPSIS

Cited by 17 publications

References 31 publications

Health Risk and Resilience Assessment with Respect to the Main Air Pollutants in Sichuan

Health Risk and Resilience Assessment with Respect to the Main Air Pollutants in Sichuan

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

Multi-Attribute Decision-Making Methods as a Part of Mathematical Optimization

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