A Multi-Stage Fuzzy Decision Analytics Framework for Evaluating Climate-Induced Risk Factors of Catastrophic Healthcare Expenditure

Sanjib Biswas; Biplab Biswas; Aparajita Sanyal; Prasenjit Chatterjee; Dragan Pamucar

doi:10.67334/cds31202628

Authors

Sanjib Biswas Amity Business School, Amity University Kolkata, Newtown, West Bengal, India Author https://orcid.org/0000-0002-9243-2403
Biplab Biswas Amity Business School, Amity University Kolkata, Newtown, West Bengal, India Author https://orcid.org/0000-0001-5869-4998
Aparajita Sanyal Amity Business School, Amity University Kolkata, Newtown, West Bengal, India Author https://orcid.org/0000-0002-9968-5817
Prasenjit Chatterjee Department of Mechanical Engineering, MCKV Institute of Engineering, West Bengal, India Author https://orcid.org/0000-0002-7994-4252
Dragan Pamucar 1) Department of Applied Mathematical Science, College of Science and Technology, Korea University, Sejong, Republic of Korea; 2) School of Engineering and Technology, Sunway University, Selangor, Malaysia; 3) UNEC Applied Artificial Intelligence Research Center, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan; 4) Transport and Logistics Competence Centre, Vilnius Gediminas Technical University, Vilnius, Lithuania Author https://orcid.org/0000-0001-8522-1942

DOI:

https://doi.org/10.67334/cds31202628

Keywords:

Catastrophic Healthcare Expenditure, Climate Change, Rural Health Vulnerability, Health Economics, q-Rung Ortho Pair Fuzzy Number, Fine-Kinney Framework, MCDM

Abstract

Climate change is increasingly intensifying health and financial vulnerabilities in rural communities, often leading to catastrophic healthcare expenditures due to rising disease burden, income instability, and reduced adaptive capacity. Identifying and prioritizing the most critical climate-induced risk factors is therefore essential for designing effective health financing and social protection strategies. This study proposes an integrated fuzzy decision analytics framework to evaluate climate-related determinants of catastrophic healthcare expenditure under conditions of uncertainty and imprecise expert judgment. The traditional Fine-Kinney risk assessment framework was extended using q-rung orthopair fuzzy sets to represent ambiguity in expert evaluations. The Comparisons Between Ranked Criteria (COBRAC) method was employed to estimate risk scores for the Fine-Kinney dimensions, while the modified Preference Selection Index (MPSI) and Simple Additive Weighting (SAW) methods were used to prioritize climate-induced healthcare risk factors. Expert assessments from sixteen specialists were aggregated using the Einstein weighted aggregation operator. To verify the robustness and reliability of the proposed framework, a three-stage validation procedure and sensitivity analysis were performed. The findings reveal that increased debt burden (0.1328), disease burden (0.1299), and challenges in maintaining livelihoods (0.1291) are the most influential contributors to catastrophic healthcare expenditure in climate-vulnerable rural communities. The proposed framework provides important implications for health economics and healthcare management by supporting evidence-based prioritization of climate adaptation policies, financial risk protection measures, and rural healthcare planning aimed at reducing the economic consequences of climate-related health shocks.

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