Mental disorders contribute substantially to global disability and economic burden, yet relatively little is known about how macro-level national indicators relate to cross-country differences in mental health burden. We constructed a country–year panel for 183 countries from 1990 to 2023 by combining data from the Global Burden of Disease, the World Bank, and Our World in Data. Two composite indices were derived from age-standardized prevalence and DALY rates for depressive disorders, anxiety disorders, bipolar disorder, eating disorders, and schizophrenia. The analysis is exploratory and predictive rather than causal. With a time-based split, the models got trained on the 1990–2010 slice and then assessed using 2011–2023 observations. For the non-lagged macro variants, they were able to explain a notable part of the cross-national variation, but the lagged versions mostly ended up reflecting strong temporal carryover in mental health burden. Across the setups, GDP per capita, urbanization, life expectancy, alcohol consumption, unemployment, PM2.5 exposure, and refugee outflows consistently emerged as important predictive correlates. Regional models show heterogeneity. In East Asia and the Pacific, income, pollution, and alcohol use looked more prominent. In contrast, for Europe, the Middle East and North Africa, refugee flows and development indicators were the more prominent predictors. Robustness checks included alternative temporal splits, complete case analysis, bootstrap uncertainty intervals, calibration assessment, feature rank stability, and panel-style benchmarks. Overall the results point toward development, labour market conditions, environmental exposure, and displacement as important macro-level variables associated with aggregate mental health burden, even if the interpretation stays ecological and not causal. Clinical trial registration: Not applicable. This study was not a clinical trial.
Imad Shahid, G. Benrhmach, K. El Moutaouakil et al.· Discover Analytics· 0 citations
In this paper, we describe a new hybrid framework called FIS-CSP-OM that combines Fuzzy Inference Systems (FIS), Constraint Satisfaction Problems (CSP), and Optimization Modeling (OM) together for analysing and interpreting imbalanced datasets. The goal of the hybrid framework is to provide a means to integrate interpretable rule-based models (e.g., FIS) and numerical constraint-based reasoning (e.g., CSP and OM) in order to better represent imbalanced datasets and to understand the relationships among the variables used to create the model. Using triangular membership functions, we convert all of the numerical features into their corresponding linguistic variables, so that we can represent the dataset in a fuzzy manner. After the linguistic representation of the data is generated, we use the Apriori algorithm to perform association rule mining which enables us to create a set of high-confidence rules that can be used to describe and characterise the minority class. Finally, we can use the set of rules to develop a Linguistic CSP model that consists of symbolic variables that represent all of the linguistic features of the data and that have constraints that describe the relationships that were uncovered using the high-confidence rules. Following the transformation of the linguistic model into a Numerical CSP by identifying fuzzy modalities with continuous intervals, a tractable question will be derived from Constraint Solvers. Validation of the methodology is performed on the imbalanced dataset where the focus is on the minority class, ‘young.’ Experimental results demonstrate that there is a high degree of confidence in the extracted rules and a great deal of structural coherence between them, yielding an accurate, interpretable characterization of the target class. In addition, the CSP allows for a geometric interpretation of feasible regions in the feature space and the production of synthetic instances consistent with the constraints learned. Overall, the FIS-CSP-OM framework offers a powerful and interpretable approach for handling imbalanced classification problems by combining fuzzy logic, data mining, and constraint-based modeling. It opens new perspectives for explainable artificial intelligence, knowledge extraction, and constraint-driven data generation.
A. Al-Shaery, M. Roudani, Karim El Moutaouakil et al.· IEEE Access· 0 citations
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