Multimodal Analysis of Factors Associated with Respiratory Symptoms in Colombian Informal Waste Pickers: A Study Based on Statistical Models and Machine Learning Algorithm

Abstract

Objective: This study aimed to assess the prevalence of respiratory symptoms among informal waste pickers in Colombia and identify the contributing demographic, environmental, and occupational risk factors. Methods: A cross-sectional study was conducted with 179 informal waste pickers from four Colombian cities. Respiratory symptoms and demographic data were collected through questionnaires. Statistical methods including logistic regression, principal component analysis (PCA), Random Forest modelling, and K-means clustering were applied to identify predictors and patterns related to respiratory health. Results: The most frequently reported symptoms were cough (46.8%), phlegm (18.4%), and shortness of breath (19.6%). Logistic regression identified age as a significant predictor of respiratory symptoms, while Random Forest analysis highlighted cough as the strongest predictor, followed by age, race, and education level. K-means clustering revealed three groups, with older workers showing the highest prevalence of symptoms. PCA indicated that respiratory symptoms and demographic factors explained significant variance in health outcomes. Conclusions: Informal waste pickers in Colombia are at elevated risk for respiratory health issues, particularly older workers exposed to prolonged occupational hazards. Targeted interventions, including improved use of protective measures and policies addressing informal work conditions, are needed to mitigate these risks and improve workers’ health and safety.