A Predictive Model Linking Food-Borne Heavy-Metal Exposure to Antimicrobial Resistance Co-Selection

Abstract

This study has examined a predictive model linking food-borne heavy-metal exposure to antimicrobial resistance co-selection in a selected food-system case context. The problem has been that heavy-metal contamination and antimicrobial resistance are often studied separately, although contaminated food environments may create non-antibiotic selective pressure that supports resistant bacteria. The purpose of the study has been to test whether food-borne heavy-metal exposure, food contamination pathways, food safety monitoring weakness, and regulatory control have predicted antimicrobial resistance co-selection. The study has used a quantitative, cross-sectional, case-based research design with 220 valid respondents from food safety, public health, laboratory, agriculture, environmental monitoring, academic, and food industry-related sectors. Data have been collected through a structured questionnaire using Likert’s five-point scale and analyzed through descriptive statistics, reliability testing, correlation analysis, and multiple regression modeling. The key variables have included food-borne heavy-metal exposure, food contamination pathways, food safety monitoring weakness, regulatory control, and antimicrobial resistance co-selection. The findings have shown that food contamination pathways have recorded the highest mean score of 3.94 with SD = 0.58, followed by food-borne heavy-metal exposure at 3.89 with SD = 0.62 and antimicrobial resistance co-selection at 3.87 with SD = 0.64. Food safety monitoring has recorded a moderate mean of 3.41, while regulatory control has recorded 3.36. Reliability has been strong, with Cronbach’s alpha values ranging from 0.81 to 0.88 and overall reliability at 0.91. Correlation results have shown significant relationships between antimicrobial resistance co-selection and heavy-metal exposure (r = 0.64), contamination pathways (r = 0.61), monitoring weakness (r = 0.49), and regulatory control (r = -0.43), all at p < 0.001. Regression results have shown that the model has explained 57.4% of the variance in antimicrobial resistance co-selection, with R² = 0.574 and adjusted R² = 0.566. The study has implied that stronger food safety monitoring, contamination control, and regulatory enforcement can reduce co-selection risk in food systems.