DETECTION AND QUANTIFICATION OF ANTIBIOTIC RESIDUES IN MILK SAMPLES AND ASSOCIATED HEALTH RISKS

Abstract

Background: Retail milk frequently harbors multidrug-resistant (MDR) pathogens and antibiotic residues, posing significant public health risks. However, the direct epidemiological correlation between these simultaneous chemical and biological hazards lacks multivariable evaluation. This study investigates the association between antibiotic concentrations and MDR isolation. The precise point-of-sale magnitude of this hazard correlation remains unknown. Methods: An eco-epidemiological cross-sectional study in Lahore, Pakistan, sampled unflavored retail fluid milk. The defining exposure was standardized quantification of beta-lactam and tetracycline residues, compared against established Maximum Residue Limits (MRLs). The primary outcome was point-of-sale isolation of MDR Escherichia coli or Staphylococcus aureus (100% acquisition completeness), assessed utilizing multivariable logistic regression adjusted for processing matrix and vendor type. Surrogate toxicological safety was monitored using deterministic dietary Hazard Quotients. Results: More than the required sample was screened to obtain the 264 independent analytic milk specimens (164 raw, 100 pasteurized). Residues exceeded MRLs in 98/264 (37.1%) samples. The primary outcome of MDR pathogen isolation occurred in 68/264 (25.8%) specimens. Exceeding MRLs was independently associated with an elevated likelihood of MDR isolation (adjusted odds ratio 2.85, 95% CI 1.52 to 5.34, p=0.001). Direct clinical adverse events were inapplicable in vitro; therefore, surrogate toxicological surveillance confirmed 0/264 samples breached systemic thresholds, with maximum pediatric hazard quotients (0.77) remaining safely below 1.0.

Conclusions: Antibiotic residues exceeding regulatory limits were independently associated with MDR pathogen contamination in retail milk, though acute surrogate toxicological hazards remained comprehensively within safe boundaries. Given the limitation of this cross-sectional observational design, future longitudinal genomic tracking is necessary to determine agricultural transmission causality