Modeling the antimicrobial resistance of enterobacteria responsible for Urinary Tract Infections in Benin: another way to Control or survey Antimicrobial Resistance

Abstract Background: Infectious diseases are serious public health issue both in developing countries and industrialized countries. In developing countries, they are the main cause of high mortality rates. In the second group, existing resistance to antibiotics is developing growing at an alarming rate. The purpose of this study was to produce data of national interest to implement sustainable control of antimicrobial resistance as well as it spreads. Methods: One hundred ninety (190) urine samples were collected in several hospitals in Benin from patients suspected of having a urinary tract infection. After getting the inform consent from patients, samples collections were performed under aseptic conditions and were further subjected to bacteriological tests in the laboratory. The resistance profile of the bacterial strains identified was then established. The search for betalactamase production was performed by the synergy test between amoxicillin + clavulanic acid and cephalosporins. Mathematical modeling of the resistance of the strains identified by 2024 was finally carried out using compartmental deterministic models. Results: Two hundred thirty (230) strains were identified from urine samples. Male individuals were the most affected by urinary tract infections. Individuals in the 21-30 age groups were predominant. Escherichia coli was the most isolated bacterial species (32.43%) in this study followed by Klebsiella pneumoniae (26.85%) and Enterobacter cloaceae (25.92%). The susceptibility testing of isolates bacteria to antibiotics showed a strong resistance of strains to amoxicillin (91.82%). The lowest resistance obtained was observed with imipenem (2%). The betalactamase was produced by 24.03% of the strains identified. Escherichia coli (32.43%) was indeed the most productive of betalactamase followed by Klebsiella pneumoniae (31.03%). Mathematical modeling revealed a rampant rise in the resistance of bacteria to the antibiotics tested. Conclusions: These results provide important data for public health. They deserve constructive advocacy so that more specific actions are taken in relation to antimicrobial resistance.