Antibiotic use and hygiene interact to influence the distribution of antimicrobial-resistant bacteria in low-income communities in Guatemala

Abstract

To examine the effects of poor sanitation and hygiene on the prevalence of antimicrobial-resistant bacteria, we surveyed households in two rural and two urban communities in Guatemala (N = 196 randomly selected households). One adult (≥ 18-years old) and, when available, one child (≤ 5 yearsold) provided a stool sample. Up to 48 presumptive Escherichia coli isolates were collected from each stool sample (n = 21,256 total) and were subjected to breakpoint assays for ten antibiotics. Mixedeffects logistic models were used to identify potential factors influencing the likelihood of harboring antibiotic-resistant bacteria. For nine out of ten antibiotics, the odds of detecting resistant bacteria decreased by ~ 32% (odds ratios, OR 0.53-0.8, P < 0.001) for every unit of improvement of a hygiene scale. Hygiene differences between households had a greater impact on prevalence compared to antibiotic use differences. The likelihood of detecting resistant isolates was lower for five antibiotics among households that boiled raw milk before consumption (OR 0.31-0.69), and higher for nine antibiotics in urban households (OR > 1.89-9.6). Poor hygiene conditions likely obscure effects of individual antibiotic use, presumably due to enhanced microbial transmission. Consequently, efforts to improve antibiotic stewardship should be coupled with improving hygiene conditions. Antimicrobial-resistant infections contribute to hundreds-of-thousands of deaths worldwide 1. To address this threat, public and private organizations are engaged in programs to improve surveillance for antimicrobialresistant organisms, to improve antibiotic stewardship, and to identify novel therapeutics, all of which are considered priority actions for combating antimicrobial resistance (AMR) 2-4. The strategies adopted to limit AMR will vary globally, particularly across countries with different degrees of wealth and development. The most effective intervention will likely depend on prevailing conditions. For example, high-income countries and low-income countries can differ in antimicrobial resistance patterns, antimicrobial use practices, access to healthcare services (human and animal), sanitation and regulation infrastructures 5-9. Studies conducted within high-income countries, particularly those concerning healthcare facilities 10-12 and the agricultural sector 13-15 , have associated reductions in antimicrobial use and improved stewardship with decreases in the prevalence of antimicrobial resistance. In contrast, studies examining global patterns of AMR find that antibiotic use is not positively correlated with resistance in all cases and may instead be correlated with

Antimicrobial Resistance in Escherichia coli Strains Isolated from Humans and Pet Animals

“…Low hygiene levels, lack of clean water, or poor sanitary conditions can create perfect conditions for the development and transmission of infections [ 66 ]. In addition to that, Farhani et al [ 67 ] have total of 80 E. coli isolates, separated into 51 different genotypes.…”

Carriage of antimicrobial-resistant bacteria in a high-density informal settlement in Kenya is associated with environmental risk-factors

“…Genotypically, we compared breakpoint assay results with wholegenome sequence data for > 730 E. coli isolates and found that for the most common antibioticresistance phenotypes (ampicillin, streptomycin, sulfamethoxazole, tetracycline and trimethoprim), diagnostic sensitivity varied between 0.75 0.93 while diagnostic speci city ranged from 0.94 and 0.99 [34]. Phenotypically, we found a correlation (r) of 0.98 between a panel of E. coli isolates by breakpoint and Kirby Bauer disc diffusion test [37]. Lastly, there was some mismatch between the antibiotics tested in this study and those used by the study community.…”

“…Lastly, there was some mismatch between the antibiotics tested in this study and those used by the study community. We used identical methods in a different study, which included both antibiotics, and found a correlation of 0.73 between the two resistance phenotypes [37]. Antibiotics that were not included in the assays were those that were either redundant to the panel of antibiotics being used, (e.g., tetracycline vs. doxycycline, ceftazidime vs. other cephalosporins) or were not expected to affect E. coli (e.g., metronidazole).…”

Carriage of antimicrobial-resistant bacteria in a high-density informal settlement in Kenya is associated with environmental risk-factors

“…Colony morphology, while not a reliable diagnostic for species identity, was 99.2% consistent with E. coli based on a random subset of 248 isolates. We have successfully used this method for selecting E. coli for high-throughput assessment of antibiotic resistance in other studies [16,[36][37][38]. Thirdly, the "breakpoint" assay, which is not considered a diagnostic tool in a clinical microbiology lab, provides a low-cost means to analyze many isolates.…”

“…Thirdly, the "breakpoint" assay, which is not considered a diagnostic tool in a clinical microbiology lab, provides a low-cost means to analyze many isolates. We have assessed the validity of this method genotypically [36] and phenotypically [38].…”