Abstract
Background
The emergence and spread of antimicrobial resistance is a serious global public health crisis. Drug-resistant Gram-negative bacteria, like Escherichia coli, are particularly ...concerning given their significant morbidity and mortality. Despite the increasing prevalence of drug-resistant Gram-negative bacteria worldwide, there are significant knowledge gaps in low resource countries. We aimed to characterize the prevalence, phenotypes, and risk factors of drug-resistant E. coli carriage in children up to age 5 from stool collected in the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) birth cohort study in rural Tanzania.
Methods
Two hundred sixty-two children were enrolled in the MAL-ED Tanzania site. We randomly selected 100 children who had E. coli specimens archived every 6 months through 60 months. Up to five lactose-fermenting colonies were selected from growth on MacConkey agar. Drug susceptibility testing of 18 antibiotics was performed by disk diffusion. CLSI interpretive criteria were used for determination of resistance. Generalized estimating equations were used to create a multivariate Poisson regression model for drug resistance risk factors.
Results
Eight hundred twenty-three E. coli specimens were available for testing. The highest rates of resistance were to ampicillin, cefazolin, and cotrimoxazole, respectively. No carbapenem resistance was found. 1.8% met criteria for extended-spectrum β-lactamase production based on combination disk testing. 696 (84.6%) specimens met criteria for multi-drug resistance (nonsusceptible to at least 1 drug in at least three drug categories). In terms of dynamic risk factors, there was no association between antibiotic use or episodes of diarrhea and antibiotic resistance. For static risk factors, there was an association between higher income and increased antibiotic resistance.
Conclusion
Antibiotic resistance carriage is an under recognized problem in this setting. Resistance rates at 6 months of age are higher than expected, with surprisingly little variance explained by individual-level risk factors for resistance in this community.
Disclosures
All authors: No reported disclosures.
: Growth trajectories are highly variable between children, making epidemiological analyses challenging both to the identification of malnutrition interventions at the population level and also risk ...assessment at individual level. We introduce stochastic differential equation (SDE) models into child growth research. SDEs describe flexible dynamic processes comprising: drift - gradual smooth changes - such as physiology or gut microbiome, and diffusion - sudden perturbations, such as illness or infection.
: We present a case study applying SDE models to child growth trajectory data from the Haydom, Tanzania and Venda, South Africa sites within the MAL-ED cohort. These data comprise n=460 children aged 0-24 months. A comparison with classical curve fitting (linear mixed models) is also presented.
: The SDE models offered a wide range of new flexible shapes and parameterizations compared to classical additive models, with performance as good or better than standard approaches. The predictions from the SDE models suggest distinct longitudinal clusters that form distinct 'streams' hidden by the large between-child variability.
: Using SDE models to predict future growth trajectories revealed new insights in the observed data, where trajectories appear to cluster together in bands, which may have a future risk assessment application. SDEs offer an attractive approach for child growth modelling and potentially offer new insights.
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