Abstract
BackgroundThe relationship between antimicrobial use and resistance is complex, making it difficult to understand and predict the impact of antimicrobial policies. Here, we examine trends of antimicrobial pressure and pathogen rates using novel metrics.MethodsData were extracted from 2007 through 2016. GEE-negative binomial regression modeled incident (within a year) hospital-onset (HO) pathogen rates, defined as the number of unique positive isolates between hospital day 3 and discharge, offset by patient-days at risk (eliminating the first 2 hospital days from the denominator, etc.). As predictors, we used pathogen-specific AM pressure metrics, summing the selection pressure of each AM regimen, given to a patient in a day, for and against the pathogen by each facility and year (e.g., if a regimen was 70% active by antibiogram then 0.7 was counted as selection against and 0.3 for the pathogen; different regimens would contribute differentially). We also adjusted by facility complexity index and pathogen admission prevalence.ResultsAll HO-pathogen rates declined significantly after adjustment (raw rates in Figure 1), except Bacteroides. Admission prevalence trends were variable (Table 1 and Figure 2). Figure 3 demonstrates the trend of the log ratio of AM pressure for and against pathogens. Significant negative associations with AM pressure against 5 pathogens and for 1 were observed (Table 1).ConclusionThere was a broad decrease in adjusted hospital pathogen rates. The negative association with selection pressure against pathogens suggests that (a) AM resistance among pathogens is decreasing, (b) it causes a decrease in infection rates, or (c) both. While residual confounding and endogeneity still exist, our findings highlight the possibility that new metrics might better predict AM effects, including potential protective effects of some patterns of AM use. It is also notable that the measured associations were not large enough nor AM pressure trends consistent enough to explain the decreases in HO-pathogen rates. This suggests that other factors not measured in this analysis, including infection prevention, likely played a large role in observed trends. Interpretation of these results should be nuanced; we are not advocating broad-spectrum AM use. Disclosures All authors: No reported disclosures.
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