Operationalizing wastewater-based surveillance of multidrug-resistant bacteria

实施基于废水的多重耐药细菌监测

• 批准号: 10679007
• 负责人: Medini Annavajhala
• 金额: $ 13.08万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-08 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10679007
• 关键词: Affect; Antibiotic Resistance; Antibiotic-resistant organism; Antibiotics; Antimicrobial Resistance; Biological Models; Biomedical Engineering; Bioreactors; Carbapenems; Cephalosporin Resistance; Cephalosporins; Chromatin; Circulation; Clinical; Clinical Data; Collection; Communities; Data; Decision Making; Detection; Development; Disease Outbreaks; Early Diagnosis; Future; Gene Exchanges; Genetic; Genotype; Growth; Guidelines; Health Care Costs; Horizontal Gene Transfer; Hospitalization; Hospitals; Individual; Infection; Link; Literature; Lung infections; Medical center; Metagenomics; Methods; Modeling; Multi-Drug Resistance; Multiple Bacterial Drug Resistance; Neighborhoods; Non-linear Models; Nosocomial pneumonia; Outcome; Patient Isolation; Patients; Pattern; Phylogenetic Analysis; Plants; Population; Population Heterogeneity; Population Surveillance; Predisposition; Prevalence; Public Health; Reporting; Research; Resistance; Risk; Risk Assessment; Sampling; Series; Stream; Surveillance Modeling; Techniques; Temperature; Testing; Time; bacterial resistance; carbapenem resistance; clinically relevant; cohort; cost; cost effective; data acquisition; design; epidemiological model; experimental study; gene interaction; gut colonization; high risk; improved; mathematical model; molecular marker; mortality; multi-drug resistant pathogen; novel; pathogen; pathogenic bacteria; pathogenic virus; patient population; pressure; prevent; primary outcome; research clinical testing; residence; resistance allele; resistance gene; resistance mechanism; sociodemographics; tool; transmission process; trend; viral outbreak; wastewater epidemiology; wastewater samples; wastewater sampling; wastewater surveillance; wastewater testing

Multidrug-resistant organisms (MDRO) pose a significant risk to public health. Infections with MDRO are associated with high mortality rates and healthcare costs, particularly related to hospital-acquired pneumonia. Current approaches to control and prevent transmission of these pathogens focus primarily on clinical testing of infectious patient isolates. This is costly, labor-intensive, and fails to account for asymptomatic carriage. Wastewater testing can overcome many of the limitations posed by patient-based surveillance by enabling cost-effective population-level data acquisition, which can subsequently be used to model and forecast infectious outbreaks. To date, wastewater-based testing has been successfully used for surveillance of pathogenic viruses, but barriers remain in applying this approach to MDRO. While pathogenic bacteria and antibiotic resistance genes (ARGs) have been detected in wastewater treatment plants, several factors currently limit the utility and accuracy of wastewater as a marker for overall burden and diversity of antibiotic resistance. Here, we aim to better operationalize metagenomic wastewater-based epidemiology by understanding the dynamics of multidrug-resistant bacteria during wastewater flow, as well as the relationship between wastewater and clinical detection of MDRO. First, we will design wastewater MDRO model systems by constructing plug-flow reactors and testing the effects of flow parameters such as hydraulic retention time, pH, and temperature, as well as antibiotic pressure, on the prevalence and diversity of MDRO and ARG genotypes. This will account for dynamics in growth rates and potential ARG exchange across species along the wastewater flow, which could significantly affect the accuracy of wastewater-based surveillance models. These bioreactor model systems will enable future experiments testing conditions relevant to specific MDRO species or wastewater streams. In Aim 2, we will take advantage of our ongoing longitudinal wastewater sampling at a major hospital center and the surrounding community to correlate MDRO in wastewater with clinical MDRO and existing patient surveillance cohorts. Through chromatin-linked metagenomics and long- read sequencing we will elucidate phylogenetic links between MDRO in hospital and community wastewater with infectious patient isolates, and potential differences in evolutionary patterns of MDRO in patient versus wastewater collections. Lastly, in Aim 3 we will interrogate different approaches to wastewater-based epidemiological modeling to estimate MDRO burden in a given community. We will contrast linear and nonlinear additive regression models with dynamic mathematical modeling approaches. We will incorporate wastewater flow parameters and community sociodemographics as well as molecular biomarker data, as normalization factors to improve model accuracy. Risk assessment techniques will be applied to these wastewater models to inform development of future public health decision making tools. If successful, the results of this study would enable wastewater surveillance as a tool to inform targeted mitigation strategies to prevent the spread of antibiotic multidrug-resistance.

耐多药生物（MDRO）对公共卫生构成重大风险。感染MDRO是