Analysis of multiple bacterial species and antibiotic classes reveals large variation in the association between seasonal antibiotic use and resistance.

Understanding how antibiotic use drives resistance is crucial for guiding effective strategies to limit the spread of resistance, but the use-resistance relationship across pathogens and antibiotics remains unclear. We applied sinusoidal models to evaluate the seasonal use-resistance relationship across 3 species (Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae) and 5 antibiotic classes (penicillins, macrolides, quinolones, tetracyclines, and nitrofurans) in Boston, Massachusetts. Outpatient use of all 5 classes and resistance in inpatient and outpatient isolates in 9 of 15 species-antibiotic combinations showed statistically significant amplitudes of seasonality (false discovery rate (FDR) < 0.05). While seasonal peaks in use varied by class, resistance in all 9 species-antibiotic combinations peaked in the winter and spring. The correlations between seasonal use and resistance thus varied widely, with resistance to all antibiotic classes being most positively correlated with use of the winter peaking classes (penicillins and macrolides). These findings challenge the simple model of antibiotic use independently selecting for resistance and suggest that stewardship strategies will not be equally effective across all species and antibiotics. Rather, seasonal selection for resistance across multiple antibiotic classes may be dominated by use of the most highly prescribed antibiotic classes, penicillins and macrolides.

Viral dynamics of acute SARS-CoV-2 infection and applications to diagnostic and public health strategies.

SARS-CoV-2 infections are characterized by viral proliferation and clearance phases and can be followed by low-level persistent viral RNA shedding. The dynamics of viral RNA concentration, particularly in the early stages of infection, can inform clinical measures and interventions such as test-based screening. We used prospective longitudinal quantitative reverse transcription PCR testing to measure the viral RNA trajectories for 68 individuals during the resumption of the 2019-2020 National Basketball Association season. For 46 individuals with acute infections, we inferred the peak viral concentration and the duration of the viral proliferation and clearance phases. According to our mathematical model, we found that viral RNA concentrations peaked an average of 3.3 days (95% credible interval [CI] 2.5, 4.2) after first possible detectability at a cycle threshold value of 22.3 (95% CI 20.5, 23.9). The viral clearance phase lasted longer for symptomatic individuals (10.9 days [95% CI 7.9, 14.4]) than for asymptomatic individuals (7.8 days [95% CI 6.1, 9.7]). A second test within 2 days after an initial positive PCR test substantially improves certainty about a patient's infection stage. The effective sensitivity of a test intended to identify infectious individuals declines substantially with test turnaround time. These findings indicate that SARS-CoV-2 viral concentrations peak rapidly regardless of symptoms. Sequential tests can help reveal a patient's progress through infection stages. Frequent, rapid-turnaround testing is needed to effectively screen individuals before they become infectious.

Real-Time Use of a Dynamic Model To Measure the Impact of Public Health Interventions on Measles Outbreak Size and Duration - Chicago, Illinois, 2024.

Measles is a highly infectious, vaccine-preventable disease that can cause severe illness, hospitalization, and death. A measles outbreak associated with a migrant shelter in Chicago occurred during February-April 2024, in which a total of 57 confirmed cases were identified, including 52 among shelter residents, three among staff members, and two among community members with a known link to the shelter. CDC simulated a measles outbreak among shelter residents using a dynamic disease model, updated in real time as additional cases were identified, to produce outbreak forecasts and assess the impact of public health interventions. As of April 8, the model forecasted a median final outbreak size of 58 cases (IQR = 56-60 cases); model fit and prediction range improved as more case data became available. Counterfactual analysis of different intervention scenarios demonstrated the importance of early deployment of public health interventions in Chicago, with a 69% chance of an outbreak of 100 or more cases had there been no mass vaccination or active case-finding compared with only a 1% chance when those interventions were deployed. This analysis highlights the value of using real-time, dynamic models to aid public health response, set expectations about outbreak size and duration, and quantify the impact of interventions. The model shows that prompt mass vaccination and active case-finding likely substantially reduced the chance of a large (100 or more cases) outbreak in Chicago.

Salt-responsive gut commensal modulates TH17 axis and disease.

A Western lifestyle with high salt consumption can lead to hypertension and cardiovascular disease. High salt may additionally drive autoimmunity by inducing T helper 17 (TH17) cells, which can also contribute to hypertension. Induction of TH17 cells depends on gut microbiota; however, the effect of salt on the gut microbiome is unknown. Here we show that high salt intake affects the gut microbiome in mice, particularly by depleting Lactobacillus murinus. Consequently, treatment of mice with L. murinus prevented salt-induced aggravation of actively induced experimental autoimmune encephalomyelitis and salt-sensitive hypertension by modulating TH17 cells. In line with these findings, a moderate high-salt challenge in a pilot study in humans reduced intestinal survival of Lactobacillus spp., increased TH17 cells and increased blood pressure. Our results connect high salt intake to the gut-immune axis and highlight the gut microbiome as a potential therapeutic target to counteract salt-sensitive conditions.

The role of "spillover" in antibiotic resistance.

Antibiotic use is a key driver of antibiotic resistance. Understanding the quantitative association between antibiotic use and resulting resistance is important for predicting future rates of antibiotic resistance and for designing antibiotic stewardship policy. However, the use-resistance association is complicated by "spillover," in which one population's level of antibiotic use affects another population's level of resistance via the transmission of bacteria between those populations. Spillover is known to have effects at the level of families and hospitals, but it is unclear if spillover is relevant at larger scales. We used mathematical modeling and analysis of observational data to address this question. First, we used dynamical models of antibiotic resistance to predict the effects of spillover. Whereas populations completely isolated from one another do not experience any spillover, we found that if even 1% of interactions are between populations, then spillover may have large consequences: The effect of a change in antibiotic use in one population on antibiotic resistance in that population could be reduced by as much as 50%. Then, we quantified spillover in observational antibiotic use and resistance data from US states and European countries for three pathogen-antibiotic combinations, finding that increased interactions between populations were associated with smaller differences in antibiotic resistance between those populations. Thus, spillover may have an important impact at the level of states and countries, which has ramifications for predicting the future of antibiotic resistance, designing antibiotic resistance stewardship policy, and interpreting stewardship interventions.

Shiga Toxin-Producing Escherichia coli Transmission via Fecal Microbiota Transplant.

Fecal microbiota transplantation (FMT) is recommended therapy for multiply recurrent Clostridioides difficile infection. We report adverse events in 7 patients who received FMT from a stool donor who was colonized with Shiga toxin-producing Escherichia coli (STEC). No patients died of FMT-transmitted STEC. Improved screening can likely avoid future transmission.

Estimating the proportion of bystander selection for antibiotic resistance among potentially pathogenic bacterial flora.

Bystander selection-the selective pressure for resistance exerted by antibiotics on microbes that are not the target pathogen of treatment-is critical to understanding the total impact of broad-spectrum antibiotic use on pathogenic bacterial species that are often carried asymptomatically. However, to our knowledge, this effect has never been quantified. We quantify bystander selection for resistance for a range of clinically relevant antibiotic-species pairs as the proportion of all antibiotic exposures received by a species for conditions in which that species was not the causative pathogen ("proportion of bystander exposures"). Data sources include the 2010-2011 National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, the Human Microbiome Project, and additional carriage and etiological data from existing literature. For outpatient prescribing in the United States, we find that this proportion over all included antibiotic classes is over 80% for eight of nine organisms of interest. Low proportions of bystander exposure are often associated with infrequent bacterial carriage or concentrated prescribing of a particular antibiotic for conditions caused by the species of interest. Applying our results, we roughly estimate that pneumococcal conjugate vaccination programs result in nearly the same proportional reduction in total antibiotic exposures of Streptococcus pneumoniae, Staphylococcus aureus, and Escherichia coli, despite the latter two organisms not being targeted by the vaccine. These results underscore the importance of considering antibiotic exposures of bystanders, in addition to the target pathogen, in measuring the impact of antibiotic resistance interventions.

Azithromycin Susceptibility Among Neisseria gonorrhoeae Isolates and Seasonal Macrolide Use.

Rising azithromycin nonsusceptibility among Neisseria gonorrhoeae isolates threatens current treatment recommendations, but the cause of this rise is not well understood. We performed an ecological study of seasonal patterns in macrolide use and azithromycin resistance in N. gonorrhoeae, finding that population-wide macrolide use is associated with increased azithromycin nonsusceptibility. These results, indicative of bystander selection, have implications for antibiotic prescribing guidelines.

Levels of outpatient prescribing for four major antibiotic classes and rates of septicemia hospitalization in adults in different US states - a statistical analysis.

BACKGROUND: Rates of sepsis/septicemia hospitalization in the US have risen significantly during recent years. Antibiotic resistance and use may contribute to those rates through various mechanisms, including lack of clearance of resistant infections following antibiotic treatment, with some of those infections subsequently devolving into sepsis. At the same time, there is limited information on the effect of prescribing of certain antibiotics vs. others on the rates of septicemia and sepsis-related hospitalizations and mortality. METHODS: We used multivariable linear regression to relate state-specific rates of outpatient prescribing overall for oral fluoroquinolones, penicillins, macrolides, and cephalosporins between 2011 and 2012 to state-specific rates of septicemia hospitalization (ICD-9 codes 038.xx present anywhere on a discharge diagnosis) in each of the following age groups of adults: (18-49y, 50-64y, 65-74y, 75-84y, 85 + y) reported to the Healthcare Cost and Utilization Project (HCUP) between 2011 and 2012, adjusting for additional covariates, and random effects associated with the ten US Health and Human Services (HHS) regions. RESULTS: Increase in the rate of prescribing of oral penicillins by 1 annual dose per 1000 state residents was associated with increases in annual septicemia hospitalization rates of 0.19 (95% CI (0.02,0.37)) per 10,000 persons aged 50-64y, of 0.48(0.12,0.84) per 10,000 persons aged 65-74y, and of 0.81(0.17,1.40) per 10,000 persons aged 74-84y. Increase by 1 in the percent of African Americans among state residents in a given age group was associated with increases in annual septicemia hospitalization rates of 2.3(0.32,4.2) per 10,000 persons aged 75-84y, and of 5.3(1.1,9.5) per 10,000 persons aged over 85y. Average minimal daily temperature was positively associated with septicemia hospitalization rates in persons aged 18-49y, 50-64y, 75-84y and over 85y. CONCLUSIONS: Our results suggest positive associations between the rates of prescribing for penicillins and the rates of hospitalization with septicemia in US adults aged 50-84y. Further studies are needed to better understand the potential effect of antibiotic replacement in the treatment of various syndromes, including the potential impact of the recent US FDA guidelines on restriction of fluoroquinolone use, as well as the potential effect of changes in the practices for prescribing of penicillins on the rates of sepsis-related hospitalization and mortality.

Racial/Ethnic Disparities in Antimicrobial Drug Use, United States, 2014-2015.

Using a US nationwide survey, we measured disparities in antimicrobial drug acquisition by race/ethnicity for 2014-2015. White persons reported twice as many antimicrobial drug prescription fills per capita as persons of other race/ethnicities. Characterizing antimicrobial drug use by demographic might improve antimicrobial drug stewardship and help address antimicrobial drug resistance.

Assessment of sewer connectivity in the United States and its implications for equity in wastewater-based epidemiology.

Wastewater-based epidemiology is a promising public health tool that can yield a more representative view of the population than case reporting. However, only about 80% of the U.S. population is connected to public sewers, and the characteristics of populations missed by wastewater-based epidemiology are unclear. To address this gap, we used publicly available datasets to assess sewer connectivity in the U.S. by location, demographic groups, and economic groups. Data from the U.S. Census' American Housing Survey revealed that sewer connectivity was lower than average when the head of household was American Indian and Alaskan Native, White, non-Hispanic, older, and for larger households and those with higher income, but smaller geographic scales revealed local variations from this national connectivity pattern. For example, data from the U.S. Environmental Protection Agency showed that sewer connectivity was positively correlated with income in Minnesota, Florida, and California. Data from the U.S. Census' American Community Survey and Environmental Protection Agency also revealed geographic areas with low sewer connectivity, such as Alaska, the Navajo Nation, Minnesota, Michigan, and Florida. However, with the exception of the U.S. Census data, there were inconsistencies across datasets. Using mathematical modeling to assess the impact of wastewater sampling inequities on inferences about epidemic trajectory at a local scale, we found that in some situations, even weak connections between communities may allow wastewater monitoring in one community to serve as a reliable proxy for an interacting community with no wastewater monitoring, when cases are widespread. A systematic, rigorous assessment of sewer connectivity will be important for ensuring an equitable and informed implementation of wastewater-based epidemiology as a public health monitoring system.

Uses of mathematical modeling to estimate the impact of mass drug administration of antibiotics on antimicrobial resistance within and between communities.

BACKGROUND: Antibiotics are a key part of modern healthcare, but their use has downsides, including selecting for antibiotic resistance, both in the individuals treated with antibiotics and in the community at large. When evaluating the benefits and costs of mass administration of azithromycin to reduce childhood mortality, effects of antibiotic use on antibiotic resistance are important but difficult to measure, especially when evaluating resistance that "spills over" from antibiotic-treated individuals to other members of their community. The aim of this scoping review was to identify how the existing literature on antibiotic resistance modeling could be better leveraged to understand the effect of mass drug administration (MDA) on antibiotic resistance. MAIN TEXT: Mathematical models of antibiotic use and resistance may be useful for estimating the expected effects of different MDA implementations on different populations, as well as aiding interpretation of existing data and guiding future experimental design. Here, strengths and limitations of models of antibiotic resistance are reviewed, and possible applications of those models in the context of mass drug administration with azithromycin are discussed. CONCLUSIONS: Statistical models of antibiotic use and resistance may provide robust and relevant estimates of the possible effects of MDA on resistance. Mechanistic models of resistance, while able to more precisely estimate the effects of different implementations of MDA on resistance, may require more data from MDA trials to be accurately parameterized.

Infectious Disease Modeling: Recommendations for Public Health Decision-Makers.

Infectious disease modeling plays an important role in the response to infectious disease outbreaks, perhaps most notably during the coronavirus disease 2019 (COVID-19) pandemic. In our experience working with state and local governments during COVID-19 and previous public health crises, we have observed that, while the scientific literature focuses on models' accuracy and underlying assumptions, an important limitation on the effective application of modeling to public health decision-making is the ability of decision-makers and modelers to work together productively. We therefore propose a set of guiding principles, informed by our experience, for working relationships between decision-makers and modelers. We hypothesize that these guidelines will improve the utility of infectious disease modeling for public health decision-making, irrespective of the particular outbreak in question and of the precise modeling approaches being used.

Pilot study of autologous fecal microbiota transplants in nursing home residents: Feasibility and safety.

Introduction: Antibiotic resistant bacterial infections (ARBIs) are extremely common in nursing home residents. These infections typically occur after a course of antibiotics, which eradicate both pathological and beneficial organisms. The eradication of beneficial organisms likely facilitates subsequent ARBIs. Autologous fecal microbiota transplant (aFMT) has been proposed as a potential treatment to reduce ARBIs in nursing home residents. Our objective was to determine the feasibility and safety of aFMT in a nursing home population. Methods: Pilot clinical trial. We evaluated feasibility as total number of stool samples collected for aFMT production and safety as the number and relatedness of serious (SAE) and non-serious adverse events (AE). Results: We screened 468 nursing home residents aged ≥18 years for eligibility; 67 enrolled, distributed among three nursing homes. Participants were 62.7% female and 35.8% Black. Mean age was 82.2 ± 8.5 years. Thirty-three participants underwent successful stool collection. Seven participants received antibiotics; four participants underwent aFMT. There were 40 SAEs (17 deaths) and 11 AEs. In the aFMT group, there were 3 SAEs (2 deaths) and 10 AEs. All SAEs and AEs were judged unrelated to the study intervention. Conclusions: In this pilot study of aFMT in nursing home residents, less than half were able to provide adequate stool samples for aFMT. There were no related SAEs or AEs during the study. In sum, we conclude aFMT has limited feasibility in a nursing home population due to logistic and technical challenges but is likely safe. Trial registration: ClinicalTrials.gov Identifier: NCT03061097.

Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States.

Wastewater-based epidemiology has emerged as a promising technology for population-level surveillance of COVID-19. In this study, we present results of a large nationwide SARS-CoV-2 wastewater monitoring system in the United States. We profile 55 locations with at least six months of sampling from April 2020 to May 2021. These locations represent more than 12 million individuals across 19 states. Samples were collected approximately weekly by wastewater treatment utilities as part of a regular wastewater surveillance service and analyzed for SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA concentrations were normalized to pepper mild mottle virus, an indicator of fecal matter in wastewater. We show that wastewater data reflect temporal and geographic trends in clinical COVID-19 cases and investigate the impact of normalization on correlations with case data within and across locations. We also provide key lessons learned from our broad-scale implementation of wastewater-based epidemiology, which can be used to inform wastewater-based epidemiology approaches for future emerging diseases. This work demonstrates that wastewater surveillance is a feasible approach for nationwide population-level monitoring of COVID-19 disease. With an evolving epidemic and effective vaccines against SARS-CoV-2, wastewater-based epidemiology can serve as a passive surveillance approach for detecting changing dynamics or resurgences of the virus.

A conserved Bacteroidetes antigen induces anti-inflammatory intestinal T lymphocytes.

The microbiome contributes to the development and maturation of the immune system. In response to commensal bacteria, intestinal CD4+ T lymphocytes differentiate into functional subtypes with regulatory or effector functions. The development of small intestine intraepithelial lymphocytes that coexpress CD4 and CD8αα homodimers (CD4IELs) depends on the microbiota. However, the identity of the microbial antigens recognized by CD4+ T cells that can differentiate into CD4IELs remains unknown. We identified ß-hexosaminidase, a conserved enzyme across commensals of the Bacteroidetes phylum, as a driver of CD4IEL differentiation. In a mouse model of colitis, ß-hexosaminidase-specific lymphocytes protected against intestinal inflammation. Thus, T cells of a single specificity can recognize a variety of abundant commensals and elicit a regulatory immune response at the intestinal mucosa.

Single molecules reveal the dynamics of heterogeneities in a polymer at the glass transition.

The notion of heterogeneous dynamics in glasses, that is, the spatial and temporal variations of structural relaxation rates, explains many of the puzzling features of glass dynamics. The nature and the dynamics of these heterogeneities, however, have been very controversial. Single rhodamine B molecules in poly(vinyl acetate) at the glass transition reorient through sudden jumps. With a statistical search for the most likely break points in the logarithm of the ratio of the two perpendicular fluorescence polarizations, we determine the times of these angular jumps. We interpret these jumps as an indication for individual glass rearrangements in the vicinity of the probe molecule. Time-series analysis of the resulting sequence of waiting times between jumps shows that dynamic heterogeneities in the matrix exist, but are short lived. From the correlation of the logarithm of the waiting time between subsequent jumps, we determine an upper limit for the lifetime of heterogeneities in the sample. The correlation time of τ(het) = 32 s is three times shorter than the orientational correlation time of the probe molecule, τ(orient) = 90 s, in the sample at this temperature, but 13 times longer than the structural relaxation time, τ(α) = 2.5 s, estimated for this sample from dielectric experiments. We present a model for glass dynamics in which each rearrangement in one region causes a random change in the barrier height for subsequent rearrangements in a neighboring region. This model, which equates the dynamics of the heterogeneities with the dynamics of the glass itself and thus implies a factor of one between heterogeneity lifetime and structural relaxation time, successfully reproduces the statistics of the experimentally observed waiting time sequences.