Seth Blumberg

Background: Although healthcare-associated transmission of Clostridioides difficile is a recognized public health concern, community-onset infections represent an important component of the overall disease burden. This paradox likely reflects the underappreciated interplay between these settings. We aimed to quantify in-hospital transmission and the hospital’s contribution to community colonization by estimating the intrinsic reproduction number (R _i ) and introducing the colonization amplification index (A _i ), defined as the ratio of colonized patients at discharge to those at admission. Given the potential contribution of external cases, we also evaluated interventions targeting asymptomatic carriers at admission to reduce disease burden. Methods and findings: We developed a compartmental model informed by data from UCSF Medical Center to capture C. difficile transmission dynamics among symptomatic and asymptomatic patients. Across simulations, the median R _i was 0.61 (Q1–Q3: 0.53, 0.71), consistently indicating limited sustained in-hospital transmission (R _i < 1). In contrast, A _i was 1.9 (Q1–Q3: 1.7, 2.1), suggesting substantial amplification of colonization during hospital stay. Amplification of colonization persisted in sensitivity analyses, with R _i exceeding 1 under boundary values of low colonization prevalence at admission, a low proportion of colonized patients progressing to symptomatic disease, and prolonged incubation periods. Redefining healthcare-associated C. difficile infection (CDI) using thresholds from ≥1-day post-admission instead of the standard threshold of >3 days post-admission increased A _i and R _i by 11% and 21%, respectively. A threshold of ≥5 days post-admission reduced these metrics by 5% and 8%, respectively. Interventions targeting asymptomatic carriers through contact precautions and/or prophylactic treatment reduced both A _i and CDI incidence, with combined interventions yielding the greatest reductions, followed by contact precautions alone. Our main limitation stems from uncertainty in some parameters describing the disease’s natural history, particularly colonization prevalence at admission, progression to symptomatic disease, and the incubation period, which may affect the precision of our estimates despite sensitivity analyses. Conclusions: Our findings indicate that in most potential scenarios, in-hospital transmission of C. difficile is limited (R _i < 1) and likely sustained by continuous importation of cases from the community. Nevertheless, hospitalization amplifies colonization (A _i > 1), which potentially contributes to community transmission. These results underscore the importance of interventions addressing asymptomatic carriers, a currently overlooked source of spread. Our study highlights the need to broaden metrics beyond R _i to capture hospitals’ contribution to the C. difficile burden. Future infection control strategies should address colonization dynamics at admission and potentially at discharge to mitigate transmission and reduce the overall burden of C. difficile.

Large-scale, placebo-controlled, cluster-randomized trials in high-mortality settings in sub-Saharan Africa demonstrated a 14–18% reduction in childhood mortality following twice-annual mass drug administration (MDA) of azithromycin among children aged 1–59 months. Azithromycin MDA also selected for antimicrobial resistance (AMR), particularly macrolide resistance. It is unknown whether the AMR from azithromycin MDA could spill over to neighboring untreated populations. If present, such geographic spillover effects could lead trials to underestimate AMR risks. We assess between-village geographic spillover effects of genotypic macrolide resistance using metagenomic deep sequencing in rectal swabs collected from 300 children in 30 monitoring villages in Niger after two years of MDA in 594 surrounding villages. Conditional permutation tests assess associations between proximal azithromycin treatment intensity and resistance gene abundance. We find no evidence of geographic spillover of macrolide resistance in untreated villages, as the genetic load of AMR remains at baseline levels in placebo-treated villages regardless of surrounding azithromycin treatment intensity (Spearman ρ = −0.05, P = 0.83). Sensitivity analyses confirm robustness across metrics, and no spillover effects are detected for other antibiotic classes. Azithromycin MDA-induced macrolide resistance appears localized to treated villages, mitigating some concerns about geographic spillover of AMR to nearby untreated villages at 24 months.

Asymptomatic carriers of antimicrobial-resistant organisms (AMROs) can unwittingly transmit these pathogens in hospitals, contributing to the burden of healthcare-associated infections (HAIs). Surveillance in hospitals can involve different types of observations; however, a framework to coherently synthesize these datasets to identify AMRO carriers is lacking. Here, we develop a new inference framework combining a data-driven mechanistic transmission model and multimodal observations from clinical cultures, electronic health records, patient mobility, and genomic data. Using extensive simulated outbreaks, we validate the inference framework for AMROs with various levels of community importation and hospital transmission and evaluate the utility of different combinations of data sources. Inference results show that using multimodal observations consistently improves the accuracy in identifying AMRO carriers. We apply the inference framework to carbapenem-resistant Klebsiella pneumoniae (CRKP) at an urban quaternary care hospital in New York City, United States and find that the addition of even sparsely sampled genome sequence data to patient characteristics supports more accurate identification of CRKP carriers. Model simulations suggest that inference-guided targeted isolation leads to a greater reduction of AMRO burdens compared to alternative, heuristic approaches. Thus, the synergistic effect of utilizing multimodal observations for estimating AMRO carriage risk may inform improved interventions in hospital settings.

Large-scale, placebo-controlled, cluster-randomized trials in high-mortality settings in several African countries demonstrated a 14-18% reduction in childhood mortality following twice-annual mass drug administration (MDA) of azithromycin among children aged 1–59 months [1–3]. Azithromycin MDA also selects for antimicrobial resistance (AMR), particularly macrolide resistance in treated populations [4–6]. It is unknown whether the genetic selection of AMR from azithromycin MDA could spill over to neighboring untreated populations. If present, such geographic spillover effects could lead the trials to underestimate the risks of AMR selection from azithromycin MDA. Here, we assessed between-village geographic spillover effects of genotypic resistance to macrolides and other antibiotic classes in rectal swabs collected from 1200 children in 30 monitoring villages in Niger after two years of MDA in 594 surrounding villages. We found no evidence of geographic spillover of macrolide resistance in untreated villages, as the genetic load of AMR remained at baseline levels in placebo-treated villages regardless of surrounding azithromycin treatment intensity. Sensitivity analyses confirmed the robustness of findings to the metric used to quantify the effect of proximal azithromycin MDAs on macrolide AMR, and no geographic spillover effects were detected for AMR to other antibiotic classes. Our results suggest that azithromycin MDA-induced selection of macrolide AMR is localized to treated villages without extending to children in neighboring, untreated villages, mitigating some concerns about geographic spillover of AMR to untreated populations. This analysis illustrates the value of randomized trial designs in assessing indirect effects of large-scale public health interventions.

Trachoma is targeted for global elimination as a public health problem by 2030. Measurement of IgG antibodies in children is being considered for surveillance and programmatic decision-making. There are currently no programmatic guidelines based on serology, which represents a generalizable problem in seroepidemiology and disease elimination. Here, we collate Chlamydia trachomatis Pgp3 and CT694 IgG measurements from 48 serosurveys across Africa, Latin America, and the Pacific Islands (41,168 children ages 1–5 years) and propose a novel approach to estimate the probability that population C. trachomatis transmission is below or above levels requiring ongoing programmatic action. We determine that trachoma programs could halt control measures with >90% certainty when seroconversion rates (SCRs) are ≤2.2 per 100 person-years. Conversely, SCRs ≥4.5 per 100 person-years correspond with >90% certainty that further control interventions are needed. More extreme SCR thresholds correspond with higher levels of confidence of elimination (lower SCR) or ongoing action needed (higher SCR). This study demonstrates a robust approach for using trachoma serosurveys to guide elimination program decisions.

Early investigation revealed a reduced risk of SARS-CoV-2 infection among social contacts of COVID-19 vaccinated individuals, referred to as indirect protection. However, indirect protection from SARS-CoV-2 infection-acquired immunity and its comparative strength and durability to vaccine-derived indirect protection in the current epidemiologic context of high levels of vaccination, prior infection, and novel variants are not well characterized. Here, we show that both vaccine-derived and infection-acquired immunity independently yield indirect protection to close social contacts with key differences in their strength and waning. Analyzing anonymized SARS-CoV-2 surveillance data from 9,625 residents in California state prisons from December 2021 to December 2022, we find that vaccine-derived indirect protection against Omicron SARS-CoV-2 infection is strongest within three months of COVID-19 vaccination [30% (95% confidence interval: 20–38%)] with subsequent modest protection. Infection-acquired immunity provides 38% (24–50%) indirect protection for 6 months after SARS-CoV-2 infection, with moderate indirect protection persisting for over one year. Variant-targeted vaccines (bivalent formulation including Omicron subvariants BA.4/BA.5) confer strong indirect protection for at least three months [40% (3–63%)]. These results demonstrate that both vaccine-derived and infection-acquired immunity can reduce SARS-CoV-2 transmission which is important for understanding long-term transmission dynamics and can guide public health intervention, especially in high-risk environments such as prisons.

: Azithromycin mass drug administration decreases child mortality but also selects for antibiotic resistance. Herein, we evaluate macrolide resistance of nasopharyngeal Streptococcus pneumoniae after azithromycin MDA. In a cluster-randomized trial, children 1–59 months received azithromycin or placebo biannually. Fifteen villages from each arm were randomly selected for antimicrobial resistance testing, and 10–15 randomly selected swabs from enrolled children at each village were processed for S. pneumoniae isolation and resistance testing. The primary prespecified outcome was macrolide resistance fraction for azithromycin versus placebo villages at 36 months. Secondary non-prespecified outcomes were comparisons of azithromycin and placebo for: 1) macrolide resistance at 12, 24, and 36 months; 2) nonmacrolide resistance at 36 months; and 3) suspected-erm mutation. At 36 months, 423 swabs were obtained and 322 grew S. pneumoniae, (azithromycin: 146/202, placebo: 176/221). Mean resistance prevalence was non-significantly higher in treatment than placebo (mixed-effects model: 14.6% vs. 8.9%; OR = 2.0, 95% CI: 0.99–3.97). However, when all time points were evaluated, macrolide resistance prevalence was significantly higher in the azithromycin group (β = 0.102, 95% CI: 0.04–0.167). For all nonmacrolides, resistance prevalence at 36 months was not different between the two groups. Azithromycin and placebo were not different for suspected-erm mutation prevalence. Macrolide resistance was higher in the azithromycin group over all time points, but not at 36 months. Although this suggests resistance may not continue to increase after biannual MDA, more studies are needed to clarify when MDA can safely decrease mortality and morbidity in lower- and middle-income countries.

COVID-19 outbreaks in congregate settings remain a serious threat to the health of disproportionately affected populations such as people experiencing incarceration or homelessness, the elderly, and essential workers. An individual-based model accounting for individual infectiousness over time, staff work schedules, and testing and isolation schedules was developed to simulate community transmission of SARS-CoV-2 to staff in a congregate facility and subsequent transmission within the facility that could cause an outbreak. Systematic testing strategies in which staff are tested on the first day of their workweek were found to prevent up to 16% more infections than testing strategies unrelated to staff schedules. Testing staff at the beginning of their workweek, implementing timely isolation following testing, limiting test turnaround time, and increasing test frequency in high transmission scenarios can supplement additional mitigation measures to aid outbreak prevention in congregate settings.

The explosive outbreaks of COVID-19 seen in congregate settings such as prisons and nursing homes, has highlighted a critical need for effective outbreak prevention and mitigation strategies for these settings. Here we consider how different types of control interventions impact the expected number of symptomatic infections due to outbreaks. Introduction of disease into the resident population from the community is modeled as a stochastic point process coupled to a branching process, while spread between residents is modeled via a deterministic compartmental model that accounts for depletion of susceptible individuals. Control is modeled as a proportional decrease in the number of susceptible residents, the reproduction number, and/or the proportion of symptomatic infections. This permits a range of assumptions about the density dependence of transmission and modes of protection by vaccination, depopulation and other types of control. We find that vaccination or depopulation can have a greater than linear effect on the expected number of cases. For example, assuming a reproduction number of 3.0 with density-dependent transmission, we find that preemptively reducing the size of the susceptible population by 20% reduced overall disease burden by 47%. In some circumstances, it may be possible to reduce the risk and burden of disease outbreaks by optimizing the way a group of residents are apportioned into distinct residential units. The optimal apportionment may be different depending on whether the goal is to reduce the probability of an outbreak occurring, or the expected number of cases from outbreak dynamics. In other circumstances there may be an opportunity to implement reactive disease control measures in which the number of susceptible individuals is rapidly reduced once an outbreak has been detected to occur. Reactive control is most effective when the reproduction number is not too high, and there is minimal delay in implementing control. We highlight the California state prison system as an example for how these findings provide a quantitative framework for understanding disease transmission in congregate settings. Our approach and accompanying interactive website (https://phoebelu.shinyapps.io/DepopulationModels/) provides a quantitative framework to evaluate the potential impact of policy decisions governing infection control in outbreak settings.