Human challenge experiments could accelerate tuberculosis vaccine development. This requires a safe Mycobacterium tuberculosis (Mtb) strain that can both replicate in the host and be reliably cleared. Here we genetically engineered Mtb strains encoding up to three kill switches: two mycobacteriophage lysin operons negatively regulated by tetracycline and a degron domain-NadE fusion, which induces ClpC1-dependent degradation of the essential enzyme NadE, negatively regulated by trimethoprim. The triple-kill-switch (TKS) strain showed similar growth kinetics and antibiotic susceptibilities to wild-type Mtb under permissive conditions but was rapidly killed in vitro without trimethoprim and doxycycline. It established infection in mice receiving antibiotics but was rapidly cleared upon cessation of treatment, and no relapse was observed in infected severe combined immunodeficiency mice or Rag mice. The TKS strain had an escape mutation rate of less than 10 per genome per generation. These findings suggest that the TKS strain could be a safe, effective candidate for a human challenge model.

Medicare beneficiaries are increasingly enrolling in Medicare Advantage (MA), which employs a wide range of practices around restriction of the networks of providers that beneficiaries visit. Though Medicare beneficiaries highly value provider choice, it is unknown whether the MA contract quality metrics which beneficiaries use to inform their contract selection capture the restrictiveness of contracts’ provider networks.

Further evaluation of the impact of long-term exposure to the gaseous air pollutants nitrogen dioxide (NO) and ozone (O) on child lung function, and of NO or O on eosinophilic airway inflammation, is needed.

The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises. However, previous studies on the early warning capabilities of geosocial media data have largely been constrained by coarse spatial resolutions or short temporal scopes, with limited understanding of how local political beliefs may influence these capabilities.

During the coronavirus disease (COVID-19) pandemic, researchers attempted to estimate the number of averted and avertible outcomes due to vaccination campaigns to quantify public health impact. However, the estimands used in these analyses have not been previously formalized. It is also unclear how these analyses relate to the broader framework of direct, indirect, total, and overall causal effects under interference. Here, using potential outcome notation, we adjust the direct and overall effects to accommodate analyses of averted and avertible outcomes. We use this framework to interrogate the commonly held assumption that vaccine-averted outcomes via direct impact among vaccinated individuals (or vaccine-avertible outcomes via direct impact among unvaccinated individuals) is a lower bound on vaccine-averted (or -avertible) outcomes overall. To do so, we describe a susceptible-infected-recovered-death model stratified by vaccination status. When vaccine efficacies wane, the lower bound fails for vaccine-avertible outcomes. When transmission or fatality parameters increase over time, the lower bound fails for both vaccine-averted and -avertible outcomes. Only in the simplest scenario where vaccine efficacies, transmission, and fatality parameters are constant over time, outcomes averted via direct impact among vaccinated individuals (or outcomes avertible via direct impact among unvaccinated individuals) is a lower bound on overall impact. In conclusion, the lower bound can fail under common violations to assumptions on time-invariant vaccine efficacy, pathogen properties, or behavioral parameters. In real data analyses, estimating what seems like a lower bound on overall impact through estimating direct impact may be inadvisable without examining the directions of indirect effects.

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