The Microbiome And Pneumonia Disease Severity In Asthmatic Children

Background: Pnuemonia is a leading cause of morbidity and mortality worldwide and children diagnosed with asthma have been shown to be at greatly increased risk of recurrent Community-Acquired Pneumonia (CAP). CAP in asthmatic children can incur nearly double the healthcare costs and lead to poorer outcomes during the course of the pneumonia infection. Objective: This study seeks to determine if sputum (SP) samples may be used in the pediatric population to better understand the microbiome environment during severe pneumonia in place or in conjunction with the more commonly used nasopharyngeal (NP) samples. Additionally, this study seeks to identify features of the microbiome associated with pneumonia severity in asthmatic children. Methods: Sputum and nasopharyngeal/oropharyngeal (NP/OP) samples were collected from asthmatic children diagnosed with asthma upon admission to a hospital. Bacterial cultures for known CAP pathogens using sputum samples, and PCR detection for viral pneumonia pathogens on the NP/OP samples were performed. To study the microbiome, 16s rRNA analysis of sputum and nasopharyngeal samples was performed and analysis conducted using a variety of single and community-based analyses. Outcomes of interest were LOS \u3e 4 days and admission to the ICU. Results: High relative abundance of CAP pathogens, including Moraxella and Haemophilus, were associated with poorer CAP outcomes in both age groups for both ICU admission and longer LOS. Similarly, a positive sputum culture result for Staphylococcus aureus was found to be significantly associated with more severe pneumonia. Bacteroidetes was associated with shorter LOS and Rothia association with longer LOS in several of the analyses. Both conclusions are consistent with previous characterizations of the bacteria in the onset of pneumonia and asthma. Moraxella was consistently associated with longer LOS and increased risk of ICU admission, consistent with its characterization as a minor CAP pathogen, but was protective against longer LOS in the younger age group. Conclusions: First, our study demonstrates that sputum samples may be used in a pediatric population. Our findings demonstrate that many of the microbiome features previously identified as being predictive of, or associated with, CAP, also serve to predict severe pneumonia outcomes in this pediatric population, including longer Length of Stay (LOS) and Intensive Care Unit (ICU) admission. However, certain inconsistencies in the trends in our data highlight the need to perform microbiome analyses using many different approaches to fully understand the complex relationships between the diverse commensal and pathogenic bacteria that comprise the microbiome

Projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018.

As of May 27, 2018, 6 suspected, 13 probable and 35 confirmed cases of Ebola virus disease (EVD) had been reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the total outbreak size and duration with and without vaccine use. We modeled Ebola virus transmission using a stochastic branching process model that included reproduction numbers from past Ebola outbreaks and a particle filtering method to generate a probabilistic projection of the outbreak size and duration conditioned on its reported trajectory to date; modeled using high (62%), low (44%), and zero (0%) estimates of vaccination coverage (after deployment). Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize the Thiel-Sen regression model predicting the outbreak size from the number of observed cases from April 4 to May 27. We used these techniques on probable and confirmed case counts with and without inclusion of suspected cases. Probabilistic projections were scored against the actual outbreak size of 54 EVD cases, using a log-likelihood score. With the stochastic model, using high, low, and zero estimates of vaccination coverage, the median outbreak sizes for probable and confirmed cases were 82 cases (95% prediction interval [PI]: 55, 156), 104 cases (95% PI: 58, 271), and 213 cases (95% PI: 64, 1450), respectively. With the Thiel-Sen regression model, the median outbreak size was estimated to be 65.0 probable and confirmed cases (95% PI: 48.8, 119.7). Among our three mathematical models, the stochastic model with suspected cases and high vaccine coverage predicted total outbreak sizes closest to the true outcome. Relatively simple mathematical models updated in real time may inform outbreak response teams with projections of total outbreak size and duration

Measles transmission during a large outbreak in California.

A large measles outbreak in 2014-2015, linked to Disneyland theme parks, attracted international attention, and led to changes in California vaccine policy. We use dates of symptom onset and known epidemic links for California cases in this outbreak to estimate time-varying transmission in the outbreak, and to estimate generation membership of cases probabilistically. We find that transmission declined significantly during the course of the outbreak (p = 0.012), despite also finding that estimates of transmission rate by day or by generation can overestimate temporal decline. We additionally find that the outbreak size and duration alone are sufficient in this case to distinguish temporal decline from time-invariant transmission (p = 0.014). As use of a single large outbreak can lead to underestimates of immunity, however, we urge caution in interpretation of quantities estimated from this outbreak alone. Further research is needed to distinguish causes of temporal decline in transmission rates

Measles transmission during a large outbreak in California.

A large measles outbreak in 2014-2015, linked to Disneyland theme parks, attracted international attention, and led to changes in California vaccine policy. We use dates of symptom onset and known epidemic links for California cases in this outbreak to estimate time-varying transmission in the outbreak, and to estimate generation membership of cases probabilistically. We find that transmission declined significantly during the course of the outbreak (p = 0.012), despite also finding that estimates of transmission rate by day or by generation can overestimate temporal decline. We additionally find that the outbreak size and duration alone are sufficient in this case to distinguish temporal decline from time-invariant transmission (p = 0.014). As use of a single large outbreak can lead to underestimates of immunity, however, we urge caution in interpretation of quantities estimated from this outbreak alone. Further research is needed to distinguish causes of temporal decline in transmission rates

The Impact of Different Types of Violence on Ebola Virus Transmission During the 2018-2020 Outbreak in the Democratic Republic of the Congo.

BackgroundOur understanding of the different effects of targeted versus nontargeted violence on Ebola virus (EBOV) transmission in Democratic Republic of the Congo (DRC) is limited.MethodsWe used time-series data of case counts to compare individuals in Ebola-affected health zones in DRC, April 2018-August 2019. Exposure was number of violent events per health zone, categorized into Ebola-targeted or Ebola-untargeted, and into civilian-induced, (para)military/political, or protests. Outcome was estimated daily reproduction number (Rt) by health zone. We fit linear time-series regression to model the relationship.ResultsAverage Rt was 1.06 (95% confidence interval [CI], 1.02-1.11). A mean of 2.92 violent events resulted in cumulative absolute increase in Rt of 0.10 (95% CI, .05-.15). More violent events increased EBOV transmission (P = .03). Considering violent events in the 95th percentile over a 21-day interval and its relative impact on Rt, Ebola-targeted events corresponded to Rt of 1.52 (95% CI, 1.30-1.74), while civilian-induced events corresponded to Rt of 1.43 (95% CI, 1.21-1.35). Untargeted events corresponded to Rt of 1.18 (95% CI, 1.02-1.35); among these, militia/political or ville morte events increased transmission.ConclusionsEbola-targeted violence, primarily driven by civilian-induced events, had the largest impact on EBOV transmission

The Impact of Different Types of Violence on Ebola Virus Transmission During the 2018-2020 Outbreak in the Democratic Republic of the Congo.

BackgroundOur understanding of the different effects of targeted versus nontargeted violence on Ebola virus (EBOV) transmission in Democratic Republic of the Congo (DRC) is limited.MethodsWe used time-series data of case counts to compare individuals in Ebola-affected health zones in DRC, April 2018-August 2019. Exposure was number of violent events per health zone, categorized into Ebola-targeted or Ebola-untargeted, and into civilian-induced, (para)military/political, or protests. Outcome was estimated daily reproduction number (Rt) by health zone. We fit linear time-series regression to model the relationship.ResultsAverage Rt was 1.06 (95% confidence interval [CI], 1.02-1.11). A mean of 2.92 violent events resulted in cumulative absolute increase in Rt of 0.10 (95% CI, .05-.15). More violent events increased EBOV transmission (P = .03). Considering violent events in the 95th percentile over a 21-day interval and its relative impact on Rt, Ebola-targeted events corresponded to Rt of 1.52 (95% CI, 1.30-1.74), while civilian-induced events corresponded to Rt of 1.43 (95% CI, 1.21-1.35). Untargeted events corresponded to Rt of 1.18 (95% CI, 1.02-1.35); among these, militia/political or ville morte events increased transmission.ConclusionsEbola-targeted violence, primarily driven by civilian-induced events, had the largest impact on EBOV transmission