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 epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019.

BackgroundAs of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak.MethodsFor short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott's rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts.ResultsDuring validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872-1054) and 955 cases by March 4 (95% prediction interval: 874-1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876-933) and 898 (95% prediction interval: 877-983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013-2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone.ConclusionsOur projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges

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

Modeling the transmission of Ebola and GU Chlamydia in Sub-Saharan African countries under both epidemic and endemic settings

In this dissertation, I used various methods to model the transmission of two infectious diseases, Ebola in an epidemic setting and GU Chlamydia in an endemic setting within in Sub-Saharan Africa. Since 2015, there have been five outbreaks of Ebola Virus Disease (EVD) in several dierent countries in Sub-Saharan Africa, one of which became the second largest EVD outbreak in history in the setting of a longstanding conflict zone. It is suspected that after violent events occur, EVD transmission will increase; however, empirical studies to understand the impact of violence on transmission are lacking. In my first chapter, I used spatial and temporal trends of EVD case counts to compare transmission rates between health zones that have versus have not experienced recent violent events during the outbreak. In my second chapter, I also sought to use modeling to make outbreak projections, looking at the 2020 outbreak in The Democratic Republic of Congo. I made short and long-term projections for the outbreak in an eort to assess the potential to provide more accurate forecasting for an ongoing outbreak. I also evaluated how the outbreak’s timing and course affected the accuracy of such forecasts. Lastly in my third chapter, I focused on trachoma endemic areas of Sub-Saharan Africa and modeling the impact of annual Trachoma Mass Drug Administration (MDA) with azithromycin upon the prevalence of genitourinary (GU) chlamydia using a compartmental model. Communities that are especially hard hit with Trachoma are almost exclusively poor commu- nities with poor access to sanitation, screening and antibiotics to treat the infection; conditions that may allow for STDs to maintain a high chain of transmission. The dosing of azithromycin for the Trachoma MDA is consistent with dosing given clinically to treat GU chlamydial (GUC) disease, and recent evidence has suggested it reduces the population prevalence.In my first chapter investigating the potential impact of violent events upon local instability and increased EVD transmission, I collected daily EVD case counts from DRC Ministry of Health for the 2018 outbreak in the Democratic Republic of Congo (DRC). A time-varying indicator of recent violence in each health zone was derived from events documented in the WHO situation reports. I used the Wallinga-Teunis technique to estimate the reproduction number R for each case by day per zone in the 2018–2019 outbreak. I fit an exponentially decaying curve to estimates of R overall and by health zone, for comparison to past outbreaks.As of 16 April 2019, the mean overall R for the entire outbreak was 1.11. I found evidence of an increase in the estimated transmission rates in health zones with recently reported violent events versus those without (p = 0.008). The average R was estimated as between 0.61 and 0.86 in regions not aected by recent violent events, and between 1.01 and 1.07 in zones aected by violent events within the previous 21 days, leading to an increase in R between 0.17 and 0.53. Within zones with recent violent events, the mean estimated quenching rate was lower than for all past outbreaks except the 2013–2016 West African outbreak. The difference in the estimated transmission rates between zones affected by recent violent events suggests that violent events contributed to increased transmission and the prolonged nature of the second largest EVD outbreak in history.In my second chapter performing EVD outbreak projections, several mathematical models were used to predict the final outbreak size and weekly incidence for the 2020 DRC outbreak. Projections were commenced prospectively mid-way through the outbreak, and retrospectively applied for the early out- break. Short-term forecasts were made using two different models: (i) a particle-filter branching-process model and (ii) a naive auto-regression. Final outbreak size predictions were made using four different models: (i) the particle-filter branching-process model, (ii) Theil-Sen regression, (iii) Gott’s Law and (iv) a novel Bayesian branching process model parameterized using prior outbreak sizes and contingent on the current outbreak size. The Bayesian model examined final size distributions across a range of current outbreak sizes, allowing for an examination of parameter fits.Overall, there were reasonable amounts of variability in the forecasts created by different models. For short-term, auto-regression models showed relatively stable steady-state growth in the outbreak, with somewhat larger confidence intervals while the particle-filter branching model projected an outbreak slowly ending in the same period. Final outbreak size predictions increased overall as the outbreak continued. The median expectation among models increased between 2.5–4.0 fold in September over initial expectations from June as the outbreak grew from 34 to 128 cases. The branching-process model was overall the most stable consistent performer, though the Bayesian model was a close second. Including the West Africa outbreak, easily the largest to date, increased the range of predicted outcomes for the DRC outbreak between 40–50%.In predicting the 2020 Ebola outbreak, the most consistent performing model was the branching process particle-filter model though the Bayesian model did nearly as well, despite being agnostic to the trajectory of the outbreak. Our short-term models consistently predicted the outbreak would grow, though models disagreed over the slowing pace; it will be important to evaluate the performance of these models in future outbreaks to understand these uncertainties. The growth of the outbreak to well over a hundred cases underscores the real risk EBOV poses to the region and the need for improved understanding of outbreak trajectories even with the presence of three approved vaccines.In my third chapter, I analyzed the impact of Trachoma MDA upon GUChlamydia prevalence using an extended compartmental SIS model, accounting for the natural history of GUC, risk structure, and gender. The model includes slowly developing partial immunity. MDA was modelled as an impulsively forced treatment with varying coverage and efficacy.My model showed that three years of MDA at current levels reduced the prevalence of GUC in all populations by at least 15%. Between annual MDA, the prevalence partially rebounded to pre-treatment levels. With Coverage x Efficacy ≥ 0.80, the time between MDA treatments was insufficient to sustain transmission, allowing for GUC burden to be suppressed below 1 in 10,000 after 5 rounds for starting prevalence less than 9.2%. When serial non-compliance is increased from 20% to 80%, this target is achieved for starting prevalences below 4.7%, down from 9.2%. Targeting azithromycin treatment only to high-risk individuals reduces the starting prevalences for which target is reached to 1.8%.My model suggests that MDA could reduce the prevalence of GUC to less than 1 in 10,000 within 5 years time. This reinforces the suggestions of potential additional health benefits of trachoma MDA and points to potential value of screening and disease treatment even in impoverished areas, and suggests testable hypotheses regarding prevalence in endemic areas under treatment

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

Lifecourse Educational Trajectories and Hypertension in Midlife: An Application of Sequence Analysis

BackgroundHigher educational attainment predicts lower hypertension. Yet, associations between nontraditional educational trajectories (eg, interrupted degree programs) and hypertension are less well understood, particularly among structurally marginalized groups who are more likely to experience these non-traditional trajectories.MethodsIn National Longitudinal Survey of Youth 1979 cohort data (N = 6 317), we used sequence and cluster analyses to identify groups of similar educational sequences-characterized by timing and type of terminal credential-that participants followed from age 14-48 years. Using logistic regression, we estimated associations between the resulting 10 educational sequences and hypertension at age 50. We evaluated effect modification by individual-level indicators of structural marginalization (race, gender, race and gender, and childhood socioeconomic status [cSES]).ResultsCompared to terminal high school (HS) diploma completed at traditional age, terminal GED (OR: 1.32; 95%CI: 1.04, 1.66) or Associate degree after ConclusionsBoth type and timing to terminal credential matter for hypertension but effects may vary by experiences of structural marginalization. Documenting the nuanced ways in which complex educational trajectories are associated with health could elucidate underlying mechanisms and inform systems-level interventions for health equity