Visualizing Results From Infection Transmission Models: A Case Against “Confidence Intervals”

Stochastic transmission models are highly important in infectious disease epidemiology. The quantity of data produced by these models is challenging to display and communicate. A common approach is to display the model results in the familiar form of a mean or median and 95% interval, plotted over time. This approach has drawbacks, however, including the potential for ambiguity and misinterpretation of model results. Instead, we propose two alternative approaches for visualizing results from stochastic models. These proposed approaches convey the information provided by the median and 95% interval, as well as information about unexpected outcomes that may be of particular interest for stochastic epidemic models

Mathematical modeling of clostridium difficile transmission in healthcare settings

Clostridium difficile is a frequent source of healthcare-associated infection, especially among patients on antibiotics or proton pump inhibitors (PPIs). The rate of C. difficile infection (CDI) has been steadily rising since 2000 and now represents a major burden on the healthcare system in terms of both morbidity and mortality. However, despite its public health importance, there are few mathematical models of C. difficile which might be used to evaluate our current evidence base or new control measures. Three different data sources were analyzed to provide parameters for a mathematical model: a cohort of incident CDI cases in the Duke Infection Control Outreach Network (DICON), a hospital-level surveillance time series, also from DICON, and inpatient records from UNC Healthcare, all from 7/1/2009 to 12/31/2010. Using estimates from these data, as well as from the literature, a pair of compartmental transmission models, one deterministic and the other stochastic, were created to evaluate the potential effect of the use of fecal transplantation as a treatment to prevent CDI. The analysis of the cohort of incident cases suggested that ICU patients experience a greater burden of mortality while infected with C. difficile and have longer lengths of stay and times until death, suggesting this population as one of special interest. Two interventions were simulated using the stochastic model: the use of fecal transplantation to treat CDI and prevent recurrent cases and the use of fecal transplantation after treatment with antibiotics or PPIs to prevent the development of CDI. Simulation results showed that treating patients with CDI was effective in preventing recurrence but not in reducing the overall number of incident cases of CDI. Transplantation after treatment with antibiotics or PPIs had no effect on preventing recurrence and a statistically significant reduction in incident cases that did not reach clinical significance. These results suggest that routine fecal transplantation for patients with CDI may be an effective treatment to prevent recurrence. Mathematical models such as the one described in this dissertation are powerful tools to evaluate potential interventions, suggest new directions for study, and understand the dynamics of infection on a population level.Doctor of Philosoph

Impact of Change to Molecular Testing for Clostridium difficile Infection on Healthcare Facility–Associated Incidence Rates

Background. Change from nonmolecular to molecular testing techniques is thought to contribute to the increasing trend in incidence of Clostridium difficile infection (CDI); however the degree of effect attributed to this versus other time-related epidemiologic factors is unclear. Methods. We compared the relative change in incidence rate (IRR) of healthcare facility–associated (HCFA) CDI among hospitals in the Duke Infection Control Outreach Network before and after the date of switch from nonmolecular tests to polymerase chain reaction (PCR) using prospectively collected surveillance data from July 2009 to December 2011. Data from 10 hospitals that switched and 22 control hospitals were included. Individual hospital estimates were determined using Poisson regression. We used an interrupted time series approach to develop a Poisson mixed-effects model. Additional regression adjustments were made for clustering and proportion of intensive care unit patient-days. The variable for PCR was treated as a fixed effect; other modeled variables were random effects. Results. For those hospitals that switched to PCR, mean incidence rate of HCFA CDI before the switch was 6.0 CDIs per 10,000 patient-days compared with 9.6 CDIs per 10,000 patient-days after the switch. Estimates of hospital-specific IRR that compared after the switch with before the switch ranged from 0.89 (95% confidence interval [CI], 0.32–2.44) to 6.91 (95% CI, 1.12–42.54). After adjustment in the mixed-effects model, the overall IRR comparing CDI incidence after the switch to before the switch was 1.56 (95% CI, 1.28–1.90). Time-trend variables did not reach statistical significance. Conclusion. Hospitals that switched from nonmolecular to molecular tests experienced an approximate 56% increase in the rate of HCFA CDI after testing change

Transportability without positivity: a synthesis of statistical and simulation modeling

When estimating an effect of an action with a randomized or observational study, that study is often not a random sample of the desired target population. Instead, estimates from that study can be transported to the target population. However, transportability methods generally rely on a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomized trials, may lead to violations of this assumption. Two common approaches to address positivity violations are restricting the target population and restricting the relevant covariate set. As neither of these restrictions are ideal, we instead propose a synthesis of statistical and simulation models to address positivity violations. We propose corresponding g-computation and inverse probability weighting estimators. The restriction and synthesis approaches to addressing positivity violations are contrasted with a simulation experiment and an illustrative example in the context of sexually transmitted infection testing uptake. In both cases, the proposed synthesis approach accurately addressed the original research question when paired with a thoughtfully selected simulation model. Neither of the restriction approaches were able to accurately address the motivating question. As public health decisions must often be made with imperfect target population information, model synthesis is a viable approach given a combination of empirical data and external information based on the best available knowledge

Gastric colonisation with a restricted commensal microbiota replicates the promotion of neoplastic lesions by diverse intestinal microbiota in the Helicobacter pylori INS-GAS mouse model of gastric carcinogenesis

Objectives: Gastric colonisation with intestinal flora (IF) has been shown to promote Helicobacter pylori (Hp)-associated gastric cancer. However, it is unknown if the mechanism involves colonisation with specific or diverse microbiota secondary to gastric atrophy. Design: Gastric colonisation with Altered Schaedler's flora (ASF) and Hp were correlated with pathology, immune responses and mRNA expression for proinflammatory and cancer-related genes in germ-free (GF), Hp monoassociated (mHp), restricted ASF (rASF; 3 species), and specific pathogen-free (complex IF), hypergastrinemic INS-GAS mice 7 months postinfection. Results: Male mice cocolonised with rASFHp or IFHp developed the most severe pathology. IFHp males had the highest inflammatory responses, and 40% developed invasive gastrointestinal intraepithelial neoplasia (GIN). Notably, rASFHp colonisation was highest in males and 23% developed invasive GIN with elevated expression of inflammatory biomarkers. Lesions were less severe in females and none developed GIN. Gastritis in male rASFHp mice was accompanied by decreased Clostridum species ASF356 and Bacteroides species ASF519 colonisation and an overgrowth of Lactobacillus murinus ASF361, supporting that inflammation-driven atrophy alters the gastric niche for GI commensals. Hp colonisation also elevated expression of IL-11 and cancer-related genes, Ptger4 and Tgf-β, further supporting that Hp infection accelerates gastric cancer development in INS-GAS mice. Conclusions: rASFHp colonisation was sufficient for GIN development in males, and lower GIN incidence in females was associated with lower inflammatory responses and gastric commensal and Hp colonisation. Colonisation efficiency of commensals appears more important than microbial diversity and lessens the probability that specific gastrointestinal pathogens are contributing to cancer risk.National Institutes of Health (U.S.) (grant R01 AI37750)National Institutes of Health (U.S.) (grant R01 CA093405)National Institutes of Health (U.S.) (grant P30-ES02109)National Institutes of Health (U.S.) (grant P01 CA028842)National Institutes of Health (U.S.) (grant T32 RR07036