RISK OF INFLUENZA UNDER REALISTIC INFLUENZA VACCINATION INTERVENTIONS AMONG UNIVERSITY STUDENTS

Seasonal influenza causes substantial morbidity and mortality each year. One important group to consider is university students since their vaccination uptake is low, they experience high attack rates, they suffer substantial negative impacts on their well-being, and they may be important for further transmission. Rather than assess direct vaccine effectiveness, this work aimed to estimate the risk of influenza under large-scale changes in the distribution of influenza vaccination.Using self-reported contact data from the eX-FLU cluster randomized trial on three-day self-isolation, we applied a recent extension of the targeted maximum likelihood estimation (TMLE) framework for dependent data. Hypothetical policies to increase vaccination focused on educational information regarding influenza, reducing non-financial barriers, and reducing financial barriers were compared. Each policy was further compared across a range of plausible shifts in the log-odds of influenza vaccination receipt. To guide the application of TMLE and approaches to account for measurement error of self-reported edges within eX-FLU, two simulation studies were conducted. To assess imputation and Bayesian procedures for measurement error, we conducted a simulation study of three network generative models with non-informative and informative measurement error. To assess TMLE for dependent data, we simulated four data-generating mechanisms in three different networks. Both imputation and Bayesian approaches reduced bias and improved confidence limit coverage for all parameters in most scenarios. The TMLE for dependent data performed wellacross scenarios with interference, but issues manifested when policies were not well-supported by the observed data. In the application of TMLE to the eX-FLU data, a reduction in the risk of laboratory-confirmed influenza was observed across policies. However, the estimates were consistent with little-to-no reduction. When accounting for measurement error of self-reported contacts via the Bayesian approach, a greater reduction in risk was observed but differences between policies were minor.Our results are a robust analysis for a parameter of public health importance. The results of our simulations and analyses will serve as an example for future applications.Doctor of Philosoph

Identifying and estimating effects of sustained interventions under parallel trends assumptions

Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions typically require a measured covariate set sufficient to control confounding, which can be questionable in observational studies. Differences-in-differences relies instead on the parallel trends assumption, allowing for some types of time-invariant unmeasured confounding. However, most existing difference-in-differences implementations are limited to point treatments in restricted subpopulations. We derive identification results for population effects of sustained treatments under parallel trends assumptions. In particular, in settings where all individuals begin follow-up with exposure status consistent with the treatment plan of interest but may deviate at later times, a version of Robins' g-formula identifies the intervention-specific mean under SUTVA, positivity, and parallel trends. We develop consistent asymptotically normal estimators based on inverse-probability weighting, outcome regression, and a double robust estimator based on targeted maximum likelihood. Simulation studies confirm theoretical results and support the use of the proposed estimators at realistic sample sizes. As an example, the methods are used to estimate the effect of a hypothetical federal stay-at-home order on all-cause mortality during the COVID-19 pandemic in spring 2020 in the United States.Comment: 15 pages, 2 figure

A Comprehensive Review and Tutorial on Confounding Adjustment Methods for Estimating Treatment Effects Using Observational Data

Controlling for confounding bias is crucial in causal inference. Causal inference using data from observational studies (e.g., electronic health records) or imperfectly randomized trials (e.g., imperfect randomization or compliance) requires accounting for confounding variables. Many different methods are currently employed to mitigate bias due to confounding. This paper provides a comprehensive review and tutorial of common estimands and confounding adjustment approaches, including outcome regression, g-computation, propensity score, and doubly robust methods. We discuss bias and precision, advantages and disadvantages, and software implementation for each method. Moreover, approaches are illustrated empirically with a reproducible case study. We conclude that different scientific questions are better addressed by certain estimands. No estimand is uniformly more appropriate. Upon selecting an estimand, decisions on which estimator can be driven by performance and available background knowledge

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

Streptococcus pneumoniae outbreaks and implications for transmission and control: a systematic review

Abstract Background Streptococcus pneumoniae is capable of causing multiple infectious syndromes and occasionally causes outbreaks. The objective of this review is to update prior outbreak reviews, identify control measures, and comment on transmission. Methods We conducted a review of published S. pneumoniae outbreaks, defined as at least two linked cases of S. pneumoniae. Results A total of 98 articles (86 respiratory; 8 conjunctivitis; 2 otitis media; 1 surgical site; 1 multiple), detailing 94 unique outbreaks occurring between 1916 to 2017 were identified. Reported serotypes included 1, 2, 3, 4, 5, 7F, 8, 12F, 14, 20, and 23F, and serogroups 6, 9, 15, 19, 22. The median attack rate for pneumococcal outbreaks was 7.0% (Interquartile range: 2.4%, 13%). The median case-fatality ratio was 12.9% (interquartile range: 0%, 29.2%). Age groups most affected by outbreaks were older adults (60.3%) and young adults (34.2%). Outbreaks occurred in crowded settings, such as universities/schools/daycares, military barracks, hospital wards, and long-term care facilities. Of outbreaks that assessed vaccination coverage, low initial vaccination or revaccination coverage was common. Most (73.1%) of reported outbreaks reported non-susceptibility to at least one antibiotic, with non-susceptibility to penicillin (56.0%) and erythromycin (52.6%) being common. Evidence suggests transmission in outbreaks can occur through multiple modes, including carriers, infected individuals, or medical devices. Several cases developed disease shortly after exposure (< 72 h). Respiratory outbreaks used infection prevention (55.6%), prophylactic vaccination (63.5%), and prophylactic antibiotics (50.5%) to prevent future cases. PPSV23 covered all reported outbreak serotypes. PCV13 covered 10 of 16 serotypes. For conjunctival outbreaks, only infection prevention strategies were used. Conclusions To prevent the initial occurrence of respiratory outbreaks, vaccination and revaccination is likely the best preventive measure. Once an outbreak occurs, vaccination and infection-prevention strategies should be utilized. Antibiotic prophylaxis may be considered for high-risk exposed individuals, but development of antibiotic resistance during outbreaks has been reported. The short period between initial exposure and development of disease indicates that pneumococcal colonization is not a prerequisite for pneumococcal respiratory infection

Beyond the Boxes: Guiding Questions for Thoughtfully Measuring and Interpreting Race in Population Health Research

Race and ethnicity are key constructs underpinning social stratification and health in the US, but the use of race and ethnicity in population health research can be ritualistic and lacking careful consideration. Our team collaborated on a research project evaluating how population health research conceptualizes and uses race. We drew from lessons learned during this project to develop a blog series for the Interdisciplinary Association of Population Health Science. This six part series proposes guiding questions and considerations for how researchers can more thoughtfully define, measure, code, analyze, and interpret race and ethnicity in their own work

Resource Packet for "Clear communication of race and ethnicity for public health: best practices & common failings"

This work was created as a resource packet for attendees of the Interdisciplinary Association for Population Health Science (IAPHS)'s pre-conference workshop "Clear communication of race and ethnicity for public health: Best practices & common failings." The pre-conference workshop was held virtually on September 23rd, 2021 and facilitated by the author team

Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial.

The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize the study participants to a particular treatment, therefore observational study designs may be used. There are major challenges with observational studies; one of which is confounding. Controlling for confounding is commonly performed by direct adjustment of measured confounders; although, sometimes this approach is suboptimal due to modeling assumptions and misspecification. Recent advances in the field of causal inference have dealt with confounding by building on classical standardization methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where new estimators were developed to overcome the limitations of the previous estimators (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R, and Python for researchers to adapt in their own observational study. The code can be accessed at https://github.com/migariane/Tutorial_Computational_Causal_Inference_Estimators