Faculty Bibliography

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 rely 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 stable unit treatment value assumption, 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.

OBJECTIVES/OBJECTIVE:We sought to assess if adding a biopsy proven histologic subtype to a model that predicts overall survival that includes variables representing competing risks in observed, biopsy proven, T1a renal cell carcinomas, enhances the model's performance. METHODS:The National Cancer Database was assessed (years 2004-2015) for patients with observed T1a renal cell carcinoma who had undergone renal mass biopsy. Kaplan-Meier curves were utilized to estimate overall survival stratified by histologic subtype. We utilized C-index from a Cox proportional hazards model to evaluate the impact of adding histologic subtypes to a model to predict overall survival for each stage. RESULTS:Of 132 958 T1a renal masses identified, 1614 had biopsy proven histology and were managed non-operatively. Of those, 61% were clear cell, 33% papillary, and 6% chromophobe. Adjusted Kaplan-Meier curves demonstrated a difference in overall survival between histologic subtypes (P = 0.010) with greater median overall survival for patients with chromophobe (85.1 months, hazard rate 0.45, P = 0.005) compared to clear cell (64.8 months, reference group). Adding histology to a model with competing risks alone did not substantially improve model performance (C-index 0.65 vs 0.64 respectively). CONCLUSIONS:Incorporation of histologic subtype into a risk stratification model to determine prognostic overall survival did not improve modeling of overall survival compared with variables representing competing risks in patients with T1a renal cell carcinoma managed with observation. These results suggest that performing renal mass biopsy in order to obtain tumor histology may have limited utility. Future studies should further investigate the overall utility of renal mass biopsy for observed T1a kidney cancers.

Purpose of Review/UNASSIGNED:Racial and socioeconomic inequities in respiratory pandemics have been consistently documented, but little official guidance exists on effective action to prevent these. We systematically reviewed quantitative evaluations of (real or simulated) interventions targeting racial and socioeconomic inequities in respiratory pandemic outcomes. Recent Findings/UNASSIGNED: = 2) communities. Results are suggestive that these interventions might be effective at reducing racial and/or SES disparities in pandemics. Summary/UNASSIGNED:There is a dearth of research on strategies to reduce pandemic disparities. We provide theory-driven, concrete suggestions for incorporating equity into intervention research for pandemic preparedness, including a focus on social and economic policies.

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

INTRODUCTION/BACKGROUND:The natural history of T1b (4-7 cm) or T2a (> 7-10 cm) kidney cancers managed with observation is not well-understood. The aim of our study was to determine if the addition of histologic subtype to a predictive model of overall survival (OS) that includes covariates for competing risks in observed, biopsy-proven, T1b and T2a renal cell carcinomas (RCCs) improves the model's performance. MATERIALS AND METHODS/METHODS:We queried the National Cancer Database for patients with biopsy-proven stage T1b or T2a RCC and managed nonoperatively between 2004 and 2015. OS was estimated by Kaplan-Meier curves based on histologic subtype. The concordance index (c-index) from a Cox proportional hazards model was used to estimate the extent to which histologic subtypes predict survival for each stage when included in a model along with competing risks of age, gender, race/ethnicity, insurance status, area-level socioeconomic indicators, Charlson-Deyo index, and tumor grade. RESULTS:A total of 937 patients (754 with T1b and 185 with T2a) with biopsy-proven RCC were identified. Kaplan-Meier analysis suggested differences in OS by histologic subtype where sarcomatoid, followed by clear cell, papillary, and chromophobe, had the highest mortality risk at 1, 3, and 5 years. However, there was marginal improvement in the multivariable model of OS using competing risks and histology (c-index, 0.64 and 0.697) compared with competing risks alone (c-index, 0.631 and 0.671) for T1b and T2a RCCs, respectively. CONCLUSIONS:In patients with T1b or T2a RCC managed with observation, incorporation of histologic subtype into a risk-stratification model to determine prognostic OS did not improve modeling of OS compared with variables representing competing risks. Histologic subtype of observed T1b and T2a RCC appears to have prognostic OS value when not considering competing risks. These findings may impact the usefulness of renal biopsy to inform decision-making when managing patients with T1b and T2a renal tumors with observation.

Animal studies have shown that the gut microbiome can influence memory, social behavior, and anxiety-like behavior. Several human studies show similar results where variation in the gut microbiome is associated with dementia, depression, and personality traits, though most of these studies are limited by small sample size and other biases. Here, we analyzed fecal samples from 313 participants in the Wisconsin Longitudinal Study, a randomly selected population-based cohort of older adults, with measured psycho-cognitive dimensions (cognition, mood, and personality) and key confounders. 16s V4 sequencing showed that Megamonas is associated with all measured psycho-cognitive traits, Fusobacterium is associated with cognitive and personality traits, Pseudoramibacter_Eubacterium is associated with mood and personality traits, Butyvibrio is associated with cognitive traits, and Cloacibacillus is associated with mood traits. These findings are robust to sensitivity analyses and provide novel evidence of shared relationships between the gut microbiome and multiple psycho-cognitive traits in older adults, confirming some of the animal literature, while also providing new insights. While we addressed some of the weaknesses in prior studies, further studies are necessary to elucidate temporal and causal relationships between the gut microbiome and multiple psycho-cognitive traits in well-phenotyped, randomly-selected population-based samples.

Emerging links between gut microbiota and diseases of aging point to possible shared immune, metabolic, and cellular damage mechanisms, operating long before diseases manifest. We conducted 16S rRNA sequencing of fecal samples collected from a subsample (n=668) of Add Health Wave V, a nationally representative longitudinal study of adults aged 32-42. An overlapping subsample (n=345) included whole blood RNA-seq. We examined associations between fecal taxonomic abundances and dried blood spot-based markers of lipid and glucose homeostasis and C-reactive protein (measured in Wave IV), as well as gene expression markers of inflammation, cellular damage, immune cell composition, and transcriptomic age (measured in Wave V), using Bayesian hierarchical models adjusted for potential confounders. We additionally estimated a co-abundance network between inflammation-related genes and bacterial taxa using penalized Gaussian graphical models. Strong and consistent microbiota associations emerged for HbA1c, glucose, C-reactive protein, and principal components of genes upregulated in inflammation, DNA repair, and reactive oxygen species, with Streptococcus infantis, Pseudomonas spp., and Peptoniphilus as major players for each. This pattern was largely echoed (though attenuated) for immunologic cell composition gene sets, and only Serratia varied meaningfully by transcriptomic age. Network co-abundance indicated relationships between Prevotella sp., Bacteroides sp., and Ruminococcus sp. and gut immune/metabolic regulatory activity, and Ruminococcussp, Dialister, and Butyrivibriocrossotus with balance between Th1 and Th2 inflammation. In conclusion, many common associations between microbiota and major physiologic aging mechanisms are evident in early-mid adulthood, and suggest avenues for early detection and prevention of accelerated aging.

The human microbiome represents a new frontier in understanding the biology of human health. While epidemiology in this area is still in its infancy, its scope will likely expand dramatically over the coming years. To rise to the challenge, we argue that epidemiology should capitalize on its population perspective as a critical complement to molecular microbiome research, allowing for the illumination of contextual mechanisms that may vary more across populations rather than among individuals. We first briefly review current research on social context and the gut microbiome, focusing specifically on socioeconomic status (SES) and race/ethnicity. Next, we reflect on the current state of microbiome epidemiology through the lens of one specific area, the association of the gut microbiome and metabolic disorders. We identify key methodological shortcomings of current epidemiological research in this area, including extensive selection bias, the use of noncompositionally robust measures, and a lack of attention to social factors as confounders or effect modifiers.

Disease surveillance systems are a cornerstone of public health tracking and prevention. This review addresses the use, promise, perils, and ethics of social media- and Internet-based data collection for public health surveillance. Our review highlights untapped opportunities for integrating digital surveillance in public health and current applications that could be improved through better integration, validation, and clarity on rules surrounding ethical considerations. Promising developments include hybrid systems that couple traditional surveillance data with data from search queries, social media posts, and crowdsourcing. In the future, it will be important to identify opportunities for public and private partnerships, train public health experts in data science, reduce biases related to digital data (gathered from Internet use, wearable devices, etc.), and address privacy. We are on the precipice of an unprecedented opportunity to track, predict, and prevent global disease burdens in the population using digital data.