Short online conferences at CUNY

Epidemiology and Biostatistics Forum: 2/10/21

Overview

  • Keynote: Professor Levi Waldron
  • Speaker: Jiaqi Zhu, PhD candidate
  • Networking

Conference Summary

The SPH Epi/Bios Conference is a forum for students, staff, alumni, faculty, and other interested attendees to learn about recent research in the CUNY community, talk to representatives of the department, school, and related institutes, and meet each other in random 1:1 networking using a full online conference platform. Make sure to register below, and contact us if you’re interested in organizing, contributing, or doing peer review for future events.

 

Program Details

Taming the Wild West of the Human Microbiome Literature

Each person harbors a microbiome of approximately 10x more microbial cells than their own cells, containing some 100x more genetic material than their own. The human-associated microbiome has roles in essential physiological processes and in the development and progression of some diseases, but the rate of data collection far outpaces our ability to fully interpret, replicate, or even compare the results of new studies to existing data. I will start with an introduction to the methods of human microbiome research, then summarize our efforts to elucidate the roles of bacteria, viruses, and microbial function in health and disease through standardization, integration, and re-analysis of published data

Levi Waldron is an Associate Professor in the department of Epidemiology and Biostatistics and in the  CUNY Institute for Implementation Science in Public Health. His research group has developed more than a dozen R/Bioconductor packages and served on the Technical Advisory Board of the Bioconductor project for open-source Bioinformatics since 2013. His overarching research goal is to enable a broad research community to make more efficient use of publicly available cancer -omics data, by creating analytical methods and standardized resources. 

https://waldronlab.io 

Evaluating WHO’s Treat-All Guideline on Clinical Staging and Mortality for HIV-infected People from Large Scale Observational Data by Target Trial and Multi-State Modelling. Jiaqi Zhu

Background: By the end of 2018, nearly all countries in Central Africa have adopted WHO’s “Treat All” guideline that eliminates the eligibility threshold for HIV infected people to receive antiretroviral therapy (ART). Little is known about the impact of “Treat All” guideline on HIV patients’ WHO clinical staging symptoms’ progression and mortality. 

Methods: We utilized a “target trial” design on the longitudinal patient data collected between 2013 and 2019 from five Central Africa countries out of an observational database, the International epidemiology Databases to Evaluate AIDS (IeDEA). Multi-state Models (MSMs) were applied to infer the hazard of transitions between progressive WHO clinical stages, as well as the transition to death. The effects, measured by hazard ratio (HR) and adjusted hazard ratio (AHR) after controlling covariates, of adopting “Treat All” guideline on the transition probabilities were estimated. Sensitivity analysis was done using multiple imputation in CD4 count, viral load and marital status. 

Results: “Treat All” policy was found to have significant impact on the transition from WHO stage 1 to death (AHR = 0.42, 95% CI 0.19 to 0.96) and the transition from stage 2 to stage 3 (AHR = 0.68, 95% CI 0.47 to 0.98). Sensitivity analysis shows that the impact on the transition from WHO stage 1 to death is still holds after we added imputed marital status, CD4 count and viral load in the multi-state models.

Conclusions: The adoption of “Treat All” guideline had a positive impact, e.g., reducing the likelihood disease progression, especially for people with no or mild clinical symptoms at enrollment of the target trial. These findings support “Treat All” policies as a strategy to combat the global HIV epidemic.

Jiaqi Zhu is a first year Ph.D. student in epidemiology at CUNY SPH. She has a background in quantitative methods and social sciences, and interest in public health, especially mental health. She is currently working as a biostatistician at Hospital for Special Surgery. In this role, she provides research design consultant on RCT and cohort studies, statistical analysis for orthopedic studies, and NIH grant proposal writing. Jiaqi also worked as a research associate at CUNY SPH on a variety public health research projects including mental health and substance use, built environment and urban health, food security and HIV “Treat-All” cascade. Her current work with Dr.Hongbin Zhang applied multistate modeling in drawing causal inference for the impact of “Treat-All’’ policy on HIV patients’ WHO staging outcomes

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