Assistant Professor Hongbin Zhang, an investigator with the CUNY Institute for Implementation Science in Population Health, was awarded an R21 grant from the National Institute of Health to work with the New York City Department of Health and Mental Hygiene (DOHMH) developing a change-point model on the timing of anti-retroviral treatment (ART) initiation after HIV diagnosis.
The primary goal of public health efforts to control HIV epidemics is to diagnose and treat people as soon as possible after infection. The timing of the first ART treatment after HIV diagnosis is therefore an important population-level indicator of the effectiveness of HIV care programs and policies at local and national levels. However, there are no population-based estimates of the timeliness of ART initiation in the US because data on the timing of ART initiation cannot feasibly and efficiently be collected as part of routine jurisdictional HIV surveillance activities.
Zhang and team will develop a statistical model for the estimation of the timing of ART initiation following HIV diagnosis using routinely collected, population-based data on laboratory tests from all persons diagnosed with HIV infection from the DOHMH. The model will be validated with antiretroviral therapy prescription information, and the methods will be disseminated through a free software package for use by health departments, public health practitioners, and policymakers. The method and software will be strategically important in influencing policies and programs to expand access to HIV prevention and treatment services and to monitor and inform the ending of HIV epidemics at the local and national level.
The proposal’s initial efforts were supported by Dean Ayman El-Mohandes’ Mentored Research Grant with Distinguished Professor Denis Nash as mentor, now a co-investigator in the project, together with Associate Professor Levi Waldron who will provide insights on R package development. The study was motivated by a pioneer work led by Dr. Sarah Braunstein at the DOHMH. Zhang will extend the work by bringing in new statistical methods.
“It is quite exciting,” Zhang says of the opportunity. “The change point based methods we are going to develop will use longitudinal viral load data to optimize the estimation of ART initiation timing by detecting a time point that induces a change to the underlying biological process.”