# Obtaining population-based estimates of the timing of antiretroviral therapy initiation using HIV surveillance data: Change-point model development, validation, and dissemination

> **NIH NIH R21** · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · 2020 · $243,230

## Abstract

PROJECT SUMMARY/ABSTRACT
The primary goal of public health efforts which aim to control HIV epidemics in the United States and around the
world is to diagnose and treat people with HIV infection as soon as possible after seroconversion. The timing of
the first antiretroviral therapy (ART) treatment after HIV diagnosis is therefore an important population-level
indicator that can be used to measure 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. In this project, we propose to develop a statistical model for the estimation of the timing
of ART initiation following HIV diagnosis. We will use routinely collected, population-based data on laboratory
tests from all persons diagnosed with HIV infection, including biomarkers such as viral load (VL) and CD4 count,
from the New York City (NYC) Department of Health and Mental Hygiene (DOHMH). We will develop a change-
point model in which the VL and CD4 counts are jointly modeled, where the ART initiation time is treated as a
random change point that induces simultaneous trajectory changes to the biological process of both VL and CD4
counts. Several data complexities challenge the model development. First, variability in CD4 count or VL
monitoring by providers leads to imbalanced VL and CD4 count reporting times at the individual level. Second,
the instrument detection limit leads to data censoring for VL. Third, the study population is heterogeneous, such
that some individuals start ART right after the diagnosis (test and treat), while others delay treatment (delay
treatment) or remain untreated (no treatment). We will develop statistical methods to address these data
complications. The methodology will be built upon likelihood and approximated-likelihood inferences coupled
with missing data handling and mixture population modeling. Leveraging ART prescription information from
Medicaid claims data linked at the individual level to the HIV surveillance data, we will cross-validate our model-
based estimations and refine the modeling. Finally, we will disseminate a free R package to share the methods
and the toolset with researchers. Following the completion of this project, we hope to substantially improve the
ability to obtain reliable population-based estimates on the timing of ART initiation for people living with HIV. We
expect the methods and the toolset will be used to inform policies and programs in NYC and other jurisdictions
and will help these jurisdictions improve access to HIV prevention and treatment services and facilitate initiatives
to better control HIV epidemics.

## Key facts

- **NIH application ID:** 9845365
- **Project number:** 1R21AI147933-01
- **Recipient organization:** GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY
- **Principal Investigator:** Hongbin Zhang
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $243,230
- **Award type:** 1
- **Project period:** 2020-02-07 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9845365

## Citation

> US National Institutes of Health, RePORTER application 9845365, Obtaining population-based estimates of the timing of antiretroviral therapy initiation using HIV surveillance data: Change-point model development, validation, and dissemination (1R21AI147933-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9845365. Licensed CC0.

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