# Data-informed Stepped Care (DiSC) to Improve Adolescent HIV Outcomes

> **NIH NIH UH3** · UNIVERSITY OF WASHINGTON · 2022 · $299,700

## Abstract

ABSTRACT
This administrative supplement requests funds to provide viral load assays for DiSC participants to optimize
utility of stepped tool and to retain cluster a RCT design that includes viral suppression as an outcome. Our data-
informed stepped care (DiSC) intervention assigns adolescents and young adults (AYA) in HIV care in Kenya to
different intensities of health services according to their need. To assign AYA to specific services, health care
workers use a risk assessment tool to identify AYA needing intensified care. They also identify AYA thriving in
HIV care needing less intensive services to enable programs to appropriately allocate limited resources.
Unfortunately, viral load supplies have been limited in Kenya resulting in absence of viral load assays for over a
year for many AYA HIV care clients. We request budgetary support to cover viral load assays for participating
AYA at two points: at enrollment into the trial to inform step allocation and at exit for secondary outcome of viral
suppression. This will provide important insights on the effectiveness of the intervention and mechanisms of
effectiveness and will add important value to the DiSC RCT. Scientific aims are unchanged.

## Key facts

- **NIH application ID:** 10579767
- **Project number:** 3UH3HD096906-04S1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Grace C. John-Stewart
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $299,700
- **Award type:** 3
- **Project period:** 2022-04-06 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10579767

## Citation

> US National Institutes of Health, RePORTER application 10579767, Data-informed Stepped Care (DiSC) to Improve Adolescent HIV Outcomes (3UH3HD096906-04S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10579767. Licensed CC0.

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