# Developing Clinical Prediction Tools to Define Strategies for Differentiated Service Delivery in Children and Adolescents Living with HIV in sub-Saharan Africa.

> **NIH NIH K01** · BAYLOR COLLEGE OF MEDICINE · 2024 · $133,691

## Abstract

SUMMARY STATEMENT
 The International Research Scientist Development Award affords an ideal mentorship opportunity to
support the transition to independence of the candidate, an internist, pediatrician and pediatric infectious
diseases physician with expertise caring for children and adults with HIV and tuberculosis (TB). The candidate’s
long-term goal is to become an independent investigator, generating much needed evidence to inform child HIV
and TB policy and practice. The candidate is based full-time in Eswatini and will work across the Baylor
International Pediatric AIDS Initiative (BIPAI) network of clinics in Eswatini, Lesotho, Botswana, Malawi,
Tanzania and Uganda to implement this multinational research study and accomplish the stated aims.
 The proposed research will guide implementation of differentiated service delivery for children and
adolescents living with HIV (CALHIV) at both high and low risk of adverse HIV and HIV associated TB outcomes.
Aim 1 will derive and validate a mortality clinical prediction tool for CALHIV to predict the risk of death 12 months
after the initiation of antiretroviral therapy. This tool will be derived and validated using retrospective data from
across the BIPAI network. Aim 2 of the proposal will develop a clinical prediction tool for viral non-suppression
12 months from study enrollment using prospectively collected clinical data and considering additional
psychosocial variables, describing the myriad of factors impacting antiretroviral therapy adherence. Aim 3 will
also harness the strength of the BIPAI network to retrospectively determine TB incidence and mortality rates in
CALHIV associated with the different isoniazid preventive therapy (IPT) strategies implemented according to
evolving national guidelines across the BIPAI network. This data is urgently needed to i) inform evidence-based
implementation of IPT in children receiving antiretroviral therapy, and ii) design prospective studies of new
shorter-course tuberculosis preventive therapy regimens.
 Complementing the described research, the candidate will take courses in data science, data
management, epidemiology, and clinical informatics, culminating in a master’s degree in epidemiology. Further,
the candidate will benefit from direct supervision and guidance from a strong mentorship team that will be led by
Dr. Mandalakas and Dr. Chiao (co-primary mentors). Dr. Mandalakas has over two decades of experience
mentoring young physician scientists in global child health research and Dr. Chiao is an internationally
recognized expert in the epidemiology of HIV associated malignancies. Dr. Lukhele (LMIC mentor and Executive
Director at the Baylor Children’s Foundation-Eswatini) has extensive experience implementing public health
research in Eswatini. Dr. Kirchner (co-mentor) will supervise the statistical analysis employed in this proposal,
Dr. Skinner (co-mentor) will guide qualitative methods, and Dr. Maldonado (co-mentor) will provide technical
exp...

## Key facts

- **NIH application ID:** 10898770
- **Project number:** 5K01TW011482-05
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Alexander William Kay
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $133,691
- **Award type:** 5
- **Project period:** 2020-09-18 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10898770

## Citation

> US National Institutes of Health, RePORTER application 10898770, Developing Clinical Prediction Tools to Define Strategies for Differentiated Service Delivery in Children and Adolescents Living with HIV in sub-Saharan Africa. (5K01TW011482-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10898770. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*