# Spatial and Decision Analytic Models for Addressing Challenges in Pediatric Tuberculosis Control and Care

> **NIH NIH F30** · YALE UNIVERSITY · 2022 · $51,753

## Abstract

Project Summary/Abstract: Tuberculosis is among the top ten causes of global mortality among children <5
years. Each year, over 1 million tuberculosis cases occur among children <15 years worldwide, and nearly one
quarter of those children die. Approximately 80% of those deaths occur among children <5 years. Alleviating
the burden of pediatric tuberculosis and mortality requires 1) enhanced efforts to prevent transmission to
children and 2) treating more children for tuberculosis.
Identifying hotspots of tuberculosis transmission can inform spatially-targeted, community-level tuberculosis
screening interventions to limit transmission to children. While national tuberculosis programs maintain
tuberculosis surveillance registers which represent a potential source of data to investigate transmission
patterns, high local tuberculosis incidence may not provide a reliable signal for transmission. In Aim 1, I will
investigate whether local overrepresentation of young children in national tuberculosis surveillance data
produces a signal for transmission hotspots. To test this hypothesis, I will interrogate spatial models to identify
locations where young children are locally overrepresented in surveillance data from the Republic of Moldova. I
will compare alignment of these locations to locations with spatial and genomic evidence of transmission.
It is estimated that 96% of global childhood mortality due to tuberculosis occurs among children not receiving
treatment. Identifying and treating more children with tuberculosis at peripheral health facilities provides an
opportunity to reduce child mortality. Preliminary evidence suggests that a majority of antituberculosis
treatment-decisions can be made on the basis clinical signs and symptoms alone. In Aim 2, I will optimize
treatment decision-making for pediatric tuberculosis at peripheral health facilities in high-burden settings. I will
estimate the contribution of clinical evidence to diagnosis by analyzing pediatric tuberculosis diagnostic
evaluation data sourced from multiple cohorts in different settings. I will use decision analytic models to inform
when clinical diagnosis is sufficient and when additional diagnostic investigation informs decision-making.
This training plan proposes to improve the care of pediatric tuberculosis by applying modeling methods to
address questions in pediatric tuberculosis control (Aim 1) and care (Aim 2), reflecting the applicant’s public
health MD-PhD training. Upon completion of this fellowship, the applicant will be prepared for a career as an
independently productive physician-scientist specializing in pediatric infectious disease. This training in spatial
and decision analytic modeling; clinical care for children with tuberculosis and other infectious disease; and
research conduct will prepare the applicant to study at the intersection of infectious disease and health equity.

## Key facts

- **NIH application ID:** 10397391
- **Project number:** 5F30HD105440-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Kenneth Suranga Gunasekera
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $51,753
- **Award type:** 5
- **Project period:** 2021-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10397391

## Citation

> US National Institutes of Health, RePORTER application 10397391, Spatial and Decision Analytic Models for Addressing Challenges in Pediatric Tuberculosis Control and Care (5F30HD105440-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10397391. Licensed CC0.

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