# Preventing antimicrobial resistance and infections in hospitalized neonates in low resource settings

> **NIH NIH K23** · JOHNS HOPKINS UNIVERSITY · 2020 · $170,696

## Abstract

Project Summary/Abstract
Annually, 2.5 million babies die within the first four weeks of life, nearly a quarter due to infectious causes.
Newborns admitted to the Neonatal Intensive Care Unit (NICU) are especially vulnerable, due to such factors as
prematurity, an immature immune system, and need for life-sustaining invasive procedures and devices. In low
and middle income countries (LMIC), an increasing number of NICUs care for premature and critically ill
newborns. Healthcare-associated bloodstream infections (HA-BSI) in LMIC are more common due to inadequate
infection prevention and control (IPC) and more difficult to treat due to high rates of antimicrobial resistance
(AMR). Previous research in this setting focuses primarily on outbreak investigations and does not adequately
describe risk factors for HA-BSI. Healthcare facilities lack effective tools to assess maternal and neonatal IPC
and create improvement strategies. Preliminary data from the applicant's ongoing prospective cohort study that
has enrolled over 6600 neonates in three NICUs in Pune, India, reinforces the high incidence of HA-BSI in this
setting with a rate of 7.6 per 1000 patient-days, as well as high rates of AMR. Among Klebsiella pneumoniae
isolates, the most common BSI pathogen, 96% are resistant to third-generation cephalosporins and 38% to
carbapenems. Among neonates with BSI, mortality is 22%. Within the framework of this study, the following are
proposed: (1) To identify modifiable risk factors for HA-BSI in the NICU; (2) To develop a model for predicting
infection with carbapenem-resistant organisms (CRO); and (3) To develop and pilot a novel tool to assess IPC
practices in the NICU and Labor & Delivery. Identifying risk factors for HA-BSI in the NICU will promote
development of targeted IPC strategies. Creation of a prediction model using a decision tree algorithm will help
identify babies at highest risk of CRO infections. Such a model can support NICU clinicians in selecting the right
antibiotics when infection is suspected, reducing time to appropriate therapy and decreasing unnecessary use
of last resort antibiotics such as colistin. Development of an IPC assessment tool that incorporates human factors
engineering (HFE) principles will enable healthcare facilities to optimize IPC and reduce risk of hospital-acquired
infections and associated mortality. This mentored research will train the applicant in advanced epidemiologic
methods and application of IPC in LMIC. The applicant is a neonatologist at Johns Hopkins University committed
to patient-oriented research in resource-limited settings. Her long-term goals are to become a leader in neonatal
IPC in low resource settings and devise interventions to reduce global burden of HA-BSI and associated
mortality. This K23 will facilitate skill development in longitudinal data analysis, prediction models, survey
development, HFE, and qualitative data analysis. Training will include formal coursework, supervised data
an...

## Key facts

- **NIH application ID:** 9871398
- **Project number:** 1K23HD100594-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Julia Johnson
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $170,696
- **Award type:** 1
- **Project period:** 2020-07-13 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9871398

## Citation

> US National Institutes of Health, RePORTER application 9871398, Preventing antimicrobial resistance and infections in hospitalized neonates in low resource settings (1K23HD100594-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9871398. Licensed CC0.

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