# Résistance Evaluée contre la Vie des Enfants au Niger-Implementation et Recherche (REVENIR). Community antimicrobial resistance after azithromycin distribution: selection, spillover, co-selection

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $790,839

## Abstract

PROJECT SUMMARY/ABSTRACT
Azithromycin mass drug administration (MDA) to children 1-59 months old reduces child mortality and is being
considered for inclusion in child survival programs. However, azithromycin MDA leads to emergence of
antimicrobial resistance (AMR). The World Health Organization (WHO) thus suggests azithromycin MDA be
limited to children 1-11 months old to reduce the risk of AMR. Several high mortality West African countries
have since initiated azithromycin MDA, though key questions about its impact on AMR remain. In particular,
the impact of long-term selection pressure is not well understood – trachoma studies treating all ages found
that AMR continues to increase with additional distributions, but studies treating only children suggest that
resistance may plateau after an initial increase. Understanding AMR patterns with long-term MDA is essential
to define the duration of future programs. In addition, spillover of AMR from treated to untreated groups is
plausible, though has yet to be demonstrated in this context. If present, the risks of AMR with this intervention
may be greater than anticipated. Similarly, previous studies on the impact of azithromycin MDA on co-selection
for resistance in non-macrolide antibiotic classes have had mixed results. The emergence of co-selection with
azithromycin MDA would amplify the risks of this intervention as well, threatening the efficacy of other essential
antibiotics. The Bill & Melinda Gates Foundation-funded AVENIR trial is a large trial that randomizes more than
3,000 communities in Niger to 4 years of biannual MDA of 1) azithromycin to 1-11-month-olds with placebo to
12-59-month-olds, 2) azithromycin to 1-59-month-olds, or 3) placebo to 1-59-month-olds. AVENIR’s primary
endpoints are mortality and AMR compared across the 3 arms. The trial will also collect rectal and
nasopharyngeal samples from several treated and untreated groups in 150 communities after 2 and 4 years of
distributions. In addition, AVENIR includes a subset of communities that received 5 years of azithromycin MDA
in a prior study, resulting in very long-term distributions between the two studies. This presents a unique
opportunity to study key questions beyond the scope of the main trial but essential to understanding how MDA
drives community AMR. Our large sample size enables adequate power to elucidate the relationship between
antibiotic use and population-level AMR emergence, including long-term effects, spillover effects to non-target
groups, and co-selection in other antibiotic classes. Moreover, this project proposes metagenomic deep
sequencing to characterize the respiratory and gut resistome in order to complement the proposed phenotypic
AMR monitoring. We propose to leverage this trial-based infrastructure, large sample bank, and our lab’s high-
throughput genomic approaches to provide evidence to directly impact mass azithromycin programs and the
WHO guidelines on this intervention.

## Key facts

- **NIH application ID:** 10801608
- **Project number:** 1R01AI175250-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** THOMAS M LIETMAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $790,839
- **Award type:** 1
- **Project period:** 2023-12-06 → 2028-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10801608

## Citation

> US National Institutes of Health, RePORTER application 10801608, Résistance Evaluée contre la Vie des Enfants au Niger-Implementation et Recherche (REVENIR). Community antimicrobial resistance after azithromycin distribution: selection, spillover, co-selection (1R01AI175250-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10801608. Licensed CC0.

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