In Uganda, Malawi, and Zambia, researchers will swab the noses and rectums of hundreds of febrile children under five to map where antibiotic resistance is concentrated. The team is developing a rapid, low-cost surveillance method—adapted from industrial quality-control sampling—to identify local hotspots of resistance without the expense of full-scale lab testing. This matters because doctors in these countries often prescribe antibiotics for childhood fevers without knowing whether the drugs will work, which fuels the spread of resistant bacteria. The project will also interview mothers, clinicians, and traditional healers to understand why antibiotics are overused, and will feed that data back to health officials. If successful, the metric could help district health teams target stewardship efforts to the specific neighbourhoods or clinics where misuse is worst, slowing resistance without waiting for national policy changes. The work is applied and implementation-focused: it aims to build a practical tool for routine use, not to uncover fundamental biological mechanisms.
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Research Question: Can regular surveillance of antimicrobial resistance (AMR) and antibiotic use (ABU) improve health worker prescribing practices and patient ABU leading to lower AMR prevalence? Background: Inappropriate ABU are key AMR drivers. Improving ABU is critical to reduce AMR. WHO’s AMR Global Action Plan highlights the need to increase surveillance to improve antimicrobial stewardship. We build on our health systems research in Uganda, Malawi, and Zambia to improve the quality of healthcare (QoC) and model AMR transmission drivers. Aims: Develop a metric & evidence use process to improve local management of childhood febrile illness. Objectives: To 1) develop a rapid, recurrent surveillance system of ABU and AMR prevalence and community voices to improve health system strategies, 2) investigate associations of QoC in Health Facilities with AMR to key antibiotics used for childhood febrile illnesses, 3) investigate community ABU and AMR drivers focusing on febrile illnesses and 4) assess AMR risk development by analysing genomic traits from nasal and rectal samples and determine future risk of resistance. Methods Febrile illness of children <5-years, the most common clinical presentation in Africa, is often empirically treated with antibiotics risking AMR spread. Microbiological surveillance of AMR is unsustainable due to cost and logistics, a more feasible approach is identifying differences in AMR prevalence in local areas to understand and address local AMR drivers and improve ABU. We develop a rapid assessment metric and evidence use process with 4 Research Packages: RP1) Adapts Lot Quality Assurance Sampling (LQAS) to investigate ABU as a local AMR driver; we assess maternal and clinician ABU for febrile children in urban, peri-urban, and rural areas per country (each area minimum n=44 febrile children). RP2) Microbiological surveillance using LQAS to detect areas with >20% AMR (minimum n=44 swabs, a<0.047,ß<0.092). We validate results (minimum sample n=240). Swabs cultured on chromogenic agar containing antibiotics for MRSA (nasal) and ESBL producing Enterobacteriaceae (rectal). Selected isolates will be whole genome sequenced for contemporary AMR profiles of bacteria enabling informed decisions for ABU based on circulating resistance genes and associated mobile genetic elements. RP3) Assesses ABU drivers. A probability survey in each area (n>95, max.95%CI ±0.10) measures service coverage, treatment seeking and costs. Ethnographies in a sequential explanatory design explain RP1 and survey results and perceptions of health, illness, and antibiotics. They advance community-driven solutions to improve ABU. In each countries’ area with the worst QoC, we conduct 12 In-Depth Interviews (clinicians, pharmacists); 1 Community Dialogue (CD) with 3 groups (fathers, mothers, and health workers); and 6 Key Informant Interviews (traditional practitioners, community leaders). RP4) Intervention development using CDs and national and district stakeholder engagement using the RAPID Outcome Mapping Engagement and Policy-Influence approach. Timelines: RP1-2 surveillance 1st round: Q1-3 yr1; 2nd round: Q4 yr3 & Q1 yr4. Evolutionary work continuous from Q2 yr2. RP3 Survey Q2-4 yr1; qualitative research Q4 yr1 through yr2. RP4 community dialogue Q3-4 yr1. Impact & Dissemination: With rapid our AMR/ABU surveillance metric, ABU drivers of AMR are better understood; Health System strategies improve ABU. 3 PhD. Publications, conferences.
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