Why interoperability matters for AMR surveillance in Uganda
Antimicrobial resistance (AMR) is an urgent global public health threat that complicates the treatment of infectious diseases. Worldwide, it is responsible for an estimated 1.3 million deaths each year. In low- and medium-income countries, including Uganda, the burden is disproportionately high — in 2019, AMR-associated mortality in Uganda surpassed deaths due to malaria, HIV, and tuberculosis.
Against that backdrop, the case for action is clear. The Global Action Plan on AMR (2015) and Uganda’s National Action Plans on AMR (2018–2023 and 2024–2029) both identify surveillance — the systematic collection and use of data on AMR and antimicrobial use — as a core intervention. Such data are essential for effective clinical management, for public health surveillance, and for the design and implementation of AMR control strategies.
But the data we have is not, yet, the data we need.
The fragmentation problem
AMR data in many low- and medium-income countries, including Uganda, remain fragmented, incomplete, and underutilised for decision-making. This is despite significant investment in microbiology laboratory capacity, and despite active surveillance programmes at facility, district, and national levels. The gap has been highlighted at the United Nations General Assembly High-Level Meeting on AMR, in the Africa CDC Landmark Report, and in Uganda’s own National Action Plan (2024–2029).
Why does this gap persist?
Three challenges stand out. First, there is a shortage of skilled personnel at facility and national levels — people who can capture, clean, and interpret AMR data consistently. Second, the data systems that do exist are disjointed. Laboratories use different software; surveillance programmes use different reporting formats; stewardship activities use different registers. Each system works for its own purpose, but the data rarely flows between them. Third, there is limited clarity about what national AMR data needs actually are — which data points matter, at what frequency, for which decisions, and in which formats.
The consequence is that AMR data, when it is needed most — during an outbreak, a policy review, a treatment guideline update — is often incomplete, out of date, or simply not available in a usable form.
What interoperability means
Interoperability is not a feature. It is a property of a system — the ability of different information systems, applications, and devices to access, exchange, and cooperatively use data in a coordinated manner. In the AMR context, that means:
- A microbiology laboratory can send its culture and susceptibility results to a national surveillance platform without manually re-entering them.
- A stewardship programme can pull antimicrobial consumption data from a hospital pharmacy system without bespoke integration for every facility.
- A policy-maker reviewing resistance trends can see data from multiple districts, multiple labs, and multiple sectors — human, animal, environmental — in a common format, with common definitions.
Interoperability is what turns a collection of disconnected systems into a surveillance network.
Why NIAMR is building an interoperable layer, not another database
Uganda does not need another AMR database. It has microbiology LIMS systems, DHIS2, WHONET, Fleming Fund-supported platforms, and several programme-specific tools. Each of these serves a real purpose, and replacing them is neither feasible nor desirable.
What is missing is the connective tissue — the interoperable layer that can bring data from these existing systems together, harmonise it using shared standards and terminologies, and make it available for timely detection, monitoring, and evidence-based decision-making.
That is what NIAMR is building. Our approach combines:
- Standards-based data integration — using medical terminologies and data exchange standards that support national and global AMR reporting requirements.
- Co-design with the user — working directly with the Ministry of Health, the National Health Laboratories and Diagnostics Services, implementing partners, and the research community to ensure the platform meets real-world needs.
- A scalable architecture — designed from the start to extend beyond human health into animal health, water, wildlife, and environmental surveillance, in line with One Health principles.
What is next
NIAMR will be implemented over three years, in five sequential phases. Phase 1, now underway, is a national situation analysis — a systematic mapping of where AMR data in Uganda currently lives, how it flows, what standards are in use, and where the gaps are. That baseline will shape everything that follows: the platform design in Phase 2, the pilot implementation, the impact evaluation, the system performance assessment, and the costing study that will inform national scale-up.
We will share what we learn at each phase — through this site, through peer-reviewed publications, through policy briefs, and through the outputs and resources that emerge from the work.
Interoperability will not, on its own, solve the AMR problem. But without it, every other intervention — stewardship, infection prevention, vaccine deployment, policy reform — is working with partial information. Fixing the data foundation is where NIAMR begins.
