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On strengthening digital health systems, disease surveillance, emergency preparedness and response, M&E, and what working inside these systems actually teaches you.
Featured Series
Nine essays on what nine years inside WHO Nigeria and Nigeria's health systems actually teach you — on data, coordination, community, and what persists when the funding ends.
A career in global health M&E is one of the most intellectually demanding and practically consequential roles in international development. It sits at the intersection of epidemiology, programme design, data systems, and strategic management and it directly determines whether health programmes produce the outcomes they are designed for.
Fifty-three thousand case investigations. COVID-19. Cholera. Mpox across 27 local government areas. Circulating vaccine-derived poliovirus type 2. Each of these responses taught something different not about the pathogen, but about the systems designed to contain it.
An indicator is only as useful as the decision it informs. A results framework filled with indicators that no one uses to make decisions is not a monitoring system it is a reporting burden. Yet most global health programmes design indicators backwards: starting with what is easy to count, then constructing a rationale for why it matters.
The 60% reduction in outbreak confirmation time I helped achieve across WHO programmes in Nigeria did not come from faster technology. It came from designing a system where the right data reached the right person at the right time and where that person had a defined protocol for what to do when it arrived.
Most global health programmes measure what they do. Results-Based Management demands that they measure what changes because of what they do. That distinction between activity tracking and outcome accountability is the difference that determines whether a programme learns, adapts, and improves, or simply reports and repeats.
Every African Union member state has formally adopted the Integrated Disease Surveillance and Response framework. Most have IDSR-aligned reporting tools, IDSR-trained personnel, and IDSR-based national surveillance guidelines. Yet when outbreaks occur across West Africa, response gaps appear at precisely the points the IDSR framework was designed to close.
The logical framework or logframe is probably the most universally required and least effectively used tool in global health M&E. Almost every WHO, USAID, GAVI, and Global Fund programme produces one. Very few programmes use it as it was intended: as a living document that links planning decisions to monitoring data and drives management action.
Moving immunisation data quality from 54% to 81% across all reporting sites in the WHO polio programmes did not come from training health workers to enter data more carefully. It came from redesigning the system so that poor data quality was harder to produce than good data quality. Data quality is a design problem with a design solution.
Monitoring and evaluation has a language problem. The same term means different things in different frameworks. This glossary establishes working definitions for the terms used throughout this blog series and across the WHO programmes I support so that every article builds on a shared foundation.
The 60% reduction in outbreak confirmation time achieved across WHO programmes in Nigeria did not come from better technology. It came from identifying and fixing the structural failures that were slowing detection, investigation, and response mistakes so common they have become invisible.
The question I ask every time I review a DHIS2 instance: who opens this dashboard at 7am on a Monday morning, and what decision do they need to make within the next hour? If the person who built the dashboard cannot answer that, it is not a decision-support tool. It is a data display.
DHIS2 is the most widely deployed health information system in the world. More than 100 countries use it to manage health data. Nigeria runs its entire national disease surveillance infrastructure on it. The WHO programmes I support depend on it daily and yet experienced global health professionals are routinely uncertain what it actually is.
Most outbreak surveillance failures are not failures of data collection. They are failures of system design in situations where data captured never reaches the people who need to act on it, in the form they need it, at the time it matters.
Africa's healthcare challenges are immense, but so is the opportunity to leapfrog legacy systems with digital-first health innovation. Here's why now.
Lessons from investigating 231 suspected Mpox cases across 27 LGAs in Imo State, and what the data tells us about the future of disease surveillance in Nigeria.
How digital M&E tools are compressing the feedback loop between health programme implementation and strategic decisions, and what it takes to get the transition right.
After over nine years spanning polio eradication, disease surveillance, immunisation, and health systems strengthening across Nigeria, here is an honest account of what the work has taught me.
Lesson 8 of 9: In public health, the technical solutions are rarely the bottleneck. What slows us down is the human and institutional challenge of getting multiple organisations to move in the same direction at the same time.
Lesson 7 of 9: The global polio eradication programme is one of the most complex public health endeavours ever attempted. Working inside it taught me that you cannot solve a systems problem by optimising one part of the system.
Lesson 6 of 9: Disease surveillance works only when communities participate willingly. That participation is not automatic. It is earned, and it can be lost.
Lesson 5 of 9: One of the most damaging assumptions in global health is that communities are blank slates waiting to receive interventions. They are not. They have knowledge, priorities, and agency that any effective programme must engage with.
Lesson 4 of 9: Field practitioners often see policy as bureaucratic obstruction. After working at the intersection of policy and implementation for over nine years, I have come to see it differently.
Lesson 3 of 9: Health policies designed at the national level are only as effective as their implementation at the community level. The "last mile" is not a delivery problem. It is a design problem.
Lesson 2 of 9: In over nine years of working with health data across Nigerian states, the most dangerous errors I have seen were not caused by bad data. They were caused by good data interpreted without adequate context.
Lesson 1 of 9: Over nine years into a public health career, the lesson I return to most often is the one that took the longest to truly understand: passion is not a strategy.
More articles in progress. Topics include community health systems, eHealth implementation, and lessons from outbreak response.
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