PMNCH workshop puts data literacy at the center of health advocacy
PMNCH is preparing a capacity-building workshop designed to sharpen how advocates, communications teams, policy staff, and youth representatives read and use numbers. Scheduled for 3 November 2026 from 15:00 to 16:00 CET, the session builds on an earlier discussion about data storytelling and moves one step upstream: checking whether the figures themselves are being read correctly before they are turned into reports, campaigns, speeches, or policy messages.
The focus is practical and immediate. In maternal health, poverty, and global development, a single statistic can travel quickly once it enters a briefing slide or social post. If the number is misunderstood at the start, the error becomes part of the message, then part of the policy conversation. The workshop is built to interrupt that chain before it hardens.
Reading a number is not the same as understanding it
Participants will be asked to examine what a data point actually measures, what it leaves out, and which assumptions sit behind it. A counted figure and a modelled estimate are not interchangeable, yet they are often repeated as if they carry the same weight. In public health communication, that difference can change how much confidence a decision-maker places in the evidence.
A single statistic rarely stands alone. Definitions shift, baseline years change, and levels of aggregation can alter the meaning of a trend without changing the underlying facts on the ground. A population-level pattern may look stable or dramatic depending on whether it is being described by district, country, age group, or time period. Misreading those design choices can produce a persuasive story that is technically wrong.
One number can mislead when the context is stripped away.
The workshop also plans to confront a few of the most common analytic traps in advocacy work. Correlation can be mistaken for causation. Findings about places can be applied to individuals. Missing data can be treated as zero. Each of those errors sounds small in isolation, but each can distort the public narrative around maternal mortality, poverty, and access to care.
Fragile settings and the problem of missing evidence
The session gives particular attention to data gaps in fragile and conflict-affected settings, where the burden of disease and hardship is often highest and the evidence base is thinnest. That is a familiar problem across global health. The places most in need of visibility are frequently the places least able to produce consistent data, which leaves advocates trying to describe severe need with incomplete numbers.
In communication terms, that forces a discipline that is easy to promise and harder to maintain. Missing data cannot be quietly filled in with optimism. It must be acknowledged as missing, weighed for its likely direction of bias, and described in a way that does not turn uncertainty into false precision. For campaign teams and policy writers, that is not a technical nicety; it changes what kind of claim can be made in the first place.
The workshop’s framing suggests a stronger standard for public-facing health messaging. A number should not be repeated simply because it is available. It should be checked for what it includes, what it excludes, and whether the comparison being drawn is actually valid. In a field where numbers often carry moral force, accuracy becomes part of the intervention.
A five-question check before any number is repeated
The session will introduce a simple five-question checklist to use before publishing or repeating any figure. The exact checklist is meant to keep communicators from moving too quickly from data to message. First, what does the number measure? Second, what is missing? Third, is it a modelled estimate or a counted figure? Fourth, has the definition, baseline, or aggregation level changed? Fifth, is the interpretation staying within the limits of the evidence?
That kind of discipline is especially relevant for teams working across social media, speeches, briefings, and advocacy materials. Fast-moving communications environments reward speed, but speed can flatten nuance. A workshop like this is trying to reverse that habit and replace it with a simpler question: is this number being used accurately enough to support the action it is supposed to prompt?
The training is open to advocates, communications and campaign teams, programme and policy staff, youth and civil society representatives, and PMNCH partners. No background in statistics is needed. That detail matters because the workshop is not being positioned as a specialist seminar for analysts alone. It is meant for the people who turn evidence into public language, often under deadline and in front of non-technical audiences.
Why data storytelling depends on getting the reading right first
The workshop follows an earlier PMNCH session on data storytelling, which focused on how to tell stories with data. This second session starts earlier in the chain, with the reading of the numbers themselves. That sequencing is sensible. A compelling narrative built on a misread indicator can mislead faster than a dull one, because polished delivery can make weak evidence sound certain.
Data storytelling in maternal health and child health carries unusual responsibility because the stakes are not abstract. Numbers are used to argue for services, financing, accountability, and political attention. When a figure is used to describe who is affected, where the burden sits, or whether progress is real, a small interpretive error can change the policy recommendation that follows.
The workshop’s central promise is simple: question first, narrate second. For public health communicators, that is a useful correction to the habit of treating data as self-explanatory. Data do not speak without context. They need careful reading before they can support a credible story.
By the end of the session, participants are expected to leave with practical tools for reading data critically, identifying common traps, understanding why gaps cluster in the hardest-hit settings, and converting a single number into a narrative that remains faithful to cause, context, and affected populations. The constraint is clear: accuracy has to survive the telling.