CLM: Community-led monitoring
HIV programmes are being scaled back worldwide. All are under pressure to prove the effectiveness of their services. At the same time, governments are coming to power that deliberately refrain from collecting health data in order to conceal the impact on key populations. Community-led monitoring (CLM) is becoming indispensable.
26th International AIDS Conference 2026 in Rio de Janeiro
Contribution by Florian Vock
Juliana Cesar from the Brazilian NGO Gestos highlighted how crucial it is for communities to control their own data. We must keep data collection and analysis in our own hands, particularly in politically turbulent times. Data is not an end in itself, but the foundation for political advocacy. We must make the realities of affected populations visible through qualitative and quantitative analysis and use this information strategically. In doing so, we must bear in mind:
- Data collection and analysis must be funded and carried out as independently as possible so that it remains within the community’s control.
- Findings must be ‘translated’ into the language of politics so that they can be understood.
- Findings must be able to ‘travel’ into policy areas other than HIV – such as equality, human rights or youth support – so that they can have a broad impact.
- Findings must be defended as a democratic foundation: there is a right to collect information, and it is a duty to listen to it (Data Justice).
It was unanimously pointed out that data collection and analysis are not rocket science. Countless NGOs around the world collect and analyse information. Monitoring by NGOs can not only reveal what is happening (e.g. infection rates) or what is being done (e.g. testing); it also highlights who is not being reached by interventions (e.g. people living in poverty).
It is also the role of NGOs to make data accessible and understandable to people. Data must be used, shared and published: infographics, simple download options, dashboards or thematic focus areas help with this. AIDSvu in the USA or HIVlens in the UK are successful examples of this.
And last but not least, it is about using data to enable modelling. How can we use the knowledge we have to calculate which interventions are particularly effective: is it worth focusing on long-acting PrEP to reduce HIV transmission? Does the incidence of syphilis decrease if we invite certain groups to be tested? The University of Bristol offers an online course introducing modelling with data.