FGM is associated with serious health risks and is illegal in the UK. NHS England wanted NEL CSU to help them understand the extent of the practice in London to enable them to reduce the risks and likelihood of unsafe practices and the long term health effects.
Data collection in this area is new, so understanding the trends is very important for future planning. Since 2015, healthcare professionals and other agencies are by law expected to record all suspected cases of FGM. This information is anonymised before it is used for service planning.
The project brought together stakeholders from the London Safeguarding Board and London CCGs via the London Information Exchange Group, the Department of Health, Metropolitan Police and NHS Digital. A number of new data sets needed to be managed, getting information from partners and shaping it into insight that everyone could use to improve support for those affected by FGM.
NEL CSU Analytics used its ability to work at scale across London to collect and analyse data and liaise with all the stakeholders to make sure the project ran smoothly. They then represented the incidence of FGM cases across the 32 London CCGs and presented this via maps which showed type age group, number of police reports and Trust and hospital reports across London. The team extended the most recent research study on FGM prevalence to model the current and future cases by CCG over the next five years.
NHS England and their partners have maps, analysed data and a methodology for future data analysis in order to track trends in FGM. This allows the health and police services to target their interventions around FGM effectively across London, using the insight that we have generated.
NHS England said:
“Dr Georgios Ketsetzis and CSU team helped the London FGM task and finish group translate and triangulate a range of data into a striking and attractive data model which gave us an opportunity to engage with stakeholders in London to stimulate discussion on FGM prevalence, health impact, engagement, commissioning and provision. The CSU team quickly understood our ambition and were able to develop the model, adapt through the pilot phase and support the work in clinical forums during conception. The model has been very well received and there are plans to offer to other regions and discussions about using similar model for other datasets”.
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