Is it getting hot in here?

Lisa Allen said MenopauseX came out of Women in Data, which was set up to address barriers to women taking up employment.

A high proportion of women drop out in their 50s, as they go through perimenopause or menopause. So, MenopauseX is looking for data that can explain this. “We are looking for a smoking gun,” Lisa said.

“We publish stats for population, and aging, but there are other data sets that might be useful, like how many women go part-time, or drop out? “How can we get data out of companies to help them tackle these issues? What I want to get out of this session is help – how do we do this?”

One participant suggested one challenge is that it can be hard to disentangle issues connected with the menopause from issues associated with mid-life in general – weariness, caring for elderly relatives, careers stalling.

Another argued that might mean men should be a control group – if there are differences between data between men and women in the relative age groups, the issues might be menopause related.

However, a third noted that men aren’t a good control group – because they’re more likely to be well established than women who have, for example, taken time out to care for children.

Looking for data 

Sickness absence data might help. As might data held by unions or ACAS, who handle work disputes. The Office of National Statistics asks questions about wellbeing and happiness, so a question in that bank might help. And it has a Labour Force Survey, so that might be a good place to start.

The NHS will have some information, but most women who seek help go to a GP, and GP data is limited (although some aggregate data sets are published as open data). There are private health and wellness apps, like Zoe, although they have limitations, and don’t always share their data.

The session – which included a number of women affected – also considered what kind of data and information might be useful for them.

Tips on handling symptoms – and not just HRT. Guidance on accessing NHS services. Perhaps, participants suggested, this might be a really good use case for the generative AI that the camp had discussed earlier.

The session also considered why it is so hard for many women to raise this issue with managers and team members. Fear of being seen as less professional or productive, or of being misunderstood by younger colleagues – female as well as male – came up.

Safe spaces for discussion, and education are needed. But, Lisa argued, this is the reason data is so important. “Companies are more likely to be willing to look at the data” – and they do, and the data shows there might be issues, they can think about how to raise them.

If issues are raised, though, workplaces will want to know what to do. So information and data on things that work – from making fans or cool drinks available, to changing when meetings are scheduled, is needed – recognising that different things will work for different women.

Overall, one participant argued, menopause is “a classic accounting problem.” It isn’t measured, so its impact is underestimated. “What we need is new ways of counting to address the bias.”