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Loyalty card data could identify ovarian cancer symptoms



Loyalty card data on over-the-counter medicine purchases could help spot cases of ovarian cancer earlier according to a new study funded by Cancer Research UK.

The first-of-its-kind Cancer Loyalty Card Study (CLOCS) study looked at whether there is a link between a diagnosis of ovarian cancer and a history of buying over-the-counter pain and indigestion medicines.

The study of almost 300 women found that pain and indigestion medication purchases were higher in women who were subsequently diagnosed with ovarian cancer, compared to women who did not have the condition.

Research shows that 93 per cent of people diagnosed with ovarian cancer survive their disease for five years or more if diagnosed at stage 1 compared to just 13 per cent when diagnosed at stage 4.

Dr Yasemin Hirst of Lancaster University’s Medical School who led the preliminary study which paved the way for this latest research, said:

“This data is very exciting for behavioural scientists to further explore life-style changes, dietary behaviours and perhaps exploring other datasets (e.g. biosensors) that can provide more information about self-care and health outcomes.”

Symptoms of ovarian cancer can be unclear during the early stages of the disease, which leads to some people buying medication from a local pharmacy to alleviate their symptoms instead of visiting their GP.

This means that many women are diagnosed late, often when the cancer has already spread, and when their likelihood of survival has greatly reduced.

Studies show that one in five women with ovarian cancer are diagnosed in A&E and many do not receive any treatment for their disease, often because they are too unwell by the time they are diagnosed.

The study used loyalty card data from two UK-based high street retailers of 283 women.

Of these participants, 153 were women who had been diagnosed with ovarian cancer, while 120 were women who had not.

The researchers analysed six years’ worth of purchase histories from the women.

The participants were also asked to complete a short questionnaire about ovarian cancer risk factors, along with the symptoms they experienced (if any) and the number of visits to their GP in the year leading up to cancer referral or diagnosis.

On average, participants with ovarian cancer began to recognise their symptoms about four and a half months before diagnosis.

Of those who visited a GP to check their symptoms, the first visit occurred, on average, about three and a half months before diagnosis.

The researchers said that more research is needed to confirm their findings, and hope that larger studies with patients diagnosed at different stages will be able to support and strengthen these results.

It is also hoped that this research could lead to the future development of an alert system for individuals to help them to seek medical attention for symptoms of cancer, or other diseases, sooner than they otherwise might do.

Dr David Crosby, Head of Prevention and Early Detection Research at Cancer Research UK, said:

“Today, in the digital age, we live with a wealth of data at our fingertips. Studies like this are a great example of how we can harness this information for good and help us detect cancer earlier.

“It’s incredible to think that this innovative study using loyalty cards, something most of us carry in our wallets, could help women with ovarian cancer which is often diagnosed late and mimics the symptoms of other, more benign conditions.

“Whilst further research with more patients is needed, this study indicates exciting potential for a new way to detect cancer earlier and save lives.”

Fiona Murphy, an ovarian cancer patient representative who helped develop the study, said:

“I lived on Gaviscon for 18 months prior to my ovarian cancer diagnosis, it went everywhere with me due to severe acid reflux. Had this been associated with ovarian cancer, I would have had a faster diagnosis, far less surgeries and better fertility options.

“I wanted to help with developing this study because I had the wrong diagnosis for nearly two years. If there is a way to get an earlier diagnosis, I want to help people who are in the same position I was in.”

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