Success in reducing mortality from cardiovascular disease combined with improvements in cancer survival mean that the number of people at risk of being diagnosed, or living with cancer, is increasing. In a few years, half of all Britons will be likely to be diagnosed with cancer at some time in their life. By 2040 a quarter of all pensioners will be cancer survivors.

These statistics are great for concentrating the mind, but what are the solutions? We should continue to pursue technological innovations that will enable the advent of better cancer tests and treatments such as new genetic biomarkers and drug therapies. However, we can also improve cancer outcomes by trying to reduce population wide variation in cancer prevention, diagnosis, and treatment. Of course the two paradigms are complementary. Every year, I greatly enjoy attending the “Cancer Outcomes” conference, which is a conference “home” for the large number of cancer charities, clinicians, researchers, and policy makers who support population based approaches to improving cancer outcomes.

Inspirational talks by Jem Rashbass and Sir Mike Richards at the start of the conference have reminded the delegates that the field of cancer intelligence is currently witnessing nothing short of a revolution. This is happening because of rapid improvements in three different dimensions.

First, because of the modernisation of cancer registration we now have much better “old-type” data (e.g. increasingly timely and complete information on critical items such as stage at diagnosis of cancer).

Second, successive waves of quality improvement initiatives for cancer services in recent years have generated a plethora of “new-type” data (ranging from patient experience or patient reported outcomes, to modern pathology, waiting times, and clinical audit datasets). Population based nationwide collections for those new sorts of cancer data have never existed previously and arguably the UK is leading the way in this field internationally.

Third, we now have greater opportunities for linking data (of both the “old” and the “new” type) than ever before. For example, using anonymous data, it is possible to link information from hospital episode statistics data along with primary care, cancer registration, and patient reported data. This generates ever greater opportunities for “high definition” studies of variations in care pathways and care quality.

Indeed, such has been the pace of innovation in data generation that many people now believe that the field is experiencing something similar to the “lags in translation” phenomenon observed in genetic research. The availability of data exceeds our current capacity for timely analysis and intelligent interpretation of the findings, and their implications for policy and research. How to realise the opportunities for generating evidence to help improve patient outcomes as soon as possible is now a great challenge for both researchers and funders.

Whilst talking of lags in research translation, Peter Sasieni gave an excellent talk on the long history of delays in implementing research findings about cervical cancer prevention. Although intervals between innovation and implementation in this field have been shorter in recent periods, it was worth remembering that the Papanicolaou cervical smear test was first described in the literature in mid-1920s and then again in the middle of the second world war. The subsequent introduction of population wide cervical screening programmes lagged by decades (and is in fact still lacking in high incidence African and Latin American countries).

The increasing availability of more and better data (“old,” “new,” and linked) comes with challenging new questions, as we were reminded by Mick Peake. Which aspects of cancer care quality do patients really value the most? For example, what is the association (and perhaps potential trade offs) between better patient experience and better clinical outcomes, and under what circumstances might this association change? Similarly, there are now great opportunities for comparing the performance of different hospitals and clinical teams, but also concerns about the reliability of organisational or small area population comparisons. This theme was also covered by Manuela Quaresma in her talk on use of funnel plots and “smooth-mapping” of geographical variations. We need innovations in metrics of cancer survival, and Paul Lambert gave an insightful talk about how inequalities in excess mortality after the diagnosis of cancer can be better understood in relation to inequalities in background mortality from causes other than cancer.

Just one day after the suspension of the reconfiguration of paediatric cardiac surgery units by Jeremy Hunt, it was entirely topical to listen to research by Henrik Moller, Mick Peake, Paul Beckett, and other colleagues who (in different presentations) explored associations between measures of volume of hospital activity and survival in the context of cancer surgery. On the same day there were also interesting talks on the emergency diagnosis of cancer by Paul Aylin (time trends appear to have gone down in the early/mid 2000s, but have been relatively stable in more recent periods) and the impact of the “be clear on cancer” cancer awareness campaigns by Carolynn Gildea (clear signals of effectiveness as evidenced by interim outcomes such as increase in referrals for investigation).

I talked earlier about patient reported outcomes and Heather Kinnear and colleagues presented an “all-Ireland” patient reported outcomes survey of prostate cancer survivors. There are many treatment modalities for localised prostate cancer, but all have one thing in common—there is a high risk of urinary, bowel, or sexual dysfunction, and other side effects with all of them. However the actual “menu” of risks seems to vary by treatment type. This kind of information could be very useful to men with localised prostate cancer when deciding on their treatment options—and about potentially acceptable to them “trade offs” between different risks. This is the first study to measure patient reported outcomes for prostate cancer in a large population based sample of prostate survivors. England and other countries should follow this example soon.

Relatively little is known about cancer outcomes for patients with prior mental health morbidity. A great study by Anna Gavin and colleagues from Northern Ireland indicates that people with mental health morbidity are more likely to be diagnosed at advanced stage and to have poorer survival. Awareness among patients and professionals caring for patients with mental health morbidity should be raised.

David Forman has provided a “global health” dimension to the debate. There is a large global health inequality in infrastructures required for cancer registration, and new resources and technical assistance are needed to develop cancer registries in resource poor countries. And for the part of the globe that benefits from already developed cancer intelligence systems, international standardisation and harmonising of data items and collection systems would be very useful.

Lastly, and as the BMJ has recently given its support for the “patient revolution.” I estimate that at least 50% of questions to speakers were asked by patients or patient representatives. I wonder how many other national conferences in any field of medicine or public health could match that “performance metric.”

Georgios Lyratzopoulos is a public health and health services researcher working at the Cambridge Centre for Health Services Research of the University of Cambridge. His research focuses on the earlier diagnosis of cancer, and other aspects of cancer healthcare quality and epidemiology. He has previously trained and worked in the NHS in either public health (1999-2007) or clinical posts (1994-1999), and also for NICE’s Interventional Procedures Programme (part-time 2005-11).

Funding declaration: GL is supported by a post doctoral fellowship award by the National Institute for Health Research (NIHR PDF-2011-04-047).  The views expressed in this publication are those of the author and not necessarily those of the NHS, the National Institute for Health Research, or the Department of Health.

Conflict of interest statement: I declare that that I have read and understood the BMJ Group policy on declaration of interests and I hereby declare the following interests:
I am an academic whose research focuses on cancer. I declare membership of the following professional groups: a) NICE Guidelines Development Group for “Referral for Suspected Cancer” (May 2012 – expected end of 2014) b) NCRI Clinical Studies Group for Primary Care (March 2013 onwards). I have presented research by myself and colleagues at the conference which I cover in this blog, and I professionally know and/or jointly work with several presenters and/or organisers of the conference.