Background: Understanding population-level exposure to cancer risk factors is vital when devising risk-reduction policies. By reducing exposure to cancer risk factors, many cancers could be prevented. But what impact on cancer incidence do these risk factors have? And what proportion of cancers could be prevented if these risk factors are avoided? Aim: The aim of this analysis was to update the estimates of the number and proportion of theoretically preventable cancers in the UK to reflect the changing behavior as assessed in representative national surveys, and new epidemiologic evidence. Separate estimates were also calculated for England, Wales, Scotland, and Northern Ireland because prevalence of risk factor exposure varies between them. Methods: Population attributable fractions (PAFs) were calculated for combinations of risk factor and cancer type with sufficient/convincing evidence of a causal association. Relative risks (RRs) were drawn from meta-analyses of cohort studies where possible. Prevalence of exposure to risk factors was obtained from nationally representative population surveys. Cancer incidence data for 2015 were sourced from national data releases and, where needed, personal communications. Results: Around four in ten (38%) cancer cases in 2015 in the UK were attributable to known risk factors. The proportion was around two percentage points higher in UK males (39%) than UK females (37%). Comparing UK countries, the attributable proportion for persons was highest in Scotland (41%) and lowest in England (37%). Tobacco smoking contributed by far the largest proportion of attributable cancer cases, followed by overweight and obesity, accounting for 15% and 6%, respectively, of all cases in the UK in 2015. Conclusion: Around four in ten (38%) cancer cases in the UK could be prevented. Tobacco and obesity remain the top contributors of attributable cancer cases. Tobacco smoking has the highest PAF because it greatly increases cancer risk and has a large number of cancer types associated with it. Obesity has the second-highest PAF because it affects a high proportion of the UK population and is also linked with many cancer types. Public health policy may seek to reduce the level of harm associated with exposure or reduce exposure levels - both approaches may be effective in preventing cancer. The variation in PAFs between UK countries is affected by sociodemographic differences which drive differences in exposure to theoretically avoidable 'lifestyle' factors. PAFs at UK country level have not been available previously and they should be used by policymakers in the devolved nations to develop more targeted public health measures. This analysis demonstrates the importance of nationally representative exposure prevalence data and cancer registration in informing evidence-based public health policy.

Background: Although inequalities in cancer survival are thought to reflect inequalities in stage at diagnosis, little evidence exists about the size of potential survival gains from eliminating inequalities in stage at diagnosis.Methods: We used data on patients diagnosed with malignant melanoma in the East of England (2006-2010) to estimate the number of deaths that could be postponed by completely eliminating socioeconomic and sex differences in stage at diagnosis after fitting a flexible parametric excess mortality model.Results: Stage was a strong predictor of survival. There were pronounced socioeconomic and sex inequalities in the proportion of patients diagnosed at stages III-IV (12 and 8% for least deprived men and women and 25 and 18% for most deprived men and women, respectively). For an annual cohort of 1025 incident cases in the East of England, eliminating sex and deprivation differences in stage at diagnosis would postpone approximately 24 deaths to beyond 5 years from diagnosis. Using appropriate weighting, the equivalent estimate for England would be around 215 deaths, representing 11% of all deaths observed within 5 years from diagnosis in this population.Conclusions: Reducing socioeconomic and sex inequalities in stage at diagnosis would result in substantial reductions in deaths within 5 years of a melanoma diagnosis.

Background: Typically, lifetime risk is calculated by the period method using current risks at different ages. Here, we estimate the probability of being diagnosed with cancer for individuals born in a given year, by estimating future risks as the cohort ages.Methods: We estimated the lifetime risk of cancer in Britain separately for men and women born in each year from 1930 to 1960. We projected rates of all cancers (excluding non-melanoma skin cancer) and of all cancer deaths forwards using a flexible age-period-cohort model and backwards using age-specific extrapolation. The sensitivity of the estimated lifetime risk to the method of projection was explored.Results: The lifetime risk of cancer increased from 38.5% for men born in 1930 to 53.5% for men born in 1960. For women it increased from 36.7 to 47.5%. Results are robust to different models for projections of cancer rates.Conclusions: The lifetime risk of cancer for people born since 1960 is >50%. Over half of people who are currently adults under the age of 65 years will be diagnosed with cancer at some point in their lifetime.

INTRODUCTION:Long-term lung cancer survival in England has improved little in recent years and is worse than many countries. The Department of Health funded a campaign to raise public awareness of persistent cough as a lung cancer symptom and encourage people with the symptom to visit their GP. This was piloted regionally within England before a nationwide rollout.METHODS:To evaluate the campaign's impact, data were analysed for various metrics covering public awareness of symptoms and process measures, through to diagnosis, staging, treatment and 1-year survival (available for regional pilot only).RESULTS:Compared with the same time in the previous year, there were significant increases in metrics including: public awareness of persistent cough as a lung cancer symptom; urgent GP referrals for suspected lung cancer; and lung cancers diagnosed. Most encouragingly, there was a 3.1 percentage point increase (P<0.001) in proportion of non-small cell lung cancer diagnosed at stage I and a 2.3 percentage point increase (P<0.001) in resections for patients seen during the national campaign, with no evidence these proportions changed during the control period (P=0.404, 0.425).CONCLUSIONS:To our knowledge, the data are the first to suggest a shift in stage distribution following an awareness campaign for lung cancer. It is possible a sustained increase in resections may lead to improved long-term survival.

Sir, The recent well-publicised Marmot review (Independent UK Panel on Breast Cancer Screening, 2012; Marmot et al, 2013) provides much-needed insight into the estimated harms and benefits of breast screening but, as it is an intention-to-treat analysis, sets out its main findings in terms of women invited to screening. Such an analysis does not need to adjust for either non-compliance or loss to follow-up, and is useful for assessing the effect of the intervention on society, but it is not designed to portray the likely impact of screening on an individual. In practice, women do not accrue benefits or harms of breast screening merely by receiving a screening invitation–these occur only during or after mammography itself. Thus, here we present an alternative set of statistics (derived from those within the Marmot review) estimating the risks and benefits to women of attending breast screening. It is clear in the Marmot review that the figures are subject to a large amount of uncertainty, which also applies to the figures presented here. The absolute reduction in risk of dying from breast cancer for women attending screening can be estimated by adjusting the absolute risk reduction in those invited to screening (0.43%) for the coverage (coverage rate in the NHSBSP is 77% for England in 2010–2011 (The Health and Social Care Information Centre)). Thus, as presented in the Marmot review, the estimated breast cancer mortality reduction is 0.56% (0.43% divided by 0.77%), or 56 breast cancer deaths prevented per 10 000 women attending screening. We have applied a similar calculation to the estimated percentage of women with an overdiagnosis (1.29%) based on the intention-to-treat analysis. This gives an estimate of 1.68% (1.29% divided by 0.77%) overdiagnosis, or 168 per 10 000 women attending screening. However, the same calculation cannot simply be repeated to estimate the number of breast cancers and DCIS diagnosed in women attending breast screening in the United Kingdom, as socioeconomic deprivation is associated with both the risk of breast cancer and the likely attendance at screening (both are higher in more affluent populations) (Maheswaran et al, 2006; National Cancer Intelligence Network, 2008). We have therefore assumed that the risk of breast cancer in women who attend breast screening is 10% higher than generally for the whole population of women invited for screening. We feel this is reasonable, as a large range of estimates of the increased risk in attenders versus non-attenders has been reported–this figure is roughly in the middle of these estimates (Collette et al, 1984; van Dijck et al, 1996; Puliti et al, 2008). Thus, we estimate there will be 749 breast cancers diagnosed in 10 000 women attending screening (681 breast cancers per 10 000 women in those invited for screening from the Marmot review+10%). To demonstrate these harms and benefits we have compared what would happen to 10 000 women attending screening in the United Kingdom after 20 years with what would happen had they not been able to attend if there were no screening programme available to them (numbers are rounded to whole numbers). Seven hundred and forty-nine women will be diagnosed with breast cancer and will receive treatment. Of these 749 women, One hundred and fifty-seven will die from breast cancer, 56 fewer than in the group not attending screening. Of the 592 who survive, Fifty-six patients will have their life extended by screening, see calculation above. One hundred and sixty-eight will be diagnosed and treated for a cancer that would not have caused problems in their lifetime (‘overdiagnosed'), see calculation above. Three hundred and sixty-nine, the remainder, will be diagnosed with and treated for a cancer that would have been picked up later without screening. If the same 10 000 women were not able to attend screening, then after 20 years Five hundred and eighty-two will be diagnosed with breast cancer, and will receive treatment, that is, the 749 minus the number of overdiagnosed cases. One hundred and sixty-eight women will have a breast cancer they never know about and that will not cause any harm during their lifetime. Of the 582 women diagnosed, Two hundred and thirteen women will die from breast cancer, 56 more than in the group attending screening. Three hundred and sixty-nine, the remainder, will be treated for cancer and will survive. These figures give further insight into the benefits and harms of breast screening, but from the individual point of view. We hope they will inform the millions of women invited for screening in the United Kingdom each year, and those around the world, and help to make their decision about whether or not to go for screening. Further information can be found on Cancer Research UK's website (Cancer Research UK, 2012).

Background: The ‘lifetime risk’ of cancer is generally estimated by combining current incidence rates with current all-cause mortality (‘current probability’ method) rather than by describing the experience of a birth cohort. As individuals may get more than one type of cancer, what is generally estimated is the average (mean) number of cancers over a lifetime. This is not the same as the probability of getting cancer. Methods: We describe a method for estimating lifetime risk that corrects for the inclusion of multiple primary cancers in the incidence rates routinely published by cancer registries. The new method applies cancer incidence rates to the estimated probability of being alive without a previous cancer. The new method is illustrated using data from the Scottish Cancer Registry and is compared with ‘gold-standard’ estimates that use (unpublished) data on first primaries. Results: The effect of this correction is to make the estimated ‘lifetime risk’ smaller. The new estimates are extremely similar to those obtained using incidence based on first primaries. The usual ‘current probability’ method considerably overestimates the lifetime risk of all cancers combined, although the correction for any single cancer site is minimal. Conclusion: Estimation of the lifetime risk of cancer should either be based on first primaries or should use the new method.