Manuela Quaresma

Population-based cancer survival trends in England and Wales up to 2007: an assessment of the NHS cancer plan for England

BACKGROUND The National Health Service (NHS) cancer plan for England was published in 2000, with the aim of improving the survival of patients with cancer. By contrast, a formal cancer strategy was not implemented in Wales until late 2006. National data on cancer patient survival in England and Wales up to 2007 thus offer the opportunity for a first formal assessment of the cancer plan in England, by comparing survival trends in England with those in Wales before, during, and after the implementation of the plan. METHODS We analysed population-based survival in 2.2 million adults diagnosed with one of 21 common cancers in England and Wales during 1996-2006 and followed up to Dec 31, 2007. We defined three calendar periods: 1996-2000 (before the cancer plan), 2001-03 (initialisation), and 2004-06 (implementation). We estimated year-on-year trends in 1-year relative survival for patients diagnosed during each period, and changes in those trends between successive periods in England and separately in Wales. Changes between successive periods in mean survival up to 5 years after diagnosis were analysed by country and by government office region of England. Life tables for single year of age, sex, calendar year, deprivation category, and government office region were used to control for background mortality in all analyses. FINDINGS 1-year survival in England and Wales improved for most cancers in men and women diagnosed during 1996-2006 and followed until 2007, although not all trends were significant. Annual trends were generally higher in Wales than in England during 1996-2000 and 2001-03, but higher in England than in Wales during 2004-06. 1-year survival for patients diagnosed in 2006 was over 60% for 12 of 17 cancers in men and 13 of 18 cancers in women. Differences in 3-year survival trends between England and Wales were less marked than the differences in 1-year survival. North-South differences in survival trends for the four most common cancers were not striking, but the North West region and Wales showed the smallest improvements during 2001-03 and 2004-06. INTERPRETATION The findings indicate slightly faster improvement in 1-year survival in England than in Wales during 2004-06, whereas the opposite was true during 2001-03. This reversal of survival trends in 2001-03 and 2004-06 between England and Wales is much less obvious for 3-year survival. These different patterns of survival suggest some beneficial effect of the NHS cancer plan for England, although the data do not so far provide a definitive assessment of the effectiveness of the plan.

40-year trends in an index of survival for all cancers combined and survival adjusted for age and sex for each cancer in England and Wales, 1971–2011: a population-based study

BACKGROUND Assessment of progress in cancer control at the population level is increasingly important. Population-based survival trends provide a key insight into the overall effectiveness of the health system, alongside trends in incidence and mortality. For this purpose, we aimed to provide a unique measure of cancer survival. METHODS In this observational study, we analysed trends in survival with population-based data for 7·2 million adults diagnosed with a first, primary, invasive malignancy in England and Wales during 1971-2011 and followed up to the end of 2012. We constructed a survival index for all cancers combined using data from the National Cancer Registry and the Welsh Cancer Intelligence and Surveillance Unit. The index is designed to be independent of changes in the age distribution of patients with cancer and of changes in the proportion of lethal cancers in each sex. We analysed trends in the cancer survival index at 1, 5, and 10 years after diagnosis for the selected periods 1971-72, 1980-81, 1990-91, 2000-01, 2005-06, and 2010-11. We also estimated trends in age-sex-adjusted survival for each cancer. We define the difference in net survival between the oldest (75-99 years) and youngest (15-44 years) patients as the age gap in survival. We evaluated the absolute change (%) in the age gap since 1971. FINDINGS The overall index of net survival increased substantially during the 40-year period 1971-2011, both in England and in Wales. For patients diagnosed in 1971-72, the index of net survival was 50% at 1 year after diagnosis. 40 years later, the same value of 50% was predicted at 10 years after diagnosis. The average 10% survival advantage for women persisted throughout this period. Predicted 10-year net survival adjusted for age and sex for patients diagnosed between 2010 and 2011 ranged from 1·1% for pancreatic cancer to 98·2% for testicular cancer. Net survival for the oldest patients (75-99 years) was persistently lower than for the youngest (15-44 years), even after adjustment for the much higher mortality from causes other than cancer in elderly people. INTERPRETATION These findings support substantial increases in both short-term and long-term net survival from all cancers combined in both England and Wales. The net survival index provides a convenient, single number that summarises the overall patterns of cancer survival in any one population, in each calendar period, for young and old men and women and for a wide range of cancers with very disparate survival. The persistent sex difference is partly due to a more favourable cancer distribution in women than men. The very wide differences in survival for different cancers, and the persistent age gap in survival, suggest the need for renewed efforts to improve cancer outcomes. Future monitoring of the cancer survival index will not be possible unless the current crisis of public concern about sharing of individual data for public health research can be resolved. FUNDING Cancer Research UK.

Socioeconomic inequalities in cancer survival in England after the NHS cancer plan

Background:Socioeconomic inequalities in survival were observed for many cancers in England during 1981–1999. The NHS Cancer Plan (2000) aimed to improve survival and reduce these inequalities. This study examines trends in the deprivation gap in cancer survival after implementation of the Plan.Materials and method:We examined relative survival among adults diagnosed with 1 of 21 common cancers in England during 1996–2006, followed up to 31 December 2007. Three periods were defined: 1996–2000 (before the Cancer Plan), 2001–2003 (initialisation) and 2004–2006 (implementation). We estimated the difference in survival between the most deprived and most affluent groups (deprivation gap) at 1 and 3 years after diagnosis, and the change in the deprivation gap both within and between these periods.Results:Survival improved for most cancers, but inequalities in survival were still wide for many cancers in 2006. Only the deprivation gap in 1-year survival narrowed slightly over time. A majority of the socioeconomic disparities in survival occurred soon after a cancer diagnosis, regardless of the cancer prognosis.Conclusion:The recently observed reduction in the deprivation gap was minor and limited to 1-year survival, suggesting that, so far, the Cancer Plan has little effect on those inequalities. Our findings highlight that earlier diagnosis and rapid access to optimal treatment should be ensured for all socioeconomic groups.

Life tables for world-wide comparison of relative survival for cancer (CONCORD study)

Background The CONCORD study compares population-based relative survival from cancer using data from cancer registries in five continents. To estimate relative survival, general mortality life tables are required. Available statistics are incomplete, so various approaches are used to construct complete life tables. This article outlines how the life tables were constructed for CONCORD; it compares life expectancy at birth between 101 populations covered by cancer registries in 31 countries and compares the impact of two approaches to the deployment of life tables in relative survival analysis. Methods The CONCORD approach, using specific mathematical methods, produced complete (single-year-of-age) life tables by sex, cancer registry area, calendar year (1990–1999) and race (only in the USA). In order to study the impact of different approaches, we compared relative survival in the USA using the US national life table, centered on the relevant census years, and the CONCORD approach. We estimated relative survival in each American participating cancer registry for patients diagnosed with breast (women), colorectal or prostate cancer during 1990–1994 and followed up to 1999. Results Average life expectancy at birth during 1990–1999 varied in CONCORD cancer registry areas from 64 to 78 years in males and from 71 to 84 years in females. It increased during the 1990s more in men than in women. In the USA, it was lower in blacks than in whites. Relative survival in American populations was lower with the CONCORD approach, which incorporates trends and geographic variation in background mortality, than with the USA census life tables. Conclusions International variation in background mortality by geographic area, calendar time, race, age and sex is wide. We suggest that in international comparisons of cancer relative survival, complete life tables that are specific for cancer registry area, calendar year and race should be used.

Geographical variation in cancer survival in England, 1991–2006: an analysis by Cancer Network

Background Reducing geographical inequalities in cancer survival in England was a key aim of the Calman–Hine Report (1995) and the NHS Cancer Plan (2000). This study assesses whether geographical inequalities changed following these policy developments by analysing the trend in 1-year relative survival in the 28 cancer networks of England. Methods Population-based age-standardised relative survival at 1 year is estimated for 1.4 million patients diagnosed with cancer of the oesophagus, stomach, colon, lung, breast (women) or cervix in England during 1991–2006 and followed up to 2007. Regional and deprivation-specific life tables are built to adjust survival estimates for differences in background mortality. Analysis is divided into three calendar periods: 1991–5, 1996–2000 and 2001–6. Funnel plots are used to assess geographical variation in survival over time. Results One-year relative survival improved for all cancers except cervical cancer. There was a wide geographical variation in survival with generally lower estimates in northern England. This north–south divide became less marked over time, although the overall number of cancer networks that were lower outliers compared with the England value remained stable. Breast cancer was the only cancer for which there was a marked reduction in geographical inequality in survival over time. Conclusion Policy changes over the past two decades coincided with improved relative survival, without an increase in geographical variation. The north–south divide in relative survival became less pronounced over time but geographical inequalities persist. The reduction in geographical inequality in breast cancer survival may be followed by a similar trend for other cancers, provided government recommendations are implemented similarly.

Funnel plots for population‐based cancer survival: principles, methods and applications

Funnel plots are graphical tools designed to detect excessive variation in performance indicators by simple visual inspection of the data. Their main use in the biomedical domain so far has been to detect publication bias in meta-analyses, but they have also been recommended as the most appropriate way to display performance indicators for a vast range of health-related outcomes. Here, we extend the use of funnel plots to population-based cancer survival and several related measures. We present three applications to familiarise the reader with their interpretation. We propose funnel plots for various cancer survival measures, as well as age-standardised survival, trends in survival and excess hazard ratios. We describe the components of a funnel plot and the formulae for the construction of the control limits for each of these survival measures. We include three transformations to construct the control limits for the survival function: complementary log-log, logit and logarithmic transformations. We present applications of funnel plots to explore the following: (i) small-area and temporal variation in cancer survival; (ii) racial and geographical variation in cancer survival; and (iii) geographical variation in the excess hazard of death. Funnel plots provide a simple and informative graphical tool to display geographical variation and trend in a range of cancer survival measures. We recommend their use as a routine instrument for cancer survival comparisons, to inform health policy makers in planning and assessing cancer policies. We advocate the use of the complementary log-log or logit transformation to construct the control limits for the survival function.

Inequalities in non-small cell lung cancer treatment and mortality

Background Non-small cell lung cancer (NSCLC) comprises approximately 85% of all lung cancer cases, and surgery is the preferred treatment for patients. The National Health Service established Primary Care Trusts (PCTs) in 2002 to manage local health needs. We investigate whether PCTs with a lower uptake of surgical treatment are those with above-average mortality 1 year after diagnosis. The applied methods can be used to monitor the performance of any administrative bodies responsible for the management of patients with cancer. Methods All adults diagnosed with NSCLC lung cancer during 1998–2006 in England were identified. We fitted mixed effect logistic models to predict surgical treatment within 6 months after diagnosis, and mortality within 1 year of diagnosis. Results Around 10% of the NCSLC patients received curative surgery. Older deprived patients and those who did not receive surgery had much higher odds of death 1 year after being diagnosed with cancer. In total, 69% of the PCTs were below the lower control limit of surgery and have predicted random intercepts above the mean value of zero of the random effect for mortality, whereas 40% were above the upper control limit of mortality within 1 year. Conclusions Our main results suggest the presence of clear geographical variation in the use of surgical treatment of NSCLC and mortality. Mixed-effects models combined with the funnel plot approach were useful for assessing the performance of PCTs that were above average in mortality and below average in surgery.

Is cancer survival associated with cancer symptom awareness and barriers to seeking medical help in England? An ecological study.

Background:Campaigns aimed at raising cancer awareness and encouraging early presentation have been implemented in England. However, little is known about whether people with low cancer awareness and increased barriers to seeking medical help have worse cancer survival, and whether there is a geographical variation in cancer awareness and barriers in England.Methods:From population-based surveys (n=35 308), using the Cancer Research UK Cancer Awareness Measure, we calculated the age- and sex-standardised symptom awareness and barriers scores for 52 primary care trusts (PCTs). These measures were evaluated in relation to the sex-, age-, and type of cancer-standardised cancer survival index of the corresponding PCT, from the National Cancer Registry, using linear regression. Breast, lung, and bowel cancer survival were analysed separately.Results:Cancer symptom awareness and barriers scores varied greatly between geographical regions in England, with the worst scores observed in socioeconomically deprived parts of East London. Low cancer awareness score was associated with poor cancer survival at PCT level (estimated slope=1.56, 95% CI: 0.56; 2.57). The barriers score was not associated with overall cancer survival, but it was associated with breast cancer survival (estimated slope=−0.66, 95% CI: −1.20; −0.11). Specific barriers, such as embarrassment and difficulties in arranging transport to the doctor’s surgery, were associated with worse breast cancer survival.Conclusions:Cancer symptom awareness and cancer survival are associated. Campaigns should focus on improving awareness about cancer symptoms, especially in socioeconomically deprived areas. Efforts should be made to alleviate barriers to seeking medical help in women with symptoms of breast cancer.

Do cancer survival statistics for every hospital make sense

1192 www.thelancet.com/oncology Vol 17 September 2016 In recent years, there has been increasing pressure from politicians for researchers to produce cancer survival estimates for every hospital and health-care provider in England, to inform national cancer strategy and patient choice. Here we set out why cancer survival statistics for each hospital would not be useful for either of those purposes. Population-based cancer survival fi gures, derived from cancer registry data for all patients with cancer living in a defi ned region, have been used to highlight geographic, socioeconomic, and international inequalities in survival for many years. Such survival fi gures have underpinned every national cancer strategy in the UK since 1995, and trends in survival have been used to evaluate the eff ect of those strategies. Unlike hospital-specifi c survival fi gures, populationbased fi gures include all patients with cancer. They are unbiased, and they take precise account of the risk of death from causes other than cancer, allowing survival from the cancer to be compared between populations. Therefore, population-based fi gures are ideal for studying trends in survival, and for making regional and international comparisons. Persistent inequalities in survival have led politicians to demand survival estimates for ever-smaller geographies, such as for the 209 Clinical Commissioning Groups in England (population 65 000–880 000 per group). However, producing survival estimates for every Clinical Commissioning Group that are suffi ciently robust to be interpreted for managerial purposes, year on year, is extremely diffi cult, even for the most common cancers. This approach can be taken for 1-year but not 5-year survival (as there are more deaths in the fi rst year after diagnosis), and even that requires modelling the entire national dataset of several million patients with cancer diagnosed over more than 10 years. Failure to understand the diff erence between the robustness of population-based survival fi gures, and the unreliability of hospital-specifi c survival fi gures has led the Department of Health and NHS England to demand routine production of hospital survival fi gures to judge the performance of every health service provider (ie, a single hospital, or a group of hospitals that provide cancer services). It has even been suggested that hospital-specifi c cancer survival statistics should be produced every year, for each type of cancer, and for each stage at diagnosis, as a way of informing “patient choice”. A proposal to that eff ect was removed from the 2015 cancer strategy only at a very late stage of drafting. We argue that provider-level survival statistics cannot be usefully interpreted for surveillance of hospital performance: they are prone to referral bias, and to random fl uctuation because of small numbers; they can lead to spurious comparisons and inappropriate management decisions; and they do not support patient choice. The fact that more than 1 million people in England have opted out from the use of their health data other than for their own clinical management will introduce bias and further undermine the utility of such statistics. Annual snapshots of outcome data cannot be safely interpreted in isolation. For example, 90-day mortality rates are published each year for individual colorectal surgeons, but the warnings for patients are striking: “the results this year will not tell us the real situation and will not be completely accurate for some surgeons”; “very few [surgeons] do a suffi cient number [of procedures], even over the four-year period, for these data to allow reliable comparison between surgeons”; “[it is] hard to identify one surgeon as being in charge in one operation” [because surgeons work in teams]”. Readmission rates, and 30-day or 90-day mortality rates, are short-term outcome measures that might relate to a single surgeon or hospital, but fair comparisons require adjustment for patient case-mix, and suffi cient numbers to produce statistically robust results. One consequence could be misplaced confi dence when evidence of poor performance is not strong, which can be misinterpreted as refl ecting adequate performance. By contrast, population-based outcome measures, such as 1-year, 5-year, or 10-year survival, refl ect the overall eff ectiveness of the health service in managing all patients with cancer in the longer term. Variation in such metrics cannot generally be ascribed to a single surgeon or hospital, or a single component of the quality of care. In addition, it is not straightforward to assign a patient with cancer to a particular hospital for survival For information on NHS clinical care choices see http://digital. nhs.uk/catalogue/PUB20527 Do cancer survival statistics for every hospital make sense?

Persistent inequalities in 90-day colon cancer mortality: an English cohort study

Background:Variation in colon cancer mortality occurring shortly after diagnosis is widely reported between socio-economic status (SES) groups: we investigated the role of different prognostic factors in explaining variation in 90-day mortality.Methods:National cancer registry data were linked with national clinical audit data and Hospital Episode Statistics records for 69 769 adults diagnosed with colon cancer in England between January 2010 and March 2013. By gender, logistic regression was used to estimate the effects of SES, age and stage at diagnosis, comorbidity and surgical treatment on probability of death within 90 days from diagnosis. Multiple imputations accounted for missing stage. We predicted conditional probabilities by prognostic factor patterns and estimated the effect of SES (deprivation) from the difference between deprivation-specific average predicted probabilities.Results:Ninety-day probability of death rose with increasing deprivation, even after accounting for the main prognostic factors. When setting the deprivation level to the least deprived group for all patients and keeping all other prognostic factors as observed, the differences between deprivation-specific averaged predicted probabilities of death were greatly reduced but persisted. Additional analysis suggested stage and treatment as potential contributors towards some of these inequalities.Conclusions:Further examination of delayed diagnosis, access to treatment and post-operative care by deprivation group may provide additional insights into understanding deprivation disparities in mortality.