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Incidence (or numbers of cases)

Cancer incidence is defined as the number of new cases of cancer diagnosed in a specific population within a specific period of time, usually a year. It only refers to primary cancers and does not include secondary cancers or recurrences.

Cancer incidence data are collected by a comprehensive network of regional and national cancer registries across the UK.1-5 Cancer Research UK Statistical Information Team collate these data into incidence statistics for Great Britain and the UK.

section reviewed 31/10/13
section updated 31/10/13

Mortality (or numbers of deaths)

Cancer mortality is defined as the number of deaths from cancer in a specific population within a specific period of time, usually a year. It only includes deaths where cancer is mentioned as an underlying cause of death on death certificates.

Cancer mortality data are derived from the registration of deaths certified by a doctor or coroner in the UK.6-8 Cancer Research UK Statistical Information Team collate these data into mortality statistics for Great Britain and the UK.

section reviewed 31/10/13
section updated 31/10/13

Incidence and mortality rates

Cancer incidence and mortality rates provide a standard measure of the frequency of cases/deaths within a specific period of time relative to a fixed population size, usually 100,000 people or expressed per million if rare. Rates are important because they allow comparisons between populations to be made.

Cancer Research UK Statistical Information Team routinely report three types of cancer rates: ‘crude rates’, ‘age-specific rates’ and ‘age-standardised rates’. These are often calculated for only the latest year, but sometimes, where the numbers are small and/or there is a lot of fluctuation in the year-on-year values it is better to calculate these rates averaged over a three (or more) years.

Crude rates

Crude rates are the simplest method for comparing cases/deaths whilst accounting for population size. Since, however, cancer is generally more common in the elderly crude rates are greatly influenced by the proportions of older people in the populations being studied. This makes comparisons between two areas (or between two time periods for the same area) with different population profiles potentially misleading. Age-standardised rates are better for this type of comparison.

Crude rates are calculated using a simple formula in which the number of cases/deaths is divided by the corresponding population and multiplied by 100,000.

Age-specific rates

Age-specific rates are generally used to show how incidence/mortality changes with age. These are often reported in five year age groups.

Age-specific rates are calculated using a similar formula to crude rates, but using specific age groups.

Age-standardised rates

Age-standardised rates are the most common way of reporting cancer statistics and are generally used to compare populations to overcome the problems caused by different age profiles in those populations. This is to try to identify real differences in cancer risk between populations. They provide unbiased comparisons of incidence and mortality rates with respect to age (for example, over time, between sexes or between geographical areas). This means that any differences between age-standardised rates are not due to different age profiles and other factors will explain the differences, such as socio-economic deprivation or ethnicity, for example.

There are two methods of age-standardisation in widespread use in cancer statistics – the direct and indirect methods. Cancer Research UK Statistical Information Team calculate all rates using the direct method.

There are two commonly used standard populations, the World standard population9 and the European standard population.10 More weight is given to older people in the European standard population and because cancer is generally more common in older people, this leads to the European age-standardised rates being higher generally than the World age-standardised rates for the same population.

section reviewed 31/10/13
section updated 31/10/13

Survival

Cancer survival is defined as the percentage of people still alive after a specified amount of time (often 1, 5 or 10 years) subsequent to a diagnosis of cancer at a specific time (e.g. 2010-11). Survival percentages are not ‘rates’ because they are not the number of people in a specified population who survive for the specified period. 

Cancer survival statistics are calculated from records collected by a comprehensive network of regional and national cancer registries across the UK.1-5 Individual patients are followed up using national death registration systems to provide information on survival. Generally, if a patient is diagnosed with more than one cancer, only the first malignant neoplasm is included in the survival calculation. Groups of patients are often excluded from the analysis, for example, due to incomplete or conflicting information.

Two types of cancer survival are often reported in cancer epidemiology: ‘observed survival’ and ‘relative survival’:

Observed survival

Observed survival is the actual percentage of people diagnosed with cancer who are still alive after a specified amount of time. This means it includes death from any cause.

Observed survival is calculated using a simple formula in which the number of people alive at the end of a specified time period is divided by the total number of people alive at the start.

Observed survival is used when the actual numbers of people surviving are required. It is, however, not generally used because a certain proportion of those people who develop cancer are likely to have died anyway within the time period (because cancer patients are commonly elderly) and this underlying background mortality into account.

Relative survival

Relative survival compares the survival experience of individuals with cancer to those in the general population (ideally it would be to those without cancer, but this baseline is difficult to obtain). It is similar to the probability of survival from cancer without including any other cause of death. It was until recently the most commonly used method of calculating and reporting population-based cancer survival.

Relative survival is generally calculated by dividing the observed survival by the expected survival of a similar group of people from the general population.

There are various underlying statistical methodologies to calculate relative survival,11-13 which can cause discrepancies when comparing rates across publications. 

Expected survival

Expected survival is estimated using actuarial life tables, which are usually stratified by year, sex and age; sometimes the life tables are further stratified (for example, by geographical area and socio-economic status), depending on the analysis. The choice of life table is very important since the underestimation of the expected survival will result in the overestimation of the relative survival and vice versa.12

Net survival

Net survival is an estimate of the probability of survival from the cancer alone. Estimation of net survival has in the past been calculated using a relative survival approach. Direct estimation of net survival has become possible in the last two years as an improvement over indirect estimation using relative survival.1 Survival content on these pages uses the new improved net survival methodology.

Net survival estimates the number of people who survive their cancer rather than calculating the number of people diagnosed with cancer who are still alive. It can be interpreted as the survival of cancer patients after taking into account the background mortality that the patients would have experienced if they had not had cancer. 

Life tables stratified at least by age, sex and calendar time are used to estimate the expected probability of death among the cancer patients if they were only to experience the hazard of death observed in the general population. It is, therefore, good at measuring the impact on cancer specific survival due to improvements such as early diagnosis and treatment but also means that deaths from other causes are not included in the survival figure and it cannot be directly used to calculate the number of people alive after a cancer diagnosis.

Period survival

Survival has traditionally been estimated using cohort survival analysis. This method takes all the people diagnosed in particular calendar year and follows them up for five or ten years and estimates survival from this data. The disadvantage of this method is that we have to wait a long time for the data to be available and treatments may have moved on. Period survival was therefore developed to take account of this perceived lack of timeliness.

Period survival takes the most recent year of follow up and uses the data from the patients diagnosed in different years. It can take into account the information from patients diagnosed more recently than the cohort approach.

Net, relative and period survival can be directly age-standardised in a way similar to incidence and mortality, but using different weighting populations.15

section reviewed 28/04/14
section updated 28/04/14

Prevalence

Cancer prevalence refers to the number (or percentage) of people who are alive on a particular date, having previously been diagnosed with cancer. It provides a ‘snapshot’ of people either living with, or cured from cancer at that particular point in time.

section reviewed 31/10/13
section updated 31/10/13

Confidence intervals

Confidence intervals provide a measure of the reliability of an estimate from the data. The level of confidence is expressed as a percentage. Cancer Research UK Statistical Information Team routinely report 95% lower and upper confidence limits, which give the range in which the statistic in question would fall 19 out of 20 times, were it possible to repeat the analysis.

Confidence intervals can also be used to statistically compare figures between two populations (for example, comparing age-standardised rates or survival rates). If the ranges of the two sets of confidence intervals do not overlap, then there is evidence that the two populations are statistically significantly different. However, if the confidence intervals do overlap, then no conclusion can be drawn since the difference between the two populations could be due purely by chance, and formal significance testing would need to be undertaken.

section reviewed 31/10/13
section updated 31/10/13

Risk

In terms of cancer, risk is the likelihood of developing or dying from cancer, either within a specified period of time (e.g. in the next ten years, up to age 64 or over a lifetime).

The effects that exposure to particular risk factors (e.g. smoking or drinking alcohol) can have on the risk of developing cancer are also reported.

There are many different types of statistical risk but Cancer Research UK Statistical Information Team commonly report "lifetime risk", "relative risk", "odds ratio" and "attributable risk":

Lifetime risk

Cancer lifetime risk is the likelihood of a person developing cancer in their lifetime. This is either taken to be from birth up to a particular age, or within a particular age span (assuming that the person is cancer-free at the start of the age span).

Lifetime risk is calculated using statistical models which apply sex-/age-specific incidence rates to the person-years at risk, derived from actuarial life tables.15 This methodology takes into account the likelihood of dying from causes other than cancer, and also makes allowances for the fact that once a person has been diagnosed with cancer, they are no longer at risk of developing cancer for the first time. This is thought to produce more realistic risk estimates than other methodologies (such as the cumulative risk method).15

Lifetime risk estimates are usually expressed as the odds of developing cancer (‘1 in x’) or as a percentage.

Cancer Stats uses two methods used to calculate lifetime risk; Current Probability and Adjusted for Multiple Primaries (AMP).  

  • The Current Probability method was developed by Goldberg and colleagues in 1956, but only termed Current Probability by Esteve and colleagues in 1994. It was an improvement over older methods because it takes into account the risk of death from causes other than cancer (also known as competing risks).
  • The AMP or Sasieni method was developed by Sasieni and colleagues in 2011. It is similar to the Current Probability method but also takes into account that for some cancers (e.g. breast cancer) it is possible to have more than one diagnosis over the course of a lifetime. Not adjusting for this can overestimate the risk in some cancer types.

Relative risk

Relative risk (RR) is the likelihood of cancer developing in people exposed to a particular risk factor, compared with the likelihood of cancer developing in people not exposed to that risk factor. RR can also mean ‘risk ratio’, this is the same as relative risk. In this context, ‘exposed to’ can also mean ‘born with’ (e.g. a medical condition, or a specific sex), not just exposure to external risk factors. The results of cohort studies and randomised controlled trials (RCTs) are often expressed as RRs, because in these study designs, researchers ‘work forward’ from the risk factor exposure to see if cancer develops in future.

For example: a study shows that the RR of cancer in people exposed to ‘risk factor A’ is 1.95. This means that people exposed to ‘risk factor A’ have a 95% higher risk of developing cancer, compared with people not exposed to ‘risk factor A’.

RRs are calculated by dividing the likelihood of developing cancer for people exposed to a particular risk factor, by the likelihood of developing cancer for people not exposed to this risk factor (Figure 1.1). The best-quality studies also take into account (‘adjust’ or ‘control’ for) exposure to other potential risk factors (‘confounders’); for example adjusting for alcohol use when comparing smokers with non-smokers. Failure to adjust for confounders can result in overestimation or underestimation of the effect of the risk factor being studied.

Figure 1.1: Calculating relative risks

relative risk calc.png

RRs are usually expressed as a number or percentage:

  • RR = 1, or RR = 100%: no difference in cancer risk between people exposed to the risk factor and people not exposed to it
  • RR less than 1, or RR = 0-100%: cancer risk is lower in people exposed to the risk factor compared with people not exposed to it; exposed people are less likely to develop cancer
  • RR greater than 1, or RR = 100%+: cancer risk if higher in people exposed to the risk factor compared with people not exposed to it; exposed people are more likely to develop cancer

Odds ratio

An odds ratio (OR) is the likelihood that people with cancer have been exposed to a particular risk factor, compared with the likelihood that people with cancer have not been exposed to that risk factor. A retrospective study is designed to understand the impact of prior exposure to a risk factor in this way, e.g. comparing between cancer patients. Thought of another way, it is the likelihood that people with cancer have been exposed to a particular risk factor, compared with the likelihood that people without cancer have been exposed to that factor. A case-control study is designed to understand the impact of prior exposure in this way, e.g. comparing between cancer patients and healthy controls. In this context, ‘exposed to’ can also mean ‘born with’ (e.g. a medical condition, or a specific sex), not just exposure to external risk factors.

The results of case-control studies and retrospective studies are often expressed as ORs, because in these study designs researchers ‘work backwards’ from the development of cancer to look for exposure to risk factors in the past. It is important to note that this type of study cannot ascertain causality (i.e. this type of study cannot show that exposure to the risk factor caused cancer to develop), it can only show whether there is an association.

For example: a study shows that the OR of exposure to ‘risk factor B’ in people with cancer is 4.3. This means that people exposed to ‘risk factor B’ have a 330% higher risk of developing cancer, compared with people not exposed to ‘risk factor B’.

ORs are calculated by dividing the likelihood of exposure to a particular risk factor among people with cancer, by the likelihood of no exposure this risk factor among people with cancer; or by dividing the likelihood of exposure to a particular risk factor among people with cancer, by the likelihood of exposure to this risk factor among people without cancer (Figure 1.2). Again, adjustment for potential confounders is important in these calculations.

Figure 1.2: Calculating odds ratios

Odds ratio calc.png

For example: if the odds of developing cancer in people exposed to ‘risk factor B’ is around 5 to 2 and the odds of developing cancer in people not exposed to ‘risk factor B’ is less than 1 to 2, then the odds in the group exposed to ‘risk factor B’ is 4.3 times the odds in the group not exposed to ‘risk factor B’.

ORs are less intuitive to understand than RRs; they are similar to RRs in that both RRs and ORs measure associations between cancer and risk factors; however the two should not be confused because ORs do not provide a direct measure of risk.

ORs are usually expressed as a number:

  • OR = 1: no difference in odds of having cancer between people exposed to the risk factor and people not exposed to it
  • OR less than 1: odds of having cancer are lower in people exposed to the risk factor compared with people not exposed to it; exposure to the risk factor may decrease the odds of developing cancer;
  • OR more than 1: odds of having cancer are higher in people exposed to the risk factor compared with people not exposed to it; exposure to the risk factor may increase the odds of developing cancer.

Attributable risk

Cancer attributable risk is the proportion of cancer cases or deaths in a specific population that is attributable to a particular risk factor. This proportion is also known as the population attributable fraction (PAF).

For example: A study shows that the cancer PAF of ‘risk factor C’ is 0.08. This means that in the population studied, 8% of cancer cases are caused by ’risk factor C’.

Attributable risk is calculated by multiplying the proportion of the population exposed to the risk factor in question (often based on population surveys), by the RR associated with that risk factor (often based on meta-analyses) (Figure 1.3).19

Figure 1.3: Calculating attributable risk/Population attributable fraction

attributable_risk_calc.png

Usually the calculation takes into account a delay (lag) between exposure to the risk factor and cancer diagnosis, e.g. using exposure 10 years ago to calculate PAF for current cancer cases; this is often based on the lag between exposure and cancer diagnosis seen in the study from which the RR is taken.

‘Exposure’ may be defined as any exposure (versus none), or as exposure above/below an optimum level (that level is sometimes defined using Government guidelines).

If a risk factor is known to account for almost all cases of a particular cancer, but prevalence of exposure to that risk factor in the population is not known, then a ‘notional prevalence’ can be calculated. This is done by comparing observed cancer incidence rates in the population overall, with expected cancer incidence rates in an unexposed population.20

Each cancer type may have multiple risk factors, but summing the PAFs for all those risk factors would overestimate the total attributable proportion for that cancer type, because there is overlap between exposure to different factors. PAFs for a cancer type can be combined by applying the ‘risk factor B’ PAF only to the proportion of cases not attributable to ‘risk factor A’, and then applying the ‘risk factor C’ PAF only to the proportion of cases not attributable to ‘risk factor A’ or ‘risk factor B’, and so on until all the risk factors have been combined (risk factors can be added in any order) (Figure 1.4).

Figure 1.4: Combining PAFs

Risk factor A PAF (RFA) = 10%, Risk factor B PAF (RFB) = 5%, Risk factor C PAF (RFC)= 3%

1. Calculate ‘% not attributable to RFA’ (‘not RFA’) 100% - 10% = 90%
2. Apply RFB to ‘not RFA’, to get % of ‘not RFA’ which is attributable to RFB (‘not RFA but RFB’) 5% * 90% = 4.5%
3. Subtract this from ‘not RFA’ to get % not attributable to RFA or RFB (‘not RFA or RFB’) 90% – 4.5% = 85.5%
4. Apply RFC to ‘not RFA or RFB’, to get % of ‘not RFA or RFB’ which is attributable to RFC (‘not RFA or RFB but RFC’) 3% * 85.5% = 2.565%
5. Subtract this from ‘not RFA or RFB’ to get % not attributable to RFA or RFB or RFC (‘not RFA or RFB or RFC’) 85.5% – 2.565% = 82.935%
6. Subtract ‘not RFA or RFB or RFC’ from 100% to get % attributable to RFA or RFB or RFC (‘RFA or RFB or RFC’) 100% – 82.935% = 17.065%


Simply summing would give 18%.

Theoretically all cancer cases attributable to a risk factor could be prevented by removing exposure to that risk factor. However we acknowledge that it is very difficult to completely remove a risk factor at population level, and so the total number of ‘preventable cancer cases’ based on PAFs is a very ambitious target.

PAFs can be expressed as a percentage, a proportion, or an absolute number of cases or deaths.

section reviewed 26/08/14
section updated 26/08/14

Ethnicity

The 2009 joint report by the National Cancer Intelligence Network and Cancer Research UK investigated cancer incidence and survival by major ethnic group, covering patients diagnosed with cancer in England between 2002 and 2006. In this analysis, ethnicity was self-reported for 76% of patients, largely at the time of admission as an inpatient of day case; 24% of patients had no ethnic group recorded. Because the ethnicity of some patients was unknown three different methods were used to account for this: firstly assuming that cases with an unknown ethnic group had the same distribution as those whose ethnicity was known, secondly that all cases with unknown ethnicity were from the White ethnic group, and thirdly that all unknown cases were from the non-White ethnic group (to create two possible extremes). Age-standardised rates are presented in ranges, where the lower and upper bounds are the lowest and highest confidence intervals (respectively) identified using each of the three methods. Full details can be found in the report. 

section reviewed 16/04/14
section updated 16/04/14

Data quality worldwide

Cancer registration has a long history in many countries of the world, particularly in the more affluent regions such as the UK, but nearly 80% of the world’s populations live in regions that are not covered by such systems. Data for deaths is better, but still only covers around 65% of the world. The International Agency for Research on Cancer (IARC) routinely uses the available data to estimate worldwide cancer statistics. However, data quality varies greatly by region, and this should be considered when interpreting these estimated statistics.

section reviewed 11/02/14
section updated 11/02/14

Calculating gaps

Gaps are calculated to show the differences between two points. It is simply the arithmetic difference between two specified values. It is commonly used to show a gap between categories. For example, a deprivation gap is the difference between the least and most deprived group.

section reviewed 28/04/14
section updated 28/04/14

Calculating excess cases or deaths

Excess cases or deaths are calculated to show the difference in the number of cases/deaths if there were no differences in the rates between categories. The rates for one category (e.g. least deprived) is applied to the underlying population in the other categories in the group (e.g. most deprived) and the expected number of cases/deaths are calculated. This figure is subtracted from the actual number of cases/deaths in that category resulting in either excess or negative cases/deaths. 

section reviewed 28/04/14
section updated 28/04/14

New children smokers

The number of new children smokers was calculated by the Statistical Information Team by comparing smoking rates of 'current smokers' at each age from the Smoking, Drinking and Drug Use among Young People in England reports with the smoking rates of the same cohort in the year before.23 So, 13 year-olds in 2012 where compared with 12 year-olds in 2011. 'Current smokers' include both regular smokers (one or more cigarettes per week) and occasional smokers (less than one cigarette per week).

If, from a thousand aged 12 in 2011, 10 smoked regularly, 20 smoked occasionally and 20 used to smoke, and from a thousand children aged 13 in 2012, 30 smoked regularly, 20 smoked occasionally and 40 used to smoke, there are clearly an additional 20 smokers in 2012 than 2011 (current smokers have increased from 30 to 50). But, in addition, 20 of the 12 year-old smokers in 2011 have given up. To take account of these children that used to smoke, an equivalent number of children much also have started smoker (or else the 20 smokers giving up and starting would cancel each other out), so there are actually 40 new children smoking.

This calculation was repeated for all the other age groups to give the number of new children aged 11-15 who started smoking in the UK from 2001 to 2011.

section reviewed 22/03/13
section updated 22/03/13

Why there can be more deaths than cases

Mortality may be higher than case numbers because of the way death certification and cancer registration works, and what data is available and when.  

For example, if a patient has died from cancer but the official documenting the death on the certificate can’t confirm what type of cancer caused the death, it may be recorded as a non-specific cancer type (for example Cancer of Unknown Primary (CUP)). However, on receipt of the death certificate the cancer registries may then be able to identify other information about that patients’ history and determine what type of cancer it was, and update the record regarding their case diagnosis. The data would therefore show a patient recorded as having a known type of cancer for their case data, e.g. being a stomach cancer case in the incidence data, but as a different cancer type in the mortality data, e.g. being a CUP death in the mortality data. This inconsistency will remain because the death certificate cannot be changed and the effect is potentially inflated mortality statistics for non-specific cancers like CUP at the detriment of the mortality statistics for specific cancers.

section reviewed 20/09/13
section updated 14/06/13

Data sources and timings – why are these the latest stats?

Incidence statistics presented on our Cancer Statistics website are compiled from data produced by the regional cancer registries in England, and the three national registries in Wales, Scotland and Northern Ireland which means that before we can publish UK statistics we have to wait until all of the data has been published by each country. The process of registering a cancer is complex and there are a number of processes in place to ensure the data is of a high quality but this means there is usually a delay of around 12-18 months before the data is complete enough for them to be published.

Mortality statistics are derived from the statutory death registrations in the countries of the UK. As it is a legal requirement to register deaths quickly, the mortality data for the UK can be compiled more quickly, but there is still a delay of around 12 months before we are able to publish the data.

The survival data we mainly use are calculated using the official data from the Office for National Statistics for England and/or England and Wales, and are produced approximately on an annual basis.

Worldwide and European incidence and mortality statistics are calculated every 4-6 years by the International Agency for Research on Cancer. Collecting cancer data from around the world takes some time, and like UK statistics, processes are implemented to ensure the data are as complete as possible. This means additional delays before the worldwide and European data are published.

Full references to the data are at the foot of each web page.

To notify our users when the data on our web pages have been updated, and to highlight recent press stories, and our latest publications, we have an e-newsletter that we send out around 6 times a year. You can sign up to the newsletter to receive this information. The latest Cancer Stats publications including the latest Cancer Stats Newsletter are available on the Latest Reports page and in our Publications section.

section reviewed 11/02/14
section updated 11/02/14

Differences in published incidence data

Cancer registrations are usually completed within two years of diagnosis but, in a small proportion of cases, the process can take longer. Consequently the cancer registration databases held in the UK, and by the Office for National Statistics (ONS), are continually being updated.

Cancer Research UK uses an extract of data from ONS as the source of the data for England to collate the UK figures for cancer incidence.

Other extracts from ONS, or from alternative sources, will have been taken at different times and, thus, will have minor differences between them and as registrations are updated and revised any resulting presentation of data may differ slightly from other published data relating to the same time period. Although the revisions are good for the quality of data such differences are nearly always trivial in terms of numbers.

section reviewed 20/09/13
section updated 20/09/13

Differences in published mortality data

There are two ways of counting mortality statistics; through death registrations or through death occurrences. Mortality statistics we present are death registrations. This means that all deaths from cancer registered in a particular year are included. Occasionally a death may occur at the end of one year but be registered in another year, for example, a death occurring on December 31st 2012 will probably be registered in January 2013. Mortality statistics published by the Office for National Statistics are occurrences of deaths rather than registrations. Therefore there may be some small discrepancies with those published on this web site.

section reviewed 20/09/13
section updated 20/09/13

We focus mainly on exposures classified by the International Agency for Research on Cancer (IARC) and/or the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) as being causally linked with the cancer type. IARC and WCRF/AICR evaluations are internationally-recognised, and are considered the gold standard in cancer epidemiology.

IARC and WCRF/AICR base their classifications on reviews of all the available evidence, taking into account the amount, quality and consistency of evidence. Exposures with the strongest evidence are classified as sufficient/Group 1 (IARC) or convincing (WCRF/AICR), and we use these factors in our key statistics. Exposures classified as having weaker evidence are also covered in the in-depth risk factors content.

IARC evaluates evidence on the carcinogenic risk to humans of a number of exposures including tobacco, alcohol, infections, radiation (ionising and ultraviolet), occupational exposures, and medications (including exogenous hormones). WCRF evaluates evidence for other exposures including diet, overweight and obesity, and physical exercise.

Where possible Meta-analyses and systematic reviews are cited where available, as they provide the best overview of all available research and most take study quality into account. Individual case-control and cohort studies are reported where such aggregated data are lacking.

Differences in downloads and online

Our printed reports and publications are collations of the information we hold at a particular point in time, and they are all dated and referenced so it’s clear when all of the information was up to that date and what period it’s for. There will be occasions where we will have updated the information on the website since we have issued a report or publication and this will mean that the content on the web pages and that in the reports will be different as we work through updating everything. If there is a difference, the web pages will be the more up to date content.

section reviewed 20/09/13
section updated 20/09/13

 

Cancer Stats and health professionals

The information on these pages of the Cancer Research UK website was originally adapted from the charity's successful series of ‘CancerStats’ Reports, which  were designed for use by health professionals. Since then the web pages and publications have expanded but we still write for people who already have a fairly good knowledge of cancer and/or statistics.

The information on our pages and publications are designed to provide a detailed summary of the data and epidemiological evidence for health professionals but we do have a series of Key Stats pages and publications which are less complicated and for all audiences.

Many of the terms used on our page and publications can be found in our glossary and information and explanations on terminology used for statistics and reporting of cancer, and the methods used to calculate some of the statistics are also available.

Of course, our information can be viewed by anyone who is interested, but other areas of Cancer Research UK's web site might be more relevant and useful. Reliable, easy to understand information for patients/families is provided by CancerHelp, there is prevention and lifestyle information for everyone, or you might be interested in our research on cancer.

section reviewed 20/09/13
section updated 20/09/13

How to reference Cancer Stats content

When Cancer Research UK material is used, we encourage a donation to our life-saving research. Our research is entirely funded by the public, so please donate today and together we can bring forward the day when all cancers are cured.

If you would like to reference us as a primary source then please use the following:

Cancer Research UK, followed by the web address in full of the page you want to use, and then the month and year you accessed the site.
So, for example, if you were referencing some data on the incidence of skin cancer which you read on the 1st of June 2014 the reference would be: Cancer Research UK, http://www.cancerresearchuk.org/cancer-info/cancerstats/types/skin/incidence, June 2014.

Alternatively all the content on these pages are fully referenced with the primary sources from which we have collated it.

If you would like to reference the published paper copy of a Cancer Stats report or any of our other publications, (which are all free and downloadable from the publications site) we suggest this format:

Cancer Research UK (year of publication), Cancer Stats report - Name of report, Cancer Research UK. So, for example, if you were referencing our July 2013 Skin cancer report the reference would be: Cancer Research UK (2013). Cancer Stats report - Skin Cancer, Cancer Research UK.

section reviewed 20/09/13
section updated 20/09/13

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References

  1. United Kingdom and Ireland Association of Cancer Registries (UKIACR). 2011.
  2. Office for National Statistics. Cancer Statistics Registrations Series MB1, England: London: Office for National Statistics. 2011.
  3. Welsh Cancer Intelligence Surveillance Unit, Public Health Wales. Cancer Incidence in Wales. 2011.
  4. Information Services Division, NHS National Services Scotland. SD Cancer Information Programme. 2011.
  5. Northern Ireland Cancer Registry, Queen's University, Belfast. Online Statistics. 2011.
  6. Office for National Statistics. Mortality statistics: Deaths registered in England and Wales (Series DR). London: Office for National Statistics. 2011.
  7. General Register Office for Scotland. Vital Events Reference Tables. 2011.
  8. Northern Ireland Statistics and Research Agency. Registrar General Annual Reports. 2011.
  9. Doll R, Payne P, Waterhouse JAH, ed. Cancer Incidence in Five Continents, Vol. I: UICC: Switzerland: Springer-Verlag; 1966.
  10. Waterhouse J, Muir, C, Correa, P, Powell, J, ed. Cancer incidence in five continents, Vol. III: IARC Scientific Publications No. 15: Lyon: IARC; 1976.
  11. Parkin DM, Hakulinen T. Analysis of Survival: Ch. 12: 159-76. In: Cancer Registration: Principles and Methods: IARC Scientific Publications No. 132: Lyon: IARC; 1991.
  12. Coleman MP, Babb PJ, Damiecki P, et al. Cancer survival trends in England and Wales, 1971–1995: deprivation and NHS region: Studies in Medical and Population Subjects No.61. London: The Stationery Office; 1999.
  13. Brenner H, Gefeller O. Deriving more up-to-date estimates of long-term patient survival. J Clin Epidemiol 1997;50:211-216.
  14. Perme MP, Stare J, Esteve J. On estimation in relative survival. Biometrics 2012; 68: 113–20.
  15. Corazziari I, Quinn M, Capocaccia R. Standard cancer patient population for age standardising survival ratios. Eur J Cancer 2004;40:2307-16.
  16. Schouten LJ, Straatman H, Kiemeney LA, et al. Cancer incidence: life table risk versus cumulative risk. J Epidemiol Community Health 1994;48:596-600.
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Updated: 26 August 2014