Our calculations explained

We have used an age-period-cohort model to calculate these projections. This approach assumes that the probability that someone will get cancer, or die of cancer will depend upon their age, the year that they are born in (period), and which cohort they are in (which depends upon the period). 

For example, a 55-year-old today has a different chance of getting mesothelioma than a 55 year old 30 years ago, as there was much greater exposure to asbestos in the work environment 30 years ago than there is now. 

We fit this model to historical cancer incidence and mortality rates, with parameters for each of these age, period, and cohort effects. The model fitted to the data calculates a trend which is then used to extrapolate the data further into the future. The impact of this trend is reduced over time, as we don’t want to assume that the same trends will continue forever.   

To get the number of cancer cases or deaths from the projected rates, we calculate this as a proportion of the projected population.

Risk factors have been modelled implicitly in this analysis. This means that rather than directly adjusting for, say, smoking rates changing over time, our approach uses the trends seen in the rates of cases and deaths (which are affected by trends in risk factors) to make its projections. 

The same is true for the effects of new and improved treatments over time on mortality rates, and other variables such as changes to early diagnosis. The effects of screening was also implicitly modelled for bowel and cervix cancers, and explicitly modelled for breast and prostate cancers.  

It is not possible to assess the statistical significance of changes between 2014 (observed) and 2035 (projected) figures. Confidence intervals are not calculated for the projected figures. Projections are by their nature uncertain because unexpected events in future could change the trend. It is not sensible to calculate a boundary of uncertainty around these already uncertain point estimates. Changes are described as ‘increase’ or ‘decrease’ if there is any difference between the point estimates.

For full details of the methods used in these projections, see Smittenaar et al (2016).[1]

References

  1. Smittenaar CR, Petersen KA, Stewart K, Moitt N. Cancer Incidence and Mortality Projections in the UK Until 2035. Brit J Cancer 2016.
Last reviewed:

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).[1]

Calculating attributable risk/Population attributable fraction

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.[2]

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).

Combining Population attributable fractions

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%

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

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.

References

  1. Rockhill B, Newman B, Weinburg C. Use and misuse of population attributable fractions. Am J Public Health 1988;88:15-9.
  2. Peto R, Lopez AD, Boreham J, et al. Mortality from tobacco in developed countries: indirect estimation from national vital statistics. Lancet 1992;339(8804):1268-78
Last reviewed:

The impact of improved survival is calculated in three parts:

  • First: the number of patients diagnosed in a particular time period, multiplied by the survival from that same time period – (what has happened?)
  • Second: multiply the same number of patients diagnosed in the time period, by the survival from a previous time period – (what would have happened?)
  • Third: subtract the first number from the second to identify how many more people have survived.

(N of patients diagnosed with cancer in most recent time period (T1) x survival in T1 period)

minus

(N of patients diagnosed with cancer in T1 period x survival in a previous time period (T2))

Last reviewed:

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.

Last reviewed:

The number of new children smokers was calculated 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[1] 'Current smokers' include both regular smokers (one or more cigarettes per week) and occasional smokers (less than one cigarette per week).

For example, if, from a thousand children 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, then we can calculate that there were 20 more smokers in 2012 than 2011 and 20 of the 12 year-old smokers in 2011 have given up. As we know that there are children in each dataset who ‘used to smoke’ we add an equivalent number as an estimate of children who are likely to have started smoking and conclude that there are actually 40 new children smoking.

References

  1. Health & Social Care Information Centre. Smoking, Drinking and Drug Use among Young People in England.
Last reviewed:

A deprivation gradient shows the difference between socio-economic groups on a measure, such as cancer incidence or cancer mortality. They are a calculation which compare the most deprived (poorest) and least deprived (richest) groups to show if there is a real difference between them. They are relevant to health statistics because typically the poorer a person is, the worse their health outcomes.

Last reviewed:

Excess cases or deaths can be calculated to show the difference between what actually is happening and what would happen if there were no differences 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.

Last reviewed:

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

Last reviewed:

Various methods exist in order to estimate the lifetime risk of developing cancer.

The “cumulative risk method" uses the number of cases of cancer (incidence) and the population estimates for each age.[1] Whilst simple to calculate, this method over-estimates the risk of developing cancer during one’s lifetime as it does not take into account that people die from other causes at different ages.

The ”current probability” method (Esteve et al[2]) takes into account that people die from other causes. In addition it uses the number of cases of cancer (incidence), population estimates for each age and data on deaths from all causes (from life tables). This method gives a better estimate of the lifetime risk of cancer than the cumulative risk method produces but it is still an overestimation of the lifetime risk of cancer if the data includes multiple primaries so is best used when these have been excluded from the data.

The “adjusted for multiple primaries (AMP)” method or “Sasieni” method (Sasieni et al[1]) addresses the issues of multiple primary tumours by assuming that the risk of developing a new primary diagnosis of cancer is the same for an individual who has not had a previous diagnosis as it is for an individual who has had a previous diagnosis of cancer, and adjusts the data accordingly.[1] Where possible, we use the AMP method for calculating lifetime risk where subsequent primary tumours are likely (e.g. breast cancer) as this avoids over estimating the lifetime risk of developing cancer.

These methods are usually calculated using a period approach, that is they are based on current incidence and mortality rates and therefore they assume that the current rates (at all ages) will remain constant. It is also possible to calculate lifetime risk using a cohort approach.[3] The cohort method uses either known or projected incidence and mortality rates for each age group as it ages. For example, for a cohort born in 1960 the incidence and mortality rate for 25 year olds will be based on data from 1985, rates for 50 year olds will be based on data from 2010 and rates for 75 year olds will be based on projections for 2035.

Methods of calculating lifetime risk

Data available Cancer sites Recommended method
Population and cancer incidence Any "Cumulative risk"
Population, all cause death, cancer death, cancer incidence (excluding multiple primary tumours) Any "Current probability"
Population, all cause death, cancer death, cancer incidence (including multiple primary tumours) Sites where multiple primary tumours are not likely "Current probability"
Population, all cause death, cancer death, cancer incidence (including multiple primary tumours) All cancers combined, sites where multiple primary tumours are likely "Adjusted for multiple primaries"

Risk terminology explained

Want to create your own calculations? Download our lifetime risk template to calculate the lifetime risk for a UK population using the current probability and adjusted for multiple primary (AMP) methods.

References

  1. Sasieni P, Shelton J, Ormiston-Smith N, et al. What is the lifetime risk of developing cancer?: The effect of adjusting for multiple primaries. Brit J Cancer 2011;105(3):460-5
  2. Esteve J, Benhamou E, Raymond L. Descriptive Epidemiology (IARC Scientific Publications No.128), Lyon, International Agency for Research on Cancer, 1994:67-68
  3. Ahmad AS, Ormiston-Smith N, Sasieni PD. Trends in the lifetime risk of developing cancer in Great Britain: Comparison of risk for those born in 1930 to 1960. Br J Cancer 2015;bjc.2014:606.
Last reviewed:

ORs are calculated by dividing the likelihood of exposure to a particular risk factor among people with cancer, by the likelihood of no exposure of 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.

Calculating odds ratios

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.

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.

Last reviewed:

We use the direct method to calculate rates, as we have age profiles of the populations and this is better than the estimates used by indirect methodology (although this is often used in cancer statistics when age profiles are not known).

Last reviewed:

Relative Risks 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.

Calculating relative risks

 

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

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.

Last reviewed:

We mainly use net survival estimates for our survival data as it estimates the number of people who survive their cancer, taking background mortality in to account. It uses life-times and measures the impact on cancer specific survival due to improvements such as early diagnosis and treatment. It excludes deaths from other causes and it cannot be directly used to calculate the number of people alive after a cancer diagnosis.

Last reviewed:

Local Cancer Statistics

Find and compare local statistics and information in the UK by healthboard, Local Authority or postcode.

Citation

You are welcome to reuse this Cancer Research UK statistics content for your own work.

Credit us as authors by referencing Cancer Research UK as the primary source. Suggested styles are:

Web content: Cancer Research UK, full URL of the page, Accessed [month] [year]. 

Publications: Cancer Research UK ([year of publication]), Name of publication, Cancer Research UK. 

Acknowledgements

We would like to acknowledge the essential work of the cancer registries in the United Kingdom and Ireland Association of Cancer Registries, without which there would be no data.

Rate this page:

Currently rated: 2.7 out of 5 based on 6 votes
Thank you!
We've recently made some changes to the site, tell us what you think

Share this page