Cancer Registration Statistics, England, 2024

Counts of cancer incidence are presented for cancers diagnosed during 2024. 

The crude rate is the number of events in a specific population during a time period per 100,000 people. When the events are tumour diagnoses the measure is an incidence rate. A crude rate is calculated using the following equation: 

(total number of events)/(total population) X 100,000 

In this publication and the accompanying data tables, the non-standardised rate refers to the crude rate for all ages combined, whereas the age-specific rate refers to the crude rate for individual age bands. 

Age-standardised cancer incidence rates are presented for males and females. Age and gender-standardised rates are presented for persons. An age-standardised rate is a weighted average of the age-specific rates, where the weights are age-specific proportions of a standardised population, European Standard Population 2013 (ESP). Standardising rates with the ESP accounts for the differing age structure of different populations. This means that geographical and time comparisons of the rates can be made. ONS recommends that Accredited Official Statistics outputs use the 2013 ESP aggregated to 90 and over, due to the availability of population estimates of assured quality for this age band. 

The confidence intervals (CIs) for the ASRs are calculated using the Dobson’s method¹ with Byar’s method². This methodology is recommended by Fingertips following a research study³. The guidance can be found in Public Health Technical Guidance – Overview of standardisation. 

[1] Dobson, A., Kuulasmaa, K., Eberle, E., and Scherer, J. (1991). Confidence intervals for weighted sums of poisson parameters. Statistics in Medicine 10: 457–462.

[2] Breslow, N., and Day, N. (1987). Statistical methods in cancer research, volume II: The design and analysis of cohort studies (World Health Organization).

[3] Morris, J.K., Tan, J., Fryers, P., and Bestwick, J. (2018). Evaluation of stability of directly standardized rates for sparse data using simulation methods. Population Health Metrics 16