1. Is the rate of biological ageing, as measured by age at diagnosis of cancer, socio-economically patterned? Dr. Jean Adams School of Population and Health Sciences University of Newcastle upon Tyne

2. Hypothesised causal pathway SEP social stress Psycho-social stress Health related behaviours Environmental risks and hazards Rate of biological ageing Health

3. Biological ageing Progressive decrease in ability to meet physiological demands Progressive decrease in ability to meet physiological demands Due to accumulation of cellular damage Due to accumulation of cellular damage Balance between damage and repair Balance between damage and repair Some factors causing damage socio- economically patterned Some factors causing damage socio- economically patterned Many factors causing damage also associated with disease Many factors causing damage also associated with disease

4. Measuring biological ageing Cancers due to mutations in genes that control cell growth Cancers due to mutations in genes that control cell growth Genetic mutations are one form of cellular damage Genetic mutations are one form of cellular damage Chronological age at development of cancer may be a good comparative marker of rate of biological ageing Chronological age at development of cancer may be a good comparative marker of rate of biological ageing

5. Hypothesis Age at development of cancer is not socio- economically patterned Age at development of cancer is not socio- economically patterned Individuals living in more socio- economically deprived circumstances develop cancer earlier in life Individuals living in more socio- economically deprived circumstances develop cancer earlier in life

6. Methods All individuals registered with NYCRIS 1986-95 inclusive with: All individuals registered with NYCRIS 1986-95 inclusive with: Colorectal cancer (ICD-10 C18, C19, C20) Colorectal cancer (ICD-10 C18, C19, C20) Breast cancer (ICD-10 C50) Breast cancer (ICD-10 C50) Prostate cancer (ICD-10 C61) Prostate cancer (ICD-10 C61) Lung cancer (ICD-10 C33, C34) Lung cancer (ICD-10 C33, C34) Age at diagnosis=date at incidence-date of birth Age at diagnosis=date at incidence-date of birth SEP=Townsend Deprivation Score of enumeration district (1991 census) SEP=Townsend Deprivation Score of enumeration district (1991 census)

7. Exclusions Key data missing Key data missing Death certification only registration Death certification only registration Second primary Second primary Men with breast cancer Men with breast cancer Youngest 25% from each group Youngest 25% from each group 144 627 registrations 144 627 registrations 39 301 (27.2%) met one or more exclusion criteria 39 301 (27.2%) met one or more exclusion criteria 105 326 included in analysis 105 326 included in analysis

8. Results - descriptive GroupN Median age Median TDS Prostate 12 828 77.63-0.35 Breast 25 697 67.84-0.50 Color. men 12 656 78.050.03 Color. women 13 538 73.670.06 Lung men 14 165 72.461.28 Lung women 26 442 72.981.09

9. Results - analytical Groupcoefficient p- value Change in age/TDS IQR* Prostate-0.073<0.001 -0.36 years Breast0.149<0.001 0.72 years Colo. men -0.0420.039 -0.21 years Colo. Women -0.0630.001 -0.33 years Lung men -0.214<0.001 -1.07 years Lung women -0.161<0.001 -0.82 years *change in age at diagnosis of cancer across IQR of TDS from most affluent to most deprived quintile

10. Comment Age at diagnosis not necessarily a good proxy age at development of cancer Age at diagnosis not necessarily a good proxy age at development of cancer SE variations in diagnostic delay? SE variations in diagnostic delay? Controlling for stage/grade at diagnosis has no effect on results Controlling for stage/grade at diagnosis has no effect on results Age at diagnosis of cancer may be poor proxy for rate of biological ageing Age at diagnosis of cancer may be poor proxy for rate of biological ageing Small effect size throughout Small effect size throughout Cancer not necessarily homogenous across age and SEP or by ICD category Cancer not necessarily homogenous across age and SEP or by ICD category

11. Conclusions Tested a complex model of aetiology using routine data from cancer registry Tested a complex model of aetiology using routine data from cancer registry Those from more deprived areas tend to develop prostate, colorectal and lung cancer earlier in life Those from more deprived areas tend to develop prostate, colorectal and lung cancer earlier in life Those from more deprived areas tend to develop breast cancer later in life Those from more deprived areas tend to develop breast cancer later in life ?due to breast cancer screening programme ?due to breast cancer screening programme Rate of biological ageing may be socio- economically patterned Rate of biological ageing may be socio- economically patterned

12. Acknowledgements Co-authors Co-authors Dr Martin White (Newcastle University) Dr Martin White (Newcastle University) Prof. David Forman (NYCRIS & Leeds University) Prof. David Forman (NYCRIS & Leeds University) Funding Funding Faculty of Public Health/BUPA Joint Research Fellowship (2001-04) Faculty of Public Health/BUPA Joint Research Fellowship (2001-04)