Interpretation of time trends in disease rates in the presence of generation effects.

A comparison is made of indices used for assessing trends in mortality rates over time, and results for four types of cancer are used as examples. The approaches contrasted as measures of change over calendar years of time are the use of crude death rates, standardized death rates (both direct and indirect approaches) and period effects estimated from an age-period-cohort model (including and excluding the cohort factor). The use of these indices is also compared with examination of the original age-specific rates. Similar analyses are made of the equivalent indices for examining changes between different birth cohorts over time. Submodels of the age-period-cohort model are shown to be algebraically and empirically related to age-standardized indices. The use of the full model, as contrasted to standardization methods, can result in important modifications to the interpretation in some cases. However, there are difficulties with this approach, as with all models, if applied uncritically. It is a wise precaution to examine the age-specific rates in each instance to ensure that any summary index is appropriate.

[1]  S. Walter,et al.  The Use of Age-Specific Mean Cohort Slopes in the Analysis of Epidemiological Incidence and Mortality Data , 1976 .

[2]  B. E. Cooper Algorithm AS 4: An Auxiliary Function for Distribution Integrals: Corrigenda , 1968 .

[3]  Lawrence L. Kupper,et al.  Age-period-cohort analysis: an illustration of the problems in assessing interaction in one observation per cell data , 1983 .

[4]  M. Segal,et al.  On a method of mortality analysis incorporating age--year interaction, with application to prostate cancer mortality. , 1982, Biometrics.

[5]  J. Osborn,et al.  A Multiplicative Model for the Analysis of Vital Statistics Rates , 1975 .

[6]  S. Epstein,et al.  Fallacies of lifestyle cancer theories , 1981, Nature.

[7]  B. Armstrong,et al.  An analysis of trends in mortality from malignant melanoma of the skin in Australia , 1980, International journal of cancer.

[8]  P Fraser,et al.  Methods for age-adjustment of rates. , 1983, Statistics in medicine.

[9]  M. Gardner,et al.  Interpretation of disease time trends: is cancer on the increase? A simple cohort technique and its relationship to more advanced models. , 1983, Journal of epidemiology and community health.

[10]  W. O. Kermack,et al.  Death-Rates in Great Britain and Sweden: Expression of Specific Mortality Rates as Products of Two Factors, and some Consequences thereof , 1934, Epidemiology and Infection.

[11]  M J Gardner,et al.  Age, period and cohort models applied to cancer mortality rates. , 1982, Statistics in medicine.

[12]  M. Gardner,et al.  Analysis of trends in cancer mortality in England and Wales during 1951-80 separating changes associated with period of birth and period of death. , 1982, British medical journal.

[13]  M. Segal,et al.  Recent trends in mortality from prostate cancer in male populations of Australia and England and Wales. , 1981, British Journal of Cancer.

[14]  R. Doll,et al.  Trends in tar, nicotine, and carbon monoxide yields of UK cigarettes manufactured since 1934. , 1981, British medical journal.

[15]  G. Kalton,et al.  Standardization: A Technique to Control for Extraneous Variables , 1968 .

[16]  M. C. Sheps,et al.  A Technique for Analyzing Some Factors Affecting the Incidence of Syphilis , 1950 .

[17]  Thomas W. Pullum,et al.  Parametrizing age, period, and cohort effects: an application to United States delinquency rates, 1964-1973 , 1978 .

[18]  S H Moolgavkar,et al.  Temporal trends in breast cancer. , 1982, American journal of epidemiology.

[19]  V. Beral Cancer of the cervix: a sexually transmitted infection? , 1974, Lancet.

[20]  C. Spicer The generation method of analysis applied to mortality from respiratory tuberculosis , 1954, Journal of Hygiene.

[21]  N E Day,et al.  Indirect standardization and multiplicative models for rates, with reference to the age adjustment of cancer incidence and relative frequency data. , 1975, Journal of chronic diseases.

[22]  T R Holford,et al.  The estimation of age, period and cohort effects for vital rates. , 1983, Biometrics.

[23]  J. Cuzick,et al.  International variations and temporal trends in mortality from multiple myeloma , 1983, International journal of cancer.

[24]  N Mantel,et al.  Computation of indirect-adjusted rates in the presence of confounding. , 1968, Biometrics.

[25]  J. Barrett Age, time and cohort factors in mortality from cancer of the cervix , 1973, Journal of Hygiene.