Correction for under-ascertainment in cancer cases in the very elderly (aged 75+): external reference method.
OBJECTIVE
Any fall-off in the age-specific cancer incidence curve in oldest age groups may be suggestive of under-ascertainment. This article introduces a correction method for under-ascertainment of cancer cases in the very elderly (aged 75+).


METHODS
Corrected age-specific rates for a certain cancer are calculated by summing the age-specific rates of that cancer for two lower age groups multiplied by a corresponding coefficient from an external reference cancer registry (sex-, site-, and age-specific coefficients from the Finnish Cancer Registry, a nation-wide population-based registry with high data validity). In order to examine this new method, Lithuanian Cancer Registry data for the period 1993-97 was chosen as an example.


RESULTS
Age-specific incidence curves of cancers in both sexes and most of cancer sites in Lithuania demonstrated the presence of under-ascertainment in the oldest age groups (over 75). Comparison of the corrected incidence rates for 1995 with the original ones showed that without correction for under-ascertainment, crude rates (and consequently number of new cases) were underestimated by around 10%. Underestimation for the year 2050 in Lithuania was approximately 20%. Depending on cancer site and sex, the percentage of underestimation varied substantially.


CONCLUSIONS
In many cancer registries, after correction for under-ascertainment in elderly age groups, the estimates of number of new cases and incidence rates increase. With respect to prepare future projections of incidence, when the effects of an aging population need to be taken into account, accurate age-specific rates in the upper age groups are important. Without correction, cancer occurrence measures may be substantially underestimated, which may lead to inadequate resource allocations for cancer control.
Fallah
Mahdi
M
Tampere School of Public Health, University of Tampere, 33014, Tampere, Finland. Mahd.Fallah@gmail.com
Kharazmi
Elham
E
D017677:Age Distribution; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015982:Bias; D005260:Female; D005387:Finland; D006801:Humans; D015994:Incidence; D008097:Lithuania / Q000453:epidemiology; D008297:Male; D009369:Neoplasms / Q000453:epidemiology* / Q000517:prevention & control; D012015:Reference Standards; D012042:Registries / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity