Search

Background: To gain more insight into the relation between socioeconomic status (SES) and breast cancer risk, we have studied that association, before and after adjustment for traditional risk factors for breast cancer, in a prospective cohort study on lifestyle and cancer that started in 1986 in the Netherlands amongst 62,573 women aged 55-69 years.Methods: At baseline, data on SES, diet, reproductive factors and other covariates were collected by means of a self-administered questionnaire. For data-analysis a case-cohort approach was used. After 3.3 years of follow-up, 471 incident cases were available for analysis.Results: We did not find a higher age-adjusted risk of breast cancer for those with a higher level of education (RR highest/lowest level of education = 0.94, 95% confidence interval [CI]: 0.63-1.39, trend-P = 0.15). Although upper white-collar workers had a slightly higher breast cancer risk than blue-collar workers (RR = 1.16, 95% CI: 0.83-1.62, trend-P = 0.34), women with a profession of higher social standing did not have a higher risk (RR highest/lowest social standing = 0.73, 95% CI: 0.24-2.23, trend-P = 0.86). Additional adjustment for traditional risk factors did not alter the association between SES and breast cancer risk.Conclusions: We did not find an association between SES and breast cancer risk. This is not in agreement with studies conducted in other European countries and North America. [ABSTRACT FROM AUTHOR]

A method has been developed to determine the optimal linkage key for record linkage between the cancer registry and a large-scale prospective cohort study in the Netherlands. The proposed linkage procedure is a two-stage process in which the initial computerized linkage using a particular linkage key is followed by visual inspection with additional information to separate the computer matches into true and false positives. In the determination of the optimal key, both informativeness and susceptibility to error of personal identifiers were taken into account. The performance of the various keys in the linkage was expressed in terms of sensitivity and predictive value of a reported computer match. The key, consisting of date of birth, first four characters of the family name and gender was the optimal choice, with a sensitivity of 98% and an initial predictive value of a computer match of 98%. When additional information on migration, place of birth and first initial was collected in the second stage, it was possible to eliminate the false positives from the reported computer matches without loss of true positives. Thus, the sensitivity remained constant whereas the secondary predictive value of accepted matches was maximized.