Background: Missing data in cancer surveillance records are common; however, little information exists on the types of cases most likely to have missing data, or how missing data influence research or policy. Two clinical elements often missing in surveillance data are histologic grade and stage of disease. Missing data are either not clinically ascertained or not successfully abstracted. Methods: Prostate cancer cases (N=22,217) reported to the Maryland Cancer Registry during 1992-1997 were geocoded by residence and analyzed. Multi-level logistic regression was used to examine case attributes and area-level demographic, economic, and health services characteristics predictive of either missing stage or grade. A scanning statistic was used to explore geographic clustering of high and low rates of missing stage and grade within the state, before and after adjustment for significant variables from multi-level models. Results: Older age, black race, missing grade, and higher county-level median income increased the likelihood of missing stage, whereas more recent year of diagnosis, higher blockgroup-level median income, and county-level rurality decreased the likelihood. Older age, missing or later stage, higher blockgroup-level median income, and more urologists per case in one's county of residence increased the likelihood of missing tumor grade, and more recent year of diagnosis, higher county-level median income, and rurality decreased the likelihood. Adjustment reduced statistically significant clusters of missing stage from six to two, and clusters of missing grade from three to zero. Conclusions: Results suggest systematic influences on missing stage and grade, which could be investigated with case-control follow-back studies.
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health