Despite continuing advances in cancer treatment, social disparities in cancer survival persist in the United States. Race/ethnicity and socioeconomic status (SES) have been identified as important predictors that independently and jointly shape patterns of cancer survival. Efforts to monitor, reduce, and ultimately eliminate health disparities depend on accurate and unbiased estimates of survival from population cancer registries. Since accurate information on cause of death is not always available in population registries, researchers look at relative survival, calculated as the ratio of the observed survival in a population of cancer cases to the expected survival in a comparable population. An important aspect of this method is the use of life-tables that accurately estimate the expected survival in the source population that gave rise to the cancer cases. In the United States, life-tables used in calculating expected survival are stratified only by age, sex, year of diagnosis, and race/ethnicity, despite ample evidence that SES is an important independent predictor of longevity. This can lead to difl'erential bias in the estimation of relafive survival by race/ethnicity, thereby hampering efforts to understand the joint contribufions of race/ethnicity and SES to cancer survival In this exploratory study, we propose to develop novel life-tables that stratify for SES using geocoding and area-based socioeconomic measures. We will use these life-tables to estimate and model relative survival up to five years for lung cancer cases in Massachusetts, in order to examine the joint contributions of race/ethnicity and SES to lung cancer survival, and to explore how disparifies vary by gender and stage at diagnosis. Our proposed project also introduces and tests innovative model-based approaches esfimating stratum specific mortality rates, with the aim of improving efficiency. Our proposed exploratory study is the first to calculate relafive survival adjusting for SES in the United States, and will contribute to efforts to monitor, investigate, and ulfimately reduce social disparities in cancer survival.
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