Background. The aim of this study was to evaluate the effect on 5‐year survival of patients with invasive breast cancer relative to demographic and clinical variables (age, residence, and disease ...diffusion) and to early diagnostic procedures performed in the area.
Methods. The observed (Kaplan‐Meier method) and relative 5‐year survival in 1263 patients with invasive breast cancer in the Province of Florence, Italy, between 1985–1986 (source: Tuscany Cancer Registry) are presented. The results were compared with those of other European areas and of the Surveillance, Epidemiology, and End Results Program. The Cox model is used to evaluate the effects of age at diagnosis (5‐year age groups), disease diffusion (localized, regional, distant, unspecified), residence (Municipality of Florence, screening area, other municipalities), and source of diagnosis (Center for the Study and Prevention of Cancer, hospitals) on observed survival.
Results. Observed 5‐year survival was 68.4% (Kaplan‐Meier method) and relative 5‐year survival was 75.4%. Relative survival for patients younger than age 35 at diagnosis was high (82.3%); it decreased slowly from 80.5% in the group of patients 35–44 years of age, to 74.0% in those 65–74 years of age, and steeply decreased to 68.1% in those 75 years of age and older. Relative 5‐year survival in Florence was lower only than that observed in Switzerland (Geneva) and in the USA (whites). Five‐year prognosis was worse in women 70 years of age or older, in advanced stages, in residents of municipalities not involved in the screening program, and in cases diagnosed in hospitals. The gain in survival may be explained partially by lead‐time effect and by length bias due to early diagnosis both in self‐referred women and in screening‐detected cases. In these cases, though, the better prognosis, although attenuated, persisted after adjustment by disease diffusion.
Conclusions. The results suggest that the early diagnosis of breast cancer in self‐referred women affects prognosis, at least concerning 5‐year survival. Moreover, although lead‐time effect and length bias cannot be excluded in this study, screening by personal invitation may reduce the disadvantage in the survival of patients with breast cancer often observed in rural areas.
Mortality rates by stage-at-diagnosis are not possible from usual mortality data. Normally, mortality data are based solely on information provided on death certificates, and stage-at-diagnosis is ...not generally reported on these documents. However, mortality rates by stage are possible when one can link diagnostic data with mortality data. Population-based cancer registries collect these types of data routinely in determining the survival of cancers in their registry. Thus, using cancer registry data, mortality rates by stage-at-diagnosis can be calculated. We report stage-specific mortality rates for the Surveillance, Epidemiology, and End Results Program, representing 10% of the cancers in the U.S. for the four sites with the largest mortality rates for females and for males. For an individual site, the stage-specific mortality rates allow one to determine how the different stages are contributing to the trends in all stage mortality. For example, distant disease mortality is the largest contributor to all stage cancer mortality for female and male lung cancer, female colorectal cancer, ovarian cancer, prostatic cancer and male pancreatic cancer. In contrast, regional disease mortality is the largest contributor to all stage cancer mortality for breast cancer and male colorectal cancer. In addition, localized disease mortality is the second largest contributor to all stage mortality for breast cancer and prostate cancer.
