br Taking advantage of data with years of
Taking advantage of data with 23 years of follow-up, the excess hazard of patients diagnosed since 23 years or more was assumed to remain asymptotically constant at the value observed around 2010 and estimated by moving averages. Other methods can be used for extrapolating survival beyond the available follow-up time. Hakama et al.  assumed excess mortality to reach zero (statistical cure) or to stabilise to a constant. Andersson et al.  used a flexible parametric model and Fang et al.  used a semi-parametric distribution for survival. Nonetheless, a non-parametric estimation method was preferred as it is simpler and free from model specifications and other parametric assumptions. By prioritising the use of information from the latest follow-up years, the Cycloheximide approach provides more reliable predictions than the cohort method, which does not provide sufficient follow-up for more recently diagnosed patients. Despite these advantages, the LE estimates of patients diagnosed before 2011 can change in future scenarios, as the prognosis of many cancers is ever improving . Unfortunately, in this database, the information on cancer stage, cancer treatment, lifestyle, and socio-economic status was not available, although it also plays an important role in determining cancer patients’ LE .
A limitation was related to the representativeness of the present results at the national level, as the long-established cancer registries contributing to this study covered only 10% of Italy. Variability of LE across regions cannot be excluded, although the cancer registries were well distributed across all Italian areas . The generalisation of the results herein presented to other countries requires caution albeit the Italian survival levels were similar to those of most central and southern European countries .
For cancer patients, the consideration of quality of life is also very important, even more so than the length of life itself , but unfortunately this indicator could not be retrieved from population-based cancer registries.
Survivorship care is an important research topic ; country-specific detailed estimates and projections of the numbers of persons living after different cancer diagnoses , cancer cure , time to cure , and “real-word” estimates of the impact of cancer on specific populations are particularly relevant to policy makers. Changes in LE during the course of the disease can provide a different and complementary point of view in investigating cancer cures with respect to the RS-based criteria, providing helpful information of the lifetime impact of a cancer diagnosis.
Providing quantitative data is essential to better define clinical follow-up, plan health care resources allocation, and optimal long-term cancer surveillance. The longer the time since diagnosis, the higher the impact of other factors, in addition to the tumour itself, on cancer survivors’ duration (and quality) of life. These “real-world” indicators are easily understandable, and therefore, Streptolydigins become useful measures to be adopted in the clinician-patient communication, especially after many years since diagnosis.
This study was funded by the Italian Association for Cancer Research (AIRC) (grant no. 21879). The authors thank Luigina Mei for editorial assistance.
Role of funding source
The funding sources had no role in the study design, collection, analysis, or interpretation of the data, the writing of the report, or the decision to submit the article for publication.
Ethical approval and consent to participate
1. Chang CL. The Life Table and its applications. Malabar (FL), USA: R.E. Krieger Publishing Company; 1984.
2. Keyfitz N, Caswell H. Applied Mathematical Demography. New York, USA: Springer-Verlag; 2005.
3. Baade PD, Youlden DR, Andersson TM, Youl PH, Kimlin MG, Aitken JF, et al. Estimating the change in life expectancy after a diagnosis of cancer among the Australian population. BMJ Open. 2015; 5(4):e006740.
4. Licher S, Heshmatollah A, van der Willik KD, Stricker BHC, Ruiter R, de Roos EW, et al. Lifetime risk and multimorbidity of non-communicable diseases and disease-free life expectancy in the general population: A population-based cohort study. PLoS Med. 2019 Feb;16(2):e1002741.
5. Andersson TM, Dickman PW, Eloranta S, Lambe M, Lambert PC. Estimating the loss in expectation of life due to cancer using flexible parametric survival models. Stat Med 2013; 32: 5286-5300.
6. Andersson TM, Dickman PW, Eloranta S, Sjövall A, Lambe M, Lambert PC. The loss in expectation of life after colon cancer: a population-based study. BMC Cancer 2015; 15: 412.