Search

Background: The reported incidence of Philadelphia-negative myeloproliferative neoplasms (MPNs) differs substantially between previous reports, likely due to true regional differences in incidence and/or variations in the quality and coverage of the cancer registers.Objective: We therefore assessed MPN incidence in Sweden during recent years using prospectively collected information captured in Swedish health registers.Methods: Patients with MPNs were identified through the Swedish Cancer Register and Swedish Blood Cancer Register between 2000 and 2014. Information on the Swedish population was obtained from the Human Mortality Database. Crude and age-standardized incidence rates of MPNs with 95% confidence intervals (CIs) were calculated.Results: A total of 6281 MPN cases were reported to the Swedish Cancer Register and Swedish Blood Cancer Register during 2000-2014. The age-standardized, to the Swedish population in 2000, incidence for all MPNs was 4.45 (95% confidence interval [CI] 4.34-4.56)/100 000 person-years. The age-standardized incidence for polycythemia vera was 1.48 (1.42-1.54), for essential thrombocythemia 1.60 (1.53-1.66) and for primary myelofibrosis 0.52 (0.48-0.56)/100 000 person-years, respectively. The incidence rate of MPNs was substantially higher in the older compared to the younger age groups. The incidence increased during the study period, likely to do better reporting and increasing age of the general population.Conclusion: The reported MPN incidences in our study, which were in the higher interval of previously published studies, are likely more accurate compared to previous reports due to the population-based setting and high level of coverage in the Swedish Cancer and Blood Cancer Registers. [ABSTRACT FROM AUTHOR]

Monitoring survival of cancer patients using data collected by population‐based cancer registries is an important component of cancer control. In this setting, patient survival is often summarized using net survival, that is survival from cancer if there were no other possible causes of death. Although net survival is the gold standard for comparing survival between groups or over time, it is less relevant for understanding the anticipated real‐world prognosis of patients. In this review, we explain statistical concepts targeted towards patients, clinicians and healthcare professionals that summarize cancer patient survival under the assumption that other causes of death exist. Specifically, we explain the appropriate use, interpretation and assumptions behind statistical methods for competing risks, loss in life expectancy due to cancer and conditional survival. These concepts are relevant when producing statistics for risk communication between physicians and patients, planning for use of healthcare resources, or other applications when consideration of both cancer outcomes and the competing risks of death is required. To reinforce the concepts, we use Swedish population‐based data of patients diagnosed with cancer of the breast, prostate, colon and chronic myeloid leukaemia. We conclude that when choosing between summary measures of survival it is critical to characterize the purpose of the study and to determine the nature of the hypothesis under investigation. The choice of terminology and style of reporting should be carefully adapted to the target audience and may range from summaries for specialist readers of scientific publications to interactive online tools aimed towards lay persons. [ABSTRACT FROM AUTHOR]

Background: Socioeconomic differences in cancer patient survival are known to exist for women diagnosed with breast cancer. Standard metrics tend not to place great emphasis on evaluating the actual impact of these differences. Methods: We used two alternative, but related, methods of reporting the impact of socioeconomic differences for breast cancer patients in England and Wales. We calculated the average gain in life years for each patient should socioeconomic differences in relative survival be removed and show how this is related to the number of all-cause deaths that could be postponed by removing socioeconomic differences in cancer patient survival. Results: Our results indicate that deprivation differences for women with breast cancer exist and result in women from more deprived areas losing a larger proportion of their life due to a diagnosis of cancer. We also estimate that on average 1.1 years could be gained for a 60 year old breast cancer patient in the most deprived group by improving their relative survival to match the least deprived group. However, our results also show that deprivation differences in general survival have a large impact on life expectancy; showing that over two-thirds of the gap in differential life expectancy is explained by differences in other-cause survival. Conclusion: Socioeconomic differences in relative survival have an impact on life expectancy for patients and result in higher early mortality for more deprived patients. However, differences in general survival across socioeconomic groups explain a larger proportion of the deprivation gap in life expectancy for breast cancer patients. [ABSTRACT FROM AUTHOR]

Background: Cure models can provide improved possibilities for inference if used appropriately, but there is potential for misleading results if care is not taken. In this study, we compared five commonly used approaches for modelling cure in a relative survival framework and provide some practical advice on the use of these approaches. Patients and methods: Data for colon, female breast, and ovarian cancers were used to illustrate these approaches. The proportion cured was estimated for each of these three cancers within each of three age groups. We then graphically assessed the assumption of cure and the model fit, by comparing the predicted relative survival from the cure models to empirical life table estimates. Results: Where both cure and distributional assumptions are appropriate (e.g., for colon or ovarian cancer patients aged <75 years), all five approaches led to similar estimates of the proportion cured. The estimates varied slightly when cure was a reasonable assumption but the distributional assumption was not (e.g., for colon cancer patients <75 years). Greater variability in the estimates was observed when the cure assumption was not supported by the data (breast cancer). Conclusions: If the data suggest cure is not a reasonable assumption then we advise against fitting cure models. In the scenarios where cure was reasonable, we found that flexible parametric cure models performed at least as well, or better, than the other modelling approaches. We recommend that, regardless of the model used, the underlying assumptions for cure and model fit should always be graphically assessed. [ABSTRACT FROM AUTHOR]