Search

Overall, women have better cancer-specific survival than men following haematological malignancies. The effect of reproductive factors on prognosis in women remains unknown and population-based studies are needed. A nationwide cohort of 21,237 Swedish women with a recorded haematological malignancy at ages 18–69 years was identified in the Swedish Cancer Register 1970–2018. Pre-diagnosis childbirths for each woman were linked to the Swedish Multigeneration Register. Net survival and excess hazard ratios for parity, age at first birth, time since the latest birth, and sex of offspring were estimated using flexible parametric models adjusted for age, year, and educational level. In unadjusted analyses, parity (p = 0.0012) and high age at first birth (p < 0.0001) were associated with better survival. After co-adjustments for reproductive factors and confounders, the associations were attenuated. The adjusted association with parity was mainly observed among women aged above 40 years at diagnosis (p = 0.0033). The associations with reproductive factors were non-significant across subtypes of haematological malignancy. There was a tendency of higher excess mortality for an increasing number of boys compared to girls, although only significant for women with three or more children (p = 0.0126). Reproductive factors were in part associated with survival following diagnosis of a haematological malignancy. However, the effect sizes were small with inconsistent association patterns, and thus reproductive factors may only partly contribute to the survival advantage of women over men. [ABSTRACT FROM AUTHOR]

Abstract Background Relative survival is commonly used for studying survival of cancer patients as it captures both the direct and indirect contribution of a cancer diagnosis on mortality by comparing the observed survival of the patients to the expected survival in a comparable cancer-free population. However, existing methods do not allow estimation of the impact of isolated conditions (e.g., excess cardiovascular mortality) on the total excess mortality. For this purpose we extend flexible parametric survival models for relative survival, which use restricted cubic splines for the baseline cumulative excess hazard and for any time-dependent effects. Methods In the extended model we partition the excess mortality associated with a diagnosis of cancer through estimating a separate baseline excess hazard function for the outcomes under investigation. This is done by incorporating mutually exclusive background mortality rates, stratified by the underlying causes of death reported in the Swedish population, and by introducing cause of death as a time-dependent effect in the extended model. This approach thereby enables modeling of temporal trends in e.g., excess cardiovascular mortality and remaining cancer excess mortality simultaneously. Furthermore, we illustrate how the results from the proposed model can be used to derive crude probabilities of death due to the component parts, i.e., probabilities estimated in the presence of competing causes of death. Results The method is illustrated with examples where the total excess mortality experienced by patients diagnosed with breast cancer is partitioned into excess cardiovascular mortality and remaining cancer excess mortality. Conclusions The proposed method can be used to simultaneously study disease patterns and temporal trends for various causes of cancer-consequent deaths. Such information should be of interest for patients and clinicians as one way of improving prognosis after cancer is through adapting treatment strategies and follow-up of patients towards reducing the excess mortality caused by side effects of the treatment.

As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population‐level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multistate models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, allowing us to directly quantify the excess risk. This article proposes a combination of these two frameworks, providing one approach to address (1) to (3). Using recently developed methods in multistate modeling, we incorporate estimation of excess hazards into a multistate model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user‐friendly Stata software with accompanying example code. [ABSTRACT FROM AUTHOR]

While Hodgkin lymphoma (HL) survival has improved, treatment‐related complications remain a concern. As a measure of treatment‐related diseases of the circulatory system (DCS) we report excess incidence of DCS and absolute risks among HL patients diagnosed in the modern treatment era. From the Swedish Cancer Register, we identified all HL patients diagnosed 1985 through 2013, at ages 18–80 years. Excess incidence rate ratios (EIRRs) with 95% confidence intervals (CIs) comparing excess DCS incidence between calendar periods were estimated overall, and at 5 and 10 years after diagnosis using flexible parametric models. Model‐based predictions were used to obtain probabilities of being diagnosed with DCS, in the presence of competing risks. During follow‐up, 726 (16%) of the 4,479 HL patients experienced DCS. Overall, the excess DCS incidence was lower during all calendar periods compared to the first (2009–2013 vs. 1985–1988: EIRR = 0.63, 95% CI: 0.42–0.95). The 5‐ and 10‐year excess incidence of DCS decreased between 1985 and 1994 for 25‐year‐olds (5‐year‐EIRR1994 = 0.32, 95% CI: 0.12–0.92) and 60‐year‐olds (5‐year‐EIRR1994 = 0.45, 95% CI: 0.24–0.88), but remained stable thereafter. No improvements were observed among 75‐year‐olds. The probability of excess DCS remained the same throughout the study period. In 2009, the percentage of patients aged 25, 60 and 75 experiencing excess DCS within 5 years was 3.4, 15.0 and 17.0% (males) and 2.3, 10.8 and 12.6% (females). Treatment‐related incidence of DCS has declined since the mid‐1980s, but more recent improvements are absent and an excess risk remains. Continued efforts towards less toxic treatments are warranted, alongside primary prevention strategies. What's new? Treatment for Hodgkin lymphoma saves lives, but also leads to additional, treatment‐related diseases. Here, the authors calculated the excess risk of diseases of the circulatory system (DCS) experienced by HL patients, compared with the risk expected for patients who have not had HL. Looking at patients diagnosed between 1985‐2013, they found that incidence of treatment‐related DCS decreased from 1985 through 1994, but leveled off after that. Patients treated since 2000 have elevated risk of a treatment‐related DCS for up to 10 years. Less toxic treatment, or better prevention strategies, would be worth pursuing. [ABSTRACT FROM AUTHOR]

A useful summary measure for survival data is the expectation of life, which is calculated by obtaining the area under a survival curve. The loss in expectation of life due to a certain type of cancer is the difference between the expectation of life in the general population and the expectation of life among the cancer patients. This measure is used little in practice as its estimation generally requires extrapolation of both the expected and observed survival. A parametric distribution can be used for extrapolation of the observed survival, but it is difficult to find a distribution that captures the underlying shape of the survival function after the end of follow-up. In this paper, we base our extrapolation on relative survival, because it is more stable and reliable. Relative survival is defined as the observed survival divided by the expected survival, and the mortality analogue is excess mortality. Approaches have been suggested for extrapolation of relative survival within life-table data, by assuming that the excess mortality has reached zero (statistical cure) or has stabilized to a constant. We propose the use of flexible parametric survival models for relative survival, which enables estimating the loss in expectation of life on individual level data by making these assumptions or by extrapolating the estimated linear trend at the end of follow-up. We have evaluated the extrapolation from this model using data on four types of cancer, and the results agree well with observed data. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Large age-dependant differences in temporal trends in 1- and 5-year relative survival have been observed in patients with acute myeloid leukaemia (AML) in Sweden. This investigation used an alternative approach to studying patient survival that simultaneously estimated the proportion of patients cured from their cancer and the survival of the ‘uncured’. We conducted a population-based study including 6439 AML patients aged 19–80 years in Sweden between 1973 and 2001. Mixture cure models were estimated, with age at diagnosis categorised (19–40, 41–60, 61–70 and 71–80) and year of diagnosis modelled using splines. In 1975 the cure proportion was ≤6% in all age groups and the median survival time for ‘uncured’ patients was <0·5 years. In 2000 the cure proportion was 68% (95% confidence interval 56–77%) in the youngest group, and 32% (25–39%), 8% (3–21%), and 4% (2–8%) in the other groups, respectively. The median survival times for ‘uncured’ were 0·74 (0·43–1·26), 0·71 (0·53–0·97), 0·69 (0·51–0·95) and 0·37 (0·31–0·44) years, respectively. A dramatic improvement in the cure proportion was seen in younger patients, whereas improvement in older ages was mainly within the survival of the ‘uncured’. This novel approach of analysing survival data could be a valuable tool for physicians, patients, health care planners and health economists. [ABSTRACT FROM AUTHOR]

Objectives: A large proportion of patients with cutaneous malignant melanoma (CMM) do not experience excess mortality due to their disease. This group of patients is referred to as the cure proportion. Few studies have examined the possibility of cure for CMM. The aim of this study was to estimate the cure proportion of patients with CMM in a Swedish population. Methods: We undertook a population-based study of 5850 CMM patients in two Swedish health care regions during 1996-2005. We used flexible parametric cure models to estimate cure proportions and median survival times (MSTs) of uncured by stage, sex, age and anatomical site. Results: Disease stage at diagnosis was the most important factor for the probability of cure, with a cure proportion of approximately 1.0 for stage IA. While the probability of cure decreased with older age, the influence of age was smaller on the MST of uncured. Differences in prognosis between males and females were mainly attributed to differences in cure as opposed to differences in MST of uncured. Conclusions: This population-based study showed approximately 100% cure among stage IA disease. Almost 50% of patients had stage IA disease and the high cure proportion for this large patient group is reassuring. [ABSTRACT FROM AUTHOR]