scholarly journals Investigating changes over time in socioeconomic gaps in cancer survival: using differences in relative survival versus differences in excess mortality rates can give different answers

2012 ◽  
Vol 23 (1) ◽  
pp. 278-279 ◽  
Author(s):  
T. Blakely ◽  
M. Soeberg ◽  
D. Sarfati
Keyword(s):  
Excess Mortality ◽  
Cancer Survival ◽  
Relative Survival ◽  
Mortality Rates ◽  
Socioeconomic Gaps ◽  
Changes Over Time ◽  
Over Time

scholarly journals Reference-adjusted and standardized all-cause and crude probabilities as an alternative to net survival in population-based cancer studies

2020 ◽  
Vol 49 (5) ◽  
pp. 1614-1623
Author(s):  
Paul C Lambert ◽  
Therese M-L Andersson ◽  
Mark J Rutherford ◽  
Tor Åge Myklebust ◽  
Bjørn Møller
Keyword(s):  
Excess Mortality ◽  
Cancer Survival ◽  
Age Distribution ◽  
Relative Survival ◽  
Reference Population ◽  
Mortality Rates ◽  
Population Based ◽  
Population Groups ◽  
Net Survival ◽  
Common Reference

Abstract Background In population-based cancer survival studies, the most common measure to compare population groups is age-standardized marginal relative survival, which under assumptions can be interpreted as marginal net survival; the probability of surviving if it was not possible to die of causes other than the cancer under study (if the age distribution was that of a common reference population). The hypothetical nature of this definition has led to confusion and incorrect interpretation. For any measure to be fair in terms of comparing cancer survival, then differences between population groups should depend only on differences in excess mortality rates due to the cancer and not differences in other-cause mortality rates or differences in the age distribution. Methods We propose using crude probabilities of death and all-cause survival which incorporate reference expected mortality rates. This makes it possible to obtain marginal crude probabilities and all-cause probability of death that only differ between population groups due to excess mortality rate differences. Choices have to be made regarding what reference mortality rates to use and what age distribution to standardize to. Results We illustrate the method and some potential choices using data from England for men diagnosed with melanoma. Various marginal measures are presented and compared. Conclusions The new measures help enhance understanding of cancer survival and are a complement to the more commonly used measures.

scholarly journals Patterns of Survival Among Patients With Myeloproliferative Neoplasms Diagnosed in Sweden From 1973 to 2008: A Population-Based Study

2012 ◽  
Vol 30 (24) ◽  
pp. 2995-3001 ◽  
Author(s):  
Malin Hultcrantz ◽  
Sigurdur Yngvi Kristinsson ◽  
Therese M.-L. Andersson ◽  
Ola Landgren ◽  
Sandra Eloranta ◽  
...  
Keyword(s):  
General Population ◽  
Life Expectancy ◽  
Excess Mortality ◽  
Myeloproliferative Neoplasms ◽  
Treatment Options ◽  
Large Population ◽  
Relative Survival ◽  
Population Based ◽  
Population Based Study ◽  
Over Time

PurposeReported survival in patients with myeloproliferative neoplasms (MPNs) shows great variation. Patients with primary myelofibrosis (PMF) have substantially reduced life expectancy, whereas patients with polycythemia vera (PV) and essential thrombocythemia (ET) have moderately reduced survival in most, but not all, studies. We conducted a large population-based study to establish patterns of survival in more than 9,000 patients with MPNs.Patients and MethodsWe identified 9,384 patients with MPNs (from the Swedish Cancer Register) diagnosed from 1973 to 2008 (divided into four calendar periods) with follow-up to 2009. Relative survival ratios (RSRs) and excess mortality rate ratios were computed as measures of survival.ResultsPatient survival was considerably lower in all MPN subtypes compared with expected survival in the general population, reflected in 10-year RSRs of 0.64 (95% CI, 0.62 to 0.67) in patients with PV, 0.68 (95% CI, 0.64 to 0.71) in those with ET, and 0.21 (95% CI, 0.18 to 0.25) in those with PMF. Excess mortality was observed in patients with any MPN subtype during all four calendar periods (P < .001). Survival improved significantly over time (P < .001); however, the improvement was less pronounced after the year 2000 and was confined to patients with PV and ET.ConclusionWe found patients with any MPN subtype to have significantly reduced life expectancy compared with the general population. The improvement over time is most likely explained by better overall clinical management of patients with MPN. The decreased life expectancy even in the most recent calendar period emphasizes the need for new treatment options for these patients.

scholarly journals Correcting inaccurate background mortality in excess hazard models through breakpoints

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Robert Darlin Mba ◽  
◽  
Juste Aristide Goungounga ◽  
Nathalie Grafféo ◽  
Roch Giorgi
Keyword(s):  
Excess Mortality ◽  
Cancer Survival ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Relative Survival ◽  
Population Based ◽  
Deprivation Level ◽  
Parameter Estimates ◽  
Background Mortality ◽  
Proposed Model

Abstract Background Methods for estimating relative survival are widely used in population-based cancer survival studies. These methods are based on splitting the observed (the overall) mortality into excess mortality (due to cancer) and background mortality (due to other causes, as expected in the general population). The latter is derived from life tables usually stratified by age, sex, and calendar year but not by other covariates (such as the deprivation level or the socioeconomic status) which may lack though they would influence background mortality. The absence of these covariates leads to inaccurate background mortality, thus to biases in estimating the excess mortality. These biases may be avoided by adjusting the background mortality for these covariates whenever available. Methods In this work, we propose a regression model of excess mortality that corrects for potentially inaccurate background mortality by introducing age-dependent multiplicative parameters through breakpoints, which gives some flexibility. The performance of this model was first assessed with a single and two breakpoints in an intensive simulation study, then the method was applied to French population-based data on colorectal cancer. Results The proposed model proved to be interesting in the simulations and the applications to real data; it limited the bias in parameter estimates of the excess mortality in several scenarios and improved the results and the generalizability of Touraine’s proportional hazards model. Conclusion Finally, the proposed model is a good approach to correct reliably inaccurate background mortality by introducing multiplicative parameters that depend on age and on an additional variable through breakpoints.

Ischemic Stroke with Troponin Elevation: Patient Characteristics, Resource Utilization, and In-Hospital Outcomes

2016 ◽  
Vol 42 (3-4) ◽  
pp. 213-223 ◽  
Author(s):  
Krishi Peddada ◽  
Salvador Cruz-Flores ◽  
Larry B. Goldstein ◽  
Eliahu Feen ◽  
Kevin F. Kennedy ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Hospital Mortality ◽  
Clinical Characteristics ◽  
Causes Of Death ◽  
Mortality Rates ◽  
Hospital Death ◽  
Stroke Patients ◽  
Troponin Elevation ◽  
Changes Over Time ◽  
Over Time

Background: Among patients hospitalized for acute ischemic stroke, abnormal serum troponins are associated with higher risk of short-term mortality. However, most findings have been reported from European hospitals. Whether troponin elevation after stroke is independently associated with death among a more heterogeneous US population remains unclear. Furthermore, only a few studies have evaluated the association between the magnitude of troponin elevation and subsequent mortality, patterns of dynamic troponin changes over time, or whether troponin elevation is related to specific causes of death. Methods: Using data collected in the American Heart Association's ‘Get With The Guidelines' stroke registry between 2008 and 2012 at a tertiary care US hospital, we used logistic regression to evaluate the independent relationship between troponin elevation and mortality after adjusting for demographic and clinical characteristics. We then assessed whether the magnitude of troponin elevation was related to in-hospital mortality by calculating mortality rates according to tertiles of peak troponin levels. Dynamic troponin changes over time were evaluated as well. To better understand whether troponin elevation identified patients most likely to die due to a specific cause of death, investigators blinded from troponin values reviewed all in-hospital deaths, and the association between troponin elevation and mortality was evaluated among patients with cardiac, neurologic, or other causes of death. Results: Of 1,145 ischemic stroke patients, 199 (17%) had elevated troponin levels. Troponin-positive patients had more cardiovascular risk factors, more intensive medical therapy, and greater use of cardiac procedures. These individuals had higher in-hospital mortality rates than troponin-negative patients (27 vs. 8%, p < 0.001), and this association persisted after adjustment for 13 clinical and management variables (OR 4.28, 95% CI 2.40-7.63). Any troponin elevation was associated with higher mortality, even at very low peak troponin levels (mortality rates 24-29% across tertiles of troponin). Patients with persistently rising troponin levels had fewer anticoagulant and antiatherosclerotic therapies, with markedly worse outcomes. Furthermore, troponin-positive patients had higher rates of all categories of death: neurologic (17 vs. 7%), cardiac (5 vs. <1%), and other causes of death (5 vs. <1%; p < 0.001 for all comparisons). Conclusions: Ischemic stroke patients with abnormal troponin levels are at higher risk of in-hospital death, even after accounting for demographic and clinical characteristics, and any degree of troponin elevation identifies this higher level of risk. Troponins that continue to rise during the hospitalization identify stroke patients at markedly higher risk of mortality, and both neurologic and non-neurologically mediated mortality rates are higher when troponin is elevated.

Differences in prostate cancer survival between Denmark, Iceland, and Sweden: A high-resolution study.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 159-159
Author(s):  
Inga Jona Ingimarsdottir
Keyword(s):  
Prostate Cancer ◽  
High Resolution ◽  
Metastatic Disease ◽  
Excess Mortality ◽  
Relative Survival ◽  
Patient Characteristics ◽  
Mortality Rates ◽  
Population Based ◽  
The Difference ◽  
Resolution Study

159 Background: Register-based studies have demonstrated large differences in relative survival and excess mortality following the diagnosis of PC in the Nordic countries. These differences may reflect differences in patient characteristics, diagnostics (PSA-use) and treatment strategies. This high-resolution study explores the background for the differences in PC survival between Denmark, Iceland and Sweden. Methods: Patients with newly diagnosed PC in 1997 were identified through population-based national cancer registers in Denmark and Iceland. Information on clinical findings was retrieved by reviewing hospital files. In Sweden information was gathered from two regional population-based prostate cancer registers providing both time of diagnosis and clinical information. Country specific excess mortality rates were compared adjusting for available information on known prognostic factors. Results: The overall relative survival in the cohorts was comparable to population-based results previously published. Across countries significant differences in excess mortality rates were seen. These differences were largely explained by differences in patient characteristics at diagnosis, and when adjusting for differences in patient characteristics i.e. metastatic / non-metastatic disease, clinical T-stage, and PSA level at diagnosis, the differences in excess mortality diminished or disappeared. The difference in percentage of patients with metastatic disease at diagnosis was the one factor responsible for the major differences in mortality-rates across countries. Conclusions: Register-based studies on relative survival and excess mortality following a PC diagnosis may be influenced by national differences in clinical presentation at diagnosis. Differences between countries in the proportion of patients with metastatic spread at diagnosis apparently explains most of the difference in relative survival previously reported. Further studies and cross country comparisons of survival and excess mortality for PC should adjust for differences in patient characteristics, mainly TNM.

scholarly journals Socioeconomic inequality in cancer survival – changes over time. A population-based study, Denmark, 1987–2013

2019 ◽  
Vol 58 (5) ◽  
pp. 737-744 ◽  
Author(s):  
Susanne Oksbjerg Dalton ◽  
Maja Halgren Olsen ◽  
Christoffer Johansen ◽  
Jørgen H. Olsen ◽  
Kaae Klaus Andersen
Keyword(s):  
Cancer Survival ◽  
Socioeconomic Inequality ◽  
Population Based ◽  
Population Based Study ◽  
Changes Over Time ◽  
Over Time

scholarly journals Conditional Relative Survival of Ovarian Cancer: A Korean National Cancer Registry Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Dong Wook Shin ◽  
Jaeman Bae ◽  
Johyun Ha ◽  
Kyu-Won Jung
Keyword(s):  
Ovarian Cancer ◽  
Cancer Registry ◽  
Excess Mortality ◽  
Social Deprivation ◽  
Relative Survival ◽  
First Year ◽  
Relative Survival Rate ◽  
Probability Of Death ◽  
Central Cancer Registry ◽  
Over Time

ObjectiveConditional relative survival (CRS) rates, which take into account changes in prognosis over time, are useful estimates for survivors and their clinicians as they make medical and personal decisions. We aimed to present the 5-year relative conditional survival probabilities of patients diagnosed with ovarian cancer from 1997–2016.MethodsThis nationwide retrospective cohort study used data from the Korean Central Cancer Registry. Patients diagnosed with ovarian cancer between 1997 and 2016 were included. CRS rates were calculated stratified by age at diagnosis, cancer stage, histology, treatment received, year of diagnosis, and social deprivation index.ResultsThe 5-year relative survival rate at the time of diagnosis was 61.1% for all cases. The probability of surviving an additional 5 years, conditioned on having already survived 1, 2, 3, 4, and 5 years after diagnosis was 65.0, 69.5, 74.6, 79.3, and 83.9%, respectively. Patients with poorer initial survival estimates (older, distant stage, serous histology) generally showed the largest increases in CRS over time. The probability of death was highest in the first year after diagnosis (11.8%), and the conditional probability of death in the 2nd, 3rd, 4th, and 5th years declined to 9.4%, 7.9%, 6.1%, and 5.2%, respectively.ConclusionCRS rates for patients with ovarian cancer increased with each year they survived, but this did not reach the level of ‘no excess mortality’ even 5 years after diagnosis. The largest improvements in CRS were observed in patients with poorer initial prognoses. Our findings provide updated prognosis to ovarian cancer survivors and clinicians.

scholarly journals Correcting inaccurate background mortality in excess hazard models through breakpoints

2020 ◽  
Author(s):  
Robert Darlin Mba ◽  
Juste Aristide Goungounga ◽  
Nathalie Grafféo ◽  
Roch Giorgi
Keyword(s):  
Excess Mortality ◽  
Cancer Survival ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Relative Survival ◽  
Population Based ◽  
Deprivation Level ◽  
Parameter Estimates ◽  
Background Mortality ◽  
Proposed Model

Abstract Background : Methods for estimating relative survival are widely used in population-based cancer survival studies. These methods are based on splitting the observed (the overall) mortality into excess mortality (due to cancer) and background mortality (due to other causes, as expected in the general population). The latter is derived from life tables usually stratified by age, sex, and calendar year but not by other covariates (such as the deprivation level or the socioeconomic status) which may lack though they would influence background mortality. The absence of these covariates leads to inaccurate background mortality, thus to biases in estimating the excess mortality. These biases may be avoided by adjusting the background mortality for these covariates whenever available. Methods : In this work, we propose a regression model of excess mortality that corrects for potentially inaccurate background mortality by introducing age-dependent multiplicative parameters through breakpoints, which gives some flexibility. The performance of this model was first assessed with a single and two breakpoints in an intensive simulation study, then the method was applied to French population-based data on colorectal cancer. Results: The proposed model proved to be interesting in the simulations and the applications to real data; it limited the bias in parameter estimates of the excess mortality in several scenarios and improved the results and the generalizability of Touraine’s proportional hazards model. Conclusion: Finally, the proposed model is a good approach to correct reliably inaccurate background mortality by introducing multiplicative parameters that depend on age and on an additional variable through breakpoints.

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