Mètodes estadístics aplicats a un registre de càncer de base poblacional. Incidència, mortalitat, supervivència i prevalença del càncer de bufeta urinària a Tarragona. 1982-2002.

L'objectiu principal d'aquest treball es exposar quins són i com es calculen els principals indicadors que mesuren l'impacte del càncer en la societat, així com aplicar aquests indicadors per mesurar l'impacte del càncer i la seva evolució a la demarcació provincial de Tarragona, fent un especial èmfasi en el càncer de bufeta urinària. Els resultats d'aquest estudi inclouen la incidència i mortalitat del període 1998-2002, les tendències temporals de la incidència i la mortalitat en el període 1982-2002, les projeccions de la incidència pels anys 2009 i 2015, la supervivència dels pacients diagnosticats de càncer de bufeta urinària entre 1995 i 1999, l'evolució de la supervivència al llarg del període comprès entre 1985 i l'any 2002 i l'estimació de la prevalença de càncer a data 31 de desembre de 2002. Anàlisi de l'Incidència, la Mortalitat, la Supervivència i la Prevalença

Trends in lung cancer incidence by age, sex and histology from 2012 to 2025 in Catalonia (Spain)

Lung cancer remains one the most common cancers in Europe and ranks frst in terms of cancer mortality in both sexes. Incidence rates vary by region and depend above all on the prevalence of tobacco consumption. In this study we describe recent trends in lung cancer incidence by sex, age and histological type in Catalonia and project changes according to histology by 2025. Bayesian age period-cohort models were used to predict trends in lung cancer incidence according to histological type from 2012 to 2025, using data from the population-based Catalan cancer registries. Data suggest a decrease in the absolute number of new cases in men under the age of 70 years and an increase in women aged 60 years or older. Adenocarcinoma was the most common type in both sexes, while squamous cell carcinoma and small cell carcinoma were decreasing signifcantly among men. In both sexes, the incident cases increased by 16% for patients over 70 years. Increases in adenocarcinoma and rising incidence in elderly patients suggest the need to prioritize strategies based on multidisciplinary teams, which should include geriatric specialist

Cancer incidence estimation from mortality data: a validation study within a population-based cancer registry

Background: Population-based cancer registries are required to calculate cancer incidence in a geographical area, and several methods have been developed to obtain estimations of cancer incidence in areas not covered by a cancer registry. However, an extended analysis of those methods in order to confirm their validity is still needed. Methods: We assessed the validity of one of the most frequently used methods to estimate cancer incidence, on the basis of cancer mortality data and the incidence-to-mortality ratio (IMR), the IMR method. Using the previous 15-year cancer mortality time series, we derived the expected yearly number of cancer cases in the period 2004– 2013 for six cancer sites for each sex. Generalized linear mixed models, including a polynomial function for the year of death and smoothing splines for age, were adjusted. Models were fitted under a Bayesian framework based on Markov chain Monte Carlo methods. The IMR method was applied to five scenarios reflecting different assumptions regarding the behavior of the IMR. We compared incident cases estimated with the IMR method to observed cases diagnosed in 2004–2013 in Granada. A goodness-of-fit (GOF) indicator was formulated to determine the best estimation scenario. Results: A total of 39,848 cancer incidence cases and 43,884 deaths due to cancer were included. The relative differences between the observed and predicted numbers of cancer cases were less than 10% for most cancer sites. The constant assumption for the IMR trend provided the best GOF for colon, rectal, lung, bladder, and stomach cancers in men and colon, rectum, breast, and corpus uteri in women. The linear assumption was better for lung and ovarian cancers in women and prostate cancer in men. In the best scenario, the mean absolute percentage error was 6% in men and 4% in women for overall cancer. Female breast cancer and prostate cancer obtained the worst GOF results in all scenarios. Conclusion: A comparison with a historical time series of real data in a population-based cancer registry indicated that the IMR method is a valid tool for the estimation of cancer incidence. The goodness-of-fit indicator proposed can help select the best assumption for the IMR based on a statistical argument.Subprogram "Cancer surveillance" of the CIBER of Epidemiology and Public Health (CIBERESP)MINECO/FEDER PGC2018-098860-B-I00Andalusian Department of Health Research, Development and Innovation PI-0152/201

Ten-Year Probabilities of Death Due to Cancer and Cardiovascular Disease among Breast Cancer Patients Diagnosed in North-Eastern Spain

Mortality from cardiovascular disease (CVD), second tumours, and other causes is of clinical interest in the long-term follow-up of breast cancer (BC) patients. Using a cohort of BC patients (N = 6758) from the cancer registries of Girona and Tarragona (north-eastern Spain), we studied the 10-year probabilities of death due to BC, other cancers, and CVD according to stage at diagnosis and hormone receptor (HR) status. Among the non-BC causes of death (N = 720), CVD (N = 218) surpassed other cancers (N = 196). The BC cohort presented a significantly higher risk of death due to endometrial and ovarian cancers than the general population. In Stage I, HR- patients showed a 1.72-fold higher probability of all-cause death and a 6.11-fold higher probability of breast cancer death than HR+ patients. In Stages II-III, the probability of CVD death (range 3.11% to 3.86%) surpassed that of other cancers (range 0.54% to 3.11%). In Stage IV patients, the probability of death from any cancer drove the mortality risk. Promoting screening and preventive measures in BC patients are warranted, since long-term control should encompass early detection of second neoplasms, ruling out the possibility of late recurrence. In patients diagnosed in Stages II-III at an older age, surveillance for preventing late cardiotoxicity is crucial

Using population-based data to evaluate the impact of adherence to endocrine therapy on survival in breast cancer through the web-application BreCanSurvPred

We show how the use and interpretation of population-based cancer survival indicators can help oncologists talk with breast cancer (BC) patients about the relationship between their prognosis and their adherence to endocrine therapy (ET). The study population comprised a population-based cohort of estrogen receptor positive BC patients (N = 1268) diagnosed in Girona and Tarragona (Northeastern Spain) and classified according to HER2 status (+ / -), stage at diagnosis (I/II/III) and five-year cumulative adherence rate (adherent > 80%; non-adherent <= 80%). Cox regression analysis was performed to identify significant prognostic factors for overall survival, whereas relative survival (RS) was used to estimate the crude probability of death due to BC (PBC). Stage and adherence to ET were the significant factors for predicting all-cause mortality. Compared to stage I, risk of death increased in stage II (hazard ratio [HR] 2.24, 95% confidence interval [CI]: 1.51-3.30) and stage III (HR 5.11, 95% CI 3.46-7.51), and it decreased with adherence to ET (HR 0.57, 95% CI 0.41-0.59). PBC differences were higher in non-adherent patients compared to adherent ones and increased across stages: stage I: 6.61% (95% CI 0.05-13.20); stage II: 9.77% (95% CI 0.59-19.01), and stage III: 22.31% (95% CI 6.34-38.45). The age-adjusted survival curves derived from this modeling were implemented in the web application BreCanSurvPred (https://pdocomputation.snpstats.net/BreCanSurvPred). Web applications like BreCanSurvPred can help oncologists discuss the consequences of non-adherence to prescribed ET with patients

No Excess Mortality up to 10 Years in Early Stages of Breast Cancer in Women Adherent to Oral Endocrine Therapy: A Probabilistic Graphical Modeling Approach

Breast cancer (BC) is globally the most frequent cancer in women. Adherence to endocrine therapy (ET) in hormone-receptor-positive BC patients is active and voluntary for the first five years after diagnosis. This study examines the impact of adherence to ET on 10-year excess mortality (EM) in patients diagnosed with Stages I to III BC (N = 2297). Since sample size is an issue for estimating age- and stage-specific survival indicators, we developed a method, ComSynSurData, for generating a large synthetic dataset (SynD) through probabilistic graphical modeling of the original cohort. We derived population-based survival indicators using a Bayesian relative survival model fitted to the SynD. Our modeling showed that hormone-receptor-positive BC patients diagnosed beyond 49 years of age at Stage I or beyond 59 years at Stage II do not have 10-year EM if they follow the prescribed ET regimen. This result calls for developing interventions to promote adherence to ET in patients with hormone receptor-positive BC and in turn improving cancer survival. The presented methodology here demonstrates the potential use of probabilistic graphical modeling for generating reliable synthetic datasets for validating population-based survival indicators when sample size is an issue

Excess mortality among breast cancer patients in early stages in Tarragona and Gerona

Objetivo: analizar la supervivencia poblacional del cáncer de mama (CM) en estadios precoces, estimando la tendencia temporal del exceso de mortalidad (EM) a largo plazo en periodos anuales y quinquenales, y determinando, si es posible, una proporción de pacientes que puedan considerarse curadas. Método: Se incluyó la cohorte de pacientes diagnosticadas de CM en estadios I y II antes de los 60 anos de edad en Gerona y Tarragona (N = 2453). Se calcularon la supervivencia observada (SO) y la supervivencia relativa (SR) al CM hasta los 20 aaños de seguimiento. Para valorar el EM se estimó la SR a intervalos anuales (SRI) y quinquenales (SR5). Los resultados se presentan por grupos de edad (≤49 y 50-59), estadio (I/II) y periodo de diagnóstico (1985-1994 y 1995-2004). Resultados: en el estadio I, la SO y la SR fueron mayores en 1995-2004 que en 1985-1994: 3,5% a los 15 años de seguimiento y 4,5% a los 20 años. La SO superó el 80% en el estadio I y se mantuvo inferior al 70% en el estadio II. Sin embargo, el EM a largo plazo no desapareció (SRI <1) independientemente del grupo de edad, el estadio y el periodo de diagnóstico. A los 15 años de seguimiento, el EM a 5 años osciló entre el 1-5% en el estadio I (SR5 ≥0,95) y el 5-10% en el estadio II. Conclusiones: En nuestra cohorte, a los 15 años de seguimiento se detectó que el EM anual no desapareció y el quinquenal fue del 1-10%. Por ello, no se pudo determinar una proporción de curación del CM durante el periodo de estudio

WebSurvCa: web-based estimation of death and survival probabilities in a cohort

La supervivencia relativa se ha utilizado habitualmente como medida de la evolución temporal del exceso de riesgo de mortalidad en cohortes de pacientes diagnosticados de cáncer, teniendo en cuenta la mortalidad de una población de referencia. Una vez estimado el exceso de riesgo de mortalidad pueden calcularse tres probabilidades acumuladas a un tiempo T: 1) la probabilidad de fallecer asociada a la causa de diagnóstico inicial (enfermedad en estudio), 2) la probabilidad de fallecer asociada a otras causas, y 3) la probabilidad de supervivencia absoluta en la cohorte a un tiempo T. Este trabajo presenta la aplicación WebSurvCa (https://shiny.snpstats.net/WebSurvCa/), mediante la cual los registros de cáncer de base hospitalaria y poblacional, y los registros de otras enfermedades, estiman dichas probabilidades en sus cohortes seleccionando como población de referencia la mortalidad de la comunidad autónoma que consideren

Trends in lung cancer incidence by age, sex and histology from 2012 to 2025 in Catalonia (Spain)

Lung cancer remains one the most common cancers in Europe and ranks first in terms of cancer mortality in both sexes. Incidence rates vary by region and depend above all on the prevalence of tobacco consumption. In this study we describe recent trends in lung cancer incidence by sex, age and histological type in Catalonia and project changes according to histology by 2025. Bayesian age-period-cohort models were used to predict trends in lung cancer incidence according to histological type from 2012 to 2025, using data from the population-based Catalan cancer registries. Data suggest a decrease in the absolute number of new cases in men under the age of 70 years and an increase in women aged 60 years or older. Adenocarcinoma was the most common type in both sexes, while squamous cell carcinoma and small cell carcinoma were decreasing significantly among men. In both sexes, the incident cases increased by 16% for patients over 70 years. Increases in adenocarcinoma and rising incidence in elderly patients suggest the need to prioritize strategies based on multidisciplinary teams, which should include geriatric specialists

Missing data imputation and synthetic data simulation through modeling graphical probabilistic dependencies between variables (ModGraProDep): An application to breast cancer survival

Background Two common issues may arise in certain population-based breast cancer (BC) survival studies: I) missing values in a survivals’ predictive variable, such as “Stage” at diagnosis, and II) small sample size due to “imbalance class problem” in certain subsets of patients, demanding data modeling/simulation methods. Methods We present a procedure, ModGraProDep, based on graphical modeling (GM) of a dataset to overcome these two issues. The performance of the models derived from ModGraProDep is compared with a set of frequently used classification and machine learning algorithms (Missing Data Problem) and with oversampling algorithms (Synthetic Data Simulation). For the Missing Data Problem we assessed two scenarios: missing completely at random (MCAR) and missing not at random (MNAR). Two validated BC datasets provided by the cancer registries of Girona and Tarragona (northeastern Spain) were used. Results In both MCAR and MNAR scenarios all models showed poorer prediction performance compared to three GM models: the saturated one (GM.SAT) and two with penalty factors on the partial likelihood (GM.K1 and GM.TEST). However, GM.SAT predictions could lead to non-reliable conclusions in BC survival analysis. Simulation of a “synthetic” dataset derived from GM.SAT could be the worst strategy, but the use of the remaining GMs models could be better than oversampling. Conclusion Our results suggest the use of the GM-procedure presented for one-variable imputation/prediction of missing data and for simulating “synthetic” BC survival datasets. The “synthetic” datasets derived from GMs could be also used in clinical applications of cancer survival data such as predictive risk analysis.Postprint (published version