The role of multimorbidity in short-term mortality of lung cancer patients in Spain: a population-based cohort study

Nikšić, Maja; Redondo-Sánchez, Daniel; Chang, Yoe-Ling; Rodriguez‐Barranco, Miguel; Expósito‐Hernández, José; Marcos‐Gragera, Rafael; Oliva-Poch, Ester; Bosch-Barrera, Joaquim; Sánchez, María‐José; Luque-Fernandez, Miguel Ángel

doi:10.1186/s12885-021-08801-9

Cited by 8 publications

(3 citation statements)

References 42 publications

(69 reference statements)

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“…In accordance with other studies ( 14 , 35 ), in the multivariable analysis restricted to the CRs providing the relevant data, comorbidity and smoking habit were independently and positively associated with RER.…”

Section: Discussionsupporting

confidence: 87%

Patterns of care and survival for lung cancer: Results of the European population-based high-resolution study

Sant

Daidone²,

Innos³

et al. 2023

Front. Epidemiol.

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ObjectivesTo investigate differences in lung cancer (LC) management and survival using data from European population cancer registries.MethodsWe analysed 4,602 lung cancer cases diagnosed in 2010–2013, followed-up to 2019 in five countries. Multivariable logistic regression was used to calculate the Odds Ratio (OR) of surgery for stages I–II LC or chemo- or radiotherapy for stages III–IV LC. Relative survival (RS) was estimated by the actuarial method; Relative Excess Risk of death (RER), with 95% CI, was calculated by generalized linear models.ResultsDiagnostic work-up was extensive for 65.9% patients (range 57%, Estonia, Portugal - 85% (Belgium). Sixty-six percent of stages I–II patients underwent surgery; compared to non-operated, their adjusted OR decreased with age and was associated with main bronchus cancer (OR vs. lobes 0.25, CI, 0.08–0.82), stage II (OR vs. stage I: 0.42, CI, 0.29–0.60), comorbidity (OR vs. absent: 0.55, CI, 0.33–0.93), country (ORs: Estonia 1.82, CI, 1.28–2.60; Belgium 0.62, CI, 0.42–0.91; Portugal 0.69, CI, 0.52–0.93).Almost half of stages III–IV patients received chemo- or radiotherapy only; the adjusted OR vs. non receiving decreased with age and was associated with unspecified cancer topography or morphology. The adjusted five-year RER increased with age and stage and was lower for women (0.78, CI, 0.72–0.86), above the reference for main bronchus cancer (1.37, CI, 1.21–1.54) and unspecified morphology (1.17, CI, 1.05–1.30). Surgery carried the lowest mortality (RS 56.9; RER 0.13, CI, 0.11–0.15) with RER above the mean in Estonia (1.20, CI, 1.10–1.30), below it in Portugal (0.88, CI, 0.82–0.93) and Switzerland (0.91, CI, 0.84–0.99). Comorbidity (1.21, CI, 1.09–1.35) and not smoking (0.68, CI, 0.57–0.81) were associated with RER.ConclusionsThe survival benefit of early diagnosis, allowing curative surgery, was evident at the population level. Screening for subjects at risk and adhesion to standard care should be incremented across the EU by funding better equipment and training health personnel.

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Section: Discussionsupporting

confidence: 87%

Patterns of care and survival for lung cancer: Results of the European population-based high-resolution study

Sant

Daidone²,

Innos³

et al. 2023

Front. Epidemiol.

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“…Some of these comorbidities are related to preventive lifestyle behaviors such as smoking and drinking. Smoking contributes to over 80% of lung cancers in high-income countries [ 36 ]. This information has clinical value as well as it sheds light on the interplay of comorbidities with lung and colorectal cancer and their effect on population survival.…”

Section: Discussionmentioning

confidence: 99%

Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain

Rubio

Álvares

Redondo-Sánchez

et al. 2022

BMC Med Res Methodol

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Cancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by several factors, such as comorbidities, that may interact with the cancer biology. Moreover, it is interesting to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities on the overall survival of cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages and . We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer.

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“…Smoking contributes to over 80% of lung cancers in highincome countries. [33] Supplementary Figures 3 and 4 show the summary measures for the other (dichotomous) variables from LL PH (early stage = I-III) and PGW PH (late stage = IV) models for lung data.…”

Section: Applicationsmentioning

confidence: 99%

Bayesian variable selection and survival modeling: Assessing the most important comorbidities that impact lung and colorectal cancer survival in Spain

Rubio

Álvares

Redondo‐Sánchez

et al. 2021

Preprint

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Cancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by a number of factors, such as comorbidities, that may interact with the cancer tumour. Moreover, it is of interest to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to a worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities in the overall survival in cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages mombf and rstan. We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer.

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The role of multimorbidity in short-term mortality of lung cancer patients in Spain: a population-based cohort study

Cited by 8 publications

References 42 publications

Patterns of care and survival for lung cancer: Results of the European population-based high-resolution study

Patterns of care and survival for lung cancer: Results of the European population-based high-resolution study

Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain

Bayesian variable selection and survival modeling: Assessing the most important comorbidities that impact lung and colorectal cancer survival in Spain

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