Analysis of excess mortality by cancer : modelling adjusting the absence of additional covariates in the life tables

La survie nette est la survie qui serait observée en l’absence de mortalité due à d’autres causes que la pathologie d’intérêt, ici le cancer. C’est un indicateur qui permet de s’affranchir des différences de mortalité qui seraient imputables à des causes autres que le cancer étudié. Les modèles développés dans le cadre de l’estimation de la survie nette considèrent la mortalité observée comme la résultante de deux forces de mortalité : la mortalité due au cancer étudié (excès de mortalité) et la mortalité due aux autres causes (mortalité attendue). Cette dernière découle des tables de mortalité de la population générale stratifiées sur un nombre limité de variables. Cependant, elles n’incluent pas certaines variables pouvant influencer la mortalité en excès. Ce manque d’information dans ces tables donne des estimations biaisées des effets sur la mortalité en excès. L’objectif de cette thèse était de proposer de nouveaux modèles pour estimer la mortalité en excès due au cancer en cas de tables de mortalité insuffisamment stratifiées. Un modèle à risques attendus non proportionnels a été proposé. Il autorise la variation dans le temps de l’effet d’une variable sur la mortalité dans la population générale, en fonction de points de rupture. Ensuite, un modèle à classes latentes a été proposé. Il permet d’identifier des sous-groupes non-observés (latents) de patients. Les performances de ces modèles ont été évaluées et une application a été faite sur les données du cancer colorectal du réseau français des registres de cancer. Les modèles proposés se sont avérés être de bonnes approches pour estimer la mortalité en excès en cas de tables de mortalité insuffisamment stratifiées.Net survival is the survival that would be observed if the only possible cause of death was the disease of interest, here the cancer. It eliminates the differences in mortality due to other causes than studied cancer. In estimating net survival, models are based on splitting the observed mortality into the excess mortality and the expected (or background) mortality. The latter is derived from life tables stratified on a limited number of covariates. However, life tables do not always include some covariates that may influence the excess mortality. The absence of such covariates leads to biased estimates of their effects on the excess mortality. The aim of this thesis was to propose new models for estimating excess mortality due to cancer on population-based data, in the case of insufficiently stratified life tables. We firstly developed a model with non-proportional expected hazards. This model provides a time-dependant effect of a covariate on background mortality, according to one or more breakpoints. Secondly, we proposed a latent class model to estimate excess mortality. This model makes it possible to identify non-observed (latent) sub-groups of patients. The performance of the developed models was assessed and the methods were applied to colorectal cancer data from the French network of cancer registries. The proposed models proved to be good approaches for estimating excess mortality on population-based data, in the case of insufficiently stratified life tables

Modelling factors associated with therapeutic inertia in hypertensive patients: Analysis using repeated data from a hospital registry in West Africa

International audienceThe proportion of poorly controlled hypertensives still remains high in the general African population. This is largely due to therapeutic inertia (TI), defined as the failure to intensify or modify treatment in a patient with poorly controlled blood pressure (BP). The objective of this study was to identify the determinants of TI. We conducted a retrospective cohort study from March 2012 to February 2014 of hypertensive patients followed during 4 medical visits. The TI score was the number of visits with TI divided by the number of visits where a therapeutic change was indicated. A random-effects logistic model was used to identify the determinants of TI. A total of 200 subjects were included, with a mean age of 57.98 years and 67% men. The TI score was measured at 85.57% (confidence interval [CI] 95% = [82.41-88.92]). Measured individual heterogeneity was significantly significant (0.78). Three factors were associated with treatment inertia, namely the number of antihypertensive drugs (odd ratios [OR] = 1.27; CI = [1.02-1.58]), the time between consultations (OR = 0.94; CI = [0.91-0.97]), and treatment noncompliance (OR = 15.18; CI = [3.13-73.70]). The random-effects model performed better in predicting high-risk patients with TI than the classical logistic model (P value < .001). Our study showed a high TI score in patients followed in cardiology in Burkina Faso. Reduction of the TI score through targeted interventions is necessary to better control hypertension in our cohort patient

Modelling factors associated with therapeutic inertia in hypertensive patients: Analysis using repeated data from a hospital registry in West Africa

International audienceThe proportion of poorly controlled hypertensives still remains high in the general African population. This is largely due to therapeutic inertia (TI), defined as the failure to intensify or modify treatment in a patient with poorly controlled blood pressure (BP). The objective of this study was to identify the determinants of TI. We conducted a retrospective cohort study from March 2012 to February 2014 of hypertensive patients followed during 4 medical visits. The TI score was the number of visits with TI divided by the number of visits where a therapeutic change was indicated. A random-effects logistic model was used to identify the determinants of TI. A total of 200 subjects were included, with a mean age of 57.98 years and 67% men. The TI score was measured at 85.57% (confidence interval [CI] 95% = [82.41-88.92]). Measured individual heterogeneity was significantly significant (0.78). Three factors were associated with treatment inertia, namely the number of antihypertensive drugs (odd ratios [OR] = 1.27; CI = [1.02-1.58]), the time between consultations (OR = 0.94; CI = [0.91-0.97]), and treatment noncompliance (OR = 15.18; CI = [3.13-73.70]). The random-effects model performed better in predicting high-risk patients with TI than the classical logistic model (P value < .001). Our study showed a high TI score in patients followed in cardiology in Burkina Faso. Reduction of the TI score through targeted interventions is necessary to better control hypertension in our cohort patient

Modelling factors associated with therapeutic inertia in hypertensive patients: Analysis using repeated data from a hospital registry in West Africa

International audienceThe proportion of poorly controlled hypertensives still remains high in the general African population. This is largely due to therapeutic inertia (TI), defined as the failure to intensify or modify treatment in a patient with poorly controlled blood pressure (BP). The objective of this study was to identify the determinants of TI. We conducted a retrospective cohort study from March 2012 to February 2014 of hypertensive patients followed during 4 medical visits. The TI score was the number of visits with TI divided by the number of visits where a therapeutic change was indicated. A random-effects logistic model was used to identify the determinants of TI. A total of 200 subjects were included, with a mean age of 57.98 years and 67% men. The TI score was measured at 85.57% (confidence interval [CI] 95% = [82.41-88.92]). Measured individual heterogeneity was significantly significant (0.78). Three factors were associated with treatment inertia, namely the number of antihypertensive drugs (odd ratios [OR] = 1.27; CI = [1.02-1.58]), the time between consultations (OR = 0.94; CI = [0.91-0.97]), and treatment noncompliance (OR = 15.18; CI = [3.13-73.70]). The random-effects model performed better in predicting high-risk patients with TI than the classical logistic model (P value < .001). Our study showed a high TI score in patients followed in cardiology in Burkina Faso. Reduction of the TI score through targeted interventions is necessary to better control hypertension in our cohort patient

Modelling factors associated with therapeutic inertia in hypertensive patients: Analysis using repeated data from a hospital registry in West Africa

International audienceThe proportion of poorly controlled hypertensives still remains high in the general African population. This is largely due to therapeutic inertia (TI), defined as the failure to intensify or modify treatment in a patient with poorly controlled blood pressure (BP). The objective of this study was to identify the determinants of TI. We conducted a retrospective cohort study from March 2012 to February 2014 of hypertensive patients followed during 4 medical visits. The TI score was the number of visits with TI divided by the number of visits where a therapeutic change was indicated. A random-effects logistic model was used to identify the determinants of TI. A total of 200 subjects were included, with a mean age of 57.98 years and 67% men. The TI score was measured at 85.57% (confidence interval [CI] 95% = [82.41-88.92]). Measured individual heterogeneity was significantly significant (0.78). Three factors were associated with treatment inertia, namely the number of antihypertensive drugs (odd ratios [OR] = 1.27; CI = [1.02-1.58]), the time between consultations (OR = 0.94; CI = [0.91-0.97]), and treatment noncompliance (OR = 15.18; CI = [3.13-73.70]). The random-effects model performed better in predicting high-risk patients with TI than the classical logistic model (P value < .001). Our study showed a high TI score in patients followed in cardiology in Burkina Faso. Reduction of the TI score through targeted interventions is necessary to better control hypertension in our cohort patient