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1. A new cure model that corrects for increased risk of non-cancer death: analysis of reliability and robustness, and application to real-life data

Author

Laura Botta, Juste Goungounga, Riccardo Capocaccia, Gaelle Romain, Marc Colonna, Gemma Gatta, Olayidé Boussari, and Valérie Jooste

Subjects
Cure model, Increased non-cancer mortality, Population-based data, Robustness, Reliability, Life tables, Medicine (General), R5-920
Abstract

Abstract Background Non-cancer mortality in cancer patients may be higher than overall mortality in the general population due to a combination of factors, such as long-term adverse effects of treatments, and genetic, environmental or lifestyle-related factors. If so, conventional indicators may underestimate net survival and cure fraction. Our aim was to propose and evaluate a mixture cure survival model that takes into account the increased risk of non-cancer death for cancer patients. Methods We assessed the performance of a corrected mixture cure survival model derived from a conventional mixture cure model to estimate the cure fraction, the survival of uncured patients, and the increased risk of non-cancer death in two settings of net survival estimation, grouped life-table data and individual patients’ data. We measured the model’s performance in terms of bias, standard deviation of the estimates and coverage rate, using an extensive simulation study. This study included reliability assessments through violation of some of the model’s assumptions. We also applied the models to colon cancer data from the FRANCIM network. Results When the assumptions were satisfied, the corrected cure model provided unbiased estimates of parameters expressing the increased risk of non-cancer death, the cure fraction, and net survival in uncured patients. No major difference was found when the model was applied to individual or grouped data. The absolute bias was

Published
2023
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2. Behavioral cost & overdominance in Anopheles gambiae.

Author

Malal M Diop, Nicolas Moiroux, Fabrice Chandre, Hadrien Martin-Herrou, Pascal Milesi, Olayidé Boussari, Angélique Porciani, Stéphane Duchon, Pierrick Labbé, and Cédric Pennetier

Subjects
Medicine, Science
Abstract

In response to the widespread use of control strategies such as Insecticide Treated Nets (ITN), Anopheles mosquitoes have evolved various resistance mechanisms. Kdr is a mutation that provides physiological resistance to the pyrethroid insecticides family (PYR). In the present study, we investigated the effect of the Kdr mutation on the ability of female An. gambiae to locate and penetrate a 1cm-diameter hole in a piece of netting, either treated with insecticide or untreated, to reach a bait in a wind tunnel. Kdr homozygous, PYR-resistant mosquitoes were the least efficient at penetrating an untreated damaged net, with about 51% [39-63] success rate compared to 80% [70-90] and 78% [65-91] for homozygous susceptible and heterozygous respectively. This reduced efficiency, likely due to reduced host-seeking activity, as revealed by mosquito video-tracking, is evidence of a recessive behavioral cost of the mutation. Kdr heterozygous mosquitoes were the most efficient at penetrating nets treated with PYR insecticide, thus providing evidence for overdominance, the rarely-described case of heterozygote advantage conveyed by a single locus. The study also highlights the remarkable capacity of female mosquitoes, whether PYR-resistant or not, to locate holes in bed-nets.

Published
2015
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3. Dry season determinants of malaria disease and net use in Benin, West Africa.

Author

Nicolas Moiroux, Olayidé Boussari, Armel Djènontin, Georgia Damien, Gilles Cottrell, Marie-Claire Henry, Hélène Guis, and Vincent Corbel

Subjects
Medicine, Science
Abstract

BackgroundTo achieve malaria eradication, control efforts have to be sustained even when the incidence of malaria cases becomes low during the dry season. In this work, malaria incidence and its determinants including bed net use were investigated in children of under 5 years of age in 28 villages in southern Benin during the dry season.Methods and findingsMean malaria clinical incidence was measured in children aged 0-5 years by active case detection in 28 villages of the Ouidah-Kpomasse-Tori Bossito sanitary district between November 2007 and March 2008. Using Poisson mixed-effect models, malaria incidence was assessed according to the level of transmission by different vector species, and Long-Lasting Insecticide-treated mosquito Nets (LLIN) use and ownership. Then, a Binomial mixed-effect model was developed to assess whether nighttime temperature (derived from MODIS remote sensing data), biting nuisance and LLIN ownership are good predictors of LLIN use >60%. Results suggested that Anopheles funestus (Incidence Rates Ratio (IRR) = 3.38 [IC95 1.91-6]) rather than An. gambiae s.s. is responsible for malaria transmission. A rate of LLIN use >60% was associated with a lower risk of malaria (IRR = 0.6 [IC95 0.37-0.99]). Low nocturnal temperature and high biting nuisance were good predictors of LLIN use >60%.ConclusionsAs recommended by the Malaria Eradication (MalERA) Consultative Group on Modelling, there is a need to understand better the effects of seasonality on malaria morbidity. This study highlights the need to take into account the specificity of malaria epidemiology during the dry-hot season and get a better understanding of the factors that influence malaria incidence and net use. These findings should help National Malaria Control Programmes to implement more effective and sustainable malaria control strategies in Africa.

Published
2012
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4. Use of a mixture statistical model in studying malaria vectors density.

Author

Olayidé Boussari, Nicolas Moiroux, Jean Iwaz, Armel Djènontin, Sahabi Bio-Bangana, Vincent Corbel, Noël Fonton, and René Ecochard

Subjects
Medicine, Science
Abstract

Vector control is a major step in the process of malaria control and elimination. This requires vector counts and appropriate statistical analyses of these counts. However, vector counts are often overdispersed. A non-parametric mixture of Poisson model (NPMP) is proposed to allow for overdispersion and better describe vector distribution. Mosquito collections using the Human Landing Catches as well as collection of environmental and climatic data were carried out from January to December 2009 in 28 villages in Southern Benin. A NPMP regression model with "village" as random effect is used to test statistical correlations between malaria vectors density and environmental and climatic factors. Furthermore, the villages were ranked using the latent classes derived from the NPMP model. Based on this classification of the villages, the impacts of four vector control strategies implemented in the villages were compared. Vector counts were highly variable and overdispersed with important proportion of zeros (75%). The NPMP model had a good aptitude to predict the observed values and showed that: i) proximity to freshwater body, market gardening, and high levels of rain were associated with high vector density; ii) water conveyance, cattle breeding, vegetation index were associated with low vector density. The 28 villages could then be ranked according to the mean vector number as estimated by the random part of the model after adjustment on all covariates. The NPMP model made it possible to describe the distribution of the vector across the study area. The villages were ranked according to the mean vector density after taking into account the most important covariates. This study demonstrates the necessity and possibility of adapting methods of vector counting and sampling to each setting.

Published
2012
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5. Impact of time-varying cumulative bevacizumab exposures on survival: re-analysis of data from randomized clinical trial in patients with metastatic colo-rectal cancer

Author

Michal Abrahamowicz, Adrien Guilloteau, Karine Le Malicot, Olayidé Boussari, Thomas Aparicio, Christine Binquet, Valérie Jooste, Catherine Quantin, Equipe EPICAD (LNC - U1231), Lipides - Nutrition - Cancer [Dijon - U1231] (LNC), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Bourgogne Franche-Comté [COMUE] (UBFC), CHU Dijon, Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), McGill University = Université McGill [Montréal, Canada], Fédération Francophone de la Cancérologie Digestive, FFCD, Hopital Saint-Louis [AP-HP] (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Paris Diderot - Paris 7 (UPD7), Université Sorbonne Paris Cité (USPC), Centre d'Investigation Clinique 1432 (Dijon) - Module Plurithématique : Périnatalité Cancérologie Handicap et Ophtalmologie (CIC-P803), Université de Bourgogne (UB)-Direction Générale de l'Organisation des Soins (DGOS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Malbec, Odile, Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Biostatistique, Biomathématique, Pharmacoépidémiologie et Maladies Infectieuses (B2PHI), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Oncology, medicine.medical_specialty, Randomization, Bevacizumab, Survival, Epidemiology, [SDV]Life Sciences [q-bio], Cumulative Exposure, Health Informatics, 01 natural sciences, law.invention, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Randomized controlled trial, Time varying cumulative exposure to maintenance treatment, law, Internal medicine, Antineoplastic Combined Chemotherapy Protocols, medicine, Humans, 0101 mathematics, lcsh:R5-920, Cumulative dose, Proportional hazards model, business.industry, Rectal Neoplasms, Hazard ratio, Confounding, Colorectal cancer, 3. Good health, [SDV] Life Sciences [q-bio], 030220 oncology & carcinogenesis, Colonic Neoplasms, business, lcsh:Medicine (General), Colorectal Neoplasms, medicine.drug, Research Article
Abstract

Background As cancer treatment, biotherapies can be as effective as chemotherapy while reducing the risk of secondary effects, so that they can be taken over longer periods than conventional chemotherapy. Thus, some trials aimed at assessing the benefit of maintaining biotherapies during chemotherapy-free intervals (CFI). For example, the recent PRODIGE9 trial assessed the effect of maintaining bevacizumab during CFI in metastatic colorectal cancer (mCRC) patients. However, its analysis was hindered by a small difference of exposure to the treatment between the randomized groups and by a large proportion of early drop outs, leading to a potentially unbalanced distribution of confounding factors among the trial completers. To address these limitations, we re-analyzed the PRODIGE9 data to assess the effects of different exposure metrics on all-cause mortality of patients with mCRC using methods originally developed for observational studies. Methods To account for the actual patterns of drug use by individual patients and for possible cumulative effects, we used five alternative time-varying exposure metrics: (i) cumulative dose, (ii) quantiles of the cumulative dose, (iii) standardized cumulative dose, (iv) Theoretical Blood Concentration (TBC), and (v) Weighted Cumulative Exposure (WCE). The last two metrics account for the timing of drug use. Treatment effects were estimated using adjusted Hazard Ratio from multivariable Cox proportional hazards models. Results After excluding 112 patients who died during the induction period, we analyzed data on 382 patients, among whom 320 (83.8%) died. All time-varying exposures improved substantially the model’s fit to data, relative to using only the time-invariant randomization group. All exposures indicated a protective effect for higher cumulative bevacizumab doses. The best-fitting WCE and TBC models accounted for both the cumulative effects and the different impact of doses taken at different times. Conclusions All time-varying analyses, regardless of the exposure metric used, consistently suggested protective effects of higher cumulative bevacizumab doses. However, the results may partly reflect the presence of a confusion bias. Complementing the main ITT analysis of maintenance trials with an analysis of potential cumulative effects of treatment actually taken can provide new insights, but the results must be interpreted with caution because they do not benefit from the randomization. Trial registration clinicaltrials.gov, NCT00952029. Registered 8 August 2009.

Published
2021
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6. Modeling excess hazard with time‐to‐cure as a parameter

Author

Laurent Remontet, Valérie Jooste, Olayidé Boussari, Marc Colonna, Nadine Bossard, Laurent Bordes, Gaëlle Romain, Registre Bourguignon des Cancers Digestifs, Lipides - Nutrition - Cancer (U866) (LNC), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon (ENSBANA)-Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon (ENSBANA)-Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Equipe EPICAD (LNC - U1231), Lipides - Nutrition - Cancer [Dijon - U1231] (LNC), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Registre du Cancer de l’Isère, Grenoble University Hospital, Grenoble, France, Réseau Français des Registres de Cancers, FRANCIM, Toulouse, France, Fédération Francophone de Cancérologie Digestive, Département de Méthodologie, and Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Statistics and Probability, Hazard (logic), Computer science, Maximum likelihood, Probability density function, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, 010104 statistics & probability, 03 medical and health sciences, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Neoplasms, Econometrics, Humans, Fraction (mathematics), 0101 mathematics, ComputingMilieux_MISCELLANEOUS, Proportional Hazards Models, 030304 developmental biology, Event (probability theory), Simple (philosophy), Estimation, Likelihood Functions, 0303 health sciences, Models, Statistical, General Immunology and Microbiology, Applied Mathematics, Regression analysis, General Medicine, Survival Analysis, 3. Good health, General Agricultural and Biological Sciences
Abstract

Cure models have been widely developed to estimate the cure fraction when some subjects never experience the event of interest. However, these models were rarely focused on the estimation of the time-to-cure, that is, the delay elapsed between the diagnosis and "the time from which cure is reached," an important indicator, for instance, to address the question of access to insurance or loans for subjects with personal history of cancer. We propose a new excess hazard regression model that includes the time-to-cure as a covariate-dependent parameter to be estimated. The model is written similarly to a Beta probability distribution function and is shown to be a particular case of the non-mixture cure models. Parameters are estimated through a maximum likelihood approach and simulation studies demonstrate good performance of the model. Illustrative applications to three cancer data sets are provided and some limitations as well as possible extensions of the model are discussed. The proposed model offers a simple and comprehensive way to estimate more accurately the time-to-cure.

Published
2020
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7. Net survival in recurrence-free colon cancer patients

Author

Anne-Marie Bouvier, Olayidé Boussari, Valérie Jooste, Côme Lepage, Antoine Drouillard, Jean Faivre, Sylvain Manfredi, Gaëlle Romain, Registre Bourguignon des Cancers Digestifs, Lipides - Nutrition - Cancer (U866) (LNC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bourgogne (UB)-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon (ENSBANA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bourgogne (UB)-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon (ENSBANA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Equipe EPICAD (LNC - U1231), Lipides - Nutrition - Cancer [Dijon - U1231] (LNC), and Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

Subjects
Adult, Male, Cancer Research, medicine.medical_specialty, Epidemiology, Colorectal cancer, [SDV]Life Sciences [q-bio], Population, Disease, 03 medical and health sciences, 0302 clinical medicine, Quality of life, Internal medicine, Medicine, Humans, 030212 general & internal medicine, Survivors, Stage (cooking), Intestinal Cancer, education, Aged, education.field_of_study, business.industry, Middle Aged, medicine.disease, Prognosis, Survival Analysis, 3. Good health, Cancer registry, Oncology, 030220 oncology & carcinogenesis, Colonic Neoplasms, Quality of Life, Female, business
Abstract

Purpose Conditional net survival in recurrence-free patients (CNS-RF) provides relevant clinical information and has never been assessed yet in a non-selected colon cancer population. We aimed to estimate conditional 5-year net survival in recurrence-free patients with colon cancer in the population-based Digestive Cancer Registry of Burgundy (France). Methods CNS-RF was estimated in the 3736 patients resected for cure for primary colon cancer between 1976 and 2006, using a flexible parametric model of net survival for every additional year survived at diagnosis and from 1 to 5 years thereafter. Results The net probability of surviving 5 more years increased from 72% at diagnosis to 92% for recurrence-free patients who survived 5 years after diagnosis. CNS-RF was over 90% 3 years after diagnosis in patients aged 75 and below. CNS-RF was over 95% in patients diagnosed after 2000 who were recurrence-free 3, 4 or 5 years after diagnosis. CNS-RF was similar between patients with stage I and II disease from 2 years after diagnosis and patients with stage III disease from 5 years after diagnosis. The initial differences in net survival related to gross features, clinical presentation, number of harvested nodes in stage II, and number of involved nodes in stage III disappeared after 2 years. Conclusions CNS-RF is a relevant measure of prognosis in patients who have already achieved a period of remission. Providing an updated estimation of prognosis in the years following diagnosis may improve the survivors’ quality of life and access to credit or insurance.

Published
2019
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8. A new approach to estimate time-to-cure from cancer registries data

Author

Gaëlle Romain, Morgane Mounier, Anne-Marie Bouvier, Valérie Jooste, Nadine Bossard, M. Colonna, Laurent Remontet, Olayidé Boussari, Christine Binquet, Registre Bourguignon des Cancers Digestifs, Lipides - Nutrition - Cancer (U866) (LNC), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon (ENSBANA)-Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon (ENSBANA)-Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Equipe EPICAD (LNC - U1231), Lipides - Nutrition - Cancer [Dijon - U1231] (LNC), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire d'Excellence : Lipoprotéines et Santé : prévention et Traitement des maladies Inflammatoires et du Cancer (LabEx LipSTIC), Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université Paris-Sud - Paris 11 (UP11)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Gustave Roussy (IGR)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon)-Université de Bourgogne (UB)-Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon)-Centre Régional de Lutte contre le cancer Georges-François Leclerc [Dijon] (UNICANCER/CRLCC-CGFL), UNICANCER-UNICANCER-Institut National de la Santé et de la Recherche Médicale (INSERM)-Fédération Francophone de la Cancérologie Digestive, FFCD-Université de Montpellier (UM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Etablissement français du sang [Bourgogne-Franche-Comté] (EFS BFC)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Service de Biostatistiques [Lyon], Hospices Civils de Lyon (HCL), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Biostatistiques santé, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Registre des hémopathies malignes de Côte d'Or, Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Centre d'Investigation Clinique 1432 (Dijon) - Epidemiologie Clinique/Essais Cliniques (CIC-EC), Université de Bourgogne (UB)-Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon)-Institut National de la Santé et de la Recherche Médicale (INSERM), Registre du Cancer de l'Isère, Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université Paris-Sud - Paris 11 (UP11)-École pratique des hautes études (EPHE)-Institut Gustave Roussy (IGR)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Université de Bourgogne (UB)-Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon)-Centre Régional de Lutte contre le cancer - Centre Georges-François Leclerc (CRLCC - CGFL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Fédération Francophone de la Cancérologie Digestive, FFCD-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Etablissement français du sang [Bourgogne-France-Comté] (EFS [Bourgogne-France-Comté])-Centre National de la Recherche Scientifique (CNRS)-Centre Hospitalier Régional Universitaire [Besançon] (CHRU Besançon)-Université de Franche-Comté (UFC)-Université de Montpellier (UM), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université Paris-Sud - Paris 11 (UP11)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Gustave Roussy (IGR)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Université de Bourgogne (UB)-Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon)-Centre Régional de Lutte contre le cancer Georges-François Leclerc [Dijon] (UNICANCER/CRLCC-CGFL), UNICANCER-UNICANCER-Institut National de la Santé et de la Recherche Médicale (INSERM)-Fédération Francophone de la Cancérologie Digestive, FFCD-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Etablissement français du sang [Bourgogne-Franche-Comté] (EFS [Bourgogne-Franche-Comté])-Centre National de la Recherche Scientifique (CNRS)-Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université de Montpellier (UM), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM), Lipides - Nutrition - Cancer (U866) ( LNC ), Université de Bourgogne ( UB ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon ( ENSBANA ) -Université de Bourgogne ( UB ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon ( ENSBANA ) -Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand ( CHU Dijon ), Lipides - Nutrition - Cancer [Dijon - U1231] ( LNC ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Laboratoire d'Excellence : Lipoprotéines et Santé : prévention et Traitement des maladies Inflammatoires et du Cancer ( LabEx LipSTIC ), Institut National de la Recherche Agronomique ( INRA ) -Université Montpellier 2 - Sciences et Techniques ( UM2 ) -Université Paris-Sud - Paris 11 ( UP11 ) -École pratique des hautes études ( EPHE ) -Institut Gustave Roussy ( IGR ) -Centre Hospitalier Régional Universitaire de Nancy ( CHRU Nancy ) -Université de Bourgogne ( UB ) -Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand ( CHU Dijon ) -Centre Régional de Lutte contre le cancer - Centre Georges-François Leclerc ( CRLCC - CGFL ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Fédération Francophone de la Cancérologie Digestive, FFCD-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Etablissement français du sang [Bourgogne-France-Comté] ( EFS [Bourgogne-France-Comté] ) -Centre National de la Recherche Scientifique ( CNRS ) -Centre Hospitalier Régional Universitaire [Besançon] ( CHRU Besançon ) -Université de Franche-Comté ( UFC ), Hospices Civils de Lyon ( HCL ), Laboratoire de Biométrie et Biologie Evolutive ( LBBE ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand ( CHU Dijon ), Centre d'Investigation Clinique 1432 (Dijon) - Epidemiologie Clinique/Essais Cliniques ( CIC-EC ), Université de Bourgogne ( UB ) -Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand ( CHU Dijon ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale ( INSERM ), and UNICANCER-UNICANCER-Institut National de la Santé et de la Recherche Médicale (INSERM)-Fédération Francophone de la Cancérologie Digestive, FFCD-Université de Montpellier (UM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Etablissement français du sang [Bourgogne-Franche-Comté] (EFS [Bourgogne-Franche-Comté])-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC)

Subjects
Adult, Male, Cancer Research, Databases, Factual, Epidemiology, Colorectal cancer, Population, Context (language use), Breast Neoplasms, Time-to-cure, [SDV.CAN]Life Sciences [q-bio]/Cancer, [ SDV.CAN ] Life Sciences [q-bio]/Cancer, 03 medical and health sciences, Net survival, 0302 clinical medicine, Breast cancer, Pancreatic cancer, Statistics, medicine, Humans, Fraction (mathematics), 030212 general & internal medicine, Registries, Thyroid Neoplasms, education, Thyroid cancer, Aged, education.field_of_study, business.industry, Cancer, Middle Aged, medicine.disease, Survival Analysis, 3. Good health, Pancreatic Neoplasms, Survival Rate, Oncology, 030220 oncology & carcinogenesis, Cure models, Female, business, Colorectal Neoplasms, Probability of being cured
Abstract

IF 2.343; International audience; BackgroundCure models have been adapted to net survival context to provide important indicators from population-based cancer data, such as the cure fraction and the time-to-cure. However existing methods for computing time-to-cure suffer from some limitations.MethodsCure models in net survival framework were briefly overviewed and a new definition of time-to-cure was introduced as the time TTC at which P(t), the estimated covariate-specific probability of being cured at a given time t after diagnosis, reaches 0.95. We applied flexible parametric cure models to data of four cancer sites provided by the French network of cancer registries (FRANCIM). Then estimates of the time-to-cure by TTC and by two existing methods were derived and compared. Cure fractions and probabilities P(t) were also computed.ResultsDepending on the age group, TTC ranged from to 8 to 10 years for colorectal and pancreatic cancer and was nearly 12 years for breast cancer. In thyroid cancer patients under 55 years at diagnosis, TTC was strikingly 0: the probability of being cured was >0.95 just after diagnosis. This is an interesting result regarding the health insurance premiums of these patients. The estimated values of time-to-cure from the three approaches were close for colorectal cancer only.ConclusionsWe propose a new approach, based on estimated covariate-specific probability of being cured, to estimate time-to-cure. Compared to two existing methods, the new approach seems to be more intuitive and natural and less sensitive to the survival time distribution.

Published
2018
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9. Operational Assessment of Long-Lasting Insecticidal Nets by Using an Anopheles Salivary Biomarker of Human–Vector Contact

Author

Martin Akogbeto, Georgia Damien, Papa M. Drame, Evelyne Chaffa, Olayidé Boussari, Mahoutin H Noukpo, Franck Remoue, Vincent Corbel, André B. Sagna, Emmanuel Elanga-N'Dille, Centre de Recherche Entomologique de Cotonou (CREC), Ministère de la Santé, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut Pierre Richet (IPR), Programme National de Lutte contre le Paludisme (PNLP), Vector Control Group (MIVEGEC-VCG), Evolution des Systèmes Vectoriels (ESV), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])

Subjects
0301 basic medicine, Long lasting, Time Factors, 030106 microbiology, 030231 tropical medicine, Biology, [SDV.IMM.II]Life Sciences [q-bio]/Immunology/Innate immunity, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Virology, Anopheles, parasitic diseases, Animals, Benin, Humans, Insecticide-Treated Bednets, Salivary biomarkers, Saliva, Physical integrity, Feeding Behavior, Articles, biology.organism_classification, Peptide antigen, 3. Good health, Malaria, Infectious Diseases, Vector (epidemiology), Immunoglobulin G, Immunology, Parasitology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Malaria control, Biomarkers, Field conditions
Abstract

International audience; The widespread implementation of long-lasting insecticidal nets (LLINs) is a major intervention method for malaria control. Although the LLINs coverage increases, information available on the physical integrity (PI) of implemented LLINs is incomplete. This study aimed to validate human IgG antibody (Ab) response to Anopheles gSG6-P1 salivary peptide antigen, previously demonstrated as a pertinent biomarker of human exposure to Anopheles bites, for evaluating the PI of LLINs in field conditions. We analyzed data from 262 randomly selected children (< 5 years of age) in health districts of Benin. Anti-gSG6-P1 IgG responses were assessed and compared with the PI of LLINs that these same children slept under, and evaluated by the hole index (HI). Specific IgG levels were positively correlated to LLINs HI (r = 0.342; P < 0.0001). According to antipeptide IgG level (i.e., intensity of vector exposure), two categories of LLINs PI were defined: 1) group “HI: [0, 100]” corresponding to LLINs with “good” PI and 2) “HI > 100” corresponding to LLINs with “bad” PI. These results suggest that human Ab response to salivary peptide could be a complementary tool to help defining a standardized threshold of efficacy for LLINs under field use.

Published
2016
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10. Modeling the seasonality of Anopheles gambiae s.s. biting rates in a South Benin sanitary zone

Author

Noël Fonton, André Garcia, Armel Djènontin, Jean Iwaz, Fabien Subtil, Nicolas Moiroux, Olayidé Boussari, René Ecochard, Jean-François Etard, Vincent Corbel, Université d’Abomey-Calavi (UAC), Biostatistiques santé, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), and Université d’Abomey-Calavi = University of Abomey Calavi (UAC)

Subjects
Veterinary medicine, Mosquito Control, [SDV]Life Sciences [q-bio], South Benin, Normalized Difference Vegetation Index, Altitude, Anopheles gambiae S, Anopheles, parasitic diseases, medicine, Animals, Benin, Humans, Malaria vector, ComputingMilieux_MISCELLANEOUS, 2. Zero hunger, Models, Statistical, biology, Public Health, Environmental and Occupational Health, Insect Bites and Stings, Latent trajectory, modeling, General Medicine, Seasonality, biology.organism_classification, medicine.disease, Classification, Malaria, 3. Good health, Biting rate, Infectious Diseases, Biting, Anopheles gambiae s.s, Malaria vectors, Vector (epidemiology), Parasitology, Seasons
Abstract

BACKGROUND Efficient malaria vector control requires knowledge of spatio-temporal vector dynamics. We have classified village groups according to the biting rate profiles of both Anopheles coluzzii and An. gambiae, the major malaria vectors in these villages. METHODS Mosquitoes were captured by human bait in 28 South Benin villages during 2009. Both An. coluzzii and An. gambiae counts in each village were standardized to focus on changes in the vector biting rate over time. Latent class trajectory modeling, allowing for random intercept at the 'village' level, was adjusted to standardized values. RESULTS The villages could be classified into two groups with distinct vector biting rate profiles (continuous/transient). This classification helped creating a map of vector biting rates in the area. The biting rate profiles were found to be significantly correlated with mean rainfall, altitude, average number of larval sites, and average normalized difference vegetation index. CONCLUSIONS In highly malaria-prone regions, knowledge of vector biting rate profiles is important to improve vector control interventions. A similar methodology may be applied to study the biting rate profiles of other vector-borne infections.

Published
2014
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11. Dry season determinants of malaria disease and net use in Benin, West Africa

Author

Gilles Cottrell, Nicolas Moiroux, Georgia Damien, Marie-Claire Henry, Armel Djènontin, Olayidé Boussari, Vincent Corbel, Hélène Guis, Vector Control Group (MIVEGEC-VCG), Evolution des Systèmes Vectoriels (ESV), Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre de Recherche Entomologique de Cotonou (CREC), Ministère de la Santé, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Mère et enfant en milieu tropical : pathogènes, système de santé et transition épidémiologique (MERIT - UMR_D 216), Institut de Recherche pour le Développement (IRD)-Université Paris Descartes - Paris 5 (UPD5), Institut des Sciences Biomédicales Appliquées (ISBA), Contrôle des maladies animales exotiques et émergentes (UMR CMAEE), Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Laboratoire de Lutte contre des Insectes Nuisibles (LIN), Institut Recherche Développement, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Mère et enfant face aux infections tropicales (MERIT - UMR_D 216), University of Abomey Calavi (UAC), Université de Montpellier (UM) - Centre National de la Recherche Scientifique (CNRS) - Institut de Recherche pour le Développement (IRD [France-Sud]), Université Paris Descartes - Paris 5 (UPD5) - Institut de Recherche pour le Développement (IRD), Université d'Abomey Calavi, Contrôle des maladies animales exotiques et émergentes [Montpellier] (CMAEE), and Institut National de la Recherche Agronomique (INRA) - Centre de coopération internationale en recherche agronomique pour le développement [CIRAD] : UMR15

Subjects
Male, Rural Population, Veterinary medicine, Anopheles gambiae, Population Modeling, L73 - Maladies des animaux, law.invention, 0302 clinical medicine, Risk Factors, law, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Dry season, Benin, Saison chaude, 030212 general & internal medicine, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Mosquito Nets, Multidisciplinary, Geography, biology, Incidence (epidemiology), 1. No poverty, Droughts, 3. Good health, Épidémiologie, Africa, Western, [SDV.EE] Life Sciences [q-bio]/Ecology, environment, Infectious Diseases, Transmission (mechanics), Child, Preschool, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Medicine, Female, Public Health, Seasons, SDV:MHEP:MI, Nuisance, L72 - Organismes nuisibles des animaux, Research Article, SDV:EE, Science, 030231 tropical medicine, Lower risk, Microbiology, Saison sèche, 03 medical and health sciences, Environmental health, parasitic diseases, Parasitic Diseases, medicine, Humans, Biology, Lutte anti-insecte, Infant, Newborn, Computational Biology, Infant, Vectors and Hosts, medicine.disease, biology.organism_classification, Malaria, Vector (epidemiology)
Abstract

International audience; BACKGROUND: To achieve malaria eradication, control efforts have to be sustained even when the incidence of malaria cases becomes low during the dry season. In this work, malaria incidence and its determinants including bed net use were investigated in children of under 5 years of age in 28 villages in southern Benin during the dry season. METHODS AND FINDINGS: Mean malaria clinical incidence was measured in children aged 0-5 years by active case detection in 28 villages of the Ouidah-Kpomasse-Tori Bossito sanitary district between November 2007 and March 2008. Using Poisson mixed-effect models, malaria incidence was assessed according to the level of transmission by different vector species, and Long-Lasting Insecticide-treated mosquito Nets (LLIN) use and ownership. Then, a Binomial mixed-effect model was developed to assess whether nighttime temperature (derived from MODIS remote sensing data), biting nuisance and LLIN ownership are good predictors of LLIN use >60%. Results suggested that Anopheles funestus (Incidence Rates Ratio (IRR) = 3.38 [IC95 1.91-6]) rather than An. gambiae s.s. is responsible for malaria transmission. A rate of LLIN use >60% was associated with a lower risk of malaria (IRR = 0.6 [IC95 0.37-0.99]). Low nocturnal temperature and high biting nuisance were good predictors of LLIN use >60%. CONCLUSIONS: As recommended by the Malaria Eradication (MalERA) Consultative Group on Modelling, there is a need to understand better the effects of seasonality on malaria morbidity. This study highlights the need to take into account the specificity of malaria epidemiology during the dry-hot season and get a better understanding of the factors that influence malaria incidence and net use. These findings should help National Malaria Control Programmes to implement more effective and sustainable malaria control strategies in Africa.

Published
2012
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12. Use of a mixture statistical model in studying malaria vectors density

Author

Sahabi Bio-Bangana, Olayidé Boussari, Vincent Corbel, Nicolas Moiroux, Noël Fonton, Armel Djènontin, Jean Iwaz, René Ecochard, Université d’Abomey-Calavi (UAC), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre de Recherche Entomologique de Cotonou (CREC), Ministère de la Santé, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université d’Abomey-Calavi = University of Abomey Calavi (UAC)

Subjects
Mosquito Control, Epidemiology, Climate, Rain, Poisson distribution, 01 natural sciences, 010104 statistics & probability, 0302 clinical medicine, Statistics, Benin, Poisson Distribution, 2. Zero hunger, [STAT.AP]Statistics [stat]/Applications [stat.AP], Multidisciplinary, Ecology, Regression analysis, Random effects model, Infectious Diseases, symbols, Medicine, Probability distribution, Seasons, Public Health, Research Article, Disease Ecology, Infectious Disease Control, Science, 030231 tropical medicine, Biostatistics, Biology, Infectious Disease Epidemiology, 03 medical and health sciences, symbols.namesake, Overdispersion, Anopheles, Covariate, Animals, Humans, Poisson regression, 0101 mathematics, Population Density, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, Models, Statistical, Population Biology, Tropical Diseases (Non-Neglected), Vectors and Hosts, Statistical model, 15. Life on land, Insect Vectors, Malaria, Cattle, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Infectious Disease Modeling, Zoology, Entomology, Mathematics
Abstract

International audience; Vector control is a major step in the process of malaria control and elimination. This requires vector counts and appropriate statistical analyses of these counts. However, vector counts are often overdispersed. A non-parametric mixture of Poisson model (NPMP) is proposed to allow for overdispersion and better describe vector distribution. Mosquito collections using the Human Landing Catches as well as collection of environmental and climatic data were carried out from January to December 2009 in 28 villages in Southern Benin. A NPMP regression model with “village” as random effect is used to test statistical correlations between malaria vectors density and environmental and climatic factors. Furthermore, the villages were ranked using the latent classes derived from the NPMP model. Based on this classification of the villages, the impacts of four vector control strategies implemented in the villages were compared. Vector counts were highly variable and overdispersed with important proportion of zeros (75%). The NPMP model had a good aptitude to predict the observed values and showed that: i) proximity to freshwater body, market gardening, and high levels of rain were associated with high vector density; ii) water conveyance, cattle breeding, vegetation index were associated with low vector density. The 28 villages could then be ranked according to the mean vector number as estimated by the random part of the model after adjustment on all covariates. The NPMP model made it possible to describe the distribution of the vector across the study area. The villages were ranked according to the mean vector density after taking into account the most important covariates. This study demonstrates the necessity and possibility of adapting methods of vector counting and sampling to each setting.

Published
2012
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13. Impact of time-varying cumulative bevacizumab exposures on survival: re-analysis of data from randomized clinical trial in patients with metastatic colo-rectal cancer

Author

Adrien Guilloteau, Michal Abrahamowicz, Olayide Boussari, Valérie Jooste, Thomas Aparicio, Catherine Quantin, Karine Le Malicot, and Christine Binquet

Subjects
Time varying cumulative exposure to maintenance treatment, Survival, Colorectal cancer, Bevacizumab, Medicine (General), R5-920
Abstract

Abstract Background As cancer treatment, biotherapies can be as effective as chemotherapy while reducing the risk of secondary effects, so that they can be taken over longer periods than conventional chemotherapy. Thus, some trials aimed at assessing the benefit of maintaining biotherapies during chemotherapy-free intervals (CFI). For example, the recent PRODIGE9 trial assessed the effect of maintaining bevacizumab during CFI in metastatic colorectal cancer (mCRC) patients. However, its analysis was hindered by a small difference of exposure to the treatment between the randomized groups and by a large proportion of early drop outs, leading to a potentially unbalanced distribution of confounding factors among the trial completers. To address these limitations, we re-analyzed the PRODIGE9 data to assess the effects of different exposure metrics on all-cause mortality of patients with mCRC using methods originally developed for observational studies. Methods To account for the actual patterns of drug use by individual patients and for possible cumulative effects, we used five alternative time-varying exposure metrics: (i) cumulative dose, (ii) quantiles of the cumulative dose, (iii) standardized cumulative dose, (iv) Theoretical Blood Concentration (TBC), and (v) Weighted Cumulative Exposure (WCE). The last two metrics account for the timing of drug use. Treatment effects were estimated using adjusted Hazard Ratio from multivariable Cox proportional hazards models. Results After excluding 112 patients who died during the induction period, we analyzed data on 382 patients, among whom 320 (83.8%) died. All time-varying exposures improved substantially the model’s fit to data, relative to using only the time-invariant randomization group. All exposures indicated a protective effect for higher cumulative bevacizumab doses. The best-fitting WCE and TBC models accounted for both the cumulative effects and the different impact of doses taken at different times. Conclusions All time-varying analyses, regardless of the exposure metric used, consistently suggested protective effects of higher cumulative bevacizumab doses. However, the results may partly reflect the presence of a confusion bias. Complementing the main ITT analysis of maintenance trials with an analysis of potential cumulative effects of treatment actually taken can provide new insights, but the results must be interpreted with caution because they do not benefit from the randomization. Trial registration clinicaltrials.gov, NCT00952029 . Registered 8 August 2009.

Published
2021
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14. Evaluation of the effectiveness of malaria vector control measures in urban settings of Dakar by a specific anopheles salivary biomarker.

Author

Papa Makhtar Drame, Abdoulaye Diallo, Anne Poinsignon, Olayide Boussari, Stephanie Dos Santos, Vanessa Machault, Richard Lalou, Sylvie Cornelie, Jean-Yves LeHesran, and Franck Remoue

Subjects
Medicine, Science
Abstract

Standard entomological methods for evaluating the impact of vector control lack sensitivity in low-malaria-risk areas. The detection of human IgG specific to Anopheles gSG6-P1 salivary antigen reflects a direct measure of human-vector contact. This study aimed to assess the effectiveness of a range of vector control measures (VCMs) in urban settings by using this biomarker approach. The study was conducted from October to December 2008 on 2,774 residents of 45 districts of urban Dakar. IgG responses to gSG6-P1 and the use of malaria VCMs highly varied between districts. At the district level, specific IgG levels significantly increased with age and decreased with season and with VCM use. The use of insecticide-treated nets, by drastically reducing specific IgG levels, was by far the most efficient VCM regardless of age, season or exposure level to mosquito bites. The use of spray bombs was also associated with a significant reduction of specific IgG levels, whereas the use of mosquito coils or electric fans/air conditioning did not show a significant effect. Human IgG response to gSG6-P1 as biomarker of vector exposure represents a reliable alternative for accurately assessing the effectiveness of malaria VCM in low-malaria-risk areas. This biomarker tool could be especially relevant for malaria control monitoring and surveillance programmes in low-exposure/low-transmission settings.

Published
2013
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15. Dynamics of insecticide resistance in malaria vectors in Benin: first evidence of the presence of L1014S kdr mutation in Anopheles gambiae from West Africa

Author

Thibaud Martin, Innocent Djègbè, Olayidé Boussari, Fabrice Chandre, Vincent Corbel, Aboubakar Sidick, Hilary Ranson, and Martin Akogbeto

Subjects
Insecticides, Veterinary medicine, lcsh:Arctic medicine. Tropical medicine, Genotype, lcsh:RC955-962, Anopheles gambiae, Population, Mutation, Missense, Bendiocarb, Disease Vectors, Polymerase Chain Reaction, lcsh:Infectious and parasitic diseases, Fenitrothion, Insecticide Resistance, chemistry.chemical_compound, Gene Frequency, Anopheles, parasitic diseases, medicine, Animals, Benin, lcsh:RC109-216, education, education.field_of_study, Pyrethroid, biology, Research, medicine.disease, biology.organism_classification, Virology, Deltamethrin, Infectious Diseases, chemistry, Insect Proteins, Female, Parasitology, Malaria, Permethrin, medicine.drug
Abstract

Background Insecticide resistance monitoring is essential to help national programmers to implement more effective and sustainable malaria control strategies in endemic countries. This study reported the spatial and seasonal variations of insecticide resistance in malaria vectors in Benin, West Africa. Methods Anopheles gambiae s.l populations were collected from October 2008 to June 2010 in four sites selected on the basis of different use of insecticides and environment. WHO susceptibility tests were carried out to detect resistance to DDT, fenitrothion, bendiocarb, permethrin and deltamethrin. The synergist piperonyl butoxide was used to assess the role of non-target site mechanisms in pyrethroid resistance. Anopheles gambiae mosquitoes were identified to species and to molecular M and S forms using PCR techniques. Molecular and biochemical assays were carried out to determine kdr and Ace.1R allelic frequencies and activity of the detoxification enzymes. Results Throughout the surveys very high levels of mortality to bendiocarb and fenitrothion were observed in An. gambiae s.l. populations. However, high frequencies of resistance to DDT and pyrethroids were seen in both M and S form of An. gambiae s.s. and Anopheles arabiensis. PBO increased the toxicity of permethrin and restored almost full susceptibility to deltamethrin. Anopheles gambiae s.l. mosquitoes from Cotonou and Malanville showed higher oxidase activity compared to the Kisumu susceptible strain in 2009, whereas the esterase activity was higher in the mosquitoes from Bohicon in both 2008 and 2009. A high frequency of 1014F kdr allele was initially showed in An. gambiae from Cotonou and Tori-Bossito whereas it increased in mosquitoes from Bohicon and Malanville during the second year. For the first time the L1014S kdr mutation was found in An. arabiensis in Benin. The ace.1R mutation was almost absent in An. gambiae s.l. Conclusion Pyrethroid and DDT resistance is widespread in malaria vector in Benin and both metabolic and target site resistance are implicated. Resistance was not correlated with a change of malaria species and/or molecular forms. The 1014S kdr allele was first identified in wild population of An. arabiensis hence confirming the expansion of pyrethroid resistance alleles in Africa.

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16. Goodness-of-fit tests for parametric excess hazard rate models with covariates

Author

Bordes , Laurent, Boussari , Olayidé, Jooste , Valérie, Université de Pau et des Pays de l'Adour (UPPA), Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Registre Bourguignon des Cancers Digestifs, Lipides - Nutrition - Cancer (U866) (LNC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bourgogne (UB)-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon (ENSBANA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bourgogne (UB)-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon (ENSBANA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), This work was partially funded by grant from INCa (INCa SHSESP 16-064). Olayidé Boussari was supported by the Fondation ARC pour la recherche sur le cancer., ANR: 11-LABX-0021,Lipstic,Lipoprotéines et santé : prévention et traitement des maladies inflammatoires non vasculaires et du(2011), Université de Pau et des Pays de l'Adour ( UPPA ), Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Chrono-environnement ( LCE ), Université Bourgogne Franche-Comté ( UBFC ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Franche-Comté ( UFC ), Institut National de la Santé et de la Recherche Médicale ( INSERM ), Lipides - Nutrition - Cancer (U866) ( LNC ), Université de Bourgogne ( UB ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon ( ENSBANA ) -Université de Bourgogne ( UB ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon ( ENSBANA ) -Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand ( CHU Dijon ), ANR : Labex,LipSTIC,ANR-11-LABX-0021-01-LipSTIC, BORDES, Laurent, and Laboratoires d'excellence - Lipoprotéines et santé : prévention et traitement des maladies inflammatoires non vasculaires et du cancer - - Lipstic2011 - ANR-11-LABX-0021 - LABX - VALID

Subjects
[STAT]Statistics [stat], Proportional excess hazards model, Semiparametric estimation, [ STAT ] Statistics [stat], Maximum likelihood estimation, Nonparametric estimation, colon cancer data, Covariates, Excess hazard model, [STAT] Statistics [stat]
Abstract

In this paper we propose a general methodology for testing the null hypothesis that an excess hazard rate model, with or without covariates, belongs to a parametric family. Estimating the excess hazard rate function parametrically through the maximum likelihood method and non-parametrically (or semi-parametrically) we build a discrepancy process which is shown to be asymptotically Gaussian under the null hypothesis. Based on this result we are able to build some statistical tests in order to decide wether or not the null hypothesis is acceptable. We illustrate our results by the construction of chi-square tests which the behavior is studied through a Monte-Carlo study. Then the testing procedure is applied to a population based colon cancer data.

Published
2017

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