Un des points cruciaux en pharmacoépidémiologie concerne le suivi longitudinal de l’exposition médicamenteuse dans les bases de données médico-administratives. Le parcours médicamenteux dans la « vraie » vie étant rarement linéaire, les trajectoires d’exposition médicamenteuses sont particulièrement complexes (notamment pour les médicaments psychoactifs) et difficilement mesurable par les méthodologies habituelles. Dans un premier temps nous avons ainsi étudié les trajectoires d’exposition à la méthadone notamment entre ses 2 formes galéniques à partir du SNIIRAM en région PACA-Corse. Cependant, la complexité des trajectoires d’exposition à la méthadone rendait leur description difficile. Pour modéliser précisément ces processus complexes un modèle multi-états a été construit. Dans ces travaux, nous avons pu identifier dans les bases de données des périodes ou l’exposition médicamenteuse ne peut être observée et pouvant engendrer un biais de classification des patients entre exposés et non exposés. Dans un second temps, nous avons donc étudié l’impact de ces périodes induites par les séjours hospitaliers a été évalué sur l’estimation du niveau d’exposition aux antipsychotiques de patients atteints de démence. En faisant l’hypothèse des « extrêmes » nous avons mis en évidence la variabilité importante induite par ces périodes. Enfin nous les avons modélisées et étudié leur impact sur la relation entre exposition aux benzodiazépines et mortalité toutes causes à 1 an à partir de l’EGB. L’ensemble de ce travail de thèse a permis de développer des méthodologies permettant une analyse plus précise de la dynamique d’exposition médicamenteuse. / In pharmacoepidemiology, one of the main concerns is analysis of drug exposure time in claim databases. In real-life settings, trajectories of patients ‘exposure are complex especially with psychoactive drugs and difficult to measure with traditional methodologies. In a first stage, we have highlighted the methadone exposure paths including between its two dosages formulations. This work underlined the multiplicity of exposure trajectories to methadone and the difficulty of making an accurate description. Consequently, we developed a multi-state model on a large claims database (SNIIR-AM) in order to investigate variations of methadone exposure with time. In this work, we identified the presence of periods or drug exposure cannot be observed in these databases. These periods lead to an unobservable or immeasurable exposure time bias in which patients are misclassified as unexposed. In a second stage, we assessed their impact on the prevalence of long-term antipsychotic use in community-dwelling patients with dementia considering hospitalization periods during which drugs administered are not available within almost all health insurance databases. Under extreme bias hypothesis the prevalence of long-term antipsychotic users almost doubled. Finally, we sought to model unobservable periods due to hospitalization and to apply several methods for addressing this bias and assess their impact on risk estimates. This approach was applied to the study of the association between benzodiazepines and mortality and was performed on the EGB database. In this thesis work we have developed methodologies for a more accurate analysis of the dynamics of drug exposure.

Using demographic multi-state, cohort-component methods, we produce projections for 120 countries (covering 93% of the world population in 2005) by five-year age groups, sex, and four levels of educational attainment for the years 2005-2050. Taking into account differentials in fertility and mortality by education level, we present the first systematic global educational attainment projections according to four widely differing education scenarios. The results show the possible range of future educational attainment trends around the world, thereby contributing to long-term economic and social planning at the national and international levels, and to the assessment of the feasibility of international education goals. (authors' abstract)

When suffering a stroke, the time to treatment is one of the key factors to increase the chance of desirable recovery. To ensure proper treatment, a diagnosis has to be made before treatment can begin. The potential consequences of treating a misdiagnosis can be severely harmful or even deadly. A Mobile Stroke Unit (MSU) is an ambulance equipped with the necessary tools to diagnose and begin treatment of stroke before reaching a hospital, reducing the time to initial treatment. We contribute a model to identify suitable locations of MSUs within a geographical region. We propose a Multi-State Particle Swarm Optimization (MBPSO) algorithm variation to solve this problem. Furthermore, we demonstrate the use of the model in a scenario created in the Southern Healthcare Region of Sweden in order to properly communicate and evaluate the model. The objective of our MBPSO variation is to find locations within a geographical region which are suitable for placing MSUs. The results of the solution shows that populations previously not covered by stroke care within one hour of an emergency call has the potential to be covered up to 81%.

Multi-state models have been widely used to analyze longitudinal event history data obtained in medical studies. The tools and methods developed recently in this area require the complete observed datasets. While, in many applications measurements on certain components of the covariate vector are missing on some study subjects. In this dissertation, several likelihood-based methodologies were proposed to deal with datasets with different types of missing covariates efficiently when applying multi-state models. Firstly, a maximum observed data likelihood method was proposed when the data has a univariate missing pattern and the missing covariate is a categorical variable. The construction of the observed data likelihood function is based on the model of a joint distribution of the response longitudinal event history data and the discrete covariate with missing values. Secondly, we proposed a maximum simulated likelihood method to deal with the missing continuous covariate when applying multi-state models. The observed data likelihood function was approximated by using the Monte Carlo simulation method. At last, an EM algorithm was used to deal with multiple missing covariates when estimating the parameters of multi-state model. The EM algorithm would be able to handle multiple missing discrete covariates in general missing pattern efficiently. All the proposed methods are justified by simulation studies and applications to the datasets from the SMART project, a consortium of 11 different high-quality longitudinal studies of aging and cognition.

CHDS State/Local / Approved for public release, distribution is unlimited / To defend American agriculture against foreign or domestic terrorism, it is essential that states build multi-state partnerships to provide for the collaborative plans, programs and operations needed to protect the nations food security. The National Homeland Security Strategy puts states on the front lines in the war against terrorism---including the struggle to secure the agriculture industry from potentially devastating attack. The issues surrounding agro-terrorism are vast and complex and the resources of the Federal government to address these issues are limited and overextended. If states attempt to address this threat independently, important opportunities to reduce vulnerability and enhance capability will be lost. To achieve the capabilities needed for agro terrorism detection, mitigation, preparedness and response, states must collaborate to build the partnerships and programs their citizens require. This thesis argues multi-state partnerships are critical to defeating this threat as well as providing a robust response to an attack. Whether intentionally introduced or naturally occurring , infectious diseases can easily cross state borders before an outbreak is even detected. States must be prepared to act quickly to mitigate the effects of any crisis. There is a significant opportunity for states to strengthen their abilities to provide for a stronger agriculture counter terrorism preparedness system. The states can further their ability to combat attacks on agriculture actively by demonstrating leadership in implementing administrative agreements and ultimately adopting compact(s) between states as well as with the private sector. / Civilian, Homeland Security Advisor and Emergency Management Administrator, Iowa Homeland Security and Emergency Management Division

CHDS State/Local / Approved for public release, distribution is unlimited / There are many potential targets for terrorists in the United States, one of which is the food supply system. An attack on the food supply system would create great need for information to many audiences, primarily the general public, about the risk resulting from such an attack. The Multi-State Partnership for Security in Agriculture, a collaborative effort of 10 states, has identified the need for development of a strategy for communicating to the public the risk resulting from an agroterrorism incident. Before the Partnership begins development of a strategy, however, it must take into consideration the factors that are important when communicating about agroterrorism risk: recognition that communication of risk about food carries with it specific challenges; the public's level of trust in government will affect how it perceives and accepts risk messages; and Americans' post-September 11, 2001 fear associated with terrorism alters perception and acceptance of risk. Recognition of the existence of these factors is not enough, however. The Partnership must recognize, as well, that these factors may present barriers to effective communication. To overcome these barriers, the Partnership should apply tried-and-true risk communication principles, tailored to specifically address the factors that make agroterrorism risk communication unique. / Civilian, Public Affairs Manager, Iowa Homeland Security and Emergency Management Division

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

L'incidence de l'infection par le VIH, définie comme le nombre de sujets nouvellement infectés par le VIH au cours du temps, est le seul indicateur permettant réellement d'appréhender la dynamique de l'épidémie du VIH/SIDA. Sa connaissance permet de prévoir les conséquences démographiques de l'épidémie et les besoins futurs de prise en charge, mais également d'évaluer l'efficacité des programmes de prévention. Jusqu'à très récemment, l'idée de base pour estimer l'incidence de l'infection par le VIH a été d'utiliser la méthode de rétro-calcul à partir des données de l'incidence du SIDA et de la connaissance de la distribution de la durée d'incubation du SIDA. L'avènement, à partir de 1996, de nouvelles combinaisons thérapeutiques très efficaces contre le VIH a contribué à modifier la durée d'incubation du SIDA et, par conséquent, à augmenter la difficulté d'utilisation de la méthode de rétro-calcul sous sa forme classique. Plus récemment, l'idée d'intégrer des informations sur les dates de diagnostic VIH a permis d'améliorer la précision des estimations. La plupart des pays occidentaux ont mis en place depuis quelques années un système de surveillance de l'infection à VIH. En France, la notification obligatoire des nouveaux diagnostics d'infection VIH, couplée à la surveillance virologique permettant de distinguer les contaminations récentes des plus anciennes a été mise en place en mars 2003. L'objectif de ce travail de thèse est de développer de nouvelles méthodes d'estimation de l'incidence de l'infection par le VIH capables de combiner les données de surveillance des diagnostics VIH et SIDA et d'utiliser les marqueurs sérologiques recueillis dans la surveillance virologique dans le but de mieux saisir l'évolution de l'épidémie dans les périodes les plus récentes. / The knowledge of the dynamics of the HIV/AIDS epidemic is crucial for planning current and future health care needs. The HIV incidence, i.e. the number of new HIV infections over time, determines the trajectory and the extent of the epidemic but is difficult to measure. The backcalculation method has been widely developed and used to estimate the past pattern of HIV infections and to project future incidence of AIDS from information on the incubation period distribution and AIDS incidence data. In recent years the incubation period from HIV infection to AIDS has changed dramatically due to increased use of antiretroviral therapy, which lengthens the time from HIV infection to the development of AIDS. Therefore, it has become more difficult to use AIDS diagnosis as the basis for back-calculation. More recently, the idea of integrating information on the dates of HIV diagnosis has improved the precision of estimates. In recent years, most western countries have set up a system for monitoring HIV infection. In France, the mandatory reporting of newly diagnosed HIV infection, coupled with virological surveillance to distinguish recent infections from older, was introduced in March 2003. The goal of this PhD thesis is to develop new methods for estimating the HIV incidence able to combine data from monitoring HIV and AIDS diagnoses and use of serologic markers collected in the virological surveillance in order to better understand the evolution of the epidemic in the most recent periods.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Infectious diseases are increasingly being recognised as significant threatening processes in conservation biology. Developing strategies to effectively manage infectious diseases in wildlife is, therefore, of the utmost importance to the maintenance of global biodiversity. The effective management of infectious diseases relies on understanding the ecology of the host, the epidemiological characteristics of the pathogen and the impacts of the pathogen on the host population. However, for most wildlife-disease systems this information remains poorly understood. This is particularly true for endangered species threatened by novel infectious agents as opportunities to observe and assess disease impacts and host-pathogen dynamics in the wild are limited. The Tasmanian devil (Sarcophilus harrisii), the world’s largest carnivorous marsupial, is threatened with extinction as a result of an epidemic of an emerging disease, a fatal infectious cancer known as Devil Facial Tumour Disease (DFTD). In this thesis I capitalised on a unique dataset from a population of Tasmanian devils where disease arrived part-way through an intensive longitudinal study, and utilised existing genetic samples collected prior to DFTD outbreak, to determine the impact of DFTD on the demography, population dynamics, genetic diversity and population genetic structure of wild Tasmanian devils. I then used this knowledge of the impacts of DFTD impacts in an unmanaged population to evaluate the effectiveness of a disease management trial involving the selective culling of infected individuals. I employed mark-recapture models to investigate the impact of DFTD on age-specific and sex-specific apparent survival rates, to examine the pattern of variation in infection rates (force of infection), and to investigate the impact of DFTD on population growth rate. I investigated demography, life-history traits and morphometric parameters of infected and uninfected individuals to determine the impacts of DFTD on age-structure and sex-structure, female fecundity and individual growth rates. I used this information to assess the population’s ability to respond to low population densities and to compensate for the detrimental impacts of DFTD. To determine the genetic consequences of disease-induced population decline I used microsatellite DNA to compare genetic diversity, population genetic structure and dispersal patterns in three Tasmanian devil populations prior to and following DFTD outbreaks. Capture-mark-recapture analyses revealed that the arrival of DFTD triggered an immediate decline in apparent survival rates of devils, the rate of which was predicted well by the increase in disease prevalence in the population over time. Transition rates of healthy individuals to the diseased class (the force of infection) increased in relation to disease prevalence, while the arrival of DFTD coincided with a marked and ongoing decline in the population growth rate. There was a significant change to the age structure following the arrival of DFTD. This shift to a younger population was caused by the loss of older individuals as a direct consequence of DFTD-driven declines in adult survival rates. Evidence of reproductive compensation in response to these disease impacts was observed via a reduction in the age of sexual maturity of females over time. However, widespread precocial breeding in devils was precluded by physiological and ecological constraints that limited the ability of one year olds to breed. Using temporally-replicated spatial genetic data, I found evidence of increased inbreeding following DFTD arrival and greater population genetic differentiation in post-disease populations. These changes appeared to be driven by a combination of selection and altered dispersal patterns of females in DFTD-affected populations. Comparison of demographic and epidemiological parameters indicative of disease progression and impact between the managed and unmanaged populations revealed that selective culling of infected individuals neither slowed the rate of disease progression nor reduced the population level impacts of this debilitating disease; with culling mortality simply compensating for disease mortality. This thesis provides one of the few direct empirical evaluations of the impact of an emerging wildlife disease epidemic on a wild population. This thesis revealed that infectious diseases can result in major demographic and genetic changes in host populations over relatively few generations and short time-scales. Results showing dramatic and ongoing population declines and very limited population compensation in DFTD-affected populations indicate that DFTD poses a significant extinction risk for wild devil populations. Hence, this study confirms that host-specific pathogens can pose a significant extinction risk for wild species, even in the absence of alternate reservoir hosts, a finding critical to our understanding of host-pathogen dynamics. My thesis also highlights the potential negative interplay between disease susceptibility and host genetic variability, which is of utmost importance to the management of novel wildlife epizootics and the conservation of threatened wildlife in general. The thorough understanding of the ecology and impacts of DFTD in the wild obtained in this study has provided a solid base from which to both rigorously assess the outcome of management strategies and also formulate recommendations for the management of this disease in the wild. The lack of evidence for successful control of the DFTD epidemic in a wild population during the first phase of a selective culling experimental adaptive management approach, points to the need to implement a multi-faceted disease management program when attempting to control a novel infectious disease in the wild. By drawing on the lessons learnt in this case study I show that it is possible to establish a set of general guidelines for the future management of infectious diseases in threatened wildlife.