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Injury now surpasses disease as the leading global cause of premature death and disability, claiming over 5.8 millions lives each year. However, unlike disease, which has been subjected to a rigorous epidemiologic approach, the field of injury prevention and control has been a relative newcomer to scientific investigation. With the distribution of injury now well described (i.e. ???who???, ???what???, ???where??? and ???when???), the underlying hypothesis is that progress in understanding ???how??? and ???why??? lies in classifying injury occurrences aetiologically. The advancement of a means of classifying injury aetiology has so far been inhibited by two related limitations: 1. Structural limitation: The absence of a cohesive and validated aetiological taxonomy for injury, and; 2. Methodological limitation: The need to manually classify large numbers of injury cases to determine aetiological patterns. This work is directed at overcoming these impediments to injury research. An aetiological taxonomy for injury was developed consistent with epidemiologic principles, along with clear conventions and a defined three-tier hierarchical structure. Validation testing revealed that the taxonomy could be applied with a high degree of accuracy (coder/gold standard agreement was 92.5-95.0%), and with high inter- and intra- coder reliability (93.0-96.3% and 93.5-96.3%). Practical application demonstrated the emergence of strong aetiological patterns which provided insight into causative sequences leading to injury, and led to the identification of effective control measures to reduce injury frequency and severity. However, limitations related to the inefficient and error-prone manual classification process (i.e. average 4.75 minute/case processing time and 5.0-7.5% error rate), revealed the need for an automated approach. To overcome these limitations, a knowledge acquisition (KA) software tool was developed, tested and applied, based on an expertsystems technique known as ripple down rules (RDR). It was found that the KA system was able acquire tacit knowledge from a human expert and apply learned rules to efficiently and accurately classify large numbers of injury cases. Ultimately, coding error rates dropped to 3.1%, which, along with an average 2.50 minute processing time, compared favourably with results from manual classification. As such, the developed taxonomy and KA tool offer significant advantages to injury researchers who have a need to deduce useful patterns from injury data and test hypotheses regarding causation and prevention.

In regression analysis, random errors in an explanatory variable cause the usual estimates of its regression coefficient to be biased. Although this problem has been studied for many years, routine methods have not emerged. This thesis investigates some aspects of this problem in the setting of analysis of epidemiological data. A major premise is that methods to cope with this problem must account for the shape of the frequency distribution of the true covariable, e.g., exposure. This is not widely recognized, and many existing methods focus only on the variability of the true covariable, rather than on the shape of its distribution. Confusion about this issue is exacerbated by the existence of two classical models, one in which the covariable is a sample from a distribution and the other in which it is a collection of fixed values. A unified approach is taken here, in which for the latter of these models more attention than usual is given to the frequency distribution of the fixed values. In epidemiology the distribution of exposures is often very skewed, making these issues particularly important. In addition, the data sets can be very large, and another premise is that differences in the performance of methods are much greater when the samples are very large. Traditionally, methods have largely been evaluated by their ability to remove bias from the regression estimates. A third premise is that in large samples there may be various methods that will adequately remove the bias, but they may differ widely in how nearly they approximate the estimates that would be obtained using the unobserved true values. A collection of old and new methods is considered, representing a variety of basic rationales and approaches. Some comparisons among them are made on theoretical grounds provided by the unified model. Simulation results are given which tend to confirm the major premises of this thesis. In particular, it is shown that the performance of one of the most standard approaches, the "correction for attenuation" method, is poor relative to other methods when the sample size is large and the distribution of covariables is skewed. / Graduation date: 1993

Injury now surpasses disease as the leading global cause of premature death and disability, claiming over 5.8 millions lives each year. However, unlike disease, which has been subjected to a rigorous epidemiologic approach, the field of injury prevention and control has been a relative newcomer to scientific investigation. With the distribution of injury now well described (i.e. ???who???, ???what???, ???where??? and ???when???), the underlying hypothesis is that progress in understanding ???how??? and ???why??? lies in classifying injury occurrences aetiologically. The advancement of a means of classifying injury aetiology has so far been inhibited by two related limitations: 1. Structural limitation: The absence of a cohesive and validated aetiological taxonomy for injury, and; 2. Methodological limitation: The need to manually classify large numbers of injury cases to determine aetiological patterns. This work is directed at overcoming these impediments to injury research. An aetiological taxonomy for injury was developed consistent with epidemiologic principles, along with clear conventions and a defined three-tier hierarchical structure. Validation testing revealed that the taxonomy could be applied with a high degree of accuracy (coder/gold standard agreement was 92.5-95.0%), and with high inter- and intra- coder reliability (93.0-96.3% and 93.5-96.3%). Practical application demonstrated the emergence of strong aetiological patterns which provided insight into causative sequences leading to injury, and led to the identification of effective control measures to reduce injury frequency and severity. However, limitations related to the inefficient and error-prone manual classification process (i.e. average 4.75 minute/case processing time and 5.0-7.5% error rate), revealed the need for an automated approach. To overcome these limitations, a knowledge acquisition (KA) software tool was developed, tested and applied, based on an expertsystems technique known as ripple down rules (RDR). It was found that the KA system was able acquire tacit knowledge from a human expert and apply learned rules to efficiently and accurately classify large numbers of injury cases. Ultimately, coding error rates dropped to 3.1%, which, along with an average 2.50 minute processing time, compared favourably with results from manual classification. As such, the developed taxonomy and KA tool offer significant advantages to injury researchers who have a need to deduce useful patterns from injury data and test hypotheses regarding causation and prevention.

Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.

Traditionally, cancer epidemiologists have investigated the causes of disease under the premise that patients with a certain site of disease can be treated as a single entity. Then risk factors associated with the disease are identified through case-control or cohort studies for the disease as a whole. However, with the rise of molecular and genomic profiling, in recent years biologic subtypes have increasingly been identified. Once subtypes are known, it is natural to ask the question of whether they share a common etiology, or in fact arise from distinct sets of risk factors, a concept known as etiologic heterogeneity. This dissertation seeks to evaluate methods for the study of etiologic heterogeneity in the context of cancer research and with a focus on methods for case-control studies. First, a number of existing regression-based methods for the study of etiologic heterogeneity in the context of pre-defined subtypes are compared using a data example and simulation studies. This work found that a standard polytomous logistic regression approach performs at least as well as more complex methods, and is easy to implement in standard software. Next, simulation studies investigate the statistical properties of an approach that combines the search for the most etiologically distinct subtype solution from high dimensional tumor marker data with estimation of risk factor effects. The method performs well when appropriate up-front selection of tumor markers is performed, even when there is confounding structure or high-dimensional noise. And finally, an application to a breast cancer case-control study demonstrates the usefulness of the novel clustering approach to identify a more risk heterogeneous class solution in breast cancer based on a panel of gene expression data and known risk factors.

L’épidémiologie de terrain peut être définie comme un ensemble de méthodes de collecte et de traitement de l’information combinant successivement les approches de l’épidémiologie descriptive mais aussi celles de l’épidémiologie analytique. La finalité de l’analyse descriptive sera de décrire et de quantifier la survenue du phénomène étudié dans une population donnée, permettant ainsi la formulation d’hypothèses préalables à la phase analytique. Phase, qui se focalisera sur les "associations" entre des "facteurs de risque" et la survenue du phénomène étudié. Dans la réponse aux questionnements posés ces deux phases les méthodes statistiques seront des outils incontournables. Afin que les résultats produits par ces analyses soient non seulement utiles mais aussi valables et utilisables, une bonne identification et une application adéquate des méthodes d’analyse s’avèreront primordiales. <p>A côté de ce constat méthodologique, il y a, dans le champ des traumatismes, tant en Belgique, qu’en pays en développement, la quasi absence d’informations pertinentes et rigoureuses pour documenter l’importance de cette problématique dans le champ de la santé. Pourtant, selon l’Organisation Mondiale de la Santé, plus de 5 millions de personnes décèdent des suites d’un traumatisme chaque année, avec 90% de ces décès survenant dans les pays à faible revenu ou à revenu intermédiaire. En Europe, les données montrent qu’une personne décède toutes les deux minutes des suites d’un traumatisme, et que pour chaque citoyen européen qui en meure, 25 personnes sont admises à l’hôpital, 145 sont traitées en ambulatoire et plus encore se font soigner ailleurs. <p> Au vu du double constat, qui est, d’une part, que les méthodes statistiques ne sont pas toujours exploitées correctement, et d’autre part, qu’il y a un manque d’informations appropriées et rigoureuses pour documenter l’ampleur du problème des traumatismes; ce travail de thèse poursuit l’objectif majeur, de montrer l’intérêt qu’il y a à appliquer de manière pertinente, adéquate et complète, des méthodes statistiques (univariées, multivariables et multivariées) adaptées aux différentes sources de données disponibles, afin de documenter l’importance des traumatismes, et des facteurs qui y sont associés, tant en pays industrialisés (exemple de la Belgique) qu’en pays en développement (exemple du Cameroun).<p>La partie classiquement appelée "résultats", correspond dans ce travail à deux chapitres distincts. Le premier fait la synthèse de ce qui a été objectivé par la revue de la littérature en termes de sources de données exploitées et de méthodes d’analyse statistique utilisées. Le second correspond à l’exploitation de quatre bases de données :une "généraliste et populationnelle" (First Health of Young People Survey - Cameroun), une "généraliste et hospitalière" (Résumé Hospitalier Minimum - Belgique), une "spécifique et populationnelle" (données issue de compagnies d’assurances belges), et une " spécifique et hospitalière" (Service SOS Enfants du CHU St Pierre - Belgique). <p>Les constats majeurs à l’issue de ce travail sont qu’il est possible de trouver dans le panel des méthodes statistiques "classiques", les méthodes nécessaires pour répondre aux questionnements de surveillance "en routine" en termes d’occurrence et de facteurs associés. L’accent devrait être mis sur une (meilleure) utilisation (justifiée, correcte et complète) de ces méthodes et sur une meilleure présentation (plus complète) des résultats. L’utilisation adéquate s’assurant d’une part, par une meilleure formation en méthodologie statistique pour les praticiens mais aussi par l’intégration, à part entière, des statisticiens dans les équipes de recherches. En ce qui concerne les sources de données utilisées, le potentiel d’information existe. Chaque source de données a ses avantages et ses inconvénients mais utilisées conjointement elles permettent d’avoir une vision plus globale du fardeau des traumatismes. L’accent devrait être mis sur l’amélioration de la disponibilité, la mise en commun mais aussi sur la qualité des données qui seraient disponibles. Dès lors, en vue de s’intégrer dans une dynamique de "Système de Surveillance des Traumatismes", une réflexion sur une utilisation globale (qu’elle soit couplée ou non) de ces différentes sources de données devrait être menée. <p>En Belgique, de nombreuses données, contenant de l’information sur les traumatismes, sont collectées en routine, au travers des données hospitalières, et ponctuellement, au travers de données d’enquêtes. Actuellement, ces données, dont la qualité reste discutable pour certaines, sont sous-utilisées dans le champ qui nous intéresse. Dans le futur, "plutôt que de ne rien savoir", il est important de continuer à exploiter l’existant pour produire et diffuser de l’information, mais cette exploitation et cette diffusion doivent s’accompagner non seulement de réflexion mais aussi d’action sur la qualité des données. En ce qui concerne l’utilisation des méthodes statistiques, nous préconisons une double approche :l’intégration et la formation. Par intégration, nous entendons le fait qu’il faut d’une part considérer le statisticien comme un professionnel ayant à la fois des compétences techniques pointues sur les méthodes, qui pourront être mises à disposition pour garantir le bon déroulement de la collecte et de l’analyse des données, mais aussi comme un chercheur capable de s’intéresser plus spécifiquement à des problématiques de santé publique, comme la problématique des traumatismes par exemple. Par formation, nous entendons le fait qu’il est essentiel d’augmenter et/ou de parfaire non seulement les connaissances des futurs professionnels de la santé (publique) en cours de formation mais aussi celles des praticiens déjà actifs sur le terrain et dès lors premiers acteurs de la collecte de l’information et de son utilisation dans une démarche de prise de décision, de détermination de priorité d’action et d’évaluation. <p>L’objectif majeur de ce travail de thèse était de montrer l’intérêt qu’il y a à appliquer de manière pertinente, adéquate et complète, des méthodes statistiques adaptées aux différentes sources de données disponibles, afin de documenter l’importance des traumatismes, et des facteurs qui y sont associés. En ayant discuté de l’existence de plusieurs sources potentielles de données en Belgique et en ayant appliqué une série de méthodes statistiques univariées, multivariables et multivariées, sur quelques-unes de celles-ci, nous avons montré qu’il était possible de documenter le fardeau des traumatismes au-travers de résultats utiles mais aussi valables et utilisables dans une approche de santé publique.<p> / Doctorat en Sciences de la santé publique / info:eu-repo/semantics/nonPublished

Measurement error is common in epidemiologic studies and can lead to biased statistical inference. It is well known, for example, that regression analyses involving measurement error in predictors often produce biased model coefficient estimates. The work in this dissertation adds to the existing vast literature on measurement error by proposing a missing data treatment of measurement error through general location models. The focus is on the case in which information about the measurement error model is not obtained from a subsample of the main study data but from separate, external information, namely the external calibration. Methods for handling measurement error in the setting of external calibration are in need with the increase in the availability of external data sources and the popularity of data integration in epidemiologic studies. General location models are well suited for the joint analysis of continuous and discrete variables. They offer direct relationships with the linear and logistic regression models and can be readily implemented using frequentist and Bayesian approaches. We use the general location models to correct for measurement error and misclassification in the context of three practical problems. The first problem concerns measurement error in a continuous variable from a dataset containing both continuous and categorical variables. In the second problem, measurement error in the continuous variable is further complicated by the limit of detection (LOD) of the measurement instrument, resulting in some measures of the error-prone continuous variable undetectable if they are below LOD. The third problem deals with misclassification in a binary treatment variable. We implement the proposed methods using Bayesian approaches for the first two problems and using the Expectation-maximization algorithm for the third problem. For the first problem we propose a Bayesian approach, based on the general location model, to correct measurement error of a continuous variable in a data set with both continuous and categorical variables. We consider the external calibration setting where in addition to the main study data of interest, calibration data are available and provide information on the measurement error but not on the error-free variables. The proposed method uses observed data from both the calibration and main study samples and incorporates relationships among all variables in measurement error adjustment, unlike existing methods that only use the calibration data for model estimation. We assume by strong nondifferential measurement error (sNDME) that the measurement error is independent of all the error-free variables given the true value of the error-prone variable. The sNDME assumption allows us to identify our model parameters. We show through simulations that the proposed method yields reduced bias, smaller mean squared error, and interval coverage closer to the nominal level compared to existing methods in regression settings. Furthermore, this improvement is pronounced with increased measurement error, higher correlation between covariates, and stronger covariate effects. We apply the new method to the New York City Neighborhood Asthma and Allergy Study to examine the association between indoor allergen concentrations and asthma morbidity among urban asthmatic children. The simultaneous occurrence of measurement error and LOD is common particularly in environmental exposures such as measurements of the indoor allergen concentrations mentioned in the first problem. Statistical analyses that do not address these two problems simultaneously could lead to wrong scientific conclusions. To address this second problem, we extend the Bayesian general location models for measurement error adjustment to handle both measurement error and values below LOD in a continuous environmental exposure in a regression setting with mixed continuous and discrete variables. We treat values below LOD as censored. Simulations show that our method yields smaller bias and root mean squared error and the posterior credible interval of our method has coverage closer to the nominal level compared to alternative methods, even when the proportion of data below LOD is moderate. We revisit data from the New York City Neighborhood Asthma and Allergy Study and quantify the effect of indoor allergen concentrations on childhood asthma when over 50% of the measured concentrations are below LOD. We finally look at the third problem of group mean comparison when treatment groups are misclassified. Our motivation comes from the Frequent User Services Engagement (FUSE) study. Researchers wish to compare quantitative health and social outcome measures for frequent jail-and-shelter users who were assigned housing and those who were not housed, and misclassification occurs as a result of noncompliance. The recommended intent-to-treat analysis which is based on initial group assignment is known to underestimate group mean differences. We use the general location model to estimate differences in group means after adjusting for misclassification in the binary grouping variable. Information on the misclassification is available through the sensitivity and specificity. We assume nondifferential misclassification so that misclassification does not depend on the outcome. We use the expectation-maximization algorithm to obtain estimates of the general location model parameters and the group means difference. Simulations show the bias reduction in the estimates of group means difference.

Internet technology has affected humans' lives in many disciplines. The search engine is one of the most important Internet tools in that it allows people to search for what they want. Search queries entered in a web search engine can be used to predict dengue incidence. This vector borne disease causes severe illness and kills a large number of people every year. This dissertation utilizes the capabilities of search queries related to dengue and climate to forecast the number of dengue cases. Several machine learning techniques are applied for data analysis, including Multiple Linear Regression, Artificial Neural Networks, and the Seasonal Autoregressive Integrated Moving Average. Predictive models produced from these machine learning methods are measured for their performance to find which technique generates the best model for dengue prediction. The results of experiments presented in this dissertation indicate that search query data related to dengue and climate can be used to forecast the number of dengue cases. The performance measurement of predictive models shows that Artificial Neural Networks outperform the others. These results will help public health officials in planning to deal with the outbreaks.