Dissertations / Theses on the topic 'Health models'

Ce manuscrit porte sur certaines extensions à des séries temporelles prenant des valeurs entières du modèle paramétrique périodique autorégressif établi pour des séries prenant des valeurs réelles. Les modèles que nous considérons sont basés sur l'utilisation de l'opérateur de Steutel et Van Harn (1979) et généralisent le processus autorégressif stationnaire à valeurs entières (INAR) introduit par Al-Osh & Alzaid (1987) à des séries de comptage périodiquement corrélées. Ces généralisations incluent l'introduction d'un opérateur périodique, la prise en compte d'une structure d’autocorrélation plus complexe dont l’ordre est supérieur à un, l'apparition d'innovations de variances périodiques mais aussi à inflation de zéro par rapport à une loi discrète donnée dans la famille des distributions exponentielles, ainsi que l’utilisation de covariables explicatives. Ces extensions enrichissent considérablement le domaine d'applicabilité des modèles de type INAR. Sur le plan théorique, nous établissons des propriétés mathématiques de nos modèles telles que l'existence, l'unicité, la stationnarité périodique de solutions aux équations définissant les modèles. Nous proposons trois méthodes d'estimation des paramètres des modèles dont une méthode des moments basée sur des équations du type Yule-Walker, une méthode des moindres carrés conditionnels, et une méthode du quasi maximum de vraisemblance (QML) basée sur la maximisation d'une vraisemblance gaussienne. Nous établissons la consistance et la normalité asymptotique de ces procédures d'estimation. Des simulations de type Monte Carlo illustrent leur comportement pour différentes tailles finies d'échantillon. Les modèles sont ensuite ajustés à des données réelles et utilisés à des fins de prédiction. La première extension du modèle INAR que nous proposons consiste à introduire deux opérateurs de Steutel et Van Harn périodiques, l'un modélisant les autocorrélations partielles d'ordre un sur chaque période et l'autre captant la saisonnalité périodique des données. Grâce à une représentation vectorielle du processus, nous établissons les conditions l'existence et d'unicité d'une solution périodiquement corrélées aux équations définissant le modèle. Dans le cas où les innovations suivent des lois de Poisson, nous étudions la loi marginale du processus. Á titre d'exemple d'application sur des données réelles, nous ajustons ce modèle à des données de comptage journalières du nombre de personnes ayant reçu des antibiotiques pour le traitement de maladies respiratoires dans la région de Vitória au Brésil. Comme les affections respiratoires sont fortement corrélées au niveau de pollution atmosphérique et aux conditions climatiques, la structure de corrélation des nombres quotidiens de personnes recevant des antibiotiques montre, entre autres caractéristiques, une périodicité et un caractère saisonnier hebdomadaire. Nous étendons ensuite ce modèle à des données présentant des autocorrélations partielles périodiques d'ordre supérieur à un. Nous étudions les propriétés statistiques du modèle, telles que la moyenne, la variance, les distributions marginales et jointes. Nous ajustons ce modèle au nombre quotidien de personnes recevant du service d'urgence de l'hôpital public de Vitória un traitement pour l'asthme. Enfin, notre dernière extension porte sur l'introduction d'innovations suivant une loi de Poisson à inflation de zéro dont les paramètres varient périodiquement, et sur l’ajout de covariables expliquant le logarithme de l'intensité de la loi de Poisson. Nous établissons certaines propriétés statistiques du modèle et nous mettons en oeuvre la méthode du QML pour estimer ses paramètres. Enfin, nous appliquons cette modélisation à des données journalières du nombre de personnes qui se sont rendues dans le service d'urgence d'un hôpital pour des problèmes respiratoires, et nous utilisons comme covariable la concentration de polluant dans la même zone géographique
This manuscript deals with some extensions to time series taking integer values of the autoregressive periodic parametric model established for series taking real values. The models we consider are based on the use of the operator of Steutel and Van Harn (1979) and generalize the stationary integer autoregressive process (INAR) introduced by Al-Osh & Alzaid (1987) to periodically correlated counting series. These generalizations include the introduction of a periodic operator, the taking into account of a more complex autocorrelation structure whose order is higher than one, the appearance of innovations of periodic variances but also at zero inflation by relation to a discrete law given in the family of exponential distributions, as well as the use of explanatory covariates. These extensions greatly enrich the applicability domain of INAR type models. On the theoretical level, we establish mathematical properties of our models such as the existence, the uniqueness, the periodic stationarity of solutions to the equations defining the models. We propose different methods for estimating model parameters, including a method of moments based on Yule-Walker equations, a conditional least squares method, and a quasi-maximum likelihood method based on the maximization of a Gaussian likelihood. We establish the consistency and asymptotic normality of these estimation procedures. Monte Carlo simulations illustrate their behavior for different finite sample sizes. The models are then adjusted to real data and used for prediction purposes.The first extension of the INAR model that we propose consists of introducing two periodic operators of Steutel and Van Harn, one modeling the partial autocorrelations of order one on each period and the other capturing the periodic seasonality of the data. Through a vector representation of the process, we establish the conditions of existence and uniqueness of a solution periodically correlated to the equations defining the model. In the case where the innovations follow Poisson's laws, we study the marginal law of the process. As an example of real-world application, we are adjusting this model to daily count data on the number of people who received antibiotics for the treatment of respiratory diseases in the Vitória region in Brazil. Because respiratory conditions are strongly correlated with air pollution and weather, the correlation pattern of the daily numbers of people receiving antibiotics shows, among other characteristics, weekly periodicity and seasonality. We then extend this model to data with periodic partial autocorrelations of order higher than one. We study the statistical properties of the model, such as mean, variance, marginal and joined distributions. We are adjusting this model to the daily number of people receiving emergency service from the public hospital of the municipality of Vitória for treatment of asthma. Finally, our last extension deals with the introduction of innovations according to a Poisson law with zero inflation whose parameters vary periodically, and on the addition of covariates explaining the logarithm of the intensity of the Poisson's law. We establish some statistical properties of the model, and we use the conditional maximum likelihood method to estimate its parameters. Finally, we apply this modeling to daily data of the number of people who have visited a hospital's emergency department for respiratory problems, and we use the concentration of a pollutant in the same geographical area as a covariate
Este manuscrito trata de algumas extensões para séries temporais de valores inteiros domodelo paramétrico periódico autorregressivo estabelecido séries temporais de valores reais. Osmodelos considerados baseiam-se no uso do operadorde Steutel e Van Harn (1979) e generalizamo processo autorregressivo depara números inteiros estacionários (INAR) introduzidos por Al-Osh & Alzaid(1987) para séries de contagem periodicamente correlacionadas. Essas generalizações incluem aintrodução de um operador periódico, a consideração de uma estrutura de autocorrelação mais complexa,cuja ordem é maior do que um, o aparecimentode inovações de variâncias periódicas, e também ainflação zero em relação a uma lei discreta dadana família de distribuições exponenciais, bem comoo uso de covariáveis explicativas. Essas extensões enriquecem muito o domínio de aplicabilidade dosmodelos do tipo INAR. No nível teórico, estabelecemospropriedades matemáticas de nossos modeloscomo a existência, a unicidade, e a estacionariedadeperiódica de soluções para as equações que definemos modelos. Propomos três métodos para estimarparâmetros de modelos, incluindo um métodode momentos baseado nas equações de Yule-Walker,um método de mínimos quadrados condicionais e ummétodo de quasi-máxima verossimilhança (QML) baseadona maximização de uma probabilidade Gaussiana. Estabelecemos a consistência e a normalidadeassintótica desses procedimentos de estimativa. Assimulações de Monte Carlo ilustram seus comportamentospara diferentes tamanhos de amostras finitas.Os modelos são então ajustados para dados reais eusados para fins de previsão. A primeira extensão domodelo INAR que propomos consiste na introdução de dois operadores periódicos de Steutel e VanHarn, o primeiro atua modelando as autocorrelações parciais de ordem um em cada período e o outro capturando a sazonalidade periódica dos dados.Através de uma representação vetorial do processo,estabelecemos as condições existência e unicidadede uma solução periodicamente correlacionada às equações que definem o modelo. No casoem que as inovações seguem as leis de Poisson,estudamos a lei marginal do processo. Como umexemplo de aplicação no mundo real, estamos ajustandoeste modelo aos dados diários de contagemdo número de pessoas que receberam antibióticos para o tratamento de doenças respiratórias na região de Vitória, Brasil. Como as condições respiratórias estão fortemente correlacionadas com a poluição doar e o clima, o padrão de correlação dos números diários de pessoas que recebem antibióticos mostra,entre outras características, a periodicidade semanale a sazonalidade. Em seguida, estendemosesse modelo para dados com autocorrelações parciaisperiódicas de ordem maior que um. Estudamosas propriedades estatísticas do modelo, como média,variância, distribuições marginais e conjuntas. Ajustamosesse modelo ao número diário de pessoascom problema respiratório que receberam atendimentode emergência no pronto-atendimento da redepública do município de Vitória. Finalmente, nossa última extensão trata da introdução de inovações de acordo com uma lei de Poisson com inflação zero cujos parâmetros variam periodicamente, e daadição de covariáveis explicando o logaritmo da intensidadeda lei de Poisson. Estabelecemos algumaspropriedades estatísticas do modelo e usamoso método QML para estimar seus parâmetros. Porfim, aplicamos essa modelagem aos dados diários sobre o número de pessoas que visitaram o departamentode emergência de um hospital por problemasrespiratórios e usamos como covariável a sérieconcentrações diárias e um poluente medido namesma área geográfica

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 53-54).
Crisis Text Line supports people with mental health issues through texting. Unfortunately, support is limited by the number of counselors and the time each counselor has for clients, as well as the cognitive load on counselors from managing multiple conversations simultaneously. We conducted a contextual inquiry with crisis counselors to find contributing problems in their work flow. We believe topic modeling can provide automatic summaries of conversation text to augment note-taking and transcript-reading. Four simple and familiar visualizations were developed to present the model data: 1) a list of conversation topics, 2) a donut chart of topic percentages, 3) a line chart of topic trends, and 4) a scatter plot of specific topic points in the text. Our hypothesis is that these visualizations will help counselors spend more time on clients without overloading the counselors. The visualizations were evaluated through a user study to determine their effectiveness against a control interface.
by Ge (Jackie) Chen.
M. Eng.

The purpose of the current study was to assess how RBF performed in terms of efficiency, effectiveness, equity and governance in the Zimbabwean context. It outlines the evolution of health systems thinking and health funding models over time to show the history and changing landscape of health care financing and their actors. General consensus is there is need to focus on results of health care investments against a background of prodigious amounts of foreign aid with marginal or no improvements in heath care delivery for decades of development assistance in developing countries. Health systems in developing countries are beset with burgeoning domestic and foreign debts as well as diminishing fiscal space that has more often put the primary health delivery system in developing nations in "comatose". The research made use of both qualitative and quantitative dimensions. Findings indicate that the pre-RBF era was characterised by poor primary health outcomes, unsound governance and a lack of confidence in the public health delivery system. However, since RBF implementation, access to health care by marginalised groups has increased, with incentives and community participation liberalising health systems to greater efficiency as shown by slight increases in post-natal care visits in rural health care centres. A trade-off between achieving efficiency and equity was found especially when scaling up health programmes under the RBF initiative. Through embracing RBF, the primary health delivery system is poised for future development attributed to community buy-in and people-centric empowerment approaches.

The aim of this study is to increase understanding about the causes of dysphoria, depression and anxiety by identifying the psychological factors that predict the development or protect the individual from developing mental health problems. A quantitative study, it is conducted over a period of a year and utilises a 3 wave observational longitudinal cohort design to investigate the relationship between the psychological variables and processes leading to mental health or ill health in a community group of female undergraduate students (N=183). Data is collected at 6 montly intervals for a period of a year from 183 female students. The participants are first year undergraduate students at a college of higher education. Characteristics from the students are collected using a battery of paper and pencil self report questionnaires in a group administration for the first wave of data and two postal questionnaires for follow up. Conceptual models are developed and tested statistically using structural equation modelling to explore the relationship between the elements identified for each model retrospectively and prospectively over a period of 12 months. Longitudinal and cross-sectional analyses are conducted for anxiety and depression separately. The elements of the models include positive and negative life events and protective and vulnerability factors for depression and dysphoria. Results from the cross-sectional and logitudinal analysis demonstrate that psychological factors have a significant effect on the development of depression and anxiety, with illness provoking factors explaining between 33-55% of the variance of depression in longitudinal analyses and 59-42% of the variance in cross-sectional analyses. They explain 45-57% of the variance in anxiety in longitudinal analyses and 28-50% in cross-sectional analyses. Health enhancing factors explain 18-19% of the variance in deprssion in longitudinal analyses and 47-49% of the variance in cross-sectional analyses. They explain 15-20% of the variance in anxiety in longitudinal analyses and 12-=20% of the variance in cross-sectional analyses. Health enhancing and illness provoking characteristics for depression and anxiety are identified in this study. They include enduring personality characteristics, cognitive styles and coping strategies and act as predictors for mental health outcomes or mediate or moderate the relationship between predictors and mental health outcomes.

Doctor of Philosophy(PhD)
This thesis consists of a series of studies of new models of multidisciplinary community health care in four compartments. These compartments are acute, subacute, outpatient and maintenance care. The purpose of the individual studies is to demonstrate the benifits of munltidisciplinary community health care in delivering alternatives to current practice by replacing hospital care or improving traditional community care.

Statistical analysis and modeling are useful for understanding the behavior of different phenomena. In this study we will focus on two areas of applications: Global warming and cancer research. Global Warming is one of the major environmental challenge people face nowadays and cancer is one of the major health problem that people need to solve. For Global Warming, we are interest to do research on two major contributable variables: Carbon dioxide (CO2) and atmosphere temperature. We will model carbon dioxide in the atmosphere data with a system of differential equations. We will develop a differential equation for each of six attributable variables that constitute CO2 in the atmosphere and a differential system of CO2 in the atmosphere. We are using real historical data on the subject phenomenon to develop the analytical form of the equations. We will evaluate the quality of the developed model by utilizing a retrofitting process. Having such an analytical system, we can obtain good estimates of the rate of change of CO2 in the atmosphere, individually and cumulatively as a function of time for near and far target times. Such information is quite useful in strategic planning of the subject matter. We will develop a statistical model taking into consideration all the attributable variables that have been identified and their corresponding response of the amount of CO2 in the atmosphere in the continental United States. The development of the statistical model that includes interactions and higher order entities, in addition to individual contributions to CO2 in the atmosphere, are included in the present study. The proposed model has been statistically evaluated and produces accurate predictions for a given set of the attributable variables. Furthermore, we rank the attributable variables with respect to their significant contribution to CO2 in the atmosphere. For Cancer Research, the object of the study is to probabilistically evaluate commonly used methods to perform survival analysis of medical patients. Our study includes evaluation of parametric, semi-parametric and nonparametric analysis of probability survival models. We will evaluate the popular Kaplan-Meier (KM), the Cox Proportional Hazard (Cox PH), and Kernel density (KD) models using both Monte Carlo simulation and using actual breast cancer data. The first part of the evaluation will be based on how these methods measure up to parametric analysis and the second part using actual cancer data. As expected, the parametric survival analysis when applicable gives the best results followed by the not commonly used nonparametric Kernel density approach for both evaluations using simulation and actual cancer data. We will develop a statistical model for breast cancer tumor size prediction for United States patients based on real uncensored data. When we simulate breast cancer tumor size, most of time these tumor sizes are randomly generated. We want to construct a statistical model to generate these tumor sizes as close as possible to the real patients' data given other related information. We accomplish the objective by developing a high quality statistical model that identifies the significant attributable variables and interactions. We rank these contributing entities according to their percentage contribution to breast cancer tumor growth. This proposed statistical model can also be used to conduct surface response analysis to identify the necessary restrictions on the significant attributable variables and their interactions to minimize the size of the breast tumor. We will utilize the Power Law process, also known as Non-homogenous Poisson Process and Weibull Process to evaluate the effectiveness of a given treatment for Stage I & II Ductal breast cancer patients. We utilize the shape parameter of the intensity function to evaluate the behavior of a given treatment with respect to its effectiveness. We will develop a differential equation that will characterize the behavior of the tumor as a function of time. Having such a differential equation, the solution of which once plotted will identify the rate of change of tumor size as a function of age. The structure of the differential equation consists of the significant attributable variables and their interactions to the growth of breast cancer tumor. Once we have developed the differential equations and its solution, we proceed to validate the quality of the proposed differential equations and its usefulness.

The ongoing digitization of healthcare, which has been much accelerated by the widespread adoption of electronic health records, generates unprecedented amounts of clinical data in a readily computable form. This, in turn, affords great opportunities for making meaningful secondary use of clinical data in the endeavor to improve healthcare, as well as to support epidemiology and medical research. To that end, there is a need for techniques capable of effectively and efficiently analyzing large amounts of clinical data. While machine learning provides the necessary tools, learning effective predictive models from electronic health records comes with many challenges due to the complexity of the data. Electronic health records contain heterogeneous and longitudinal data that jointly provides a rich perspective of patient trajectories in the healthcare process. The diverse characteristics of the data need to be properly accounted for when learning predictive models from clinical data. However, how best to represent healthcare data for predictive modeling has been insufficiently studied. This thesis addresses several of the technical challenges involved in learning effective predictive models from electronic health records. Methods are developed to address the challenges of (i) representing heterogeneous types of data, (ii) leveraging the concept hierarchy of clinical codes, and (iii) modeling the temporality of clinical events. The proposed methods are evaluated empirically in the context of detecting adverse drug events in electronic health records. Various representations of each type of data that account for its unique characteristics are investigated and it is shown that combining multiple representations yields improved predictive performance. It is also demonstrated how the information embedded in the concept hierarchy of clinical codes can be exploited, both for creating enriched feature spaces and for decomposing the predictive task. Moreover, incorporating temporal information leads to more effective predictive models by distinguishing between event occurrences in the patient history. Both single-point representations, using pre-assigned or learned temporal weights, and multivariate time series representations are shown to be more informative than representations in which temporality is ignored. Effective methods for representing heterogeneous and longitudinal data are key for enhancing and truly enabling meaningful secondary use of electronic health records through large-scale analysis of clinical data.

Mestrado em Actuarial Science
Os Modelos Lineares Generalizados (GLMs) são amplamente utilizados na precificação de seguros do ramo Não Vida. O prémio cobrado pela seguradora é calculado com base em uma tarifa. A abordagem clássica para estimar o prémio é feita assumindo a independência entre o número de sinistros e o seu custo. A partir desta independência, a frequência e a severidade dos sinistros são estimados através de GLMs separados e a tarifa é obtida combinando os dois modelos. O presente relatório fornece uma breve introdução sobre a metodologia e descreve como preparámos os dados antes da aplicação do GLM. Os modelos obtidos para os Tratamentos e Consultas de Estomatologia, uma das muitas coberturas que podem ser incluídas numa apólice de Seguro Saúde, são analisados neste relatório. O software SAS foi utilizado para construir as bases de dados e para organizar adequadamente a informação e o software R foi utilizado para o processo de modelagem. Uma vez estimados os modelos, o prémio puro foi calculado e a tarifa, para a cobertura mencionada, foi construída. Por fim, comparámos os resultados obtidos em R com as conclusões obtidas pelos meus colegas, utilizando o software implementado pela empresa. Concluímos que ambos os modelos não são significativamente diferentes, apesar de apresentarem algumas distinções estruturais.
Generalized Linear Models (GLMs) are being broadly used in the Non-Life Insurance Pricing. The premium charged by the insurance company is calculated based on a tariff. The most standard procedure to estimate the pure premium is by assuming that the claim counts and claim amounts are independent. From this independence, the claim frequency and severity can be forecasted by distinct GLMs and the Tariff is obtained by combining both models. The present report gives a brief introduction on the methodology and describes how we prepared the data prior to the GLM application. The models obtained for the Stomatology Treatments and Appointments, one of the many coverages that can be included in a Health Insurance policy, are analyzed in this report. The SAS software was used to construct the datasets and to properly organize the data and R was the software used for the modelling process. Once the models were estimated, the pure premium was calculated and a tariff for the mentioned coverage was constructed. Finally, we compared the results obtained by modelling the coverage in R with the output obtained by my colleagues, using the software implemented by the company. We conclude that both models are not significantly different, despite having some structural distinctions.
info:eu-repo/semantics/publishedVersion

Background: Nearly 750,000 adolescents and emerging adults with special health care needs (AEA-SHCN) enter into adulthood annually. The linkages to ensure the seamless transfer of care from pediatric to adult care and transition to adulthood for AEA-SHCN have yet to be realized. Purpose: The purpose of this systematic review was to investigate the state of the science of health care transition (HCT) service models as described in quantitative investigations. Methods: A four-tier screening approach was used to obtain reviewed articles published from 2004 to 2013. A total of 17 articles were included in this review. Discussion: Transfer of care was the most prominent intervention feature. Overall, using the Effective Public Health Practice Project criteria, the studies were rated as weak. Limitations included lack of control groups, rigorous designs and methodology, and incomplete intervention descriptions. Conclusion: As the findings indicate, HCT is an emerging field of practice that is largely in the exploratory stage of model development.

Health economic models are representations of judgements about the relationships between the model's input parameters and the costs and health effects that the model aims to predict. We recognise that we can rarely define with certainty a 'true' model for a particular decision problem. Building an 'incorrect' model will result in an uncertain prediction error, which we denote 'structural uncertainty'. The absence of observations on the total costs and health effects under each decision option limits the use of data driven approaches to managing structural uncertainty, such as model averaging. We therefore propose a discrepancy based approach in which we make judgements about structural error at the sub-function level within the model and introduce a series of terms to 'correct' the errors. This is deemed to be easier than making meaningful statements about the error at the level of the model output. The specification of discrepancy terms within the model also allows us to use sensitivity analysis methods to determine the relative importance of the different structural uncertainties in driving output and decision uncertainty. Following the computation of either the main effect index or the partial expected value of perfect information for each discrepancy term, we can review the structure of those parts of the model where structural uncertainty is an important source of model output or decision uncertainty. We interpret the overall expected value of perfect information for all the discrepancy terms as an upper bound on the expected value of model improvement (EVMI). We illustrate the sub-function discrepancy method in two case studies: a simple decision tree, and a more complex Markov model. Finally, we propose an efficient method for computing the main effect index and the partial expected value of perfect information when inputs and/or discrepancies are correlated.

Occupational and environmental epidemiological studies often involve ordinal data, including antibody titer data, indicators of health perceptions, and certain psychometrics. Ideally, such data should be analyzed using approaches that exploit the ordinal nature of the scale, while making a minimum of assumptions. In this work, we first review and illustrate the analytical technique of ordinal logistic regression called the "proportional odds model". This model, which is based on a constrained ordinal model, is considered the most popular ordinal model. We use hypothetical data to illustrate a situation where the proportional odds model holds exactly, and we demonstrate through derivations and simulations how using this model has better statistical power than simple logistic regression. The section concludes with an example illustrating the use of the model in avian and swine influenza research. In the middle section of this work, we show how the proportional model assumption can be relaxed to a less restrictive model called the "trend odds model". We demonstrate how this model is related to latent logistic, normal, and exponential distributions. In particular, scale changes in these potential latent distributions are found to be consistent with the trend odds assumption, with the logistic and exponential distributions having odds that increase in a linear or nearly linear fashion. Actual data of antibody titer against avian and swine influenza among occupationally- exposed participants and non-exposed controls illustrate the fit and interpretation of the proportional odds model and the trend odds model. Finally, we show how to perform a multivariable analysis in which some of the variables meet the proportional model assumption and some meet the trend odds assumption. Likert-scaled data pertaining to violence among middle school students illustrate the fit and interpretation of the multivariable proportional-trend odds model. In conclusion, the proportional odds model provides superior power compared to models that employ arbitrary dichotomization of ordinal data. In addition, the added complexity of the trend odds model provides improved power over the proportional odds model when there are moderate to severe departures from proportionality. The increase in power is of great public health relevance in a time of increasingly scarce resources for occupational and environmental health research. The trend odds model indicates and tests the presence of a trend in odds, providing a new dimension to risk factors and disease etiology analyses. In addition to applications demonstrated in this work, other research areas in occupational and environmental health can benefit from the use of these methods. For example, worker fatigue is often self-reported using ordinal scales, and traumatic brain injury recovery is measured using recovery scores such as the Glasgow Outcome Scale (GOS).

Aims: To evaluate the use of public health models in policymaking, with regard to the appropriateness and the practicalities of using such models for simulating health interventions, and the application of the results of such modelling exercises to public health policymaking. Methods: In conjunction with policymakers, existing public health models were adapted and used for simulating the effects of risk factor interventions on CHD in the England & Wales population. These models were evaluated in terms of the limitations of the input data, the assumptions underlying the methodology of the models, and problems in translating interventions to the simulation environment. Results: The simulation of CHD risk factor interventions using the Prevent and POHEM models demonstrated how public health models can be used with policy makers to estimate the future development of the health of populations; to evaluate alternative routes to achieving health goals; to demonstrate the effect of targeting health interventions at different sections of a population; to investigate the relationship between risk factors and their linked diseases; and for demonstrating the possible effect of health interventions to health practitioners. Conclusions: Public health models can be used as policy tools, although ultimately they may only inform policy, and not drive it, due to other factors which can influence the policy agenda. Such models are complex instruments that require a long term commitment in terms of funding, and they need to be developed by multidisciplinary teams, whose expertise cover the areas of computing, epidemiology and health policy, but most importantly policymakers should be involved with their development and use. Although public health models may never be validated in terms of a "gold standard", they can be used as policy tools as long as one is aware that they are unverified and that they yield results of a hypothetical nature.

The retina is the ocular tissue responsible for the detection of light. Its extensive demand for oxygen, coupled with a concomitant elevated supply, renders this tissue prone to both hypoxia and hyperoxia. In this thesis, we construct mathematical models of the retina, formulated as systems of reaction-diffusion equations, investigating its oxygen-related dynamics in healthy and diseased states. In the healthy state, we model the oxygen distribution across the human retina, examining the efficacy of the protein neuroglobin in the prevention of hypoxia. It has been suggested that neuroglobin could prevent hypoxia, either by transporting oxygen from regions where it is rich to those where it is poor, or by storing oxygen during periods of diminished supply or increased uptake. Numerical solutions demonstrate that neuroglobin may be effective in preventing or alleviating hypoxia via oxygen transport, but that its capacity for oxygen storage is essentially negligible, whilst asymptotic analysis reveals that, contrary to the prevailing assumption, neuroglobin's oxygen affinity is near optimal for oxygen transport. A further asymptotic analysis justifies the common approximation of a piecewise constant oxygen uptake across the retina, placing existing models upon a stronger theoretical foundation. In the diseased state, we explore the effect of hyperoxia upon the progression of the inherited retinal diseases, known collectively as retinitis pigmentosa. Both numerical solutions and asymptotic analyses show that this mechanism may replicate many of the patterns of retinal degeneration seen in vivo, but that others are inaccessible to it, demonstrating both the strengths and weaknesses of the oxygen toxicity hypothesis. It is shown that the wave speed of hyperoxic degeneration is negatively correlated with the local photoreceptor density, high density regions acting as a barrier to the spread of photoreceptor loss. The effects of capillary degeneration and treatment with antioxidants or trophic factors are also investigated, demonstrating that each has the potential to delay, halt or partially reverse photoreceptor loss. In addition to answering questions that are not accessible to experimental investigation, these models generate a number of experimentally testable predictions, forming the first loop in what has the potential to be a fruitful experimental/modelling cycle.

Given the sad state of obesity and lifestyle-related illnesses in the US, nurses, as the largest and most trusted health profession, must take the cause of health promotion seriously and personally. This study seeks to close the gap in knowledge of nurses’ perception of self as a role model of health and personal healthy lifestyle behaviors. This study focuses on four specific behaviors that lead to a healthy lifestyle: limit alcohol, avoid tobacco, improve nutrition, and engage in physical activity. The purpose of this study is to determine the relationship between nurses’ health practices and their perceptions of self as role models for health promotion using constructs of the Social Cognitive Theory. The final sample consisted of 804 registered nurses in the state of Tennessee. In this study, 4% report smoking, 24.9% drink alcohol, 34% are overweight and 30% obese. Approximately 70% do not meet the weekly physical activity recommendations of 150-minutes and 32.8% follow guidelines for a healthy diet only 50% of the time or less. There was a significant correlation between following a healthy diet or physical activity and the Self as a Role Model of Health Promotion (SARMHEP) scores. Based on the regression analysis, working in an acute care or ambulatory setting negatively affected the SARMHEP, as opposed to age and gender having a positive effect on the SARMHEP score. This study has the potential to provide key information that can be used to promote health among nurses.

It is standard practice to assume the discounted utility (DU) model on the part of the economic agents. This thesis tests the key axiom of the DU model (stationarity) in the health domain. Intertemporal preferences for health are of interest because of the debate over the appropriate treatment of future health effects in economic evaluation and of the relationship between intertemporal preferences and health-affecting behaviour. Social intertemporal preferences for fatal changes in health and private and social intertemporal preferences for non-fatal changes were elicited from members of the general public. Private intertemporal preferences for non-fatal changes were elicited from university students. Stationarity was violated in all three studies indicating that the DU model does not accurately describe individuals' intertemporal preferences. Psychologists dissatisfied with the DU model have developed hyperbolic models which replace the stationarity axiom by a generalised stationarity axiom. This thesis compared the descriptive properties of the DU model and hyperbolic discounting models in the health domain. The results showed that the hyperbolic discounting models fitted the data better than the DU model. This indicates that hyperbolic models should be preferred in the analyses of health affecting behaviour. Whether they should also be used in economic evaluations is likely to depend on other criteria as well as descriptive superiority. To inform the debate about the appropriate discount rate for health effects in economic evaluations this thesis investigated whether intertemporal preferences differ across outcomes within the health domain. The results showed that private and social intertemporal preferences for non-fatal changes in health are very similar. More differences were found between intertemporal preferences for fatal changes and non-fatal changes. This indicates that the debate over the relationship between individuals' preferences and the social discount rate is less important and that the debate should perhaps focus more on whether the rate should depend on the type of health outcome of the intervention.

Sound clinical decision making requires evidence-based estimates of the impact of different treatment strategies. In the absence of randomized trials, two potential approaches are agent-based models (ABMs) and the parametric g-formula. Although these methods are mathematically similar, they have generally been considered in isolation. In this dissertation, we bridge the gap between ABMs and the parametric g-formula, in order to improve the use of ABMs for causal inference. In Chapter 1, we describe bias that can occur when ABM inputs or estimates are extrapolated to new populations, and demonstrate the impact of this bias by comparison with the parametric g-formula. We describe the assumptions that are required for extrapolation of an ABM and show that violations of these assumptions produce biased estimates of the risk and causal effect. In Chapter 2, we describe an approach to provide calibration targets for ABMs, and to identify the set of parameters of the ABM that interfere with transportability of the model results to a particular population. We illustrate this approach by comparing the estimates from an existing ABM, the Cost-Effectiveness of Preventing AIDS Complications (CEPAC) model, to estimates from the parametric g-formula applied to a prospective clinical data of HIV-positive individuals under different treatment initiation strategies. In Chapter 3, we focus on the core problem of causal inference from ABMs: how to define and estimate the parameters described in Chapter 2 in light of the bias described in Chapter 1. To illustrate this problem, we consider CEPAC input parameters for opportunistic diseases. We formally define the effect of interest, describe the conditions under which this effect is or is not identifiable, and describe the assumptions required for transportability of this effect. Finally, we show that the estimation of these parameters via a naïve regression analysis approach provides implausible estimates.

This thesis designed and implemented efficient deep learning methods to solve classification and segmentation problems in two major health informatics domains, namely pervasive sensing and medical imaging. In the area of pervasive sensing, this thesis focuses only on food and related scene classification for health and nutrition analysis. This thesis used deep learning models to find the answer of two important two questions, “where we eat?’’ and ‘’what we eat?’’ for properly monitoring our health and nutrition condition. This is a new research domain, so this thesis presented entire scenarios from the scratch (e.g. create a dataset, model selection, parameter optimization, etc.). To answer the first question, “where we eat?”, it introduced two new datasets, ``FoodPlaces'', ``EgoFoodPlaces'' and models, “MACNet”, “MACNet+SA” based on multi-scale atrous convolutional networks with the self-attention mechanism. To answer the second question, “what we eat?”, it presented a new dataset, ``Yummly48K'' and model, ``CuisineNet'‘, designed by aggregating convolution layers with various kernel sizes followed by residual and pyramid pooling module with two fully connected pathway. The proposed models performed state-of-the-art classification accuracy on their related datasets. In the field of medical imaging, this thesis targets skin lesion segmentation problem in the dermoscopic images. This thesis introduced two novel deep learning models to accurately segment the skin lesions, “SLSDeep” and “MobileGAN” based on dilated residual with pyramid pooling network and conditional Generative Adversarial Networks (cGANs). Both models show excellent performance on public benchmark datasets.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 67-74).
Pancreatic cancer is the third most lethal cancer in the U.S., causing an estimated 45,750 deaths in 2019. Of all treatments, surgical resection provides the best survival rate for pancreatic cancer. This is not feasible for the majority of pancreatic cancer patients, whose cancer is typically not diagnosed until the tumor is unresectable. Most symptoms of pancreatic cancer are typically subtle, which underscores the need for better risk modeling to predict a patient's chance of pancreatic cancer well before it would usually be diagnosed. We propose a series of novel models that apply standard machine learning techniques to Electronic Health Records (EHRs) to predict risk of pancreatic cancer in advance of cancer diagnosis. On the test dataset, two of our models achieved AUROCs of 0.85 (CI 0.81 - 0.90) and 0.79 (CI 0.77 - 0.82) with a 365-day lead time.
by Jennifer McCleary.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

[EN] Healthcare domain produces and consumes big quantities of people's health data. Although data exchange is the norm rather than the exception, being able to access to all patient data is still far from achieved. Current developments such as personal health records will introduce even more data and complexity to the Electronic Health Records (EHR). Achieving semantic interoperability is one of the biggest challenges to overcome in order to benefit from all the information contained in the distributed EHR. This requires that the semantics of the information can be understood by all involved parties. It has been stablished that three layers are needed to achieve semantic interoperability: Reference models, clinical models (archetypes), and clinical terminologies. As seen in the literature, information models (reference models and clinical models) are lacking methodologies and tools to improve EHR systems and to develop new systems that can be semantically interoperable. The purpose of this thesis is to provide methodologies and tools for advancing the use of archetypes in three different scenarios: - Archetype definition over specifications with no dual model architecture native support. Any EHR architecture that directly or indirectly has the notion of detailed clinical models (such as HL7 CDA templates) can be potentially used as a reference model for archetype definition. This allows transforming single-model architectures (which contain only a reference model) into dual-model architectures (reference model with archetypes). A set of methodologies and tools has been developed to support the definition of archetypes from multiple reference models. - Data transformation. A complete methodology and tools are proposed to deal with the transformation of legacy data into XML documents compliant with the archetype and the underlying reference model. If the reference model is a standard then the transformation is a standardization process. The methodologies and tools allow both the transformation of legacy data and the transformation of data between different EHR standards. - Automatic generation of implementation guides and reference materials from archetypes. A methodology for the automatic generation of a set of reference materials is provided. These materials are useful for the development and use of EHR systems. These reference materials include data validators, example instances, implementation guides, human-readable formal rules, sample forms, mindmaps, etc. These reference materials can be combined and organized in different ways to adapt to different types of users (clinical or information technology staff). This way, users can include the detailed clinical model in their organization workflow and cooperate in the model definition. These methodologies and tools put clinical models as a key part of the system. The set of presented methodologies and tools ease the achievement of semantic interoperability by providing means for the semantic description, normalization, and validation of existing and new systems.
[ES] El sector sanitario produce y consume una gran cantidad de datos sobre la salud de las personas. La necesidad de intercambiar esta información es una norma más que una excepción, aunque este objetivo está lejos de ser alcanzado. Actualmente estamos viviendo avances como la medicina personalizada que incrementarán aún más el tamaño y complejidad de la Historia Clínica Electrónica (HCE). La consecución de altos grados de interoperabilidad semántica es uno de los principales retos para aprovechar al máximo toda la información contenida en las HCEs. Esto a su vez requiere una representación fiel de la información de tal forma que asegure la consistencia de su significado entre todos los agentes involucrados. Actualmente está reconocido que para la representación del significado clínico necesitamos tres tipos de artefactos: modelos de referencia, modelos clínicos (arquetipos) y terminologías. En el caso concreto de los modelos de información (modelos de referencia y modelos clínicos) se observa en la literatura una falta de metodologías y herramientas que faciliten su uso tanto para la mejora de sistemas de HCE ya existentes como en el desarrollo de nuevos sistemas con altos niveles de interoperabilidad semántica. Esta tesis tiene como propósito proporcionar metodologías y herramientas para el uso avanzado de arquetipos en tres escenarios diferentes: - Definición de arquetipos sobre especificaciones sin soporte nativo al modelo dual. Cualquier arquitectura de HCE que posea directa o indirectamente la noción de modelos clínicos detallados (por ejemplo, las plantillas en HL7 CDA) puede ser potencialmente usada como modelo de referencia para la definición de arquetipos. Con esto se consigue transformar arquitecturas de HCE de modelo único (solo con modelo de referencia) en arquitecturas de doble modelo (modelo de referencia + arquetipos). Se han desarrollado metodologías y herramientas que faciliten a los editores de arquetipos el soporte a múltiples modelos de referencia. - Transformación de datos. Se propone una metodología y herramientas para la transformación de datos ya existentes a documentos XML conformes con los arquetipos y el modelo de referencia subyacente. Si el modelo de referencia es un estándar entonces la transformación será un proceso de estandarización de datos. La metodología y herramientas permiten tanto la transformación de datos no estandarizados como la transformación de datos entre diferentes estándares. - Generación automática de guías de implementación y artefactos procesables a partir de arquetipos. Se aporta una metodología para la generación automática de un conjunto de materiales de referencia de utilidad en el desarrollo y uso de sistemas de HCE, concretamente validadores de datos, instancias de ejemplo, guías de implementación , reglas formales legibles por humanos, formularios de ejemplo, mindmaps, etc. Estos materiales pueden ser combinados y organizados de diferentes modos para facilitar que los diferentes tipos de usuarios (clínicos, técnicos) puedan incluir los modelos clínicos detallados en el flujo de trabajo de su sistema y colaborar en su definición. Estas metodologías y herramientas ponen los modelos clínicos como una parte clave en el sistema. El conjunto de las metodologías y herramientas presentadas facilitan la consecución de la interoperabilidad semántica al proveer medios para la descripción semántica, normalización y validación tanto de sistemas nuevos como ya existentes.
[CAT] El sector sanitari produeix i consumeix una gran quantitat de dades sobre la salut de les persones. La necessitat d'intercanviar aquesta informació és una norma més que una excepció, encara que aquest objectiu està lluny de ser aconseguit. Actualment estem vivint avanços com la medicina personalitzada que incrementaran encara més la grandària i complexitat de la Història Clínica Electrònica (HCE). La consecució d'alts graus d'interoperabilitat semàntica és un dels principals reptes per a aprofitar al màxim tota la informació continguda en les HCEs. Açò, per la seua banda, requereix una representació fidel de la informació de tal forma que assegure la consistència del seu significat entre tots els agents involucrats. Actualment està reconegut que per a la representació del significat clínic necessitem tres tipus d'artefactes: models de referència, models clínics (arquetips) i terminologies. En el cas concret dels models d'informació (models de referència i models clínics) s'observa en la literatura una mancança de metodologies i eines que en faciliten l'ús tant per a la millora de sistemes de HCE ja existents com per al desenvolupament de nous sistemes amb alts nivells d'interoperabilitat semàntica. Aquesta tesi té com a propòsit proporcionar metodologies i eines per a l'ús avançat d'arquetips en tres escenaris diferents: - Definició d'arquetips sobre especificacions sense suport natiu al model dual. Qualsevol arquitectura de HCE que posseïsca directa o indirectament la noció de models clínics detallats (per exemple, les plantilles en HL7 CDA) pot ser potencialment usada com a model de referència per a la definició d'arquetips. Amb açò s'aconsegueix transformar arquitectures de HCE de model únic (solament amb model de referència) en arquitectures de doble model (model de referència + arquetips). S'han desenvolupat metodologies i eines que faciliten als editors d'arquetips el suport a múltiples models de referència. - Transformació de dades. Es proposa una metodologia i eines per a la transformació de dades ja existents a documents XML conformes amb els arquetips i el model de referència subjacent. Si el model de referència és un estàndard llavors la transformació serà un procés d'estandardització de dades. La metodologia i eines permeten tant la transformació de dades no estandarditzades com la transformació de dades entre diferents estàndards. - Generació automàtica de guies d'implementació i artefactes processables a partir d'arquetips. S'hi inclou una metodologia per a la generació automàtica d'un conjunt de materials de referència d'utilitat en el desenvolupament i ús de sistemes de HCE, concretament validadors de dades, instàncies d'exemple, guies d'implementació, regles formals llegibles per humans, formularis d'exemple, mapes mentals, etc. Aquests materials poden ser combinats i organitzats de diferents maneres per a facilitar que els diferents tipus d'usuaris (clínics, tècnics) puguen incloure els models clínics detallats en el flux de treball del seu sistema i col·laborar en la seua definició. Aquestes metodologies i eines posen els models clínics com una part clau del sistemes. El conjunt de les metodologies i eines presentades faciliten la consecució de la interoperabilitat semàntica en proveir mitjans per a la seua descripció semàntica, normalització i validació tant de sistemes nous com ja existents.
Boscá Tomás, D. (2016). DETAILED CLINICAL MODELS AND THEIR RELATION WITH ELECTRONIC HEALTH RECORDS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/62174
TESIS

When I was younger I always conceived of a room where all these (strategic) concepts were worked out for the whole, company. Later I didn't find any such room .... The strategy (of the company) may not even exist in the mind of one man. I certainly don't know where it is written down. It is simply transmitted in the series of decisions made (Quinn 1978: 7). How do organisations in the Australian Catholic Health and Aged Care sector transform shared strategic thinking into formulated strategy? This research has investigated strategy formation, which can be defined as the process whereby the insights and thoughts of the key players in Catholic health and aged care are converted into formulated strategies. Specifically, the research analysed a major strategic amalgamation of the health and aged care operations of the Catholic Church in Australia, identified as Integration 2000. The concept of social constructs of meaning for the key actors is the fundamental perspective of this research. This required a constructivist ontology. The epistemology is interpretivist, and set out to provide a description of perceptions of the key actors as they engage in the formation of strategy. Defenders of interpretivism argued that the human sciences aim to understand human action (Schwandt, 2000:191). A qualitative methodology has been used to provide a plausible interpretation of the conversion process commonly referred to as strategy formation.A purposive sample was obtained. The data collection methods included qualitative interviews, attendance as an observer at two of the three day National Conferences of Catholic Health Australia and document analysis (see Chapter Three).A key focus of the research was the identification of planning models used to set the strategic context of organisations in Catholic health. The research showed that the prescriptive design and planning models were not used to plan broad strategy, but to implement strategies already formed by an emergent/learning process which, in Mintzberg et al's (1998) terms, would fit the learning, cultural and environmental schools of thought. Pinpointing a strategy school may not be a particularly fruitful exercise in this particular arena. It assumes that the distinctive act of deciding the future shape and the strategic management context of organisations charged with fulfilling a sacred mission can be classified into one school or another. The research also explored the perceptions of the Integration 2000 process, including the compatibility between the espoused philosophies and values of Catholic health and aged care and the behaviours evidenced during the Integration 2000 process. A diagnostic model was used to perform this evaluation. Rather than uncovering major discrepancies, this revealed some differences and some potential challenges.The espoused philosophies and values of Catholic health and aged care are those of compassion, collaboration, sense of community and, of course, financial viability. Pre-Integration 2000, particularly in health care, theories of organising and practices reflected values of independence and competitiveness, both between and even within religious orders. The findings from post-Integration 2000 suggested that theories of organising and practices were becoming more aligned with the original and continuing values, at the same time as responsibility for sustaining these values was being handed over from religious to lay trusteeship. There are still some outstanding issues before the Integration 2000 process achieves its objectives. The progress to date in bringing together so many components of such a disparate sector attests to the strength of the underlying value systems of Catholic health and aged care.

Being in the era of Big data, the applicability and importance of data-driven models like artificial neural network (ANN) in the modern statistics have increased substantially. In this dissertation, our main goal is to contribute to the development and the expansion of these ANN models by incorporating Bayesian learning techniques. We have demonstrated the applicability of these Bayesian ANN models in interdisciplinary research including health and cybersecurity. Breast cancer is one of the leading causes of deaths among females. Early and accurate diagnosis is a critical component which decides the survival of the patients. Including the well known ``Gail Model", numerous efforts are being made to quantify the risk of diagnosing malignant breast cancer. However, these models impose some limitations on their use of risk prediction. In this dissertation, we have developed a diagnosis model using ANN to identify the potential breast cancer patients with their demographic factors and the previous mammogram results. While developing the model, we applied the Bayesian regularization techniques (evidence procedure), along with the automatic relevance determination (ARD) prior, to minimize the network over-fitting. The optimal Bayesian network has 81\% overall accuracy in correctly classifying the actual status of breast cancer patients, 59\% sensitivity in accurately detecting the malignancy and 83\% specificity in correctly detecting non-malignancy. The area under the receiver operating characteristic curve (0.7940) shows that this is a moderate classification model. We then present a new Bayesian ANN model for developing a nonlinear Poisson regression model which can be used for count data modeling. Here, we have summarized all the important steps involved in developing the ANN model, including the forward-propagation, backward-propagation and the error gradient calculations of the newly developed network. As a part of this, we have introduced a new activation function into the output layer of the ANN and error minimizing criterion, using count data. Moreover, we have expanded our model to incorporate the Bayesian learning techniques. The performance our model is tested using simulation data. In addition to that, a piecewise constant hazard model is developed by extending the above nonlinear Poisson regression model under the Bayesian setting. This model can be utilized over the other conventional methods for accurate survival time prediction. With this, we were able to significantly improve the prediction accuracies. We captured the uncertainties of our predictions by incorporating the error bars which could not achieve with a linear Poisson model due to the overdispersion in the data. We also have proposed a new hybrid learning technique, and we evaluated the performance of those techniques with a varying number of hidden nodes and data size. Finally, we demonstrate the suitability of Bayesian ANN models for time series forecasting by using an online training algorithm. We have developed a vulnerability forecast model for the Linux operating system by using this approach.

Thesis (Ph.D.)--Indiana University, Dept. of Economics, 2006.
"Title from dissertation home page (viewed July 16, 2007)." Source: Dissertation Abstracts International, Volume: 67-10, Section: A, page: 3907. Adviser: Pravin Trivedi.

With the increase in quality of life (QoL) research in recent years and its relationship to physical and mental health, building a model of these relationships is an important pursuit for researchers with the aim of creating targeted social policy and programs. Two studies were designed to test a model of the relationship between quality of life, physical health, and mental health on two different groups in the National-Capital region: a housed sample and a homeless and vulnerably housed sample. Study 1 consisted of 1,339 adults who took part in the 2007-2008 Canadian Community Health Survey and were stably housed in either a rented or owned residence. Study 2 consisted of 395 single adults who participated in the City of Ottawa baseline measure of the Health and Housing in Transition (HHiT) study (Hwang, Aubry, et al., 2011) and were homeless or vulnerably housed. Subjective levels of various physical and mental health indicators were measured for each of the samples, along with subjective quality of life indicators. Of interest in each of the studies was: 1) The effect that physical and mental health factors have on quality of life in each of the samples, and 2) determining if mental health or physical health is a better predictor of quality of life. Structural analysis of the housed sample model resulted in both physical and mental health having a significant positive effect on QoL, although neither physical nor mental health was a better predictor of quality of life. For the homeless and vulnerably housed model, structural analysis determined mental health to have a significant direct positive effect on QoL, while physical health showed a non-significant negative effect. Mental health was determined to be a significantly better predictor of QoL in the homeless and vulnerably housed model, accounting for 30.47 percent of the variance in quality of life. Implications of this research are discussed.

The neuropathogenicity of two retroviruses was investigated. The human immunodeficiency virus, in addition to its profound effect on the immune system, also causes degenerative changes in the brain, the spinal cord and peripheral nerves. In order to elucidate how it affects the nervous system, transgenic mice were generated that express the entire HIV genome in neurons in the anterior thalamus and in the anterior horn of the spinal cord, and examined clinically, neuropsychologically, electrophysiologically and histologically. Animals developed a neurological syndrome characterized by hypoactivity and weakness, and by axonal degeneration in peripheral nerves. These results provide evidence for a role of HIV in affecting both the central and peripheral nervous systems.
In a second project, pathological effects associated with a disease determining region contained in the gp70 envelope protein of the Cas-Br-E murine leukemia virus, were investigated. In infected mice, this virus causes hind limb paralysis and a spongiform myeloencephalopathy with gliosis and neuronal loss. Stably transfected fibroblasts that express gp70 were injected into the brains of mice, and the animals were examined for histopathological changes attributable to the effects of gp70. While gp70 protein was detected at the implantation site, this was not accompanied by any specific histological changes. These data suggest that the intracerebral expression of the neuropathogenic gp70 protein alone is not sufficient to cause disease, and lend indirect support to a model according to which gp70 causes disease by altering the cytokine profile of infected mononuclear cells in the central nervous system.

This research set out to develop one or more conceptual models of diagnostic radiography based on the ways in which clinical radiographers themselves viewed their skills. The report analyses the historical relationship between radiographers and radiologists and the limited attempts at theory development prior to this research. This context is set against selected literature from nursing metatheory. This comparison is made since radiography had in part developed from the nursing profession and the historical and gendered background of both professions may be seen to be similar. •The methodology is largely qualitative and the use of computers for qualitative data analysis is discussed in some detail. The data collection was completed in three major phases- a diagnostic phase; a theory development phase and an applications phase, utilising several brainstorming groups and two questionnaires as well as action research in the third phase. In the diagnostic (first) phase of the research categories representing parts of the radiographic process were derived. Two models (or theoretical frameworks) were developed and their various concepts were explored and refined. In the second phase of theory development the research was extended to develop a statement of the role of the diagnostic radiographer and several concepts from the model were explored. In the final phase, the concept of holism was explored together with the relevance of the models for the education of student radiographers. In the final sections of the report there is a detailed evaluation of the potential impact of the models including a reflexive analysis. The conclusions are that the models provide a valid conceptual representation of the radiographic process and philosophy and that they have a role to play in education of student radiographers.

Evidence of changing patterns of food consumption behavior is presented. Previous attempts at explaining these changes are critically reviewed and the need for an alternate approach is identified. A model of consumer behavior in which utility from food consumption is maximized subject to outlay for foods and limits on the consumption of fat, cholesterol, sodium, and/or other food components is proposed. This model yields a system of demands that are functions of prices and outlay as well as the composition of food and limits on the consumption of these components. The structure of this model is examined and restrictions on consumer food demands are derived. The derivation of individual demands based on the proposed model is demonstrated using a specific indirect utility function. Tests of the joint hypotheses that fat or cholesterol consumption determines food demand are defined. The computation of aggregate food demand elasticities with respect to changes in prices and changes in attributes such as fat or cholesterol consumption is demonstrated. Data necessary for estimating the parameters of the model and testing hypotheses are identified. The model proposed in this study allows tests of the hypothesis that food demands are not affected by food composition as well as measurement of these effects
Ph. D.

When I was younger I always conceived of a room where all these (strategic) concepts were worked out for the whole, company. Later I didn't find any such room .... The strategy (of the company) may not even exist in the mind of one man. I certainly don't know where it is written down. It is simply transmitted in the series of decisions made (Quinn 1978: 7). How do organisations in the Australian Catholic Health and Aged Care sector transform shared strategic thinking into formulated strategy? This research has investigated strategy formation, which can be defined as the process whereby the insights and thoughts of the key players in Catholic health and aged care are converted into formulated strategies. Specifically, the research analysed a major strategic amalgamation of the health and aged care operations of the Catholic Church in Australia, identified as Integration 2000. The concept of social constructs of meaning for the key actors is the fundamental perspective of this research. This required a constructivist ontology. The epistemology is interpretivist, and set out to provide a description of perceptions of the key actors as they engage in the formation of strategy. Defenders of interpretivism argued that the human sciences aim to understand human action (Schwandt, 2000:191). A qualitative methodology has been used to provide a plausible interpretation of the conversion process commonly referred to as strategy formation.
A purposive sample was obtained. The data collection methods included qualitative interviews, attendance as an observer at two of the three day National Conferences of Catholic Health Australia and document analysis (see Chapter Three).A key focus of the research was the identification of planning models used to set the strategic context of organisations in Catholic health. The research showed that the prescriptive design and planning models were not used to plan broad strategy, but to implement strategies already formed by an emergent/learning process which, in Mintzberg et al's (1998) terms, would fit the learning, cultural and environmental schools of thought. Pinpointing a strategy school may not be a particularly fruitful exercise in this particular arena. It assumes that the distinctive act of deciding the future shape and the strategic management context of organisations charged with fulfilling a sacred mission can be classified into one school or another. The research also explored the perceptions of the Integration 2000 process, including the compatibility between the espoused philosophies and values of Catholic health and aged care and the behaviours evidenced during the Integration 2000 process. A diagnostic model was used to perform this evaluation. Rather than uncovering major discrepancies, this revealed some differences and some potential challenges.
The espoused philosophies and values of Catholic health and aged care are those of compassion, collaboration, sense of community and, of course, financial viability. Pre-Integration 2000, particularly in health care, theories of organising and practices reflected values of independence and competitiveness, both between and even within religious orders. The findings from post-Integration 2000 suggested that theories of organising and practices were becoming more aligned with the original and continuing values, at the same time as responsibility for sustaining these values was being handed over from religious to lay trusteeship. There are still some outstanding issues before the Integration 2000 process achieves its objectives. The progress to date in bringing together so many components of such a disparate sector attests to the strength of the underlying value systems of Catholic health and aged care.

South Africa possesses a highly fragmented health system with wide disparities in health spending and inequitable distribution of both health care professionals and resources. The national health system (NHI) of South Africa consists of a large public sector and small private sectors which are overused and under resourced and a smaller private sector which is underused and over resourced. In broad terms, the NHI promises a health care system in which everyone, regardless of income level, can access decent health services at a cost that is affordable to them and to the country as a whole. The relevance of this study is to contribute to the NHI debate while simultaneously providing insights from other countries which have implemented national health care systems. As such, the South African government can then appropriately implement as well as finance the new NHI system specific to South Africa’s current socio-economic status. The objective of this study was to examine health care financing models in different countries in order to draw lessons for South Africa when implementing the NHI. A case study was conducted by examining ten countries with a national health insurance system, in order to evaluate the health financing models in each country. The following specific objectives are pursued: firstly, to review the current health management system and the policy proposed for NHI; secondly, to examine health financing models in a selected number of countries around the world and lastly to draw lessons to inform the South African NHI policy debate. The main findings were firstly, wealthier nations tend to have a much healthier population; this is the result of these developed countries investing significantly in their public health sectors. Secondly, the governments in developing nations allocate a smaller percentage of their GDP and government expenditure on health care. Lastly, South Africa is classified as an upper middle income developing country; however, the health status of South Africans mirrors that of countries which perform worse than South Africa on health matters. In other words the health care in South Africa is not operating at the standard it should be given the resources South Africa possesses. The cause of this may be attributed to South Africa being stuck in what is referred to as the “middle income trap” amongst other reasons.

South Africa possesses a highly fragmented health system with wide disparities in health spending and inequitable distribution of both health care professionals and resources. The national health system (NHI) of South Africa consists of a large public sector and small private sectors which are overused and under resourced and a smaller private sector which is underused and over resourced. In broad terms, the NHI promises a health care system in which everyone, regardless of income level, can access decent health services at a cost that is affordable to them and to the country as a whole. The relevance of this study is to contribute to the NHI debate while simultaneously providing insights from other countries which have implemented national health care systems. As such, the South African government can then appropriately implement as well as finance the new NHI system specific to South Africa’s current socio-economic status. The objective of this study was to examine health care financing models in different countries in order to draw lessons for South Africa when implementing the NHI. A case study was conducted by examining ten countries with a national health insurance system, in order to evaluate the health financing models in each country. The following specific objectives are pursued: firstly, to review the current health management system and the policy proposed for NHI; secondly, to examine health financing models in a selected number of countries around the world and lastly to draw lessons to inform the South African NHI policy debate. The main findings were firstly, wealthier nations tend to have a much healthier population; this is the result of these developed countries investing significantly in their public health sectors. Secondly, the governments in developing nations allocate a smaller percentage of their GDP and government expenditure on health care. Lastly, South Africa is classified as an upper middle income developing country; however, the health status of South Africans mirrors that of countries which perform worse than South Africa on health matters. In other words the health care in South Africa is not operating at the standard it should be given the resources South Africa possesses. The cause of this may be attributed to South Africa being stuck in what is referred to as the “middle income trap” amongst other reasons.

Background: Uncertainty in parameters is present in many risk assessment and decision making problems and leads to uncertainty in model predictions. Therefore an analysis of the degree of uncertainty around the model inputs is often needed. Importance analysis involves use of quantitative methods aiming at identifying the contribution of uncertain input model parameters to output uncertainty. Expected value of partial perfect information (EVPPI) measure is a current gold- standard technique for measuring parameters importance in health economics models. The current standard approach of estimating EVPPI through performing double Monte Carlo simulation (MCS) can be associated with a long run time. Objective: To investigate different importance analysis techniques with an aim to find alternative technique with shorter run time that will identify parameters with greatest contribution to uncertainty in model output. Methods: A health economics model was updated and served as a tool to implement various importance analysis techniques. Twelve alternative techniques were applied: rank correlation analysis, contribution to variance analysis, mutual information analysis, dominance analysis, regression analysis, analysis of elasticity, ANCOVA, maximum separation distances analysis, sequential bifurcation, double MCS EVPPI,EVPPI-quadrature and EVPPI- single method. Results: Among all these techniques, the dominance measure resulted with the closest correlated calibrated scores when compared with EVPPI calibrated scores. Performing a dominance analysis as a screening method to identify subgroup of parameters as candidates for being most important parameters and subsequently only performing EVPPI analysis on the selected parameters will reduce the overall run time.