Relevant bibliographies by topics / Health models

Academic literature on the topic 'Health models'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Health models"

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Lisboa, Salime Donida Chedid, Rodrigo Sudatti Delevatti, Ana Carolina Kanitz, Thais Reichert, Cláudia Gomes Bracht, Alexandra Ferreira Vieira, and Luiz Fernando Martins Kruel. "Health-Related Physical Fitness in Female Models." Health 08, no. 02 (2016): 163–72. http://dx.doi.org/10.4236/health.2016.82019.

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Dushanova, Juliana. "Diagnostics, rehabilitation and models of Parkinson’s disease." Health 04, no. 11 (2012): 1200–1217. http://dx.doi.org/10.4236/health.2012.431178.

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Ebi, K., and J. Gamble. "MODELS, SCENARIOS AND HEALTH: HEALTHY PEOPLE 2100." Epidemiology 15, no. 4 (July 2004): S222. http://dx.doi.org/10.1097/00001648-200407000-00593.

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Hurwicz, Margo-Lea, and Margaret Rose. "Older Adults’ Explanatory Models of Colds and Flu." Health 07, no. 09 (2015): 1183–95. http://dx.doi.org/10.4236/health.2015.79133.

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Hurwicz, Margo-Lea, and Margaret Rose. "Older Adults’ Explanatory Models of High Blood Pressure." Health 08, no. 07 (2016): 680–93. http://dx.doi.org/10.4236/health.2016.87072.

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Strand, Justine. "Health Belief Models." Journal of Physician Assistant Education 13, no. 1 (2002): 43–45. http://dx.doi.org/10.1097/01367895-200213010-00009.

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Drummond, M. F. "Health economic models." Rheumatology 39, suppl_2 (December 2000): 29–32. http://dx.doi.org/10.1093/rheumatology/39.suppl_2.29.

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Lisboa, Salime Donida Chedid, Rodrigo Sudatti Delevatti, Ana Carolina Kanitz, Felipe Barreto Schuch, Cláudia Gomes Bracht, and Luiz Fernando Martins Kruel. "Quality of Life and Depressive Symptoms in Female Models." Health 08, no. 11 (2016): 1040–48. http://dx.doi.org/10.4236/health.2016.811107.

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Northridge, Mary E., and Jennifer A. Ellis. "Applying Population Health Models." American Journal of Public Health 93, no. 3 (March 2003): 365. http://dx.doi.org/10.2105/ajph.93.3.365.

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Shock, Lisa P. "Models of Population Health." Primary Care: Clinics in Office Practice 46, no. 4 (December 2019): 595–602. http://dx.doi.org/10.1016/j.pop.2019.07.011.

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Dissertations / Theses on the topic "Health models"

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Prezotti, Filho Paulo Roberto. "Periodic models and variations applied to health problems." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC015.

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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
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Wood, David L. "New Models of Health and Social Determinants of Health." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/5180.

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Forster, Martin. "Economics, inequalities in health and health-related behaviour." Thesis, University of York, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245870.

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Chen, Ge (Ge Jackie). "Visualizations for mental health topic models." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91306.

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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.
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Mutopo, Yvonne. "Rethinking health care financing models: the case of Zimbabwe's health sector." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27236.

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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.
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Hipwell, Michele. "Models of health enhancing and illness provoking factors in mental health." Thesis, Queen Margaret University, 2005. https://eresearch.qmu.ac.uk/handle/20.500.12289/7351.

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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.
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Wilson, Stephen Francis. "New models of multidisciplinary community health care." University of Sydney, 2005. http://hdl.handle.net/2123/895.

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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.
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Xu, Yong. "Statistical Models for Environmental and Health Sciences." Scholar Commons, 2011. http://scholarcommons.usf.edu/etd/3414.

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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.
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Zhao, Jing. "Learning Predictive Models from Electronic Health Records." Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-137936.

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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.
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Valente, Ana Beatriz Marques Cabral. "Health insurance pricing with generalised linear models." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20988.

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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
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Books on the topic "Health models"

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Christensen, Karl Bang, Svend Kreiner, and Mounir Mesbah, eds. Rasch Models in Health. Hoboken, NJ USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118574454.

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Davidson, Gavin, Jim Campbell, Ciarán Shannon, and Ciaran Mulholland. Models of Mental Health. London: Macmillan Education UK, 2016. http://dx.doi.org/10.1007/978-1-137-36591-0.

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Carol, Tannahill, and Tannahill Andres, eds. Health promotion: Models and values. 2nd ed. Oxford: Oxford University Press, 1996.

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Carol, Tannahill, and Tannahill Andrew, eds. Health promotion: Models and values. Oxford: Oxford University Press, 1990.

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The dimensions of health: Conceptual models. Sudbury, Mass: Jones and Bartlett, 2010.

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Hjelm, John R. The dimensions of health: Conceptual models. Sudbury, Mass: Jones and Bartlett, 2010.

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Gibbens, Brad. State rural health policy advocacy models. North Dakota: University of North Dakota Rural Health Research Center, 1991.

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Hjelm, John R. The dimensions of health: Conceptual models. Sudbury, Mass: Jones and Bartlett, 2010.

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Mahmood, Tahir, Allan Templeton, and Charnjit Dhillon, eds. Models of Care in Women's Health. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9781107478299.

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Sexton, Adrienne B. Occupational regulation models: Health-related boards. Salem, Or: Legislative Committee Office, 1992.

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Book chapters on the topic "Health models"

1

Benson, Tim. "Models." In Health Informatics, 35–53. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-803-2_3.

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Parnis, J. Mark, and Donald Mackay. "Human Health." In Multimedia Environmental Models, 263–68. Third edition. | Boca Raton, FL : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780367809829-8.

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Benson, Tim, and Grahame Grieve. "Models." In Principles of Health Interoperability, 37–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30370-3_3.

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Benson, Tim, and Grahame Grieve. "Models." In Principles of Health Interoperability, 379–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56883-2_20.

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Bria, William F., and Nancy B. Finn. "New Healthcare Models." In Health Informatics, 21–41. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-355-6_3.

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Benson, Tim. "Constrained Information Models." In Health Informatics, 129–43. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-803-2_8.

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McPake, Barbara, Charles Normand, Samantha Smith, and Anne Nolan. "Basic market models." In Health Economics, 39–48. 4th edition. | Abingdon, Oxon ; New York, NY : Routledge, [2020]: Routledge, 2020. http://dx.doi.org/10.4324/9781315169729-6.

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Pintilie, Melania. "Competing Risk Models." In Health Services Evaluation, 433–46. New York, NY: Springer US, 2019. http://dx.doi.org/10.1007/978-1-4939-8715-3_30.

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Pintilie, Melania. "Competing Risk Models." In Health Services Research, 1–14. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4939-6704-9_2-1.

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Pitacco, Ermanno. "Actuarial Models for Sickness Insurance." In Health Insurance, 69–94. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12235-9_5.

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Conference papers on the topic "Health models"

1

Barb, Adrian S., and Chi-Ren Shyu. "Semantic models for ranking medical images using Dirichlet non-parametric mixture models." In 2011 IEEE 13th International Conference on e-Health Networking, Applications and Services (Healthcom 2011). IEEE, 2011. http://dx.doi.org/10.1109/health.2011.6026776.

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Gruber, Sebastian, Bernd Neumayr, Michael Schrefl, and Josef Niebauer. "Towards Multi-level Modeling of Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00084.

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Sulkowski, Lukasz. "HEALTH MARKETING - CONCEPTS, MODELS AND RESEARCH." In 4th International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgemsocial2017/15/s05.057.

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Shou, Xiao, Georgios Mavroudeas, Alexander New, Kofi Arhin, Jason N. Kuruzovich, Malik Magdon-Ismail, and Kristin P. Bennett. "Supervised Mixture Models for Population Health." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983339.

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"INTEGRATING R MODELS WITH WEB TECHNOLOGIES." In International Conference on Health Informatics. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001430104110415.

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Chistova, Elena, and Alexander Tyrsin. "Health Status of Working Pensioners." In Proceedings of the Ecological-Socio-Economic Systems: Models of Competition and Cooperation (ESES 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200113.015.

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Starke, E., M. Krause, G. Pfeifer, and W. J. Fischer. "Applying network models to improve FE-models." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Mehrdad N. Ghasemi-Nejhad. SPIE, 2011. http://dx.doi.org/10.1117/12.885635.

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"Public Health Protection in a Social Welfare State." In XII Ural Demographic Forum “Paradigms and models of demographic development”. Institute of Economics of the Ural Branch of the Russian Academy of Sciences, 2021. http://dx.doi.org/10.17059/udf-2021-4-19.

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Hunn, N. "Making Mobile Health work - an alternative look at Mobile Health business models." In IET Seminar on Assisted Living 2011. IET, 2011. http://dx.doi.org/10.1049/ic.2011.0037.

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"BSN MIDDLEWARE - Abstracting Resources to Human Models." In International Conference on Health Informatics. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001545802450250.

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Reports on the topic "Health models"

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Collins, Chris, Denise Levis Hewson, Richard Munger, and Torlen Wade. Evolving Models of Behavioral Health. Milbank Memorial Fund, May 2010. http://dx.doi.org/10.1599/evolvingcare2010.

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Rieger, Oya. Academic Health Sciences Libraries: Structural Models and Perspectives. Ithaka S+R, October 2020. http://dx.doi.org/10.18665/sr.314248.

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Abrahamson, S., M. Bender, S. Book, C. Buncher, C. Denniston, E. Gilbert, F. Hahn, V. Hertzberg, H. Maxon, and B. Scott. Health effects models for nuclear power plant accident consequence analysis: Low LET radiation: Part 2, Scientific bases for health effects models. Office of Scientific and Technical Information (OSTI), May 1989. http://dx.doi.org/10.2172/5901689.

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Abrahamson, S., M. A. Bender, B. B. Boecker, B. R. Scott, and E. S. Gilbert. Health effects models for nuclear power plant accident consequence analysis: Modifications of models resulting from recent reports on health effects of ionizing radiation. Office of Scientific and Technical Information (OSTI), August 1991. http://dx.doi.org/10.2172/6196411.

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Bound, John. Self-Reported vs. Objective Measures of Health in Retirement Models. Cambridge, MA: National Bureau of Economic Research, June 1989. http://dx.doi.org/10.3386/w2997.

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Truong, Kris, and Michell Dolfini-Reed. Behavioral Health Care Delivery Models and Examples: Contractual to Functional Integration. Fort Belvoir, VA: Defense Technical Information Center, November 2000. http://dx.doi.org/10.21236/ada401079.

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Klasky, Hilda, Ozgur Ozmen, Olufemi Omitaomu, Mohammed Olama, Laura Pullum, Addi Thakur Malviya, and Teja Kuruganti. Comparative Assessment of Data-driven Process Models in Health Information Technology. Office of Scientific and Technical Information (OSTI), May 2021. http://dx.doi.org/10.2172/1824947.

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Evans, J. S., D. W. Moeller, and D. W. Cooper. Health effects model for nuclear power plant accident consequence analysis. Part I. Introduction, integration, and summary. Part II. Scientific basis for health effects models. Office of Scientific and Technical Information (OSTI), July 1985. http://dx.doi.org/10.2172/6299241.

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Bogner, Hillary, Fran Barg, and Dawei Xie. Using Statistical Models to Predict Worsening Health Among Older People With Disabilities. Patient-Centered Outcomes Research Institute® (PCORI), May 2020. http://dx.doi.org/10.25302/05.2020.ad.12114567.

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Hammond, Terry. Feasible Models of Universal Health Insurance in Oregon According to Stakeholder Views. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.500.

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