[Tuncated abstract] This research thesis consists of five sections. Section one provides the background information (chapter 1) and a description of characteristics of the cohort and the methods of analysis (chapter 2). The Acute Physiology and Chronic Health Evaluation (APACHE) II scoring system is one of commonly used severity of illness scoring systems in many intensive care units (ICUs). Section two of this thesis includes an assessment of the performance of the APACHE II scoring system in an Australian context. First, the performance of the APACHE II scoring system in predicting hospital mortality of critically ill patients in an ICU of a tertiary university teaching hospital in Western Australia was assessed (Chapter 3). Second, a simple modification of the traditional APACHE II scoring system, the 'admission APACHE II scoring system', generated by replacing the worst first 24-hour data by the ICU admission physiological and laboratory data was assessed (Chapter 3). Indigenous and Aboriginal Australians constitute a significant proportion of the population in Western Australia (3.2%) and have marked social disadvantage when compared to other Australians. The difference in the pattern of critical illness between indigenous and non-indigenous Australians and also whether the performance of the APACHE II scoring system was comparable between these two groups of critically ill patients in Western Australia was assessed (Chapter 4). Both discrimination and calibration are important indicators of the performance of a prognostic scoring system. ... The use of the APACHE II scoring system in patients readmitted to ICU during the same hospitalisation was evaluated and also whether incorporating events prior to the ICU readmission to the APACHE II scoring system would improve its ability to predict hospital mortality of ICU readmission was assessed in chapter 10. Whilst there have been a number of studies investigating predictors of post-ICU in-hospital mortality none have investigated whether unresolved or latent inflammation and sepsis may be an important predictor. Section four examines the role of inflammatory markers measured at ICU discharge on predicting ICU re- 4 admission (Chapter 11) and in-hospital mortality during the same hospitalisation (Chapter 12) and whether some of these inflammatory markers were more important than organ failure score and the APACHE II scoring system in predicting these outcomes. Section five describes the development of a new prognostic scoring system that can estimate median survival time and long term survival probabilities for critically ill patients (Chapter 13). An assessment of the effects of other factors such as socioeconomic status and Aboriginality on the long term survival of critically ill patients in an Australian ICU was assessed (Chapter 14). Section six provides the conclusions. Chapter 15 includes a summary and discussion of the findings of this thesis and outlines possible future directions for further research in this important aspect of intensive care medicine.

CONTEXTUALIZAÇÃO: A preocupação com a qualidade da assistência tem aumentado nas últimas décadas em todo o mundo. O aumento da demanda, aliado à escassez de recursos financeiros e ao desenvolvimento e incorporação de novas tecnologias, tem suscitado reflexões e pesquisas que busquem avaliar a assistência hospitalar prestada em termos de custo-efetividade. Os estudos têm utilizado, na sua grande maioria, taxas de mortalidade hospitalar, que é um indicador tradicional de desempenho hospitalar. A análise comparativa de indicadores de desempenho pressupõe que as taxas de mortalidade sejam ajustadas às características dos pacientes e ao perfil do hospital, que também contribui na probabilidade de óbito hospitalar. Muitos autores têm utilizado bases de dados administrativas para avaliar estabelecimentos de saúde, principalmente pelo baixo custo e fácil disponibilidade. Diversos estudos internacionais têm analisado a eficiência dos serviços hospitalares de forma intensa e constante. No Brasil, os estudos ainda são poucos e a maioria tem avaliado diagnósticos específicos ou faixas-etárias específicas. Além disso, são poucos os que agregam o perfil dos hospitais na análise de predição do óbito hospitalar. OBJETIVO: O objetivo desta tese é desenvolver um índice de risco para óbito hospitalar ajustado pelas características das internações e pelo perfil dos hospitais a partir dos dados disponíveis no Sistema de Informações Hospitalar (SIH-SUS), com a finalidade de comparação de desempenho entre hospitais. É também objetivo desenvolver um modelo preditivo de probabilidade de óbito hospitalar utilizando a metodologia de modelo multinível. MÉTODOS: Trata-se de um estudo transversal com dados de 453.515 Autorizações de Internação Hospitalar (AIHs) do Sistema de Informações Hospitalares do Sistema Único de Saúde (SIH-SUS) do Rio Grande do Sul no ano de 2005. Utilizou-se regressão logística tradicional a fim de desenvolver um modelo preditivo das chances de óbito hospitalar considerando as características das internações. A seguir, foi realizada modelagem multinível buscando desenvolver um modelo preditivo das chances de óbito hospitalar considerando as características das internações e o perfil dos hospitais. Após o ajuste do modelo, foi calculado o Índice de Risco (IR), que permitiu o cálculo das probabilidades de óbitos hospitalares esperados (E), que foram comparados aos óbitos observados (O). O ordenamento do desempenho dos estabelecimentos foi realizado através da razão O/E em função da incorporação das características das internações (nível individual) e do perfil dos hospitais (nível contextual) conjuntamente no modelo preditivo. RESULTADOS: A taxa bruta de mortalidade para o conjunto dos 332 hospitais (453.515 AIHs) foi de 6,3%. A mortalidade foi maior para os homens. As doenças infecciosas e parasitárias, neoplasias, doenças do sistema nervoso, do aparelho circulatório e respiratório e, ainda, diagnósticos informados como sinais e sintomas anormais foram os que apresentaram significativamente maior número de óbitos do que o esperado através do teste Qui-quadrado. A especialidade clínica médica apresentou maior número de óbitos em comparação à especialidade cirurgia. A maioria das internações ocorreu em hospitais privados, enquanto que a taxa bruta de mortalidade foi maior nos hospitais públicos. Através da modelagem por regressão logística, utilizando o perfil das internações, obteve-se um Índice de Risco (IR) para mortalidade hospitalar. A partir do modelo preditivo foram calculados os óbitos esperados para os hospitais. Dos 206 hospitais analisados, a razão O/E (óbito observado/óbito esperado) mostrou 40 hospitais com mortalidade significativamente superior à esperada e 58 hospitais com mortalidade significativamente inferior à esperada. A partir do modelo preditivo multinível, formado por variáveis explicativas referentes à internação (primeiro nível) e variáveis explicativas referentes ao hospital (segundo nível), verificou-se que o perfil dos hospitais tem papel importante na predição do óbito hospitalar. As variáveis uso de UTI, seguida por idade foram as principais preditoras para óbito hospitalar no nível individual e porte do hospital, seguida por natureza jurídica, o foram no nível contextual respectivamente. A razão O/E baseada no modelo multinível mostrou que os hospitais de pequeno porte tem pior desempenho, os de grande porte melhoram seu desempenho e os de médio porte mantiveram-se praticamente sem modificações, quando comparados ao desempenho medido pela razão O/E obtida apenas para as características das internações. Constatou-se, ainda, um melhor desempenho dos estabelecimentos públicos, para todos os portes, e pior desempenho para os hospitais privados CONCLUSÕES: O índice de risco construído a partir das características da internação e do perfil dos estabelecimentos por modelos multinível pode ser empregado na análise de desempenho dos hospitais do SIH-SUS. O IR construído permitirá calcular a probabilidade de óbito e assim obter a taxa ajustada de mortalidade, a ser usada como um indicador de desempenho. Esta metodologia mostrou-se útil para rastrear hospitais que merecem uma atenção maior por parte de gestores, prestadores de serviços, profissionais e comunidade. A ordenação dos hospitais utilizando apenas a taxa de mortalidade bruta não é igual à ordenação quando se utiliza o ranking ajustado pelo modelo preditivo de probabilidade para o nível de internações, e esse último também não é igual quando se adiciona o nível dos hospitais. Recomenda-se que, ao comparar hospitais, seja utilizado o ajuste pelo modelo preditivo de probabilidade de risco que incorpora tanto o nível das internações, quanto dos hospitais. Estudos acrescentando outras variáveis do nível de internações, do nível hospitalar, além da região, poderão contribuir para o aprimoramento do modelo e do índice de risco. O desenvolvimento de uma série histórica de acompanhamento, bem como a discussão com representantes de várias instâncias envolvidas no processo de avaliação hospitalar poderão aumentar a eficiência do método. / CONTEXTUALIZATION: The concern with the quality of care has increased in recent decades throughout the world. Increased demand, combined with the scarcity of financial resources and the development and incorporation of new technologies, has raised debate and research that seek to evaluate the hospital care provided in terms of costeffectiveness. Studies have mostly used hospital mortality rates, which is a traditional indicator of hospital performance. Comparative analysis of performance indicators means that mortality rates are adjusted to the characteristics of patients and to the hospital profile, which also contributes to the risk of death in hospital. Many authors have used administrative databases to assess health institutions, especially for their low cost and easy availability. Several international studies have analyzed the efficiency of hospital services in intense and constant way. In Brazil, studies are still few and most have evaluated specific diagnoses or specific age ranges. Moreover, few studies add the profile of hospitals to the analysis of prediction of hospital death. OBJECTIVE: The objective of this thesis is to develop a risk index for hospital death adjusted by characteristics of hospital admissions and by the profile of hospitals, using the available data in the SIH-SUS, for the purpose of comparison of performance between SUS hospitals. It also aims to develop a multilevel model of hospital risk of death. METHODS: This is a cross-sectional study with data from 453.515 Authorization Form for Hospital Admittance (AIHs) of the Hospital Information System of the Unified Health System (SIH-SUS) in Rio Grande do Sul in 2005. A traditional logistic regression was used to develop a predictive model of the chances of hospital death considering the characteristics of hospital admissions. Additionally a multilevel modeling was employed to develop a predictive model of the chances of death considering the characteristics of hospital admissions and hospital profiles. After fitting the model, the risk index (IR) was calculated, which allowed for the calculation of the likelihood of hospital expected deaths (E), which were then compared to the observed deaths (O). The performance ranking of the establishments was conducted through the ratio O/E depending on the incorporation of characteristics of hospital (individual level) and the profiles of hospitals (contextual level) together in the predictive model. RESULTS: The crude death rate for all 332 hospitals (453.515 AIHs) was 6.3%. Mortality was higher for men. Infectious and parasitic diseases, neoplasms, diseases of the nervous system, of the circulatory and of the respiratory apparatus, and also informed diagnoses as abnormal signs and symptoms were those that had significantly more deaths than expected by the chi-square test. Higher number was observed for the speciality medical clinic of deaths compared to surgery. Most hospitalizations occurred in private hospitals, while the crude death rate was higher in public hospitals. Through the RL model, by using the profile of hospitalizations, a Risk Index (IR) was obtained for hospital mortality. From the predictive model were calculated expected deaths for hospitals. In 40 out of the 206 hospitals studied, the ratio O/E (observed deaths / expected deaths) showed mortality rates significantly higher than expected and, in 58 hospitals the mortality rates were significantly lower than expected. As for the multilevel predictive model, consisting of explanatory variables related to hospitalization (first level) and explanatory variables for the hospital (second level), the profiles of hospitals had an important role in prediction of hospital death. The variable use of Intensive Care Unit (UTI), followed by patient age, were the main predictors for hospital death at the individual level and size of the hospital, followed by a legal nature were the more important variables for the contextual level. The ratio O/E based on the multilevel model showed that small hospitals had a worse their performance, large institutions had better performances and those of medium size virtually unchanged when compared to the ratio O/E only for the characteristics of admissions It was also verified an improvement of performance of the public hospitals, for all sizes, and worsening of performance for private hospitals. CONCLUSIONS: The risk index constructed from the characteristics of hospitalization and the profile of establishments by multilevel models can be used in the analysis of performance of the SIH-SUS hospitals. The presently developed IR will yield a probability of death and thereby an adjusted rate of mortality, to be used as an indicator of performance. This methodology proved to be useful to track hospitals that deserve greater attention from managers, providers, professionals and community. The ordering of the hospitals using only the crude mortality rate is not equal to the ordering that uses the ranking set by the predictive model of probability for the level of admissions, and the latter is not equal when it adds the level of hospitals. When comparing hospitals, it is recommended the use of adjustment of the predictive model of probability of risk that incorporates both the levels of admissions and of the hospitals. Studies adding other variables in the level of admissions, the hospital level, as well as the region, could contribute to the improvement of the model and the risk index. The development of a historical series of monitoring and discussion with representatives of various groups involved in hospital evaluation will add validity to the assessment method.

CONTEXTUALIZAÇÃO: A preocupação com a qualidade da assistência tem aumentado nas últimas décadas em todo o mundo. O aumento da demanda, aliado à escassez de recursos financeiros e ao desenvolvimento e incorporação de novas tecnologias, tem suscitado reflexões e pesquisas que busquem avaliar a assistência hospitalar prestada em termos de custo-efetividade. Os estudos têm utilizado, na sua grande maioria, taxas de mortalidade hospitalar, que é um indicador tradicional de desempenho hospitalar. A análise comparativa de indicadores de desempenho pressupõe que as taxas de mortalidade sejam ajustadas às características dos pacientes e ao perfil do hospital, que também contribui na probabilidade de óbito hospitalar. Muitos autores têm utilizado bases de dados administrativas para avaliar estabelecimentos de saúde, principalmente pelo baixo custo e fácil disponibilidade. Diversos estudos internacionais têm analisado a eficiência dos serviços hospitalares de forma intensa e constante. No Brasil, os estudos ainda são poucos e a maioria tem avaliado diagnósticos específicos ou faixas-etárias específicas. Além disso, são poucos os que agregam o perfil dos hospitais na análise de predição do óbito hospitalar. OBJETIVO: O objetivo desta tese é desenvolver um índice de risco para óbito hospitalar ajustado pelas características das internações e pelo perfil dos hospitais a partir dos dados disponíveis no Sistema de Informações Hospitalar (SIH-SUS), com a finalidade de comparação de desempenho entre hospitais. É também objetivo desenvolver um modelo preditivo de probabilidade de óbito hospitalar utilizando a metodologia de modelo multinível. MÉTODOS: Trata-se de um estudo transversal com dados de 453.515 Autorizações de Internação Hospitalar (AIHs) do Sistema de Informações Hospitalares do Sistema Único de Saúde (SIH-SUS) do Rio Grande do Sul no ano de 2005. Utilizou-se regressão logística tradicional a fim de desenvolver um modelo preditivo das chances de óbito hospitalar considerando as características das internações. A seguir, foi realizada modelagem multinível buscando desenvolver um modelo preditivo das chances de óbito hospitalar considerando as características das internações e o perfil dos hospitais. Após o ajuste do modelo, foi calculado o Índice de Risco (IR), que permitiu o cálculo das probabilidades de óbitos hospitalares esperados (E), que foram comparados aos óbitos observados (O). O ordenamento do desempenho dos estabelecimentos foi realizado através da razão O/E em função da incorporação das características das internações (nível individual) e do perfil dos hospitais (nível contextual) conjuntamente no modelo preditivo. RESULTADOS: A taxa bruta de mortalidade para o conjunto dos 332 hospitais (453.515 AIHs) foi de 6,3%. A mortalidade foi maior para os homens. As doenças infecciosas e parasitárias, neoplasias, doenças do sistema nervoso, do aparelho circulatório e respiratório e, ainda, diagnósticos informados como sinais e sintomas anormais foram os que apresentaram significativamente maior número de óbitos do que o esperado através do teste Qui-quadrado. A especialidade clínica médica apresentou maior número de óbitos em comparação à especialidade cirurgia. A maioria das internações ocorreu em hospitais privados, enquanto que a taxa bruta de mortalidade foi maior nos hospitais públicos. Através da modelagem por regressão logística, utilizando o perfil das internações, obteve-se um Índice de Risco (IR) para mortalidade hospitalar. A partir do modelo preditivo foram calculados os óbitos esperados para os hospitais. Dos 206 hospitais analisados, a razão O/E (óbito observado/óbito esperado) mostrou 40 hospitais com mortalidade significativamente superior à esperada e 58 hospitais com mortalidade significativamente inferior à esperada. A partir do modelo preditivo multinível, formado por variáveis explicativas referentes à internação (primeiro nível) e variáveis explicativas referentes ao hospital (segundo nível), verificou-se que o perfil dos hospitais tem papel importante na predição do óbito hospitalar. As variáveis uso de UTI, seguida por idade foram as principais preditoras para óbito hospitalar no nível individual e porte do hospital, seguida por natureza jurídica, o foram no nível contextual respectivamente. A razão O/E baseada no modelo multinível mostrou que os hospitais de pequeno porte tem pior desempenho, os de grande porte melhoram seu desempenho e os de médio porte mantiveram-se praticamente sem modificações, quando comparados ao desempenho medido pela razão O/E obtida apenas para as características das internações. Constatou-se, ainda, um melhor desempenho dos estabelecimentos públicos, para todos os portes, e pior desempenho para os hospitais privados CONCLUSÕES: O índice de risco construído a partir das características da internação e do perfil dos estabelecimentos por modelos multinível pode ser empregado na análise de desempenho dos hospitais do SIH-SUS. O IR construído permitirá calcular a probabilidade de óbito e assim obter a taxa ajustada de mortalidade, a ser usada como um indicador de desempenho. Esta metodologia mostrou-se útil para rastrear hospitais que merecem uma atenção maior por parte de gestores, prestadores de serviços, profissionais e comunidade. A ordenação dos hospitais utilizando apenas a taxa de mortalidade bruta não é igual à ordenação quando se utiliza o ranking ajustado pelo modelo preditivo de probabilidade para o nível de internações, e esse último também não é igual quando se adiciona o nível dos hospitais. Recomenda-se que, ao comparar hospitais, seja utilizado o ajuste pelo modelo preditivo de probabilidade de risco que incorpora tanto o nível das internações, quanto dos hospitais. Estudos acrescentando outras variáveis do nível de internações, do nível hospitalar, além da região, poderão contribuir para o aprimoramento do modelo e do índice de risco. O desenvolvimento de uma série histórica de acompanhamento, bem como a discussão com representantes de várias instâncias envolvidas no processo de avaliação hospitalar poderão aumentar a eficiência do método. / CONTEXTUALIZATION: The concern with the quality of care has increased in recent decades throughout the world. Increased demand, combined with the scarcity of financial resources and the development and incorporation of new technologies, has raised debate and research that seek to evaluate the hospital care provided in terms of costeffectiveness. Studies have mostly used hospital mortality rates, which is a traditional indicator of hospital performance. Comparative analysis of performance indicators means that mortality rates are adjusted to the characteristics of patients and to the hospital profile, which also contributes to the risk of death in hospital. Many authors have used administrative databases to assess health institutions, especially for their low cost and easy availability. Several international studies have analyzed the efficiency of hospital services in intense and constant way. In Brazil, studies are still few and most have evaluated specific diagnoses or specific age ranges. Moreover, few studies add the profile of hospitals to the analysis of prediction of hospital death. OBJECTIVE: The objective of this thesis is to develop a risk index for hospital death adjusted by characteristics of hospital admissions and by the profile of hospitals, using the available data in the SIH-SUS, for the purpose of comparison of performance between SUS hospitals. It also aims to develop a multilevel model of hospital risk of death. METHODS: This is a cross-sectional study with data from 453.515 Authorization Form for Hospital Admittance (AIHs) of the Hospital Information System of the Unified Health System (SIH-SUS) in Rio Grande do Sul in 2005. A traditional logistic regression was used to develop a predictive model of the chances of hospital death considering the characteristics of hospital admissions. Additionally a multilevel modeling was employed to develop a predictive model of the chances of death considering the characteristics of hospital admissions and hospital profiles. After fitting the model, the risk index (IR) was calculated, which allowed for the calculation of the likelihood of hospital expected deaths (E), which were then compared to the observed deaths (O). The performance ranking of the establishments was conducted through the ratio O/E depending on the incorporation of characteristics of hospital (individual level) and the profiles of hospitals (contextual level) together in the predictive model. RESULTS: The crude death rate for all 332 hospitals (453.515 AIHs) was 6.3%. Mortality was higher for men. Infectious and parasitic diseases, neoplasms, diseases of the nervous system, of the circulatory and of the respiratory apparatus, and also informed diagnoses as abnormal signs and symptoms were those that had significantly more deaths than expected by the chi-square test. Higher number was observed for the speciality medical clinic of deaths compared to surgery. Most hospitalizations occurred in private hospitals, while the crude death rate was higher in public hospitals. Through the RL model, by using the profile of hospitalizations, a Risk Index (IR) was obtained for hospital mortality. From the predictive model were calculated expected deaths for hospitals. In 40 out of the 206 hospitals studied, the ratio O/E (observed deaths / expected deaths) showed mortality rates significantly higher than expected and, in 58 hospitals the mortality rates were significantly lower than expected. As for the multilevel predictive model, consisting of explanatory variables related to hospitalization (first level) and explanatory variables for the hospital (second level), the profiles of hospitals had an important role in prediction of hospital death. The variable use of Intensive Care Unit (UTI), followed by patient age, were the main predictors for hospital death at the individual level and size of the hospital, followed by a legal nature were the more important variables for the contextual level. The ratio O/E based on the multilevel model showed that small hospitals had a worse their performance, large institutions had better performances and those of medium size virtually unchanged when compared to the ratio O/E only for the characteristics of admissions It was also verified an improvement of performance of the public hospitals, for all sizes, and worsening of performance for private hospitals. CONCLUSIONS: The risk index constructed from the characteristics of hospitalization and the profile of establishments by multilevel models can be used in the analysis of performance of the SIH-SUS hospitals. The presently developed IR will yield a probability of death and thereby an adjusted rate of mortality, to be used as an indicator of performance. This methodology proved to be useful to track hospitals that deserve greater attention from managers, providers, professionals and community. The ordering of the hospitals using only the crude mortality rate is not equal to the ordering that uses the ranking set by the predictive model of probability for the level of admissions, and the latter is not equal when it adds the level of hospitals. When comparing hospitals, it is recommended the use of adjustment of the predictive model of probability of risk that incorporates both the levels of admissions and of the hospitals. Studies adding other variables in the level of admissions, the hospital level, as well as the region, could contribute to the improvement of the model and the risk index. The development of a historical series of monitoring and discussion with representatives of various groups involved in hospital evaluation will add validity to the assessment method.

CONTEXTUALIZAÇÃO: A preocupação com a qualidade da assistência tem aumentado nas últimas décadas em todo o mundo. O aumento da demanda, aliado à escassez de recursos financeiros e ao desenvolvimento e incorporação de novas tecnologias, tem suscitado reflexões e pesquisas que busquem avaliar a assistência hospitalar prestada em termos de custo-efetividade. Os estudos têm utilizado, na sua grande maioria, taxas de mortalidade hospitalar, que é um indicador tradicional de desempenho hospitalar. A análise comparativa de indicadores de desempenho pressupõe que as taxas de mortalidade sejam ajustadas às características dos pacientes e ao perfil do hospital, que também contribui na probabilidade de óbito hospitalar. Muitos autores têm utilizado bases de dados administrativas para avaliar estabelecimentos de saúde, principalmente pelo baixo custo e fácil disponibilidade. Diversos estudos internacionais têm analisado a eficiência dos serviços hospitalares de forma intensa e constante. No Brasil, os estudos ainda são poucos e a maioria tem avaliado diagnósticos específicos ou faixas-etárias específicas. Além disso, são poucos os que agregam o perfil dos hospitais na análise de predição do óbito hospitalar. OBJETIVO: O objetivo desta tese é desenvolver um índice de risco para óbito hospitalar ajustado pelas características das internações e pelo perfil dos hospitais a partir dos dados disponíveis no Sistema de Informações Hospitalar (SIH-SUS), com a finalidade de comparação de desempenho entre hospitais. É também objetivo desenvolver um modelo preditivo de probabilidade de óbito hospitalar utilizando a metodologia de modelo multinível. MÉTODOS: Trata-se de um estudo transversal com dados de 453.515 Autorizações de Internação Hospitalar (AIHs) do Sistema de Informações Hospitalares do Sistema Único de Saúde (SIH-SUS) do Rio Grande do Sul no ano de 2005. Utilizou-se regressão logística tradicional a fim de desenvolver um modelo preditivo das chances de óbito hospitalar considerando as características das internações. A seguir, foi realizada modelagem multinível buscando desenvolver um modelo preditivo das chances de óbito hospitalar considerando as características das internações e o perfil dos hospitais. Após o ajuste do modelo, foi calculado o Índice de Risco (IR), que permitiu o cálculo das probabilidades de óbitos hospitalares esperados (E), que foram comparados aos óbitos observados (O). O ordenamento do desempenho dos estabelecimentos foi realizado através da razão O/E em função da incorporação das características das internações (nível individual) e do perfil dos hospitais (nível contextual) conjuntamente no modelo preditivo. RESULTADOS: A taxa bruta de mortalidade para o conjunto dos 332 hospitais (453.515 AIHs) foi de 6,3%. A mortalidade foi maior para os homens. As doenças infecciosas e parasitárias, neoplasias, doenças do sistema nervoso, do aparelho circulatório e respiratório e, ainda, diagnósticos informados como sinais e sintomas anormais foram os que apresentaram significativamente maior número de óbitos do que o esperado através do teste Qui-quadrado. A especialidade clínica médica apresentou maior número de óbitos em comparação à especialidade cirurgia. A maioria das internações ocorreu em hospitais privados, enquanto que a taxa bruta de mortalidade foi maior nos hospitais públicos. Através da modelagem por regressão logística, utilizando o perfil das internações, obteve-se um Índice de Risco (IR) para mortalidade hospitalar. A partir do modelo preditivo foram calculados os óbitos esperados para os hospitais. Dos 206 hospitais analisados, a razão O/E (óbito observado/óbito esperado) mostrou 40 hospitais com mortalidade significativamente superior à esperada e 58 hospitais com mortalidade significativamente inferior à esperada. A partir do modelo preditivo multinível, formado por variáveis explicativas referentes à internação (primeiro nível) e variáveis explicativas referentes ao hospital (segundo nível), verificou-se que o perfil dos hospitais tem papel importante na predição do óbito hospitalar. As variáveis uso de UTI, seguida por idade foram as principais preditoras para óbito hospitalar no nível individual e porte do hospital, seguida por natureza jurídica, o foram no nível contextual respectivamente. A razão O/E baseada no modelo multinível mostrou que os hospitais de pequeno porte tem pior desempenho, os de grande porte melhoram seu desempenho e os de médio porte mantiveram-se praticamente sem modificações, quando comparados ao desempenho medido pela razão O/E obtida apenas para as características das internações. Constatou-se, ainda, um melhor desempenho dos estabelecimentos públicos, para todos os portes, e pior desempenho para os hospitais privados CONCLUSÕES: O índice de risco construído a partir das características da internação e do perfil dos estabelecimentos por modelos multinível pode ser empregado na análise de desempenho dos hospitais do SIH-SUS. O IR construído permitirá calcular a probabilidade de óbito e assim obter a taxa ajustada de mortalidade, a ser usada como um indicador de desempenho. Esta metodologia mostrou-se útil para rastrear hospitais que merecem uma atenção maior por parte de gestores, prestadores de serviços, profissionais e comunidade. A ordenação dos hospitais utilizando apenas a taxa de mortalidade bruta não é igual à ordenação quando se utiliza o ranking ajustado pelo modelo preditivo de probabilidade para o nível de internações, e esse último também não é igual quando se adiciona o nível dos hospitais. Recomenda-se que, ao comparar hospitais, seja utilizado o ajuste pelo modelo preditivo de probabilidade de risco que incorpora tanto o nível das internações, quanto dos hospitais. Estudos acrescentando outras variáveis do nível de internações, do nível hospitalar, além da região, poderão contribuir para o aprimoramento do modelo e do índice de risco. O desenvolvimento de uma série histórica de acompanhamento, bem como a discussão com representantes de várias instâncias envolvidas no processo de avaliação hospitalar poderão aumentar a eficiência do método. / CONTEXTUALIZATION: The concern with the quality of care has increased in recent decades throughout the world. Increased demand, combined with the scarcity of financial resources and the development and incorporation of new technologies, has raised debate and research that seek to evaluate the hospital care provided in terms of costeffectiveness. Studies have mostly used hospital mortality rates, which is a traditional indicator of hospital performance. Comparative analysis of performance indicators means that mortality rates are adjusted to the characteristics of patients and to the hospital profile, which also contributes to the risk of death in hospital. Many authors have used administrative databases to assess health institutions, especially for their low cost and easy availability. Several international studies have analyzed the efficiency of hospital services in intense and constant way. In Brazil, studies are still few and most have evaluated specific diagnoses or specific age ranges. Moreover, few studies add the profile of hospitals to the analysis of prediction of hospital death. OBJECTIVE: The objective of this thesis is to develop a risk index for hospital death adjusted by characteristics of hospital admissions and by the profile of hospitals, using the available data in the SIH-SUS, for the purpose of comparison of performance between SUS hospitals. It also aims to develop a multilevel model of hospital risk of death. METHODS: This is a cross-sectional study with data from 453.515 Authorization Form for Hospital Admittance (AIHs) of the Hospital Information System of the Unified Health System (SIH-SUS) in Rio Grande do Sul in 2005. A traditional logistic regression was used to develop a predictive model of the chances of hospital death considering the characteristics of hospital admissions. Additionally a multilevel modeling was employed to develop a predictive model of the chances of death considering the characteristics of hospital admissions and hospital profiles. After fitting the model, the risk index (IR) was calculated, which allowed for the calculation of the likelihood of hospital expected deaths (E), which were then compared to the observed deaths (O). The performance ranking of the establishments was conducted through the ratio O/E depending on the incorporation of characteristics of hospital (individual level) and the profiles of hospitals (contextual level) together in the predictive model. RESULTS: The crude death rate for all 332 hospitals (453.515 AIHs) was 6.3%. Mortality was higher for men. Infectious and parasitic diseases, neoplasms, diseases of the nervous system, of the circulatory and of the respiratory apparatus, and also informed diagnoses as abnormal signs and symptoms were those that had significantly more deaths than expected by the chi-square test. Higher number was observed for the speciality medical clinic of deaths compared to surgery. Most hospitalizations occurred in private hospitals, while the crude death rate was higher in public hospitals. Through the RL model, by using the profile of hospitalizations, a Risk Index (IR) was obtained for hospital mortality. From the predictive model were calculated expected deaths for hospitals. In 40 out of the 206 hospitals studied, the ratio O/E (observed deaths / expected deaths) showed mortality rates significantly higher than expected and, in 58 hospitals the mortality rates were significantly lower than expected. As for the multilevel predictive model, consisting of explanatory variables related to hospitalization (first level) and explanatory variables for the hospital (second level), the profiles of hospitals had an important role in prediction of hospital death. The variable use of Intensive Care Unit (UTI), followed by patient age, were the main predictors for hospital death at the individual level and size of the hospital, followed by a legal nature were the more important variables for the contextual level. The ratio O/E based on the multilevel model showed that small hospitals had a worse their performance, large institutions had better performances and those of medium size virtually unchanged when compared to the ratio O/E only for the characteristics of admissions It was also verified an improvement of performance of the public hospitals, for all sizes, and worsening of performance for private hospitals. CONCLUSIONS: The risk index constructed from the characteristics of hospitalization and the profile of establishments by multilevel models can be used in the analysis of performance of the SIH-SUS hospitals. The presently developed IR will yield a probability of death and thereby an adjusted rate of mortality, to be used as an indicator of performance. This methodology proved to be useful to track hospitals that deserve greater attention from managers, providers, professionals and community. The ordering of the hospitals using only the crude mortality rate is not equal to the ordering that uses the ranking set by the predictive model of probability for the level of admissions, and the latter is not equal when it adds the level of hospitals. When comparing hospitals, it is recommended the use of adjustment of the predictive model of probability of risk that incorporates both the levels of admissions and of the hospitals. Studies adding other variables in the level of admissions, the hospital level, as well as the region, could contribute to the improvement of the model and the risk index. The development of a historical series of monitoring and discussion with representatives of various groups involved in hospital evaluation will add validity to the assessment method.

Orientador: Ricardo Carlos Cordeiro / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas / Made available in DSpace on 2018-08-18T15:08:16Z (GMT). No. of bitstreams: 1 Mafra_AnaCarolinaCintraNunes_D.pdf: 19877794 bytes, checksum: a74a4b2bf9bccffacddd691b458d1fd3 (MD5) Previous issue date: 2011 / Resumo: A busca em compreender determinados fenômenos epidemiológicos muitas vezes envolve uma ferramenta denominada análise espacial do risco. O estudo do espaço em que ocorrem determinados desfechos permite ao pesquisador considerar informações não coletadas através de questionários ou prontuários médicos. Também insere questões sobre o que faz com que determinada área dentro da região de estudo se associe com maior risco ou proteção para o desfecho estudado. Existem muitos métodos para obter análises espaciais do risco, como os modelos aditivos generalizados, que permitem incluir nestas análises outras informações de interesse dos indivíduos estudados. Porém, atualmente, os estudos epidemiológicos que consideram a distribuição espacial do risco são analisados apenas com desfechos dicotômicos como, por exemplo, quando se classifica o indivíduo em doente ou não-doente. Esta é uma limitação que este trabalho visa superar ao apresentar um processo analítico da distribuição espacial do risco quando se tem uma variável resposta multinomial. Além de apresentar esta nova ferramenta, este trabalho analisou dois desfechos epidemiológicos: o primeiro é proveniente de um estudo caso-controle sobre acidentes de trabalhado na cidade de Piracicaba em que a resposta foi: casos graves, casos leves ou controles; outra ilustração provém de um estudo transversal sobre criadouros de mosquitos no Distrito Sul de Campinas, onde se encontrou muitos criadouros, poucos criadouros ou nenhum criadouro. Primeiramente, faz-se necessária uma discussão sobre a adequação de cada modelo multinomial a alguns estudos epidemiológicos. Também se discute a escolha de um entre diversos modelos multinomiais e apresenta-se a maneira de interpretar os resultados da análise. Para tornar este método acessível a outros pesquisadores, são apresentadas funções computacionais para o processo analítico / Abstract: The search for understanding some epidemiological phenomena often involves an tool called spatial analysis of risk. The study of space in which certain outcomes occur allows the researcher to consider information that can not be collected through questionnaires or medical records. It also puts questions about what makes a certain area within the study region was associated with greater risk or protection for the outcome studied. Many techniques are used for this kind of study as the generalized additive models that fit the spatial analysis of the risk with others informations of interest. But now, epidemiological studies that consider the spatial distribution of risk are analyzed only with dichotomous outcomes, such as when it classifies the individual in case or control. This is a limitation that this study aims to overcome when presenting an analytical process of the spatial distribution of risk when you have a multinomial response variable. In addition to presenting this new tool, this study analyzed two outcomes: first, from a case-control study of precarious workers in the city of Piracicaba in which the response was: severe cases, mild cases or controls. Another illustration comes from a cross-sectional study on mosquito breeding sites in the Southern District of Campinas, where we met many breeding sites, few or no breeding sites. First, it is necessary a discussion on the appropriateness of each multinomial model to some epidemiological studies. It also discusses the choice of one among several multinomial models and shows the way to interpret the results of the analysis. We present the computational functions for the analytical process to make this method accessible to other researchers / Doutorado / Epidemiologia / Doutor em Saude Coletiva

Health policy makers are struggling to manage health care and spending. To identify strategies for improving health quality and reducing health spending, policy makers need to first understand health risks and outcomes. Despite lacking some desirable clinical detail, existing health care databases, such as national health surveys and claims and enrollment data for insured populations, are often rich in information relating patient characteristics to heath risks and outcomes. They typically encompass more inclusive populations than can feasibly be achieved with new data collection and are valuable resources for informing health policy. This dissertation illustrates how the Medicare Current Beneficiary Survey (MCBS) and MassHealth data can be used to develop models that provide useful estimates of risks and health quality measures. It provides insights into: 1) the benefits of a proxy for the Framingham cardiovascular disease (CVD) risk score, that relies only on variables available in the MCBS, to target health interventions to policy-relevant subgroups, such as elderly Medicare beneficiaries, based on their risk of developing CVD, 2) the importance of setting appropriate risk-adjusted quality of care standards for accountable care organizations (ACOs) based on the characteristics of their enrolled members, and 3) the outsized effect of high- frequency hospital users on re-admission measures and possibly other quality measures. This work develops tools that can be used to identify and support care of vulnerable patients to both improve their health outcomes and reduce spending – an important step on the road to health equity.

The research in this thesis has been undertaken to develop statistical tools for monitoring adverse events in hospitals that adjust for varying patient risk. The studies involved a detailed literature review of risk adjustment scores for patient mortality following cardiac surgery, comparison of institutional performance, the performance of risk adjusted CUSUM schemes for varying risk profiles of the populations being monitored, the effects of uncertainty in the estimates of expected probabilities of mortality on performance of risk adjusted CUSUM schemes, and the instability of the estimated average run lengths of risk adjusted CUSUM schemes found using the Markov chain approach. The literature review of cardiac surgical risk found that the number of risk factors in a risk model and its discriminating ability were independent, the risk factors could be classified into their "dimensions of risk", and a risk score could not be generalized to populations remote from its developmental database if accurate predictions of patients' probabilities of mortality were required. The conclusions were that an institution could use an "off the shelf" risk score, provided it was recalibrated, or it could construct a customized risk score with risk factors that provide at least one measure for each dimension of risk. The use of report cards to publish adverse outcomes as a tool for quality improvement has been criticized in the medical literature. An analysis of the report cards for cardiac surgery in New York State showed that the institutions' outcome rates appeared overdispersed compared to the model used to construct confidence intervals, and the uncertainty associated with the estimation of institutions' out come rates could be mitigated with trend analysis. A second analysis of the mortality of patients admitted to coronary care units demonstrated the use of notched box plots, fixed and random effect models, and risk adjusted CUSUM schemes as tools to identify outlying hospitals. An important finding from the literature review was that the primary reason for publication of outcomes is to ensure that health care institutions are accountable for the services they provide. A detailed review of the risk adjusted CUSUM scheme was undertaken and the use of average run lengths (ARLs) to assess the scheme, as the risk profile of the population being monitored changes, was justified. The ARLs for in-control and out-of-control processes were found to increase markedly as the average outcome rate of the patient population decreased towards zero. A modification of the risk adjusted CUSUM scheme, where the step size for in-control to out-of-control outcome probabilities were constrained to no less than 0.05, was proposed. The ARLs of this "minimum effect" CUSUM scheme were found to be stable. The previous assessment of the risk adjusted CUSUM scheme assumed that the predicted probability of a patient's mortality is known. A study of its performance, where the estimates of the expected probability of patient mortality were uncertain, showed that uncertainty at the patient level did not affect the performance of the CUSUM schemes, provided that the risk score was well calibrated. Uncertainty in the calibration of the risk model appeared to cause considerable variation in the ARL performance measures. The ARLs of the risk adjusted CUSUM schemes were approximated using simulation because the approximation method using the Markov chain property of CUSUMs, as proposed by Steiner et al. (2000), gave unstable results. The cause of the instability was the method of computing the Markov chain transition probabilities, where probability is concentrated at the midpoint of its Markov state. If probability was assumed to be uniformly distributed over each Markov state, the ARLs were stabilized, provided that the scores for the patients' risk of adverse outcomes were discrete and finite.

No / To assess the validity of case mix adjustment methods used to derive standardised mortality ratios for hospitals, by examining the consistency of relations between risk factors and mortality across hospitals. DESIGN: Retrospective analysis of routinely collected hospital data comparing observed deaths with deaths predicted by the Dr Foster Unit case mix method. SETTING: Four acute National Health Service hospitals in the West Midlands (England) with case mix adjusted standardised mortality ratios ranging from 88 to 140. PARTICIPANTS: 96 948 (April 2005 to March 2006), 126 695 (April 2006 to March 2007), and 62 639 (April to October 2007) admissions to the four hospitals. MAIN OUTCOME MEASURES: Presence of large interaction effects between case mix variable and hospital in a logistic regression model indicating non-constant risk relations, and plausible mechanisms that could give rise to these effects. RESULTS: Large significant (P<or=0.0001) interaction effects were seen with several case mix adjustment variables. For two of these variables-the Charlson (comorbidity) index and emergency admission-interaction effects could be explained credibly by differences in clinical coding and admission practices across hospitals. CONCLUSIONS: The Dr Foster Unit hospital standardised mortality ratio is derived from an internationally adopted/adapted method, which uses at least two variables (the Charlson comorbidity index and emergency admission) that are unsafe for case mix adjustment because their inclusion may actually increase the very bias that case mix adjustment is intended to reduce. Claims that variations in hospital standardised mortality ratios from Dr Foster Unit reflect differences in quality of care are less than credible.

The main objective of the study was to determine the exchange rate volatility regime for carry trade profitability when using the South African Rand as the target currency. The study used the Logistic Smooth Transition Regression (LSTR) model to test the uncovered interest rate parity (UIP). The Sharpe ratio and the risk adjusted forward premium were used as the transition variables. The transition variable is a function of the transition function, which is used to determine the regime for the UIP. The LSTR model is characterised by three regimes, i.e. the lower regime, the middle regime and the upper regime. The LSTR model was tested for the short-term forward rate maturity of less than one year. The results show that the UIP hypothesis holds in the middle regime for the Rand/USD and the Rand/GBP when using the Sharpe ratio as the transition variable. Meanwhile, the UIP hypothesis does not hold for the Rand/Yen when using the Sharpe ratio as the transition variable for the forward rate maturity of one month, and it does hold for other short-term forward rate maturity of less than one year. The results for the risk adjusted forward premium as the transition variable show that the UIP hypothesis does not hold for all three currencies at various short-term forward rate maturities of less than one year. The research provides the following contributions to new knowledge: (1) Uncovered interest parity hypothesis holds in the middle regime for all periods for the Rand/USD and the Rand/GBP when using the Sharpe ratio as the transition variable with a short-term forward rate maturity of less than one year. (2) Currency carry trade profit taking for the Rand/USD and the Rand/GBP can be achieved in the upper regime. (3) The results for the Rand/Yen are mixed, in that the UIP hypothesis does not hold for other crisis periods as a result of negative Sharpe ratios. However, for the calm periods, UIP hypothesis holds in the middle regime for the Rand/Yen for short-term forward rate maturity of more than one month but less than one year when using the Sharpe ratio as the transition variable. The overall contribution of this study is that for the South African Rand as the target currency, the UIP hypothesis holds for the short-term horizon when using the Sharpe ratio as the transition variable and that this mostly depends more on currency than on horizon. Contrary to other researchers who found that the UIP holds in the long-term maturity with higher Sharpe ratios in the upper regime, this study proved that the UIP holds in the short-term maturity horizon. / Business Management / D.B.L.

Contexte : Les médecins spécialistes peuvent participer aux soins ambulatoires des personnes atteintes de maladies chroniques (MCs) et comorbidité comme co-gestionnaire ou consultant selon qu’ils sont responsables ou non du suivi du patient. Il y a un manque d’évidences sur les déterminants et l’impact du type d’implication du médecin spécialiste, ainsi que sur la façon optimale de mesurer la comorbidité pour recueillir ces évidences. Objectifs : 1) déterminer chez les patients atteints de MCs les facteurs associés à la cogestion en spécialité, dont les caractéristiques des organisations de première ligne et la comorbidité; 2) évaluer si le type d’implication du spécialiste influence le recours à l’urgence; 3) identifier et critiquer les méthodes de sélection d’un indice de comorbidité pour la recherche sur l’implication des spécialistes dans le suivi des patients. Méthodologie : 709 adultes (65 +/- 11 ans) atteints de diabète, d’arthrite, de maladie pulmonaire obstructive chronique ou d’insuffisance cardiaque furent recrutés dans 33 cliniques de première ligne. Des enquêtes standardisées ont permis de mesurer les caractéristiques des patients (sociodémographiques, comorbidité et qualité de vie) et des cliniques (modèle, ressources). L’utilisation des services de spécialistes et de l’urgence fut mesurée avec une base de données médico-administratives. Des régressions logistiques multivariées furent utilisées pour modéliser les variables associées à la cogestion et comparer le recours à l’urgence selon le type d’implication du spécialiste. Une revue systématique des études sur l’utilisation des services de spécialistes, ainsi que des revues sur les indices de comorbidité fut réalisée pour identifier les méthodes de sélection d’un indice de comorbidité utilisées et recommandées. Résultats : Le tiers des sujets a utilisé les services de spécialistes, dont 62% pour de la cogestion. La cogestion était associée avec une augmentation de la gravité de la maladie, du niveau d’éducation et du revenu. La cogestion diminuait avec l’âge et la réception de soins dans les cliniques avec infirmière ayant un rôle innovateur. Le recours à l’urgence n’était pas influencé par l’implication du spécialiste, en tant que co-gestionnaire (OR ajusté = 1.06, 95%CI = 0.61-1.85) ou consultant (OR ajusté = 0.97, 95%CI = 0.63-1.50). Le nombre de comorbidités n’était pas associé avec la cogestion, ni l’impact du spécialiste sur le recours à l’urgence. Les revues systématiques ont révélé qu’il n’y avait pas standardisation des procédures recommandées pour sélectionner un indice de comorbidité, mais que 10 critères concernant principalement la justesse et l’applicabilité des instruments de mesure pouvaient être utilisés. Les études sur l’utilisation des services de spécialistes utilisent majoritairement l’indice de Charlson, mais n’en expliquent pas les raisons. Conclusion : L’implication du spécialiste dans le suivi des patients atteints de MCs et de comorbidité pourrait se faire essentiellement à titre de consultant plutôt que de co-gestionnaire. Les organisations avec infirmières ayant un rôle innovateur pourraient réduire le besoin pour la cogestion en spécialité. Une méthode structurée, basée sur des critères standardisés devrait être utilisée pour sélectionner l’indice de comorbidité le plus approprié en recherche sur les services de spécialistes. Les indices incluant la gravité des comorbidités seraient les plus pertinents à utiliser. / Background: Medical specialist physicians can be involved either as comanagers (responsible for follow-up of patients) or consultants (provide advice/specialized interventions) in the care of patients with chronic diseases (CDs) managed in a primary health care (PHC) setting. Evidences concerning determinants and impact of type of specialist involvement are currently lacking, in particular the influence of comorbidity and how best to measure this factor. Objectives: The objectives were 1) to determine clinical, patient and PHC organizational characteristics associated with type of specialist involvement in patients with CDs; 2) to assess whether type of specialist involvement is associated with emergency department (ED) use and; 3) to identify methods for selecting a comorbidity index for specialist services research. Methods: 709 adults (65 +/- 11 years) with diabetes, heart failure, arthritis, or chronic obstructive pulmonary disease were recruited in 33 PHC practices. Standardized surveys were used to measure patient (gender, age, education, income, comorbidity, quality of life) and practice characteristics (model, remuneration mode, resources, role of nurse). Information on specialist services and ED use was procured from the Quebec physician claims database. We used multivariate logistic regression to 1) model variables associated with being comanaged and 2) compare ED use among persons with different types of specialist involvement. We conducted two systematic reviews: 1) review articles on comorbidity indices to identify proposed selection procedures and 2) studies on specialist services utilization to identify selection processes actually used. Results: One third of our sample saw a specialist; the majority (62%) was as a comanager. Comanagement was associated with higher disease severity, younger age, higher education level and income and primary care management in practices without a nurse in advanced practice role. There was no difference in rates of ED use over one year between patients with or without specialist involvement, either as a comanager (adjusted OR = 1.06, 95%CI = 0.61-1.85) or as a consultant (adjusted OR = 0.97, 95%CI = 0.63-1.50). Quantity of comorbidity was not associated with either comanagement or impact of specialist involvement on ED use. Our systematic review revealed no standardized selection process of a comorbidity index. However, 10 distinct criteria related to accuracy and applicability of a measurement scale or validity of reported studies were compiled. Studies on specialist services utilization mostly used the Charlson comorbidity index, but none justified their choice. Conclusion: Specialist support in the management of patients with CDs and comorbidity should be provided on a consultant basis. The PHC practice model with a nurse in an advanced practice role may reduce the need for specialist comanagement. When adjusting for comorbidity, researchers should use a structured process to select the appropriate index based on standard criteria such as validity and applicability. Indices considering severity of comorbidities may be more useful than sole disease count in specialist services research.