Tr?s alternativas estoc?sticas para modelar morbimortalidade por doen?as respirat?rias e cardiovasculares via vari?veis atmosf?ricas

Autor: Gomes, Ana Carla dos Santos
Jazyk: portugalština
Rok vydání: 2015
Předmět:
Zdroj: Repositório Institucional da UFRNUniversidade Federal do Rio Grande do NorteUFRN.
Druh dokumentu: Doctoral Thesis
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Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES
O clima e a polui??o do ar, dentre outros, s?o fatores respons?veis pelo aumento da vulnerabilidade da sa?de das popula??es residentes nos grandes centros urbanos. Altera??es clim?ticas combinadas a altas concentra??es de poluentes atmosf?ricos est?o associadas a doen?as respirat?rias e cardiovasculares. Neste sentido, o objetivo principal da pesquisa ? modelar de diferentes formas a rela??o clima e sa?de, especificamente para a popula??o de crian?as e idosos residentes em S?o Paulo. Para tanto, foram utilizados dados de vari?veis meteorol?gicas, poluentes atmosf?ricos, interna??es e ?bitos por doen?as respirat?rias e cardiovasculares no per?odo de 11 anos (2000 a 2010). Por interm?dio de Modelos via equa??es de estima??o generalizadas, estimou-se o risco relativo. Com a regress?o din?mica, foi poss?vel prever o n?mero de ?bitos por meio das vari?veis atmosf?ricas e por meio do modelo beta-binomial-poisson estimouse o n?mero de ?bitos e foi poss?vel projetar cen?rios. Os resultados mostraram que o risco das interna??es por asma aumenta aproximadamente duas vezes para crian?as expostas ?s altas concentra??es do material particulado do que crian?as que n?o s?o expostas. O risco de morte por infarto agudo do mioc?rdio de idosos aumenta em 3%, 6%, 4% e 9% devido ?s concentra??es de CO, SO2, O3 e PM10, respectivamente. Com rela??o ? modelagem via regress?o din?mica, os resultados mostraram que os ?bitos por doen?as respirat?rias podem ser previstos de forma consistente. O modelo betabinomial-poisson conseguiu retratar o n?mero m?dio de ?bitos por insufici?ncia card?aca. Na regi?o de Santo Amaro o n?mero observado foi de 2,462 e o simulado de 2,508 na regi?o da S?, o observado foi de 4,308 e o simulado de 4,426 o que possibilitou a gera??o de cen?rios que possam servir como par?metro para a an?lise destinada ? tomada de decis?o. A partir dos resultados obtidos, ? poss?vel contribuir com metodologias que possam auxiliar a compreens?o da rela??o clima e sa?de fornecendo subs?dios aos gestores no planejamento de pol?ticas de sa?de p?blica e ambiental.
Climate and air pollution, among others, are responsible factors for increase of health vulnerability of the populations that live in urban centers. Climate changes combined with high concentrations of atmospheric pollutants are usually associated with respiratory and cardiovascular diseases. In this sense, the main objective of this research is to model in different ways the climate and health relation, specifically for the children and elderly population which live in S?o Paulo. Therefore, data of meteorological variables, air pollutants, hospitalizations and deaths from respiratory and cardiovascular diseases a in 11-year period (2000-2010) were used. By using modeling via generalized estimating equations, the relative risk was obtained. By dynamic regression, it was possible to predict the number of deaths through the atmospheric variables and the betabinomial-poisson model was able to estimate the number of deaths and simulate scenarios. The results showed that the risk of hospitalizations due to asthma increases approximately twice for children exposed to high concentrations of particulate matter than children who are not exposed. The risk of death by acute myocardial infarction in elderly increase in 3%, 6%, 4% and 9% due to high concentrations CO, SO2, O3 and PM10, respectively. Regarding the dynamic regression modeling, the results showed that deaths by respiratory diseases can be predicted consistently. The beta-binomial-poisson model was able to reproduce an average number of deaths by heart insufficiency. In the region of Santo Amaro the observed number was 2.462 and the simulated was 2.508, in the S? region 4.308 were observed and 4.426 simulated, which allowed for the generation of scenarios that may be used as a parameter for decision. Making with these results, it is possible to contribute for methodologies that can improve the understanding of the relation between climate and health and proved support to managers in environmental planning and public health policies.
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