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Master Thesis

Pollution atmosphérique dans la vallée de l’Arve et admissions aux urgences de l’hôpital de Sallanches : méthodologie d’une analyse de séries temporelles

Abstract : WHO guidelines as well as the current french legislation upper threshold are frequently exceeded in the Arve Valley. The aim of this thesis was to study the feasibility and defining the methodology for a study on short-term effect of atmospheric pollution in the Arve Valley. The method proposed is a time series analysis of hospital admissions to the emergency department in Sallanches. The study period was from 2007-2015, during which daily admissions of pollution-related pathologies was determined. Daily levels of PM10, NO, NO², O3 were collected. Number of tourist nights was used as an adjustment factor in the analysis of the time series to account for tourist seasonal fluctuations. Weather conditions were collected. We found that daily events followed a generalized linear model by a lognormal and negative binomial distribution as the most suitable statistical model. Concerning cardiovascular and cerebrovascular disease, the number of daily events was: 1 (25th and 75th centile, 0-2), and 0 (25th and 75th centile 0-1). Concerning respiratory disease, the average number of daily events was 2 (25th and 75th centile 1-4). The Arve Valley small population size does not allow us to have enough admissions for a statistically significant analysis of short term pollution effect on emergency department admissions, with regards to cardiovascular and cerebrovascular disease. Using information about tourist activity for statistical analysis is a novel approach. Concerning the Arve Valley, our model is pertinent but unfortunately it does not have enough statistical power to draw any conclusions. Increasing the length of the study time could increase statistical power.
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Master Thesis
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https://dumas.ccsd.cnrs.fr/dumas-01499656
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Julie Paget. Pollution atmosphérique dans la vallée de l’Arve et admissions aux urgences de l’hôpital de Sallanches : méthodologie d’une analyse de séries temporelles. Médecine humaine et pathologie. 2016. ⟨dumas-01499656⟩

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