Banca de QUALIFICAÇÃO: Alessandra Batista de Campos

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : Alessandra Batista de Campos
DATE: 21/11/2023
TIME: 14:00
LOCAL: teams
TITLE:

Association of air pollution, climatic factors, and mental disorders: a geospatial perspective

KEY WORDS:

Wildfire, Air Pollution, Particulate Matter, Mental Disorder, Remote Sensing, Climate Change

PAGES: 29
BIG AREA: Outra
AREA: Multidisciplinar
SUMMARY:

The focus of the analysis undertaken lies in atmospheric pollution resulting from wildfires, climate changes, and hospitalizations due to mental disorders. Its general objective is to analyze the association between particulate matter resulting from wildfires and mental disorders, especially depression, anxiety, and suicide attempts in the Federal District (DF). Particulate matter, considered one of the main pollutants, has become a global concern, posing significant risks to environmental health, adversely impacting human health with severe and fatal consequences, and reducing the quality of life. In light of the above, the implementation of this study becomes relevant, aiming for continuous integration between the Health and Environmental Sectors, so that they can utilize interdisciplinary tools and direct their conduct and actions toward the development of public policies. This is a descriptive study with an ecological time series design. Data collection will be carried out through the DATASUS database and the DF Health Department, collecting data from patients seen and hospitalized in public health units in the DF with psychiatric disorders, considering the period from 2000 to 2021. For the identification of wildfire locations and the evaluation of pollution levels, particularly particulate matter, the research will be conducted using remote sensing information, AOD MODIS. With the collected data, a database will be created, and it will be spatialized using GIS modeling techniques. Subsequently, descriptive statistics will be performed, calculating measures of central tendency for continuous variables, such as mean, median, and standard deviation, and calculating absolute and relative percentage frequencies for discontinuous variables. Following that, statistical tests will be applied, such as Pearson's linear correlation coefficients (r), multiple linear regression models, and for the estimation of spatial autocorrelation, the Global Moran's Index (GMI) will be used. For all comparative tests, a p-value less than or equal to 0.05 will be considered significant.

COMMITTEE MEMBERS:
Presidente - ***.054.061-** - EDER DE SOUZA MARTINS - UnB
Interna - 3049879 - ERINA VITORIO RODRIGUES
Externa à Instituição - FERNANDA MONTEIRO DE CASTRO FERNANDES - UCB
Interno - 1644727 - LUIZ FABRICIO ZARA