Araújo Zanotta Ray 2024

Methods:

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The data spanned from January of 2018 to July of 2023 and due to the lack of georeferencing limitations (missing address on the database) and duplicate records, our analysis evaluated a sample of 5,769 cases. Subsequently, we create a Mental Health Index (MHI) adapted from the descriptive measures of incidence and prevalence which have been largely used in epidemiology.

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The researchers collected mental health diagnosis records from 2018 to 2023. After removing repeated records and cases that did not have location information, they analyzed 5,769 cases. They then created a Mental Health Index to show how common mental health diagnoses were in each neighborhood based on population size.

Introduction:

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Currently, there exists a lack of comprehensive information regarding the influence of different land use categories on the mental well-being of individuals in the southern region. ed on emergency initiatives and symptomatic solutions that are temporary, costly, and often not effective. Given these prevailing circumstances, it is imperative to better comprehend the relationship between various land use patterns, specifically focusing on the presence of green spaces within urban settings and their potential impact on the mental health of the local population.

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There is not enough information about how different types of land use affect mental health in southern regions. Because current solutions are often short-term and not very effective, the researchers wanted to better understand how green spaces in cities may affect people’s mental health.

Results:

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Fig. 3 confirm the direct and indirect effects of the Tree cover percentage and the Human Development Index on the Mental Health Index. Higher Tree cover and the Human Development Index are associated with a reduction of the Mental Health Index in the neighborhoods.

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The results showed that neighborhoods with more trees tended to have better mental health outcomes. Areas with higher tree cover and higher development levels had fewer diagnosed mental health cases.

Discussion:

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Specifically, an increase in neighborhood Tree cover above 50% was associated with a decrease in the Mental Health Index. To the best of our knowledge, our study is the first one to test directly, through machine learning and a modelling technique, the influence of different LULC classes and the HDI on the number of diagnosed on mental health cases in a low- to middle-income city.

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The findings suggest that having more trees in a neighborhood may help reduce mental health problems. This study is important because it directly examined how land use and development are connected to mental health in a low- to middle-income city.

Future studies could look at how close people live to green spaces and how often they use them. Researchers could also study other cities and see how these environmental factors affect mental health over a longer period of time.

The most difficult part to understand was how the machine learning models were used to analyze the data and decide which environmental factors were most important. I have not been exposed to these models before, so it was a learning experience.