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The dataset selected for this assignment is “Health expenditure and suicide rates [2000-2019]” by Fernando Retamales. The author’s goal is to promote awareness of preventable death causes by showing the correlation between suicides and countries’ average costs of healthcare services (Retamales, 2022). It contains annual statistics for nations that provided their information to the World Health Organization (WHO) regarding mental and physical health payments per citizen, alongside the number of deaths by self-harm per 100,000 citizens.
The dataset can be used by governments in policymaking processes and by healthcare providers in creating accessible options for patients who are at risk of taking their lives. It can also serve as a basis for scientific research that studies depression, suicides, and overall happiness in different populations. Trends that are apparent in the dataset can be used in a statistical analysis of efficient prevention strategies. However, this large table may require additional manipulations to become a viable source of evidence for a particular country. For example, a nation must compare health expenditures per person with their costs and average salaries. It might be challenging to evaluate the impact of depression without knowing its causes, making it essential for scientists to incorporate outside sources into a study that is based on the author’s work.
There is a need to expand the list of parameters in the table to receive a clear picture of the situation within each community. Organizations can utilize this dataset for the proposed purposes by adding information from other sources to complement the statistics with different socioeconomic and health-related parameters. In conclusion, the dataset regarding health expenditures and their correlation to suicide rates provides an apparent link between these two arguments, although it cannot be viewed as a complete tool for policymaking purposes.
Reference
Retamales, F. (2022). Health expenditure and suicide rates [2000-2019]. Kaggle: Your Machine Learning and Data Science Community. Web.
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