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Over the course of time, lifestyles have been stable for most cultures. However, in the last 40 years, with the advent of globalization, the pace of change has quickened across the globe.
The following indicators of population health and progress were examined in this analysis.
Indicators Rural Population (as a Percentage of the total), Health Expenditures, Life Expectancy, Diabetes Prevalence)
Time Spans of Interest 1960-2020
Regions
"Arab World", "Caribbean small states", "East Asia & Pacific", "Euro area", "Europe & Central Asia"
In October 2020, this project was embarked on by OPA Towobola to explore and organize data and analysis on world trends. The main spheres of inqiry were in energy (production, consumption), health (longevity and disease rates) and development (urbanization and GDP).
Population dispersal has been changing over the course of the period from 1960-2000. Across various world regions, the percentage of the population that is rural has been declining.
The initial intent was to link these, so as to determine linkages. One question that could be answered is whether urbanization trends are promoting better or worse health. A similar question that could be answered is whether promiximity to energy sources (power lines) is linked to health trends for any demograhic group in particular.
Observations of trend plots show that generally infant mortality drops with advancements in technology and development, such as higher Gross Domestic Product (GDP)
However, there appear to be some side-effects associated with development trends. Studies of this are still underway. One possible indicator of this is the rising rate of Autism diagnoses. Shown below is an apparent rise in the impact of non-communicable diseases.
In order to further understand the impact of development, it was necessary to examine the associated rise in health expenditures. The graph below shows how life expectancy has increased as countries have spent more on health care (whether from direct individual payment or from third-party payments)
The scatter matrix below shows a more detailed view of health expenditure on various measures of health.
- Data retrieval Data was retrieved from various sources, as referenced above.
- Review Review existing literature.
- Import Pandas turned out to be useful. After importing the datasets into dataframes, it was straightforward to make plots
- Examination Quick preview of data in python and in a spreadsheet.
- In-Dept Analysis A more detailed review of the data was performed.