Data Study on US Health
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
The motivation of this data study is to explore and visualize the connection between specific health factors like obesity, physical activity and nutrition alongside state level health policies and environments.
According to the Center for Disease Control and Prevention (CDC), data shows more than a third of US adults are obese, being this condition, one of the most important preventable factors in chronic diseases and healthcare costs. Healthcare spending is estimated between $147 and $210 billion per year and with projections to increase 5.5% per year – as per the Center for Medicare and Medical Services (CMS)- it will account for almost 20% of the total US GDP by 2026.
In 2010 the US federal government announce the prevention and treatment of obesity as important part of its campaign to improve health of Americans. Since then several initiatives and programs have been implemented in order to revert the increasing trend in obesity that has doubled in the last 40 years. These initiatives and programs range from nutritional education, food labeling through specific funding to improve the food system at local, several policies and legislation at state and federal level. Nevertheless, it is still questionable if any significant progress has being made.
The Division of Nutrition, Physical Activity and Obesity (DNPAO) under the Centers for Disease Control and Prevention (CDC) is one of the main federal entities at forefront of decreasing obesity among US population. The DNPAO has developed a comprehensive dataset of information from which the following datasets were used for this project:
1) Behavioral and Risk Factors Surveillance System: This database includes 9 variables with more than 50K records
2) Environmental and Policy Support: This database includes more than 30 variables in approximately 4K records.
To simplify the visualization of different variables the project was divided in 3 sections:
Risk Behavioral Factors
The first section includes high risk behavioral factors: obesity, no-physical activity and potential not ideal nutrition (assessed by lack of vegetable portions in daily diet).
State Level Policies
The second section includes policies that have been implemented at state level. These policies have been recommended as positive influences in nutrition and health of communities. The Food Council policy aims to improve food systems by coordinating actions across different levels and stakeholders. The policy for Healthier Food Retailer seeks to make healthier foods more accessible among underserved populations. The Complete Street Policy works with local governments in designing streets/sidewalks that are friendlier and safer for pedestrians, bicyclist, motorist and transit riders of all ages and abilities.
The third section compares relative accessibility to favorable environmental factors against obesity levels at state level. These environmental factors are: 1) the percentage of population living half mile of a park, 2) the percentage of youth with parks or playground areas, community centers and sidewalks or walking paths available in their neighborhood, 3) the percentage of census tracts (neighborhoods) that have at least one healthier food retailer located within the tract or within half mile of tract boundaries
- It seems there are no major changes in obesity, physical activity and nutrition in last years when exploring at state level.
- The Food Policy Councils and Healthier Food Retailers are not as implemented as the Complete Street Policy at state level.
- In general states with lower percentage of obesity have better access to favorable environmental factors such as parks, community centers, playgrounds, sidewalks and healthier food retailers.
- MA, DC and CO seem to outperform other states in terms of lower percentage of obesity and better environmental support for healthier lifestyle.
To check the shiny app. please visit: https://antoniosalvador.shinyapps.io/project_v1/