Gathering Materials

CONTEXT/GAP

After the Road to Approval for my research project, I can now start the process of Gathering Materials (or data collection). First, as context to the current academic conversation, one scholarly article that adds to the professional conversation surrounding this research question is “Cross-national Comparison of Childhood Obesity: The Epidemic and the Relationship Between Obesity and Socioeconomic Status,” authored by Youfa Wang and published within the International Journal of Epidemiology. This article essentially uses a logistic regression methodology to examine the relationship between socioeconomic status (like household income) and childhood obesity in China, Russia, and the United States. This was a cross-national comparison for childhood obesity rates (1).

A second scholarly article within the existing literature is “Changes in State-Specific Childhood Obesity and Overweight Prevalence in the United States from 2003 to 2007,” authored by Singh, Kogan, and vanDyck and published by JAMA, a peer-reviewed international medical journal. Within this article, Singh and his colleagues use a multivariate logistic regression methodology to examine multiple factors that contribute to childhood obesity within U.S. states. Not only did they examine socioeconomic status per state, but they also looked at sex, ethnicity, age, state of residence, and overweight qualifications (2).

Now, a major question to consider is WHERE IS THE GAP in obesity-related research? To answer this question, there are little-to-no studies that examine obesity-related data in children. There is also little research comparing childhood obesity rates to socioeconomic status. Lastly, there is NO research comparing adolescent obesity rates to socioeconomic status between U.S. STATES (Colorado, California) and U.S. TERRITORIES (Puerto Rico, Guam). Therefore, this leads to the research question: How do adolescent obesity rates in the pacific U.S. territories differ from U.S. states based on differences in socioeconomic status within the past decade?

PROCESS

For my methodology, I will first collect and organize my data (including obesity percentages and socioeconomic status data - which will be family income for my project). Second, I will compare my data with a binary logistic regression analysis. Third, I will analyze my data and determine the modern trend, rates of change, and similarities and differences between each state or territory.

Within my methodology, I will have a few main points of comparison: socioeconomic status (specifically analyzing family income) where I will get the data from the U.S. Census Bureau of Investigation, obesity percentage where I will get the data from the U.S. Centers for Disease Control and Prevention, age where I will compare the children 2-4 (as this is the age group with the most available data), and time per year where I will analyze every other year from the past decade (the most recent years of data availability). Also, I will look at the geographic factor - comparing the 3 highest, lowest, and moderate SES U.S. states to the three inhabited U.S. territories with the most available data (Puerto Rico, Guam, and the U.S. Virgin Islands). Finally, my NEW points of data will be the rates of change over time and a comparison between states and territories (like an increase or decrease).

PROGRESS

As an update to my data collection process, I have found success in organizing my data. I have formed multiple tables (based on previous scholarly articles) to organize and analyze my data. This includes a "Main characteristics of each sample" table to outline each data source. Additionally, this includes a "Prevalence (%) of obesity among children in the U.S. states and territories" and "Average socioeconomic status (SES) of family income, rounded to the nearest $" table to organize all data points for later comparison. My main goal in data collection is to fully complete these tables. So far, I have completed the first two: "Main characteristics..." and "Prevalence (%) of obesity..." I aim to finish data collection and Gathering Materials by February.

I came across many unexpected struggles as I officially started the data collection process. First, the previous U.S. territory data source that I had planned to use did not include multiple years of data. This was problematic because the point of my research question was to analyze the TREND of childhood obesity between states and territories. So, to overcome this struggle, I needed to do more research to find a U.S. territory source with the five inhabited territories over multiple years.

After two weeks of extensive research, I finally found a potential data source. This U.S. territory source would cover the territories I needed (American Samoa, Northern Mariana Islands). However, another problem surfaced. This data source only analyzed children from 2-8 years old, while my U.S. state data analyzed children from 2-4 years old. Even though the U.S. state data source included a few U.S. territories, such as Puerto Rico, Guam, and the Virgin Islands, it did not include all five territories that I planned to analyze. So, due to limited data in the U.S. territories, I made the decision to stick to the age group of 2-8 (for U.S. states, Puerto Rico, Guam, Virgin Islands) and 2-4 (American Samoa, Northern Mariana Islands), and I will define this difference in age as a limitation to my research paper.

CITATIONS

Youfa Wang. “Cross-National Comparison of Childhood Obesity: The Epidemic and the Relationship between Obesity and Socioeconomic Status.” International Journal of Epidemiology 30, no. 5 (2001): 1129–36. https://doi.org/10.1093/ije/30.5.1129.
Gopal Singh, Michael Kogan, and Peter van Dyck. “Changes in State-Specific Childhood Obesity and Overweight Prevalence in the United States from 2003 to 2007.” Archives of Pediatrics & Adolescent Medicine 164, no. 7 (2010). https://doi.org/10.1001/archpediatrics.2010.84.

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