Obesity Prevelance Amongst African American Adolescents
Obesity Prevelance Amongst African American Adolescents
Obesity is an epidemic affecting nearly one third of all Americans in the United States today. Obesity is determined by using Body Mass Index(BMI) measurements, in which BMI is defined as the; weight in kilograms by the square of the height in meters( kg/m^2). A BMI of 25 is considered overweight and a BMI of 30, Obese. Demographic and health research has shown consistently higher obesity prevalence amongst lower income minority populations; establishing a negative relationship between Socio-economic status (SES) and obesity. In general individuals of lower socioeconomic levels face greater barriers to maintaining healthy Body Mass Indexes(BMIs). The most alarming trend is the rising prevalence of obesity amongst adolescents, particularly lower income minority populations. In fact the prevalence among adolescents aged 12-19 years, has risen from 5% in 1985 to 18.1% by 2008(WHO). Knowing that a disproportionate number of African Americans comprise lower income demographics, while considering onset obesity begins during adolescent years; This paper will focus on the prevalence of Obesity amongst lower income African American Adolescents, 12-17 years. The intent of this study is to find a correlation between higher levels of Obesity Prevalence amongst African American Adolescents as opposed to non-African American samples. Through Categorical analysis, the results should provide underlying disparities between, race and SES specific, obesity trends amongst adolescents.
This paper will use research findings and independent studies based on aggregate data samples from: U.S. Census Bureau, Center of Disease Control, California Health Impact Survey 2003, and the National Health and Nutrition Examination Survey I-III. It is important to determine first, if lower income levels promote higher obesity prevalence. Once having determined this, we can narrow the focus on African American specific environmental factors and demographics.
The first analysis utilizes CHIS(Figure 1) findings on Poverty Level Income and Obesity prevalence. The 2003 CHIS uses a sample size of n=4,029 adolescents (12-17 yrs). CHIS findings categorized participants family incomes relative to the Federal Poverty Level (FPL) of $19,971, for a family of four. Family Incomes were categorized within four FPL levels: (I)100% or below FPL, (II)100-199% FPL, (III) 200-299% FPL, and (IV)300% and above FPL. Calculating the occurrence of obesity within the FPL categories(I-IV) yielded significant prevalence inequity in adolescents(12-17) within categories; Of these, 21% were Obese in Category I(100% or below FPL), whereas those 200% to 300% of FPL, yielded lower figures, 16% and 8% respectively. These income comparisons support the claim that lower SES yields higher adolescent obesity prevalence. The chart provides the correlation between the level of fast food outlets and obesity prevalence for given FPL. Consolidating both results according to respective FPLs, the regression analysis revealed a correlation of R2=.877. Clearly this figure yields a high correlation in the number of fast food outlets in low income neighborhoods with prevalence of obesity ( FIGURE 2 and 3).
Next this study will determine and analyze health barriers common to lower income/obese prone demographics. This analysis will provide the overall food quality and accessibility, in lower income neighborhoods. Lower income neighborhoods tend to have a higher concentration of “limited service “outlets and convenience stores, as opposed to “full service” and chain supermarkets in higher income neighborhoods. Supporting this trend, we provide analysis of 28,050 zip codes and the raw count of food outlets categorized as: 1) chain/non-chain supermarkets, 2) grocery outlets, 3) convenience stores. The raw data count of outlets was provided by D&B, through utilizing marketplace software. Using the data we were able to determine the proportion and mean prevalence of specific types of food outlets within different income zip codes. We assume Supermarkets offer the highest level of healthy food accessibility at affordable prices and Convenience Stores offering the lowest level of healthy food accessibility and affordability. Using these respective means, I was able to determine the ratio of low income categorical means, to the averaged sum of middle and high income categorical means. The results show that on average, low income zip codes offer; 49% fewer supermarkets, 14% more grocery outlets and 3% more convenience stores to higher income level zip codes. Even though the data provides compelling evidence, the geographic analysis was limited to results within zip codes, excluding factors amongst adjoining zip codes. Such limitations may result in an overrepresentation in the outlet prevalence, thereby overinflating results for our covariates of interest, namely, income by zip code. However these results provide enough support in claiming that