Adolescent Overweight and Obesity

Narrative

Over the past 30 years, the prevalence of obesity has quadrupled among adolescents in the United States.1 In 2011–2012, 20.5 percent of youth aged 12–19 years were obese, 14.0 percent were overweight, 61.9 percent were of normal weight, and 3.6 percent were underweight. Overweight and obesity in adolescence is associated with overweight and obesity in adulthood, putting obese adolescents at increased risk of several adverse health conditions, including overweight and obesity later in life, high cholesterol and blood pressure, prediabetes, bone and joint problems, cancer, and other social and psychological health outcomes.2

Adolescent weight status varies by several factors, including sex, race and ethnicity, and poverty status. In 2011–2012, nearly 40 percent of non-Hispanic Black and Hispanic youth were reportedly overweight or obese, compared to 31.2 percent of non-Hispanic White youth. Racial and ethnic differences varied by sex and were particularly pronounced among males, such that 21.4 percent of non-Hispanic Black males and 23.9 percent of Hispanic males were obese, compared to 18.3 percent of non-Hispanic White males (Figure 1).

weight status of children by race-ethnicity and sex

Figure 1 Source

The prevalence of overweight and obesity also varies by poverty status. In 2011–2012, nearly 41 percent of youth living in households with incomes below 100 percent of poverty were overweight or obese. By comparison, 28.2 percent of youth living in households with incomes of 300 percent or more of poverty were overweight or obese. These differences were only notable among females: 17.1 and 25.9 percent of females living in households with incomes below 100 percent of poverty were overweight and obese, respectively, compared to 9.0 and 10.8 percent of their female counterparts living in households with incomes of 300 percent or more of poverty (Figure 2).

overweight and obese by sex and poverty status

Figure 2 Source

The Community Preventive Services Task Force recommends several strategies for preventing obesity in community settings. For example, behavioral interventions for reducing screen time (e.g., time spent watching television, playing computer games, or browsing the Internet) have improved weight-related outcomes among children and adolescents.

Data Sources

Figure 1. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Health and Nutrition Examination Survey, 2011–2012. Unpublished estimates. Analyses conducted by the National Center for Health Statistics.

Figure 2. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Health and Nutrition Examination Survey, 2011–2012. Unpublished estimates. Analyses conducted by the National Center for Health Statistics.

Endnotes

1 U.S. Department of Health and Human Services, National Center for Health Statistics. Health, United States, 2011: With Special Features on Socioeconomic Status and Health. Hyattsville, MD: U.S. Department of Health and Human Services; 2012.

2 U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. Adolescent and School Health: Childhood Obesity Facts. 2012. Accessed September 5, 2014.

Data

Statistical Significance Test

Calculate the difference between two estimates:

Calculated Z-Test Result 0.9567433 Not statistically significant

We follow statistical conventions in defining a significant difference by a p-value less than 0.05 where there is a less than 5% probability of observing a difference of that magnitude or greater by chance alone if there were really no difference between estimates. The 95% confidence interval includes a plausible range of values for the observed difference; 95% of random samples would include the true difference with fewer than 5% of random samples failing to capture the true difference.

This website allows comparisons between two estimates using the independent z-test for differences in rates or proportions. This test is appropriate for comparing independent populations across years (e.g., 2011 versus 2012) or subgroups (e.g., Male versus Female) on corresponding measures. To the extent possible, the functionality of this application has limited estimate comparisons based on appropriate use of the independent z-test. However, some tables present subgroup categories within broader categories that will allow comparisons between non-independent populations (e.g., low birth weight and very low birth weight). Users should exercise caution when interpreting these test results, which will frequently overstate statistical significance.

For some tables, the website does not allow for comparisons between two estimates, even though the data represent independent populations. Generally, this is because the standard errors were not publicly available at the time this website was created.

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