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Differences in Body Mass Index (BMI) Trends Across American Ethnicities

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This study aims to determine if there are differences in body mass index (BMI) across ethnic groups in the United States. Modern medicine is increasingly going the way of personalized medicine, and existing literature has begun to suggest that cultural

This study aims to determine if there are differences in body mass index (BMI) across ethnic groups in the United States. Modern medicine is increasingly going the way of personalized medicine, and existing literature has begun to suggest that cultural differences may have an effect on physical health. Initially, this study was to explore anorexia nervosa prevalence, but the data is simply not there; this led to a shift in focus to exploring health differences in terms of BMI. The data analyzed is from the National Health and Nutritional Examination Survey (NHANES) collected by the Centers of Disease Control and Prevention (CDC) from 1999-2013. The subjects used were aged 13-25, and the ethnicities compared were African American, Caucasian American, Mexican American, Other Hispanic American, Asian American, and Other (including multiracial). Statistical tests were run through the software program SAS and included ANOVA tests, t-tests, and z-tests. It was found that there are differences across ethnicities, and that there are far more differences among females than among males. Asian American males and Mexican American males appear to be the groups that caused males to have significant differences. Asian Americans were also found to have the lowest average BMI by far. On the other hand, African Americans and Mexican Americans appeared to have the highest average BMIs. Although these findings and others detailed in the paper are intriguing, the BMI data is not strictly normal, and is still not normalized even by transforming the variable into a log of BMI. The data is still right skewed, and must be attacked in the future with different transformations and non-parametric tests to increase the accuracy and strength of these findings.

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Date Created
2018-05

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Ethnic Discrimination, Socioeconomic Status, and Health in India

Description

Significant health inequalities exist between different castes and ethnic communities in India, and identifying the roots of these inequalities is of interest to public health research and policy. Research on caste-based health inequalities in India has historically focused on general,

Significant health inequalities exist between different castes and ethnic communities in India, and identifying the roots of these inequalities is of interest to public health research and policy. Research on caste-based health inequalities in India has historically focused on general, government-defined categories, such as “Scheduled Castes,” “Scheduled Tribes,” and “Other Backward Classes.” This method obscures the diversity of experiences, indicators of well-being, and health outcomes between castes, tribes, and other communities in the “scheduled” category. This study analyzes data on 699,686 women from 4,260 castes, tribes and communities in the 2015-2016 Demographic and Health Survey of India to: (1) examine the diversity within and overlap between general, government-defined community categories in both wealth, infant mortality, and education, and (2) analyze how infant mortality is related to community category membership and socioeconomic status (measured using highest level of education and household wealth). While there are significant differences between general, government-defined community categories (e.g., scheduled caste, backward class) in both wealth and infant mortality, the vast majority of variation between communities occurs within these categories. Moreover, when other socioeconomic factors like wealth and education are taken into account, the difference between general, government-defined categories reduces or disappears. These findings suggest that focusing on measures of education and wealth at the household level, rather than general caste categories, may more accurately target those individuals and households most at risk for poor health outcomes. Further research is needed to explain the mechanisms by which discrimination affects health in these populations, and to identify sources of resilience, which may inform more effective policies.

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Created

Date Created
2022-05