The analysis focused on the results derived from the use of weighted data, a strategy intended to reflect a more accurate representation of Native Hawaiians in the State of Hawai‘i and across the different islands. To address the two major questions for this investigation a two-model logistic regression analysis was conducted. The first model included the independent variables of gender, age, income, education level, and years in Hawai‘i. The second model consisted of adding the two types of discrimination, overt and covert acts of everyday discrimination to Model 1 to determine their unique contribution to explaining obesity/overweight. The results of this logistic regression are presented in .
| Table 2Hierarchical Logistic Regression Examining Demographic Factors and Everyday Discrimination on Being Overweight and/or Obese Among Native Hawaiians (Weighted Data) |
In the logistic regression analysis of Model 1, inclusive of the demographic variables (age, education, income, and length of time in Hawai‘i), revealed significant odds ratios for all, thus confirming their importance in explaining the variability in the Hawaiians' health risks of obesity/overweight. The analysis confirmed good model fit (Omnibus test of Model Coefficients (χ2 = 20921.157, P = .0005) with the demographic predictors combined pseudo R2 indices to explaining between 13.0% (Cox & Snell pseudo R2) and 18.3% (Nagelkerke pseudo R2) of the variability in the criterion of obesity/overweight. The classification analysis reveals that Model 1 is able to predict the correct category (obesity/overweight) for the respondents in 74.8% of the cases.
Of importance, however, within this set of predictors, the statistically significant demographic predictors varied in the nature of their association with their relationship to the criterion of being obese/overweight. Specifically, being male (OR=2.73), between the ages of 35 and 54 (OR = 1.29), having a household income of $35,000 or less (OR = 2.45), and living in the Hawaiian Islands 6–20 years (OR = 1.29) were positively related to being obese/overweight. In contrast, being female, in the age group of 18–34 (OR = .41), with a household income of $35,000–$74,999 (OR = .89), and having an education level of high school diploma or less (OR = .84) or some college education (OR = .77) was negatively related to obesity/overweight. The odds of a Hawaiian reporting being obese/overweight decreases by being a female, between the ages of 18–34, with a household income between $35,000 and $74,999 and having an education level of high school or less or some college experience.
Model 2 inclusive of overt and covert discrimination, while controlling for gender, age, education, household income and length of time living in Hawai‘i, reveal the salience of both overt and covert discrimination in explaining the variability in Hawaiians reporting being obese/overweight. The logistic regression analysis of Model 2, revealed significant odds ratios for both overt and covert discrimination thus confirming their importance in explaining the variability in the Hawaiians' health risks of obesity/overweight. The analysis confirmed good model fit (Omnibus test of Model Coefficients (χ2 = 26743.275, P = .0005) with the demographic predictors combined pseudo R2 indices to explaining between 16.3% (Cox & Snell pseudo R2) and 22.9% (Nagelkerke pseudo R2) of the variability in the criterion of obesity/overweight. The classification analysis reveals that Model 2 is able to predict the correct category (obesity/overweight) for the respondents in 75.7% of the cases. As was true for demographic predictors however, the nature of the influence of overt and covert discrimination varies. Overt discrimination (OR = 3.08) is positively related to obesity/overweight and thus a factor in promoting or reinforcing obesity/overweight, all other factors being equal. In contrast, covert discrimination (OR = −.66) is negatively associated with obesity/overweight and thus a predictor of not reporting being obese/overweight. The results of the logistic regression with unweighted data is included in for comparative purposes. The logistic regression reveals significant odds being limited to two predictors. Specifically, household income of $35,000 or less (OR = 1.88) and overt discrimination (OR = 2.19) are the only statistically significant predictors.
| Table 3Hierarchical Logistic Regression Examining Demographic Factors and Everyday Discrimination on Being Overweight and/or Obese Among Native Hawaiians (Unweighted Data) |