Developing a Predictive Model for Measuring Health Literacy in Hawai’i’s Adult Populations Lubimir, Karen T.
dc.contributor.department Biomedical Sciences 2019-05-28T19:34:38Z 2019-05-28T19:34:38Z 2017-08
dc.subject Health Literacy
dc.subject Predictive Model
dc.subject Hawai’i
dc.title Developing a Predictive Model for Measuring Health Literacy in Hawai’i’s Adult Populations
dc.type Thesis
dcterms.abstract Background: Adequate health literacy is necessary to effectively participate in one’s own health care, yet many American adults lack the health literacy skills to navigate the US health care system. Lower health literacy has been associated with poorer health status, higher health care utilization, and greater health disparities in studies conducted both nationally and in Hawai’i. Other factors associated with inadequate health literacy include older age, immigrant status, ethnic minority status, poverty and fewer years of education. Successful health interventions aimed at reducing disparities and improving health outcomes should be based on adequately identifying the unique characteristics of the population, including health literacy skills. Direct measurement of health literacy is labor intensive and controversial, hindering its study in population-based research. National health literacy predictive models have been developed to help address this problem. To date, the applicability of national health literacy predictive models in Hawai’i’s ethnically diverse population has not been studied. Objective: To identify demographic variables associated with low health literacy and develop a multivariable predictive model for low health literacy in Hawai’i’s adult population. Methods: We completed a cross-sectional analysis of a representative sample of Hawai’i’s adult population from the annual telephone based Hawai’i Health Survey (HHS) data, years 2008 and 2010. The sample included 11,941 respondents. Descriptive analysis was used to identify individual socio-demographic variables associated with low health literacy. Predictors included age, gender, marital status, educational attainment, race/ethnicity, insurance status, poverty status, rural residence and migration status. We used multivariable logistic regression models, Thesis: Developing a Predictive Model for Measuring Health Literacy in Hawai’i’s Adult Populations Karen T. Lubimir, Candidate for Masters in Clinical Research including sub-group analysis for age over 65 years and migration status, to predict the probability of inadequate health literacy. Results: In the descriptive results, 18% had low health literacy, which was significantly associated with extremes of age (<25 or >84 years old, compared to those ages 25-64 and 64-84 years old ), less than high school education (compared to high school graduates or post high school education), non-white ethnicity, being poor, lack of health insurance, rural residence, and being born outside the U.S. (p<0.01). In the multivariable logistic regression model with the full population, Chinese, Filipinos, Japanese and Pacific Islanders were significantly more likely to have low health literacy compared to whites, even after control for the socio-demographic factors of education, poverty, insurance and rural residence. In the models including migration status, controlling for socio-demographic factors, low health literacy was associated with non-U.S. born, rural residence, lacking health insurance and Hawaiian, Chinese, Filipino, Japanese and “Other” ethnicities (p<0.05). Within the age 65+ year models, only age 85+ years and Pacific Islander ethnicity had increased odds for low health literacy (p<0.05). Across all models, those with less than high school education had twice the odds of having lower health literacy compared to those who completed high school or post-secondary education (p<0.02). Predictive accuracy for all models achieved AUROC between 0.624-0.689. Conclusions: Several of Hawai’i’s Asian American and Pacific Islander ethnic groups have significantly lower health literacy compared to White residents. Based on comparison of Hawai’i and national models’ statistical results, this study found a lack of predictive accuracy for Hawai’i health literacy models, patterned after national models, when applied to Hawai’is’ adult Thesis: Developing a Predictive Model for Measuring Health Literacy in Hawai’i’s Adult Populations Karen T. Lubimir, Candidate for Masters in Clinical Research population. A locally derived multivariable model may lead to better detection of community specific information than using individual predictors of low health literacy, and could supply Hawai’i’s healthcare stakeholders with an additional tool to help focus health interventions.
dcterms.description M.S. Thesis. University of Hawaiʻi at Mānoa 2017.
dcterms.language eng
dcterms.publisher University of Hawaiʻi at Mānoa
dcterms.rights All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
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