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Overlapping geographic clusters of food security and health: Where do social determinants and health outcomes converge in the U.S?

Description: This article identifies geographic clusters of food insecurity and health across U.S. counties to identify potential shared mechanisms for geographic disparities in health and food insecurity.
Date: June 14, 2018
Creator: Leonard, Tammy; Hughes, Amy; Donegan, Connor; Santillan, Alejandro & Pruitt, Sandi L.
Partner: UNT College of Arts and Sciences

Exploratory Analysis of Social E-health Behavior

Description: Extant literature has documented well that people seek health information via the internet as patients and consumers. Much less, however, is known about interaction and creation behaviors in the development of new online health information and knowledge. More specifically, generalizable sociodemographic data on who engages in this online health behavior via social media is lacking in the sociological literature. The term “social e-health” is introduced to emphasize the difference between seeking behaviors and interaction and creation behaviors. A 2010 dataset of a large nationally representative and randomly sampled telephone survey made freely available from the Pew Research Center is used to examine social e-health behavior according to respondents’ sociodemographics. The dependent variable of social e-health behavior is measured by 13 survey questions from the survey. Gender, race, ethnicity, age, education, and income are used as independent variables. Logistic regression analysis was used to determine the odds of engagement in social e-health behavior based on the sociodemographic predictors. The social determinants of health and digital divide frameworks are used to help explain why socioeconomic variances exist in social e-health behavior. The findings of the current study suggest that predictable sociodemographic patterns along the dimensions of gender, race, age, education, and income exist for those who report engaging in social e-health behavior. This study is important because it underscores the fact that engagement in social e-health behavior is differentially distributed in the general U.S. population according to patterned sociodemographics.
Date: May 2014
Creator: Acadia, Spencer
Partner: UNT Libraries

Assessing Social Determinants of Severe Mental Illness in High-Risk Groups

Description: The primary objective of this research was to explore the impact of possible social factors on non-institutionalized adults 18 years of age or older residing in the United States who exhibited severe mental illness (SMI). A holistic sociological model was developed to explain SMI by incorporating elements of social learning theory, social disorganization theory, and gender socialization theory with social demographic factors. Based on the holistic sociological model, the following factors were investigated: demographic aspects of age, education, income and gender; gender socialization; influence of neighborhood area; social network influence based on communication and interaction among peers and family members; and socially deviant behaviors such as frequently smoking cigarettes, drinking alcohol and using drugs specifically marijuana. The impact of these factors on SMI was examined. A sample of 206 respondents drawn from National Survey on Drug Use and Health, 2003 was assessed. These respondents had answered all the questions related to SMI; social deviant behaviors; neighborhood environment; and communications among peers, family members and friends; and the other studied factors. Ordinary linear regression with interaction terms was employed as a statistical tool to assess the impact of social determinants on SMI. Being female, living a disorganized neighborhood, and frequent and high levels of smoking cigarettes and drinking alcohol had a significant influence on SMI. This reevaluation and reexamination of the role of gender socialization path, socially deviant behaviors like smoking and drinking, and community construction on SMI provided additional insights. This research is one of the first to develop a more holistic sociological model on SMI and explored the previously untested interactive relationships. The limitations of this study suggest the need to test a potential recursive research model and explore additional bi-directional associations.
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Date: May 2014
Creator: Sun, Qi
Partner: UNT Libraries

The Influence of Disease Mapping Methods on Spatial Patterns and Neighborhood Characteristics for Health Risk

Description: This thesis addresses three interrelated challenges of disease mapping and contributes a new approach for improving visualization of disease burdens to enhance disease surveillance systems. First, it determines an appropriate threshold choice (smoothing parameter) for the adaptive kernel density estimation (KDE) in disease mapping. The results show that the appropriate threshold value depends on the characteristics of data, and bandwidth selector algorithms can be used to guide such decisions about mapping parameters. Similar approaches are recommended for map-makers who are faced with decisions about choosing threshold values for their own data. This can facilitate threshold selection. Second, the study evaluates the relative performance of the adaptive KDE and spatial empirical Bayes for disease mapping. The results reveal that while the estimated rates at the state level computed from both methods are identical, those at the zip code level are slightly different. These findings indicate that using either the adaptive KDE or spatial empirical Bayes method to map disease in urban areas may provide identical rate estimates, but caution is necessary when mapping diseases in non-urban (sparsely populated) areas. This study contributes insights on the relative performance in terms of accuracy of visual representation and associated limitations. Lastly, the study contributes a new approach for delimiting spatial units of disease risk using straightforward statistical and spatial methods and social determinants of health. The results show that the neighborhood risk map not only helps in geographically targeting where but also in tailoring interventions in those areas to those high risk populations. Moreover, when health data is limited, the neighborhood risk map alone is adequate for identifying where and which populations are at risk. These findings will benefit public health tasks of planning and targeting appropriate intervention even in areas with limited and poor-quality health data. This study not only fills the identified ...
Date: December 2017
Creator: Ruckthongsook, Warangkana
Partner: UNT Libraries