Investigating the relationship between dental cavities and protective factors among children aged 0-5 years.

The purpose of this study was to determine the protective factors that contribute to the prevention of children aged 0-5 years from developing dental cavities. The oral hygiene practices of 266 children aged 0-5 years were assessed through surveys administered from 2019 to 2022 to identify clinical, dietary, social and parental factors. The Partial Least Squares (PLS) Regression and Artificial Neuron Networks (ANN) Models were used to determine protective factors associated with the prevention of dental cavities in children. The race distribution of the children as identified by caregivers is as follows: (1) Black or African-American (53.4%); (2) Asian (25.9%); (3) White (18.4%); and (4) Native American (2.3%). We found behavioral protective factors to significantly affect the oral health outcome (cavities) among children aged 0-5 years (p < 0.05). We also found that children whose parents/caregivers flossed their teeth were less likely to develop cavities. In addition, children were least likely to have cavities if their parents/caregivers used toothpaste and mouthwash, avoided sharing chewed food, and refrained from drinking 100% juice. In contrast, children were more likely to obtain cavities if their parents/caregivers had a lower education level, rarely cleaned their teeth, and often consumed marijuana, cow or goat milk, juice drinks and sugary beverages. The education level of parents, and on the contrary, oral hygiene practices of the family, play a significant role in influencing the prevalence of cavities in children aged 0-5 years.

At-Risk Stakeholder Behavior Change with Community-Based Health Events.

Community-based interventions such as health fairs feature diagnostic and preventive services that are useful to address health disparities in underserved stakeholders. Quantitative evaluation of these events presents challenges. This study applied the contemplation stage of the Trans-Theoretical Model of Behavioral Change (TTM-C) to evaluate health disparity-focused community interventions. Most of the 412 event participants self-identified as lower socioeconomic status (54%), overweight (74%), female (74%), and African American (83%). After event participation, stakeholders demonstrated significant improvement in overall TTM-C scores based on a standard t-test, where pre-event scores (M=30.8, SD=7.0) and post-event scores (M=32.3, SD=6.9) conditions t(346)=5.167, p=.001. For health disparity-related community events, underserved African American women are most likely to recognize a specific health issue and seek ideas about how to address it during these events. This study validates TTM-C behavioral change as a novel means to evaluate community health events.

Altered Emotional Intelligence through a Health Disparity Curriculum: Early Results.

INTRODUCTION: Emotional intelligence (EI) interests medical schools as a predictive factor in their graduates' clinical success. Historically black college and university (HBCU) academic health centers produce professionals to address health disparities. This preliminary study evaluated a health disparity reduction curriculum's effect on EI. METHODS: Thirty-one undergraduates participating in a Meharry Medical College health disparity reduction program voluntarily self-reported demographic and EI data before and after service-learning training. RESULTS: Paired sample t-test results demonstrated significant improvement in EI subscales of total score (p = .004), self-awareness (p=.001), self-confidence (p=.007), self-control (p=.041), motivation (p=.020), and social competence (p=.036). Multiple linear analyses confirmed African American race significantly predicted EI-Motivation (F [1,29] = 5.858, p =.022). CONCLUSIONS: These preliminary data support a beneficial effect in African Americans of a health disparity curriculum to improve EI, particularly the relevance of race to motivation. Future research should examine EI in HBCU medical school students.

Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.