PHS Graduate Student Conference Papers

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    Understanding equitable access to interpretive services in healthcare in Kansas
    (Wichita State University, 2023-04-14) Medina, Melissa; Colcher, Drew; Showstack, Rachel E.; Keene Woods, Nikki
    Introduction: There is an increasing need in Kansas for qualified healthcare interpreters as the demographics continue to become more diverse. Patients with limited English proficiency are considered a vulnerable population. They experience medical errors with worse clinical outcomes compared to English speakers. Per the Affordable Care Act, most healthcare facilities must provide qualified interpreters for individuals with limited English proficiency (LEP). A community-based project funded by the U.S. Office of Minority Health works to understand barriers and improve language access across Kansas. Purpose: To determine how health service providers in southeast, south-central, and southwest Kansas provide interpretive services and to better ensure effective communication for LEP patients. Methods: A cross-sectional survey was developed based on a previous survey by the National Health Law Program (NHeLP). The pilot survey included 18 questions on how health organizations are paid or reimbursed for interpretation services and the quality of the service each individual receives. The survey will be administered to primary care providers, health service providers, and health service organizations. A snowball recruiting strategy will be used to recruit participants via email and newsletters. The study timeframe will be from January 2023 to April 2023. Descriptive statistics will be used to summarize the findings. Results: The anticipated findings include additional information on the quantity and quality of interpretive services available across the southern regions of Kansas. We aim to determine whether language access plans that provide certified interpretive services during visits are in place at clinics and hospitals in Southern Kansas. We expect to find more information on the barriers to interpreting services in a clinical setting. Conclusion: To ensure better healthcare services and health outcomes for individuals with limited English proficiency, it is essential to increase the availability of interpretive services in healthcare settings. Education and training for healthcare workers and organizations can help improve health equity in an increasingly diverse community.
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    Validation of three question health literacy screener in determining health literacy as compared to existing STOFLA
    (Wichita State University, 2021-04-02) Petersen, Emily; Hansford, Hayley; Burkhart, Katie; Reyes, Jared; Woods, Nikki Keene; Chesser, Amy K.
    INTRODUCTION: Low health literacy has been associated with poor health outcomes. Identifying those with low health literacy would allow for appropriate interventions. There are currently several validated health literacy assessments, however, many of these are not feasible due to time, access and embarrassment to the patient. The three-question screener (3Q-Screener) has been proven to detect low health literacy in an outpatient setting. This modality was quick, practical and available electronically. Current studies have shown efficacy using the 3Q-Screener but more research was needed to compare accuracy between this newer efficient modality with the wildly used and accepted Short Test of Functional Health Literacy (STOFLA). PURPOSE: Our goal was to identify a quick and accurate health literacy assessment tool that would help identify low health literacy. The objective of this study was to determine if these two screening modalities are comparably valid. METHODS: An electronic survey was created that combined both the 3Q-Screener and the STOFLA. Consenting participants, solicited via oral communication, were directed to complete both the 3Q-Screener and STOFLA in no particular order and without knowledge of modality order. Demographics were collected. Inclusion criteria included English speaking adults in Kansas with the ability to read and understand questions and use an electronic device. RESULTS: Among the 225 participants, frequencies of inadequate and adequate health literacy as measured by the 3Q-Screener were 83.6% and 16.4% respectively as compared to the STOFHLA at 2.2% and 97.8% respectively. Sensitivities and specificities as well as positive and negative predicted values indicated that the 3Q-Screener was sensitive at detecting inadequate health literacy but lacks specificity (16.74%). A McNemar test revealed there were only four participants that both the STOFHLA and 3Q-Screener identified as both having inadequate health literacy (4/225, p< 0.001), and zero participants were marked as having adequate health literacy by the 3Q-Screener that the STOFHLA identified as having inadequate health literacy (0/225, p< 0.001). CONCLUSION: Our findings suggest that the 3Q-Screener identified those with inadequate health literacy but was prone to falsely label a competent patient when compared to the STOFLA. Due to the unexpected high number of inadequacies identified via the 3Q-Screener, we concluded that these two modalities are likely assigning health literacy in different ways. The STOFLA assesses objective literacy while the 3Q-Screener assesses the patients perceived health literacy. Identifying perceived inadequate health literacy may prove more valuable in improving health outcomes. Future studies should analyze if there are other modalities that predict "perceived" health literacy as well and whether perceived vs objective health literacy leads to better health outcomes.
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    Obesity classification from facial images using deep learning
    (Wichita State University, 2021-04-02) Siddiqui, Hera; Siddiqui, Hera; Rattani, Ajita; Cure Vellojin, Laila N.; Woods, Nikki Keene; Lewis, Rhonda K.; Twomey, Janet M.; Smith-Campbell, Betty; Hill, Twyla J.
    INTRODUCTION: Obesity is a serious health problem that is on the rise both in the United States and globally. Obesity is frequently defined using the clinical Body Mass Index (BMI) ratio of height and weight. Overweight individuals have a BMI between 25-30, and those over 30 are classified as obese. Obesity can lead to heart disease, type 2 diabetes, and many other serious health conditions. Self-diagnostic face-based solutions are being investigated for obesity classification and monitoring. PURPOSE: To classify obesity status based on facial images using deep learning-based convolutional neural networks (CNNs). METHODS: The four CNNs (VGG16, ResNet50, DenseNet121, and MobileNetV2) used in this study were pre-trained on three public datasets (ImageNet, VGGFace, and VGGFace2). Using the above CNNs, we extracted deep features from the FIW-BMI and VisualBMI datasets annotated with BMI information. The deep features from 8298 images in the FIW-BMI dataset along with BMI values were then used to train a Support Vector Classification (SVC) classifier. The trained SVC model was tested on 4206 different images from the VisualBMI dataset for the validation. RESULTS: CNNs trained on ImageNet dataset obtained an initial accuracy (percentage of correct obese and non-obese classifications) in the range 64% to 72%. Accuracy of 84% to 86% was obtained by using CNNs trained on VGGFace dataset. 86% accuracy was obtained by concatenating features from pre-trained (VGGFace) and fine-tuned (FIW-BMI) model. ResNet-50 trained on VGGFace2 dataset obtained an accuracy of 91% when features from the original image datasets were used and 92% accuracy when features were fused from the original image with the horizontally flipped image. The fused image modifications resulted in a model with Sensitivity, Specificity, and Precision of 0.90, 0.94, and 0.95, respectively. Mean Absolute Error (MAE) of this model in predicting BMI is 3.16 and area under the curve (AUC) is 0.97. CONCLUSION: Obesity can be predicted from facial images using deep learning models with a promising accuracy. SVC models trained on deep features extracted from models pre-trained on VGGFace2 dataset performed better than models pre-trained on ImageNet dataset. ResNet-50 (pre-trained on VGGFace2) obtained the highest accuracy of 92% by combining features from the original image and horizontally flipped image. These models when deployed on smartphones can help individuals in monitoring their obesity status, BMI, and weight changes.
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    Understanding the prevalence of gestational diabetes in urban and rural communities in Kansas
    (Wichita State University, 2021-04-02) Ali, Umama; Woods, Nikki Keene; Cure, Laila; Rattani, Ajita; Hill, Twyla J.
    INTRODUCTION: Gestational diabetes is one of the most common medical conditions women encounter during pregnancy and is the leading cause of maternal morbidity and mortality, perinatal and neonatal morbidity, and long-term consequences for both mother and baby. The percentage of pregnant women with gestational diabetes increased by 56% in the last ten years in the United States. Maternal obesity is a significant risk factor for gestational diabetes. In 2020, the Kansas obesity rate was 30-35%, which is expected to rise to more than 55% by 2030. Additionally, maternal and child health disparities are experienced to a greater extent in rural areas including obesity, in which rates are higher in rural counties than in large urban counties. It is hypothesized most rural counties in Kansas will have a higher prevalence of gestational diabetes. PURPOSE: Analyze the prevalence of gestational diabetes in rural and urban counties in Kansas. METHODS: Kansas Department of Health and Environment (KDHE) was used to gather diabetes and pre-pregnancy BMI data from 2005 to 2019. The data was analyzed by creating trend graphs and examining the prevalence in categorized peer groups. RESULTS: There is an increasing trend in rates and number of cases of gestational onset diabetes and pre-pregnancy BMI classified as overweight and obese. Urban counties have a higher prevalence of gestational diabetes, however rural counties have a higher prevalence of pre-pregnancy BMI overweight and obese cases. Rural counties have experienced a growing increase over the years in the prevalence of gestational diabetes. CONCLUSION: Similar national studies from the Centers for Disease Control and Prevention have reported an increased prevalence of gestational diabetes from 3.7% in 2012 to 5.8% in 2016. In Kansas gestational diabetes increased from 5.8% in 2012 to 6.4% in 2016. Observed increases in the prevalence of gestational diabetes could be reflective of recent increases in the prevalence of pre-pregnancy obesity that is experienced to a greater extent in rural communities. Future efforts to address this growing health disparity are needed.
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    Assessment of health care professionals' attitudes and knowledge about older adults
    (Wichita State University, 2020-05-01) Heath, Kathleen; Smith, Alysia; Green, Jacie; Rogers, Nicole L.; Chesser, Amy K.
    INTRODUCTION: The aging population is continuing to increase in size and so are their healthcare needs. Healthcare professionals' knowledge of and attitudes toward the health and well-being of this population may affect the quality of care these patients receive. PURPOSE: The purpose of this pilot study is to evaluate the relationship between the level of knowledge regarding the aging population and ageist attitudes among healthcare workers. METHODS: This pilot study used a test-survey distributed to a convenience sample of healthcare professionals working in aging communities. Data collected from a compilation of three surveys assessed the following factors: ageist attitudes, knowledge level regarding older adults, and demographic information. The primary outcome of interest in this study is the relationship between participants' level of knowledge of older adults and level of ageist attitudes. RESULTS: Fifty-seven healthcare workers participated in the survey, while fifty-three completed it entirely. Correlation coefficients were computed among knowledge, ageism, gender and age. Data analysis revealed no significant correlations among the categories. There were no significant correlations between the level of ageism and knowledge of older adults, r(57) = -.422, p < .05. Additionally, correlation coefficients calculated between gender and level of ageism as well as gender and knowledge of older adults revealed no significant values, r(57) = -.056, p < .05, r(57) = -.105, p < .05. Furthermore, there was no significant correlation between the level of knowledge of older adults and participants' age, r(53) = .084, p > .05 . Finally, there was no significant correlation between level of ageism and increased age, r(53) = .095, p < .05. CONCLUSION: The lack of significant correlations is likely due to the small sample size of this study. Additionally, small sample size limited the ability to analyze data into further subgroups. It is possible the external validity of this study is compromised due to the limited number of participants. There are a variety of factors contributing to limited participation. Despite inconclusive results, proper training is crucial to the quality of care providers can give their patients. Future research will be valuable to explore this research question further as well as explore actions to implement appropriate workplace training and education to reduce ageist attitudes among healthcare providers.