Student Research Projects

The UNMC College of Public Health celebrates the research endeavors of our students, which is why we want to feature them.

Explore the spectrum of topics, methodologies, and impacts that characterize our students’ research journeys.

Evaluating the efficacy of praziquantel in curing Schistosoma haematobium infection and reversing associated undernutrition using Fluke Finder

Group:

Louis Fok, MPH, PhD Student, Department of Epidemiology 

Abstract:

Schistosomiasis is the second most devastating parasitic infection worldwide, causing untold suffering for millions of persons worldwide. Widespread in much of the underdeveloped world, schistosomiasis is a disease of poverty that disables more than it kills, with malnutrition, stunted development and growth retardation being its primary adverse effects.

FlukeFinder® has been used for decades for detecting parasite eggs in the feces of farm animals. While it has been tested – and has shown promise – under controlled environments for detecting parasite eggs in human feces, it has never been studied for the detection of Schistosoma eggs in human urine. In addition, there is very limited clinical evidence to demonstrate that praziquantel treatment can reverse schistosomiasis-associated malnutrition.

This study aims to determine the current efficacy of praziquantel at curing urinary schistosomiasis; to examine praziquantel’s short-term effect at potentially reversing schistosomiasis-associated malnutrition in children; and to evaluate the performance of a FlukeFinder®, a veterinary diagnostic tool, at detecting schistosomiasis in children’s urine.

Where is Dinner?: A Mixed-Method Food Insecurity Analysis of Latino Communities across Rural Nebraska

Group:

Natalia Santos, MPH, PhD Candidate, Department of Health Promotion

Abstract:

Food insecurity is caused by socioeconomic and systemic inequitable problems and is linked to rising rates of obesity, diabetes, hypertension, and other diet-related. These issues are exacerbated in minority and rural communities. However, methodological differences in variables measured, analysis types, and geography has led researchers to contend with the extent of food environments' direct impact on obesity. This gap in practical approaches has impacted how we define the need for food environment implementation programs, where nutrition and wellness programs take place, the strategies used, how to recruit participants, and, ultimately, the impact of food environments on community health.

This study is based on the NIH's NIMHD Minority Health and Health Disparities Research Framework and the Social Ecological Model. It addresses the influence of behavioral, sociocultural, and physical environments at individual and community levels on objective and perceived food insecurity experienced by Latinos in rural areas. The PI will conduct a geospatial analysis of SNAP and WIC food retailer data combining local socioeconomic variables identified through the literature to develop a Food Insecurity Vulnerability Index at the census tract in Nebraska. We will then conduct interviews with Latino community members to understand their perception of food insecurity in rural Nebraska. Lastly, store assessments will provide quantitative insight into the availability of healthy food items found within food retailers and the price, promotion, and placement of foods.

Epidemiology and Risk Factors for Hookworm Infection and Response to Treatment: A Field Study in Beposo, Ghana

Group:

Taylor Clarkson, MPH Student, Department of Epidemiology 

Abstract:

Background: Hookworms are soil-transmitted helminths that infect over 600 million people worldwide, primarily those in low and middle-income countries. Infections with hookworms can cause anemia, malnutrition, and stunted development. There are significant gaps in our understanding of critical host-parasite factors that mediate hookworm infection, reinfection, and response to deworming treatment in communities where the parasite is endemic.

Results: We hope the results of our fieldwork will shed light on the specific host risk factors for hookworm infection and response to deworming, and that this information can be used to guide future intervention strategies.

Examining Conflicts Between Multidisciplinary Teams in Cancer Care

Group:

Aatiya Ahmad, PhD student, Department of Health Services Research & Administration 

Abstract:

Cancer is an intricate and complex disease that requires multiple specialist teams to work closely together to deliver care. A multidisciplinary team promotes more precise and comprehensive care to cancer patients, which is why it has grown in popularity in the last decade. Multidisciplinary teams work as a team-of-teams, or multiteam systems, which can create challenges both within and between teams for care coordination due to specialization differentiation and goal discordance. This paper proposes a theoretical framework for understanding how goal alignment impacts cancer care delivery and care coordination both within and between care teams. The objective of this research is to develop a framework for goal alignment within multidisciplinary teams by 1. Defining goal conflicts that commonly arise between teams, 2. Why they occur, and 3. Discuss ways to minimize or resolve them.

We conducted a review of prominent frameworks and seminal papers relating to goal discordance, goal concordant care, and goal conflict for multidisciplinary teams in cancer care. We identified common themes and examined how these conflicts impacted patient outcomes.

We found four main drivers of goal discordance between teams, including communication failures, lack of shared mental models, lack of understanding of patient preferences, and interoperability of the electronic health record. We organized our results under an organizational theory framework. The poster will define each of these drivers, provide a case study example, and proposed solution.  

As the prevalence of cancer continues to rise, there is an increased demand for well-coordinated cancer care delivery across multidisciplinary teams. To address this need, we developed a framework that identifies common barriers related to goal alignment across care teams. Our research can shed light on common causes of conflict between teams using organizational theory and ways that teams can reduce discordance while promoting positive patient outcomes.

Stability Selection Ensemble Learning Framework for Identifying Biomarkers for Early Cancer Detection

Group:

Apu Chandra Das, MS, PhD Student, Department of Biostatistics

Abstract:

A growing number of biological markers are being tested to identify sensitive and specific biomarkers for early cancer detection, tumor regrowth, or metastasis. The biomarkers can be obtained by simple blood tests, making them an inexpensive, noninvasive method for detecting cancer. Modern biomarker analysis often result in high-dimensional data sets with many more biomarkers than subjects (𝑛≪𝑝). However, there is no guarantee that all the signal variables will be selected by existing regularization approaches. Since ovarian cancer and endometrial cancer have poor prognoses, we aim to identify biomarkers that show excellent performance in both the discovery and validation phases of early cancer detection.

The study of biomarkers often involves a large number of measured variables (such as proteins, autoantibodies, etc.), but not all of them are predictive to the response. Model overfitting occurs when a lot of noise variables are erroneously selected, leading to poor performance upon validation. In addition, selection results may be highly sensitive to model misspecification. Stability selection methods have been used to prevent overfitting and to select true signals reliably. Ensemble learning methods have been developed to build robust methods against model misspecification. We built a novel framework consisting of a biomarker selection stage using stability selection and a prediction stage using an ensemble of machine learning methods, namely the stability selection ensemble learning. We incorporate a collection of ML methods including LASSO, random forest, and support vector machine for cross-sectional biomarker studies. We apply the new approaches to simulated and real datasets. 

We have used 869 subjects from PLCO data, including 432 having either endometrial or ovarian cancer and 437 controls. When LASSO was integrated with stability selection, Geotaxin, GCSF, SILRII, and VEGF biomarkers were selected as predictive of cancer in a multivariable model. Later, the performance was investigated through logistic regression model, RF, and SVM techniques using STABEL framework.

Our results demonstrate the power of integrating ensemble learning with stability selection for selecting endometrial and ovarian cancer biomarkers. This technique has a broad range of applications in biomarker selection for any cancer.

Understanding Perceptions of Tick-Borne Disease Risk and Prevention in Agricultural Operators and Their Healthcare Providers in the Plains States

Group:

Juliana Monono, MPH Student, Department of Epidemiology 

Abstract:

Ticks are rapidly expanding their geographical ranges across the U.S., including into new regions in the central plain’s states. Specific Aim 1- Develop and deploy a questionnaire to understand agricultural operators’ perceptions of tick-borne disease risk and prevention. Specific Aim 2- Adapt and expand an existing questionnaire aimed at understanding healthcare provider and veterinary professionals’ knowledge of tick-borne diseases to additional plains states.

Three unique surveys have been designed for agricultural operators, healthcare providers, and veterinary practitioners. For agricultural operators specifically, we have designed the survey to assess i) their perception on occupational exposures to ticks and TBD, ii) their current tick bite prevention practices and willingness to take precautions to reduce tick bites, and iii) their history of exposures to tick-bites. The healthcare and veterinary workers survey was adapted from a previous survey of healthcare providers in Nebraska. For healthcare providers, we have included questions to assess i) their knowledge of symptoms of and evidence-based treatments for various TBDs and Alpha-Gal syndrome, ii) whether they have sought diagnostic testing and treatment options for TBDs, iii) whether they provide their patients with information on tick bite prevention. In the veterinary survey the questions were adapted for ticks and TBD that affect animals in the plain’s states. All surveys were designed using Qualtrics and consist of questions with binary yes/no, check all that apply, and Likert scale formats. The survey design process began in January 2023 after conducting the background analysis and literature review regarding ticks and TBD in the study catchment area. The questionnaires have been reviewed extensively by our collaborators at the UNL Bureau of Sociological Research, Meghan Brashear (Qualtrics expert), Cheryl Bessler, and Ellen Duysen.

Data collection is ongoing. Initial surveys for healthcare providers and veterinary workers have been sent out by BOSR. We anticipate sending out agricultural operator’s surveys in September.

TBDs represent an emerging threat in the great plains’ region served by CS-CASH. This study represents the first comprehensive assessment of agricultural operators, health care providers, and veterinary workers knowledge, attitudes, and perceptions of TBDs in our region.