Mapping and Geographical Risk Factors for Cancer in Iowa According to the Iowa Cancer Registry 2024 Cancer in Iowa Report, Iowa continues to have the 2nd highest and fastest growing rate of new cancers in the U.S. The Institute for Public Health Practice, Research and Policy has been working with the Iowa Cancer Registry to produce age-adjusted cancer rate estimates for every county in Iowa. We are interested in identifying potential risk factors related to these increases in cancer rates. New estimates of risk are available through PLACES produced by the CDC. In this project, we will relate cancer rates to various risk factors to identify potential risk factors of cancer incidence in Iowa and how those risk factors might change geographically.
Deep Learning to Predict Enrollment at the University of Iowa The University of Iowa, like many large institutions, has substantial data infrastructure regarding prospective, current, and former students. This resource is actively used by researchers in the Department of Biostatistics to help the university with recruitment, planning, resource allocation, and promoting student success and retention. This project will seek to expand the methods used to predict enrollment at the university by implementing recurrent neural network (RNN) models, and applying them to time-series of real student-level data. We will explore neural networks generally, as well as the use of graphics processing unit (GPU) accelerators and high-performance computing (HPC) resources to improve computation time.
Predicting antibiotics usage for improved healthcare-associated infection risk assessment Healthcare-associated infections (HAIs) impact an estimated 1 in 31 hospital patients and 1 in 43 nursing home residents. Many HAIs are caused by pathogens resistant to antibiotic treatments, while other HAIs arise from antibiotics suppressing probiotics (helpful microorganisms). An example is Clostridioidis difficile which in 2017 led to 223,900 infections and 12,800 deaths in the U.S. It is imperative to develop a better understanding of how these pathogens are transmitted within healthcare facilities. To help answer this question, our research group has published research on which antibiotics lead to a higher risk of a C. difficile infection (CDI), as well as how other patients with a CDI in the same facility increases one’s own risk. These two research lines come from the following two data sources respectively: (D1) over 26 million hospital encounters in which antibiotics usage is available, and (D2) over 16.8 million hospital encounters in which each patient’s healthcare facility is available. To accurately understand how facility-level features and concurrent patients with CDIs (found only in D2) combine to foster varying risk environments to newly admitted patients, we also must know the antibiotic usage of patients (found only in D1). This project, therefore, aims to use machine learning techniques to build predictive models for antibiotics found to lead to high CDI risk using D1; these will then be used to predict antibiotic usage of those patients in D2 in order to better understand how C. difficile spreads within hospitals.
Multiple Structural Breaks Detection through Genetic Algorithm Under stimuli and workloads, the human body tends to display discomforts and covert proximal cognitions that can manifest through physiological responses. One such response can be in a form of skin sweats, easily capturable via wearable sensors. The sensors capturing electrodermal activities (EDA) record big data serially (4Hz, 8Hz), per subject, over a duration of an experiment (approximately 35 min). This project aims to study serial EDA data collected on subjects from an experiment being conducted in neuropsychology at the University of Iowa, with the goal to detect structural breaks corresponding to epochs of learning activities and assess the role that biofeedback plays in efforts to engage people in a learner space. The project mobilizes technological innovations in neuroimaging (fNIRS), wearable sensors monitoring covert cognitive activity, monitoring arousal states under workload, and video data outputs to address learners’ emotional discomfort. The project will focus on structural breaks detection though a genetic algorithm stochastic search across the spectrum of EDA data.
Radionuclide therapy and immortal time bias in patients with neuroendocrine tumors Peptide receptor radionuclide therapy is a potentially promising therapy for treating neuroendocrine tumors. However, assessing its benefit is tricky: for much of the past decade, it was approved for use in Europe but not in the US, some patients took it, others didn’t, all at different times, etc. In analyzing this type of data, simple approaches fall victim to a phenomenon called “immortal time bias”. In this project, you will learn about time-to-event modeling, what immortal time bias is, how to avoid it, and how to adjust for the various thorny issues described above. Some approaches overestimate the benefit of PRRT, some underestimate its benefit, but (hopefully!) this project will produce an unbiased picture of its benefit (or lack thereof) for these patients.
Burnout Among Servicemen: A case of the Russian-Ukraine War Studies of war veterans estimated that 95% of military members consider burnout to be the leading cause of separation, retirement, and interpersonal disorders. Burnout, defined as a syndrome of emotional exhaustion and feelings of workplace failure that occurs in response to chronic exposure to occupational stressors, if not attended to and properly addressed, can trigger, or induce emotional disorders, feeling of discouragement, frustrations, worthlessness, and depression among servicemen. The current project involves researchers from the University of Iowa and investigators in the Psychology Department at Tara Shevchenko University in Kiev, Ukraine. The data were gathered on Ukraine’s servicemen at the frontline of the Russian-Ukraine war (n = 400). Burnout Assessment Tools (BAT), Interpersonal Guilt Rating Scale Self Report (IGRS-SR), Basic Psychological Need Satisfaction and Frustration Scale measurements (BPNSFS), will be cross-studied as functions of servicemen socio-demographic characteristics such as: gender, age, education, marital status, number of children, combat operation and other characteristics. Specific hypotheses will be studied and tested, and recommendations will be made with direct policy implications regarding burnouts among frontline servicemen.
Understanding HIV continuum among MSM in Iowa Of the estimated 36,136 new HIV diagnoses in the US in 2021, 71% were among men who have sex with other men (MSM) including Black/African American MSM (36%). Similarly in Iowa, MSM accounted for 73% of the 120 new HIV diagnoses and 54% of the estimated 3,228 people living with HIV in 2022. According to the Iowa Department of Health and Human Services 2023 surveillance report, Non-Hispanic Black/African Americans were over 10 times more likely to be diagnosed with HIV than Non-Hispanic white Iowans. To address this disparity in HIV incidence and prevalence, one of the four strategic goals of the US government’s “Ending the HIV Epidemic” (EHE) initiative is to expand coverage of pre-exposure prophylaxis (PrEP) by 2030 especially among populations at increased risk of HIV acquisition. It is important for researchers and policy makers to understand the HIV care continuum in Iowa to be able to achieve the EHE goals. This project seeks to understand HIV care continuum among MSM across races/ethnic groups and geographical locations in Iowa.
An Investigation of Factors Influencing Post-Resuscitation Survival and Functional Recovery for Patients Experiencing Out-of-Hospital Cardiac Arrest Roughly 350,000 Americans annually experience out-of-hospital cardiac arrest (OHCA). OHCA is unfortunately associated with very poor survival rates, with the majority of victims perishing in the pre-hospital setting. However, a substantial proportion of OHCA-related deaths occur in hospitalized patients following the return of spontaneous circulation (ROSC) (i.e., during the post-resuscitation phase), since resuscitated patients remain at risk for anoxic brain injury and multi-organ failure. For this project, using data from the Cardiac Arrest Registry to Enhance Survival (CARES) registry, we will investigate the factors that impact post-resuscitation survival and functional recovery for patients experiencing OHCA. We will consider the following explanatory variables: (1) patient factors (age, gender, medical comorbidities), (2) cardiac arrest characteristics (location of arrest, arrest witness status, first rhythm type), (3) bystander response (bystander automated external defibrillator and CPR use), (4) EMS response characteristics (arrival time, on-scene time, resuscitation time), and hospital-level interventions (coronary angiography and targeted temperature management). We hypothesize the existence of significant variation in post-resuscitation survival among hospitals, which should be partly explained by hospital utilization of recommended post-resuscitation measures.