Creating County Health Rankings The overall health of the population is of vital importance to a civilization. Monitoring the overall health in the United States has taken on several forms including the Healthy People 2000, 2010, and 2020. The Iowa Department of Public Health took this format and created the Healthy Iowans 2000, 2010, and 2020. To take this healthy initiative one step further, we have seen the creation of The Healthiest State Initiative for Iowa, and communities being ranked according to health status. In this project, we will participate in health assessment by ranking the 99 counties in Iowa from the most healthy to the least healthy according to a variety of health factors. We will first select the health factors that the rankings should be based upon. Due to the rural nature of Iowa, many of these measures will have small counts leading to highly variable estimates. We will use Bayesian spatial smoothing techniques to arrive at reliable estimates of our determined measures of health. We will then combine the various measures in a statistical model to provide health status ranks for the 99 counties in Iowa.
How Comparable are Clinical Trials Results Reporting on ClinicalTrials.gov versus High-Impact Neurological Journals? Since the 2007 FDA Amendments Act, requirements for reporting results of clinical trials to ClinicalTrials.gov have been expanded. For all FDA-regulated medical products, the results of a clinical trial must be reported within 12 months of trial completion. A recent study (Becker et al 2014) investigated the accuracy of ClinicalTrials.gov-reported results by comparing what is reported on ClinicalTrials.gov to what is reported in the corresponding peer-reviewed publications. For the primary endpoints of these trials, they found that 23% could not be compared because the information did not show up in both sources. They also found that 16% were discrepant between the sources and that in 29% of the discrepancies, the discrepancy actually changed the interpretation of the result. At the University of Iowa Clinical Trials Statistical and Data Management Center, we work on many neurological studies and almost all of these studies are registered on ClinicalTrials.gov. While the Becker review spans many disease areas, analyses of more specialized areas (such as the leading neurological journals) is warranted. We will look to see just how well researchers in the field of neurology are reporting their results to these two important sources.
Modeling Complex Epidemics with Environmental Reservoirs: Studying Cholera Transmission on the Island of Hispaniola with Bayesian Epidemic Models Vibrio cholera is a bacterium responsible for serious illness in humans, and is primarily transmitted via contaminated water sources. In 2010, during the aftermath of a major earthquake on the island of Hispaniola which devastated areas of Haiti and the Dominican Republic, cholera was introduced to the island by aid workers from the UN. Particularly in Haiti, the lack of public health infrastructure and access to clean water has had a devastating human cost. This project seeks to better understand Cholera transmission on the island of Hispaniola, particularly in relation to precipitation and surface water flow. Cholera case data is available from the Pan-American Health Organization (PAHO), and the project will be completed using the open source ABSEIR epidemic modeling software. This project will include the study of Bayesian hierarchical models, and advanced computing techniques including Approximate Bayesian Computation, and will explore the unique challenges presented by epidemic models in general and spatial epidemic models specifically.
Prevention of Graft-versus-Host Disease in Bone Marrow Transplant Patients Graft-versus-host disease (GvHD) is a medical complication occurring in 30-70% of patients receiving bone marrow transplants. The disease occurs when immune cells from the transplanted tissue (graft) recognize the recipient (host) as foreign, and attack cells in the host’s body. GvHD is the leading cause of subsequent illness and death following transplantation. In order to reduce the risk of GvHD and improve health outcomes, a better understanding of disease risk factors and better treatments to prevent the disease are needed. In this project, we will study a potential preventative treatment for GvHD in a group of patients who received bone marrow transplants for blood disorders, including leukemia and myelodysplastic syndromes. Statistical methods will be employed to examine the effects of patient characteristics on occurrence of GvHD, relapse, and death. Regression and machine learning techniques will be employed to identify important characteristics, develop multivariable statistical models for the health outcomes, and evaluate the performance of the models.
Genetic Risk Factors for Preterm Birth Preterm birth affects 5-18% of pregnancies worldwide, and is the leading cause of infant mortality. Both genetic and environmental factors play a role in preterm birth, with research suggesting that approximately 40% of the responsibility is with genetic factors. Nevertheless, few specific genetic variants have been identified. In this study, the exomes of 93 pairs and 2 triads of sisters with a history of premature birth were sequenced in an effort to find such variants. This project offers a chance to learn about preterm birth as well as a chance to learn about modern genetic studies and the discovery of rare variants that recent advances in genetic sequencing have made possible.
The Effect of Inter-health Care Facility Patient Sharing Network on Clostridium difficile Infection Rates According to the CDC, Clostridium difficile (C.diff) caused almost half a million infections in the U.S. alone, directly leading to 15,000 deaths annually. This bacterium is listed as an “Urgent Threat” not only due to its prevalence but also its antibiotic resistance. People can become infected if they touch contaminated surfaces and then touch their mouth, nose, eyes, etc. Healthcare workers can spread the bacteria or contaminate surfaces through hand contact. Hospitals commonly share patients with other hospitals via direct transfers and indirectly via readmissions. Thus, patient sharing between hospitals may serve as a means for the dissemination of C.diff and other healthcare-associated infections. This project’s focus is on understanding the effect of connectivity patterns between hospitals via patient sharing on CDI incidence rates. We will conduct some exploratory analyses on these patient sharing networks, and use a more formal linear network autocorrelation model to estimate the network effect on C.diff infection incidence.
Floods and Tornadoes: Trends in Extreme Weather Events in the U.S. Heartland In addition to economic impacts, floods and tornadoes may have severe public health effects in the form of deaths, injuries, and disease. This project will investigate whether these types of extreme weather events have increased in frequency and/or intensity since 1950. We will also determine whether time trends differ in different parts of the U.S. heartland and what geographic characteristics are associated with greater risk of floods or tornadoes.
Is the Full Dynamic Range of Standard Automated Perimetry Useful for Clinical Investigation of Glaucoma Progression? Perimetry is a systematic measurement of visual field function. Visual field testing helps quantify this visual function. The test is carried by sending a stimulus with various degrees of intensity into a visual field and assessing the patient’s response. The dynamic range in perimetry is the range of the smallest and largest values of the visual stimulus that a device is capable of displaying. It has been recognized that not all values from a perimeter are useful—as some have little effect on disease detection; and sometimes values below a certain threshold may not be useful when detecting glaucoma during perimetry. Therefore, it may be more appropriate to focus on Effective Dynamic Range (EDR) during perimetry testing. EDR is defined as the part of the physical range that is physiologically meaningful and clinically useful. Some authors have called it Useful Dynamic Range (UDR). This study will use statistical tools, such as permutation tests, to assess the EDR (or UDR) in the Iowa Variability in Perimetry (VIP) study, where 120 glaucoma subjects and 60 normal subjects were tested every six months for 4 years using the Standard Automated Perimetry with Goldmann III stimuli.