Data Science Commons^™

Health And Healthcare: Designing For The Social Determinants Of Health And Blue Zones In North Nashville, Rebecca Tonguis, Honor Thomas, Olivia Hobbs

Belmont University Research Symposium (BURS)

Owned by North Nashville’s First Community Church, a now empty site in the Osage-North Fisk neighborhood of North Nashville has been identified as a potential site for a new location of The Store, in addition to a community-centric architectural development based on the social determinants of health and informed by the principles behind Blue Zones, the locations with the highest lifespans in the world. Opened by Brad Paisley and Kimberly Williams-Paisley, The Store is a free grocery store that “allow[s] people to shop for their basic needs in a way that protects dignity and fosters hope”, for which North Nashville …

The Psychological Science Accelerator's Covid-19 Rapid-Response Dataset, Erin M. Buchanan, Andree Hartanto

Research Collection School of Social Sciences

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with …

Surveillance Systems In Western Kenya: Methods, Perceptions, And Effectiveness, Marissa Duffy

Independent Study Project (ISP) Collection

Surveillance is an important tool in monitoring and evaluating infectious disease patterns and trends. Surveillance is vital because it aids public health officials and medical professionals in creating better prevention methods and efficiently managing outbreaks. Kenya is home to many noncommunicable diseases making it an important location to conduct disease surveillance. Within Kenya, each county has its own surveillance unit which tracks and controls outbreaks. In addition, government run surveillance systems were established to determine disease burden, incidence, and patterns in specific at-risk communities around Kenya. One of these major surveillance systems is Population-Based Infectious Disease Surveillance (PBIDS) which has …

Predicting Micronutrient Deficiency With Publicly Available Satellite Data, Elizabeth Bondi-Kelly, Haipeng Chen, Christopher D. Golden, Nikhil Behari, Milind Tambe

Arts & Sciences Articles

Micronutrient deficiency (MND), which is a form of malnutrition that can have serious health consequences, is difficult to diagnose in early stages without blood draws, which are expensive and time-consuming to collect and process. It is even more difficult at a public health scale seeking to identify regions at higher risk of MND. To provide data more widely and frequently, we propose an accurate, scalable, low-cost, and interpretable regional-level MND prediction system. Specifically, our work is the first to use satellite data, such as forest cover, weather, and presence of water, to predict deficiency of micronutrients such as iron, Vitamin …

Determining The Proportionality Of Ischemic Stroke Risk Factors To Age, Elizabeth Hunter, John D. Kelleher

Articles

While age is an important risk factor, there are some disadvantages to including it in a stroke risk model: age can dominate the risk score and lead to over-or under-predictions in some age groups. There is evidence to suggest that some of these disadvantages are due to the non-proportionality of other risk factors with age, eg, risk factors contribute differently to stroke risk based on an individual’s age. In this paper, we present a framework to test if risk factors are proportional with age. We then apply the framework to a set of risk factors using Framingham heart study data …

The Daily Patterns Of Emergency Medical Events, Mary E. Helander, Margaret K. Formica, Dessa K. Bergen-Cico

Social Science - All Scholarship

This study examines population level daily patterns of time-stamped emergency medical service (EMS) dispatches to establish their situational predictability. Using visualization, sinusoidal regression, and statistical tests to compare empirical cumulative distributions, we analyzed 311,848,450 emergency medical call records from the U.S. National Emergency Medical Services Information System (NEMSIS) for years 2010 through 2022. The analysis revealed a robust daily pattern in the hourly distribution of distress calls across 33 major categories of medical emergency dispatch types. Sinusoidal regression coefficients for all types were statistically significant, mostly at the p < 0.0001 level. The coefficient of determination ($R^2$) ranged from 0.84 and 0.99 for all models, with most falling in the 0.94 to 0.99 range. The common sinusoidal pattern, peaking in mid-afternoon, demonstrates that all major categories of medical emergency dispatch types appear to be influenced by an underlying daily rhythm that is aligned with daylight hours and common sleep/wake cycles. A comparison of results with previous landmark studies revealed new and contrasting EMS patterns for several long-established peak occurrence hours--specifically for chest pain, heart problems, stroke, convulsions and seizures, and sudden cardiac arrest/death. Upon closer examination, we also found that heart attacks, diagnosed by paramedics in the field via 12-lead cardiac monitoring, followed the identified common daily pattern of a mid-afternoon peak, departing from prior generally accepted morning tendencies. Extended analysis revealed that the normative pattern prevailed across the NEMSIS data when re-organized to consider monthly, seasonal, daylight-savings vs civil time, and pre-/post- COVID-19 periods. The predictable daily EMS patterns provide impetus for more research that links daily variation with causal risk and protective factors. Our methods are straightforward and presented with detail to provide accessible and replicable implementation for researchers and practitioners.

Estimating The Health Effects Of Adding Bicycle And Pedestrian Paths At The Census Tract Level: Multiple Model Comparison, Ross J. Gore, Christopher Lynch, Craig Jordan, Andrew Collins, R. Michael Robinson, Gabrielle Fuller, Pearson Ames, Prateek Keerthi, Yash Kandukuri

VMASC Publications

Background: Adding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions.

Objective: Our work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question “To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents’ health outcomes over time?” …

Predictive Modeling And Estimation Of The Doubling Time Of Confirmed Cases Of Covid-19 In Niger, Ibrahim Sidi Zakari, Hadiza Galadima

Community & Environmental Health Faculty Publications

Modeling is increasingly used to assess scenarios and make projections on the future course of new coronavirus disease. This allows for better planning of care as well as a relaxation or tightening of the restrictive measures decreed by the government and the health authorities. The data analyzed in this study covers the period from March 19 to June 05, 2020 and allowed predictions of new cases of COVID-19 based on a growth model with a growth rate that changes linearly over time. In addition, we calculated and predicted the doubling time of the number of positive cases in each region …

Analyzing Tweets On New Norm: Work From Home During Covid-19 Outbreak, Swapna Gottipati, Kyong Jin Shim, Hui Hian Teo, Karthik Nityanand, Shreyansh Shivam

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic triggered a large-scale work-from-home trend globally in recent months. In this paper, we study the phenomenon of “work-from-home” (WFH) by performing social listening. We propose an analytics pipeline designed to crawl social media data and perform text mining analyzes on textual data from tweets scrapped based on hashtags related to WFH in COVID-19 situation. We apply text mining and NLP techniques to analyze the tweets for extracting the WFH themes and sentiments (positive and negative). Our Twitter theme analysis adds further value by summarizing the common key topics, allowing employers to gain more insights on areas of …

Semantics Of The Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable And Explainable?, Manas Gaur, Keyur Faldu, Amit Sheth

Publications

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to …

Prospects And Challenges Of Population Health With Online And Other Big Data In Africa; Understanding The Link To Improving Healthcare Service Delivery, Rowland Edet, Bolarinwa Afolabi

Department of Sociology: Faculty Publications

Big data analytics offers promises to many health care service challenges and can provide answers to many population health issues. Big data is having a positive impact in almost every sphere of life in more advanced world while developing countries are striving to meet up. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa and identify its peculiar needs. The purpose of this review was to summarize the challenges faced by …

Population Data Centre Profile - The Western Australian Data Linkage Branch, Steve Hodges, Tom Eitelhuber, Alexandra Merchant, Janine Alan

Research outputs 2014 to 2021

Established in 1995, the Western Australian Data Linkage Branch (DLB) is Australia’s longest running data linkage agency. The Western Australian Data Linkage System (WADLS) employs an enduring linkage model spanning over 60 data collections supported by internally developed and supported software and IT infrastructure. DLB has delivered, and continues to deliver, a range of significant data linkage innovations, many of which have been adopted elsewhere. A current restructure within the Western Australian Department of Health (which we will refer to as the Department of Health) will provide an improved funding model geared toward addressing issues with staff retention, capacity and …

Degradation Science: Mesoscopic Evolution And Temporal Analytics Of Photovoltaic Energy Materials, Roger H. French, Rudolf Podgornik, Timothy J. Peshek, Laura S. Bruckman, Yifan Xu, Nicholas R. Wheeler, Abdulkerim Gok, Yang Hu, Mohammad A. Hossain, Devin A. Gordon, Pei Zhao, Jiayang Sun, Guo-Qiang Zhang

Faculty Scholarship

Based on recent advances in nanoscience, data science and the availability of massive real-world datastreams, the mesoscopic evolution of mesoscopic energy materials can now be more fully studied. The temporal evolution is vastly complex in time and length scales and is fundamentally challenging to scientific understanding of degradation mechanisms and pathways responsible for energy materials evolution over lifetime. We propose a paradigm shift towards mesoscopic evolution modeling, based on physical and statistical models, that would integrate laboratory studies and real-world massive datastreams into a stress/mechanism/response framework with predictive capabilities. These epidemiological studies encompass the variability in properties that affect performance …

Exploration Of Preterm Birth Rates Using The Public Health Exposome Database And Computational Analysis Methods, Anne D. Kershenbaum, Michael A. Langston, Robert S. Levine, Arnold M. Saxton, Tonny J. Oyana, Barbara J. Kilbourne, Gary L. Rogers, Lisaann S. Gittner, Suzanne H. Baktash, Patricia Matthews-Juarez, Paul D. Juarez

Sociology Faculty Research

Recent advances in informatics technology has made it possible to integrate, manipulate, and analyze variables from a wide range of scientific disciplines allowing for the examination of complex social problems such as health disparities. This study used 589 county-level variables to identify and compare geographical variation of high and low preterm birth rates. Data were collected from a number of publically available sources, bringing together natality outcomes with attributes of the natural, built, social, and policy environments. Singleton early premature county birth rate, in counties with population size over 100,000 persons provided the dependent variable. Graph theoretical techniques were used …

Scalable Combinatorial Tools For Health Disparities Research, Michael A. Langston, Robert S. Levine, Barbara J. Kilbourne, Gary L. Rogers Jr., Anne D. Kershenbaum, Suzanne H. Baktash, Steven S. Coughlin, Arnold M. Saxton, Vincent K. Agboto, Darryl B. Hood, Maureen Y. Litchveld, Tonny J. Oyana, Patricia Matthews-Juarez, Paul D. Juarez

Sociology Faculty Research

Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual’s genetic endowment, particularly to environmental exposures experienced across his or her life course. New analytical approaches are clearly required as investigators turn to complicated systems theory and ecological, place-based and life-history perspectives in order to understand more clearly the relationships between social determinants, environmental exposures and health disparities. While traditional data analysis techniques remain foundational to health disparities research, they are easily overwhelmed by the ever-increasing size …