Epidemiology Commons^™

Predicting Dietary Behavior In Obese Adult Diabetics Using Social Cognitive Theory, Precious Mueller

Walden Dissertations and Doctoral Studies

Predicting Dietary Behavior in Obese Adult Diabetics Using Social Cognitive Theory by Precious Mueller MA, University of Phoenix, 2009 BS, University of Iowa, 2006 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Public Health Walden University November 2024 “ Diabetes mellitus Type II is a prevalent and serious condition affecting millions globally, particularly among obese adults. The association between dietary behaviors and diabetes management is critical, as it directly impacts health outcomes and quality of life for affected individuals. This study aims to identify the determinants of dietary behaviors that influence Type II …

A Cancer Education Needs Assessment: Informing Middle-Aged Female Patients About The Relationships Between Obesity And Women’S Health Concerns In The Reproductive System, Breast, And Endometrial Health, Batul Mirza

MUSC Theses and Dissertations

Obesity significantly impacts women’s health, particularly among middle-aged women, by increasing the risk of hormone-sensitive cancers such as breast, endometrial, and reproductive system cancers. This study examines the educational needs of this demographic group regarding obesity-related cancer risks and explores effective intervention strategies. Obesity-induced mechanisms – hormonal imbalances, chronic inflammation, and insulin resistance – drive cancer susceptibility, emphasizing the need for targeted health education. The study employs a qualitative design, which includes interviews with subject matter experts (SMEs) and surveys of middle-aged women. The goal is to assess awareness, perceived barriers, and preferred learning methods. Findings suggest that with many …

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Analyzing Environmental Health: Air Pollution And Human Health Using Geospatial Data Science, Yanhong Huang

Geography ETDs

Air pollution from industrial emissions and wildfire smoke poses growing threats to public health. This dissertation employs geospatial data science to examine these challenges through three interrelated studies. The first study investigates the relationship between maternal residential exposure to industrial pollutants and low birth weight, identifying five chemicals as significant risk factors. The second study assesses the impact of industrial air pollution on lung and bronchus cancer survival, finding that exposure to 1,1,1-trichloroethane and cobalt is associated with reduced survival. The third study explores disparities in wildfire smoke PM_2.5 exposure and its association with asthma exacerbations. Results show disparities …

Spatial Proximity Of Immune Cell Pairs To Cancer Cells In The Tumor Microenvironment As Biomarkers For Patient Stratification, Jian-Rong Li, Xingxin Pan, Yupei Lin, Yanding Zhao, Yanhong Liu, Yong Li, Christopher I Amos, Chao Cheng

Faculty, Staff and Students Publications

Background/objectives: The tumor microenvironment (TME) plays a critical role in cancer progression by shaping immune responses and influencing patient outcomes. We hypothesized that the relative proximity of specific immune cell pairs to cancer cells within the TME could help predict their pro- or anti-tumor functions and reflect clinically relevant immune dynamics.

Methods: We analyzed imaging mass cytometry (IMC) data from lung adenocarcinoma (LUAD) and triple-negative breast cancer (TNBC) cohorts. For each immune cell pair, we calculated a relative distance (RD) score, which quantifies the spatial difference in proximity to cancer cells. We assessed the prognostic and predictive significance of these …

The Need For Targeted Prevention And Screening In Gastrointestinal Cancer Disparities, Jason C. Lin, John C. Lin, Joy Zhao

Division of Gastroenterology and Hepatology Faculty Papers

No abstract provided.

Cannabis And Covid: An Interrupted Time Series Analysis Of Their Impact On Mental Health-Related Hospital Visits In Rural Arizona (2016-2023), Jennifer Megan Suter

Walden Dissertations and Doctoral Studies

Arizona’s rural communities face shortages in mental health care services. In 2020, the nearly simultaneous onset of the COVID-19 pandemic and the legalization of recreational cannabis introduced overlapping public health stressors that may have disproportionately affected these vulnerable populations. However, their combined impact on rural mental health remains understudied. This three-manuscript dissertation employed interrupted time series analyses to assess how these events influenced monthly hospital visits for schizophrenia, psychosis, cannabis-induced psychosis, and suicidal behaviors among rural Arizona adults aged 21 and older. A total of 16,468 records from the Arizona State Inpatient and Emergency Department Databases (SID and SEDD) met …

Latent Tuberculosis Infection Treatment Adherence Among Black/African American People Living In Houston, Texas, Napoleon Ayibo Wokoma

Walden Dissertations and Doctoral Studies

Early detection and treatment of latent tuberculosis infection (LTBI) is crucial in preventing active TB and supporting elimination efforts. However, adherence to LTBI treatment is challenging, especially among racial and ethnic minorities, with Black/African Americans showing particularly low rates. A quantitative retrospective chart review was conducted to examine sociodemographic factors affecting LTBI adherence in Black/African American individuals and to evaluate various adherence strategies. Using the health belief model, 435 deidentified secondary data records were analyzed from the Houston Health Department from 2012 to 2022, covering both native and foreign-born Black/African American patients undergoing LTBI treatment. Descriptive and inferential statistics, including …

Exploring Sex-Specific Clinical Features In Chinese Patients With Takotsubo Syndrome, Hong-Yi Duan, Ling-Lin Wang, Ling-Chun Lyu, Zhen-Hua Shi, Xiao-Ping Lin, Zheng-Yang Yu, Guo-Hua Fan, Jing Chen, Xin-Hong Yang, Wei Deng, Chang-Wu Xu, Yan Huang, Qing Zhou, Jin-Ling Chen, Hong-Ning Song, Dong Hu, Jin-Qiu Liu, Hector Barajas-Martinez, Charles Antzelevitch, Kan Liu, Hong Jiang, Dan Hu

Department of Medicine Faculty Papers

BACKGROUND: Takotsubo syndrome (TTS) is considered as a transient acute heart failure syndrome predominant in females. However, data on Chinese TTS patients are limited. Therefore, this study aimed to investigate the clinical characteristics of Chinese TTS patients, identify prognostic markers.

METHODS: A total of 258 TTS patients were enrolled. Data on clinical characteristics and in-hospital major adverse cardiovascular events (MACE) were collected and analyzed. The latter included severe heart failure, shock, malignant arrhythmia, and death.

RESULTS: The average age of TTS patients was 59.3 ± 19.2 years. Female patients accounted for 68.9%. MACE occurred in 41.4% of patients during hospitalization. …

Modeling, Analysis, And Prediction Of Covid-19 Dynamics With Interacting Subpopulations And Implicit Behavior Using Physics-Informed Neural Networks, Naima Aubry-Romero, Alonso Ogueda-Oliva, Padmanabhan Seshaiyer

Spora: A Journal of Biomathematics

In this paper, we consider an extended SEIR compartmental model that incorporates young and old interacting subpopulations, allowing for cross-group transmission dynamics. Implicit behavioral changes are included to determine the influence of social behavior on coronavirus transmission dynamics. The basic reproduction number, the average number of secondary cases of infection produced by a single primary case, is derived for both the explicit and implicit model using the next-generation matrix method. We solve the associated differential equation systems and estimate useful parameters in the explicit model using physics-informed neural networks (PINNs). Our results point to how the PINNs approach offers an …

Evaluating Nutrition, Exercise, And Education, On Mental Health And Quality Of Life In Menopause, Jill R. Stone

Walden Dissertations and Doctoral Studies

Physical inactivity, poor nutrition, and a lack of understanding of menopause adversely affect mental health and quality of life in women entering menopause. Metabolic changes during this period can lead to an increase in visceral fat, loss of muscle mass, depression, and anxiety. These metabolic changes are linked to a higher risk for cardiovascular disease, type two diabetes, and metabolic syndrome. This cross-sectional quantitative study, guided by the health belief model, explored the contribution of adopting specific nutrition strategies, exercise, and education in women during menopause transition to help manage mental health (as measured by depression and tension), and quality …

Sociodemographic Influences On Substance Use In Psychosis In An African Cohort, Megan L. Campbell, Raymond Odokonyero, Dickens Akena, Melkam Alemayehu, Lukoye Atwoli, Lori B. Chibnik, Bizu Gelaye, Stella Gichuru, Symon M. Kariuki, Karestan C. Koenen

Internal Medicine, East Africa

Background: Substance use is common among individuals with psychotic disorders, but limited research exists on the variations in substance use across countries in Africa. This study aims to investigate the frequency of alcohol, tobacco, cannabis, and khat consumption in individuals with bipolar disorder (BD) and schizophrenia (SCZ) across four African countries: South Africa, Ethiopia, Kenya, and Uganda.

Methods: We utilized data from the Neuropsychiatric Genetics of African Populations-Psychosis project, a large case-control study which will soon have genetic data on over 42,000 participants, half with psychosis. Information on substance use was collected using the Alcohol, Smoking and Substance Involvement Screening …

Common Sources, Perceived Communication Efficacy And Sufficiency Of Cervical Cancer Information, Education And Communication Materials For Women With Hiv/Aids In Rural Kenya, Scholastic Musakali Adeli, Elkanah Orang'o, Violet Naanyu, Ann Mwangi, Peter Itsura, Barry Rosen, Patrick Loehrer

Obstetrics and Gynaecology, East Africa

Introduction: Cervical cancer is the greatest cause of cancer-related death among women in Sub-Saharan Africa, with particularly high incidence and mortality rates in rural regions. In Kenya, Women with HIV/AIDS (WLHA) are at a greater risk of developing cervical cancer though screening rate remains low. Information, education and communication (IEC) materials play a crucial role in raising awareness, promoting screening, and supporting early detection and treatment; however, little is known about the accessibility, content, and perceived effectiveness of these materials from the perspective of WLHA.

Methods: The objective of this study was to determine the common sources, perceived communication efficacy …

Unsuitable Underlying Cause-Of-Death Statements: Implementation And Evaluation Of A Structured Process For Healthcare Providers Certifying Natural Causes Of Death In An Outpatient Setting, Naida Rutherford

Doctor of Nursing Practice Projects

Background: Accuracy in death certification is crucial for effective public health initiatives. Inaccuracies in unsuitable Underlying Cause-of-Death (UCOD) statements can distort mortality statistics, influence health policy, and impact resource allocation. In South Carolina, changes in the death registration process and the introduction of an unsuitable cause-of-death list highlighted an urgent need for structured training among certifiers to enhance accuracy in death documentation.

Purpose: The purpose of this project was to implement a standardized training program for healthcare providers certifying natural causes of death and assessing the impact of reducing the unsuitable underlying cause-of-death inaccuracies in an outpatient hospice setting.

Project …

Using The Observational Medical Outcomes Partnership Common Data Model For A Multi-Registry Intensive Care Unit Benchmarking Federated Analysis: Lessons Learned, Aasiyah Rashan, Daniel P Püttmann, Nicolette F De Keizer, Dave A Dongelmans, Ronald Cornet, Otavio Ranzani, Wangari Siika, Matthew Smith, Steve Harris, Abi Beane

Anaesthesiology, East Africa

Objective: Federated analysis is a method that allows data analysis to be performed on similar datasets without exchanging any data, thus facilitating international research collaboration while adhering to strict privacy laws. This study aimed to evaluate the feasibility of using federated analysis to benchmark mortality in 2 critical care quality registry databases converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), describing challenges to and recommendations for performing federated analysis on data transformed to OMOP CDM.

Materials and Methods: To identify as many challenges as possible and to be able to complete the benchmarking phase, …

The Effect Of Individual Variation On Epidemiological Dynamics: From Super-Spreaders To Landscapes Of Risk, Alexis Shannon Beagle

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Individuals that make up a population always display some level of variation between members. At the smallest scale, individuals always maintain genetic variation, but variation continues to arise at other scales of biological organization from physiological or immunological variation within individuals to variation in behavior between individuals that can be both genetically and environmentally mediated. The role of individual variation is particularly important in the context of infectious diseases because there is strong evidence that infections are highly heterogenous, indicating that patterns of individual variation drive a significant portion of the epidemiological patterns we observe. Individual variation leads to heterogeneity …

Shared Genetic Architecture Of Posttraumatic Stress Disorder With Cardiovascular Imaging, Risk, And Diagnoses, Jie Shen, Wander Valentim, Eleni Friligkou, Cassie Overstreet, Karmel W. Choi, Dora Koller, Christopher J. O’Donnell, Murray B. Stein, Joel Gelernter, Haitao Lv, Ling Sun, Guido J. Falcone, Renato Polimanti, Gita A. Pathak, Edward Trapido, Ariane Rung, Et Al.

School of Public Health Faculty Publications

Patients with post-traumatic stress disorder face increased cardiovascular risk. This study examines shared genetic regions between post-traumatic stress disorder and 246 cardiovascular conditions across electronic health records, 82 cardiac imaging, and health behaviors defined by Life’s Essential 8. Post-traumatic stress disorder is genetically correlated with cardiovascular diagnoses in 33 regions, imaging traits in 4 regions, and health behaviors in 44 regions. Potentially shared causal variants between post-traumatic stress disorder and 17 cardiovascular conditions were observed in 11 regions. Subsequent observational analysis in AllofUS cohort showed post-traumatic stress disorder is associated with 13 diagnoses even after accounting for socioeconomic factors and …

Functional Time Transformation Model With Applications To Digital Health, Rahul Ghosal Ph.D., Marcos Matabuena, Sujit K. Ghosh

Faculty Publications

The advent of wearable and sensor technologies now leads to functional predictors which are intrinsically infinite dimensional. While the existing approaches for functional data and survival outcomes lean on the well-established Cox model, the proportional hazard (PH) assumption might not always be suitable in real-world applications. Motivated by physiological signals encountered in digital medicine, we develop a more general and flexible functional time-transformation model for estimating the conditional survival function with both functional and scalar covariates. A partially functional regression model is used to directly model the survival time on the covariates through an unknown monotone transformation and …

Validation Of Established Thyroid Ultrasound Volume Norms In A Chernobyl Cohort, Lydia B. Zablotska, Robert J. Mcconnell, Aleksandr V. Rozhko, Patrick O'Kane, Vasilina Yauseyenka, Mark P. Little, Victor Minenko, Vladimir Drozdovitch, Tamara Moskvicheva, Maureen Hatch, Tamara Yeudachkova, Kiyohiko Mabuchi, Elizabeth K. Cahoon

Department of Radiology Faculty Papers

OBJECTIVE: To establish thyroid ultrasound volume norms appropriate for studies of diffuse goiter in a cohort of children and adolescents from an iodine-deficient population exposed to 131I by the Chernobyl fallout.

METHODS: A cohort of 11,970 Belarusians aged ≤18 years at the time of the 1986 Chernobyl accident with individual thyroid radiation dose estimates was screened 10-18 years later. From these, a low-dose subset of 2,392 with no thyroid diseases was selected to construct age- and sex-specific normative values for thyroid ultrasound volume, compared to Belarusian Ministry of Health (MOH) norms and existing WHO and European standards.

RESULTS: Cohort-specific values …

Vitamin D Status And Risk For Breast Cancer In Kenya, Ronald Wasike, Victor Mobegi, Esther Maina, Francis Makokha, Robert Armstrong, Annastacia Mbithi

General Surgery, East Africa

Background: Vitamin D plays a critical role in bone metabolism, calcium regulation, and various cellular processes, including those linked to cancer. The biologically active form, 1,25-dihydroxyvitamin D [1,25(OH)2D], works through the Vitamin D Receptor (VDR). Polymorphisms in VDR and related genes have been associated with cancer risk, including breast cancer. This case-control study aimed to investigate the relationship between serum vitamin D levels and breast cancer among Kenyan women.

Methods: Serum 25-hydroxyvitamin D [25(OH)D] levels were measured in 112 breast cancer patients and 106 controls using electrochemiluminescence.

Results: Results indicated that 81.2% of participants had deficient or insufficient vitamin D …