Medicine and Health Sciences Commons^™

The Impact Of Covid-19 On Hiv Treatment And Research: A Call To Action, Tiffany Chenneville, Kemesha Gabbidon, Patricia Hanson, Cashea Holyfield

USF St. Petersburg campus Faculty Publications

The impact of the COVID-19 pandemic is far reaching, with devastating effects on individuals, communities, and societies across the world. People with chronic health conditions may be at greater risk of contracting or experiencing complications from COVID-19. In addition to illness or death for those who contract the virus, the physical distancing required to flatten the curve of new cases is having a negative impact on the economy, the effects of which intersect with mental health and other existing health concerns, thus affecting marginalized communities. Given that HIV also has a disproportionate impact on marginalized communities, COVID-19 is affecting people …

Role Of Healthcare Providers' Recommendation Style In Hpv Vaccine-Decision-Making Among Haitian Parents And Female Patients, Dudith Pierre-Victor, Dionne P. Stephens, Rachel Clarke, Kemesha Gabbidon, Purnima Madhivanan

USF St. Petersburg campus Faculty Publications

Background and Purpose: The strong influence of physician recommendation on vaccine uptake is well established in the literature. However, its influence on HPV vaccine decision-making among young Haitian women is understudied. This study investigated the role of healthcare providers’ recommendation style in Haitian parents’ and female patients’ HPV vaccine decision-making. Methods: Thirty selfidentified Haitian women aged 17-26 years were recruited from a large university campus in the Southeastern United States (N=30). They completed in-depth face-to-face interviews regarding their HPV vaccine decision-making process. Transcripts were analyzed using thematic analysis. Results: Provider recommendation was cited as a major factor that influenced HPV …

Does Survival Vary For Breast Cancer Patients In The United States? A Study From Six Randomly Selected States, Hafiz Mohammad Rafiqullah Khan, Lisaann S. Gittner, Abhilash Perisetti, Anshul Saxena, Aamrin Rafiq, Kemesha Gabbidon, Sarah Mende, Maria Lyuksyutova

USF St. Petersburg campus Faculty Publications

Background . Breast cancer is the most common cancer in women. Disparities in some characteristics of breast cancer patients and their survival data for six randomly selected states in the US were examined. Materials and Methods . A probability random sampling method was used to select the records of 2,000 patients from each of six randomly selected states. Demographic and disease characteristics were extracted from the Surveillance Epidemiology and End Results (SEER) database. To evaluate relationships between variables, we employed a Cox Proportional Regression to compare survival times in the different states. Results . Iowa had the highest mean age …

Does Breast Cancer Drive The Building Of Survival Probability Models Among States? An Assessment Of Goodness Of Fit For Patient Data From Seer Registries, Hafiz Mohammad Rafiqullah Khan, Anshul Saxena, Abhilash Perisetti, Aamrin Rafiq, Kemesha Gabbidon, Sarah Mende, Maria Lyuksyutova, Kandi Quesada, Summre Blakely, Tiffany Torres, Mahlet Afesse

USF St. Petersburg campus Faculty Publications

Background:

Breast cancer is a worldwide public health concern and is the most prevalent type of cancer in women in the United States. This study concerned the best fit of statistical probability models on the basis of survival times for nine state cancer registries: California, Connecticut, Georgia, Hawaii, Iowa, Michigan, New Mexico, Utah, and Washington.

Materials and Methods:

A probability random sampling method was applied to select and extract records of 2,000 breast cancer patients from the Surveillance Epidemiology and End Results (SEER) database for each of the nine state cancer registries used in this study. EasyFit software was utilized …

Statistical Applications For The Prediction Of White Hispanic Breast Cancer Survival, Hafiz Mohammad Rafiqullah Khan, Anshul Saxena, Kemesha Gabbidon, Elizabeth Ross, Alice Shrestha

USF St. Petersburg campus Faculty Publications

Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the …

Health Disparities Between Black Hispanic And Black Non-Hispanic Cervical Cancer Cases In The Usa, Hafiz Mohammad Rafiqullah Khan, Kemesha Gabbidon, Faheema Abdool-Ghany, Anshul Saxena, Esneider Gomez, Ts Stewart

USF St. Petersburg campus Faculty Publications

Background: Globally, cervical cancer is a major public health concern. Cervical cancer is the second most common cancer among women, resulting in approximately 500,000 cases per year. The purpose of this study is to compare disease characteristics between Black Hispanic (BH) and Black non-Hispanic (BNH) women in the US. Materials and Methods: We used stratified random sampling to select cervical cancer patient records from the SEER database (1973-2009). We used Chi-square and independent samples t-test to examine differences in proportions and means. Results: The sample included 2,000 cervical cancer cases of Black non-Hispanic and 91 Black Hispanic women. There were …

Model-Based Survival Estimates Of Female Breast Cancer Data, Hafiz Mohammad Rafiqullah Khan, Anshul Saxena, Kemesha Gabbidon, Sagar Rana, Nasar Uddin Ahmed

USF St. Petersburg campus Faculty Publications

Background: Statistical methods are very important to precisely measure breast cancer patient survival times for healthcare management. Previous studies considered basic statistics to measure survival times without incorporating statistical modeling strategies. The objective of this study was to develop a data-based statistical probability model from the female breast cancer patients’ survival times by using the Bayesian approach to predict future inferences of survival times. Materials and Methods: A random sample of 500 female patients was selected from the Surveillance Epidemiology and End Results cancer registry database. For goodness of fit, the standard model building criteria were used. The Bayesian approach …

Survival Analysis For White Non-Hispanic Female Breast Cancer Patients, Hafiz Mohammad Rafiqullah Khan, Anshul Saxena, Kemesha Gabbidon, Tiffanie Shauna-Jeanne Stewart, Chintan Bhatt

USF St. Petersburg campus Faculty Publications

Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared …