scholarly journals A Data Analytics Approach for Revealing Influencing Factors of HPV-Related Cancers From Population-Level Statistics Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaoqin Du ◽  
Qi Tan
Keyword(s):  
Risk Factors ◽  
Influencing Factors ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Population Level ◽  
The United States ◽  
Clinical Test ◽  
Infection Pattern ◽  
Level Statistics ◽  
Population Statistic

Human papillomavirus (HPV) is considered as one of the major causes of multiple cancers, including cervical, anal, and vaginal cancers. Some studies analyzed the infection patterns of cancers caused by HPV using individual clinical test data, which is resource and time expensive. In order to facilitate the understanding of cancers caused by HPV, we propose to use data analytics methods to reveal the influencing factors from the population-level statistics data, which is available more easily. Particularly, we demonstrate the effectiveness of data analytics approach by introducing a predictive analytics method in studying the risk factors of cervix cancer in the United States. Besides accurate prediction of the number of infections, the predictive analytics method discovers the population statistic factors that most affect the cervical cancer infection pattern. Furthermore, we discuss the potential directions in developing more advanced data analytics approaches in studying cancers caused by HPV.

scholarly journals Risk behaviours by type of concurrency among young people in three STI clinics in the United States

Sexual Health ◽  
2012 ◽  
Vol 9 (3) ◽  
pp. 280 ◽  
Author(s):  
Kristen L. Hess ◽  
Pamina M. Gorbach ◽  
Lisa E. Manhart ◽  
Bradley P. Stoner ◽  
David H. Martin ◽  
...  
Keyword(s):  
Risk Factors ◽  
African American ◽  
Drug Use ◽  
Population Level ◽  
The United States ◽  
Risk Behaviours ◽  
Men And Women ◽  
Concurrent Sexual Partnerships ◽  
Sexually Transmissible Infections ◽  
African American Race

Background Concurrent sexual partnerships can increase sexually transmissible infections (STI) transmission on a population level. However, different concurrency types may be associated with differential risks for transmission. To investigate this, we describe the prevalence and correlates of four specific concurrency types. Methods: Between 2001 and 2004, 1098 young adults attending three STI clinics were interviewed and tested for STIs. Characteristics associated with concurrency types were identified using logistic regression. Results: Approximately one-third of respondents reported reactive (34%), transitional (36%), compensatory (32%) and experimental (26%) concurrency. Among men, reactive concurrency was associated with not identifying as heterosexual, drug use and having sex the same day as meeting a partner. Among women, reactive concurrency was associated with African-American race and having >3 lifetime partners. Transitional concurrency was associated with >3 lifetime partners for men and women. Among men, compensatory concurrency was associated with African-American race; among women, there were no associations with compensatory concurrency. Among men, experimental concurrency was associated with >3 lifetime partners and having sex the same day as meeting a partner. Among women, experimental concurrency was associated with not identifying as heterosexual, drug use and having sex the same day as meeting a partner. Conclusions: All concurrency types were common in this population and each was associated with a set of demographic and risk factors. Reactive and experimental concurrency types were associated with other high-risk behaviours, such as drug use.

Abstract P559: National Burden & Cardiovascular Risk Factor Profile of Stroke Among Young Adults in The United States

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Javier Valero Elizondo ◽  
Rohan Khera ◽  
Farhaan S Vahidy ◽  
Prachi Dubey ◽  
Haider Warraich ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Young Adults ◽  
Cardiovascular Health ◽  
Population Level ◽  
The United States ◽  
Cvd Risk ◽  
Representative Study ◽  
Insufficient Physical Activity ◽  
History Of

Introduction: Stroke is a leading cause of death and disability worldwide. While most prevalent in elderly, it’s not uncommon in the non-elderly (<65), who also experience many more years of living with disability. In this study, we aimed to describe the scope and CVD determinants of stroke among young (18-44 years) adults in a US representative population. Methods: We analyzed the National Health Interview Survey (2012-2018), a nationally representative study sample. Stroke, as well as CVD risk factors (CRF) [diabetes, hypertension, ever-smoker, insufficient physical activity, obesity and high cholesterol] were self-reported. A CRF profile was then created, with the following categories: “Optimal”, “Average” and “Poor” (0-1, 2-3 & ≥ 4 CRFs, respectively). All analyses took into consideration the survey’s complex design. Results: The 2012-2018 survey population consisted of 224,638 adults ≥ 18 yrs, ≈ 242 million US adults annually. Overall 2.8% (≈ 7 million) reported ever having history of stroke, with 45% noted in the non-elderly (< 65). Among non-elderly, 21% of stroke-history was allocated among the young (18-44 years) adults, translating to nearly 642,810 individuals reporting ever having history of stroke per year. The most common risk factors noted in these patients were insufficient physical activity (56%), current/past smoking (48%), obesity (45%), and hypertension (44%). Overall among the young (<45 years), stroke prevalence was 10-fold higher among those with poor (≈ 3.9 million young adults) vs optimal CRF profile (3.5% vs 0.3%, p < 0.001). Adjusting for demographics, all CVD risk were significantly associated with history of stroke, with participants with poor CRF reporting a 7-fold higher history of stroke (Table). Conclusion: More than half a million adults 18-44 years of age reported a history stroke in US. Individuals with sub-optimal CRF profiles are highly susceptible, and population-level strategies emphasizing cardiovascular health may significantly reduce risk of stroke among young adults in US.

scholarly journals Cardiovascular Disease Risk Factors among White-Collar Workers towards Healthy Communities in Malaysia

2020 ◽  
Vol 9 (4) ◽  
pp. 2118-2124
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Big Data ◽  
Data Analytics ◽  
Disease Risk ◽  
Heart Diseases ◽  
White Collar ◽  
The United States ◽  
White Collar Workers ◽  
Healthy Community

Cardiovascular disease (CVD) is the most common cause of mortality worldwide, including in most Western countries and Asian countries such as Malaysia. Reports by The Department of Statistics Malaysia highlighted that ischaemic heart diseases and cerebrovascular disease, which are a few of CVD, was the principal cause of death in 2016 and 2017. At the same time, big data is a part of Malaysia's fast-growing technology and has grown prominently in the six Malaysian government's public sector clustering which are profiling, social, economy, transportation, education, and also in healthcare. This paper focuses on healthcare big data, which is a prime example of how the three Vs of data, velocity (speed of generation of data), variety, and volume, are an innate aspect of the data it produces. Most healthcare data analytics has been conducted in the United States and Europe, however there were some studies in Canada and very little in Asia. This study will be conducted in Selangor, Malaysia focusing on white-collar workers among the Selangor healthy community. Interviews will be held within medical practitioner or healthcare provider in order to collected information. The information available from the National Cardiovascular Database (NCVD) published reports will be used to conduct the data analysis experiments which will lead towards the identification of CVD risk factors. The results obtained show that data crawling of social media data can be used as a means towards healthcare big data analytics. This will hence aid in the Malaysian healthcare integration process and aid the Malaysian government to provide better healthcare for the overall Malaysian healthy community and society.

Population level risk factors associated with childhood obesity

2019 ◽  
Author(s):  
Claire Beynon ◽  
Nora Pashyan ◽  
Elizabeth Fisher ◽  
Dougal Hargreaves ◽  
Linda Bailey ◽  
...  
Keyword(s):  
Risk Factors ◽  
Childhood Obesity ◽  
Population Level ◽  
Factors Associated

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

Fidelity and Its Relationship to Implementation Effectiveness, Adaptation, and Dissemination

2017 ◽  
Author(s):  
Jennifer D. Allen ◽  
Rachel C. Shelton ◽  
Karen M. Emmons ◽  
Laura A. Linnan
Keyword(s):  
Public Health ◽  
Treatment Strategies ◽  
Implementation Fidelity ◽  
Population Level ◽  
The United States ◽  
Dissemination And Implementation ◽  
Evidence Based ◽  
Health Gains ◽  
Significant Public Health ◽  
Research Continuum

There is substantial variability in the implementation of evidence-based interventions across the United States, which leads to inconsistent access to evidence-based prevention and treatment strategies at a population level. Increased dissemination and implementation of evidence-based interventions could result in significant public health gains. While the availability of evidence-based interventions is increasing, study of implementation, adaptation, and dissemination has only recently gained attention in public health. To date, insufficient attention has been given to the issue of fidelity. Consideration of fidelity is necessary to balance the need for internal and external validity across the research continuum. There is also a need for a more robust literature to increase knowledge about factors that influence fidelity, strategies for maximizing fidelity, methods for measuring and analyzing fidelity, and examining sources of variability in implementation fidelity.

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