scholarly journals Russian Twitter Accounts and the Partisan Polarization of Vaccine Discourse, 2015–2017

2020 ◽  
Vol 110 (5) ◽  
pp. 718-724 ◽  
Author(s):  
Dror Walter ◽  
Yotam Ophir ◽  
Kathleen Hall Jamieson
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Health Disparities ◽  
Network Analysis ◽  
Internet Research ◽  
Unsupervised Machine Learning ◽  
Health Implications ◽  
Vaccination Rates ◽  
Partisan Polarization ◽  
The Us

Objectives. To understand how Twitter accounts operated by the Russian Internet Research Agency (IRA) discussed vaccines to increase the credibility of their manufactured personas. Methods. We analyzed 2.82 million tweets published by 2689 IRA accounts between 2015 and 2017. Combining unsupervised machine learning and network analysis to identify “thematic personas” (i.e., accounts that consistently share the same topics), we analyzed the ways in which each discussed vaccines. Results. We found differences in volume and valence of vaccine-related tweets among 9 thematic personas. Pro-Trump personas were more likely to express antivaccine sentiment. Anti-Trump personas expressed support for vaccination. Others offered a balanced valence, talked about vaccines neutrally, or did not tweet about vaccines. Conclusions. IRA-operated accounts discussed vaccines in manners consistent with fabricated US identities. Public Health Implications. IRA accounts discussed vaccines online in ways that evoked political identities. This could exacerbate recently emerging partisan gaps relating to vaccine misinformation, as differently valenced messages were targeted at different segments of the US public. These sophisticated targeting efforts, if repeated and increased in reach, could reduce vaccination rates and magnify health disparities.

A Sociogenomic World

2018 ◽  
Author(s):  
Catherine Bliss
Keyword(s):  
Public Health ◽  
Health Disparities ◽  
Social Factors ◽  
Paradigm Shift ◽  
Large Scale ◽  
Minority Recruitment ◽  
The Us ◽  
Social Domains ◽  
Genome Projects ◽  
Genomic Sciences

This chapter discusses a paradigm shift in the genomic sciences wherein scientists have gone from ignoring race to studying it. It argues that the field has adopted a sociogenomic approach to race, in which scientists understand race as a muddled mix of genetic and social factors. Scientists responsible for seminal genome projects, who have faced pressure from the US public health establishment and an array of experts on race, now prioritize race-targeted research, minority recruitment, and analysis of genomic health disparities. As a result large-scale sequencing projects, pharmaceuticals, and postgenomic research have become ever more racialized, while race has taken on an irrevocably genomic imprimatur. This paradigm shift has occurred because of changes across a number of powerful social domains of expertise within science, medicine, and policy. This chapter thus draws upon events taking place in a variety of institutional, regulatory, and normative contexts.

Diagnosing malaria from some symptoms: a machine learning approach and public health implications

2020 ◽  
Author(s):  
Hilary I. Okagbue ◽  
Pelumi E. Oguntunde ◽  
Emmanuela C. M. Obasi ◽  
Patience I. Adamu ◽  
Abiodun A. Opanuga
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Learning Approach ◽  
Health Implications ◽  
Machine Learning Approach

scholarly journals Public Health Implications of Vaping in the US: The Smoking and Vaping Simulation Model

2020 ◽  
Author(s):  
David T Levy ◽  
Jamie Tam ◽  
Luz Maria María Sanchez-Romero ◽  
Yameng Li ◽  
Zhe Yuan ◽  
...  
Keyword(s):  
Public Health ◽  
Smoking Cessation ◽  
Smoking Initiation ◽  
Health Impact ◽  
Health Implications ◽  
Relative Risks ◽  
Life Years ◽  
Wide Range ◽  
The Us ◽  
High Degree

Abstract Background Nicotine vaping products (NVPs) are increasingly popular worldwide. They may provide public health benefits if used as a substitute for smoking, but may create public health harms if used as a gateway to smoking or to discourage smoking cessation. This paper presents the Smoking and Vaping Model (SAVM), which estimates the public health implications of NVPs in the US. Methods SAVM adopts a cohort-approach and is available on an Excel platform. We derive public health implications by comparing smoking- and NVP-attributable deaths under a No-NVP and an NVP Scenario. The No-NVP Scenario projects current, former and never smoking rates via smoking initiation and cessation rates, and incorporates excess risks of smoking. The NVP Scenario allows for NVP relative excess risks, switching from cigarette to NVP use, separate NVP and smoking initiation rates, and separate NVP and smoking cessation rates. The model is validated against recent US survey data on smoking and vaping prevalence. Results The SAVM projects that under current patterns of US NVP use and substitution, NVP use will translate into 5.8 million premature smoking- and vaping-attributable deaths avoided and 96.4 million life years gained between 2013 and 2100. Sensitivity analysis shows that parameters for NVP relative risks, NVP-related switching and smoking cessation are particularly influential in gauging public health impacts. Discussion The SAVM shows potential benefits of e-cigarette use over a wide range of parameters. However, there is a high degree of uncertainty regarding key parameters. Policymakers, researchers and other public health stakeholders can apply the SAVM to estimate the potential public health impact of NVPs in their country or region using their own data sources.

Predictive Modeling of Vaccination Uptake in U.S. Counties: A Machine Learning-based Approach (Preprint)

2021 ◽  
Author(s):  
Queena Cheong ◽  
Martin Au-yeung ◽  
Stephanie Quon ◽  
Katsy Concepcion ◽  
Jude Dzevela Kong
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Predictive Modelling ◽  
Sociodemographic Factors ◽  
Vaccine Uptake ◽  
Vaccination Rates ◽  
Vaccination Uptake ◽  
Sociodemographic Data ◽  
Public Health Officials ◽  
The U.S

BACKGROUND While the COVID-19 pandemic has left an unprecedented impact globally, countries such as the United States of America have reported the most significant incidence of COVID-19 cases worldwide. Within the U.S., various sociodemographic factors have played an essential role in the creation of regional disparities. Regional disparities have resulted in the unequal spread of disease between U.S. counties, underscoring the need for efficient and accurate predictive modelling strategies to inform public health officials and reduce the burden on healthcare systems. Furthermore, despite the widespread accessibility of COVID-19 vaccines across the U.S., vaccination rates have become stagnant, necessitating predictive modelling to identify important factors impacting vaccination uptake. OBJECTIVE To determine the association between sociodemographic factors and vaccine uptake across counties in the U.S. METHODS Sociodemographic data on fully vaccinated and unvaccinated individuals were sourced from several online databases, such as the U.S. Centre for Disease Control and U.S. Census Bureau COVID-19 Site. Machine learning analysis was performed using XGBoost and sociodemographic data. RESULTS Our model predicted COVID-19 vaccination uptake across U.S. countries with 59% accuracy. In addition, it identified location, education, ethnicity, and income as the most critical sociodemographic features in predicting vaccination uptake in U.S. counties. Lastly, the model produced a choropleth demonstrating areas of low and high vaccination rates, which can be used by healthcare authorities in future pandemics to visualize and prioritize areas of low vaccination and design targeted vaccination campaigns. CONCLUSIONS Our study reveals that sociodemographic characteristics are predictors of vaccine uptake rate across counties in the U.S. and if leveraged appropriately can assist policy makers and public health officials to understand vaccine uptake rates and craft policies to improve them.

scholarly journals Public Health Implications of Vaping in The US: The Smoking and Vaping Simulation Model

2021 ◽  
Author(s):  
David T Levy ◽  
Jamie Tam ◽  
Luz Maria Sanchez-Romero ◽  
Yameng Li ◽  
Zhe Yuan ◽  
...  
Keyword(s):  
Public Health ◽  
Sensitivity Analysis ◽  
Smoking Cessation ◽  
Relative Risk ◽  
Smoking Initiation ◽  
Mortality Rates ◽  
Base Case ◽  
Health Implications ◽  
The Public ◽  
The Us

Abstract Background: Nicotine vaping products (NVPs) are increasingly popular worldwide. They may provide public health benefits if used as a substitute for smoking, but may create public health harms if used as a gateway to smoking or to discourage smoking cessation. This paper presents the Smoking and Vaping Model (SAVM), a user-friendly model which estimates the public health implications of NVPs in the US. Methods: SAVM adopts a cohort-approach. We derive public health implications by comparing smoking- and NVP-attributable deaths and life-year lost under a No-NVP and an NVP Scenario. The No-NVP Scenario projects current, former and never smoking rates via smoking initiation and cessation rates, with their respective mortality rates. The NVP Scenario allows for smoking- and NVP-specific mortality rates, switching from cigarette to NVP use, separate NVP and smoking initiation rates, and separate NVP and smoking cessation rates. After validating the model against recent US survey data, we present the base model with extensive sensitivity analyses.Results: The SAVM projects that under current patterns of US NVP use and substitution, NVP use will translate into 1.8 million premature smoking- and vaping-attributable deaths avoided and 38.9 million life-years gained between 2013 and 2060. When the NVP relative risk is set to 5%, the results are sensitive to the level of switching and smoking cessation rates and to a lesser extent smoking initiation rates. When the NVP relative risk is raised to 40%, the public health gains in terms of averted deaths and LYL are reduced by 42% in the base case, and the results become much more sensitive to variations in the base case parameters.Discussion: Policymakers, researchers and other public health stakeholders can apply the SAVM to estimate the potential public health impact of NVPs in their country or region using their own data sources. In developing new simulation models involving NVPs, it will be important to conduct extensive sensitivity analysis and continually update and validate with new data. Conclusion: The SAVM indicates potential benefits of NVP use. However, given the uncertainty surrounding model parameters, extensive sensitivity analysis becomes particularly important.

scholarly journals Delayed Interventions, Low Compliance, and Health Disparities Amplified the Early Spread of COVID-19

2020 ◽  
Author(s):  
Aliea M. Jalali ◽  
Sumaia G. Khoury ◽  
JongWon See ◽  
Alexis M. Gulsvig ◽  
Brent M. Peterson ◽  
...  
Keyword(s):  
Public Health ◽  
Health Disparities ◽  
State Level ◽  
The United States ◽  
Health Interventions ◽  
Systematic Evaluation ◽  
Public Health Interventions ◽  
Multivariate Statistical ◽  
The Us ◽  
At Home

AbstractThe United States (US) public health interventions were rigorous and rapid, yet failed to arrest the spread of the Coronavirus Disease 2019 (COVID-19) pandemic as infections spread throughout the US. Many factors have contributed to the spread of COVID-19, and the success of public health interventions depends on the level of community adherence to preventative measures. Public health professionals must also understand regional demographic variation in health disparities and determinants to target interventions more effectively. In this study, a systematic evaluation of three significant interventions employed in the US, and their effectiveness in slowing the early spread of COVID-19 was conducted. Next, community-level compliance with a state-level stay at home orders was assessed to determine COVID-19 spread behavior. Finally, health disparities that may have contributed to the disproportionate acceleration of early COVID-19 spread between certain counties were characterized. The contribution of these factors for the disproportionate spread of the disease was analyzed using both univariate and multivariate statistical analyses. Results of this investigation show that delayed implementation of public health interventions, a low level of compliance with the stay at home orders, in conjunction with health disparities, significantly contributed to the early spread of the COVID-19 pandemic.

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