scholarly journals Measuring the Outreach Efforts of Public Health Authorities and the Public Response on Facebook During the COVID-19 Pandemic in Early 2020: Cross-Country Comparison (Preprint)

2020 ◽  
Author(s):  
Aravind Sesagiri Raamkumar ◽  
Soon Guan Tan ◽  
Hwee Lin Wee
Keyword(s):  
Public Health ◽  
United States ◽  
Social Media ◽  
Communication Strategies ◽  
The United States ◽  
Media Analysis ◽  
Public Response ◽  
The Public ◽  
Health Authorities ◽  
Public Health Authorities

BACKGROUND The coronavirus disease (COVID-19) pandemic presents one of the most challenging global crises at the dawn of a new decade. Public health authorities (PHAs) are increasingly adopting the use of social media such as Facebook to rapidly communicate and disseminate pandemic response measures to the public. Understanding of communication strategies across different PHAs and examining the public response on the social media landscapes can help improve practices for disseminating information to the public. OBJECTIVE This study aims to examine COVID-19-related outreach efforts of PHAs in Singapore, the United States, and England, and the corresponding public response to these outreach efforts on Facebook. METHODS Posts and comments from the Facebook pages of the Ministry of Health (MOH) in Singapore, the Centers for Disease Control and Prevention (CDC) in the United States, and Public Health England (PHE) in England were extracted from January 1, 2019, to March 18, 2020. Posts published before January 1, 2020, were categorized as pre-COVID-19, while the remaining posts were categorized as peri-COVID-19 posts. COVID-19-related posts were identified and classified into themes. Metrics used for measuring outreach and engagement were frequency, mean posts per day (PPD), mean reactions per post, mean shares per post, and mean comments per post. Responses to the COVID-19 posts were measured using frequency, mean sentiment polarity, positive to negative sentiments ratio (PNSR), and positive to negative emotions ratio (PNER). Toxicity in comments were identified and analyzed using frequency, mean likes per toxic comment, and mean replies per toxic comment. Trend analysis was performed to examine how the metrics varied with key events such as when COVID-19 was declared a pandemic. RESULTS The MOH published more COVID-19 posts (n=271; mean PPD 5.0) compared to the CDC (n=94; mean PPD 2.2) and PHE (n=45; mean PPD 1.4). The mean number of comments per COVID-19 post was highest for the CDC (mean CPP 255.3) compared to the MOH (mean CPP 15.6) and PHE (mean CPP 12.5). Six major themes were identified, with posts about prevention and safety measures and situation updates being prevalent across the three PHAs. The themes of the MOH’s posts were diverse, while the CDC and PHE posts focused on a few themes. Overall, response sentiments for the MOH posts (PNSR 0.94) were more favorable compared to response sentiments for the CDC (PNSR 0.57) and PHE (PNSR 0.55) posts. Toxic comments were rare (0.01%) across all PHAs. CONCLUSIONS PHAs’ extent of Facebook use for outreach purposes during the COVID-19 pandemic varied among the three PHAs, highlighting the strategies and approaches that other PHAs can potentially adopt. Our study showed that social media analysis was capable of providing insights about the communication strategies of PHAs during disease outbreaks.

scholarly journals Use of Health Belief Model–Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study (Preprint)

2020 ◽  
Author(s):  
Aravind Sesagiri Raamkumar ◽  
Soon Guan Tan ◽  
Hwee Lin Wee
Keyword(s):  
Public Health ◽  
Social Media ◽  
Public Perceptions ◽  
Perceived Barriers ◽  
Classification Models ◽  
Perceived Severity ◽  
The Public ◽  
Health Authorities ◽  
Public Health Authorities

BACKGROUND Public health authorities have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions toward such interventions should be identified to enable public health authorities to effectively address valid concerns. The Health Belief Model (HBM) has been used to characterize user-generated content from social media during previous outbreaks, with the aim of understanding the health behaviors of the public. OBJECTIVE This study is aimed at developing and evaluating deep learning–based text classification models for classifying social media content posted during the COVID-19 outbreak, using the four key constructs of the HBM. We will specifically focus on content related to the physical distancing interventions put forth by public health authorities. We intend to test the model with a real-world case study. METHODS The data set for this study was prepared by analyzing Facebook comments that were posted by the public in response to the COVID-19–related posts of three public health authorities: the Ministry of Health of Singapore (MOH), the Centers for Disease Control and Prevention, and Public Health England. The comments made in the context of physical distancing were manually classified with a Yes/No flag for each of the four HBM constructs: perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. Using a curated data set of 16,752 comments, gated recurrent unit–based recurrent neural network models were trained and validated for text classification. Accuracy and binary cross-entropy loss were used to evaluate the model. Specificity, sensitivity, and balanced accuracy were used to evaluate the classification results in the MOH case study. RESULTS The HBM text classification models achieved mean accuracy rates of 0.92, 0.95, 0.91, and 0.94 for the constructs of perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, respectively. In the case study with MOH Facebook comments, specificity was above 96% for all HBM constructs. Sensitivity was 94.3% and 90.9% for perceived severity and perceived benefits, respectively. In addition, sensitivity was 79.6% and 81.5% for perceived susceptibility and perceived barriers, respectively. The classification models were able to accurately predict trends in the prevalence of the constructs for the time period examined in the case study. CONCLUSIONS The deep learning–based text classifiers developed in this study help to determine public perceptions toward physical distancing, using the four key constructs of HBM. Health officials can make use of the classification model to characterize the health behaviors of the public through the lens of social media. In future studies, we intend to extend the model to study public perceptions of other important interventions by public health authorities.

scholarly journals Vaping Restrictions: Is Priority to the Young Justified?

2020 ◽  
Author(s):  
Monica Magalhaes
Keyword(s):  
Public Health ◽  
United States ◽  
At Risk ◽  
Young People ◽  
Harm Reduction ◽  
Ethical Dilemma ◽  
The United States ◽  
Youth Smoking ◽  
Health Authorities ◽  
Public Health Authorities

Abstract The vast majority of smokers become dependent on nicotine in youth. Preventing dependence has therefore been crucial to the recent decline in youth smoking. The advent of vaping creates an opportunity for harm reduction to existing smokers (mostly adults) but simultaneously also undermines prevention efforts by becoming a new vehicle for young people to become dependent on nicotine, creating an ethical dilemma. Restrictions to access to some vaping products enacted in response to the increase in vaping among youth observed in the United States since 2018 have arguably prioritized prevention of new cases of dependence—protecting the young—over harm reduction to already dependent adults. Can this prioritization of the young be justified? This article surveys the main bioethical arguments for prioritizing giving health benefits to the young and finds that none can justify prioritizing dependence prevention over harm reduction: any reasons for prioritizing the current cohort of young people at risk from vaping will equally apply to current adult smokers, who are overwhelmingly likely to have become nicotine-dependent in their own youth. Public health authorities’ current tendency to prioritize the young, therefore, does not seem to be ethically justified. Implications This article argues that commonsense reasons for prioritizing the young do not apply to the ethical dilemma surrounding restricting access to vaping products.

scholarly journals Syndromic Surveillance Practice in the United States 2014: Results from a Nationwide Survey

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Tera Reynolds ◽  
Scott Gordon ◽  
Paula Soper ◽  
James Buehler ◽  
Richard Hopkins ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Capacity Building ◽  
Syndromic Surveillance ◽  
Nationwide Survey ◽  
The United States ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Surveillance Practice

Presentation of the results of a nationwide survey designed to assess the syndromic surveillance practices and capacity-building assistance needs of state and territorial public health authorities in the United States.

scholarly journals Risk factors for death and illness in dogs imported into the United States, 2010--2018

2021 ◽  
Author(s):  
Emily Pieracci ◽  
Brian Maskery ◽  
Kendra Stauffer ◽  
Alida Gertz ◽  
Clive Brown
Keyword(s):  
Public Health ◽  
United States ◽  
Well Being ◽  
The United States ◽  
Emerging Diseases ◽  
International Travel ◽  
Morbidity And Mortality ◽  
Factors Associated ◽  
Health Authorities ◽  
Public Health Authorities

CDC estimates 1 million dogs are imported into the United States annually. With the movement of large numbers of animals into the United States the risk of disease importation is a concern, especially for emerging diseases. Dogs that arrive to the United States ill or dead are investigated by public health authorities to ensure dogs are not infected with diseases of concern (such as rabies). We identified factors associated with illness and death in imported dogs and estimated the initial investigation cost to public health authorities. Dog importation data from the CDC’s Quarantine Activity Reporting System were reviewed from 2010–2018. The date of entry, country of origin, port of entry, transportation method, and breed were extracted to examine factors associated with illness and death in dogs during international travel. Costs for public health investigations were estimated from data collected by the Bureau of Labor Statistics and Office of Personal Management. Death or illness was more likely to occur in brachycephalic breeds (aOR=3.88, 95%CI 2.74–5.51). Transportation of dogs via cargo (aOR=2.41, 95%CI 1.57–3.70) or as checked baggage (aOR=5.74, 95%CI 3.65–9.03) were also associated with death or illness. On average, 19 dog illnesses or deaths were reported annually from 2010–2018. The estimated annual cost to public health authorities to conduct initial public health assessments ranged from $2,071–$104,648. Current regulations do not provide adequate resources or mechanisms to monitor the rates of morbidity and mortality of imported dogs. There are growing attempts to assess animal welfare and communicable disease importation risks; however, responsibility for dogs’ health and well-being is overseen by multiple agencies. A joint federal agency approach to identify interventions that reduce dog morbidity and mortality during flights while continuing to protect U.S. borders from public health and foreign animal disease threats could be beneficial.

Portion Sizes of Ultra-Processed Foods in the United States, 2002 to 2021

2021 ◽  
Vol 111 (12) ◽  
pp. 2223-2226
Author(s):  
Lisa R. Young ◽  
Marion Nestle
Keyword(s):  
Public Health ◽  
United States ◽  
The United States ◽  
Portion Size ◽  
Processed Foods ◽  
Fast Foods ◽  
Health Authorities ◽  
The Us ◽  
Portion Sizes ◽  
Public Health Authorities

Objectives. To assess the US food industry’s response to calls from public health authorities to reduce portion sizes by comparing current with past sizes of selected examples of single-serve ultra-processed packaged and fast foods. Methods. We obtained manufacturers’ information about current portion sizes and compared it with sizes when first introduced and in 2002. Results. Few companies in our sample reduced portion sizes since 2002; all still sold portions of ultra-processed foods in up to 5-times-larger sizes than when first introduced. Conclusions. Policies and practices focused on reducing portion size could help discourage the consumption of excessive amounts of ultra-processed foods. (Am J Public Health. 2021;111(12):2223–2226. https://doi.org/10.2105/AJPH.2021.306513 )

scholarly journals Use of Health Belief Model–Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study

10.2196/20493 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e20493 ◽  
Author(s):  
Aravind Sesagiri Raamkumar ◽  
Soon Guan Tan ◽  
Hwee Lin Wee
Keyword(s):  
Public Health ◽  
Social Media ◽  
Public Perceptions ◽  
Perceived Barriers ◽  
Classification Models ◽  
Perceived Severity ◽  
The Public ◽  
Health Authorities ◽  
Public Health Authorities

Background Public health authorities have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions toward such interventions should be identified to enable public health authorities to effectively address valid concerns. The Health Belief Model (HBM) has been used to characterize user-generated content from social media during previous outbreaks, with the aim of understanding the health behaviors of the public. Objective This study is aimed at developing and evaluating deep learning–based text classification models for classifying social media content posted during the COVID-19 outbreak, using the four key constructs of the HBM. We will specifically focus on content related to the physical distancing interventions put forth by public health authorities. We intend to test the model with a real-world case study. Methods The data set for this study was prepared by analyzing Facebook comments that were posted by the public in response to the COVID-19–related posts of three public health authorities: the Ministry of Health of Singapore (MOH), the Centers for Disease Control and Prevention, and Public Health England. The comments made in the context of physical distancing were manually classified with a Yes/No flag for each of the four HBM constructs: perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. Using a curated data set of 16,752 comments, gated recurrent unit–based recurrent neural network models were trained and validated for text classification. Accuracy and binary cross-entropy loss were used to evaluate the model. Specificity, sensitivity, and balanced accuracy were used to evaluate the classification results in the MOH case study. Results The HBM text classification models achieved mean accuracy rates of 0.92, 0.95, 0.91, and 0.94 for the constructs of perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, respectively. In the case study with MOH Facebook comments, specificity was above 96% for all HBM constructs. Sensitivity was 94.3% and 90.9% for perceived severity and perceived benefits, respectively. In addition, sensitivity was 79.6% and 81.5% for perceived susceptibility and perceived barriers, respectively. The classification models were able to accurately predict trends in the prevalence of the constructs for the time period examined in the case study. Conclusions The deep learning–based text classifiers developed in this study help to determine public perceptions toward physical distancing, using the four key constructs of HBM. Health officials can make use of the classification model to characterize the health behaviors of the public through the lens of social media. In future studies, we intend to extend the model to study public perceptions of other important interventions by public health authorities.

COVID-19 Misinformation and Toxicological Consequences (Preprint)

2020 ◽  
Author(s):  
Mohammad Amrollahi-Sharifabadi
Keyword(s):  
Public Health ◽  
Social Media ◽  
Accurate Information ◽  
The Public ◽  
Corona Virus ◽  
Health Authorities ◽  
The World ◽  
Prevention And Treatment ◽  
Public Health Authorities ◽  
New Strategies

UNSTRUCTURED The new Corona virus pandemic alarmed the world. Misinformation regarding prevention and treatment for safeguarding against this pandemic seemed to be more contagious and hazardous than the Corona virus. Public health authorities in the world tried to battle this virtual virus by offering true information and correcting misinformation. However, the public misinformation through social media caused toxicological consequences in some parts of the world which provoked awareness, response, and concern of the public health authorities including the FDA and toxicology community. On the other hand, finding new strategies for the prevention and treatment of the corona virus again stress the roles of toxicology, infodemiology, and social media. Hundreds of chemicals are being tested to be prophylactic medications or healing drugs for the corona virus. Therefore, spread accurate information and edit misinformation will be crucial. Conclusively, toxicology education to the public is a necessity and conducting more toxicological infodemiology studies recommended.

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