scholarly journals Presenting or Spinning Facts? Deconstructing the U.S. Centers for Disease Control Statement on the Importance of Reopening Schools Under COVID-19

2021 ◽  
Vol 9 ◽  
Author(s):  
Habib Benzian ◽  
Marilyn Johnston ◽  
Nicole Stauf ◽  
Richard Niederman
Keyword(s):  
Public Health ◽  
Disease Control ◽  
Public Perception ◽  
Political Influence ◽  
Analysis Tool ◽  
Word Choice ◽  
The Public ◽  
Control And Prevention ◽  
Centers For Disease Control ◽  
The U.S

Credible, reliable and consistent information to the public, as well as health professionals and decision makers, is crucial to help navigate uncertainty and risk in times of crisis and concern. Traditionally, information and health communications issued by respected and established government agencies have been regarded as factual, unbiased and credible. The U.S. Centers for Disease Control and Prevention (CDC) is such an agency that addresses all aspects of health and public health on behalf of the U.S Government for the benefit of its citizens. In July 2020, the CDC issued guidelines on reopening schools which resulted in open criticism by the U.S. President and others, prompting a review and publication of revised guidelines together with a special “Statement on the Importance of Reopening Schools under COVID-19.” We hypothesize that this statement introduced bias with the intention to shift the public perception and media narrative in favor of reopening of schools. Using a mixed methods approach, including an online text analysis tool, we demonstrate that document title and structure, word frequencies, word choice, and website presentation did not provide a balanced account of the complexity and uncertainty surrounding school reopening during the COVID-19 pandemic. Despite available scientific guidance and practical evidence-based advice on how to manage infection risks when reopening schools, the CDC Statement was intentionally overriding possible parent and public health concerns. The CDC Statement provides an example of how political influence is exercised over the presentation of science in the context of a major pandemic. It was withdrawn by the CDC in November 2020.

scholarly journals Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study

10.2196/25108 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e25108
Author(s):  
Joanne Chen Lyu ◽  
Garving K Luli
Keyword(s):  
Public Health ◽  
Disease Control ◽  
The United States ◽  
Data Set ◽  
Public Discussion ◽  
The Public ◽  
Twitter Data ◽  
The World ◽  
Control And Prevention ◽  
Centers For Disease Control

Background The Centers for Disease Control and Prevention (CDC) is a national public health protection agency in the United States. With the escalating impact of the COVID-19 pandemic on society in the United States and around the world, the CDC has become one of the focal points of public discussion. Objective This study aims to identify the topics and their overarching themes emerging from the public COVID-19-related discussion about the CDC on Twitter and to further provide insight into public's concerns, focus of attention, perception of the CDC's current performance, and expectations from the CDC. Methods Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, when the World Health Organization declared COVID-19 a pandemic, to August 14, 2020. We used R (The R Foundation) to clean the tweets and retain tweets that contained any of five specific keywords—cdc, CDC, centers for disease control and prevention, CDCgov, and cdcgov—while eliminating all 91 tweets posted by the CDC itself. The final data set included in the analysis consisted of 290,764 unique tweets from 152,314 different users. We used R to perform the latent Dirichlet allocation algorithm for topic modeling. Results The Twitter data generated 16 topics that the public linked to the CDC when they talked about COVID-19. Among the topics, the most discussed was COVID-19 death counts, accounting for 12.16% (n=35,347) of the total 290,764 tweets in the analysis, followed by general opinions about the credibility of the CDC and other authorities and the CDC's COVID-19 guidelines, with over 20,000 tweets for each. The 16 topics fell into four overarching themes: knowing the virus and the situation, policy and government actions, response guidelines, and general opinion about credibility. Conclusions Social media platforms, such as Twitter, provide valuable databases for public opinion. In a protracted pandemic, such as COVID-19, quickly and efficiently identifying the topics within the public discussion on Twitter would help public health agencies improve the next-round communication with the public.

scholarly journals Improving Probabilistic Infectious Disease Forecasting Through Coherence

2019 ◽  
Author(s):  
Graham Casey Gibson ◽  
Kelly R. Moran ◽  
Nicholas G. Reich ◽  
Dave Osthus
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Disease Control ◽  
The United States ◽  
Weighted Sum ◽  
Public Health Burden ◽  
Control And Prevention ◽  
Centers For Disease Control ◽  
The U.S ◽  
Geographical Units

AbstractWith an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 90% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system’s geographical hierarchy.Author SummarySeasonal influenza causes a significant public health burden nationwide. Accurate influenza forecasting may help public health officials allocate resources and plan responses to emerging outbreaks. The U.S. Centers for Disease Control and Prevention (CDC) reports influenza data at multiple geographical units, including regionally and nationally, where the national data are by construction a weighted sum of the regional data. In an effort to improve influenza forecast accuracy across all models submitted to the CDC’s annual flu forecasting challenge, we examined the effect of imposing this geographical constraint on the set of independent forecasts, made publicly available by the CDC. We developed a novel method to transform forecast densities to obey the geographical constraint that respects the correlation structure between geographical units. This method showed consistent improvement across 90% of models and that held when stratified by targets and test seasons. Our method can be applied to other forecasting systems both within and outside an infectious disease context that have a geographical hierarchy.

scholarly journals Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Weiqin Cai ◽  
Chengyue Li ◽  
Mei Sun ◽  
Mo Hao
Keyword(s):  
Public Health ◽  
Disease Control ◽  
Gini Coefficient ◽  
Health Workforce ◽  
Public Health Workforce ◽  
County Level ◽  
The Public ◽  
Control And Prevention ◽  
Centers For Disease Control ◽  
Geographical Units

Abstract Background The public health workforce (PHW) is a key component of a country’s public health system. Since the outbreak of SARS (severe acute respiratory syndrome) in 2003, the scale of PHW in China has been continuously expanding, but policymakers and researchers still focus on the distribution of public health personnel, especially the regional inequality in such distribution. We aimed to identify the root cause of PHW inequality by decomposing different geographical units in China. Methods This study was based on data from a nationwide survey, which included 2712 county-level data. The distribution of the PHW in geographical units was evaluated by the Gini coefficient and Theil T index, and inequalities at regional, provincial, and municipal levels were decomposed to identify the root causes of inequalities in the PHW. Additionally, the contextual factors affecting the distribution of the PHW were determined through regression analysis. Results The overall inequality results show that health professional and field epidemiological investigators faced worse inequality than the staff. In particular, field epidemiological investigators had a Gini coefficient close to 0.4. Step decomposition showed that within-region inequalities accounted for 98.5% or more of overall inter-county inequality in the distribution of all PHW categories; provincial decomposition showed that at least 74% of inequality is still distributed within provinces; the overall contribution of within-municipal inequality and between-municipal inequality was basically the same. Further, the contextual factor that influenced between-municipality and within-municipality inequality for all three categories of PHWs was the agency building area per employee. Per capita GDP had a similar effect, except for between-municipality inequality of professionals and within-municipality inequality of field epidemiological investigators. Conclusions The successive decomposition showed that inequality is mainly concentrated in counties at the within-province and within-municipal levels. This study clearly suggests that the government, especially the municipal government at the provincial level, should increase financial investment in Centers for Disease Control and Prevention (CDCs) with worse resource allocation in their jurisdiction through various ways of compensation and incentives, enhance their infrastructure, and improve the salary of personnel in these institutions, to attract more public health professionals to these institutions.

scholarly journals Careers in Virology: Working at the Centers for Disease Control and Prevention

2017 ◽  
Vol 91 (9) ◽  
Author(s):  
Jennifer L. Konopka-Anstadt ◽  
Cara C. Burns
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Disease Control ◽  
Public Health Research ◽  
Early Career ◽  
Health Needs ◽  
Research Career ◽  
The Public ◽  
Control And Prevention ◽  
Centers For Disease Control

ABSTRACT As nonacademic careers in science have become less and less “alternative,” one field that has consistently attracted early-career virologists is public health research. The desire to make tangible contributions to public health needs and better protect the public from infectious disease often motivates the transition. In this career-related Gem, two academically trained virologists offer insights into pursuing a research career in public health at the Centers for Disease Control and Prevention.

scholarly journals Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study (Preprint)

2020 ◽  
Author(s):  
Joanne Chen Lyu ◽  
Garving K Luli
Keyword(s):  
Public Health ◽  
Disease Control ◽  
The United States ◽  
Data Set ◽  
Public Discussion ◽  
The Public ◽  
Twitter Data ◽  
The World ◽  
Control And Prevention ◽  
Centers For Disease Control

BACKGROUND The Centers for Disease Control and Prevention (CDC) is a national public health protection agency in the United States. With the escalating impact of the COVID-19 pandemic on society in the United States and around the world, the CDC has become one of the focal points of public discussion. OBJECTIVE This study aims to identify the topics and their overarching themes emerging from the public COVID-19-related discussion about the CDC on Twitter and to further provide insight into public's concerns, focus of attention, perception of the CDC's current performance, and expectations from the CDC. METHODS Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, when the World Health Organization declared COVID-19 a pandemic, to August 14, 2020. We used R (The R Foundation) to clean the tweets and retain tweets that contained any of five specific keywords—cdc, CDC, centers for disease control and prevention, CDCgov, and cdcgov—while eliminating all 91 tweets posted by the CDC itself. The final data set included in the analysis consisted of 290,764 unique tweets from 152,314 different users. We used R to perform the latent Dirichlet allocation algorithm for topic modeling. RESULTS The Twitter data generated 16 topics that the public linked to the CDC when they talked about COVID-19. Among the topics, the most discussed was COVID-19 death counts, accounting for 12.16% (n=35,347) of the total 290,764 tweets in the analysis, followed by general opinions about the credibility of the CDC and other authorities and the CDC's COVID-19 guidelines, with over 20,000 tweets for each. The 16 topics fell into four overarching themes: knowing the virus and the situation, policy and government actions, response guidelines, and general opinion about credibility. CONCLUSIONS Social media platforms, such as Twitter, provide valuable databases for public opinion. In a protracted pandemic, such as COVID-19, quickly and efficiently identifying the topics within the public discussion on Twitter would help public health agencies improve the next-round communication with the public.

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