scholarly journals The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

2021 ◽  
Vol 118 (51) ◽  
pp. e2111454118 ◽  
Author(s):  
Joshua A. Salomon ◽  
Alex Reinhart ◽  
Alyssa Bilinski ◽  
Eu Jing Chua ◽  
Wichada La Motte-Kerr ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Large Scale ◽  
Survey Methods ◽  
Health Priorities ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
Essential Information ◽  
Public Health Reporting ◽  
The Us

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey—over 20 million responses in its first year of operation—allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

scholarly journals The U.S. COVID-19 Trends and Impact Survey, 2020-2021: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing and vaccination

2021 ◽  
Author(s):  
Joshua A Salomon ◽  
Alex Reinhart ◽  
Alyssa Bilinski ◽  
Eu Jing Chua ◽  
Wichida La Motte-Kerr ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Large Scale ◽  
Survey Methods ◽  
Health Priorities ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
Essential Information ◽  
Public Health Reporting ◽  
The U.S

The U.S. COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, Internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey -- over 20 million responses in its first year of operation -- allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

scholarly journals Public Health Workforce Burnout in the COVID-19 Response in the U.S.

2021 ◽  
Vol 18 (8) ◽  
pp. 4369
Author(s):  
Kahler W. Stone ◽  
Kristina W. Kintziger ◽  
Meredith A. Jagger ◽  
Jennifer A. Horney
Keyword(s):  
Public Health ◽  
Health Workforce ◽  
Work Experience ◽  
Large Scale ◽  
Health Workers ◽  
Public Health Workforce ◽  
Cross Sectional Survey ◽  
Public Health Response ◽  
Cross Sectional ◽  
The U.S

While the health impacts of the COVID-19 pandemic on frontline health care workers have been well described, the effects of the COVID-19 response on the U.S. public health workforce, which has been impacted by the prolonged public health response to the pandemic, has not been adequately characterized. A cross-sectional survey of public health professionals was conducted to assess mental and physical health, risk and protective factors for burnout, and short- and long-term career decisions during the pandemic response. The survey was completed online using the Qualtrics survey platform. Descriptive statistics and prevalence ratios (95% confidence intervals) were calculated. Among responses received from 23 August and 11 September 2020, 66.2% of public health workers reported burnout. Those with more work experience (1–4 vs. <1 years: prevalence ratio (PR) = 1.90, 95% confidence interval (CI) = 1.08−3.36; 5–9 vs. <1 years: PR = 1.89, CI = 1.07−3.34) or working in academic settings (vs. practice: PR = 1.31, CI = 1.08–1.58) were most likely to report burnout. As of September 2020, 23.6% fewer respondents planned to remain in the U.S. public health workforce for three or more years compared to their retrospectively reported January 2020 plans. A large-scale public health emergency response places unsustainable burdens on an already underfunded and understaffed public health workforce. Pandemic-related burnout threatens the U.S. public health workforce’s future when many challenges related to the ongoing COVID-19 response remain unaddressed.

scholarly journals The Necessity of Mobile Phone Technologies for Public Health Surveillance in Benin

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Yaovi M. G. Hounmanou ◽  
Murielle S. S. Agonsanou ◽  
Victorien Dougnon ◽  
Mahougnon H. B. Vodougnon ◽  
Ephraim M. Achoh ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Smart Phone ◽  
Cross Sectional Study ◽  
Health Surveillance ◽  
Medical Professionals ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
The Public ◽  
Phone Calls

A cross-sectional study was conducted in March 2016 to assess the need of mobile phone technologies for health surveillance and interventions in Benin. Questionnaires were administered to 130 individuals comprising 25 medical professionals, 33 veterinarians, and 72 respondents from the public. All respondents possess cell phones and 75%, 84%, and 100% of the public, medical professionals, and veterinarians, respectively, generally use them for medical purposes. 75% of respondents including 68% of medics, 84.8% of veterinarians, and 72.2% of the public acknowledged that the current surveillance systems are ineffective and do not capture and share real-time information. More than 92% of the all respondents confirmed that mobile phones have the potential to improve health surveillance in the country. All respondents reported adhering to a nascent project of mobile phone-based health surveillance and confirmed that there is no existing similar approach in the country. The most preferred methods by all respondents for effective implementation of such platform are phone calls (96.92%) followed by SMS (49.23%) and smart phone digital forms (41.53%). This study revealed urgent needs of mobile phone technologies for health surveillance and interventions in Benin for real-time surveillance and efficient disease prevention.

Digital Health Engagement in the US Population: Insights From the 2018 Health Information National Trends Survey

2021 ◽  
pp. e1-e4
Author(s):  
Chelsea L. Ratcliff ◽  
Melinda Krakow ◽  
Alexandra Greenberg-Worisek ◽  
Bradford W. Hesse
Keyword(s):  
Public Health ◽  
Health Information ◽  
Health Care Providers ◽  
Digital Health ◽  
Smart Device ◽  
P Value ◽  
Care Providers ◽  
Cross Sectional ◽  
The Us ◽  
National Trends

Objectives. To examine prevalence and predictors of digital health engagement among the US population. Methods. We analyzed nationally representative cross-sectional data on 7 digital health engagement behaviors, as well as demographic and socioeconomic predictors, from the Health Information National Trends Survey (HINTS 5, cycle 2, collected in 2018; n = 2698–3504). We fitted multivariable logistic regression models using weighted survey responses to generate population estimates. Results. Digitally seeking health information (70.14%) was relatively common, whereas using health apps (39.53%) and using a digital device to track health metrics (35.37%) or health goal progress (38.99%) were less common. Digitally communicating with one’s health care providers (35.58%) was moderate, whereas sharing health data with providers (17.20%) and sharing health information on social media (14.02%) were uncommon. Being female, younger than 65 years, a college graduate, and a smart device owner positively predicted several digital health engagement behaviors (odds ratio range = 0.09–4.21; P value range < .001–.03). Conclusions. Many public health goals depend on a digitally engaged populace. These data highlight potential barriers to 7 key digital engagement behaviors that could be targeted for intervention. (Am J Public Health. Published online ahead of print May 20, 2021: e1–e4. https://doi.org/10.2105/AJPH.2021.306282 )

scholarly journals Data-Driven Decision Making and Proactive Citizen–Scientist Communication: A Cross-Sectional Study on COVID-19 Vaccination Adherence

Vaccines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1384
Author(s):  
Emil Syundyukov ◽  
Martins Mednis ◽  
Linda Zaharenko ◽  
Eva Pildegovica ◽  
Ieva Danovska ◽  
...  
Keyword(s):  
Public Health ◽  
Logistic Regression ◽  
Exploratory Study ◽  
Large Scale ◽  
Severe Disease ◽  
Data Driven ◽  
Personal Factors ◽  
Cross Sectional ◽  
Digital Platform ◽  
Factors Associated

Due to the severe impact of COVID-19 on public health, rollout of the vaccines must be large-scale. Current solutions are not intended to promote an active collaboration between communities and public health researchers. We aimed to develop a digital platform for communication between scientists and the general population, and to use it for an exploratory study on factors associated with vaccination readiness. The digital platform was developed in Latvia and was equipped with dynamic consent management. During a period of six weeks 467 participants were enrolled in the population-based cross-sectional exploratory study using this platform. We assessed demographics, COVID-19-related behavioral and personal factors, and reasons for vaccination. Logistic regression models adjusted for the level of education, anxiety, factors affecting the motivation to vaccinate, and risk of infection/severe disease were built to investigate their association with vaccination readiness. In the fully adjusted multiple logistic regression model, factors associated with vaccination readiness were anxiety (odds ratio, OR = 3.09 [95% confidence interval 1.88; 5.09]), feelings of social responsibility (OR = 1.61 [1.16; 2.22]), and trust in pharmaceutical companies (OR = 1.53 [1.03; 2.27]). The assessment of a large number of participants in a six-week period show the potential of a digital platform to create a data-driven dialogue on vaccination readiness.

scholarly journals Engagement with COVID-19 Public Health Measures in the United States: A Cross-Sectional Social Media Analysis from June to November 2020 (Preprint)

2020 ◽  
Author(s):  
Daisy Massey ◽  
Chenxi Huang ◽  
Yuan Lu ◽  
Alina Cohen ◽  
Yahel Oren ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Language Processing ◽  
Daily Life ◽  
The United States ◽  
Media Analysis ◽  
Prevention Measures ◽  
Cross Sectional ◽  
Health Measures ◽  
The Us

BACKGROUND The coronavirus disease 2019 (COVID-19) has continued to spread in the US and globally. Closely monitoring public engagement and perception of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs. OBJECTIVE To measure the public’s behaviors and perceptions regarding COVID-19 and its daily life effects during the recent 5 months of the pandemic. METHODS Natural language processing (NLP) algorithms were used to identify COVID-19 related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged, and sensitivity and specificity were both calculated to validate the classification of posts. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the US. RESULTS The final sample size included 9,065,733 posts, 70% of which were sourced from the US. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the US beginning in October. Additionally, counter to reports from March and April, discussion was more focused on daily life topics (69%), compared with COVID-19 in general (37%) and COVID-19 public health measures (20%). CONCLUSIONS There was a decline in COVID-19-related social media discussion sourced mainly from the US, even as COVID-19 cases in the US have increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures until a vaccine is widely available to the public.

Feasibility Study of Surveying the Adverse Drug Reaction Surveillance Systems in a Large Community of Hospitals

1992 ◽  
Vol 26 (3) ◽  
pp. 384-391 ◽  
Author(s):  
Abraham G. Hartzema ◽  
Miquel S. Porta ◽  
Hugh H. Tilson ◽  
Carlos R. Herrera ◽  
Jeffrey T. Moss ◽  
...  
Keyword(s):  
Adverse Drug Reaction ◽  
Drug Reaction ◽  
Surveillance System ◽  
Large Scale ◽  
Surveillance Systems ◽  
Harris County ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Uniform Definition ◽  
Definition Of

OBJECTIVE: To determine the feasibility of accurately assessing the types of hospital adverse drug reaction (ADR) surveillance systems. DESIGN: Cross-sectional survey by mailed, self-administered questionnaire followed by selected verification interviews. SETTING: Harris County, Texas. PARTICIPANTS: All hospitals in the county with different pharmacy directors. MAIN OUTCOME MEASURE: Self description of surveillance system and number of ADRs reported. RESULTS: Forty-nine of 61 hospitals (80 percent) responded to a questionnaire. Forty-seven (96 percent) of the responding hospitals collected information on ADRs with 11 (22 percent) describing their surveillance system as active. Those individuals most often cited as responsible for ADR surveillance included pharmacists, quality assurance personnel, and nurses. Data were verified by personal interviews for 10 hospitals. The number of ADRs reported during the interviews was significantly lower than that reported in the questionnaires. Overall, the reporting of fatal and severe ADRs were more reliable than the reporting of moderate ADRs. These differences were the result of inadequate documentation and the lack of a uniform definition of ADRs. CONCLUSIONS: These data suggest that a large-scale ongoing survey of surveillance systems and reported adverse event rates has limitations and the reliability of data derived from a questionnaire should be verified. To improve the accuracy of surveys used to monitor hospital ADR surveillance systems, it is essential to develop reliable definitions for classifying ADRs and surveillance methods, as well as accurate measures of ADR documentation procedures.

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.

scholarly journals #HIV: Alignment of HIV-Related Visual Content on Instagram with Public Health Priorities in the US

2020 ◽  
Vol 24 (7) ◽  
pp. 2045-2053 ◽  
Author(s):  
Alicia L. Nobles ◽  
Eric C. Leas ◽  
Carl A. Latkin ◽  
Mark Dredze ◽  
Steffanie A. Strathdee ◽  
...  
Keyword(s):  
Public Health ◽  
Health Priorities ◽  
Visual Content ◽  
The Us

scholarly journals A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data

2021 ◽  
pp. 096228022110619
Author(s):  
Yuanke Qu ◽  
Chun Yin Lee ◽  
KF Lam
Keyword(s):  
Public Health ◽  
Real Time ◽  
Disease Severity ◽  
Null Hypothesis ◽  
Large Scale ◽  
Mainland China ◽  
Cumulative Number ◽  
Finite Sample ◽  
Test Statistic ◽  
Multiple Groups

Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the fatality rates in real-time among different groups, say, divided by treatment, age, or area, have an important role to play in informing public health strategies. We propose a statistical test for the null hypothesis of equal real-time fatality rates across multiple groups during an ongoing epidemic. An elegant property of the proposed test statistic is that it converges to a Brownian motion under the null hypothesis, which allows one to develop a sequential testing approach for rejecting the null hypothesis at the earliest possible time when statistical evidence accumulates. This property is particularly important as scientists and clinicians are competing with time to identify possible treatments or effective interventions to combat the emerging epidemic. The method is widely applicable as it only requires the cumulative number of confirmed cases, deaths, and recoveries. A large-scale simulation study shows that the finite-sample performance of the proposed test is highly satisfactory. The proposed test is applied to compare the difference in disease severity among Wuhan, Hubei province (exclude Wuhan) and mainland China (exclude Hubei) from February to March 2020. The result suggests that the disease severity is potentially associated with the health care resource availability during the early phase of the COVID-19 pandemic in mainland China.

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