Dataset Information

The Perceived Impact of COVID-19 among Treatment-Seeking Smokers: A Mixed Methods Approach.

ABSTRACT: The consequences of the COVID-19 pandemic on behavioral health, including tobacco use, are not fully known. The current study sought to measure the perceived impact of COVID-19 and the resulting stay-at-home orders in Philadelphia, Pennsylvania and Buffalo, New York on smokers enrolled in four smoking cessation trials between March 2020 and July 2020. The survey collected quantitative data regarding life changes due to COVID-19, health/exposure status, and the impact on their cessation attempt (e.g., motivation to quit, change in triggers). The questionnaire collected qualitative data to better understand how such changes could explain changes in smoking behavior. Of the 42 participants surveyed, approximately half indicated that COVID-19 changed their motivation and ability to quit or remain quit. Among those who reported that it was easier to quit following the stay-at-home orders (n = 24), most attributed this to concerns regarding the severity of COVID-19 among smokers. Among those who reported more difficulty quitting (n = 15), most attributed this to their increased stress due to the pandemic and the inability to access activities, places, or people that could help them manage triggers. Given public health warnings of continued surges in COVID-19, these data provide insight into who may benefit from further smoking cessation support should existing restrictions or new stay-at-home orders be enacted.

SUBMITTER: Rosoff-Verbit Z

PROVIDER: S-EPMC7826630 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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The Perceived Impact of COVID-19 among Treatment-Seeking Smokers: A Mixed Methods Approach.

Rosoff-Verbit Zoe Z Logue-Chamberlain Erin E Fishman Jessica J Audrain-McGovern Janet J Hawk Larry L Mahoney Martin M Mazur Alexa A Ashare Rebecca R

International journal of environmental research and public health 20210109 2

The consequences of the COVID-19 pandemic on behavioral health, including tobacco use, are not fully known. The current study sought to measure the perceived impact of COVID-19 and the resulting stay-at-home orders in Philadelphia, Pennsylvania and Buffalo, New York on smokers enrolled in four smoking cessation trials between March 2020 and July 2020. The survey collected quantitative data regarding life changes due to COVID-19, health/exposure status, and the impact on their cessation attempt ( ...[more]

PMID: 33435462

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Project description:Vaccination is the most cost-effective way of preventing Coronavirus Disease 2019 (COVID-19) although there was a considerable delay in its institution in Tanzania. This study assessed health care workers' (HCWs) self-perceived infection risk and uptake of COVID-19 vaccines. A concurrent embedded, mixed methods design was utilized to collect data among HCWs in seven Tanzanian regions. Quantitative data was collected using a validated, pre-piloted, interviewer administered questionnaire whereas in-depth interviews (IDIs) and focus group discussions (FGDs) gathered qualitative data. Descriptive analyses were performed while chi-square test and logistic regression were used to test for associations across categories. Thematic analysis was used to analyze the qualitative data. A total of 1,368 HCWs responded to the quantitative tool, 26 participated in the IDIs and 74 in FGDs. About half of the HCW (53.6%) reported to have been vaccinated and three quarters (75.5%) self-perceived to be at a high risk of acquiring COVID-19 infection. High perceived infection risk was associated with increased COVID-19 vaccine uptake (OR 1.535). Participants perceived that the nature of their work and the working environment in the health facilities increased their infection risk. Limited availability and use of personal protective equipment (PPE) was reported to elevate the perceived infection risks. Participants in the oldest age group and from low and mid-level health care facilities had higher proportions with a high-risk perception of acquiring COVID-19 infection. Only about half of the HCWs reported to be vaccinated albeit the majority recounted higher perception of risk to contracting COVID-19 due to their working environment, including limited availability and use of PPE. Efforts to address heightened perceived-risks should include improving the working environment, availability of PPE and continue updating HCWs on the benefits of COVID-19 vaccine to limit their infection risks and consequent transmission to their patients and public.

Project description:BackgroundStudying COVID-19 misinformation on Twitter presents methodological challenges. A computational approach can analyze large data sets, but it is limited when interpreting context. A qualitative approach allows for a deeper analysis of content, but it is labor-intensive and feasible only for smaller data sets.ObjectiveWe aimed to identify and characterize tweets containing COVID-19 misinformation.MethodsTweets geolocated to the Philippines (January 1 to March 21, 2020) containing the words coronavirus, covid, and ncov were mined using the GetOldTweets3 Python library. This primary corpus (N=12,631) was subjected to biterm topic modeling. Key informant interviews were conducted to elicit examples of COVID-19 misinformation and determine keywords. Using NVivo (QSR International) and a combination of word frequency and text search using key informant interview keywords, subcorpus A (n=5881) was constituted and manually coded to identify misinformation. Constant comparative, iterative, and consensual analyses were used to further characterize these tweets. Tweets containing key informant interview keywords were extracted from the primary corpus and processed to constitute subcorpus B (n=4634), of which 506 tweets were manually labeled as misinformation. This training set was subjected to natural language processing to identify tweets with misinformation in the primary corpus. These tweets were further manually coded to confirm labeling.ResultsBiterm topic modeling of the primary corpus revealed the following topics: uncertainty, lawmaker's response, safety measures, testing, loved ones, health standards, panic buying, tragedies other than COVID-19, economy, COVID-19 statistics, precautions, health measures, international issues, adherence to guidelines, and frontliners. These were categorized into 4 major topics: nature of COVID-19, contexts and consequences, people and agents of COVID-19, and COVID-19 prevention and management. Manual coding of subcorpus A identified 398 tweets with misinformation in the following formats: misleading content (n=179), satire and/or parody (n=77), false connection (n=53), conspiracy (n=47), and false context (n=42). The discursive strategies identified were humor (n=109), fear mongering (n=67), anger and disgust (n=59), political commentary (n=59), performing credibility (n=45), overpositivity (n=32), and marketing (n=27). Natural language processing identified 165 tweets with misinformation. However, a manual review showed that 69.7% (115/165) of tweets did not contain misinformation.ConclusionsAn interdisciplinary approach was used to identify tweets with COVID-19 misinformation. Natural language processing mislabeled tweets, likely due to tweets written in Filipino or a combination of the Filipino and English languages. Identifying the formats and discursive strategies of tweets with misinformation required iterative, manual, and emergent coding by human coders with experiential and cultural knowledge of Twitter. An interdisciplinary team composed of experts in health, health informatics, social science, and computer science combined computational and qualitative methods to gain a better understanding of COVID-19 misinformation on Twitter.

Dataset Information

The Perceived Impact of COVID-19 among Treatment-Seeking Smokers: A Mixed Methods Approach.

Publications

The Perceived Impact of COVID-19 among Treatment-Seeking Smokers: A Mixed Methods Approach.

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