Trading-off privacy and utility: the Washington State experience assessing the performance of a public health digital exposure notification system for coronavirus disease 2019

ObjectiveDigital exposure notifications (DEN) systems were an emergency response to the coronavirus disease 2019 (COVID-19) pandemic, harnessing smartphone-based technology to enhance conventional pandemic response strategies such as contact tracing. We identify and describe performance measurement constructs relevant to the implementation of DEN tools: (1) reach (number of users enrolled in the intervention); (2) engagement (utilization of the intervention); and (3) effectiveness in preventing transmissions of COVID-19 (impact of the intervention). We also describe WA State’s experience utilizing these constructs to design data-driven evaluation approaches.MethodsWe conducted an environmental scan of DEN documentation and relevant publications. Participation in multidisciplinary collaborative environments facilitated shared learning. Compilation of available data sources and their relevance to implementation and operation workflows were synthesized to develop implementation evaluation constructs.ResultsWe identified 8 useful performance indicators within reach, engagement, and effectiveness constructs.DiscussionWe use implementation science to frame the evaluation of DEN tools by linking the theoretical constructs with the metrics available in the underlying disparate, deidentified, and aggregate data infrastructure. Our challenges in developing meaningful metrics include limited data science competencies in public health, validation of analytic methodologies in the complex and evolving pandemic environment, and the lack of integration with the public health infrastructure.ConclusionContinued collaboration and multidisciplinary consensus activities can improve the utility of DEN tools for future public health emergencies.     

完善公共卫生防疫体系建设：基于基层卫生“平战”防疫视角

背景　在新型冠状病毒肺炎疫情暴发之后,特大城市公共卫生突发事件的治理能力被提上政策议程,而基层卫生作为公共卫生体系的“网底”所发挥的作用十分关键。目的　探索上海市基层卫生在应对此次新型冠状病毒肺炎疫情中的实践并剖析当前存在的问题与困境,为完善公共卫生应急体系提供政策建议与决策依据。方法　本研究于2020年6-9月选取上海市具有代表性的郊区、城区与城郊结合地带,采用小组座谈形式调查了5个区10家社区卫生服务中心,以及相应卫生健康委员会与疾病防控部门。访谈工具为自行设计的半结构式访谈大纲,社区卫生服务中心的访谈内容主要包括疫情期间承接主要任务、组织架构、实际工作内容、内部协同、主要问题与建议等,相应卫生健康委员会与疾病防控部门的访谈内容主要包括社区卫生服务中心在疫情中的主要职能与实际作用、暴露的“短板”、卫生健康委员会/疾控中心如何予以支持及如何平战结合。结果　上海市基层卫生在公共卫生防疫中主要发挥了战时应急响应、区域联防联控,院内分诊筛查、保障基本医疗,借力新型冠状病毒肺炎疫情、做实签约服务的功能,存在物资与人力资源不足、心理防疫能力不足、内部协同与多部门管理不足、激励机制与活力不足四大问题。结论　针对当前基层卫生在公共卫生疫情防控中的困境,建议从四个方面理顺关系,即理顺基本医疗与公共卫生关系、理顺平时与战时关系、理顺社区与上级部门关系、理顺政府与市场关系。     

Health care worker burnout during the COVID-19 pandemic: A cross-sectional survey study in Saudi Arabia

Objectives:To estimate the prevalence of burnout among health care workers (HCWs) who are working in Saudi Arabia during the Coronavirus disease 2019 (COVID-19) pandemic, and explore individual and work-related factors associated with burnout in this population.Methods:In this cross-sectional study conducted between June to August of 2020, we invited HCWs through social channels to complete a questionnaire. The questionnaire inquired about demographics, factors related to burnout, and used the Copenhagen Burnout Inventory scale to indicate burnout. A total of 646 HCWs participated.Results:The mean (SD) age of participants was 34.1 (9.5) years. Sixty-one percent were female. The prevalence of burnout among HCWs was 75%. Significant factors associated with burnout were age, job title, years of experience, increased working hours during the pandemic, average hours of sleep per day, exposure to patients with COVID-19, number of times tested for COVID-19, and perception of being pushed to deal with COVID-19 patients.Conclusion:Health care workers as frontline workers, face great challenges during this pandemic, because of the nature of their work. Efforts should be made to promote psychological resilience for HCWs during pandemics. This study points out the factors that should be invested in and the factors that may not be influential.     

The emerging role of clinical informatics fellows in service learning during the COVID-19 pandemic

ObjectiveThe study sought to describe the contributions of clinical informatics (CI) fellows to their institutions’ coronavirus disease 2019 (COVID-19) response.Materials and MethodsWe designed a survey to capture key domains of health informatics and perceptions regarding fellows’ application of their CI skills. We also conducted detailed interviews with select fellows and described their specific projects in a brief case series.ResultsForty-one of the 99 CI fellows responded to our survey. Seventy-five percent agreed that they were “able to apply clinical informatics training and interest to the COVID-19 response.” The most common project types were telemedicine (63%), reporting and analytics (49%), and electronic health record builds and governance (32%). Telehealth projects included training providers on existing telehealth tools, building entirely new virtual clinics for video triage of COVID-19 patients, and pioneering workflows and implementation of brand-new emergency department and inpatient video visit types. Analytics projects included reports and dashboards for institutional leadership, as well as developing digital contact tracing tools. For electronic health record builds, fellows directly contributed to note templates with embedded screening and testing guidance, adding COVID-19 tests to order sets, and validating clinical triage workflows.DiscussionFellows were engaged in projects that span the breadth of the CI specialty and were able to make system-wide contributions in line with their educational milestones.ConclusionsCI fellows contributed meaningfully and rapidly to their institutions’ response to the COVID-19 pandemic.     

The value of community-integrated health information exchanges: three recent examples from El Paso,Texas

The foundational role of health information exchanges (HIEs) is to facilitate communication between clinical partners in real time. Once this infrastructure for the secure and immediate flow of patient information is built, however, HIEs can benefit community public health and clinical care in myriad other ways that are in line with their mission, goals, patient privacy, and funding structures. We encourage the development of community-integrated HIEs and list specific steps that can be taken toward community integration. We give three examples of those steps in action from a community HIE in El Paso, TX. Each local partnership, in combination with technology innovation, resulted in the development of informatics tools to address community health needs and generated long-term benefits, especially for the most vulnerable patients. Two examples relate to different aspects of the COVID-19 pandemic and a third to the Afghan refugee evacuation.     

Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study

ObjectiveTo rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team.Materials and MethodsWe developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.ResultsThe prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.DiscussionStacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction.ConclusionWe developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.     

Electronic health record–based disease surveillance systems: A systematic literature review on challenges and solutions

ObjectiveDisease surveillance systems are expanding using electronic health records (EHRs). However, there are many challenges in this regard. In the present study, the solutions and challenges of implementing EHR-based disease surveillance systems (EHR-DS) have been reviewed.Materials and MethodsWe searched the related keywords in ProQuest, PubMed, Web of Science, Cochrane Library, Embase, and Scopus. Then, we assessed and selected articles using the inclusion and exclusion criteria and, finally, classified the identified solutions and challenges.ResultsFinally, 50 studies were included, and 52 unique solutions and 47 challenges were organized into 6 main themes (policy and regulatory, technical, management, standardization, financial, and data quality). The results indicate that due to the multifaceted nature of the challenges, the implementation of EHR-DS is not low cost and easy to implement and requires a variety of interventions. On the one hand, the most common challenges include the need to invest significant time and resources; the poor data quality in EHRs; difficulty in analyzing, cleaning, and accessing unstructured data; data privacy and security; and the lack of interoperability standards. On the other hand, the most common solutions are the use of natural language processing and machine learning algorithms for unstructured data; the use of appropriate technical solutions for data retrieval, extraction, identification, and visualization; the collaboration of health and clinical departments to access data; standardizing EHR content for public health; and using a unique health identifier for individuals.ConclusionsEHR systems have an important role in modernizing disease surveillance systems. However, there are many problems and challenges facing the development and implementation of EHR-DS that need to be appropriately addressed.     

北京市社区卫生服务机构传染病防控能力现状及对策研究

背景　新型冠状病毒肺炎疫情防控期间,北京市社区卫生服务机构暴露出在传染病防控方面的一些弱项和“短板”。为提升其传染病防控能力、充分发挥其哨点监测作用,亟须摸清现状,发现问题症结,提出改进建议。目的　了解北京市社区卫生服务机构传染病防控能力,发现问题,分析原因,提出政策建议。方法　于2020年5-7月对北京市全部社区卫生服务中心开展问卷调查,对机构基本情况、科室设置情况、人员情况、基础设施情况、公共卫生服务提供情况、传染病和突发公共卫生事件应急能力等进行描述性分析。同时,利用国家疫情监测网络、2019年度北京市社区卫生工作统计资料汇编,对数据进行核对和补充。结果　截至2019年底,北京市共有社区卫生服务中心342家。其中,90家（26.32%）设有发热门诊,102家（29.82%）设有肠道门诊,54家（15.79%）同时设有发热门诊和肠道门诊。社区卫生服务人员实际在岗28 809人,2 887人（10.02%）在公共卫生岗位工作,其中高级职称178人（6.17%）。159家（46.49%）机构可开展HIV检测,11家（3.22%）可开展新型冠状病毒核酸检测。对于29种常见传染病,140家（40.94%）机构无诊治能力,135家（39.47%）可诊治1~5种,29家（8.48%）可诊治>10种。结论　北京市社区卫生服务机构在传染病防控体制机制、发热哨点诊室、诊疗能力、人才队伍建设等方面存在系列问题,应加强顶层设计,尽快弥补“短板”与不足。     

基于价值链理论的家庭医生团队在突发公共卫生事件应急防控中的现实困境及其破解路径

背景　目前家庭医生团队在发挥突发公共卫生事件应急防控“守门人”的功效上仍然面临着重重阻碍,但目前较缺乏针对家庭医生团队在突发公共卫生事件应急防控中现实困境的研究。目的　分析家庭医生团队在突发公共卫生事件应急防控中的现实困境,为保障其高效参与突发公共卫生事件应急防控工作提供理论依据和对策建议。方法　2020年6月,采用方便抽样法抽取广州市25家基层医疗卫生机构,将其一线医务人员（每家机构抽取2名）作为问卷调查对象,对2月至调查时基本公共卫生服务专线开展情况、基本医疗服务开展情况、应急防控工作开展情况进行统计调查。同时选取部分愿意配合的基层工作人员进行访谈,结合价值链理论进行分析。结果　25家机构中,19家机构停止（过）部分或全部公共卫生专线,6家机构没有确诊/疑似病例的转运通道和能力,24家机构启动应急预案,10家机构开设健康教育专线,9家机构未开放传染病及突发公共卫生事件报告和处理专线,10家机构未开设发热门诊,11家机构的所有门诊一直正常开放。访谈结合价值链理论分析显示,家庭医生团队在突发公共卫生事件应急防控中面临着内部后勤保障不到位、外部沟通协作不顺畅、监测预警功能发挥不完全、健康教育工作不深入、基本医疗服务需求得不到满足、应急基础设施不健全、应急专业人才缺口大、信息化建设步调不一致、应急物资采购机制不完善等现实困境。结论　建议优先破除阻碍开展基本工作的困境,再深入优化解决辅助工作中出现的问题,让社区成为突发公共卫生事件应急防控的坚实堡垒,保障居民的生命健康安全。     

突发公共卫生事件对公众心理的影响:2019冠状病毒病疫情期间浙江省心理援助热线来电分析

目的: 总结2019冠状病毒病疫情期间心理援助热线服务需求及公众心理状态的变化特点。方法: 采用量化统计与质性分析相结合的方法对2020年1月25日至2月29日浙江省心理援助热线（简称“热线”）来电进行分析。将疫情相关来电分为医学、心理、资讯、其他四类，采用文本分析法确定心理类来电的二级类别；按周统计来电数量并分析各类来电数量随时间变化情况；采用分层随机抽样法在各类疫情相关来电中抽取600人次进行语义分析，通过标记新增、同类合并的方式形成特征集，归纳各阶段的来电内容特征；对200名来电者进行回访，从等待时间、通话时长、解决问题的程度和结束通话的方式四方面了解其对来电过程的感受。结果: 全省热线累计接到来电13 764人次，8978人次（65.23%）为COVID-19疫情相关来电，其中医学类占12.59%（1130/8978）、心理类占26.50%（2379/8978）、资讯类占27.18%（2440/8978）、其他占33.74%（3029/8978）。各类来电数量随时间呈非线性变化，每日各类来电数量受到每日疫情情况、新增相关政策情况、热线宣传力度等因素的影响（P < 0.05或P < 0.01）；各类来电数量在不同性别和身份的来电者中的分布差异有统计学意义（均P < 0.05）。181名来电者自愿接受回访，其中51.38%（93/181）的来电者认为热线占线等待时间偏长，33.15%（60/181）的来电者认为热线通话时间不足，80.66%（146/181）的来电者认为通过拨打热线能部分/完全解决问题，39.23%（71/181）的来电者认为是由接线员单方面提出结束通话。结论: 心理援助热线来电数量和内容的变化反映出疫情期间公众心理大致经历盲目期、恐慌期、烦闷期和调整期四个阶段，各个时期呈现不同的特征。应进一步加强心理援助热线的“专线”特性，提高服务效率，针对不同阶段公众的心理特征采取相应的心理干预策略。     

Identifying patterns in administrative tasks through structural topic modeling: A study of task definitions,prevalence, and shifts in a mental health practice’s operations during the COVID-19 pandemic

ObjectiveThis case study illustrates the use of natural language processing for identifying administrative task categories, prevalence, and shifts necessitated by a major event (the COVID-19 [coronavirus disease 2019] pandemic) from user-generated data stored as free text in a task management system for a multisite mental health practice with 40 clinicians and 13 administrative staff members.Materials and MethodsStructural topic modeling was applied on 7079 task sequences from 13 administrative users of a Health Insurance Portability and Accountability Act–compliant task management platform. Context was obtained through interviews with an expert panel.ResultsTen task definitions spanning 3 major categories were identified, and their prevalence estimated. Significant shifts in task prevalence due to the pandemic were detected for tasks like billing inquiries to insurers, appointment cancellations, patient balances, and new patient follow-up.ConclusionsStructural topic modeling effectively detects task categories, prevalence, and shifts, providing opportunities for healthcare providers to reconsider staff roles and to optimize workflows and resource allocation.     

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative

ObjectiveIn response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations.Materials and MethodsWe developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements.ResultsBeyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback.DiscussionWe encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate.ConclusionBy combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.     

疫情下对全科专业住院医师规范化培训的思考与建议

为应对新型冠状病毒肺炎疫情,国家提出各地社区卫生服务中心（站）和乡镇卫生院、村卫生室要充分发挥在疫情防控中的“网底”作用。全科医生在基层发挥着无可替代的作用,同时也遇到了一些困难与问题。为此对全国10个省（自治区、直辖市）的41位全科医生进行开放式问卷调查,结果显示：此次疫情中,全科医生遇到的困难和问题主要是公共卫生、传染病学知识匮乏;此次疫情易感人群包括医疗防疫工作者、弱势高危群体和缺乏防范意识的人员;全科培训中应该强化公共卫生、传染病学、流行病学方面课程。为此建议全科专业住院医师规范化培训应强化突发公共卫生事件应对及处理、传染病防治、健康教育、心理疏导等相关内容培训,提高全科医生岗位胜任力。     

农村订单定向医学毕业生参与新型冠状病毒肺炎疫情防控情况研究

背景　新型冠状病毒肺炎（简称新冠肺炎）疫情防控期间，基层卫生工作者起到了十分重要的作用。我国于2010年发文启动实施农村订单定向医学生免费培养工作，从2015年起至今已有数届订单定向医学生完成了本科学业，其在疫情防控中的工作情况值得关注。目的　调查订单定向毕业生和其他临床五年制毕业生参与新冠肺炎疫情防控工作情况，分析订单定向毕业生在疫情防控中发挥的作用及存在的困难与挑战，并提出针对性政策建议。方法　本研究数据来源于“订单定向医学生队列研究”项目，本次随访调查于2020年9月采用电子问卷形式开展，有效随访我国中西部4所医学院校2015-2019届共1 631例订单定向毕业生和1 009例其他临床五年制毕业生，调查内容包括基本信息、基本工作情况、疫情期间工作情况等。结果　在调查期间处于工作状态的毕业生中，订单定向毕业生的疫情防控工作参与率高于其他临床五年制毕业生〔70.18%（1 113/1 586）比51.58%（293/568），P<0.05〕。订单定向毕业生中，男性参与率高于女性（P<0.05），2015-2016届参与率高于2017-2019届（P<0.05），已婚者参与率高于未婚/离异/其他婚姻状况者（P<0.05）。在参与疫情防控工作的订单定向毕业生中，参与较多的服务类别为健康宣传与教育〔88.59%（986/1 113）〕、社区防疫与管理〔85.62%（953/1 113）〕、核酸检测与筛查〔67.30%（749/1 113）〕，面临的常见挑战为缺少防护设备〔77.72%（865/1 113）〕、工作量和/或工作压力太大〔41.87%（466/1 113）〕、担心自己或家人被感染〔37.56%（418/1 113）〕，认为疫情防控期间的日常临床工作量、日常公共卫生工作量增多的人员占比分别为57.50%（640/1 113）和62.26%（693/1 113）。结论　订单定向毕业生在疫情防控过程中承担了多项重要工作，对全国的疫情防控发挥了不可忽视的作用，但是同时存在着防护设备不足、工作压力过大等问题。建议进一步改善基层卫生设施，为基层医务工作者提供更多的防护与支持。     

Why do people oppose mask wearing? A comprehensive analysis of U.S. tweets during the COVID-19 pandemic

ObjectiveFacial masks are an essential personal protective measure to fight the COVID-19 (coronavirus disease) pandemic. However, the mask adoption rate in the United States is still less than optimal. This study aims to understand the beliefs held by individuals who oppose the use of facial masks, and the evidence that they use to support these beliefs, to inform the development of targeted public health communication strategies.Materials and MethodsWe analyzed a total of 771 268 U.S.-based tweets between January to October 2020. We developed machine learning classifiers to identify and categorize relevant tweets, followed by a qualitative content analysis of a subset of the tweets to understand the rationale of those opposed mask wearing.ResultsWe identified 267 152 tweets that contained personal opinions about wearing facial masks to prevent the spread of COVID-19. While the majority of the tweets supported mask wearing, the proportion of anti-mask tweets stayed constant at about a 10% level throughout the study period. Common reasons for opposition included physical discomfort and negative effects, lack of effectiveness, and being unnecessary or inappropriate for certain people or under certain circumstances. The opposing tweets were significantly less likely to cite external sources of information such as public health agencies’ websites to support the arguments.ConclusionsCombining machine learning and qualitative content analysis is an effective strategy for identifying public attitudes toward mask wearing and the reasons for opposition. The results may inform better communication strategies to improve the public perception of wearing masks and, in particular, to specifically address common anti-mask beliefs.     

The Effect of the Covid-19 Pandemic on Nutritional Status and Anxiety Levels in Turkish Young Adults

BackgroundThe pandemic called “Coronavirus Disease 2019” (COVID-19), which first appeared in China, then spread to the whole world, has had negative consequences in many areas, especially in health. The long-term quarantine process caused by the pandemic and the experienced stress had a great impact on nutritional habits.AimIn this study, it was aimed to determine the change in anxiety levels and eating habits of young adults after the COVID-19 pandemic.MethodsThe data were obtained through an online questionnaire between April and July 2020. In the questionnaire, the general and health information of the individuals, their nutritional habits, and anthropometric measurements (height and body weight) were questioned. In addition, the food frequency questionnaire form and Beck Anxiety Inventory were applied.ResultsA total of 823 (174 males and 649 females) participants were included in the study. The median ages of males and females were 27.0 (18.0) and 26.0 (8.0), respectively. According to the results of the food frequency questionnaire; it was found that among females, the consumption of egg, cheese, milk, yogurt, pickles, fruit, onion, garlic, lemon, salad, legumes, pastry, sweets, red meat, turmeric, and herbal tea were increased significantly in the post-pandemic period; and the consumption of milk, yogurt, garlic, and lemon significantly increased in males. It was also found that the anxiety levels of the females increased statistically significantly in the post-pandemic period.ConclusionIt was determined that during the COVID-19 pandemic, there were statistically significant changes in the food intake patterns and anxiety levels of the participants. It is thought that the results obtained from this study may be a guide for further studies to determine the nutritional habits in the COVID-19 pandemic.     

突发公共卫生事件中图书馆的角色定位

分析了疫情之下图书馆的发展机遇与挑战,通过调查不同类型图书馆在新冠疫情期间的实践,进一步明确了图书馆在突发公共卫生事件中的角色定位,并针对图书馆如何应对突发事件,提出了后疫情时代图书馆发展的建议,以期进一步提升图书馆的服务效能及其社会价值。     

Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications

ObjectiveUnderstanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing-based pipeline to understand public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter across time.MethodsThis retrospective study included 609 189 US-based tweets between January 29, 2020 and November 30, 2021 on 4 drugs that gained wide public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, drug therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatment options for eligible patients. Time-trend analysis was used to understand the popularity and related events. Content and demographic analyses were conducted to explore potential rationales of people’s stances on each drug.ResultsTime-trend analysis revealed that Hydroxychloroquine and Ivermectin received much more discussion than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly politicized, related to conspiracy theories, hearsay, celebrity effects, etc. The distribution of stance between the 2 major US political parties was significantly different (P < .001); Republicans were much more likely to support Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the general population; in contrast, the general population was more likely to support Ivermectin (+14%).ConclusionOur study found that social media users with have different perceptions and stances on off-label versus FDA-authorized drug use across different stages of COVID-19, indicating that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation for promoting safe drug use. Our analysis pipeline and stance detection models are made public at https://github.com/ningkko/COVID-drug.