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1.
J Med Internet Res ; 23(2): e25429, 2021 02 09.
Article in English | MEDLINE | ID: covidwho-1575482

ABSTRACT

BACKGROUND: As the number of COVID-19 cases increased precipitously in the United States, policy makers and health officials marshalled their pandemic responses. As the economic impacts multiplied, anecdotal reports noted the increased use of web-based crowdfunding to defray these costs. OBJECTIVE: We examined the web-based crowdfunding response in the early stage of the COVID-19 pandemic in the United States to understand the incidence of initiation of COVID-19-related campaigns and compare them to non-COVID-19-related campaigns. METHODS: On May 16, 2020, we extracted all available data available on US campaigns that contained narratives and were created between January 1 and May 10, 2020, on GoFundMe. We identified the subset of COVID-19-related campaigns using keywords relevant to the COVID-19 pandemic. We explored the incidence of COVID-19-related campaigns by geography, by category, and over time, and we compared the characteristics of the campaigns to those of non-COVID-19-related campaigns after March 11, when the pandemic was declared. We then used a natural language processing algorithm to cluster campaigns by narrative content using overlapping keywords. RESULTS: We found that there was a substantial increase in overall GoFundMe web-based crowdfunding campaigns in March, largely attributable to COVID-19-related campaigns. However, as the COVID-19 pandemic persisted and progressed, the number of campaigns per COVID-19 case declined more than tenfold across all states. The states with the earliest disease burden had the fewest campaigns per case, indicating a lack of a case-dependent response. COVID-19-related campaigns raised more money, had a longer narrative description, and were more likely to be shared on Facebook than other campaigns in the study period. CONCLUSIONS: Web-based crowdfunding appears to be a stopgap for only a minority of campaigners. The novelty of an emergency likely impacts both campaign initiation and crowdfunding success, as it reflects the affective response of a community. Crowdfunding activity likely serves as an early signal for emerging needs and societal sentiment for communities in acute distress that could be used by governments and aid organizations to guide disaster relief and policy.


Subject(s)
COVID-19/epidemiology , Crowdsourcing/statistics & numerical data , Financial Support , COVID-19/economics , Cost of Illness , Cross-Sectional Studies , Crowdsourcing/economics , Government , Humans , Narration , Natural Language Processing , Pandemics , SARS-CoV-2 , United States/epidemiology
3.
JAMA Netw Open ; 4(10): e2124946, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1460117

ABSTRACT

Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. Design, Setting, and Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. Main Outcomes and Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. Conclusions and Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models.


Subject(s)
Algorithms , Benchmarking , COVID-19/diagnosis , Clinical Decision Rules , Crowdsourcing , Hospitalization/statistics & numerical data , Machine Learning , Adolescent , Adult , Aged , Aged, 80 and over , Area Under Curve , COVID-19/epidemiology , COVID-19/therapy , COVID-19 Testing , Child , Child, Preschool , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Models, Statistical , Prognosis , ROC Curve , Severity of Illness Index , Washington/epidemiology , Young Adult
4.
Sensors (Basel) ; 21(15)2021 Jul 23.
Article in English | MEDLINE | ID: covidwho-1346525

ABSTRACT

Crowdsourcing is a new mode of value creation in which organizations leverage numerous Internet users to accomplish tasks. However, because these workers have different backgrounds and intentions, crowdsourcing suffers from quality concerns. In the literature, tracing the behavior of workers is preferred over other methodologies such as consensus methods and gold standard approaches. This paper proposes two novel models based on workers' behavior for task classification. These models newly benefit from time-series features and characteristics. The first model uses multiple time-series features with a machine learning classifier. The second model converts time series into images using the recurrent characteristic and applies a convolutional neural network classifier. The proposed models surpass the current state of-the-art baselines in terms of performance. In terms of accuracy, our feature-based model achieved 83.8%, whereas our convolutional neural network model achieved 76.6%.


Subject(s)
Crowdsourcing , Neural Networks, Computer , Humans , Machine Learning
5.
Int J Med Inform ; 153: 104508, 2021 09.
Article in English | MEDLINE | ID: covidwho-1324153

ABSTRACT

BACKGROUND: The Health Sentinel (Centinela de la Salud, CDS), a mobile crowdsourcing platform that includes the CDS app, was deployed to assess its utility as a tool for COVID-19 surveillance in San Luis Potosí, Mexico. METHODS: The CDS app allowed anonymized individual surveys of demographic features and COVID-19 risk of transmission and exacerbation factors from users of the San Luis Potosí Metropolitan Area (SLPMA). The platform's data processing pipeline computed and geolocalized the risk index of each user and enabled the analysis of the variables and their association. Point process analysis identified geographic clustering patterns of users at risk and these were compared with the patterns of COVID-19 cases confirmed by the State Health Services. RESULTS: A total of 1554 COVID-19 surveys were administered through the CDS app. Among the respondents, 50.4 % were men and 49.6 % women, with an average age of 33.5 years. Overall risk index frequencies were, in descending order: no-risk 77.8 %, low risk 10.6 %, respiratory symptoms 6.7 %, medium risk 1.4 %, high risk 2.0 %, very high risk 1.5 %. Comorbidity was the most frequent vulnerability category (32.4 %), followed by the inability to keep home lockdown (19.2 %). Statistically significant risk clusters identified at a spatial scale between 5 and 730 m coincided with those in neighborhoods containing substantial numbers of confirmed COVID-19 cases. CONCLUSIONS: The CDS platform enables the analysis of the sociodemographic features and spatial distribution of individual risk indexes of COVID-19 transmission and exacerbation. It is a useful epidemiological surveillance and early detection tool because it identifies statistically significant and consistent risk clusters in neighborhoods with a substantial number of confirmed COVID-19 cases.


Subject(s)
COVID-19 , Crowdsourcing , Adult , Communicable Disease Control , Female , Humans , Male , Mexico , SARS-CoV-2 , Self Report , Surveys and Questionnaires
6.
Int J Environ Res Public Health ; 18(14)2021 07 20.
Article in English | MEDLINE | ID: covidwho-1323257

ABSTRACT

In 2020, the coronavirus pandemic devasted public health agencies and the federal government across the world. Bridging the gap between underserved populations and the healthcare system, the donation-based crowdfunding campaign has opened a new way for suffering individuals and families to access broader social network platforms for financial and non-financial assistance. Despite the growing popularity of crowdfunding during the pandemic crisis, little research has explored the various signals that attract potential donors to donate. This study explores the effects of signaling theory on the success of a crowdfunding campaign for food relief launched in GoFundMe during which the United States was severely affected by the pandemic with a surged number of coronavirus infected cases from 1 March with 134 confirmed COVID-19 infected cases to 29 July with 4,295,308 infected cases according to World Health Organization. The following results show that the three different signal success measures are important to the success of crowdfunding campaigns: (1) signals originating from the campaign (Title, Description, Spelling Error, Location, and Picture); (2) signals originating from the fundraiser (Social Network, and Update); and (3) signals originating from the social interaction of the fundraiser with the crowd (Comment, Follower, and Share). These findings provide insight and bring additional knowledge contribution to the crowdfunding literature.


Subject(s)
COVID-19 , Crowdsourcing , Fund Raising , Delivery of Health Care , Humans , SARS-CoV-2 , United States
7.
J Trauma Stress ; 34(4): 701-710, 2021 08.
Article in English | MEDLINE | ID: covidwho-1303282

ABSTRACT

As a result of the COVID-19 pandemic, many individuals have experienced disruptions in social, occupational, and daily life activities. Individuals with mental health difficulties, particularly those with elevated posttraumatic stress symptoms (PTSS), may be especially vulnerable to increased impairment as a result of COVID-19. Additionally, demographic factors, such as age, gender, and race/ethnicity, may impact individual difficulties related to the pandemic. The current study examined the concurrent and prospective associations between posttraumatic stress disorder (PTSD) symptoms, broader anxiety and depression symptoms, and COVID-19-related disability. Participants recruited through Amazon's Mechanical Turk (N = 136) completed questionnaire batteries approximately 1 month apart during the COVID-19 pandemic (i.e., Wave 1 and Wave 2). The results indicated that PTSD, anxiety, and depressive symptoms were all associated with increased COVID-19-related disability across assessment points, rs = .44-.68. PTSD symptoms, specifically negative alterations in cognition and mood, significantly predicted COVID-19-related disability after accounting for anxiety and depressive symptoms as well as demographic factors, ßs = .31-.38. Overall, these findings suggest that individuals experiencing elevated PTSS are particularly vulnerable to increased functional impairment as a result of COVID-19 and suggest a need for additional outreach and clinical care among individuals with elevated PTSD symptoms during the pandemic.


Subject(s)
COVID-19/psychology , Disabled Persons/psychology , Stress Disorders, Post-Traumatic/psychology , Adult , Anxiety/diagnosis , Anxiety/etiology , Anxiety/psychology , COVID-19/epidemiology , Crowdsourcing/methods , Depression/diagnosis , Depression/etiology , Depression/psychology , Female , Humans , Male , Middle Aged , Pandemics , SARS-CoV-2 , Stress Disorders, Post-Traumatic/diagnosis , Stress Disorders, Post-Traumatic/etiology , Surveys and Questionnaires , United States/epidemiology
8.
PLoS One ; 16(7): e0253371, 2021.
Article in English | MEDLINE | ID: covidwho-1291919

ABSTRACT

BACKGROUND: The Covid-19 pandemic has had unprecedented effects on individual lives and livelihoods as well as on social, health, economic and political systems and structures across the world. This article derives from a unique collaboration between researchers and museums using rapid response crowdsourcing to document contemporary life among the general public during the pandemic crisis in Sweden. METHODS AND FINDINGS: We use qualitative analysis to explore the narrative crowdsourced submissions of the same 88 individuals at two timepoints, during the 1st and 2nd pandemic waves, about what they most fear in relation to the Covid-19 pandemic, and how their descriptions changed over time. In this self-selected group, we found that aspects they most feared generally concerned responses to the pandemic on a societal level, rather than to the Covid-19 disease itself or other health-related issues. The most salient fears included a broad array of societal issues, including general societal collapse and fears about effects on social and political interactions among people with resulting impact on political order. Notably strong support for the Swedish pandemic response was expressed, despite both national and international criticism. CONCLUSIONS: This analysis fills a notable gap in research literature that lacks subjective and detailed investigation of experiences of the general public, despite recognition of the widespread effects of Covid-19 and its' management strategies. Findings address controversy about the role of experts in formulating and communicating strategy, as well as implications of human responses to existential threats. Based on this analysis, we call for broader focus on societal issues related to this existential threat and the responses to it.


Subject(s)
COVID-19 , Crowdsourcing , Fear , COVID-19/epidemiology , COVID-19/psychology , Humans , Longitudinal Studies , Pandemics , Sweden/epidemiology , Time Factors
9.
Soc Sci Med ; 282: 114105, 2021 08.
Article in English | MEDLINE | ID: covidwho-1272726

ABSTRACT

During the first seven months of the COVID-19 pandemic, more than 175,000 crowdfunding campaigns were established in the US for coronavirus-related needs using the platform GoFundMe. Though charitable crowdfunding has been popular in recent years, the widespread creation of COVID-19 related campaigns points to potential shifts in how the platform is being used, and the volume of needs users have brought to the site during a profound economic, social, and epidemiological crisis. This study offers a systematic examination of the scope and impacts of COVID-19 related crowdfunding in the early months of the pandemic and assesses how existing social and health inequities shaped crowdfunding use and outcomes. Using data collected from all US-based GoFundMe campaigns mentioning COVID or coronavirus, we used descriptive analysis and a series of negative binomial and linear models to assess the contributions of demographic factors and COVID-19 impacts to campaign creation and outcome. We find significant evidence of growing inequalities in outcomes for campaigners. We find that crowdfunding provides substantially higher benefits in wealthier counties with higher levels of education. People from these areas are more likely to initiate campaigns in response to adverse health and economic impacts of COVID-19, and they also receive more funding compared to people living in areas with lower income and education. Modeling also indicates differential outcomes based on the racial and ethnic composition of county population, though without more detail about who is creating and funding campaigns we cannot explain causality. A targeted qualitative analysis of the top earning COVID-19 campaigns offers further evidence of how user privilege and corporate practices contribute to highly unequal outcomes. Taken together, these findings demonstrate how a market-oriented digital technology used to respond to large-scale crisis can exacerbate inequalities and further benefit already privileged groups.


Subject(s)
COVID-19 , Crowdsourcing , Humans , Pandemics , SARS-CoV-2
10.
J Hosp Infect ; 112: 104-107, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1272537

ABSTRACT

Personal protective equipment (PPE) is essential for healthcare worker (HCW) safety. Conservation of PPE for clinical use during the COVID-19 pandemic reduced its availability for training, necessitating an innovative approach to sourcing high physical resemblance PPE (HPR-PPE). We present a case study of crowd-sourcing of HPR-PPE to train HCWs. Survey results indicated that HPR-PPE enabled high-fidelity practise of PPE application and removal, aided procedure recall, improved user confidence and was sufficiently similar to medical-grade PPE. HPR-PPE provided a novel and cost-effective alternative. We also demonstrated that medical-grade PPE can be sourced from non-medical institutions and businesses during a pandemic.


Subject(s)
COVID-19/prevention & control , Health Personnel/education , Infection Control/methods , Infectious Disease Transmission, Patient-to-Professional/prevention & control , Personal Protective Equipment/supply & distribution , Case-Control Studies , Crowdsourcing , Durable Medical Equipment , Humans , Infection Control/instrumentation , Qualitative Research , Respiratory Protective Devices , Simulation Training
12.
Int J Med Inform ; 151: 104486, 2021 07.
Article in English | MEDLINE | ID: covidwho-1224720

ABSTRACT

OBJECTIVE: There was a significant delay in compiling a complete list of the symptoms of COVID-19 during the 2020 outbreak of the disease. When there is little information about the symptoms of a novel disease, interventions to contain the spread of the disease would be suboptimal because people experiencing symptoms that are not yet known to be related to the disease may not limit their social activities. Our goal was to understand whether users' social media postings about the symptoms of novel diseases could be used to develop a complete list of the disease symptoms in a shorter time. MATERIALS AND METHODS: We used the Twitter API to download tweets that contained 'coronavirus', 'COVID-19', and 'symptom'. After data cleaning, the resulting dataset consisted of over 95,000 unique, English tweets posted between January 17, 2020 and March 15, 2020 that contained references to the symptoms of COVID-19. We analyzed this data using network and time series methods. RESULTS: We found that a complete list of the symptoms of COVID-19 could have been compiled by mid-March 2020, before most states in the U.S. announced a lockdown and about 75 days earlier than the list was completed on CDC's website. DISCUSSION & CONCLUSION: We conclude that national and international health agencies should use the crowd-sourced intelligence obtained from social media to develop effective symptom surveillance systems in the early stages of pandemics. We propose a high-level framework that facilitates the collection, analysis, and dissemination of information that are posted in various languages and on different social media platforms about the symptoms of novel diseases.


Subject(s)
COVID-19 , Crowdsourcing , Social Media , Communicable Disease Control , Communication , Humans , Pandemics , SARS-CoV-2 , United States
13.
Sci Total Environ ; 792: 148336, 2021 Oct 20.
Article in English | MEDLINE | ID: covidwho-1260859

ABSTRACT

INTRODUCTION: To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies. METHODS: We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions. RESULTS: Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels. CONCLUSION: Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.


Subject(s)
COVID-19 , Crowdsourcing , Fatigue , Humans , Pandemics , SARS-CoV-2
14.
JAMA Netw Open ; 4(5): e2110090, 2021 05 03.
Article in English | MEDLINE | ID: covidwho-1227702

ABSTRACT

Importance: Reimagining university life during COVID-19 requires substantial innovation and meaningful community input. One method for obtaining community input is crowdsourcing, which involves having a group of individuals work to solve a problem and then publicly share solutions. Objective: To evaluate a crowdsourcing open call as an approach to COVID-19 university community engagement and strategic planning. Design, Setting, and Participants: This qualitative study assessed a crowdsourcing open call offered from June 16 to July 16, 2020, that sought ideas to inform safety in the fall 2020 semester at the University of North Carolina at Chapel Hill (UNC). Digital methods (email and social media) were used for promotion, and submissions were collected online for 4 weeks. Participation was open to UNC students, staff, faculty, and others. Main Outcomes and Measures: Submissions were evaluated for innovation, feasibility, inclusivity, and potential to improve safety and well-being. Demographic data were collected from submitting individuals, and submissions were qualitatively analyzed for emergent themes on challenges with and solutions for addressing safety and well-being in the fall semester. Data were shared with UNC leadership to inform decision-making. Results: The open call received 82 submissions from 110 participants, including current UNC students (56 submissions [68%]), people younger than 30 years (67 [82%]), women (55 [67%]), and individuals identifying as a racial/ethnic minority or as multiracial/ethnic (49 [60%]). Seven submissions were identified as finalists and received cash prizes with the encouragement to use these funds toward idea development and implementation. Seventeen runner-up teams were linked to university resources for further development. Thematic analysis of submissions regarding challenges with the fall semester revealed not only physical health concerns and the limitations of remote learning but also challenges that have been exacerbated by the pandemic, such as a lack of mental health support, structural racism and inequality, and insufficient public transportation. Solutions included novel ideas to support mental health among specific populations (eg, graduate students and racial/ethnic minorities), improve health equity, and increase transit access. All 24 finalists and runners-up indicated interest in implementation after being notified of the open call results. Conclusions and Relevance: This study suggests that open calls are a feasible strategy for university community engagement on COVID-19, providing a stakeholder-driven approach to identifying promising ideas for enhancing safety and well-being. Open calls could be formally incorporated into university planning processes to develop COVID-19 safety strategies that are responsive to diverse community members' concerns.


Subject(s)
COVID-19/prevention & control , Communicable Disease Control , Crowdsourcing , Organizational Innovation , Strategic Planning , Universities/organization & administration , Adult , COVID-19/transmission , Education, Distance , Female , Health Knowledge, Attitudes, Practice , Humans , Male , Mental Health , Minority Groups/psychology , North Carolina , Pandemics/prevention & control , SARS-CoV-2 , Social Support , Students/psychology , Young Adult
15.
PLoS One ; 16(4): e0250382, 2021.
Article in English | MEDLINE | ID: covidwho-1209772

ABSTRACT

Voluntary contributions by citizen scientists can gather large datasets covering wide geographical areas, and are increasingly utilized by researchers for multiple applications, including arthropod vector surveillance. Online platforms such as iNaturalist accumulate crowdsourced biological observations from around the world and these data could also be useful for monitoring vectors. The aim of this study was to explore the availability of observations of important vector taxa on the iNaturalist platform and examine the utility of these data to complement existing vector surveillance activities. Of ten vector taxa investigated, records were most numerous for mosquitoes (Culicidae; 23,018 records, 222 species) and ticks (Ixodida; 16,214 records, 87 species), with most data from 2019-2020. Case studies were performed to assess whether images associated with records were of sufficient quality to identify species and compare iNaturalist observations of vector species to the known situation at the state, national and regional level based on existing published data. Firstly, tick data collected at the national (United Kingdom) or state (Minnesota, USA) level were sufficient to determine seasonal occurrence and distribution patterns of important tick species, and were able to corroborate and complement known trends in tick distribution. Importantly, tick species with expanding distributions (Haemaphysalis punctata in the UK, and Amblyomma americanum in Minnesota) were also detected. Secondly, using iNaturalist data to monitor expanding tick species in Europe (Hyalomma spp.) and the USA (Haemaphysalis longicornis), and invasive Aedes mosquitoes in Europe, showed potential for tracking these species within their known range as well as identifying possible areas of expansion. Despite known limitations associated with crowdsourced data, this study shows that iNaturalist can be a valuable source of information on vector distribution and seasonality that could be used to supplement existing vector surveillance data, especially at a time when many surveillance programs may have been interrupted by COVID-19 restrictions.


Subject(s)
Arthropod Vectors/classification , Citizen Science , Crowdsourcing , Culicidae/classification , Ticks/classification , Animal Distribution , Animals , Arthropod Vectors/physiology , Citizen Science/methods , Crowdsourcing/methods , Culicidae/physiology , Databases, Factual , Europe , Humans , Introduced Species , Population Density , Ticks/physiology , United Kingdom , United States
16.
Front Public Health ; 9: 632024, 2021.
Article in English | MEDLINE | ID: covidwho-1201120

ABSTRACT

Background: Infection prevention and control measures are critical for the prevention of the spread of COVID-19. Aim: In this study, we aimed to measure and evaluate the level of awareness and knowledge of the prevention, symptoms, and transmission control of COVID-19 before and after quarantine among the residents of Rabigh city and adjacent villages in Saudi Arabia. Methods: A cross-sectional online survey was conducted in two stages: the first stage took place before quarantine and the second stage took place after quarantine. The survey was filled out electronically. Results: A total of 448 participants responded and filled out the questionnaires. Females (73.70%) formed the largest number of participants for both stages. The majority of the participants were <30 years old (50.90%) and had a high education level in various sectors and levels (97.1%). It was noticeable that during the first stage, the participants' awareness of COVID-19 symptoms was not very high: 13.62% did not know about the symptoms. However, by the second stage, awareness about symptoms had increased (9.6%). Conclusion: The residents of Rabigh city and the surrounding villages had good levels of knowledge about COVID-19.


Subject(s)
COVID-19 , Crowdsourcing , Health Knowledge, Attitudes, Practice , Quarantine , Adult , Cross-Sectional Studies , Female , Humans , Male , Saudi Arabia/epidemiology
17.
J Glob Health ; 11: 09001, 2021 Mar 01.
Article in English | MEDLINE | ID: covidwho-1168062

ABSTRACT

Background: Crowdsourcing was recognized as having the potential to collect information rapidly, inexpensively and accurately. U-Report is a mobile empowerment platform that connects young people all over the world to information that will change their lives and influence decisions. Previous studies of U-Report's effectiveness highlight strengths in the timeliness, low cost and high credibility for collecting and sending information, however they also highlight areas to improve on concerning data representation. EquityTool has developed a simpler approach to assess the wealth quintiles of respondents based on fewer questions derived from large household surveys such as Multiple Indicators Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Methods: The methodology of Equity Tool was adopted to assess the socio-economic profile of U-Reporters (ie, enrolled participants of U-Report) in Bangladesh. The RapidPro flow collected the survey responses and scored them against the DHS national wealth index using the EquityTool methodology. This helped placing each U-Reporter who completed all questions into the appropriate wealth quintile. Results: With 19% of the respondents completing all questions, the respondents fell into all 5 wealth quintiles, with 79% in the top-two quintiles and only 21% in the lower-three resulting in an Equity Index of 53/100 where 100 is completely in line with Bangladesh equity distribution and 1 is the least in line. An equitable random sample of 1828 U-Reporters from among the regular and frequent respondents was subsequently created for future surveys and the sample has an Equity Index of 98/100. Conclusions: U-Report in Bangladesh does reach the poorest quintiles while the initial recruitment skews to respondents towards better off families. It is possible to create an equitable random sub-sample of respondents from all five wealth quintiles and thus process information and data for future surveys. Moving forward, U-Reporters from the poorly represented quintiles may be incentivized to recruit peers to increase equity and representation. In times of COVID-19, U-Report in combination with the EquityTool has the potential to enhance the quality of crowdsourced data for statistical analysis.


Subject(s)
Crowdsourcing/standards , Surveys and Questionnaires/standards , Bangladesh , Female , Forecasting , Humans , Male , Socioeconomic Factors
18.
J Natl Med Assoc ; 113(4): 405-413, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1164101

ABSTRACT

BACKGROUND: We used online crowdsourcing to explore public perceptions and attitudes towards virtual orthopaedic care, and to identify factors associated with perceived difficulty navigating telehealth services during the COVID-19 pandemic. METHODS: A modified version of the validated Telemedicine Satisfaction and Usefulness Questionnaire was completed by 816 individuals using crowd-sourcing methods. Multivariable logistic regression modelling was used to determine population characteristics associated with perceived difficulty using telehealth technology. RESULTS: Most respondents (85%) believed that telehealth visits would be a convenient form of healthcare delivery, and 64% would prefer them over in-person office visits. The majority (92%) agreed that telehealth would save them time, but 81% had concerns regarding the lack of physical contact during a musculoskeletal examination. More respondents would feel comfortable using telehealth for routine follow-up care (81%) compared to initial assessment visits (59%) and first postoperative appointments (60%). Roughly 1 in 15 (7%) expressed difficulty with using telehealth; these respondents were more often unmarried, lower-income, and more medically infirm, and reported greater symptoms of depression. After multivariable adjustment, lower income and poor health were retained as predictors of difficulty with navigating telehealth technology (p = 0.027,p = 0.036, respectively). CONCLUSION: The majority of the public appears receptive to telehealth for orthopaedic care for both new patient visits and follow-up appointments. The finding that people with multiple chronic conditions and psychosocial needs struggle to engage with telehealth suggests that those who arguably stand to benefit the most from continued care are the ones being unintentionally left out of this digitization boom.


Subject(s)
Health Services Accessibility , Orthopedics/methods , Public Opinion , Telemedicine , Adolescent , Adult , COVID-19 , Cross-Sectional Studies , Crowdsourcing , Female , Humans , Male , Middle Aged , Surveys and Questionnaires
20.
Appl Clin Inform ; 12(1): 170-178, 2021 01.
Article in English | MEDLINE | ID: covidwho-1127207

ABSTRACT

OBJECTIVE: This study examines the validity of optical mark recognition, a novel user interface, and crowdsourced data validation to rapidly digitize and extract data from paper COVID-19 assessment forms at a large medical center. METHODS: An optical mark recognition/optical character recognition (OMR/OCR) system was developed to identify fields that were selected on 2,814 paper assessment forms, each with 141 fields which were used to assess potential COVID-19 infections. A novel user interface (UI) displayed mirrored forms showing the scanned assessment forms with OMR results superimposed on the left and an editable web form on the right to improve ease of data validation. Crowdsourced participants validated the results of the OMR system. Overall error rate and time taken to validate were calculated. A subset of forms was validated by multiple participants to calculate agreement between participants. RESULTS: The OMR/OCR tools correctly extracted data from scanned forms fields with an average accuracy of 70% and median accuracy of 78% when the OMR/OCR results were compared with the results from crowd validation. Scanned forms were crowd-validated at a mean rate of 157 seconds per document and a volume of approximately 108 documents per day. A randomly selected subset of documents was reviewed by multiple participants, producing an interobserver agreement of 97% for documents when narrative-text fields were included and 98% when only Boolean and multiple-choice fields were considered. CONCLUSION: Due to the COVID-19 pandemic, it may be challenging for health care workers wearing personal protective equipment to interact with electronic health records. The combination of OMR/OCR technology, a novel UI, and crowdsourcing data-validation processes allowed for the efficient extraction of a large volume of paper medical documents produced during the COVID-19 pandemic.


Subject(s)
COVID-19/diagnosis , Health Information Exchange , Information Storage and Retrieval , Crowdsourcing , Humans , Physicians , User-Computer Interface
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