Contents analysis of thyroid cancer-related information uploaded to YouTube by physicians in Korea: endorsing thyroid cancer screening, potentially leading to overdiagnosis.

BACKGROUND: Thyroid cancer overdiagnosis is a major public health issue in South Korea, which has the highest incidence rate. The accessibility of information through the Internet, particularly on YouTube, could potentially impact excessive screening. This study aimed to analyze the content of thyroid cancer-related YouTube videos, particularly those from 2016 onwards, to evaluate the potential spread of misinformation. METHODS: A total of 326 videos for analysis were collected using a video search protocol with the keyword "thyroid cancer" on YouTube. This study classified the selected YouTube videos as either provided by medical professionals or not and used topic clustering with LDA (latent dirichlet allocation), sentiment analysis with KoBERT (Korean bidirectional encoder representations from transformers), and reliability evaluation to analyze the content. The proportion of mentions of poor prognosis for thyroid cancer and the categorization of advertising content was also analyzed. RESULTS: Videos by medical professionals were categorized into 7 topics, with "Thyroid cancer is not a 'Good cancer'" being the most common. The number of videos opposing excessive thyroid cancer screening decreased gradually yearly. Videos advocating screening received more favorable comments from viewers than videos opposing excessive thyroid cancer screening. Patient experience videos were categorized into 6 topics, with the "Treatment process and after-treatment" being the most common. CONCLUSION: This study found that a significant proportion of videos uploaded by medical professionals on thyroid cancer endorse the practice, potentially leading to excessive treatments. The study highlights the need for medical professionals to provide high-quality and unbiased information on social media platforms to prevent the spread of medical misinformation and the need for criteria to judge the content and quality of online health information.

Agricultural education in Africa using YouTube multilingual animations: A retrospective feasibility study assessing costs to reach language-diverse populations.

There is a critical need for widespread information dissemination of agricultural best practices in Africa. Literacy, language and resource barriers often impede such information dissemination. Culturally and linguistically localized, computer-animated training videos placed on YouTube and promoted through paid advertising is a potential tool to help overcome these barriers. The goal of this study is to assess the feasibility of reaching language-diverse populations in Africa using this new type of information dissemination channel. As a case study, cost estimates were obtained for YouTube ad campaigns of a video to prevent post-harvest loss through safe food storage using sanitized jerrycan containers. Seventy-three video variants were created for the most common 16 languages in Ghana, 35 languages in Kenya, and 22 languages in Nigeria. Using these videos, campaigns were deployed country wide or focused on zones of influence that represent economically underdeveloped regions known to produce beans suitable for jerrycan storage. Using data collected from YouTube ad campaigns, language-specific models were created for each country to estimate how many viewers could be reached per US dollar spent. Separate models were created to estimate the number of viewers who watched 25% and 75% of the video (most of video without end credits), reflecting different levels of engagement. For language campaigns with both country wide and zone of influence areas of deployment, separate region-specific models were created. Models showed that the estimated number of viewers per dollar spent varied considerably amongst countries and languages. On average, the expected number of viewers per dollar spent were 1.8 (Range = 0.2-7.3) for 25% watched and 0.8 (Range = 0.1-3.2) for 75% watched in Ghana, 1.2 (0.2-4.8) for 25% watched and 0.5 (Range = 0.1-2.0) for 75% watched in Kenya, and 0.4 (Range = 0.2-1.3) for 25% watched and 0.2 (Range = 0.1-0.5) for 75% watched in Nigeria. English versions of the video were the most cost-effective in reaching viewers in Ghana and Nigeria. In Kenya, English language campaigns ranked 28 (country wide) and 36 (zones of influence) out of 37 analyzed campaigns. Results also showed that many local language campaigns performed well, opening the possibility that targeted knowledge dissemination on topics of importance to local populations, is potentially cost effective. In addition, such targeted information dissemination appears feasible, even during regional and global crises when in-person training may not be possible. In summary, leveraging multilingual computer-animations and digital platforms such as YouTube shows promise for conducting large-scale agricultural education campaigns. The findings of the current study provides the justification to pursue a more rigorous prospective study to verify the efficacy of knowledge exchange and societal impact through this form of information dissemination channel.

Now Is the Time to Strengthen Government-Academic Data Infrastructures to Jump-Start Future Public Health Crisis Response.

During public health crises, the significance of rapid data sharing cannot be overstated. In attempts to accelerate COVID-19 pandemic responses, discussions within society and scholarly research have focused on data sharing among health care providers, across government departments at different levels, and on an international scale. A lesser-addressed yet equally important approach to sharing data during the COVID-19 pandemic and other crises involves cross-sector collaboration between government entities and academic researchers. Specifically, this refers to dedicated projects in which a government entity shares public health data with an academic research team for data analysis to receive data insights to inform policy. In this viewpoint, we identify and outline documented data sharing challenges in the context of COVID-19 and other public health crises, as well as broader crisis scenarios encompassing natural disasters and humanitarian emergencies. We then argue that government-academic data collaborations have the potential to alleviate these challenges, which should place them at the forefront of future research attention. In particular, for researchers, data collaborations with government entities should be considered part of the social infrastructure that bolsters their research efforts toward public health crisis response. Looking ahead, we propose a shift from ad hoc, intermittent collaborations to cultivating robust and enduring partnerships. Thus, we need to move beyond viewing government-academic data interactions as 1-time sharing events. Additionally, given the scarcity of scholarly exploration in this domain, we advocate for further investigation into the real-world practices and experiences related to sharing data from government sources with researchers during public health crises.

The Costs of Anonymization: Case Study Using Clinical Data.

BACKGROUND: Sharing data from clinical studies can accelerate scientific progress, improve transparency, and increase the potential for innovation and collaboration. However, privacy concerns remain a barrier to data sharing. Certain concerns, such as reidentification risk, can be addressed through the application of anonymization algorithms, whereby data are altered so that it is no longer reasonably related to a person. Yet, such alterations have the potential to influence the data set's statistical properties, such that the privacy-utility trade-off must be considered. This has been studied in theory, but evidence based on real-world individual-level clinical data is rare, and anonymization has not broadly been adopted in clinical practice. OBJECTIVE: The goal of this study is to contribute to a better understanding of anonymization in the real world by comprehensively evaluating the privacy-utility trade-off of differently anonymized data using data and scientific results from the German Chronic Kidney Disease (GCKD) study. METHODS: The GCKD data set extracted for this study consists of 5217 records and 70 variables. A 2-step procedure was followed to determine which variables constituted reidentification risks. To capture a large portion of the risk-utility space, we decided on risk thresholds ranging from 0.02 to 1. The data were then transformed via generalization and suppression, and the anonymization process was varied using a generic and a use case-specific configuration. To assess the utility of the anonymized GCKD data, general-purpose metrics (ie, data granularity and entropy), as well as use case-specific metrics (ie, reproducibility), were applied. Reproducibility was assessed by measuring the overlap of the 95% CI lengths between anonymized and original results. RESULTS: Reproducibility measured by 95% CI overlap was higher than utility obtained from general-purpose metrics. For example, granularity varied between 68.2% and 87.6%, and entropy varied between 25.5% and 46.2%, whereas the average 95% CI overlap was above 90% for all risk thresholds applied. A nonoverlapping 95% CI was detected in 6 estimates across all analyses, but the overwhelming majority of estimates exhibited an overlap over 50%. The use case-specific configuration outperformed the generic one in terms of actual utility (ie, reproducibility) at the same level of privacy. CONCLUSIONS: Our results illustrate the challenges that anonymization faces when aiming to support multiple likely and possibly competing uses, while use case-specific anonymization can provide greater utility. This aspect should be taken into account when evaluating the associated costs of anonymized data and attempting to maintain sufficiently high levels of privacy for anonymized data. TRIAL REGISTRATION: German Clinical Trials Register DRKS00003971; https://drks.de/search/en/trial/DRKS00003971. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1093/ndt/gfr456.

Dacryocystorhinostomy videos on YouTube as a source of patient education.

BACKGROUND: To determine the quality and reliability of DCR YouTube videos as patient education resources and identify any associated factors predictive of video quality. METHODS: A YouTube search was conducted using the terms "Dacryocystorhinostomy, DCR, surgery" on 12th of January 2022, with the first 50 relevant videos selected for inclusion. For each video, the following was collected: video hyperlink, title, total views, months since the video was posted, video length, total likes/dislikes, authorship (i.e. surgeon, patient experience or media companies) and number of comments. The videos were graded independently by a resident, a registrar and an oculoplastic surgeon using three validated scoring systems: the Journal of the American Medical Association (JAMA), DISCERN, and Health on the Net (HON). RESULTS: The average number of video views was 22,992, with the mean length being 488.12 s and an average of 18 comments per video. The consensus JAMA, DISCERN and HON scores were 2.1 ± 0.6, 29.1 ± 8.8 and 2.7 ± 1.0, respectively. This indicated that the included videos were of a low quality, however, only DISCERN scores had good interobserver similarity. Videos posted by surgeons were superior to non-surgeons when considering mean JAMA and HON scores. No other factors were associated with the quality of educational content. CONCLUSION: The quality and reliability of DCR related content for patient education is relatively low. Based on this study's findings, patients should be encouraged to view videos created by surgeons or specialists in preference to other sources on YouTube.

A data-driven approach to choosing privacy parameters for clinical trial data sharing under differential privacy.

OBJECTIVES: Clinical trial data sharing is crucial for promoting transparency and collaborative efforts in medical research. Differential privacy (DP) is a formal statistical technique for anonymizing shared data that balances privacy of individual records and accuracy of replicated results through a "privacy budget" parameter, ε. DP is considered the state of the art in privacy-protected data publication and is underutilized in clinical trial data sharing. This study is focused on identifying ε values for the sharing of clinical trial data. MATERIALS AND METHODS: We analyzed 2 clinical trial datasets with privacy budget ε ranging from 0.01 to 10. Smaller values of ε entail adding greater amounts of random noise, with better privacy as a result. Comparison of rates, odds ratios, means, and mean differences between the original clinical trial datasets and the empirical distribution of the DP estimator was performed. RESULTS: The DP rate closely approximated the original rate of 6.5% when ε > 1. The DP odds ratio closely aligned with the original odds ratio of 0.689 when ε ≥ 3. The DP mean closely approximated the original mean of 164.64 when ε ≥ 1. As ε increased to 5, both the minimum and maximum DP means converged toward the original mean. DISCUSSION: There is no consensus on how to choose the privacy budget ε. The definition of DP does not specify the required level of privacy, and there is no established formula for determining ε. CONCLUSION: Our findings suggest that the application of DP holds promise in the context of sharing clinical trial data.

Information videos posted on Instagram by orthopaedics and sports traumatology surgeons mostly explain surgical technique, and the least mentioned topics are injury prevention and complications.

PURPOSE: Social media has become the new information acquisition platform for all content producers. In the current literature, there are no studies examining the content quality and the strengths and weaknesses of videos on Instagram that explain anterior cruciate ligament (ACL) injuries, which is the most discussed topic of sports surgery. The aim of this study was to evaluate the quality, strengths and weaknesses of information pertaining to ACL surgery that is disseminated on Instagram. METHOD: An Instagram search was conducted from 30 May 2023 to 30 January 2024. The search encompassed six languages (English, Spanish, German, French, Italian and Turkish) and was performed by six different observers. The investigation focused on eight subheadings derived from current literature on the ACL. These subheadings were addressed in Instagram videos, covering ACL biology or biomechanics, injury mechanism, injury prevention, injury evaluation, surgical technique, injury or surgery complications, injury rehabilitation process and return to sport or work. RESULT: The content was assessed of 127 videos from 127 Instagram accounts, spanning six different languages. Across the review of eight subheadings, the average number covered for the entire group was 3 (range, 0-8). Further analysis revealed that surgical technique was the most frequently mentioned subheading for the whole group (68.5%), followed by injury evaluation (54.3%). Prevention (10.2%) and complications (19.6%) were the least mentioned subheadings. The number of followers showed a correlation with video content quality. CONCLUSION: Although the video quality scores were found to be moderate, the content often focused on surgery and evaluation subheadings. The prevalence of incomplete information underscores the importance of developing strategies to ensure more comprehensive and accurate dissemination of medical knowledge. LEVEL OF EVIDENCE: Level V.

An assessment of the informative value of data sharing statements in clinical trial registries.

BACKGROUND: The provision of data sharing statements (DSS) for clinical trials has been made mandatory by different stakeholders. DSS are a device to clarify whether there is intention to share individual participant data (IPD). What is missing is a detailed assessment of whether DSS are providing clear and understandable information about the conditions for data sharing of IPD for secondary use. METHODS: A random sample of 200 COVID-19 clinical trials with explicit DSS was drawn from the ECRIN clinical research metadata repository. The DSS were assessed and classified, by two experienced experts and one assessor with less experience in data sharing (DS), into different categories (unclear, no sharing, no plans, yes but vague, yes on request, yes with specified storage location, yes but with complex conditions). RESULTS: Between the two experts the agreement was moderate to substantial (kappa=0.62, 95% CI [0.55, 0.70]). Agreement considerably decreased when these experts were compared with a third person who was less experienced and trained in data sharing ("assessor") (kappa=0.33, 95% CI [0.25, 0.41]; 0.35, 95% CI [0.27, 0.43]). Between the two experts and under supervision of an independent moderator, a consensus was achieved for those cases, where both experts had disagreed, and the result was used as "gold standard" for further analysis. At least some degree of willingness of DS (data sharing) was expressed in 63.5% (127/200) cases. Of these cases, around one quarter (31/127) were vague statements of support for data sharing but without useful detail. In around half of the cases (60/127) it was stated that IPD could be obtained by request. Only in in slightly more than 10% of the cases (15/127) it was stated that the IPD would be transferred to a specific data repository. In the remaining cases (21/127), a more complex regime was described or referenced, which could not be allocated to one of the three previous groups. As a result of the consensus meetings, the classification system was updated. CONCLUSION: The study showed that the current DSS that imply possible data sharing are often not easy to interpret, even by relatively experienced staff. Machine based interpretation, which would be necessary for any practical application, is currently not possible. Machine learning and / or natural language processing techniques might improve machine actionability, but would represent a very substantial investment of research effort. The cheaper and easier option would be for data providers, data requestors, funders and platforms to adopt a clearer, more structured and more standardised approach to specifying, providing and collecting DSS. TRIAL REGISTRATION: The protocol for the study was pre-registered on ZENODO ( https://zenodo.org/record/7064624#.Y4DIAHbMJD8 ).

YouTube as a Source of Information on Public Health Ethics.

BACKGROUND: Public health ethics (PHE) is a dynamic area within bioethics that addresses the complex moral implications of public health measures in the face of growing health threats. YouTube is a powerful and widely used platform for disseminating health-related information. The primary objective of this study is to assess videos related to PHE on YouTube. The aim is to gauge the extent of misinformation in collecting PHE videos on the platform. METHODS: On October 25, 2023, a thorough investigation on YouTube was undertaken, employing pre-determined search phrases involving 'public health,' 'healthcare,' 'health services administration,' and 'health policy and ethics.' The research encompassed a total of 137 videos that were selected according to strict inclusion and exclusion criteria. The videos were evaluated using the Global Quality Scale to measure quality and the modified DISCERN tool to evaluate reliability. The researchers identified video sources and compared several video attributes across different quality groups. RESULTS: A total of 137 videos were analyzed, and 65 (47.45%) were classified as high quality, 52 (37.23%) as moderate quality, and 21 (15.32%) as low quality. In high-quality videos, academic, government, physician, and university-hospital sources predominated, whereas Internet users and news sources were connected with low-quality videos. Significant differences in DISCERN score, per day views, likes, and comments were seen across the quality groups (P = 0.001 for views per day and P = 0.001 for other characteristics). According to the findings, low-quality videos had higher median values for daily views, likes, and comments. CONCLUSION: Although nearly half of the videos were high-quality, low-quality videos attracted greater attention. Critical contributors to high-quality videos included academic, government, physician, and university-hospital sources. The findings highlight the importance of quality control methods on social media platforms and strategies to direct users to trustworthy health information. Authors should prioritize appropriate citations and evaluate YouTube and other comparable platforms for potential promotional low-quality information.

Community views on the secondary use of general practice data: Findings from a mixed-methods study.

INTRODUCTION: General practice data, particularly when combined with hospital and other health service data through data linkage, are increasingly being used for quality assurance, evaluation, health service planning and research. In this study, we explored community views on sharing general practice data for secondary purposes, including research, to establish what concerns and conditions need to be addressed in the process of developing a social licence to support such use. METHODS: We used a mixed-methods approach with focus groups (November-December 2021), followed by a cross-sectional survey (March-April 2022). RESULTS: The participants in this study strongly supported sharing general practice data with the clinicians responsible for their care, and where there were direct benefits for individual patients. Over 90% of survey participants (N = 2604) were willing to share their general practice information to directly support their health care, that is, for the primary purpose of collection. There was less support for sharing data for secondary purposes such as research and health service planning (36% and 45% respectively in broad agreement) or for linking general practice data to data in the education, social services and criminal justice systems (30%-36%). A substantial minority of participants were unsure or could not see how benefits would arise from sharing data for secondary purposes. Participants were concerned about the potential for privacy breaches, discrimination and data misuse and they wanted greater transparency and an opportunity to consent to data release. CONCLUSION: The findings of this study suggest that the public may be more concerned about sharing general practice data for secondary purposes than they are about sharing data collected in other settings. Sharing general practice data more broadly will require careful attention to patient and public concerns, including focusing on the factors that will sustain trust and legitimacy in general practice and GPs. PATIENT AND PUBLIC CONTRIBUTION: Members of the public were participants in the study. Data produced from their participation generated study findings. CLINICAL TRIAL REGISTRATION: Not applicable.

Characteristics of the most viewed Hybrid Assistive Limb-related videos on YouTube.

Objectives. YouTube is one of the most popular video-sharing tools and is used as a forum for sharing information about experiences with new technology-based exercise programs, such as the wearable cyborg Hybrid Assistive Limb (HAL). This study aimed to analyze the content and quality of HAL-related videos viewed by people to clarify the content required by YouTube viewers. Methods. We searched HAL-related YouTube videos and selected the top 100 most viewed videos. The number of views, video length, upload date, content, and uploaders of each video were recorded. In addition, DISCERN was used to evaluate the quality of videos. Results. Of the 100 videos, the most shared content was the training videos on HAL (n = 58). The videos uploaded by YouTubers had a significantly higher number of views than those uploaded by other uploaders (hospital, company, and municipality) (p < .001). Furthermore, they had significantly higher DISCERN Questions 16 scores, which serve as the basis for the overall evaluation of the publication's overall quality (p < .001). Conclusion. HAL-related videos on YouTube, featuring the experiences and feedback of YouTubers who are HAL users, are favored more by viewers than by service providers.

[A survey on the utilization of internet services to collect job information among medical students and initiation of video public relations activities for the recruitment of public health physicians based on the survey results].

Objectives　It is difficult for medical students to obtain information about public health physicians because there are very few public health physicians near them. To improve this situation, we surveyed the utilization of internet services to collect job information among medical students and produced six videos and conducted public relations activities for the recruitment of public health physicians based on the survey results.Methods　The subjects of the survey were medical students in their third year or above from 18 universities. Public health teachers in these 18 universities sent their students anonymous self-administered questionnaires created with Google Forms mainly by e-mail. The questionnaires included the following items "internet services used to collect job information," "desired length of each video for knowing job information," and "information you want to know about your future work." The responses were reflected in the length and the content of the videos and the settings for their distribution.Results　Responses were obtained from a total of 491 medical students, including 14 third-year students, 177 fifth-year students, and 300 sixth-year students. Homepages were the most frequently used online source for collecting job information (94.7%), followed by blogs (42.0%), Twitter (32.6%), and YouTube (18.9%). Medical students are less likely to use social networking services for collecting job information compared with non-medical job-hunting students. Regarding the length of the videos, 55.8% of the respondents preferred the length of one video to be less than 5 minutes, and 95.1% preferred it to be less than 10 minutes. Almost all of the respondents (93.1%) wanted to know the atmosphere of young public health physicians, and 74.1% also wanted to know the atmosphere of veteran physicians. Based on these results, we selected six public health physicians including young and veteran physicians and produced interview videos that conveyed the atmosphere of each doctor within 5 minutes per person. We refurbished the banner on the top page of the Japanese Association of Public Health Center Directors so that the videos uploaded to YouTube could be watched.Conclusion　We clarified the current situation of the utilization of internet services for job-hunting activities among medical students and were able to initiate video public relations activities for the recruitment of public health physicians in accordance with the needs. It is necessary to increase awareness of the video platform among medical students and clinicians by deepening cooperation with local governments, universities, and medical institutions and expanding the human network both online and in person.

Use of Tokens to Unlock Greater Data Sharing in Health Care.

This Viewpoint discusses the use of privacy-preserving record linkage, a token-based record linkage system, as a promising avenue for building a data infrastructure system that bridges isolated data.

BioChainReward: A Secure and Incentivised Blockchain Framework for Biomedical Data Sharing.

In the era of digital healthcare, biomedical data sharing is of paramount importance for the advancement of research and personalised healthcare. However, sharing such data while preserving user privacy and ensuring data security poses significant challenges. This paper introduces BioChainReward (BCR), a blockchain-based framework designed to address these concerns. BCR offers enhanced security, privacy, and incentivisation for data sharing in biomedical applications. Its architecture consists of four distinct layers: data, blockchain, smart contract, and application. The data layer handles the encryption and decryption of data, while the blockchain layer manages data hashing and retrieval. The smart contract layer includes an AI-enabled privacy-preservation sublayer that dynamically selects an appropriate privacy technique, tailored to the nature and purpose of each data request. This layer also features a feedback and incentive mechanism that incentivises patients to share their data by offering rewards. Lastly, the application layer serves as an interface for diverse applications, such as AI-enabled apps and data analysis tools, to access and utilise the shared data. Hence, BCR presents a robust, comprehensive approach to secure, privacy-aware, and incentivised data sharing in the biomedical domain.

Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data.

BACKGROUND: When data is distributed across multiple sites, sharing information at the individual level among sites may be difficult. In these multi-site studies, propensity score model can be fitted with data within each site or data from all sites when using inverse probability-weighted Cox regression to estimate overall hazard ratio. However, when there is unknown heterogeneity of covariates in different sites, either approach may lead to potential bias or reduced efficiency. In this study, we proposed a method to estimate propensity score based on covariate balance-related criterion and estimate the overall hazard ratio while overcoming data sharing constraints across sites. METHODS: The proposed propensity score was generated by choosing between global and local propensity score based on covariate balance-related criterion, combining the global propensity score fitted in the entire population and the local propensity score fitted within each site. We used this proposed propensity score to estimate overall hazard ratio of distributed survival data with multiple sites, while requiring only the summary-level information across sites. We conducted simulation studies to evaluate the performance of the proposed method. Besides, we applied the proposed method to real-world data to examine the effect of radiation therapy on time to death among breast cancer patients. RESULTS: The simulation studies showed that the proposed method improved the performance in estimating overall hazard ratio comparing with global and local propensity score method, regardless of the number of sites and sample size in each site. Similar results were observed under both homogeneous and heterogeneous settings. Besides, the proposed method yielded identical results to the pooled individual-level data analysis. The real-world data analysis indicated that the proposed method was more likely to find a significant effect of radiation therapy on mortality compared to the global propensity score method and local propensity score method. CONCLUSIONS: The proposed covariate balance-related propensity score in multi-site distributed survival data outperformed the global propensity score estimated using data from the entire population or the local propensity score estimated within each site in estimating the overall hazard ratio. The proposed approach can be performed without individual-level data transfer between sites and would yield the same results as the corresponding pooled individual-level data analysis.

FAIR, ethical, and coordinated data sharing for COVID-19 response: a scoping review and cross-sectional survey of COVID-19 data sharing platforms and registries.

Data sharing is central to the rapid translation of research into advances in clinical medicine and public health practice. In the context of COVID-19, there has been a rush to share data marked by an explosion of population-specific and discipline-specific resources for collecting, curating, and disseminating participant-level data. We conducted a scoping review and cross-sectional survey to identify and describe COVID-19-related platforms and registries that harmonise and share participant-level clinical, omics (eg, genomic and metabolomic data), imaging data, and metadata. We assess how these initiatives map to the best practices for the ethical and equitable management of data and the findable, accessible, interoperable, and reusable (FAIR) principles for data resources. We review gaps and redundancies in COVID-19 data-sharing efforts and provide recommendations to build on existing synergies that align with frameworks for effective and equitable data reuse. We identified 44 COVID-19-related registries and 20 platforms from the scoping review. Data-sharing resources were concentrated in high-income countries and siloed by comorbidity, body system, and data type. Resources for harmonising and sharing clinical data were less likely to implement FAIR principles than those sharing omics or imaging data. Our findings are that more data sharing does not equate to better data sharing, and the semantic and technical interoperability of platforms and registries harmonising and sharing COVID-19-related participant-level data needs to improve to facilitate the global collaboration required to address the COVID-19 crisis.

The relationship between e-health literacy and information technology acceptance, and the willingness to share personal and health information among pregnant women.

BACKGROUND: Many factors may affect pregnant women's willingness to accept information (IT) technology and share their personal and health information. One of these factors is their e-health literacy level. OBJECTIVE: To investigate the relationship between e-health literacy and IT acceptance, as well as the willingness of pregnant women to share their information. METHODS: This survey was conducted among pregnant women visiting hospitals and private physicians' offices in Zahedan, Iran in 2019. Data were collected using a 4-part questionnaire with 66 questions. The data were analyzed using descriptive (frequency, percentage, mean and standard deviation) and inferential (Pearson correlation coefficient and linear regression) statistics. RESULTS: The mean scores of electronic health literacy, information technology acceptance, and willingness of pregnant women to share personal and health information were 27.43 ± 5.82, 145.49 ± 25.72, and 19.16 ± 5.47, respectively. There was a significant relationship between IT acceptance and information sharing, which means that with increasing IT acceptance, people were more willing to share their information. Also, the results showed that with the decrease in economic well-being, the willingness to share personal and health information decreases. CONCLUSION: This study showed that with the increase in e-health literacy of pregnant women, their IT acceptance grows. Increasing IT acceptance improves their willingness to share their information. Setting and updating information-sharing rules and security mechanisms with the participation of people can help reduce concerns and increase public trust. Healthcare policymakers can encourage the use of health IT in the prevention and treatment of diseases by providing relevant education and informing people.