Anonymous Data Research Articles

Using anonymized mobility data to reduce inequality in the availability and use of urban parks

Abstract Parks are an integral component of cities. Ensuring city residents have equitable and easy access to parks is crucial for human well‐being. In temperate climates, park accessibility is particularly important in the summer months when these green spaces provide an area to recreate, exercise and escape indoor temperatures and heat emanating from paved and built surfaces. However, there are well‐known disparities in park accessibility in cities globally that may threaten the health of city residents, especially with global warming. We examined some of the largest city parks (>50 ha) in Toronto, Canada, by comparing park activity, housing demographics and daily weather patterns. We found that parks that provided more green space area per resident were situated in neighbourhoods that had higher proportions of single‐detached housing, higher automobile use and fewer multistorey apartments. We also found a strong correlation between park activity with population density and the number of amenities in the park. Surprisingly, we found no relationship between park activity and daily weather patterns, although park use was higher on weekends and holidays. These results suggest denser communities are at a disadvantage because they have proportionately less park area within walking distance in addition to having no private green spaces (e.g. backyards). We recommend revising municipal zoning around certain parks and the creation of new green spaces as methods to balance park provisioning in the city. Our findings suggest that designing and maintaining accessible, amenity‐rich parks is an important strategy for promoting health and well‐being in urban populations. Read the free Plain Language Summary for this article on the Journal blog.

Abstract P460: Attack of the (Survey) Bots: How We Determined That Our Anonymous Survey Was Full of False Data

Introduction: Given the nursing shortage in the US and increased focus on reducing burnout, more research on nurses' and nursing assistants’ strategies to mitigate the adverse effects of night shifts and rotating schedules is needed. However, recruitment challenges limit study sample sizes and, consequently, the generalizability of results. Social media platforms provide opportunities to reach a broader sample, yet data quality assessment requires further investigation. Purpose: This study examined the feasibility of using X (formerly Twitter) to recruit nurses and nursing assistants throughout the US to complete a survey on work schedules, fatigue countermeasures use, and sleep and general health. Methods: The anonymous survey was advertised on X with the following tweet: “Nurses and nursing assistants: How does your work schedule and sleep affect your health? What do you do to lessen your fatigue when working? Let us know by completing the Scheduling, Sleep, and Health Survey and a chance for an Amazon gift card: [survey link]. Please RT & share! #Nurse #NurseLife.” Inclusion criteria were: 1) age ≥ 18; 2) being a registered nurse, licensed practical nurse, or nursing assistant; and 3) worked ≥ 36 hours/week during the last 4 weeks. The survey included questions on sociodemographics, shift type and schedule, fatigue countermeasure use, mealtimes/location when working, and sleep health and several PROMIS measures (General Health, Fatigue, and Cognitive Function). After survey completion, respondents were directed to a new survey to provide their contact information to be entered to win a $50 Amazon gift card, with 20 randomly selected recipients. The survey incorporated data verification questions to detect potentially bot-generated or suspicious responses. Results: In under 24 hours, 512 surveys were recorded (495 contained complete data) before deactivating the survey link. Responses were categorized as: 1) likely genuine (n = 2); 2) likely bot-generated (n = 472); and 3) uncertain (i.e., possible but not likely such as worked 6 or 7 shifts in the last 7 days; n = 19). Identifying likely bot-generated responses involved evaluating four components from the data verification questions: 1) inconsistencies in mealtimes/locations compared to reported shift type in the past week (n = 243); 2) failure to meet the criterion for hours worked (i.e., shift length and number of shifts totaling < 36 hours in the last 7 days; n = 143); 3) discrepancies in reported shifts between the past week and the previous 4 weeks (n = 61); and 4) responses with large blocks of identical data (n = 27). Conclusions: Since over 95% of the responses were likely from bots, it may not be feasible to use X for valid, anonymous survey data collection. Targeted recruitment methods within a healthcare system or specialty groups (e.g., operating room or critical care nurses) may provide more valid, but less generalizable, data.

Nursing students' attitude toward euthanasia following its legalization in Spain.

Euthanasia is a controversial practice in many countries. Since Spain's Euthanasia Law came into effect on March 24, 2021, healthcare providers have faced a new challenge since they must inform patients, provide care, accompany them, and implement the law. It also represents a new stumbling block at universities, which must adapt to regulatory changes and educate future professionals accordingly. Little is known about the attitude of nursing students in Spain toward euthanasia since this law was implemented. This study aims to answer the following research questions: What is the attitude of nursing students toward euthanasia? What factors influence this attitude? A cross-sectional study was conducted using an online questionnaire. The study population comprised all nursing students at a public university in Barcelona (n = 444), Spain, during the 2022-2023 academic year. The validated Spanish version of the Euthanasia Attitude Scale was employed. A bivariate analysis was performed. The university Ethics Committee (CEEAH 6247) approved this study. All participating students signed an informed consent form. Participation was voluntary, and data anonymity and confidentiality were guaranteed. Two hundred and forty-four nursing students responded to the questionnaire. The mean total score was 79.64. Participants with religious beliefs presented lower scores, indicating a more negative attitude toward euthanasia. Participants in their second, third, or fourth year of the nursing degree scored higher, demonstrating a more positive attitude. The attitude of nursing students toward euthanasia was remarkably positive. Working on ethical content during the degree course and clinical practice are factors that help to develop a more positive attitude. In addition, nursing education should encourage professional aspects to prevail over religious beliefs in euthanasia situations.

How Long Does Contouring Really Take? Results of the Royal College of Radiologists Contouring Surveys

AimsThe success and safety of modern radiotherapy relies on accurate contouring. Understanding the time taken to complete radiotherapy contours is critical to informing workforce planning and, in the context of a workforce shortfall, advocating for investment in technology and multi-professional skills mix. We aimed to quantify the time taken to delineate target volumes for radical radiotherapy. Materials and methodsThe Royal College of Radiologists circulated two electronic surveys via email to all clinical oncology consultants in the UK. The individual case survey requested anonymous data regarding the next five patients contoured for radical radiotherapy. The second survey collected data on respondents' usual practice in radiotherapy contouring. ResultsThe median time to contour one radiotherapy case was 85 minutes (IQR = 50–131 minutes). Marked variability between and within tumour sites was evident: paediatric cancers took the most time (median = 210 minutes, IQR = 87.5 minutes), followed by head and neck and gynaecological cancers (median = 120 minutes, IQR = 71 and 72.5 minutes respectively). Breast cancer contouring required the least time (median = 43 minutes, IQR = 60 minutes). Radiotherapy technique, inclusion of nodes and 4D CT planning were associated with longer contouring times. A non-medical professional was involved in contouring in 65% of cases, but clinical oncology consultants were involved in target volume delineation in 90% of cases, and OARs in 74%. Peer review took place in 46% of cases with 56% of consultants reporting no time for peer review in their job plan. ConclusionContouring for radical radiotherapy is complex and time-consuming, and despite increasing involvement of non-medical professionals, clinical oncology consultants remain the primary practitioners. Peer review practice is variable and time is often a limiting factor. Many factors influence the time required for contouring, and departments should take these factors and the need for peer-review into account when developing job plans.

Opinions and Beliefs of Adults with Intellectual Disabilities or Visual Impairment about Research with Routine Care Data

Research with routine care data (RCD) is not always a transparent process to the people receiving care for disabilities or impairment. This study aimed to understand the point of view on secondary use of care data for research from the people with visual or intellectual disabilities themselves. In total 36 participants from the UK and the Netherlands were interviewed (20 with intellectual disabilities, 16 with visual impairment). Transcripts were analysed inductively using the Framework Approach. Interview results showed that people from both countries and disability groups assumed that RCD research took place and saw potential contributions to care quality as grounds for legitimacy. Their themes of concern were about inaccuracy and threats to anonymity of data. Interviewees made suggestions for improving conditions under which RCD research can happen with informed consent. In addition to informing data governance policies of organisations serving people with disabilities, findings underscore the contribution that people with disabilities can make to data governance.

Diagnostic Yield and Clinical Implications of Implantable Loop Recorders in Patients with Syncope in Germany: A National Database Analysis.

In patients with unexplained syncope, implantable loop recorders (ILR) are used to detect arrhythmias as a cause of syncope. This study aimed to assess the diagnostic yield and clinical implications arising from ILR implantation in patients with syncope in Germany. Secondary longitudinal data were obtained from a large German research database including anonymized data from nationwide statutory health insurances, representative for the German population. Patients ≥ 18 years with a diagnosis of syncope and ILR implantation between 2017 and 2018 were analyzed, and cardiac diagnoses and therapies during a follow-up period of two years assessed. Of 2,403,301 continuously insured persons in 2017-2018, 1360 (0.1%) received an ILR and 675 patients (45.6% female) were included. During follow-up, arrhythmias were diagnosed in 65.0%. The following antiarrhythmic therapies were established: pacemaker and defibrillator implantations in 20% and 1.5%, respectively, ablation therapy in 3.0%, and antiarrhythmic drug therapy in 4.7%. Aside from the diagnoses typically associated with syncope, atrial fibrillation or flutter was diagnosed in 37.0%, and anticoagulation therapy was initiated in 21.5%. There was a high diagnostic yield of arrhythmias following ILR implantation, leading to a relevant number of syncope-specific treatment. Arrhythmias not necessarily related to syncope were also diagnosed, leading to a high rate of anticoagulation therapy.

Surgical therapy and pathogen detection in endogenous endophthalmitis : Analysis of data from five German eye hospitals

Endogenous endophthalmitis results from hematogenous spread of bacterial or fungal infection in severely diseased patients. Specific systemic and intraocular therapy is required. The basis for this treatment is causal pathogen detection in blood culture or vitreous sample. However, functional results are limited. The current article provides practical hints for surgical therapy and pathogen detection in patients with endogenous endophthalmitis. Aretrospective analysis of anonymous data of 68male and female patients from 2018-2023 from five ophthalmology clinics in Germany was performed. Mean age of affected patients was 71.4years (31-96years). Surgical therapy included pars plana vitrectomy (ppV) and intravitreal injection (IVOM). In 44of 68patients (65%), 1-3 surgeries were performed, 4-6surgeries were required in 14/68 (21%) of patients, and 10or more surgeries were required in 4/68patients (6%). Pathogen detection was possible in 34% of vitreous specimens and in 11% of anterior chamber samples. Mean initial visual acuity was logMAR1.5. After treatment and amean follow-up of 2.5months, mean visual acuity was logMAR1.3. Preanalytical methods for specimen collection like the Freiburg endophthalmitis set to optimize pathogen detection are presented. Severe inflammatory intraocular reactions in endogenous endophthalmitis necessitate acombination of ppV and repeated IVOM. In addition to providing avitreous sample, ppV also serves to remove inflammatory fibrin membranes. Early pars plana vitrectomy with specific antibiotic or antifungal therapy should be sought in addition to the focus search and systemic therapy.

Nursing informatics competency and its associated factors among palliative care nurses: an online survey in mainland China

BackgroundNursing informatics (NI) competency is a required core competency for high-quality care in digitally enabled healthcare environments. Given the increasing reliance on digital health in palliative care settings, it becomes crucial to evaluate the NI competency of nurses to ensure the seamless integration and effective utilization of digital health in their clinical practice. This study aimed to investigate the level of NI competency and explore its associated factors among palliative care nurses in mainland China.MethodsA cross-sectional design was conducted for this study, involving a total of 409 palliative care nurses from 302 hospitals in mainland China. Anonymous data were collected through a self-designed sociodemographic questionnaire, the Nursing Informatics Competency Scale (NICS) and the Innovative Self-Efficacy Scale.ResultsThe total score of the NICS was 129.19 ± 22.02, which indicated that Chinese palliative care nurses had a moderate level of NI competency. There was a positive correlation between innovative self-efficacy and NI competency (r = 0.602, P < 0.01). The hospital level and innovative self-efficacy were identified as statistically significant factors influencing nurses’ NI competency based on multiple linear regression analysis results. These associated factors could explain 35.1% of the difference in NI competency.ConclusionsThis study found that palliative care nurses in mainland China exhibited moderate levels of NI competency and identified the hospital level and innovative self-efficacy as associated factors of nurses’ NI competency. Measures such as developing supported strategies, including targeted NI training programs by nursing education managers of primary-level hospitals and creating a positive culture of innovation by healthcare institutions can be considered to improve the level of NI competency among Chinese palliative care nurses.

Synthetic data and data protection laws

While synthetic data has the potential to address privacy concerns associated with real-world data in many scenarios — ranging from health applications to machine learning — organisations must proceed carefully to ensure they do not inadvertently violate the General Data Protection Regulation or other data protection laws. The boundaries between the processing of personal and anonymised data are sometimes overlooked, creating potential risks for individuals' rights and freedoms. This paper will start from a definition of synthetic data and, after reviewing some foreseeable use cases (also promoted by forthcoming sector legislations in the areas of artificial intelligence and data sharing), it will address the conditions set out in data protection laws (the EU General Data Protection Regulation in primis) in order to consider a set of data as properly anonymised, as well as the phases of synthetic data generation and use where personal data might still be processed, proposing some reflections towards genuine legal compliance.

Simplifying Multimodal Clinical Research Data Management: Introducing an Integrated and User-friendly Database Concept.

Clinical research, particularly in scientific data, grapples with the efficient management of multimodal and longitudinal clinical data. Especially in neuroscience, the volume of heterogeneous longitudinal data challenges researchers. While current research data management systems offer rich functionality, they suffer from architectural complexity that makes them difficult to install and maintain and require extensive user training. The focus is the development and presentation of a data management approach specifically tailored for clinical researchers involved in active patient care, especially in the neuroscientific environment of German university hospitals. Our design considers the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) principles and the secure handling of sensitive data in compliance with the General Data Protection Regulation. We introduce a streamlined database concept, featuring an intuitive graphical interface built on Hypertext Markup Language revision 5 (HTML5)/Cascading Style Sheets (CSS) technology. The system can be effortlessly deployed within local networks, that is, in Microsoft Windows 10 environments. Our design incorporates FAIR principles for effective data management. Moreover, we have streamlined data interchange through established standards like HL7 Clinical Document Architecture (CDA). To ensure data integrity, we have integrated real-time validation mechanisms that cover data type, plausibility, and Clinical Quality Language logic during data import and entry. We have developed and evaluated our concept with clinicians using a sample dataset of subjects who visited our memory clinic over a 3-year period and collected several multimodal clinical parameters. A notable advantage is the unified data matrix, which simplifies data aggregation, anonymization, and export. THIS STREAMLINES DATA EXCHANGE AND ENHANCES DATABASE INTEGRATION WITH PLATFORMS LIKE KONSTANZ INFORMATION MINER (KNIME): . Our approach offers a significant advancement for capturing and managing clinical research data, specifically tailored for small-scale initiatives operating within limited information technology (IT) infrastructures. It is designed for immediate, hassle-free deployment by clinicians and researchers.The database template and precompiled versions of the user interface are available at: https://github.com/stebro01/research_database_sqlite_i2b2.git.

Long-Term Outcomes Following Definitive or Adjuvant Proton Radiotherapy for Adenoid Cystic Carcinoma

PurposeAdenoid cystic carcinoma (ACC) is a rare malignancy accounting for 1% of all head and neck cancers. Treatment for ACC has its challenges and risks, yet few outcomes studies exist. We present long-term outcomes of patients with ACC of the head and neck treated with proton therapy (PT). Materials and MethodsUnder an institutional review board-approved, single-institutional prospective outcomes registry, we reviewed the records of 56 patients with de novo, nonmetastatic ACC of the head and neck treated with PT with definitive (n = 9) or adjuvant PT (n = 47) from June 2007 to December 2021. The median dose to the primary site was 72.6 gray relative biological equivalent (range, 64-74.4) delivered as either once (n = 19) or twice (n = 37) daily treatments. Thirty patients received concurrent chemotherapy. Thirty-one patients received nodal radiation, 30 electively and 1 for nodal involvement. ResultsWith a median follow-up of 6.2 years (range, 0.9-14.7), the 5-year local-regional control (LRC), disease-free survival, cause-specific survival, and overall survival rates were 88%, 85%, 89%, and 89%, respectively. Intracranial extension (P = .003) and gross residual tumor (P = .0388) were factors associated with LRC rates. While the LRC rate for those with a gross total resection was 96%, those with subtotal resection or biopsy alone were 81% and 76%, respectively. The 5-year cumulative incidence of clinically significant grade ≥3 toxicity was 15%, and the crude incidence at the most recent follow-up was 23% (n = 13). ConclusionThis is the largest sample size with the longest median follow-up to date of patients with ACC treated with PT. PT can provide excellent disease control for ACC of the head and neck with acceptable toxicity. T4 disease, intracranial involvement, and gross residual disease at the time of PT following either biopsy or subtotal resection were significant prognostic features for worse outcomes.

Netnography of Women´s Cyberactivism Against Environmental Denialism on Instagram

This article aims to give an account of the digital narratives used by women activists to counter environmental denialism during the 2022 election period in Brazil. There is still little research that addresses the topic, especially through women ́s lenses on a specific timeframe, such as elections. The article is based on a netnography of the publications on Instagram during the months of August and September 2022, to identify the strategies used by women cyberactivists to counter environmental denialism through their digital narratives. Data were gathered through desk research and non-participatory observation, incorporating specific inclusive criteria, and the netnography process involved a thematic analysis supported by Atlas TI. The study is constrained by limitations in the netnography method, such as ethical considerations necessitating anonymized data from a sample mostly consisting of verified accounts and the limited access to cyberactivists' demographic information. We have identified 5 main strategies from the carried out analysis: Value´s Expression, Emotional Expression, Reasoned Arguments, Collective Action, and Community Building, which was framed in the dimensions Individual, Collective, Subjective and Objective, create meaning through generating identification, action, sense of belonging and creating awareness and movement.The creation and envisioning of new realities happen in the dialogue between the Cyberactivists and their followers, and the success of their activism relies on their capacity of reaching and affecting their followers through their strategies, generating meaning

Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context

We discuss how to assess the reliability of partial, anonymized mobility data and compare two different methods to identify spatial communities based on movements: Greedy Modularity Clustering (GMC) and the novel Critical Variable Selection (CVS). These capture different aspects of mobility: direct population fluxes (GMC) and the probability for individuals to move between two nodes (CVS). As a test case, we consider movements of Italians before and during the SARS-Cov2 pandemic, using Facebook users’ data and publicly available information from the Italian National Institute of Statistics (Istat) to construct daily mobility networks at the interprovincial level. Using the Perron-Frobenius (PF) theorem, we show how the mean stochastic network has a stationary population density state comparable with data from Istat, and how this ceases to be the case if even a moderate amount of pruning is applied to the network. We then identify the first two national lockdowns through temporal clustering of the mobility networks, define two representative graphs for the lockdown and non-lockdown conditions and perform optimal spatial community identification on both graphs using the GMC and CVS approaches. Despite the fundamental differences in the methods, the variation of information (VI) between them assesses that they return similar partitions of the Italian provincial networks in both situations. The information provided can be used to inform policy, for example, to define an optimal scale for lockdown measures. Our approach is general and can be applied to other countries or geographical scales.

Leveraging big data for outdoor recreation management: A case study from the York river in Virginia

Outdoor recreation is important for improving quality of life, well-being, and local economies, but quantifying its value without direct monetary transactions can be challenging. This study explores combining non-market valuation techniques with emerging big data sources to estimate the value of recreation for the York River and surrounding parks in Virginia. By applying the travel cost method to anonymous human mobility data, we gain deeper insights into the significance of recreational experiences for visitors and the local economy. Results of a zero-inflated Negative Binomial model show a mean consumer surplus value of $26.91 per trip, totaling $15.5 million across nearly 600,000 trips observed in 2022. Further, weekends, holidays, and the summer and fall months are found to be peak visitation times, whereas those with young children and who are Hispanic or over 64 years old are less likely to visit. These findings shed light on various factors influencing visitation patterns and recreation values, including temporal effects and socio-demographics, revealing disparities that warrant targeted efforts for inclusivity and accessibility. Policymakers can use these insights to make informed and sustainable choices in outdoor recreation management, fostering the preservation of natural resources for the benefit of both visitors and the environment.

EPA-GAN: Electric Power Anonymization via Generative Adversarial Network Model

The contemporary landscape of electricity marketing data utilization is characterized by increased openness, heightened data circulation, and more intricate interaction contexts. Throughout the entire lifecycle of data, the persistent threat of leakage is ever-present. In this study, we introduce a novel electricity data anonymization model, termed EPA-GAN, which relies on table generation. In comparison to existing methodologies, our model extends the foundation of generative adversarial networks by incorporating feature encoders and feedback mechanisms. This adaptation enables the generation of anonymized data with heightened practicality and similarity to the original data, specifically tailored for mixed data types, thereby achieving a deliberate decoupling from the source data. Our proposed approach initiates by parsing the original JSON file, encoding it based on variable types and features using distinct feature encoders. Subsequently, a generative adversarial network, enhanced with information, downstream, generator losses, and the Was + GP modification, is employed to generate anonymized data. The introduction of random noise fortifies privacy protection during the data generation process. Experimental validation attests to a conspicuous reduction in both machine learning utility and statistical dissimilarity between the data synthesized by our proposed anonymization model and the original dataset. This substantiates the model’s efficacy in replacing the original data for mining analysis and data sharing, thereby effectively safeguarding the privacy of the source data.

Consumption responses to an unconditional child allowance in the United States

The COVID-19 pandemic put families in the United States under financial stress. The federal government’s largest response in 2021 was the American Rescue Plan Act, which temporarily expanded the Child Tax Credit (CTC) into a large, unconditional child allowance providing monthly payments to families with children. This study investigates consumption responses to the CTC expansion using anonymized mobile-location data and debit/credit card data that track visits and spending at 1.3 million establishments across US counties. For identification, we exploit variation in the size of households’ income gains due to the CTC across counties in a difference-in-differences framework spanning January 2021 to May 2022. Counties benefiting most from the CTC expansion experienced larger increases in visits to childcare centres and health- and personal-care establishments, and increased visits to and spending per transaction at grocery and general stores. These findings suggest that the CTC expansion increased household consumption and spending on children.

Patient weight has diverse effects on the prescribing of different antibiotics to dogs.

Various factors including body weight-associated treatment cost may influence the probability of dispensing antibiotics to dogs in first-opinion practice, but their effect on specific drug choice remains unclear. Multiple membership regression modeling was used to investigate the probability of dispensing 12 different antibiotics to dogs of different weights in the context of various disease presentations, using anonymized data obtained from electronic health records of 18 clinics between 2020 and 2022. Data from 14,259 dogs were analyzed. Treatment choice varied significantly with animal weight. Higher body weight was associated with an increased likelihood of dispensing lower cost antimicrobials such as amoxicillin and trimethoprim sulfonamide, while use of higher cost antimicrobials such as cefovecin was strongly biased to smaller animals. However, these effects were limited when restricted treatment options were available for the target condition. This work demonstrates that anticipated financial costs may result in different treatment choices for canine patients depending on their body weight. Further work is needed to understand the impact of financial pressures on veterinarians' treatment choices, and the implications for the optimization of antimicrobial stewardship in first opinion practice.

Privacy-preserving intrusion detection in Internet of medical things neural networks using a novel recurrent U-Net auto-encoder algorithm for biomedical applications

The rapid development of Internet of Things (IoT) technology has enabled the emergence of the Internet of Medical Things (IoMT), especially in body area network applications. To protect sensitive medical data, it is essential to ensure privacy preservation and detect intrusions in this context. This study proposes a novel intrusion detection system that protects the privacy of IoMT networks, specifically in the context of body area networks. For feature extraction, the system employs a recurrent U-Net autoencoder algorithm, which effectively captures temporal dependencies in IoMT data. In addition, privacy is protected through the combination of data anonymization techniques and data classification using Principal Component Analysis (PCA). Combining the recurrent U-Net autoencoder algorithm, privacy preservation mechanisms, and PCA-based data classification, the proposed system architecture comprises the U-Net autoencoder algorithm. The proposed method is superior to existing approaches in terms of accuracy, precision, recall, F-measure, and classification loss, as demonstrated by experimental evaluations. This research contributes to the field of privacy protection and intrusion detection in IoMT, specifically in body area network applications.

Willingness and understanding of the safety and effectiveness of COVID‐19 vaccines in young children: a cross‐sectional study among parents in Malaysia

AbstractBackgroundThe safety and efficacy of COVID‐19 vaccinations are crucial factors that parents consider when deciding whether to vaccinate their children.AimThis study aimed to investigate parents' understanding of COVID‐19 vaccines and its association with the degree of willingness to vaccinate their young children.MethodA cross‐sectional survey was conducted among the public with children younger than 12 years old. Information on parents' demography, understanding of COVID‐19 vaccines and their willingness to get their children vaccinated were collected. The association between the level of understanding and willingness was tested using the One‐way analysis of variance (ANOVA) test. Predictors of good understanding and high willingness were investigated using multiple linear regression. Ethics approval was granted by the Research Ethics Committee of Universiti Teknologi MARA (Reference no: REC [PH]/012/2022) and the study conforms with the Declaration of Helsinki. Informed consent was obtained from all participants. A web‐based survey link was distributed through social media platforms. The initial section of the survey included a project information sheet including the purpose of the study, length of questionnaire, identity of researchers and measures taken to ensure data confidentiality and anonymity of participants. Participants provided their consent electronically prior to being granted access to the questionnaire.ResultsOf the 386 respondents, 53.4% had a good understanding, and 74.6% had a high willingness. Only 42.5% agreed that the vaccines are effective in protecting children from getting COVID‐19, 79.5% and 73.3% misunderstood the severity and tendency to experience the side effects, respectively. A better understanding was associated with a higher willingness to vaccinate ([F(4381) = 54.417, p = 0.000). A higher understanding was significantly associated with higher educational status (postgraduate [β = 0.297, p = 0.028] and bachelor [β = 0.275, p = 0.034], work in a health‐related field [β = 0.300, p &lt; 0.001]) and have received a COVID‐19 booster (β = 0.347, p &lt; 0.001). A high willingness was prevalent among parents working in a health‐related field, had received a COVID‐19 booster and had medical condition. Low willingness was observed among parents aged above 30 years old and those who have used herbal medicines.ConclusionIt is of utmost importance to comprehend parents’ understanding of this topic to minimise hesitancy toward COVID‐19 vaccination in children.

Parkinsono liga sergančių asmenų vakcinacija nuo Sars-cov-2 viruso, remiantis covpark-lt tyrimo rezultatais

Background. COVID-19 in patients with Parkinson’s disease leads to worsening of symptoms and development of severe/critical conditions; its long-term consequences are still being investigated. Data on vaccination against SARS-Cov-2 in Parkinson’s disease are inconsistent. There are no publications on this topic regarding Lithuania.&#x0D; Materials and methods. The retrospective study COVPARK-LT was performed in the Vilnius University Hospital Santaros Clinics in 2022. Methods: analysis of anonymous data from electronic patient histories obtained during consultations for Parkinson’s disease in the out-patient department (form E025).&#x0D; Objective: To investigate COVID-19 vaccination status and associated factors in patients with Parkinson’s disease.&#x0D; Results. 173 patients were enrolled, 68 males (39.3%) and 105 females (60.7%), the average age of the cohort was 67.99±1.34 years, the duration of Parkinson’s disease was 7.73±0.54 years. The rate of vaccination against SARS-Cov-2 with at least one dose was 85.6% among study patients and 69.8% in the general population. The rate of full vaccination against SARS-Cov-2 was 81.5% in COVPARK-LT and 33.4% in the general population. The rate of laboratory-proven COVID-19 was 20% (N=5) in the non-vaccinated group of the cohort and 22.3% (N=33) in the vaccinated group (p=0.087). COVID-19 vaccine-negativity was associated with the history of vaccination against non-SARS-Cov-2 infections (odds ratio, OR: 0.18, p &lt; 0.01), vaccination against influenza (OR: 0.21, p&lt;0.01), male gender (OR: 0.68, p&lt;0.05), male age (OR: 0.88, p&lt;0.05), duration of Parkinson’s disease (OR: 1.1, p&lt;0.001), and Parkinson’s disease stage according to Hoehn-Yahr (OR: 0.51, p&lt;0.05).&#x0D; Conclusions. The rate of vaccination against SARS-Cov-2 was higher in the COVPARK-LT cohort patients than in the general population in Lithuania. Non-vaccinated status was positively associated with female gender, younger age in men and earlier stage of Parkinson’s disease according to Hoehn-Yahr staging. Vaccinated status was associated with vaccination against other infectious diseases (influenza, tick-borne encephalitis, pneumococcus). The rate of COVID-19 in the COVPARK-LT cohort did not differ between non-vaccinated and vaccinated patients with Parkinson’s disease.