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Background Vaccination is one of the most successful public health interventions for the prevention of COVID-19. Toward the end of April 2021, UNICEF (United Nations International Children’s Emergency Fund), alongside other organizations, were promoting the hashtag #VaccinesWork. Objective The aim of this paper is to analyze the #VaccinesWork hashtag on Twitter in the context of the COVID-19 pandemic, analyzing the main messages shared and the organizations involved. Methods The data set used in this study consists of 11,085 tweets containing the #VaccinesWork hashtag from the 29th to the 30th of April 2021. The data set includes tweets that may not have the hashtag but were replies or mentions in those tweets. The data were retrieved using NodeXL, and the network graph was laid out using the Harel-Koren fast multiscale layout algorithm. Results The study found that organizations such as the World Health Organization, UNICEF, and Gavi were the key opinion leaders and had a big influence on the spread of information among users. Furthermore, the most shared URLs belonged to academic journals with a high impact factor. Provaccination users had other vaccination-promoting hashtags in common, not only in the COVID-19 scenario. Conclusions This study investigated the discussions surrounding the #VaccinesWork hashtag. Social media networks containing conspiracy theories tend to contain dubious accounts leading the discussions and are often linked to unverified information. This kind of analysis can be useful to detect the optimal moment for launching health campaigns on Twitter.

BACKGROUND Vaccination is one of the most successful public health interventions for the prevention of COVID-19. Towards the end of April 2021, UNICEF, alongside other organizations, were promoting the hashtag 'VaccinesWork' OBJECTIVE The aim of this paper is to analyse the 'VaccinesWork' hashtag on Twitter in the context of the COVID-19 pandemic, analysing the main messages shared and the organizations involved METHODS The data set used in this article consists of 11,085 tweets containing the 'VaccinesWork' hashtag from the 29th to the 30th of April 2021. The dataset includes tweets that may not have the hashtag but were relipes or mentions in those tweets. The data were retrieved using NodeXL and the network graph was laid out using the Harel-Koren Fast Multiscale layout algorithm. RESULTS The study found that organizations such as WHO, UNICEF and Gavi were the key opinion leaders and had a big influence on the spread of information among users. Furthermore, the most shared URLs belong to academic journals with a high impact factor. Pro-vaccination users had other vaccination-promoting hashtags in common, not only in the COVID-19 scenario. CONCLUSIONS This study investigated the discussions surrounding the VaccinesWork hashtag. Social media networks containing conspiracy theories tend to contain dubious accounts leading the discussions and are often linked to unverified information. This kind of analysis can be useful to detect the optimal moment for launching health campaigns on Twitter. CLINICALTRIAL

SARS-CoV-2 vaccination is the most effective strategy to protect individuals with haematologic malignancies against severe COVID-19, while eliciting limited vaccine responses. We characterized the humoral responses following 3 mo after mRNA-based vaccines in individuals at different plasma-cell disease stages: monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), and multiple myeloma on first-line therapy (MM), compared with a healthy population. Plasma samples from uninfected MM patients showed lower SARS-CoV-2-specific antibody levels and neutralization capacity compared with MGUS, SMM, and healthy individuals. Importantly, COVID-19 recovered MM individuals presented significantly higher plasma neutralization capacity compared with their uninfected counterparts, highlighting that hybrid immunity elicit stronger immunity even in this immunocompromised population. No differences in the vaccine-induced humoral responses were observed between uninfected MGUS, SMM and healthy individuals. In conclusion, MGUS and SMM patients could be SARS-CoV-2 vaccinated following the vaccine recommendations for the general population, whereas a tailored monitoring of the vaccine-induced immune responses should be considered in uninfected MM patients. This work was partially funded by Grifols, the Departament de Salut of the Generalitat de Catalunya (grant DSL0016 to J Blanco and Grant DSL015 to J Carrillo), the Spanish Health Institute Carlos III (Grant PI17/01518 and PI20/00093 to J Blanco and PI18/01332 to J Carrillo), Fundació Gloria Soler, and the crowdfunding initiatives https://www.yomecorono.com, BonPreu/Esclat and Correos. M Trigueros was supported by a doctoral fellowship from the Departament de Salut from Generalitat de Catalunya (SLT017/20/000095). F Muñoz-Lopez is supported by a doctoral grant from Sorigué Foundation. E Pradenas was supported by a doctoral grant from ANID, Chile: Grant 72180406. M Massanella was granted with RYC2020-028934-I/AEI/10.13039/501100011033 from Spanish Ministry of Science and Innovation and State Research Agency, MCIN/AEI/10.13039/501100011033 and the European Social Fund “investing in your future.”

The natural history of COVID-19 and predictors of mortality in older adults need to be investigated to inform clinical operations and healthcare policy planning. A retrospective study took place in 80 long-term nursing homes in Catalonia, Spain collecting data from March 1st to May 31st, 2020. Demographic and clinical data from 2,092 RT-PCR confirmed cases of SARS-CoV-2 infection were registered, including structural characteristics of the facilities. Descriptive statistics to describe the demographic, clinical, and molecular characteristics of our sample were prepared, both overall and by their symptomatology was performed and an analysis of statistically significant bivariate differences and constructions of a logistic regression model were carried out to assess the relationship between variables. The incidence of the infection was 28%. 71% of the residents showed symptoms. Five major symptoms included: fever, dyspnea, dry cough, asthenia and diarrhea. Fever and dyspnea were by far the most frequent (50% and 28%, respectively). The presentation was predominantly acute and symptomatology persisted from days to weeks (mean 9.1 days, SD = 10,9). 16% of residents had confirmed pneumonia and 22% required hospitalization. The accumulated mortality rate was 21.75% (86% concentrated during the first 28 days at onset). A multivariate logistic regression analysis showed a positive predictive value for mortality for some variables such as age, pneumonia, fever, dyspnea, stupor refusal to oral intake and dementia (p

BACKGROUND The COVID-19 pandemic has turned the care model of health systems around the world upside down, causing the abrupt cancellation of face-to-face visits and redirection of the model toward telemedicine. Digital transformation boosts information systems—the more robust they are, the easier it is to monitor the health care system in a highly complex state and allow for more agile and reliable analysis. OBJECTIVE The purpose of this study was to analyze diagnoses from primary care visits and distinguish between those that had higher and lower variations, relative to the 2019 and 2020 periods (roughly before and during COVID-19), to identify clinical profiles that may have been most impaired from the least-used diagnostic codes for visits during the pandemic. METHODS We used a database from the Primary Care Services Information Technologies Information System of Catalonia. We analyzed the register of visits (n=2,824,185) and their International Classification of Diseases (ICD-10) diagnostic codes (n=3,921,974; mean 1.38 per visit), as approximations of the reasons for consultations, at 3 different grouping levels. The data were represented by a term frequency matrix and analyzed recursively in different partitions aggregated according to date. RESULTS The increase in non–face-to-face visits (+267%) did not counterbalance the decrease in face-to-face visits (–47%), with an overall reduction in the total number of visits of 1.36%, despite the notable increase in nursing visits (10.54%). The largest increases in 2020 were visits with diagnoses related to COVID-19 (ICD-10 codes Z20-Z29: 2.540%), along with codes related to economic and housing problems (ICD-10 codes Z55-Z65: 44.40%). Visits with most of the other diagnostic codes decreased in 2020 relative to those in 2019. The largest reductions were chronic pathologies such as arterial hypertension (ICD-10 codes I10-I16: –32.73%) or diabetes (ICD-10 codes E08-E13: –21.13%), but also obesity (E65-E68: –48.58%) and bodily injuries (ICD-10 code T14: –33.70%). Visits with mental health–related diagnostic codes decreased, but the decrease was less than the average decrease. There was a decrease in consultations—for children, adolescents, and adults—for respiratory infections (ICD-10 codes J00-J06: –40.96%). The results show large year-on-year variations (in absolute terms, an average of 12%), which is representative of the strong shock to the health system. CONCLUSIONS The disruption in the primary care model in Catalonia has led to an explosive increase in the number of non–face-to-face visits. There has been a reduction in the number of visits for diagnoses related to chronic pathologies, respiratory infections, obesity, and bodily injuries. Instead, visits for diagnoses related to socioeconomic and housing problems have increased, which emphasizes the importance of social determinants of health in the context of this pandemic. Big data analytics with routine care data yield findings that are consistent with those derived from intuition in everyday clinical practice and can help inform decision making by health planners in order to use the next few years to focus on the least-treated diseases during the COVID-19 pandemic.

BACKGROUND In Catalonia, the Fundació TIC Salut Social’s mHealth Office created the AppSalut Site to showcase to mobile apps in the field of health and social services. Its primary objective was to encourage the public to look after their health. The catalogue allows primary health care doctors to prescribe certified, connected apps, which guarantees a safe and reliable environment for their use. The generated data can be consulted by health care professionals and included in the patient's clinical history. This document presents the intervention and the major findings following a five-month pilot project conducted in the Barcelona area. OBJECTIVE The objective of the pilot study was to test, in a real, controlled environment, the implementation of AppSalut. Specifically, we tested whether (1) the procedures corresponding to the prescription, transmission, and evaluation of the data functions correctly, (2) users interact successfully and accept the tool, and (3) the data travels through existing pathways in accordance with international standards. The evaluation is not based on clinical criteria, but rather on the usability and technological reliability of the intervention and its implementation in the context of primary care. METHODS The project was presented to the Primary Care Team participants to encourage the involvement of doctors. The study involved at least 5 doctors and 5 patients per professional, chosen at their discretion and in accordance with their own clinical criteria. An initial consultation took place, during which the doctor discussed the pilot project with the patient and recommended the app. The patient was sent a text message (SMS, short message service) containing an access code. When the patient arrived home, they accessed their personal health record (PHR) to view the recommendation, download the app, and enter the access code. The patient was then able to start using the app. The data was collected in a standardized manner and automatically sent to the system. In a second visit, the patient looked at the data with their doctor on their clinical station screen. The latter was able to consult the information generated by the patient and select what to include in their electronic health record. In order to assess the performance of the system, three focus groups were performed and two ad-hoc case-specific questionnaires, one for doctors and one for patients, were sent by email. Response was voluntary. RESULTS A total of 32 doctors made 79 recommendations of apps to patients. On average, the patients uploaded data 13 times per prescribed app, accounting for a total of 16 different variables. Results show that data traveled through the established channels in an adequate manner and in accordance with international standards. This includes the prescription of an app by a doctor, the patient accessing the recommendation via the PHR, app download by the patient from the official app stores, linking of the patient to the public platform through the app, the generation and visualization of the data on the primary care workstation, and its subsequent validation by the clinician. CONCLUSIONS First, the choice of apps to be used is fundamental; the user's perception of the utility of the proposed tool being paramount. Second, thorough face-to-face support is vital for a smooth transition towards a more intense model of telemedicine. Last, a powerful limiting factor is the lack of control over people’s ability to use the apps.