Collection and Analysis of Narratives for a Values Charter of the Italian Society for Hospital Pharmacy

In recent years, the field of Narrative Pharmacy was introduced, which particularly addresses the pharmacist not only to guide a relationship of listening to and caring for the patient but also to strengthen and motivate toward the profession, improve relationships with colleagues, enhance the ability to teamwork, and understand emotions. In this paper, we report the analysis behind the construction of the Value Chart from the personal narratives of members of the Italian Society of Hospital Pharmacy. Each member's subjective professional experiences and their own view of themselves within society were collected through a semi-structured interview. Personal thinking, including experiences, feelings, opinions, desires, and regrets was classified by objective methods, from which main concepts were extracted for the Value Chart. The feedback to the survey, including activities during the Covid-19 pandemic management, is classified according to the analytical methods of Kleinman, Frank, Bury and Launer-Robinson. Regarding sentiment analysis, the emotional and subjective context of the text provides an ideal baseline to validate the result. The analysis was implemented using neural networks trained on dictionaries and natural language (i.e., Tweets). The originality of the work lies in the fact that generally value charters are built on a Society's values. In contrast, in this case, individual contributions were gathered to complement the ethical values on which the society is founded. © 2022 IEEE.

Global Trends in Social Prescribing: Web-Based Crawling Approach.

BACKGROUND: Social loneliness is a prevalent issue in industrialized countries that can lead to adverse health outcomes, including a 26% increased risk of premature mortality, coronary heart disease, stroke, depression, cognitive impairment, and Alzheimer disease. The United Kingdom has implemented a strategy to address loneliness, including social prescribing-a health care model where physicians prescribe nonpharmacological interventions to tackle social loneliness. However, there is a need for evidence-based plans for global social prescribing dissemination. OBJECTIVE: This study aims to identify global trends in social prescribing from 2018. To this end, we intend to collect and analyze words related to social prescribing worldwide and evaluate various trends of related words by classifying the core areas of social prescribing. METHODS: Google's searchable data were collected to analyze web-based data related to social prescribing. With the help of web crawling, 3796 news items were collected for the 5-year period from 2018 to 2022. Key topics were selected to identify keywords for each major topic related to social prescribing. The topics were grouped into 4 categories, namely Healthy, Program, Governance, and Target, and keywords for each topic were selected thereafter. Text mining was used to determine the importance of words collected from new data. RESULTS: Word clouds were generated for words related to social prescribing, which collected 3796 words from Google News databases, including 128 in 2018, 432 in 2019, 566 in 2020, 748 in 2021, and 1922 in 2022, increasing nearly 15-fold between 2018 and 2022 (5 years). Words such as health, prescribing, and GPs (general practitioners) were the highest in terms of frequency in the list for all the years. Between 2020 and 2021, COVID, gardening, and UK were found to be highly related words. In 2022, NHS (National Health Service) and UK ranked high. This dissertation examines social prescribing-related term frequency and classification (2018-2022) in Healthy, Program, Governance, and Target categories. Key findings include increased "Healthy" terms from 2020, "gardening" prominence in "Program," "community" growth across categories, and "Target" term spikes in 2021. CONCLUSIONS: This study's discussion highlights four key aspects: (1) the "Healthy" category trends emphasize mental health, cancer, and sleep; (2) the "Program" category prioritizes gardening, community, home-schooling, and digital initiatives; (3) "Governance" underscores the significance of community resources in social prescribing implementation; and (4) "Target" focuses on 4 main groups: individuals with long-term conditions, low-level mental health issues, social isolation, or complex social needs impacting well-being. Social prescribing is gaining global acceptance and is becoming a global national policy, as the world is witnessing a sharp rise in the aging population, noncontagious diseases, and mental health problems. A successful and sustainable model of social prescribing can be achieved by introducing social prescribing schemes based on the understanding of roles and the impact of multisectoral partnerships.

Barriers to Dissemination of Local Health Data Faced by US State Agencies: Survey Study of Behavioral Risk Factor Surveillance System Coordinators.

BACKGROUND: Advances in information technology have paved the way to facilitate accessibility to population-level health data through web-based data query systems (WDQSs). Despite these advances in technology, US state agencies face many challenges related to the dissemination of their local health data. It is essential for the public to have access to high-quality data that are easy to interpret, reliable, and trusted. These challenges have been at the forefront throughout the COVID-19 pandemic. OBJECTIVE: The purpose of this study is to identify the most significant challenges faced by state agencies, from the perspective of the Behavioral Risk Factor Surveillance System (BRFSS) coordinator from each state, and to assess if the coordinators from states with a WDQS perceive these challenges differently. METHODS: We surveyed BRFSS coordinators (N=43) across all 50 US states and the District of Columbia. We surveyed the participants about contextual factors and asked them to rate system aspects and challenges they faced with their health data system on a Likert scale. We used two-sample t tests to compare the means of the ratings by participants from states with and without a WDQS. RESULTS: Overall, 41/43 states (95%) make health data available over the internet, while 65% (28/43) employ a WDQS. States with a WDQS reported greater challenges (P=.01) related to the cost of hardware and software (mean score 3.44/4, 95% CI 3.09-3.78) than states without a WDQS (mean score 2.63/4, 95% CI 2.25-3.00). The system aspect of standardization of vocabulary scored more favorably (P=.01) in states with a WDQS (mean score 3.32/5, 95% CI 2.94-3.69) than in states without a WDQS (mean score 2.85/5, 95% CI 2.47-3.22). CONCLUSIONS: Securing of adequate resources and commitment to standardization are vital in the dissemination of local-level health data. Factors such as receiving data in a timely manner, privacy, and political opposition are less significant barriers than anticipated.

Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: - evidence from Baidu index.

BACKGROUND: New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19. METHODS: We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed. RESULTS: Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10- 6; cough: rs=0.592, p=4.485× 10- 4; fatigue: rs=0.629, p=1.494× 10- 4; sputum production: rs=0.648, p=8.206× 10- 5; shortness of breath: rs=0.656, p=6.182× 10-5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value's optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever. CONCLUSION: The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public's attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

Characteristics and Outcomes of a Sample of Patients With COVID-19 Identified Through Social Media in Wuhan, China: Observational Study.

BACKGROUND: The number of deaths worldwide caused by coronavirus disease (COVID-19) is increasing rapidly. Information about the clinical characteristics of patients with COVID-19 who were not admitted to hospital is limited. Some risk factors of mortality associated with COVID-19 are controversial (eg, smoking). Moreover, the impact of city closure on mortality and admission rates is unknown. OBJECTIVE: The aim of this study was to explore the risk factors of mortality associated with COVID-19 infection among a sample of patients in Wuhan whose conditions were reported on social media. METHODS: We enrolled 599 patients with COVID-19 from 67 hospitals in Wuhan in the study; 117 of the participants (19.5%) were not admitted to hospital. The demographic, epidemiological, clinical, and radiological features of the patients were extracted from their social media posts and coded. Telephone follow-up was conducted 1 month later (between March 15 and 23, 2020) to check the clinical outcomes of the patients and acquire other relevant information. RESULTS: The median age of patients with COVID-19 who died (72 years, IQR 66.5-82.0) was significantly higher than that of patients who recovered (61 years, IQR 53-69, P<.001). We found that lack of admission to hospital (odds ratio [OR] 5.82, 95% CI 3.36-10.1; P<.001), older age (OR 1.08, 95% CI 1.06-1.1; P<.001), diffuse distribution (OR 11.09, 95% CI 0.93-132.9; P=.058), and hypoxemia (odds ratio 2.94, 95% CI 1.32-6.6; P=.009) were associated with increasing odds of death. Smoking was not significantly associated with mortality risk (OR 0.9, 95% CI 0.44-1.85; P=.78). CONCLUSIONS: Older age, diffuse distribution, and hypoxemia are factors that can help clinicians identify patients with COVID-19 who have poor prognosis. Our study suggests that aggregated data from social media can also be comprehensive, immediate, and informative in disease prognosis.