Search

Hypertensive disorders of pregnancy pose significant risks to both maternal and fetal health. Postpartum hypertension, a common complication, often leads to emergency room (ER) visits or hospital readmissions. Despite the prevalence of these complications, there is a paucity of studies that focus on blood pressure monitoring in postpartum patients with de novo hypertensive disorders of pregnancy. This review aimed to address the gap by evaluating available evidence to compare telehealth monitoring with in‐person visits in preventing ER visits and hospital readmissions among postpartum patients with de novo hypertensive disorders of pregnancy. The study identified relevant studies by conducting a rigorous search strategy (Medline/OVID, the Cochrane Library, Scopus, and research registries such as the International Clinical Trials Registry Platform [ICTRP] and clinical trials) directed by the clinical information specialist. Two reviewers independently screened titles and abstracts, resolving discrepancies with the assistance of a third reviewer. Data extraction followed standardized protocols, and risk of bias assessments were conducted using appropriate tools. This rapid review synthesized evidence from 11 studies on telehealth for women with recent de novo hypertensive disorders of pregnancy. Findings highlighted that telemonitoring led to earlier blood pressure documentation and intervention, reduced disparities in blood pressure measurement, decreased hypertension‐related readmissions, higher rates of postpartum antihypertensive treatment initiation, and increased patient satisfaction. Telehealth emerges as a promising tool for managing postpartum hypertension among women with recent de novo hypertensive disorders of pregnancy [ABSTRACT FROM AUTHOR]

Background Clinically extremely vulnerable (CEV) individuals have a significantly higher risk of morbidity and mortality from coronavirus disease 2019 (COVID-19). This high risk is due to predispositions such as chronic obstructive pulmonary disease (COPD), diabetes mellitus, hypertension, smoking, or extreme age (≥75). The initial COVID-19 preventive measures (use of face masks, social distancing, social bubbles) and vaccine allocation prioritized this group of vulnerable individuals to ensure their continued protection. However, as countries start relaxing the lockdown measures to help prevent socio-economic collapse, the impact of this relaxation on CEVs is once again brought to light. In this study, we set out to understand the impact of policy changes on the lives of CEVs by analyzing Twitter data with the hashtag #highriskcovid used by many high-risk individuals to tweet about and express their opinions and feelings. Methodology Tweets were extracted from the Twitter API between March 01, 2022, and April 21, 2022, using the Twarc2 tool. Extracted tweets were in English and included the hashtag #highriskcovid. We evaluated the most frequently used words and hashtags by calculating term frequency-inverse document frequency, and the location of tweets using the tidygeocoder package (method = osm). We also evaluated the sentiments and emotions depicted by these tweets using the National Research Council sentiment lexicon of the Syuzhet package. Finally, we used the latent Dirichlet allocation algorithm to determine relevant high-risk COVID-19 themes. Results The vast majority of the tweets originated from the United States (64%), Canada (22%), and the United Kingdom (4%). The most common hashtags were #highriskcovid (25.5%), #covid (6.82%), #immunocompromised (4.93%), #covidisnotover (4.0%), and #Maskup (1.40%), and the most frequently used words were immunocompromised (1.64%), people (1.4%), disabled (0.97%), maskup (0.85%), and eugenics (0.85%). The tweets were more negative (19.27%) than positive, and the most expressed negative emotions were fear (13.62%) and sadness (12.47%). At the same time, trust was the most expressed positive emotion and was used in relation to belief in masks, policies, and health workers to help. Finally, we detected frequently co-tweeted words such asmass and disaster, deadly and disabling, high and risk, public and health, immunocompromised and people, mass and disaster, and deadly and disabling. Conclusions The study provides evidence regarding the concerns and fears of high-risk COVID-19 groups as expressed via social media. It is imperative that further policies be implemented to specifically protect the health and mental wellness of high-risk individuals (for example, incorporating sentiment analyses of high-risk COVID-19 individuals such as this paper to inform the evaluation of already implemented preventive measures and policies). In addition, considerable work needs to be done to educate the public on high-risk individuals., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2022, Awoyemi et al.)

Background Long COVID syndrome originated as a patient phrased terminology which was initially used to describe a group of vague symptoms that persisted after recovering from COVID-19. However, it has moved from a patient lingo to a recognized pathological entity which refers to a group of symptoms that lasts weeks or months after the COVID-19 illness. The novelty of this condition, the inadequacy of research on long COVID syndrome, and its origin as a patient-coined terminology necessitated exploring the disease's sentiments and conversations by analyzing publicly available tweets. Method Tweets were extracted using the Twarc2 tool for tweets in the English language with the keywords ( long COVID syndrome, long COVID, post-COVID syndrome, post-acute sequelae of SARS-CoV-2, long-term COVID, long haulers, and chronic COVID syndrome ) between March 25, 2022, and April 1, 2022. The analyses included frequency of the keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language approach and the latent Dirichlet allocation algorithm were used to determine the most shared tweet topics, categorize clusters, and identify themes based on keyword analysis. Results The search yielded 62,232 tweets. The tweets were reduced to 10,670 tweets after removing the duplicates. The vast majority of the tweets originated from the United States of America (38%), United Kingdom (30%), and Canada (13%), with the most common hashtags being #longcovid (36%) and #covid (6.36%), and the most frequently used word being people (1.05%). The top three emotions detected by our analysis were trust (11.68%), fear (11.26%), and sadness (9.76%). The sentiment analysis results showed that people have comparable levels of positivity (19.90%) and negativity (18.39%) towards long COVID. Conclusions Our analysis revealed comparable sentiments about long COVID syndrome, albeit slightly positive. Most tweets connoted trust (positive), fear (negative), and sadness (negative). These emotions were linked with concerns about the infection, pandemic, chronic disability , and governmental policies . We believe this study would be important in guiding information dissemination and governmental policy implementation necessary in tackling long COVID syndrome., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2022, Awoyemi et al.)