Examining Rural and Urban Sentiment Difference in COVID-19-Related Topics on Twitter: Word Embedding-Based Retrospective Study.

BACKGROUND: By the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic spread, people used social media platforms to express their opinions and concerns about COVID-19-related topics. OBJECTIVE: This study aimed to (1) identify the primary COVID-19-related topics in the contiguous United States communicated over Twitter and (2) compare the sentiments urban and rural users expressed about these topics. METHODS: We collected tweets containing geolocation data from May 2020 to January 2022 in the contiguous United States. We relied on the tweets' geolocations to determine if their authors were in an urban or rural setting. We trained multiple word2vec models with several corpora of tweets based on geospatial and timing information. Using a word2vec model built on all tweets, we identified hashtags relevant to COVID-19 and performed hashtag clustering to obtain related topics. We then ran an inference analysis for urban and rural sentiments with respect to the topics based on the similarity between topic hashtags and opinion adjectives in the corresponding urban and rural word2vec models. Finally, we analyzed the temporal trend in sentiments using monthly word2vec models. RESULTS: We created a corpus of 407 million tweets, 350 million (86%) of which were posted by users in urban areas, while 18 million (4.4%) were posted by users in rural areas. There were 2666 hashtags related to COVID-19, which clustered into 20 topics. Rural users expressed stronger negative sentiments than urban users about COVID-19 prevention strategies and vaccination (P<.001). Moreover, there was a clear political divide in the perception of politicians by urban and rural users; these users communicated stronger negative sentiments about Republican and Democratic politicians, respectively (P<.001). Regarding misinformation and conspiracy theories, urban users exhibited stronger negative sentiments about the "covidiots" and "China virus" topics, while rural users exhibited stronger negative sentiments about the "Dr. Fauci" and "plandemic" topics. Finally, we observed that urban users' sentiments about the economy appeared to transition from negative to positive in late 2021, which was in line with the US economic recovery. CONCLUSIONS: This study demonstrates there is a statistically significant difference in the sentiments of urban and rural Twitter users regarding a wide range of COVID-19-related topics. This suggests that social media can be relied upon to monitor public sentiment during pandemics in disparate types of regions. This may assist in the geographically targeted deployment of epidemic prevention and management efforts.

“Please Advise”: Understanding the Needs of Informal Caregivers of People with Alzheimer’s Disease and Related Dementia from Online Communities Through a Structured Topic Modeling Approach

The informal or family caregivers of the Alzheimer’s disease or related dementia (ADRD) patients, also known as the “invisible second patients”, are often reported experiencing emotional and behavioral hardships. In recent years, the rapid development of online communities provides these caregivers a new opportunity for seeking information and emotional support. Comparing with offline social support services which have been constrained during the COVID-19 pandemic, online support allows caregivers to reach many peers in a convenient manner. This research aimed to examine the issues faced by ADRD caregivers through performing a structural topic modeling on posts from two online communities. Results revealed that the top concerns of the caregivers include getting along with Alzheimer’s patients, family issues, patients’ internal medical issues, stages of the disease, care facilities, etc. The results may have a further implication to the future implementation of psychological and social intervention of ADRD family care.

Effect of Stellate Ganglion Block Combined with Lidocaine at Different Concentrations for Preemptive Analgesia on Postoperative Pain Relief and Adverse Reactions of Patients Undergoing Laparoscopic Cholecystectomy.

Objective: To explore the effect of stellate ganglion block (SGB) combined with lidocaine at different concentrations for preemptive analgesia on postoperative pain relief and adverse reactions of patients undergoing laparoscopic cholecystectomy (LC). Methods: Ninety patients undergoing LC in our hospital from June 2019 to June 2020 were selected as the subjects and were randomly divided into group A (30 cases), group B (30 cases), and group C (30 cases), all patients received SGB, and 10 mL of lidocaine at concentrations of 0.25%, 0.5%, and 0.75% was, respectively, administered to patients in groups A, B, and C, so as to compare the analgesic effect, adverse reactions, and clinical indicators among the three groups. Results: At T 1 and T 2, group C obtained obviously lower NRS scores than groups A and B (P < 0.001); compared with groups A and B, group A had obviously higher onset time (P < 0.001) and significantly lower duration (P < 0.001); no obvious differences in the hemodynamic indexes among the groups were observed (P > 0.05); group C obtained obviously higher BCS score than groups A and B; and the total incidence rate of adverse reactions was obviously higher in group C than in groups A and B (P < 0.05). Conclusion: Performing SGB combined with 0.5% lidocaine to patients undergoing LC achieves the optimal analgesic effect; such anesthesia plan can effectively stabilize patients' hemodynamics, present higher safety, and promote the regulation of the body internal environment. Further research will be conducive to establishing a better anesthesia plan for such patients.

Dynamically adjusting case reporting policy to maximize privacy and public health utility in the face of a pandemic.

OBJECTIVE: Supporting public health research and the public's situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, such as the Health Insurance Portability and Accountability Act of 1996 and recent state-level regulations, permits sharing deidentified person-level data; however, current deidentification approaches are limited. Namely, they are inefficient, relying on retrospective disclosure risk assessments, and do not flex with changes in infection rates or population demographics over time. In this paper, we introduce a framework to dynamically adapt deidentification for near-real time sharing of person-level surveillance data. MATERIALS AND METHODS: The framework leverages a simulation mechanism, capable of application at any geographic level, to forecast the reidentification risk of sharing the data under a wide range of generalization policies. The estimates inform weekly, prospective policy selection to maintain the proportion of records corresponding to a group size less than 11 (PK11) at or below 0.1. Fixing the policy at the start of each week facilitates timely dataset updates and supports sharing granular date information. We use August 2020 through October 2021 case data from Johns Hopkins University and the Centers for Disease Control and Prevention to demonstrate the framework's effectiveness in maintaining the PK11 threshold of 0.01. RESULTS: When sharing COVID-19 county-level case data across all US counties, the framework's approach meets the threshold for 96.2% of daily data releases, while a policy based on current deidentification techniques meets the threshold for 32.3%. CONCLUSION: Periodically adapting the data publication policies preserves privacy while enhancing public health utility through timely updates and sharing epidemiologically critical features.