Disparities and resilience: analyzing online Health information provision, behaviors and needs of LBGTQ + elders during COVID-19.

BACKGROUND: Prior studies indicate that older members of LGBTQ+ communities have specific health provision and health information needs related to coping with COVID-19, its long-term effects, and the social and economic impact of the pandemic. This study addresses the issue of a lack of timely, complete, and high-quality data about this population's healthcare and healthcare information needs and behaviors. Recognizing also that this is a diverse population made up of multiple communities and identities with different concerns and experiences, this research seeks to develop and refine a method that can provide additional nuanced data and insights that can support improved and more closely targeted health interventions and online information provision. METHODS: We use computational discourse analysis, which is based on NLP algorithms, to build and analyze a digital corpus of online search results containing rich, wide-ranging content such as quotes and anecdotes from older members of LGBTQ+ communities as well as practitioners, advice, and recommendations from policymakers and healthcare experts, and research outcomes. In our analysis, we develop and apply an innovative disparities and resilience (D&R) framework to identify external and internal perspectives and understand better disparities and resilience as they pertain to this population. RESULTS: Results of this initial study support previous research that LGBTQ+ elders experience aggravated health and related social-economic disparities in comparison to the general population of older people. We also find that LGBTQ+ elders leverage individual toughness and community closeness, and quickly adapt mentally and technologically, despite inadequate social infrastructure for sharing health information and elders' often low social economic status. The methods used therefore are able to surface distinctive resilience in the face of distinctive disparities. CONCLUSIONS: Our study provides evidence that methodological innovation in gathering and analyzing digital data relating to overlooked, disparately affected, and socially and economically marginalized intersectional communities such as LGBTQ+ elders can result in increased external and self-knowledge of these populations. Specifically, it demonstrates the potential of computational discourse analysis to surface hidden and emerging issues and trends relating to a multi-faceted population that has important concerns about public exposure in highly timely and automated ways. It also points to the potential benefits of triangulating data gathered through this approach with data gathered through more traditional mechanisms such as surveys and interviews. TRIAL REGISTRATION: Not Applicable.

Association between nursing home staff turnover and infection control citations.

OBJECTIVE: To describe the association between nursing home staff turnover and the presence and scope of infection control citations. DATA SOURCES: Secondary data for all US nursing homes between March 31, 2017, through December 31, 2019 were obtained from Payroll-Based Journal (PBJ), Nursing Home Compare, and Long-Term Care: Facts on Care in the US (LTC Focus). STUDY DESIGN: We estimated the association between nurse turnover and the probability of an infection control citation and the scope of the citation while controlling for nursing home fixed effects. Our turnover measure is the percent of the facility's nursing staff hours that were provided by new staff (less than 60 days of experience in the last 180 days) during the 2 weeks prior to the health inspection. We calculated turnover for all staff together and separately for registered nurses, licensed practical nurses (LPNs), and certified nursing assistants. DATA COLLECTION/EXTRACTION METHODS: We linked nursing homes standard inspection surveys to 650 million shifts from the PBJ data. We excluded any nursing home with incomplete or missing staffing data. Our final analytic sample included 12,550 nursing homes with 30,536 surveys. PRINCIPAL FINDINGS: Staff turnover was associated with an increased likelihood of an infection control citation (average marginal effect [AME] = 0.12 percentage points [pp]; 95% confidence interval [CI]: 0.05, 0.18). LPN (AME = 0.06 pp; 95% CI: 0.01, 0.11) turnover was conditionally associated with an infection control citation. Conditional on having at least an isolated citation for infection control, staff turnover was positively associated with receiving a citation coded as a "pattern" (AME = 0.21 pp; 95% CI: 0.10, 0.32). Conditional of having at least a pattern citation, staff turnover was positively associated with receiving a widespread citation (AME = 0.21 pp; 95% CI: 0.10, 0.32). CONCLUSIONS: Turnover was positively associated with the probability of an infection control citation. Staff turnover should be considered an important factor related to the spread of infections within nursing homes.

High Nursing Staff Turnover In Nursing Homes Offers Important Quality Information.

Nursing staff turnover has long been considered an important indicator of nursing home quality. However, turnover has never been reported on the Nursing Home Compare website, likely because of the lack of adequate data. On July 1, 2016, the Centers for Medicare and Medicaid Services began collecting auditable payroll-based daily staffing data for US nursing homes. We used 492 million nurse shifts from these data to calculate a novel turnover metric representing the percentage of hours of nursing staff care that turned over annually at each of 15,645 facilities. Mean and median annual turnover rates for total nursing staff were roughly 128 percent and 94 percent, respectively. Turnover rates were correlated with facility location, for-profit status, chain ownership, Medicaid patient census, and star ratings. Disseminating facilities' nursing staff turnover rates on Nursing Home Compare could provide important quality information for policy makers, payers, and consumers, and it may incentivize efforts to reduce turnover.

Stigmatization in social media: Documenting and analyzing hate speech for COVID-19 on Twitter.

As the COVID-19 pandemic has unfolded, Hate Speech on social media about China and Chinese people has encouraged social stigmatization. For the historical and humanistic purposes, this history-in-the-making needs to be archived and analyzed. Using the query "china+and+coronavirus" to scrape from the Twitter API, we have obtained 3,457,402 key tweets about China relating to COVID-19. In this archive, in which about 40% of the tweets are from the U.S., we identify 25,467 Hate Speech occurrences and analyze them according to lexicon-based emotions and demographics using machine learning and network methods. The results indicate that there are substantial associations between the amount of Hate Speech and demonstrations of sentiments, and state demographics factors. Sentiments of surprise and fear associated with poverty and unemployment rates are prominent. This digital archive and the related analyses are not simply historical, therefore. They play vital roles in raising public awareness and mitigating future crises. Consequently, we regard our research as a pilot study in methods of analysis that might be used by other researchers in various fields.