Risk factors for healthcare-associated candidemia in adults hospitalized with SARS-CoV-2 infection.

Analyzing data from a national deidentified electronic health record-based data set using a matched case-control study design, we found that antibiotic use and severity of illness were independent risk factors for healthcare-associated candidemia in adult patients hospitalized with SARS-CoV-2 infection. Interleukin-6 inhibitor and corticosteroid use were not independent risk factors.

Risk Factors for SARS-CoV-2 Infection and Severe Outcomes Among People With Human Immunodeficiency Virus: Cohort Study.

Background: Studies on COVID-19 in people with HIV (PWH) have had limitations. Further investigations on risk factors and outcomes of SARS-CoV-2 infection among PWH are needed. Methods: This retrospective cohort study leveraged the national OPTUM COVID-19 data set to investigate factors associated with SARS-CoV-2 positivity among PWH and risk factors for severe outcomes, including hospitalization, intensive care unit stays, and death. A subset analysis was conducted to examine HIV-specific variables. Multiple variable logistic regression was used to adjust for covariates. Results: Of 43 173 PWH included in this study, 6472 had a positive SARS-CoV-2 result based on a polymerase chain reaction test or antigen test. For PWH with SARS-CoV-2 positivity, higher odds were found for those who were younger (18-49 years), Hispanic White, African American, from the US South, uninsured, and a noncurrent smoker and had a higher body mass index and higher Charlson Comorbidity Index. For PWH with severe outcomes, higher odds were identified for those who were SARS-CoV-2 positive, older, from the US South, receiving Medicaid/Medicare or uninsured, a current smoker, and underweight and had a higher Charlson Comorbidity Index. In a subset analysis including PWH with HIV care variables (n = 5098), those with unsuppressed HIV viral load, a low CD4 count, and no antiretroviral therapy had higher odds of severe outcomes. Conclusions: This large US study found significant ethnic, racial, and geographic differences in SARS-CoV-2 infection among PWH. Chronic comorbidities, older age, lower body mass index, and smoking were associated with severe outcomes among PWH during the COVID-19 pandemic. SARS-CoV-2 infection was associated with severe outcomes, but once we adjusted for HIV care variables, SARS-CoV-2 was no longer significant; however, low CD4 count, high viral load, and lack of antiretroviral therapy had higher odds of severe outcomes.

Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only.

Introduction In January, the National Institutes of Health (NIH) implemented a Data Management and Sharing Policy aiming to leverage data collected during NIH-funded research. The COVID-19 pandemic illustrated that this practice is equally vital for augmenting patient research. In addition, data sharing acts as a necessary safeguard against the introduction of analytical biases. While the pandemic provided an opportunity to curtail critical research issues such as reproducibility and validity through data sharing, this did not materialise in practice and became an example of 'Open Data in Appearance Only' (ODIAO). Here, we define ODIAO as the intent of data sharing without the occurrence of actual data sharing (eg, material or digital data transfers).Objective Propose a framework that states the main risks associated with data sharing, systematically present risk mitigation strategies and provide examples through a healthcare lens.Methods This framework was informed by critical aspects of both the Open Data Institute and the NIH's 2023 Data Management and Sharing Policy plan guidelines.Results Through our examination of legal, technical, reputational and commercial categories, we find barriers to data sharing ranging from misinterpretation of General Data Privacy Rule to lack of technical personnel able to execute large data transfers. From this, we deduce that at numerous touchpoints, data sharing is presently too disincentivised to become the norm.Conclusion In order to move towards Open Data, we propose the creation of mechanisms for incentivisation, beginning with recentring data sharing on patient benefits, additional clauses in grant requirements and committees to encourage adherence to data reporting practices.

Analyzing an Emerging Pandemic on Twitter: Monkeypox.

Background: Social media platforms like Twitter provide important insights into the public's perceptions of global outbreaks like monkeypox. By analyzing tweets, we aimed to identify public knowledge and opinions on the monkeypox virus and related public health issues. Methods: We analyzed English-language tweets using the keyword "monkeypox" from 1 May to 23 July 2022. We reported gender, ethnicity, and race of Twitter users and analyzed tweets to identify predominant sentiment and emotions. We performed topic modeling and compared cohorts of users who self-identify as LGBTQ+ (an abreviation for lesbian, gay, bisexual, transgender, queer, and/or questioning) allies versus users who do not, and cohorts identified as "bots" versus humans. Results: A total of 48 330 tweets were written by LGBTQ+ self-identified advocates or allies. The mean sentiment score for all tweets was -0.413 on a -4 to +4 scale. Negative tweets comprised 39% of tweets. The most common emotions expressed were fear and sadness. Topic modeling identified unique topics among the 4 cohorts analyzed. Conclusions: The spread of mis- and disinformation about monkeypox was common in our tweet library. Various conspiracy theories about the origins of monkeypox, its relationship to global economic concerns, and homophobic and racial comments were common. Conversely, many other tweets helped to provide information about monkeypox vaccines, disease symptoms, and prevention methods. Discussion of rising monkeypox case numbers globally was also a large aspect of the conversation. Conclusions: We demonstrated that Twitter is an effective means of tracking sentiment about public healthcare issues. We gained insight into a subset of people, self-identified LGBTQ+ allies, who were more affected by monkeypox.

Incidence and Risk Factors for Severe Outcomes in Pediatric Patients With COVID-19.

OBJECTIVES: Throughout the pandemic, children with COVID-19 have experienced hospitalization, ICU admission, invasive respiratory support, and death. Using a multisite, national dataset, we investigate risk factors associated with these outcomes in children with COVID-19. METHODS: Our data source (Optum deidentified COVID-19 Electronic Health Record Dataset) included children aged 0 to 18 years testing positive for COVID-19 between January 1, 2020, and January 20, 2022. Using ordinal logistic regression, we identified factors associated with an ordinal outcome scale: nonhospitalization, hospitalization, or a severe composite outcome (ICU, intensive respiratory support, death). To contrast hospitalization for COVID-19 and incidental positivity on hospitalization, we secondarily identified patient factors associated with hospitalizations with a primary diagnosis of COVID-19. RESULTS: In 165 437 children with COVID-19, 3087 (1.8%) were hospitalized without complication, 2954 (1.8%) experienced ICU admission and/or intensive respiratory support, and 31 (0.02%) died. We grouped patients by age: 0 to 4 years old (35 088), and 5 to 11 years old (75 574), 12 to 18 years old (54 775). Factors positively associated with worse outcomes were preexisting comorbidities and residency in the Southern United States. In 0- to 4-year-old children, there was a nonlinear association between age and worse outcomes, with worse outcomes in 0- to 2-year-old children. In 5- to 18-year-old patients, vaccination was protective. Findings were similar in our secondary analysis of hospitalizations with a primary diagnosis of COVID-19, though region effects were no longer observed. CONCLUSIONS: Among children with COVID-19, preexisting comorbidities and residency in the Southern United States were positively associated with worse outcomes, whereas vaccination was negatively associated. Our study population was highly insured; future studies should evaluate underinsured populations to confirm generalizability.