HIV and Associated Indicators of COVID-19 Cytokine Release Syndrome: A Meta-Analysis and Meta-Regression.

The aim of this review was to evaluate the risk of COVID-19 cytokine release syndrome (CRS) with HIV infection and meta-regress for indicator covariates. Electronic databases, including Google Scholar, Cochrane Library, Web of Sciences (WOS), EMBASE, Medline/PubMed, COVID-19 Research Database, and Scopus, were systematically searched till February 30, 2022. All human studies were included, irrespective of publication date or region. Eleven studies, with a total of 2,005,274 detailing cytokine release syndrome defined by specific parameters, were included. To pool the estimate, a random-effects model with risk ratio (RR) as the effect measure was used. Moreover, publication bias and sensitivity analysis were evaluated followed by meta-regression analysis to account for any possible covariates. This systematic review, meta-analysis, and meta-regression trial was registered (CRD42021264761) on the PROSPERO register. HIV infection showed an increased risk for COVID-19 cytokine release syndrome (RR= 1.48, 95% CI (1.16, 1.88) (P=0.002)) with substantial heterogeneity (I2 > 80%) and a 4.6% cumulative incidence. The true effects size in 95% of all the comparable populations (prediction interval) fell between 0.67 to 3.29. HIV infection further showed an increased risk for intensive care unit (ICU) admission ((P<0.0001) (I² = 0%)] and mechanical ventilation (MV) ((P=0.04) (I² = 0%)) as the key indicators of cytokine release syndrome. Meta-regression analysis demonstrated that COVID-19 cytokine release syndrome was influenced by the year a study was published (R² = 0.55) and the region from where the study was conducted (R² = 0.11). On meta-regression analysis, the combined impact of all covariates in the model explained at least some of the variance in effect size (Q = 16.21, df = 6, P= 0.0127), and the proportion of variance explained by covariates on comparing the model with and without the covariates was 73 % and highly significant (Tau² = 0.1100, Tau = 0.3317, I² = 86.5%, Q = .99, df = 10, P<0.0001) (R² = 0.73). Our updated meta-analysis indicated that HIV infection was significantly associated with an increased risk for COVID-19 cytokine release syndrome, which, in addition, might be moderated by the year a study was published and the region in which the study was conducted. Further, the risk for intensive care unit (ICU) admission and mechanical ventilation (MV) were identified as the key indicators of cytokine release syndrome. We believe the updated data anchoring cytokine release syndrome will contribute to more substantiation of the findings reported by similar earlier studies.

Tackling brain drain at Chinese CDCs: understanding job preferences of public health doctoral students using a discrete choice experiment survey.

BACKGROUND: Given the demands for public health and infectious disease management skills during COVID-19, a shortage of the public health workforce, particularly with skills and competencies in epidemiology and biostatistics, has emerged at the Centers for Disease Controls (CDCs) in China. This study aims to investigate the employment preferences of doctoral students majoring in epidemiology and biostatistics, to inform policy-makers and future employers to address recruitment and retention requirements at CDCs across China. METHODS: A convenience sampling approach for recruitment, and an online discrete choice experiment (DCE) survey instrument to elicit future employee profiles, and self-report of their employment and aspirational preferences during October 20 and November 12, 2020. Attributes included monthly income, employment location, housing benefits, children's education opportunities, working environment, career promotion speed and bianzhi (formally established post). RESULTS: A total of 106 doctoral epidemiology and biostatistics students from 28 universities completed the online survey. Monthly income, employment location and bianzhi was of highest concern in the seven attributes measured, though all attributes were statistically significant and presented in the expected direction, demonstrating preference heterogeneity. Work environment was of least concern. For the subgroup analysis, employment located in a first-tier city was more likely to lead to a higher utility value for PhD students who were women, married, from an urban area and had a high annual family income. Unsurprisingly, when compared to single students, married students were willing to forgo more for good educational opportunities for their children. The simulation results suggest that, given our base case, increasing only monthly income from 10,000 ($ 1449.1) to 25,000 CNY ($ 3622.7) the probability of choosing the job in the third-tier city would increase from 18.1 to 53.8% (i.e., the location choice is changed). CONCLUSION: Monthly income and employment location were the preferred attributes across the cohort, with other attributes then clearly ranked and delineated. A wider use of DCEs could inform both recruitment and retention of a public health workforce, especially for CDCs in third-tier cities where resource constraints preclude all the strategies discussed here.

COVID-19 Management Missteps.

In December 2019, the first case of a novel coronavirus infectious disease, coronavirus disease 2019 (COVID-19), was identified in the province of Wuhan, China. Since the initial identification on March 11, 2020, by the World Health Organization (WHO), COVID-19 had rapidly spread all over the world, leading to the declaration of COVID-19 as a pandemic. In response to the exponential trend of reported confirmed cases, national governments worked quickly to devise plans to combat the spread and to soften the consequences which were to follow. Two primary approaches included limiting the spread of the virus and increasing hospital capacity. The implementation of these strategies, however, varied greatly among different governments and their respective populations. Countries developed similar guidelines in response to COVID-19, but with a variation. Many of these guidelines were similar in that they fell under the same general topics such as the use of facial masks, social distancing, and online learning. The effect of COVID-19 on public health was more reliant on the implementation of these recommendations rather than the recommendations themselves.  The medical therapies used to treat the widespread COVID-19 disease are flourishing and evolving rapidly. Ongoing research shows that the spectrum of treatment for COVID-19 varies from pharmacological and non-pharmacological therapeutic interventions. Some of the treatments that are being used in clinical practice include supportive care, antiviral drugs, immunomodulatory agents, convalescent plasma transfusion, and monoclonal antibody treatments. In addition, the most promising approach thus far is the COVID-19 vaccine developed by Pfizer-BioNTech, Moderna, and most recently Johnson & Johnson. Overall, as various treatment approaches are being explored and administered to people globally, it is important to acknowledge that there is currently no definite cure or any evidence-based treatment for COVID-19.  COVID-19 infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have brought devastating consequences to the lives of millions of people through their health effects and the failure of global initiatives to contain it. A review of many missteps that potentially could have altered the landscape for this virus to affect the lives of many is discussed with hope for a better approach going forward.

Clinical Data and Health Outcomes for HIV-Positive Patients Diagnosed With COVID-19.

Introduction As we care for patients during the coronavirus pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is important to learn and analyze the health outcomes for HIV-positive patients who have been infected with COVID-19. The clinical course and outcome of COVID-19 among patients with HIV-1 infection are still unknown and novel. Methods This is a retrospective cohort study of 34 HIV-positive patients who are diagnosed with COVID-19. The following basic demographic, clinical, and laboratory test information were collected for each patient: age, race/ethnicity, gender, CD4/viral load count before and after COVID-19 diagnosis, clinical symptoms, hospitalizations, antiretroviral medications, and comorbidities. These data were collected from the electronic health record (EHR) and recorded in the study database. Results The mean (interquartile range (IQR)) HIV viral load (RNA PCR) after COVID-19 infection was 37,170 (<20-167) copies/mL compared to 25,730 (<20-100) copies/mL before COVID-19 infection. The mean (IQR) CD4+ lymphocyte count prior to and after COVID-19 infection was 583 (101-1139) and 477 (167-821) cells/mm3, respectively. Hypertension (n = 20) was the most prevalent comorbidity found in the cohort of HIV-positive patients. Patients with HIV RNA < 20 copies/mL prior to and after COVID-19 infection were 27 (79.3%) and 17 (73.7%), respectively. Conclusion As the pandemic situation keeps on evolving, there will be new findings on how people living with HIV might be affected by SARS-CoV-2. Our findings highlight the importance of larger sample size studies to better understand the management of HIV-positive patients in a pandemic situation.