HIV infection and COVID-19 death: a population-based cohort analysis of UK primary care data and linked national death registrations within the OpenSAFELY platform

Human Immunodeficiency Virus (HIV) and outcomes from coronavirus disease 2019 (COVID-19) pneumonia: A Meta-Analysis and Meta-Regression

AIDS Research and Human Retroviruses officially retracts the Instant Online/Just Accepted version of the article entitled, "Human Immunodeficiency Virus (HIV) and Outcomes from Coronavirus Disease 2019 (COVID-19) Pneumonia: A Meta-Analysis and Meta-Regression" (epub 27 Jan 2021; doi.org/10.1089/AID.2020.0307). A technical issue caused the accepted version to post online before all plagiarism checks were finalized. Those checks determined that there was too much duplication from previously published sources which prevented the continuance to final publication. The technical issue that caused the premature posting has since been corrected. AIDS Research and Human Retroviruses and its Publisher are committed to upholding the standards of scientific publishing and the community it serves.

BACKGROUND

The number of positive and death cases from coronavirus disease 2019 (COVID-19) is still increasing until now. One of the most prone individuals, even in normal situations is patients with HIV. Currently, the evidence regarding the link between HIV and COVID-19 is still limited and conflicting. This study aims to analyze the relationship between HIV and poor outcomes of COVID-19 infection.

METHODS

We systematically searched the PubMed and Europe PMC database using specific keywords related to our aims until January 12th, 2021. All articles published on COVID-19 and HIV were retrieved. The quality of the study was assessed using the Newcastle Ottawa Scale (NOS) tool for observational studies. Statistical analysis was done using Review Manager 5.4 and Comprehensive Meta-Analysis version 3 software.

RESULTS

A total of 38 studies with 18,271,025 COVID-19 patients were included in this meta-analysis. This meta-analysis showed that HIV was not associated with composite poor outcome [OR 1.08 (95% CI 0.95 - 1.23), p = 0.26, I2 = 68%, random-effect modelling]. Meta-regression showed that the association with composite poor outcome was influenced by hypertension (p < 0.00001) and diabetes (p = 0.0007). Subgroup analysis which involves only studies from African region showed that HIV was associated with composite poor outcomes [OR 1.11 (95% CI 1.03 - 1.21), p = 0.01, I2 = 0%, random-effect modelling].

CONCLUSIONS

Patients with HIV should still be considered as a population for whom precautions are needed to prevent the COVID-19. The availability of antiretroviral therapy should be ensured.

GIS-Based Analysis Framework to Identify the Determinants of COVID-19 Incidence and Fatality in Africa

Corona virus diseases 2019 (COVID-19) pandemic is an extraordinary threat with significant implications in all aspects of human life; therefore, it represents the most immediate challenge for the countries all over the world. This study, hence, is intended to identify the best GIS-based model that can explore, quantify, and model the determinants of COVID-19 incidence and fatality. For this purpose, geospatial models were developed to estimate COVID-19 incidence and fatality rates in Africa, up to 16th of August 2020 at the national level. The models involved Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) analysis using ArcGIS. Spatial autocorrelation analysis recorded a positive spatial autocorrelation in COVID-19 incidence (Moran index 0.16, P = 0.1) and fatality (Moran index 0.26, P = 0.01) rates within different African countries. GWR model had higher R2 than OLS for prediction of incidence and mortality (58% vs 45% and 55% vs 53%). The main predictors of COVID-19 incidence rate were overcrowding, health expenditure, HIV infections, air pollution, and BCG vaccination (mean β = 3.10, 1.66, 0.01, 3.79, and -66.60 respectively, P < 0.05). The main determinants of COVID-19 fatality were prevalence of bronchial asthma, tobacco use, poverty, aging, and cardiovascular diseases fatality (mean β = 0.00162, 0.00004, -0.00025, -0.00144, and -0.00027 respectively, P < 0.05). Application of the suggested model can assist in guiding intervention strategies, particularly at the local and community level whenever the data on COVID-19 cases and predictors variables are available.

A data-driven model of the COVID-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in Italy

An epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the inter-regional mobility restored at the end of the first strong lockdown in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March-October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of the production, trade, and education activities, determined a time advance of the contagion increase since September 2020.