HIV infection and risk of COVID-19 mortality: a meta-analysis

Background: This meta-analysis aimed to estimate the association of human immunodeficiency virus (HIV) infection and risk of coronavirus disease 2019 (COVID-19) mortality.

HIV infection and risk of COVID-19 mortality: A meta-analysis.

BACKGROUND: This meta-analysis aimed to estimate the association of human immunodeficiency virus (HIV) infection and risk of coronavirus disease 2019 (COVID-19) mortality. METHODS: We systematically retrieved articles published on HIV infection and risk of COVID-19 mortality through PubMed, EMBase, China National Knowledge Infrastructure, WanFang, and Chongqing VIP databases using a predefined search strategy from December 1, 2019 to January 31, 2021. Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included studies. Cochran Q test and I2 statistics were quantified to measure heterogeneity. Odds ratio (OR) and 95% confidence intervals (CI) were computed and displayed in the form of forest plots. Subgroup analysis was performed to explore the source of heterogeneity. Funnel plot, Begg test, and Egger test were used to assess potential publication bias. Stata software version 11.0 was used to analyze all the statistical data. RESULTS: We included 10 studies with 18,122,370 COVID-19 patients, of whom 41,113 were with HIV infection and 18,081,257 were without HIV infection. The pooled overall results suggested that people living with HIV infection had a higher risk of mortality from COVID-19 than those without HIV infection (OR = 1.252, 95% CI 1.027-1.524). Subgroup analysis showed that people living with HIV infection had a higher risk of COVID-19 mortality than those without HIV infection in the United States (OR = 1.520, 95% CI 1.252-1.845) and in South Africa (OR = 1.122, 95% CI 1.032-1.220); however, no significant association was found in the United Kingdom (OR = 0.878, 95% CI 0.657-1.174). CONCLUSION: Patients with HIV infection should be the emphasis population to prevent the risk of mortality during the clinical treatment of COVID-19 patients.

Detecting Early-Warning Signals in Time Series of Visits to Points of Interest to Examine Population Response to COVID-19 Pandemic.

The objective of this paper is to examine population response to COVID-19 and associated policy interventions through detecting early-warning signals in time series of visits to points of interest (POIs). Complex systems, such as cities, would demonstrate early-warning signals (e.g., increased autocorrelation and standard deviation) when they approach phase transitions responding to external perturbation, such as crises, policy changes, and human behavior changes. In urban systems, population visits to POIs, such as restaurants, museums, and hospitals, represent a state of cities as complex systems. These states may undergo phase transitions due to population response to pandemic risks and intervention policies (e.g., social distancing and shelter-in-place orders). In this study, we conducted early-warning signal detection on population visits to POIs to examine population response to pandemic risks, and we evaluated time lags between detected early-warning dates and dates of first cases and policy interventions. We examined two early-warning signals, the increase of autocorrelation at-lag-1 and standard deviation, in time series of population visits to POIs in 17 metropolitan cities in the United States of America. We examined visits to grouped POIs according to two categories of essential services and non-essential services. The results show that: (1) early-warning signals for population response to COVID-19 were detected between February 14 and March 11, 2020 in 17 cities; (2) detected population response had started prior to shelter-in-place orders in 17 cities; (3) early-warning signals detected from the essential POIs visits appeared earlier than those from non-essential POIs; and 4) longer time lags between detected population response and shelter-in-place orders led to a less decrease in POI visits. The results show the importance of detecting early-warning signals during crises in cities as complex systems. Early-warning signals could provide important insights regarding the timing and extent of population response to crises to inform policymakers.