Association between pre-existing conditions and hospitalization, intensive care services and mortality from COVID-19, a cross sectional analysis of an international global health data repository (preprint)

Objective To investigate the association between pre-existing conditions and hospitalization, need for intensive care services (ICU) and mortality due to COVID-19. Methods We used data on all cases recorded in the Global Health Data repository up to the 10th of March 2021 to carry out a cross-sectional analysis of associations between cardiovascular diseases (CVD), hypertension, diabetes, obesity, lung diseases and kidney disease and hospitalization, ICU admission and mortality due to COVID-19. The Global Health repository reported data from 137 countries, but only Brazil, Mexico and Cuba reported more than 10 COVID-19 cases in participants with preexisting conditions. We used multivariable logistic regression to compute adjusted odds ratios (aOR) of the three outcomes for each pre-existing condition in ten-year age groups from 0-9 years and up to 110-120 years. Results The Global Health repository held 25 900 000 records of confirmed cases of COVID-19, of which 2 900 000 cases were from Brazil, Mexico and Cuba. The overall adjusted odds of hospitalization for the selected pre-existing condition were; CVD (OR 1.7, 95%CI 1.7-1.7), hypertension (OR 1.5, 95%CI 1.4-1.5), diabetes (OR 2.2, 95%CI 2.1-2.2), obesity (OR 1.7, 95%CI 1.6-1.7), kidney disease (OR 5.5, 95%CI 5.2-5.7) and lung disease (OR 1.9, 95%CI 1.8-1.9). The overall adjusted odds of ICU admission for each pre-existing condition were; CVD (OR 2.1, 95%CI 1.8-2.4), hypertension (OR 1.3, 95%CI 1.2-1.4), diabetes (OR 1.7, 95%CI 1.5-1.8), obesity (OR 2.2, 95%%CI 2.1-2.4), kidney disease (OR 1.4, 95%CI 1.2-1.7) and lung disease (OR 1.1, 95%CI 0.9-1.3). The overall adjusted odds of mortality for each pre-existing condition were; CVD (OR 1.7, 95%CI 1.6-1.7), hypertension (OR 1.3, 95%CI 1.3-1.4), diabetes (OR 2.0, 95%CI 1.9-2.0), obesity (OR 1.9, 95%CI 1.8-2.0), kidney disease (OR 2.7, 95%CI 2.6-2.9) and lung disease (OR 1.6, 95%CI 1.5-1.7). The odds of each outcome were considerably larger in children and young adults with these preexisting conditions than for adults, especially for kidney disease, CVD, and diabetes. Conclusion This analysis of a global health repository confirms associations between pre-existing diseases and clinical outcomes of COVID-19. The odds of these outcomes are especially elevated in children and young adults with these preexisting conditions.

Dynamics of anti-S IgG antibodies titers after the second dose of COVID 19 mRNA and non-mRNA vaccines in the manual and craft worker population of Qatar (preprint)

Background: There is limited sero epidemiological evidence on the magnitude and long-term durability of antibody titers of mRNA and non-mRNA vaccines in the Qatari population. This study was conducted to generate evidence on long-term anti-S IgG antibodies titers and their dynamics in individuals who have completed a primary COVID-19 vaccination schedule. Methods: A total of 300 participants who received any of the following vaccines BNT162b2/Comirnaty or mRNA-1273 or ChAdOx1-S/Covishield or COVID-19 Vaccine Janssen/Johnson or BBIBP-CorV or Covaxin were enrolled in our study. All sera samples were tested by chemiluminescent microparticle immunoassay (CMIA) for the quantitative determination of IgG antibodies to SARS-CoV-2, receptor-binding domain (RBD) of the S1 subunit of the spike protein of SARS-CoV-2. Antibodies against SARS-CoV-2 nucleocapsid (SARS-CoV-2 N-protein IgG) were also determined. Kaplan-Meier survival curves were used to compare the time from the last dose of the primary vaccination schedule to the time by which anti-S IgG antibodies titers fell into the lowest quartile (range of values collected) for the mRNA and non-mRNA vaccines. Results: Participants vaccinated with mRNA vaccines had higher median anti-S IgG antibody titers. Participants vaccinated with the mRNA-1273 vaccine had the highest median anti-S-antibody level of 13720.9 AU/mL (IQR 6426.5 to 30185.6 AU/mL) followed by BNT162b2 (median, 7570.9 AU/ml; IQR, 3757.9 to 16577.4 AU/mL); while the median anti-S antibody titer for non-mRNA vaccinated participants was 3759.7 AU/mL (IQR, 2059.7-5693.5 AU/mL). The median time to reach the lowest quartile was 3.53 months (IQR, 2.2-4.5 months) and 7.63 months (IQR, 6.3-8.4 months) for the non-mRNA vaccine recipients and Pfizer vaccine recipients, respectively. However, more than 50% of the Moderna vaccine recipients did not reach the lowest quartile by the end of the follow-up period. Conclusions: This evidence on anti-S IgG antibody titers, their durability and decay over time should be considered for the utility of these assays in transmission dynamics after the full course of primary vaccination.

Duration of COVID-19 mRNA Vaccine Effectiveness against Severe Disease (preprint)

Background: Waning immunity following administration of mRNA based COVID-19 vaccines remains a concern for many health systems. We undertook a study of SARS-CoV-2 infections, with and without requirement for intensive care to shed more light on the duration of vaccine effectiveness for protection against the need for intensive care. Methods: We used a matched case-control study design with the study base being all individuals with first infection with SARS-CoV-2 reported in the State of Qatar between 1 Jan 2021 and 20 Feb 2022. Cases were those requiring intensive care while controls were those who recovered without need for intensive care. Vaccine effectiveness against requiring intensive care and number needed to vaccinate (NNV) to prevent one more case of COVID-19 requiring intensive care were computed for the mRNA (BNT162b2 / mRNA-1273) vaccines. Results: Vaccine effectiveness against requiring intensive care was 59% (95% confidence interval [CI], 50 to 76) between the first and second dose and strengthened to 89% (95% CI, 85 to 92) between the second dose and 4 months post the second dose in persons who received a primary course of the vaccine. There was no waning of vaccine effectiveness in the period from 4 to 12 months after the second dose. Conclusions: This study demonstrates that vaccine effectiveness against requiring intensive care remains robust till at least 12 months after the second dose of mRNA based vaccines.

Can the Rapid Antigen Test for COVID-19 Replace RT-PCR: A Meta-analysis of Test Agreement (preprint)

BackgroundSeveral studies have compared the performance of reverse transcription-polymerase chain reaction (RT-PCR) and antigen rapid diagnostic tests (Ag-RDTs) as tools to diagnose SARS-CoV-2 disease (COVID-19). As the performance of Ag-RDT may vary among different products and viral load scenarios, the clinical utility of the Ag-RDT remains unclear. Our aim is to assess the diagnostic agreement between Ag-RDTs and RT-PCR in testing for COVID-19 across different products and cycle threshold (Ct) values. MethodsAn evidence synthesis and meta-analysis of Positive Percent Agreement (PPA) and Negative Percent Agreement (NPA) was conducted after an exhaustive search of five databases to locate published studies that compared Ag-RDT to RT-PCR and reported quantitative comparison results. After the screening, quality assessment, and data extraction, the synthesis of pooled estimates was carried out utilizing the quality-effects (QE) model and Freeman-Tukey double arcsine transformation (FTT) for variance stabilization. Subgroup analysis was also conducted to evaluate the tests diagnostic agreement across distinctive products and Ct-value thresholds. FindingsA total of 420 studies were screened by title and abstract, of which 39 were eventually included in the analysis. The overall NPA was 99.4% (95%CI 98.8-99.8, I2=91.40%). The PPA was higher in lower Ct groups such as groups with Ct <20 and Ct <25, which had an overall PPA of 95.9% (95%CI 92.7-98.2, I2=0%) and 96.8% (95%CI 95.2-98.0, I2=50.1%) respectively. This is in contrast to groups with higher Ct values, which had relatively lower PPA. Panbio and Roche Ag-RDTs had the best consistent overall PPA across different Ct groups especially in groups with Ct <20 and Ct <25. InterpretationThe findings of our meta-analysis support the use of Ag-RDTs in lieu of RT-PCR for decision making regarding COVID-19 control measures, since the enhanced capacity of RT-PCR to detect disease in those that are Ag-RDT negative will be unlikely to have much public health utility. This step will drastically reduce the cost and time in testing for COVID-19. FundingThis research did not receive any specific funding.

The Epidemic Volatility Index: an early warning tool for epidemics (preprint)

Background: . This paper presents, for the first time, the Epidemic Volatility Index (EVI), a conceptually simple, early warning tool for emerging epidemic waves. Methods: . EVI is based on the volatility of the newly reported cases per unit of time, ideally per day, and issues an early warning when the rate of the volatility change exceeds a threshold. Results: . Results from the COVID-19 epidemic in Italy and New York are presented here, while daily updated predictions for all world countries and each of the United States are available online. Interpretation . EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting oncoming waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act fast and optimize containment of outbreaks.

An early warning tool for predicting mortality risk of COVID-19 patients using machine learning (preprint)

COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics, and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high sensitive C-reactive protein, and age - acquired at hospital admission were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate- and high-risk groups using LNLCA cut-off values of 10.4 and 12.65 with the death probability less than 5%, 5% to 50%, and above 50%, respectively. The prognostic model, nomogram and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.