Several studies have proposed that the neutrophil–lymphocyte ratio (NLR) is one of the various biomarkers that can be useful in assessing COVID-19 disease-related outcomes. Our systematic review analyzes the relationship between on-admission NLR values and COVID-19 severity and mortality. Six different severity criteria were used. A search of the literature in various databases was conducted from 1 January 2020 to 1 May 2021. We calculated the pooled standardized mean difference (SMD) for the collected NLR values. A meta-regression analysis was performed, looking at the length of hospitalization and other probable confounders, such as age, gender, and comorbidities. A total of sixty-four studies were considered, which included a total of 15,683 patients. The meta-analysis showed an SMD of 3.12 (95% CI: 2.64–3.59) in NLR values between severe and non-severe patients. A difference of 3.93 (95% CI: 2.35–5.50) was found between survivors and non-survivors of the disease. Upon summary receiver operating characteristics analysis, NLR showed 80.2% (95% CI: 74.0–85.2%) sensitivity and 75.8% (95% CI: 71.3–79.9%) specificity for the prediction of severity and 78.8% (95% CI: 73.5–83.2%) sensitivity and 73.0% (95% CI: 68.4–77.1%) specificity for mortality, and was not influenced by age, gender, or co-morbid conditions. Conclusion: On admission, NLR predicts both severity and mortality in COVID-19 patients, and an NLR > 6.5 is associated with significantly greater the odds of mortality.

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Background:  Knowledge of facilitators and barriers regarding the uptake of COVID-19 vaccination at a global population level is critical for combating the pandemic, saving lives, and protecting the economy.  The aim of this work was to determine the proportion of people likely to accept or refuse to undergo COVID-19 vaccination.  This study also investigated (a) time trends regarding the intention to undergo COVID-19 vaccination and (b) socio-demographic risk factors influencing vaccine refusal. Methods: Databases (01 March 2020-01 March 2021) searched included PubMed, MEDLINE, and Scopus. The sample size was n ≥1000 and selected studies were those that determined vaccine ‘acceptance’, ‘refusal’ and ‘hesitancy’. A random-effects model was employed to obtain the overall odds ratio (OR) and 95% confidence interval (CI) for socio-demographic predictors for vaccine refusal. Results:  A total of 832 citations were screened and 35 studies from 21 countries (n=130,179) were analyzed.  The pooled proportion of individuals reporting an intention to vaccinate was 0.70 (95% CI: 0.65 to 0.74; I2 = 99.68%). The proportion of people intending to vaccinate decreased (regression coefficient = -0.13; p<0.001) during the study period and odds of refusal to vaccinate increased by 1.37-fold (95% CI: 1.33-1.41) during the second half. Risk factors identified for vaccine refusal included being female, rural residence, lower income, and lower level of formal education.  Conclusions: A moderate proportion of people showed an intention to receive vaccination, although this declined during the study period. A global and national multi-pronged strategic and targeted approach is urgently needed to enhance vaccination uptake amongst females, those with a relatively lower educational and socioeconomic status, and those in rural areas.

To achieve herd immunity to a disease, a large portion of the population needs to be vaccinated, which is possible only when there is broad acceptance of the vaccine within the community. Thus, policymakers need to understand how the general public will perceive the vaccine. This study focused on the degree of COVID-19 vaccine hesitancy and refusal and explored sociodemographic correlations that influence vaccine hesitancy and refusal. A cross-sectional online survey was conducted among the adult population of India. The survey consisted of basic demographic questions and questions from the Vaccination Attitudes Examination (VAX) Scale. Multinomial logistical regression was used to identify correlates of vaccine hesitancy and refusal. Of the 1582 people in the study, 9% refused to become vaccinated and 30.8% were hesitant. We found that both hesitancy and refusal predictors were nearly identical (lower socioeconomic status, female gender, and older age groups), except for three groups (subjects aged 45–64 years, those with approximate income <10,000 INR/month, and those residing in rural households) that showed slightly higher odds of vaccine hesitancy than refusal. We need to address the underlying sociodemographic determinants and formulate public awareness programs to address specific subgroups that are at higher risk of rejecting the vaccine and convert those who are undecided or hesitant into those willing to accept the vaccine.

Introduction: Coronavirus disease 2019 (COVID-19) pandemic has caused unprecedented mortality and has stretched the health infrastructure thin worldwide, especially in low- and middle-income countries. There is a need to evaluate easily available biomarkers for their clinical relevance for poor outcomes in severe cases of COVID-19. It is also known that comorbidities affect these biomarkers with or without COVID-19. We aimed to unearth the influence of comorbidities on feasible hematological predictive markers for mortality in hospitalized severe COVID-19 patients. Materials and Methods: This is a retrospective study done on severe COVID-19 hospitalized patients, diagnosed with RT polymerase chain reaction (n = 205), were investigated. Comorbidities associated with the patients were tracked and scored according to Charlson comorbidity index (CCI). CCI score of zero was grouped in A, those with CCI score 1–4 into group B and those with CCI scores ≥ 5 into group C. Correlation between hematological parameters and CCI scores was analyzed using Pearson correlation coefficient. Optimal cut-off and odds ratio was derived from receiver operating characteristic (ROC) curve analysis. Results: Among the 205 severe COVID-19 patients age, C-reactive protein (CRP), neutrophil lymphocyte ratio (NLR), derived NLR (dNLR), absolute neutrophil count (ANC) and total leukocyte count (TLC) were found to be statistically significant independent risk factors for predicting COVID-19 mortality (p < 0.01). In group A, cut off for CRP was 51.5 mg/L (odds ratio [OR]: 26.7; area under curve [AUC]: 0.867), TLC was 11,850 cells/mm³ (OR: 11.7; AUC: 0.731), NLR was 11.76 (OR: 14.3; AUC: 0.756), dNLR was 5.77 (OR: 4.89; AUC: 0.659), ANC was 13,110 cells/mm³ (OR: 1.68; AUC: 0.553). In group B, cut off for CRP was 36.5 mg/L (OR: 32.1; AUC: 0.886), TLC was 11,077 cells/mm³ (OR: 12.1; AUC: 0.722), NLR was 8.27 (OR: 18.9; AUC: 0.827), dNLR was 3.79 (OR: 9.26; AUC: 0.727), ANC was 11,420 cells/mm³ (OR: 2.42; AUC: 0.564). In group C, cut-off for CRP was 23.7 mg/L (OR: 32.7; AUC: 0.904), TLC was 10,480 cells/mm³ (OR: 21.2; AUC: 0.651), NLR was 6.29 (OR: 23.5; AUC: 0.647), dNLR was 1.93 (OR: 20.8; AUC: 0.698), ANC was 6650 cells/mm³ (OR: 2.45; AUC: 0.564). Conclusions: In severe COVID-19 patients, CRP was the most reliable biomarker to predict mortality followed by NLR. Presence, type, and number of co-morbidities influence the levels of the biomarkers and the clinically relevant cut-offs associated with mortality.