Hypercoagulability and the need for prioritizing coagulation markers for prognostic abilities have been highlighted in COVID-19. We aimed to quantify the associations of D-dimer with disease progression in patients with COVID-19. This systematic review and meta-analysis was registered with PROSPERO, CRD42020186661.We included 113 studies in our systematic review, of which 100 records (n = 38,310) with D-dimer data) were considered for meta-analysis. Across 68 unadjusted (n = 26,960) and 39 adjusted studies (n = 15,653) reporting initial D-dimer, a significant association was found in patients with higher D-dimer for the risk of overall disease progression (unadjusted odds ratio (uOR) 3.15; adjusted odds ratio (aOR) 1.64). The time-to-event outcomes were pooled across 19 unadjusted (n = 9743) and 21 adjusted studies (n = 13,287); a strong association was found in patients with higher D-dimers for the risk of overall disease progression (unadjusted hazard ratio (uHR) 1.41; adjusted hazard ratio (aHR) 1.10). The prognostic use of higher D-dimer was found to be promising for predicting overall disease progression (studies 68, area under curve 0.75) in COVID-19. Our study showed that higher D-dimer levels provide prognostic information useful for clinicians to early assess COVID-19 patients at risk for disease progression and mortality outcomes. This study, recommends rapid assessment of D-dimer for predicting adverse outcomes in COVID-19.

We used a publicly available data of 44,672 patients reported by China’s centre for disease control to study the role of age, sex, co-morbidities and health-care related occupation on COVID-19 mortality. The data is in the form of absolute numbers and proportions. Using the percentages, retrospective synthetic data of 100 survivors and 100 deaths were generated using random number libraries so that proportions of ages, genders, co-morbidities, and occupations were constant as in the original data. Logistic regression of the four predictor factors of age, sex, co-morbidities and occupation revealed that only age and comorbidities significantly affected mortality. Sex and occupation when adjusted for other factors in the equation were not significant predictors of mortality. Age and presence of co-morbidities correlated negatively with survival with co-efficient of -1.23 and -2.33 respectively. Odds ratio (OR) for dying from COVID-19 for every 10-year increase in age was 3.4 compared to the previous band of 10 years. OR for dying of COVID-19 was 10.3 for the presence of any of the co-morbidities. Our findings could help in triaging the patients in the emergency room and emphasize the need to protect the elderly and those with comorbidities from getting exposed.

Coronavirus disease 2019, i.e. COVID-19, started as an outbreak in a district of China and has engulfed the world in a matter of 3 months. It is posing a serious health and economic challenge worldwide. However, case fatality rates (CFRs) have varied amongst various countries ranging from 0 to 8.91%. We have evaluated the effect of selected socio-economic and health indicators to explain this variation in CFR. Countries reporting a minimum of 50 cases as on 14th March 2020, were selected for this analysis. Data about the socio-economic indicators of each country was accessed from the World bank database and data about the health indicators were accessed from the World Health Organisation (WHO) database. Various socioeconomic indicators and health indicators were selected for this analysis. After selecting from univariate analysis, the indicators with the maximum correlation were used to build a model using multiple variable linear regression with a forward selection of variables and using adjusted R-squared score as the metric. We found univariate regression results were significant for GDP (Gross Domestic Product) per capita, POD 30/70 (Probability Of Dying Between Age 30 And Exact Age 70 From Any of Cardiovascular Disease, Cancer, Diabetes or Chronic Respiratory Disease), HCI (Human Capital Index), GNI(Gross National Income) per capita, life expectancy, medical doctors per 10000 population, as these parameters negatively corelated with CFR (rho = -0.48 to -0.38 , p<0.05). Case fatality rate was regressed using ordinary least squares (OLS) against the socio-economic and health indicators. The indicators in the final model were GDP per capita, POD 30/70, HCI, life expectancy, medical doctors per 10,000, median age, current health expenditure per capita, number of confirmed cases and population in millions. The adjusted R-squared score was 0.306. Developing countries with a poor economy are especially vulnerable in terms of COVID-19 mortality and underscore the need to have a global policy to deal with this on-going pandemic. These trends largely confirm that the toll from COVID-19 will be worse in countries ill-equipped to deal with it. These analyses of epidemiological data are need of time as apart from increasing situational awareness, it guides us in taking informed interventions and helps policy-making to tackle this pandemic.