Background
Tocilizumab blocks pro-inflammatory activity of interleukin-6 (IL-6), involved in pathogenesis of pneumonia the most frequent cause of death in COVID-19 patients.
Methods
A multicenter, single-arm, hypothesis-driven trial was planned, according to a phase 2 design, to study the effect of tocilizumab on lethality rates at 14 and 30 days (co-primary endpoints, a priori expected rates being 20 and 35%, respectively). A further prospective cohort of patients, consecutively enrolled after the first cohort was accomplished, was used as a secondary validation dataset. The two cohorts were evaluated jointly in an exploratory multivariable logistic regression model to assess prognostic variables on survival.
Results
In the primary intention-to-treat (ITT) phase 2 population, 180/301 (59.8%) subjects received tocilizumab, and 67 deaths were observed overall. Lethality rates were equal to 18.4% (97.5% CI: 13.6–24.0, P = 0.52) and 22.4% (97.5% CI: 17.2–28.3, P < 0.001) at 14 and 30 days, respectively. Lethality rates were lower in the validation dataset, that included 920 patients. No signal of specific drug toxicity was reported. In the exploratory multivariable logistic regression analysis, older age and lower PaO2/FiO2 ratio negatively affected survival, while the concurrent use of steroids was associated with greater survival. A statistically significant interaction was found between tocilizumab and respiratory support, suggesting that tocilizumab might be more effective in patients not requiring mechanical respiratory support at baseline.
Conclusions
Tocilizumab reduced lethality rate at 30 days compared with null hypothesis, without significant toxicity. Possibly, this effect could be limited to patients not requiring mechanical respiratory support at baseline.
Registration EudraCT (2020-001110-38); clinicaltrials.gov (NCT04317092).
For countries with recurrent droughts, the design of drought impact mitigation measures could benefit from analyses of determinants of yields and prices of local crops at regional and district level. This study applies dynamic spatial panel data regression models to yields and prices of four major food crops across regions of Burkina Faso and Niger, over sample periods between 1984 and 2006. Results lend support to mainly simultaneous spatial spillovers, particularly for millet and cowpea prices and sorghum yields in Niger, and maize yields in Burkina Faso. After accounting for these effects, most crop yields are found to be weakly price-responsive, as envisaged by a supply-side geographical diffusion hypothesis. Seasonal rainfall elasticity estimates suggest that dominant food crops have slight advantage margins in terms of relative resilience to rainfall shortages. However, this result is to be weighed against low millet yields in Niger, and marked drops in sorghum yields during officially declared droughts in Burkina Faso.JEL classifications: C23, C49, Q19, R14
The strength of the adjustment towards arbitrage equilibrium can be expected to be somehow proportional to the extent of market price deviations from equilibrium. In this article, threshold and smooth transition cointegration models are applied to quarterly wheat prices of three major world suppliers over the period 1973^99. Results based on arranged autoregressions of the error term of a static regression do not prove to be robust. Although non-linear models relying on a multivariate system approach yield partly contradictory results, the main evidence from the latter suggests a weakening, rather than an outright inaction, of the adjustment process in the inner regime.
Public interventions in support of public health and housing in developing countries could benefit from better understanding of spatial heterogeneity and anisotropy. Estimation of directional variation within geographically weighted regression (GWR) faces problems of local parameter instability, border effects and, if extended to nonspatial attributes, potential endogeneity. This study formulates a GWR model where anisotropy is filtered out based on information from directional variograms. Along with classical regressions, the approach is applied to investigate child anaemia and its associations with household characteristics, sanitation and basic infrastructure in 173 regions of sub-Saharan Africa. Based on ordinary least squares (OLS) results, anaemia prevalence rates are up to three times more responsive to child morbidity (related to malaria and other diseases) than to other covariates. GWR estimates provide similar indications, but also point to poor sanitation facilities as a cofactor of severe anaemia particularly in east and southern Africa. The anisotropy-adjusted GWR is spatially stationary in residuals, and its estimated local parameters are less collinear than GWR with no adjustment. However, similar explanatory power and lack of significant bias in parameters estimated by the latter suggest that directional variation is largely captured by modelled co-movements among the variables.
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