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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
130 120-128 Disease denotes COVID-19 MESH:C000657245
131 129-135 Disease denotes deaths MESH:D003643
132 200-209 Disease denotes mortality MESH:D003643
133 300-306 Disease denotes deaths MESH:D003643
136 1809-1818 Disease denotes mortality MESH:D003643
137 2011-2020 Disease denotes mortality MESH:D003643
139 2277-2281 Gene denotes S1.2 Gene:6268

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T103 0-24 Sentence denotes 2.5 Statistical methods
T104 25-168 Sentence denotes We specified Bayesian hierarchical Poisson log-linear models to investigate the association of COVID-19 deaths and NO2 and PM2.5 independently.
T105 169-316 Sentence denotes The LSOA specific standardised mortality ratio is known to be an unstable estimator with high variance when the number of expected deaths is small.
T106 317-646 Sentence denotes To overcome this problem, we used a well-established hierarchical framework, specifying spatially structured and unstructured random effects, so that the model borrows strength from the other areas across the entire study region, as well as from the neighbouring ones (Best et al., 2005, Wakefield et al., 2000, Wakefield, 2006).
T107 647-818 Sentence denotes We model these random effects using a re-parametrisation of the Besag-York-Molliè conditional autoregressive prior distribution (Besag et al., 1991, Simpson et al., 2017).
T108 819-851 Sentence denotes We fitted four models including:
T109 852-1081 Sentence denotes 1) each pollutant (model 1), 2) each pollutant and the spatial autocorrelation term (model 2), 3) each pollutant and all confounders (model 3) and 4) each pollutant, the spatial autocorrelation term and all confounders (model 4).
T110 1082-1247 Sentence denotes All models were adjusted for age, sex and ethnicity using indirect standardisation; we used the English population as the standard population to calculate the rates.
T111 1248-1390 Sentence denotes We do not report results from the joint analysis including both pollutants since they are highly correlated (Supplemental Material Figure S5).
T112 1391-1622 Sentence denotes In order to propagate the uncertainty resulted from the sampling we used for the downscaling, we fitted the models over 100 downscaled samples and then performed Bayesian model averaging to combine the estimates (Gómez-Rubio et al.
T113 1623-1629 Sentence denotes 2020).
T114 1630-1753 Sentence denotes We performed a complete case analysis since for only 1.1% of the cases information about age, sex and ethnicity is missing.
T115 1754-1967 Sentence denotes We report results as posterior median of % increase in mortality risk for every 1 μg/m3 increase in the air-pollutants, 95% credible intervals (CrI) and posterior probability that the estimated effect is positive.
T116 1968-2164 Sentence denotes We also report posterior median of spatial mortality relative risks (exponential of the spatial autocorrelation term) and posterior probabilities that the spatial relative risks are larger than 1.
T117 2165-2282 Sentence denotes The mathematical formulation of the models and prior specifications are given in the Supplemental Material Text S1.2.
T118 2283-2325 Sentence denotes All models were fitted in INLA (Rue et al.
T119 2326-2332 Sentence denotes 2009).
T120 2333-2457 Sentence denotes Covariate data and code for running the analysis are available at https://github.com/gkonstantinoudis/COVID19AirpollutionEn.