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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
177 510-513 Chemical denotes NO2
178 901-909 Disease denotes COVID-19 MESH:C000657245
179 910-916 Disease denotes deaths MESH:D003643

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T171 0-279 Sentence denotes When LTLAs are the main geographical unit for analysis, the results are consistent, but higher in magnitude, potentially due to inadequate covariate and spatial autocorrelation adjustment due to the coarse geographical resolution (Supplemental Material Tables S6-7, Fig. S19-20).
T172 280-475 Sentence denotes Restricting the study period to March 23, 2020 (N = 698) also results in similar estimates for both pollutants, however the uncertainty is higher (Supplemental Material Tables S8-9, Fig. S21-22).
T173 476-724 Sentence denotes The latent field of model 4, with NO2 as the pollutant, is similar to the latent fields of the models with and without the disease progression variables, with a correlation coefficient of 0.94 and 0.93 respectively (Supplemental Material Fig. S23).
T174 725-832 Sentence denotes The use of quintiles of the pollutants justifies the linearity assumption (Supplemental Material Fig. S24).
T175 833-981 Sentence denotes The results are consistent, but the evidence weaker, when suspected COVID-19 deaths are included (Supplemental Material Tables S10-11, Fig. S25-26).
T176 982-1115 Sentence denotes The results are also similar when we used a 3 or a 10-year mean of the air-pollutants concentration (Supplemental Material Fig. S27).
T177 1116-1236 Sentence denotes The results are consistent when we fitted a zero-inflated Poisson (Supplemental Material Tables S12-13 and Fig. S28-29).