> top > docs > PMC:7786642 > spans > 20891-21827 > annotations

PMC:7786642 / 20891-21827 JSONTXT

Annnotations TAB JSON ListView MergeView

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T7 155-167 Phenotype denotes hypertension http://purl.obolibrary.org/obo/HP_0000822
T8 169-206 Phenotype denotes chronic obstructive pulmonary disease http://purl.obolibrary.org/obo/HP_0006510
T9 208-212 Phenotype denotes COPD http://purl.obolibrary.org/obo/HP_0006510

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
191 71-74 Chemical denotes NO2
192 673-676 Chemical denotes phe MESH:D010649
193 736-739 Chemical denotes NO2
194 78-86 Disease denotes COVID-19 MESH:C000657245
195 87-96 Disease denotes mortality MESH:D003643
196 155-167 Disease denotes hypertension MESH:D006973
197 169-206 Disease denotes chronic obstructive pulmonary disease MESH:D029424
198 208-212 Disease denotes COPD MESH:D029424
199 218-226 Disease denotes diabetes MESH:D003920
200 300-308 Disease denotes COVID-19 MESH:C000657245
201 309-318 Disease denotes mortality MESH:D003643

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T179 0-142 Sentence denotes In a post-hoc analysis we investigated if the evidence of an effect of NO2 on COVID-19 mortality can be attributed to pre-existing conditions.
T180 143-528 Sentence denotes We selected hypertension, chronic obstructive pulmonary disease (COPD) and diabetes, because of 1) indications of previous literature that they increase the COVID-19 mortality risk (Williamson et al., 2020, Yang et al., 2020), 2) previous literature that suggest an effect with long-term exposure NO2 (Balti et al., 2014, Cai et al., 2016, Zhang et al., 2018) and 3) data availability.
T181 529-721 Sentence denotes We retrieved prevalence data for these pre-existing conditions from PHE available at the GP practice level during 2018–2019 (https://fingertips.phe.org.uk/), Supplemental Material Fig. S30-32.
T182 722-936 Sentence denotes The effect of NO2 remains similar, 0.6% (95% CrI: −0.1%, 1.3%) with the posterior probability being 0.94 whereas the spatial relative risk highlights the same geographical locations, Supplemental Material Fig. S33.