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LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T7 4703-4715 Phenotype denotes hypertension http://purl.obolibrary.org/obo/HP_0000822
T8 4717-4754 Phenotype denotes chronic obstructive pulmonary disease http://purl.obolibrary.org/obo/HP_0006510
T9 4756-4760 Phenotype denotes COPD http://purl.obolibrary.org/obo/HP_0006510

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
144 309-317 Disease denotes COVID-19 MESH:C000657245
145 318-324 Disease denotes deaths MESH:D003643
149 55-63 Disease denotes COVID-19 MESH:C000657245
150 64-70 Disease denotes deaths MESH:D003643
151 204-210 Disease denotes deaths MESH:D003643
153 937-940 Chemical denotes No2
157 2147-2150 Chemical denotes NO2
158 2089-2097 Disease denotes COVID-19 MESH:C000657245
159 2098-2107 Disease denotes mortality MESH:D003643
164 1076-1079 Chemical denotes NO2
165 1298-1301 Chemical denotes NO2
166 996-1004 Disease denotes COVID-19 MESH:C000657245
167 1005-1014 Disease denotes mortality MESH:D003643
171 2909-2912 Chemical denotes NO2
172 3022-3025 Chemical denotes NO2
173 2285-2294 Disease denotes mortality MESH:D003643
177 3797-3800 Chemical denotes NO2
178 4188-4196 Disease denotes COVID-19 MESH:C000657245
179 4197-4203 Disease denotes deaths MESH:D003643
191 4619-4622 Chemical denotes NO2
192 5221-5224 Chemical denotes phe MESH:D010649
193 5284-5287 Chemical denotes NO2
194 4626-4634 Disease denotes COVID-19 MESH:C000657245
195 4635-4644 Disease denotes mortality MESH:D003643
196 4703-4715 Disease denotes hypertension MESH:D006973
197 4717-4754 Disease denotes chronic obstructive pulmonary disease MESH:D029424
198 4756-4760 Disease denotes COPD MESH:D029424
199 4766-4774 Disease denotes diabetes MESH:D003920
200 4848-4856 Disease denotes COVID-19 MESH:C000657245
201 4857-4866 Disease denotes mortality MESH:D003643

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T130 0-10 Sentence denotes 3 Results
T131 12-33 Sentence denotes 3.1 Study population
T132 34-156 Sentence denotes We identified 38,573 COVID-19 deaths with a laboratory confirmed test in England between 2nd March and 30th June (Fig. 1).
T133 157-284 Sentence denotes The age, sex and ethnicity distribution of the deaths follows patterns reported previously (Supplemental Material Tables S2-3).
T134 285-325 Sentence denotes Fig. 1 Flowchart of the COVID-19 deaths.
T135 327-340 Sentence denotes 3.2 Exposure
T136 341-418 Sentence denotes Fig. 2 shows the population weighted air-pollutants at LSOA level in England.
T137 419-563 Sentence denotes We observe that the localised variation of NO2, for instance due to the highways, is adequately captured at the spatial resolution of the LSOAs.
T138 564-696 Sentence denotes The mean of NO2 is 16.17 μg/m3 and it varies from 2.99 μg/m3 in highly rural areas to 50.69 μg/m3 in the big urban centres (Fig. 2).
T139 697-781 Sentence denotes The mean of PM2.5 is 9.84 μg/m3 with a smaller variation, 5.14–14.22 μg/m3 (Fig. 2).
T140 782-827 Sentence denotes Fig. 2 Population weighted exposure per LSOA.
T141 829-845 Sentence denotes 3.3 Confounders
T142 846-930 Sentence denotes Plots and maps of the confounders can be found in Supplemental Material, Fig. S7-17.
T143 932-940 Sentence denotes 3.4 No2
T144 941-967 Sentence denotes We observe a 2.6% (95%CrI:
T145 968-1140 Sentence denotes 2.4%, 2.7%) increase in the COVID-19 mortality risk for every 1 μg/m3 increase in the long-term exposure to NO2, based on model 1 (Fig. 3 & Supplemental Material Table S4).
T146 1141-1334 Sentence denotes There is still evidence of an effect, albeit smaller, once we adjust for spatial autocorrelation or confounders, with increases in the long-term exposure to NO2 of, respectively, 1.3% (95% CrI:
T147 1335-1362 Sentence denotes 0.8%, 1.8%), 1.8% (95% CrI:
T148 1363-1393 Sentence denotes 1.5%, 2.1%) for every 1 μg/m3.
T149 1394-1646 Sentence denotes When we adjust for both autocorrelation and confounders the evidence is less strong, with estimates of 0.5% (95% CrI: −0.2%, 1.2%) for every 1 μg/m3 (Fig. 3 & Supplemental Material Table S4) and posterior probability of a positive effect reaching 0.93.
T150 1647-1710 Sentence denotes The spatial relative risk in England varies from 0.24 (95% CrI:
T151 1711-1740 Sentence denotes 0.08, 0.69) to 2.09 (95% CrI:
T152 1741-1787 Sentence denotes 1.30, 3.11) in model 2 and from 0.30 (95% CrI:
T153 1788-1817 Sentence denotes 0.10, 0.84) to 1.87 (95% CrI:
T154 1818-1927 Sentence denotes 1.18, 2.93) in model 4, implying that the confounders explain very little of the observed variation (Fig. 3).
T155 1928-2045 Sentence denotes The variation is more pronounced in the cities and suburban areas (with posterior probability higher than 1; Fig. 3).
T156 2046-2216 Sentence denotes Fig. 3 Density strips for the posterior of COVID-19 mortality relative risk with 1 μg/m3 increase in NO2 (top panel) and PM2.5 (bottom panel) averaged long-term exposure.
T157 2218-2228 Sentence denotes 3.5 Pm2.5
T158 2229-2256 Sentence denotes We observe a 4.4% (95% CrI:
T159 2257-2422 Sentence denotes 3.7%, 5.1%) increase in the mortality risk for every 1 μg/m3 increase in the long-term exposure to PM2.5, based on model 1 (Fig. 3 & Supplemental Material Table S5).
T160 2423-2548 Sentence denotes When we adjust for spatial autocorrelation the effect increases slightly but the credible intervals are wider, 5.4% (95% CrI:
T161 2549-2629 Sentence denotes 2.5%, 8.4%), whereas it is similar when we adjust for confounding 4.9% (95% CrI:
T162 2630-2684 Sentence denotes 3.7%, 6.2%) (Fig. 3 & Supplemental Material Table S5).
T163 2685-2834 Sentence denotes The effect is weak when we account for confounders and spatial autocorrelation 1.4% (95% CrI: −2.1%, 5.1%) (Fig. 3 & Supplemental Material Table S5).
T164 2835-2932 Sentence denotes The posterior probability of a positive effect is lower than observed for NO2, and equal to 0.78.
T165 2933-3094 Sentence denotes The spatial relative risk follows similar patterns as the one reported in the models for NO2, with the posterior median relative risk varying from 0.24 (95% CrI:
T166 3095-3124 Sentence denotes 0.12, 0.46) to 2.26 (95% CrI:
T167 3125-3171 Sentence denotes 1.32, 3.85) in model 2 and from 0.30 (95% CrI:
T168 3172-3201 Sentence denotes 0.15, 0.57) to 1.90 (95% CrI:
T169 3202-3259 Sentence denotes 1.14, 3.17) in model 4 (Supplemental Material, Fig. S18).
T170 3261-3286 Sentence denotes 3.6 Sensitivity analyses
T171 3287-3566 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 3567-3762 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 3763-4011 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 4012-4119 Sentence denotes The use of quintiles of the pollutants justifies the linearity assumption (Supplemental Material Fig. S24).
T175 4120-4268 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 4269-4402 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 4403-4523 Sentence denotes The results are consistent when we fitted a zero-inflated Poisson (Supplemental Material Tables S12-13 and Fig. S28-29).
T178 4525-4547 Sentence denotes 3.7 Post-hoc analysis
T179 4548-4690 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 4691-5076 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 5077-5269 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 5270-5484 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.