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LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T10 8719-8731 Phenotype denotes hypertension http://purl.obolibrary.org/obo/HP_0000822
T11 8746-8750 Phenotype denotes COPD http://purl.obolibrary.org/obo/HP_0006510
T12 9911-9923 Phenotype denotes hypertension http://purl.obolibrary.org/obo/HP_0000822
T13 9938-9942 Phenotype denotes COPD http://purl.obolibrary.org/obo/HP_0006510

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
209 130-133 Chemical denotes NO2
210 290-293 Chemical denotes NO2
211 485-488 Chemical denotes NO2
212 164-172 Disease denotes COVID-19 MESH:C000657245
213 173-182 Disease denotes mortality MESH:D003643
214 308-316 Disease denotes COVID-19 MESH:C000657245
215 317-326 Disease denotes mortality MESH:D003643
241 859-862 Chemical denotes NO2
242 1286-1289 Chemical denotes NO2
243 1972-1975 Chemical denotes NO2
244 2276-2279 Chemical denotes NO2
245 2349-2352 Chemical denotes NO2
246 2517-2520 Chemical denotes NO2
247 866-874 Disease denotes COVID-19 MESH:C000657245
248 875-884 Disease denotes mortality MESH:D003643
249 917-923 Disease denotes deaths MESH:D003643
250 1231-1240 Disease denotes mortality MESH:D003643
251 1544-1552 Disease denotes COVID-19 MESH:C000657245
252 1553-1562 Disease denotes mortality MESH:D003643
253 1608-1616 Disease denotes COVID-19 MESH:C000657245
254 1617-1623 Disease denotes deaths MESH:D003643
255 1845-1853 Disease denotes COVID-19 MESH:C000657245
256 1854-1860 Disease denotes deaths MESH:D003643
257 1926-1934 Disease denotes COVID-19 MESH:C000657245
258 1935-1941 Disease denotes deaths MESH:D003643
259 2138-2144 Disease denotes deaths MESH:D003643
260 2227-2235 Disease denotes COVID-19 MESH:C000657245
261 2236-2245 Disease denotes mortality MESH:D003643
262 2372-2380 Disease denotes COVID-19 MESH:C000657245
263 2381-2387 Disease denotes deaths MESH:D003643
264 2459-2467 Disease denotes COVID-19 MESH:C000657245
265 2468-2477 Disease denotes mortality MESH:D003643
280 2695-2703 Disease denotes COVID-19 MESH:C000657245
281 2704-2713 Disease denotes mortality MESH:D003643
282 2787-2795 Disease denotes COVID-19 MESH:C000657245
283 2796-2805 Disease denotes mortality MESH:D003643
284 3232-3240 Disease denotes COVID-19 MESH:C000657245
285 3241-3250 Disease denotes mortality MESH:D003643
286 3403-3409 Disease denotes deaths MESH:D003643
287 3777-3785 Disease denotes COVID-19 MESH:C000657245
288 3786-3791 Disease denotes death MESH:D003643
289 3980-3988 Disease denotes COVID-19 MESH:C000657245
290 3989-3995 Disease denotes deaths MESH:D003643
291 4113-4119 Disease denotes deaths MESH:D003643
292 4193-4201 Disease denotes COVID-19 MESH:C000657245
293 4202-4211 Disease denotes mortality MESH:D003643
305 4386-4389 Chemical denotes NO2
306 4404-4412 Disease denotes COVID-19 MESH:C000657245
307 4413-4422 Disease denotes mortality MESH:D003643
308 5015-5023 Disease denotes COVID-19 MESH:C000657245
309 5024-5030 Disease denotes deaths MESH:D003643
310 5419-5427 Disease denotes COVID-19 MESH:C000657245
311 5428-5437 Disease denotes mortality MESH:D003643
312 5776-5784 Disease denotes COVID-19 MESH:C000657245
313 5785-5794 Disease denotes mortality MESH:D003643
314 5862-5870 Disease denotes COVID-19 MESH:C000657245
315 5871-5880 Disease denotes mortality MESH:D003643
326 7623-7629 Species denotes people Tax:9606
327 6812-6822 Disease denotes infections MESH:D007239
328 6860-6870 Disease denotes infections MESH:D007239
329 6935-6943 Disease denotes COVID-19 MESH:C000657245
330 7108-7114 Disease denotes deaths MESH:D003643
331 7192-7198 Disease denotes deaths MESH:D003643
332 7589-7595 Disease denotes deaths MESH:D003643
333 7900-7908 Disease denotes COVID-19 MESH:C000657245
334 8084-8092 Disease denotes COVID-19 MESH:C000657245
335 8093-8102 Disease denotes mortality MESH:D003643
346 8969-8979 Species denotes SARS-CoV-2 Tax:2697049
347 8491-8494 Chemical denotes NO2
348 8548-8557 Disease denotes mortality MESH:D003643
349 8719-8731 Disease denotes hypertension MESH:D006973
350 8733-8741 Disease denotes diabetes MESH:D003920
351 8746-8750 Disease denotes COPD MESH:D029424
352 8941-8950 Disease denotes mortality MESH:D003643
353 9046-9054 Disease denotes infected MESH:D007239
354 9229-9241 Disease denotes inflammation MESH:D007249
355 9384-9392 Disease denotes COVID-19 MESH:C000657245
358 10561-10564 Chemical denotes NO2
359 10623-10626 Chemical denotes NO2
365 9911-9923 Disease denotes hypertension MESH:D006973
366 9925-9933 Disease denotes diabetes MESH:D003920
367 9938-9942 Disease denotes COPD MESH:D029424
368 10189-10197 Disease denotes COVID-19 MESH:C000657245
369 10204-10222 Disease denotes infectious disease MESH:D003141

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T183 0-13 Sentence denotes 4 Discussion
T184 15-33 Sentence denotes 4.1 Main findings
T185 34-197 Sentence denotes This is the first nationwide study in England investigating the effect of long-term exposure to NO2 and PM2.5 during 2014–2018 on COVID-19 mortality at LSOA level.
T186 198-342 Sentence denotes The unadjusted models indicate that for every 1 μg/m3 increase in the long-term exposure to NO2 and PM2.5 the COVID-19 mortality risk increases.
T187 343-534 Sentence denotes After considering the effect of confounding and spatial autocorrelation there is still some evidence of an effect, albeit is less strong, for NO2, while for PM2.5 there is larger uncertainty.
T188 535-684 Sentence denotes The spatial relative risk has strong spatial patterns, identical for the different pollutants, potentially highlighting the effect of disease spread.
T189 686-739 Sentence denotes 4.2 Comparison with previous studies focusing on NO2
T190 740-885 Sentence denotes Our study is comparable with previous studies in the US, England and the Netherlands assessing the long-term effect of NO2 in COVID-19 mortality.
T191 886-972 Sentence denotes The study in the US focused on deaths reported by April 29, 2020, using 3122 counties.
T192 973-1158 Sentence denotes For the exposure, they calculated the mean of daily concentrations during 2010–2016 as modelled by a previously described ensemble machine learning model (R2 = 0.79) (Di et al., 2019a).
T193 1159-1205 Sentence denotes They reported a 7.1% (95% Confidence Interval:
T194 1206-1362 Sentence denotes 1.2%, 13.4%) increase in mortality per 4.5 ppb (1 ppb = 1.25 μg/m3) increase in NO2 after adjusting for confounders and spatial autocorrelation(Liang et al.
T195 1363-1418 Sentence denotes 2020)(that is approximately 1.3% increase per 1 μg/m3).
T196 1419-1574 Sentence denotes A study in England, with partly overlapping data as in our analysis, also reported a significant association between NO2 and COVID-19 mortality (p < 0.05).
T197 1575-1747 Sentence denotes For the analysis they focused on COVID-19 deaths reported in England up to April 10, 2020, used 317 LTLAs, and did not account for spatial autocorrelation (Travaglio et al.
T198 1748-1754 Sentence denotes 2020).
T199 1755-1902 Sentence denotes The study in the Netherlands using 335 municipalities, mean exposure during 2015–2019 and COVID-19 deaths up to June 5, 2020 reported 0.35 (95% CI:
T200 1903-2105 Sentence denotes 0.04, 0.66) additional COVID-19 deaths for every 1 μg/m3 increase in NO2 after adjusting for confounders and certain spatial controls, such as transmission beyond the Dutch national borders (Cole et al.
T201 2106-2112 Sentence denotes 2020).
T202 2113-2280 Sentence denotes Since the mean number of deaths in their sample is 16.86, the above estimate translates to a 2.0% increase in the COVID-19 mortality for every 1 μg/m3 increase in NO2.
T203 2281-2548 Sentence denotes An ONS report in England using 175 sampling units, 10-year averaged NO2 exposure (PCM) and COVID-19 deaths up to June 12, 2020 found a 0.6% (95% CI: −0.1%, 2.2%) increase in the COVID-19 mortality for every 1 μg/m3 increase in averaged NO2 exposure (Statistics 2020).
T204 2550-2605 Sentence denotes 4.3 Comparison with previous studies focusing on PM2.5
T205 2606-2714 Sentence denotes Our study is comparable with previous studies assessing the long-term effect of PM2.5 on COVID-19 mortality.
T206 2715-2818 Sentence denotes The aforementioned study in the US also assessed the effect of PM2.5 on COVID-19 mortality(Liang et al.
T207 2819-2825 Sentence denotes 2020).
T208 2826-2927 Sentence denotes Their exposure model was previously validated having an R2 = 0.89 for the annual estimates (Di et al.
T209 2928-2935 Sentence denotes 2019b).
T210 2936-3160 Sentence denotes The evidence for PM2.5 was weak, namely 10.8% (95% CI:-1.1%, 24.1%) per 3.4 μg/m3 increase in PM2.5 concentration (that is approximately 3.2% increase per 1 μg/m3) after adjusting for confounding and spatial autocorrelation.
T211 3161-3335 Sentence denotes The ONS report in England found a 1% (95% CI: −3%, 6%) increase in the COVID-19 mortality for every 1 μg/m3 increase in the 10-year averaged PM2.5 exposure (Statistics 2020).
T212 3336-3490 Sentence denotes Our study comes in contrast with another study in the US that used deaths reported until April 22nd, 2020 and counties as the geographical unit (Wu et al.
T213 3491-3497 Sentence denotes 2020).
T214 3498-3609 Sentence denotes For the exposure, they used previously validated monthly PM2.5 concentrations (R2 = 0.70) (Van Donkelaar et al.
T215 3610-3655 Sentence denotes 2019) and averaged them during 2000 and 2016.
T216 3656-3751 Sentence denotes After adjusting for confounding but not for spatial autocorrelation, they found an 11% (95% CI:
T217 3752-3857 Sentence denotes 6%, 17%) increase in the COVID-19 death rate for an increase of 1 μg/m3 in PM2.5 concentration (Wu et al.
T218 3858-3864 Sentence denotes 2020).
T219 3865-3958 Sentence denotes Our study comes also in contrast with the study in the Netherlands that reported 2.3 (95% CI:
T220 3959-4081 Sentence denotes 1.3, 3.0) additional COVID-19 deaths for an increase of 1 μg/m3 in the averaged long-term PM2.5 concentration (Cole et al.
T221 4082-4088 Sentence denotes 2020).
T222 4089-4267 Sentence denotes Having a mean number of deaths equal to 16.86, the above estimate translates to a 13.6% increase in the COVID-19 mortality rate for an increase of 1 μg/m3 in PM2.5 concentration.
T223 4269-4299 Sentence denotes 4.4 Strengths and limitations
T224 4300-4459 Sentence denotes Our study is the first study to examine the association between long-term exposure to NO2 and PM2.5 and COVID-19 mortality at very high geographical precision.
T225 4460-4758 Sentence denotes The spatial unit of our analysis is LSOAs, for which there are 32,844 in England (~130 000 km2), whereas previous studies have used 317 LTLAs or 175 sampling units in England, counties in the US (3 122 in an area ~9.8 million km2) and municipalities in the Netherlands (334 in an area ~41 500 km2).
T226 4759-4928 Sentence denotes Such high-resolution allows capturing the localised geographical patterns of the pollutants but also ensures adequate confounding and spatial autocorrelation adjustment.
T227 4929-5177 Sentence denotes Our study also covers, so far, the largest temporal window of the epidemic (capturing COVID-19 deaths attributable to the first wave, Supplemental Material Fig. S34), while most previous studies focused on the early to mid-stages of the first wave.
T228 5178-5230 Sentence denotes This ensures better generalisability of the results.
T229 5231-5482 Sentence denotes In addition, physical distancing and other public health interventions were introduced nationwide in England during the first epidemic, mitigating any distortion between air-pollution and COVID-19 mortality due to potential regional level differences.
T230 5483-5624 Sentence denotes Our results are also consistent in a sensitivity analysis focusing on the pre-lockdown period, in the absence of public health interventions.
T231 5625-5795 Sentence denotes Based on the scientific literature, we adjusted for several variables which would act as the confounders of the relationship between air pollution and COVID-19 mortality.
T232 5796-5986 Sentence denotes Nevertheless, since the aetiology and the factors contributing to COVID-19 mortality are not fully understood yet, we included a spatial random effect to capture unknown spatial confounding.
T233 5987-6062 Sentence denotes The spatial random effect was found to be a crucial component in the model.
T234 6063-6253 Sentence denotes Not accounting for spatial autocorrelation, when spatial autocorrelation is present, is expected to give rise to narrower credible intervals and false positive effects (Lee and Sarran 2015).
T235 6254-6290 Sentence denotes Our study has also some limitations.
T236 6291-6369 Sentence denotes The downscaling procedure will likely inflate the reported credible intervals.
T237 6370-6484 Sentence denotes However, this naturally reflects the uncertainty of the place of residence resulted from the downscaling approach.
T238 6485-6642 Sentence denotes Although we consider small areas, the study is still an ecological one and thus the reported effects do not reflect individual associations (Wakefield 2008).
T239 6643-6774 Sentence denotes Case fatality might have been a more appropriate metric for the analysis, since disease spread is accounted for in the denominator.
T240 6775-6989 Sentence denotes Nevertheless, given the asymptomatic infections and the fact that number of reported infections is not a random sample of the general population, the number of COVID-19 cases per LTLA is not reliable at this stage.
T241 6990-7199 Sentence denotes For the same reason, using the number of reported cases to adjust for disease progression and clustering of cases and deaths might not adequately capture disease progression and clustering of cases and deaths.
T242 7200-7282 Sentence denotes However, part of this clustering was captured in the spatial autocorrelation term.
T243 7283-7419 Sentence denotes We did not account for population mobility during 2014–2018 and assumed constant residence and thus levels of exposure to air-pollution.
T244 7420-7681 Sentence denotes While this is a limitation, we believe that it would have a minimal impact on the results given that 1) the exposure period is relatively short and 2) almost 93% of the deaths in our dataset occurred in people 60 years or older (Supplemental Material Table S2).
T245 7682-7786 Sentence denotes This comprises a population less likely to have moved during the past 5 years (Burgess and Quinio 2020).
T246 7787-7856 Sentence denotes We also could not account for non-residential air-pollution exposure.
T247 7857-8121 Sentence denotes Spatiotemporal variation in the strains of COVID-19 can introduce bias (Villeneuve and Goldberg 2020), however at the time of publication there was no evidence supporting that strain types can confound the relationship between COVID-19 mortality and air-pollution.
T248 8123-8142 Sentence denotes 4.5 Interpretation
T249 8143-8347 Sentence denotes Compared to the previous studies, our results are the smallest in magnitude, likely because of the high geographical precision that allows more accurate confounding and spatial autocorrelation adjustment.
T250 8348-8469 Sentence denotes In addition, we report weak evidence of an effect, which could also be due to lack of power and individual exposure data.
T251 8470-8654 Sentence denotes Nevertheless, as for NO2 we find a high posterior probability of an effect on mortality, we argue that a potential explanation might be the mediation effect of pre-existing conditions.
T252 8655-8915 Sentence denotes While in our analysis the inclusion of area-level prevalence of hypertension, diabetes and COPD did not change the results, the ecological nature of the pre-existing conditions data does not allow us to account for the mediation effect at the individual level.
T253 8916-9147 Sentence denotes Our study focuses on the mortality after contracting SARS-CoV-2, however we cannot rule out individual susceptibility to becoming infected as an explanation to the uncertainty in the effect estimates (Villeneuve and Goldberg 2020).
T254 9148-9267 Sentence denotes Such susceptibility can reflect immunosuppression, leading to later increases in inflammation (Edoardo Conticini et al.
T255 9268-9422 Sentence denotes 2020) and thus worse prognosis, or even disease spread, as recent studies have suggested that PM2.5 can proliferate COVID-19 transmission (Bianconi et al.
T256 9423-9429 Sentence denotes 2020).
T257 9430-9483 Sentence denotes Our analysis captured strong spatial autocorrelation.
T258 9484-9727 Sentence denotes The observed pattern could reflect residual variation from a potential inadequate covariate adjustment (including disease spread), spatial variation of pre-existing conditions, other unknown spatial confounders or a combination from all above.
T259 9728-9976 Sentence denotes In a sensitivity analysis, we observed that the factors associated with disease transmission left the latent field unchanged (Supplemental Material Fig. S21), as did the inclusion of hypertension, diabetes and COPD (Supplemental Material Fig. S33).
T260 9977-10147 Sentence denotes When we restricted the analysis to the pre-lockdown period, the latent field for both pollutants captured London and Birmingham, i.e. the cities with the first outbreaks.
T261 10148-10370 Sentence denotes Considering the above, and the fact that COVID-19 is an infectious disease, we believe that large variation of Fig. 4 is likely due to disease spread, which is not adequately captured in the disease progression covariates.
T262 10371-10633 Sentence denotes Fig. 4 Median posterior spatial relative risk (exponential of the spatial autocorrelation term) and posterior probability that the spatial relative risk is larger than 1 for the models with NO2 and a spatial autocorrelation term and the fully adjusted NO2 model.