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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
305 117-120 Chemical denotes NO2
306 135-143 Disease denotes COVID-19 MESH:C000657245
307 144-153 Disease denotes mortality MESH:D003643
308 746-754 Disease denotes COVID-19 MESH:C000657245
309 755-761 Disease denotes deaths MESH:D003643
310 1150-1158 Disease denotes COVID-19 MESH:C000657245
311 1159-1168 Disease denotes mortality MESH:D003643
312 1507-1515 Disease denotes COVID-19 MESH:C000657245
313 1516-1525 Disease denotes mortality MESH:D003643
314 1593-1601 Disease denotes COVID-19 MESH:C000657245
315 1602-1611 Disease denotes mortality MESH:D003643
326 3354-3360 Species denotes people Tax:9606
327 2543-2553 Disease denotes infections MESH:D007239
328 2591-2601 Disease denotes infections MESH:D007239
329 2666-2674 Disease denotes COVID-19 MESH:C000657245
330 2839-2845 Disease denotes deaths MESH:D003643
331 2923-2929 Disease denotes deaths MESH:D003643
332 3320-3326 Disease denotes deaths MESH:D003643
333 3631-3639 Disease denotes COVID-19 MESH:C000657245
334 3815-3823 Disease denotes COVID-19 MESH:C000657245
335 3824-3833 Disease denotes mortality MESH:D003643

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T223 0-30 Sentence denotes 4.4 Strengths and limitations
T224 31-190 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 191-489 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 490-659 Sentence denotes Such high-resolution allows capturing the localised geographical patterns of the pollutants but also ensures adequate confounding and spatial autocorrelation adjustment.
T227 660-908 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 909-961 Sentence denotes This ensures better generalisability of the results.
T229 962-1213 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 1214-1355 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 1356-1526 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 1527-1717 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 1718-1793 Sentence denotes The spatial random effect was found to be a crucial component in the model.
T234 1794-1984 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 1985-2021 Sentence denotes Our study has also some limitations.
T236 2022-2100 Sentence denotes The downscaling procedure will likely inflate the reported credible intervals.
T237 2101-2215 Sentence denotes However, this naturally reflects the uncertainty of the place of residence resulted from the downscaling approach.
T238 2216-2373 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 2374-2505 Sentence denotes Case fatality might have been a more appropriate metric for the analysis, since disease spread is accounted for in the denominator.
T240 2506-2720 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 2721-2930 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 2931-3013 Sentence denotes However, part of this clustering was captured in the spatial autocorrelation term.
T243 3014-3150 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 3151-3412 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 3413-3517 Sentence denotes This comprises a population less likely to have moved during the past 5 years (Burgess and Quinio 2020).
T246 3518-3587 Sentence denotes We also could not account for non-residential air-pollution exposure.
T247 3588-3852 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.