PMC:7786642 / 3027-4232 JSON TXT

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Id	Subject	Object	Predicate	Lexical cue	tao:has_database_id
66	79-87	Disease	denotes	COVID-19	MESH:C000657245
67	88-97	Disease	denotes	mortality	MESH:D003643
68	517-526	Disease	denotes	mortality	MESH:D003643
69	827-833	Disease	denotes	deaths	MESH:D003643
70	1158-1166	Disease	denotes	COVID-19	MESH:C000657245
71	1167-1173	Disease	denotes	deaths	MESH:D003643

Id	Subject	Object	Predicate	Lexical cue
T32	0-253	Sentence	denotes	Previous studies suggested an effect of long-term exposure to air-pollution on COVID-19 mortality (Cole et al., 2020, Liang et al., 2020, Travaglio et al., 2020, Wu et al., 2020), however several methodological shortcomings limit their interpretability.
T33	254-429	Sentence	denotes	They were based on data aggregated on large spatial units and thus suffer from ecological fallacy (grouped levels association do not reflect individual ones) (Wakefield 2008).
T34	430-607	Sentence	denotes	Air pollution is characterised by high spatial variability, making the availability of mortality data at the same high spatial resolution crucial (Villeneuve and Goldberg 2020).
T35	608-784	Sentence	denotes	In addition, a coarse geographical resolution might lead to inadequate adjustment for confounders, when these are available at higher resolution (Villeneuve and Goldberg 2020).
T36	785-1016	Sentence	denotes	Most previous studies assessed cumulative deaths until mid or end of April and thus the generalisability of their results is limited to the early stages of the epidemic (Liang et al., 2020, Travaglio et al., 2020, Wu et al., 2020).
T37	1017-1077	Sentence	denotes	One study had data available up to June 5, 2020 (Cole et al.
T38	1078-1205	Sentence	denotes	2020) and another up to June 12, 2020 (Statistics 2020), capturing a proportion COVID-19 deaths attributable to the first wave.