PMC:7786642 / 28083-29950 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"326","span":{"begin":1369,"end":1375},"obj":"Species"},{"id":"327","span":{"begin":558,"end":568},"obj":"Disease"},{"id":"328","span":{"begin":606,"end":616},"obj":"Disease"},{"id":"329","span":{"begin":681,"end":689},"obj":"Disease"},{"id":"330","span":{"begin":854,"end":860},"obj":"Disease"},{"id":"331","span":{"begin":938,"end":944},"obj":"Disease"},{"id":"332","span":{"begin":1335,"end":1341},"obj":"Disease"},{"id":"333","span":{"begin":1646,"end":1654},"obj":"Disease"},{"id":"334","span":{"begin":1830,"end":1838},"obj":"Disease"},{"id":"335","span":{"begin":1839,"end":1848},"obj":"Disease"}],"attributes":[{"id":"A326","pred":"tao:has_database_id","subj":"326","obj":"Tax:9606"},{"id":"A327","pred":"tao:has_database_id","subj":"327","obj":"MESH:D007239"},{"id":"A328","pred":"tao:has_database_id","subj":"328","obj":"MESH:D007239"},{"id":"A329","pred":"tao:has_database_id","subj":"329","obj":"MESH:C000657245"},{"id":"A330","pred":"tao:has_database_id","subj":"330","obj":"MESH:D003643"},{"id":"A331","pred":"tao:has_database_id","subj":"331","obj":"MESH:D003643"},{"id":"A332","pred":"tao:has_database_id","subj":"332","obj":"MESH:D003643"},{"id":"A333","pred":"tao:has_database_id","subj":"333","obj":"MESH:C000657245"},{"id":"A334","pred":"tao:has_database_id","subj":"334","obj":"MESH:C000657245"},{"id":"A335","pred":"tao:has_database_id","subj":"335","obj":"MESH:D003643"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"Our study has also some limitations. The downscaling procedure will likely inflate the reported credible intervals. However, this naturally reflects the uncertainty of the place of residence resulted from the downscaling approach. Although we consider small areas, the study is still an ecological one and thus the reported effects do not reflect individual associations (Wakefield 2008). Case fatality might have been a more appropriate metric for the analysis, since disease spread is accounted for in the denominator. 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. 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. However, part of this clustering was captured in the spatial autocorrelation term. We did not account for population mobility during 2014–2018 and assumed constant residence and thus levels of exposure to air-pollution. 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). This comprises a population less likely to have moved during the past 5 years (Burgess and Quinio 2020). We also could not account for non-residential air-pollution exposure. 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."}

{"project":"LitCovid-sentences","denotations":[{"id":"T235","span":{"begin":0,"end":36},"obj":"Sentence"},{"id":"T236","span":{"begin":37,"end":115},"obj":"Sentence"},{"id":"T237","span":{"begin":116,"end":230},"obj":"Sentence"},{"id":"T238","span":{"begin":231,"end":388},"obj":"Sentence"},{"id":"T239","span":{"begin":389,"end":520},"obj":"Sentence"},{"id":"T240","span":{"begin":521,"end":735},"obj":"Sentence"},{"id":"T241","span":{"begin":736,"end":945},"obj":"Sentence"},{"id":"T242","span":{"begin":946,"end":1028},"obj":"Sentence"},{"id":"T243","span":{"begin":1029,"end":1165},"obj":"Sentence"},{"id":"T244","span":{"begin":1166,"end":1427},"obj":"Sentence"},{"id":"T245","span":{"begin":1428,"end":1532},"obj":"Sentence"},{"id":"T246","span":{"begin":1533,"end":1602},"obj":"Sentence"},{"id":"T247","span":{"begin":1603,"end":1867},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Our study has also some limitations. The downscaling procedure will likely inflate the reported credible intervals. However, this naturally reflects the uncertainty of the place of residence resulted from the downscaling approach. Although we consider small areas, the study is still an ecological one and thus the reported effects do not reflect individual associations (Wakefield 2008). Case fatality might have been a more appropriate metric for the analysis, since disease spread is accounted for in the denominator. 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. 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. However, part of this clustering was captured in the spatial autocorrelation term. We did not account for population mobility during 2014–2018 and assumed constant residence and thus levels of exposure to air-pollution. 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). This comprises a population less likely to have moved during the past 5 years (Burgess and Quinio 2020). We also could not account for non-residential air-pollution exposure. 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."}