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LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T1 561-567 Phenotype denotes stroke http://purl.obolibrary.org/obo/HP_0001297
T2 569-581 Phenotype denotes hypertension http://purl.obolibrary.org/obo/HP_0000822
T3 879-885 Phenotype denotes stroke http://purl.obolibrary.org/obo/HP_0001297
T4 887-899 Phenotype denotes hypertension http://purl.obolibrary.org/obo/HP_0000822
T5 9107-9114 Phenotype denotes obesity http://purl.obolibrary.org/obo/HP_0001513
T6 10411-10418 Phenotype denotes Obesity http://purl.obolibrary.org/obo/HP_0001513
T7 19000-19012 Phenotype denotes hypertension http://purl.obolibrary.org/obo/HP_0000822
T8 19014-19051 Phenotype denotes chronic obstructive pulmonary disease http://purl.obolibrary.org/obo/HP_0006510
T9 19053-19057 Phenotype denotes COPD http://purl.obolibrary.org/obo/HP_0006510
T10 28502-28514 Phenotype denotes hypertension http://purl.obolibrary.org/obo/HP_0000822
T11 28529-28533 Phenotype denotes COPD http://purl.obolibrary.org/obo/HP_0006510
T12 29694-29706 Phenotype denotes hypertension http://purl.obolibrary.org/obo/HP_0000822
T13 29721-29725 Phenotype denotes COPD http://purl.obolibrary.org/obo/HP_0006510

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
48 41-49 Disease denotes COVID-19 MESH:C000657245
49 79-85 Disease denotes deaths MESH:D003643
50 234-240 Disease denotes deaths MESH:D003643
51 408-416 Disease denotes COVID-19 MESH:C000657245
52 417-426 Disease denotes mortality MESH:D003643
53 561-567 Disease denotes stroke MESH:D020521
54 569-581 Disease denotes hypertension MESH:D006973
55 586-594 Disease denotes diabetes MESH:D003920
56 709-717 Disease denotes COVID-19 MESH:C000657245
57 795-804 Disease denotes infection MESH:D007239
58 879-885 Disease denotes stroke MESH:D020521
59 887-899 Disease denotes hypertension MESH:D006973
66 1060-1068 Disease denotes COVID-19 MESH:C000657245
67 1069-1078 Disease denotes mortality MESH:D003643
68 1498-1507 Disease denotes mortality MESH:D003643
69 1808-1814 Disease denotes deaths MESH:D003643
70 2139-2147 Disease denotes COVID-19 MESH:C000657245
71 2148-2154 Disease denotes deaths MESH:D003643
75 2446-2449 Chemical denotes NO2
76 2293-2301 Disease denotes COVID-19 MESH:C000657245
77 2302-2311 Disease denotes mortality MESH:D003643
93 2797-2805 Disease denotes COVID-19 MESH:C000657245
94 2806-2812 Disease denotes deaths MESH:D003643
95 2888-2894 Disease denotes deaths MESH:D003643
96 2937-2945 Disease denotes COVID-19 MESH:C000657245
97 3010-3018 Disease denotes COVID-19 MESH:C000657245
98 3019-3025 Disease denotes deaths MESH:D003643
99 3038-3044 Disease denotes deaths MESH:D003643
100 3089-3097 Disease denotes COVID-19 MESH:C000657245
101 3105-3110 Disease denotes death MESH:D003643
102 3265-3271 Disease denotes deaths MESH:D003643
103 3285-3293 Disease denotes COVID-19 MESH:C000657245
104 3294-3300 Disease denotes deaths MESH:D003643
105 3343-3351 Disease denotes COVID-19 MESH:C000657245
106 3352-3358 Disease denotes deaths MESH:D003643
107 3471-3480 Disease denotes mortality MESH:D003643
110 4735-4741 Disease denotes deaths MESH:D003643
111 4962-4968 Disease denotes deaths MESH:D003643
113 5144-5147 Chemical denotes NO2
120 9219-9223 Gene denotes S1.1 Gene:6267
121 8533-8537 Gene denotes S1.1 Gene:6267
122 8311-8319 Disease denotes COVID-19 MESH:C000657245
123 8379-8387 Disease denotes COVID-19 MESH:C000657245
124 8496-8504 Disease denotes infected MESH:D007239
125 9107-9114 Disease denotes obesity MESH:D009765
130 10797-10805 Disease denotes COVID-19 MESH:C000657245
131 10806-10812 Disease denotes deaths MESH:D003643
132 10877-10886 Disease denotes mortality MESH:D003643
133 10977-10983 Disease denotes deaths MESH:D003643
136 12486-12495 Disease denotes mortality MESH:D003643
137 12688-12697 Disease denotes mortality MESH:D003643
139 12954-12958 Gene denotes S1.2 Gene:6268
141 13849-13853 Disease denotes fits MESH:D012640
144 14606-14614 Disease denotes COVID-19 MESH:C000657245
145 14615-14621 Disease denotes deaths MESH:D003643
149 14352-14360 Disease denotes COVID-19 MESH:C000657245
150 14361-14367 Disease denotes deaths MESH:D003643
151 14501-14507 Disease denotes deaths MESH:D003643
153 15234-15237 Chemical denotes No2
157 16444-16447 Chemical denotes NO2
158 16386-16394 Disease denotes COVID-19 MESH:C000657245
159 16395-16404 Disease denotes mortality MESH:D003643
164 15373-15376 Chemical denotes NO2
165 15595-15598 Chemical denotes NO2
166 15293-15301 Disease denotes COVID-19 MESH:C000657245
167 15302-15311 Disease denotes mortality MESH:D003643
171 17206-17209 Chemical denotes NO2
172 17319-17322 Chemical denotes NO2
173 16582-16591 Disease denotes mortality MESH:D003643
177 18094-18097 Chemical denotes NO2
178 18485-18493 Disease denotes COVID-19 MESH:C000657245
179 18494-18500 Disease denotes deaths MESH:D003643
191 18916-18919 Chemical denotes NO2
192 19518-19521 Chemical denotes phe MESH:D010649
193 19581-19584 Chemical denotes NO2
194 18923-18931 Disease denotes COVID-19 MESH:C000657245
195 18932-18941 Disease denotes mortality MESH:D003643
196 19000-19012 Disease denotes hypertension MESH:D006973
197 19014-19051 Disease denotes chronic obstructive pulmonary disease MESH:D029424
198 19053-19057 Disease denotes COPD MESH:D029424
199 19063-19071 Disease denotes diabetes MESH:D003920
200 19145-19153 Disease denotes COVID-19 MESH:C000657245
201 19154-19163 Disease denotes mortality MESH:D003643
209 19913-19916 Chemical denotes NO2
210 20073-20076 Chemical denotes NO2
211 20268-20271 Chemical denotes NO2
212 19947-19955 Disease denotes COVID-19 MESH:C000657245
213 19956-19965 Disease denotes mortality MESH:D003643
214 20091-20099 Disease denotes COVID-19 MESH:C000657245
215 20100-20109 Disease denotes mortality MESH:D003643
241 20642-20645 Chemical denotes NO2
242 21069-21072 Chemical denotes NO2
243 21755-21758 Chemical denotes NO2
244 22059-22062 Chemical denotes NO2
245 22132-22135 Chemical denotes NO2
246 22300-22303 Chemical denotes NO2
247 20649-20657 Disease denotes COVID-19 MESH:C000657245
248 20658-20667 Disease denotes mortality MESH:D003643
249 20700-20706 Disease denotes deaths MESH:D003643
250 21014-21023 Disease denotes mortality MESH:D003643
251 21327-21335 Disease denotes COVID-19 MESH:C000657245
252 21336-21345 Disease denotes mortality MESH:D003643
253 21391-21399 Disease denotes COVID-19 MESH:C000657245
254 21400-21406 Disease denotes deaths MESH:D003643
255 21628-21636 Disease denotes COVID-19 MESH:C000657245
256 21637-21643 Disease denotes deaths MESH:D003643
257 21709-21717 Disease denotes COVID-19 MESH:C000657245
258 21718-21724 Disease denotes deaths MESH:D003643
259 21921-21927 Disease denotes deaths MESH:D003643
260 22010-22018 Disease denotes COVID-19 MESH:C000657245
261 22019-22028 Disease denotes mortality MESH:D003643
262 22155-22163 Disease denotes COVID-19 MESH:C000657245
263 22164-22170 Disease denotes deaths MESH:D003643
264 22242-22250 Disease denotes COVID-19 MESH:C000657245
265 22251-22260 Disease denotes mortality MESH:D003643
280 22478-22486 Disease denotes COVID-19 MESH:C000657245
281 22487-22496 Disease denotes mortality MESH:D003643
282 22570-22578 Disease denotes COVID-19 MESH:C000657245
283 22579-22588 Disease denotes mortality MESH:D003643
284 23015-23023 Disease denotes COVID-19 MESH:C000657245
285 23024-23033 Disease denotes mortality MESH:D003643
286 23186-23192 Disease denotes deaths MESH:D003643
287 23560-23568 Disease denotes COVID-19 MESH:C000657245
288 23569-23574 Disease denotes death MESH:D003643
289 23763-23771 Disease denotes COVID-19 MESH:C000657245
290 23772-23778 Disease denotes deaths MESH:D003643
291 23896-23902 Disease denotes deaths MESH:D003643
292 23976-23984 Disease denotes COVID-19 MESH:C000657245
293 23985-23994 Disease denotes mortality MESH:D003643
305 24169-24172 Chemical denotes NO2
306 24187-24195 Disease denotes COVID-19 MESH:C000657245
307 24196-24205 Disease denotes mortality MESH:D003643
308 24798-24806 Disease denotes COVID-19 MESH:C000657245
309 24807-24813 Disease denotes deaths MESH:D003643
310 25202-25210 Disease denotes COVID-19 MESH:C000657245
311 25211-25220 Disease denotes mortality MESH:D003643
312 25559-25567 Disease denotes COVID-19 MESH:C000657245
313 25568-25577 Disease denotes mortality MESH:D003643
314 25645-25653 Disease denotes COVID-19 MESH:C000657245
315 25654-25663 Disease denotes mortality MESH:D003643
326 27406-27412 Species denotes people Tax:9606
327 26595-26605 Disease denotes infections MESH:D007239
328 26643-26653 Disease denotes infections MESH:D007239
329 26718-26726 Disease denotes COVID-19 MESH:C000657245
330 26891-26897 Disease denotes deaths MESH:D003643
331 26975-26981 Disease denotes deaths MESH:D003643
332 27372-27378 Disease denotes deaths MESH:D003643
333 27683-27691 Disease denotes COVID-19 MESH:C000657245
334 27867-27875 Disease denotes COVID-19 MESH:C000657245
335 27876-27885 Disease denotes mortality MESH:D003643
346 28752-28762 Species denotes SARS-CoV-2 Tax:2697049
347 28274-28277 Chemical denotes NO2
348 28331-28340 Disease denotes mortality MESH:D003643
349 28502-28514 Disease denotes hypertension MESH:D006973
350 28516-28524 Disease denotes diabetes MESH:D003920
351 28529-28533 Disease denotes COPD MESH:D029424
352 28724-28733 Disease denotes mortality MESH:D003643
353 28829-28837 Disease denotes infected MESH:D007239
354 29012-29024 Disease denotes inflammation MESH:D007249
355 29167-29175 Disease denotes COVID-19 MESH:C000657245
358 30344-30347 Chemical denotes NO2
359 30406-30409 Chemical denotes NO2
365 29694-29706 Disease denotes hypertension MESH:D006973
366 29708-29716 Disease denotes diabetes MESH:D003920
367 29721-29725 Disease denotes COPD MESH:D029424
368 29972-29980 Disease denotes COVID-19 MESH:C000657245
369 29987-30005 Disease denotes infectious disease MESH:D003141
373 30539-30542 Chemical denotes NO2
374 30547-30555 Disease denotes COVID-19 MESH:C000657245
375 30556-30565 Disease denotes mortality MESH:D003643

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T21 0-15 Sentence denotes 1 Introduction
T22 16-150 Sentence denotes As of 30th of June 2020, COVID-19 has caused more than 500,000 deaths globally, with an estimated case fatality of 1–4% (Hauser et al.
T23 151-157 Sentence denotes 2020).
T24 158-372 Sentence denotes The UK is one of the countries most affected, with an estimated 57,300 more deaths in England and Wales than it would be expected from mid-February to end of May 2020 had the pandemic not taken place (Kontis et al.
T25 373-379 Sentence denotes 2020).
T26 380-468 Sentence denotes Established risk factors of COVID-19 mortality include age, sex and ethnicity (Wu et al.
T27 469-475 Sentence denotes 2020).
T28 476-640 Sentence denotes Previous studies have observed a correlation between pre-existing conditions such as stroke, hypertension and diabetes (Williamson et al., 2020, Yang et al., 2020).
T29 641-808 Sentence denotes Long-term exposure to air-pollution has been hypothesised to worsen COVID-19 prognosis: either directly, as it can suppress early immune responses to the infection (E.
T30 809-825 Sentence denotes Conticini et al.
T31 826-980 Sentence denotes 2020), or indirectly, as it can increase the risk of stroke, hypertension and other pre-existing conditions (Giorgini et al., 2016, Scheers et al., 2015).
T32 981-1234 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 1235-1410 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 1411-1588 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 1589-1765 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 1766-1997 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 1998-2058 Sentence denotes One study had data available up to June 5, 2020 (Cole et al.
T38 2059-2186 Sentence denotes 2020) and another up to June 12, 2020 (Statistics 2020), capturing a proportion COVID-19 deaths attributable to the first wave.
T39 2187-2419 Sentence denotes In this nationwide study in England, we investigated the effect of long-term exposure to air pollution on COVID-19 mortality during the entire first wave of the epidemic, after accounting for confounding and spatial autocorrelation.
T40 2420-2533 Sentence denotes We focused on exposure to NO2 and PM2.5 (atmospheric particulate matter that has a diameter of less than 2.5 µm).
T41 2534-2745 Sentence denotes We downscaled the LTLA geographical information to the Lower Layer Super Output Area (LSOA) to alleviate the effect of ecological bias and exploit the variability of the exposure at high geographical resolution.
T42 2747-2757 Sentence denotes 2 Methods
T43 2759-2780 Sentence denotes 2.1 Study population
T44 2781-2873 Sentence denotes We included all COVID-19 deaths as reported to Public Health England (PHE) by June 30, 2020.
T45 2874-3123 Sentence denotes These include deaths that had a laboratory confirmed report of COVID-19 (including at post-mortem) (EpiCell 2020), as well as suspected COVID-19 deaths, defined as deaths without a positive test but with mention of COVID-19 in the death certificate.
T46 3124-3208 Sentence denotes These definitions were consistent during the study period and over the study region.
T47 3209-3272 Sentence denotes The main outcome of this study was laboratory confirmed deaths.
T48 3273-3518 Sentence denotes We selected COVID-19 deaths up to June the 30th to ensure we captured COVID-19 deaths attributable to the first wave of epidemic that in England and Wales was over by the end of May, when all-cause mortality was no longer elevated (Kontis et al.
T49 3519-3525 Sentence denotes 2020).
T50 3526-3724 Sentence denotes Individual data on age, sex, ethnicity, lower tier local authority (LTLA) of the residential address and type of residence type (i.e. nursing homes, prisons, medical facilities etc.) were available.
T51 3725-3814 Sentence denotes Population at risk in England was available through Office for National Statistics (ONS).
T52 3815-3974 Sentence denotes Information at the LSOA level about age and sex was available for 2018, whereas about ethnicity for 2011 (the most recent years available at time of analysis).
T53 3976-3992 Sentence denotes 2.2 Downscaling
T54 3993-4065 Sentence denotes There were 317 LTLAs in England in 2019 (Supplemental Material Fig. S1).
T55 4066-4180 Sentence denotes Such a coarse geographical unit is not expected to capture the strong localised spatial patterns of air-pollution.
T56 4181-4252 Sentence denotes We thus downscaled the LTLA geographical information to the LSOA level.
T57 4253-4367 Sentence denotes LSOAs are high resolution geographical units in England (32,844 units in 2011, see Supplemental Material Fig. S2).
T58 4368-4577 Sentence denotes The median population per LSOA in 2018 was 1617, varying from 591 to 14,696 (min to max) (Supplemental Material Fig. S3), and the median area per LSOA was 0.4km2, varying from 0.0002km2 to 68.4km2(min to max).
T59 4578-4654 Sentence denotes The LTLA boundaries are revised every year, whereas the LSOA ones at census.
T60 4655-4933 Sentence denotes Let l~m denote that the l-th LSOA belongs to the m-th LTLA, nijkm the number of deaths in the m-th LTLA and Pijkl the population in the i-th age group (1<, 1–4, 5–9, …, 85–90, >90), j-the sex (male or female), k-th ethnic group (White, Mixed, Asian, Black, Other) and l-th LSOA.
T61 4934-5102 Sentence denotes We sampled nijkm individual deaths at the l-th LSOA level from a Multinomial distribution with probabilities:πijkl=Pijkl/∑l~mPijkl,and repeated the procedure 100 times.
T62 5104-5117 Sentence denotes 2.3 Exposure
T63 5118-5189 Sentence denotes We considered exposure to NO2 and PM2.5 as indicators of air pollution.
T64 5190-5227 Sentence denotes We selected these pollutants because:
T65 5228-5643 Sentence denotes 1) they reflect different sources of air-pollution (NO2 reflects traffic related air-pollution, whereas PM2.5 is a combination of traffic and non-traffic sources), 2) they were considered in previous studies (Cole et al., 2020, Liang et al., 2020, Travaglio et al., 2020, Wu et al., 2020), and 3) they are responsible for the highest number of years of life lost compared to other pollutants in Europe (Ortiz 2019).
T66 5644-5767 Sentence denotes We retrieved NO2 and PM2.5 concentration in England from the Pollution Climate Mapping (PCM; https://uk-air.defra.gov.uk/).
T67 5768-5882 Sentence denotes The PCM produces annual estimates during 2001–2018 for NO2 and 2002–2018 for PM2.5 at 1x1km resolution for the UK.
T68 5883-6046 Sentence denotes The PCM model is calibrated using monitoring stations across the nation and has high predictive accuracy, R2 = 0.88 for NO2 and R2 = 0.63 for PM2.5 (Brookes 2017).
T69 6047-6197 Sentence denotes We defined long-term exposure to these compounds as the mean of the past 5 years for which data was available at the time of analysis, i.e. 2014–2018.
T70 6198-6367 Sentence denotes An alternative is calculating the median, however the distribution of the air-pollutants using any of these metrics is almost identical, (Supplemental Material Fig. S4).
T71 6368-6677 Sentence denotes We weighted the exposure using a combination of population estimates available from the fourth version of Gridded Population of the World collection at 1x1km grid as of 2020 (Center for International Earth Science Information Network - CIESIN - Columbia University 2018) and from ONS at LSOA level as of 2018.
T72 6678-6782 Sentence denotes Let Xgl be the pollutant and Pgl the population in the intersection of the g-th grid cell and l-th LSOA.
T73 6783-6946 Sentence denotes Assuming the Xg is constant (i.e. Xgl=Xg for all intersections) in the g-th grid cell, we define the population weighted version X¯lof Xgl as:X¯l=∑glP¯glXg∑glP¯gl.
T74 6947-7046 Sentence denotes To calculate P¯gl, we first compute w¯gl=wgl/∑glwgl, where wgl is the area weight per intersection.
T75 7047-7094 Sentence denotes Then calculate the population per intersection:
T76 7095-7107 Sentence denotes Pgl'=Pgw¯gl.
T77 7108-7224 Sentence denotes We then use the Pl (LSOA populations) and obtain P¯gl=vglPl, where vgl is the normalised Pgl', ie vgl= Pgl'/∑glPgl'.
T78 7226-7242 Sentence denotes 2.4 Confounders
T79 7243-7391 Sentence denotes We considered confounders related with meteorology, socio-demographics, disease spread, healthcare provision and health related variables (Table 1).
T80 7392-7605 Sentence denotes As meteorological confounders, we considered temperature and relative humidity and calculated the mean for March-June 2018 as this is the latest year with data available at 1x1km grid retrieved from the MetOffice.
T81 7606-7723 Sentence denotes We weighted temperature and relative humidity using the population weights calculated for the air-pollution exposure.
T82 7724-7853 Sentence denotes As socio-demographical confounders we considered age, sex, ethnicity, deprivation, urbanicity, population density and occupation.
T83 7854-8054 Sentence denotes Information on age (2018), sex (2018), ethnicity (2011), urbanicity (2011) and population density (2018) was available at the LSOA level from ONS (the most recent years available at time of analysis).
T84 8055-8263 Sentence denotes To adjust for deprivation, we used quintiles of the index of multiple deprivation at LSOA level in 2019 (Ministry of Housing, Communities and Local Government), excluding the dimension related to air quality.
T85 8264-8552 Sentence denotes We used estimates of occupational exposures to COVID-19, as calculated by ONS, to adjust for high risk exposure to COVID-19, defined as those with a score higher than 80/100 (corresponding to at least >1 per week exposed to someone infected, Supplemental Material Text S1.1 and Table S1).
T86 8553-8739 Sentence denotes To account for disease progression, we used the number of days since the 1st reported case and the number of positive cases in each LTLA (as of 30th of June 2020, as retrieved from PHE).
T87 8740-8901 Sentence denotes Adjustment for the latter factors is expected to attenuate geographical differences generated due to regional differences about the timing on the pandemic curve.
T88 8902-9045 Sentence denotes For healthcare provision, we used the number of intensive care unit beds per population, in February 2020 per NHS trust, as retrieved from NHS.
T89 9046-9225 Sentence denotes Last, as health-related variables, we considered smoking and obesity prevalence at the GP practice level during 2018–2019, as retrieved from PHE (Supplemental Material Text S1.1).
T90 9226-9268 Sentence denotes Table 1 Data sources used in the analysis.
T91 9269-9331 Sentence denotes Confounders Source Spatial Resolution Temporal Resolution Type
T92 9332-9415 Sentence denotes Temperature MetOfficehttps://www.metoffice.gov.uk/ 1 km2 March-June 2018 continuous
T93 9416-9505 Sentence denotes Relative humidity MetOfficehttps://www.metoffice.gov.uk/ 1 km2 March-June 2018 continuous
T94 9506-9660 Sentence denotes Index of Multiple Deprivation Ministry of Housing, Communities and Local Governmenthttps://www.gov.uk/ Lower layer super output area 2019 rank (quintiles)
T95 9661-9772 Sentence denotes Urbanicity Office for National Statisticshttps://www.ons.gov.uk/ Lower layer super output area 2011 urban/rural
T96 9773-9877 Sentence denotes Days since 1st reported case Public Health England Lower tier local authority Until 30th June continuous
T97 9878-9985 Sentence denotes Number of positive cases Public Health England Lower tier local authority Until 30th June discrete (counts)
T98 9986-10122 Sentence denotes Population density Office for National Statisticshttps://www.ons.gov.uk/ Lower layer super output area 2018 continuous (log transformed)
T99 10123-10280 Sentence denotes Number of intensive care unit beds National Health Servicehttps://www.england.nhs.uk/ National Health Service trust February 2020 continuous (per population)
T100 10281-10410 Sentence denotes Smoking Public Health Englandhttps://fingertips.phe.org.uk/ General practitioner catchment area 2018–2019 continuous (prevalence)
T101 10411-10540 Sentence denotes Obesity Public Health Englandhttps://fingertips.phe.org.uk/ General practitioner catchment area 2018–2019 continuous (prevalence)
T102 10541-10675 Sentence denotes High Risk Occupation Office for National Statisticshttps://www.ons.gov.uk/ Middle layer super output area 2011 continuous (prevalence)
T103 10677-10701 Sentence denotes 2.5 Statistical methods
T104 10702-10845 Sentence denotes We specified Bayesian hierarchical Poisson log-linear models to investigate the association of COVID-19 deaths and NO2 and PM2.5 independently.
T105 10846-10993 Sentence denotes The LSOA specific standardised mortality ratio is known to be an unstable estimator with high variance when the number of expected deaths is small.
T106 10994-11323 Sentence denotes To overcome this problem, we used a well-established hierarchical framework, specifying spatially structured and unstructured random effects, so that the model borrows strength from the other areas across the entire study region, as well as from the neighbouring ones (Best et al., 2005, Wakefield et al., 2000, Wakefield, 2006).
T107 11324-11495 Sentence denotes We model these random effects using a re-parametrisation of the Besag-York-Molliè conditional autoregressive prior distribution (Besag et al., 1991, Simpson et al., 2017).
T108 11496-11528 Sentence denotes We fitted four models including:
T109 11529-11758 Sentence denotes 1) each pollutant (model 1), 2) each pollutant and the spatial autocorrelation term (model 2), 3) each pollutant and all confounders (model 3) and 4) each pollutant, the spatial autocorrelation term and all confounders (model 4).
T110 11759-11924 Sentence denotes All models were adjusted for age, sex and ethnicity using indirect standardisation; we used the English population as the standard population to calculate the rates.
T111 11925-12067 Sentence denotes We do not report results from the joint analysis including both pollutants since they are highly correlated (Supplemental Material Figure S5).
T112 12068-12299 Sentence denotes In order to propagate the uncertainty resulted from the sampling we used for the downscaling, we fitted the models over 100 downscaled samples and then performed Bayesian model averaging to combine the estimates (Gómez-Rubio et al.
T113 12300-12306 Sentence denotes 2020).
T114 12307-12430 Sentence denotes We performed a complete case analysis since for only 1.1% of the cases information about age, sex and ethnicity is missing.
T115 12431-12644 Sentence denotes We report results as posterior median of % increase in mortality risk for every 1 μg/m3 increase in the air-pollutants, 95% credible intervals (CrI) and posterior probability that the estimated effect is positive.
T116 12645-12841 Sentence denotes We also report posterior median of spatial mortality relative risks (exponential of the spatial autocorrelation term) and posterior probabilities that the spatial relative risks are larger than 1.
T117 12842-12959 Sentence denotes The mathematical formulation of the models and prior specifications are given in the Supplemental Material Text S1.2.
T118 12960-13002 Sentence denotes All models were fitted in INLA (Rue et al.
T119 13003-13009 Sentence denotes 2009).
T120 13010-13134 Sentence denotes Covariate data and code for running the analysis are available at https://github.com/gkonstantinoudis/COVID19AirpollutionEn.
T121 13136-13161 Sentence denotes 2.6 Sensitivity analyses
T122 13162-13208 Sentence denotes We performed a series of sensitivity analyses.
T123 13209-13333 Sentence denotes First, we repeated the main analyses using data at the LTLA level with all exposures and confounding weighted by population.
T124 13334-13524 Sentence denotes Second, we examined if there is a differential effect of long-term exposure to air-pollution at the early stages of the epidemic, considering the lockdown (23rd of March 2020) as a landmark.
T125 13525-13776 Sentence denotes Third, we assessed the correlation between the latent field of the full model (model 4) with that of the model excluding or including only covariates indicating disease spread (i.e. number of tested positive cases and days since first reported cases).
T126 13777-13854 Sentence denotes Fourth, we categorised pollutants into quintiles to allow more flexible fits.
T127 13855-13932 Sentence denotes Fifth, we repeated the analysis including the suspected cases to the outcome.
T128 13933-14133 Sentence denotes Sixth, we repeated the analysis changing the definition of long-term exposure to the mean of the past 3 and 10 years for which data was available at the time of analysis, i.e. 2016–2018 and 2009–2018.
T129 14134-14295 Sentence denotes Seventh, we fitted a zero-inflated Poisson model to account for the proportion of zeros in the data (36% in the 100 samples – see Supplemental Material Fig. S6).
T130 14297-14307 Sentence denotes 3 Results
T131 14309-14330 Sentence denotes 3.1 Study population
T132 14331-14453 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 14454-14581 Sentence denotes The age, sex and ethnicity distribution of the deaths follows patterns reported previously (Supplemental Material Tables S2-3).
T134 14582-14622 Sentence denotes Fig. 1 Flowchart of the COVID-19 deaths.
T135 14624-14637 Sentence denotes 3.2 Exposure
T136 14638-14715 Sentence denotes Fig. 2 shows the population weighted air-pollutants at LSOA level in England.
T137 14716-14860 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 14861-14993 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 14994-15078 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 15079-15124 Sentence denotes Fig. 2 Population weighted exposure per LSOA.
T141 15126-15142 Sentence denotes 3.3 Confounders
T142 15143-15227 Sentence denotes Plots and maps of the confounders can be found in Supplemental Material, Fig. S7-17.
T143 15229-15237 Sentence denotes 3.4 No2
T144 15238-15264 Sentence denotes We observe a 2.6% (95%CrI:
T145 15265-15437 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 15438-15631 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 15632-15659 Sentence denotes 0.8%, 1.8%), 1.8% (95% CrI:
T148 15660-15690 Sentence denotes 1.5%, 2.1%) for every 1 μg/m3.
T149 15691-15943 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 15944-16007 Sentence denotes The spatial relative risk in England varies from 0.24 (95% CrI:
T151 16008-16037 Sentence denotes 0.08, 0.69) to 2.09 (95% CrI:
T152 16038-16084 Sentence denotes 1.30, 3.11) in model 2 and from 0.30 (95% CrI:
T153 16085-16114 Sentence denotes 0.10, 0.84) to 1.87 (95% CrI:
T154 16115-16224 Sentence denotes 1.18, 2.93) in model 4, implying that the confounders explain very little of the observed variation (Fig. 3).
T155 16225-16342 Sentence denotes The variation is more pronounced in the cities and suburban areas (with posterior probability higher than 1; Fig. 3).
T156 16343-16513 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 16515-16525 Sentence denotes 3.5 Pm2.5
T158 16526-16553 Sentence denotes We observe a 4.4% (95% CrI:
T159 16554-16719 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 16720-16845 Sentence denotes When we adjust for spatial autocorrelation the effect increases slightly but the credible intervals are wider, 5.4% (95% CrI:
T161 16846-16926 Sentence denotes 2.5%, 8.4%), whereas it is similar when we adjust for confounding 4.9% (95% CrI:
T162 16927-16981 Sentence denotes 3.7%, 6.2%) (Fig. 3 & Supplemental Material Table S5).
T163 16982-17131 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 17132-17229 Sentence denotes The posterior probability of a positive effect is lower than observed for NO2, and equal to 0.78.
T165 17230-17391 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 17392-17421 Sentence denotes 0.12, 0.46) to 2.26 (95% CrI:
T167 17422-17468 Sentence denotes 1.32, 3.85) in model 2 and from 0.30 (95% CrI:
T168 17469-17498 Sentence denotes 0.15, 0.57) to 1.90 (95% CrI:
T169 17499-17556 Sentence denotes 1.14, 3.17) in model 4 (Supplemental Material, Fig. S18).
T170 17558-17583 Sentence denotes 3.6 Sensitivity analyses
T171 17584-17863 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 17864-18059 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 18060-18308 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 18309-18416 Sentence denotes The use of quintiles of the pollutants justifies the linearity assumption (Supplemental Material Fig. S24).
T175 18417-18565 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 18566-18699 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 18700-18820 Sentence denotes The results are consistent when we fitted a zero-inflated Poisson (Supplemental Material Tables S12-13 and Fig. S28-29).
T178 18822-18844 Sentence denotes 3.7 Post-hoc analysis
T179 18845-18987 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 18988-19373 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 19374-19566 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 19567-19781 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.
T183 19783-19796 Sentence denotes 4 Discussion
T184 19798-19816 Sentence denotes 4.1 Main findings
T185 19817-19980 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 19981-20125 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 20126-20317 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 20318-20467 Sentence denotes The spatial relative risk has strong spatial patterns, identical for the different pollutants, potentially highlighting the effect of disease spread.
T189 20469-20522 Sentence denotes 4.2 Comparison with previous studies focusing on NO2
T190 20523-20668 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 20669-20755 Sentence denotes The study in the US focused on deaths reported by April 29, 2020, using 3122 counties.
T192 20756-20941 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 20942-20988 Sentence denotes They reported a 7.1% (95% Confidence Interval:
T194 20989-21145 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 21146-21201 Sentence denotes 2020)(that is approximately 1.3% increase per 1 μg/m3).
T196 21202-21357 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 21358-21530 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 21531-21537 Sentence denotes 2020).
T199 21538-21685 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 21686-21888 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 21889-21895 Sentence denotes 2020).
T202 21896-22063 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 22064-22331 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 22333-22388 Sentence denotes 4.3 Comparison with previous studies focusing on PM2.5
T205 22389-22497 Sentence denotes Our study is comparable with previous studies assessing the long-term effect of PM2.5 on COVID-19 mortality.
T206 22498-22601 Sentence denotes The aforementioned study in the US also assessed the effect of PM2.5 on COVID-19 mortality(Liang et al.
T207 22602-22608 Sentence denotes 2020).
T208 22609-22710 Sentence denotes Their exposure model was previously validated having an R2 = 0.89 for the annual estimates (Di et al.
T209 22711-22718 Sentence denotes 2019b).
T210 22719-22943 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 22944-23118 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 23119-23273 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 23274-23280 Sentence denotes 2020).
T214 23281-23392 Sentence denotes For the exposure, they used previously validated monthly PM2.5 concentrations (R2 = 0.70) (Van Donkelaar et al.
T215 23393-23438 Sentence denotes 2019) and averaged them during 2000 and 2016.
T216 23439-23534 Sentence denotes After adjusting for confounding but not for spatial autocorrelation, they found an 11% (95% CI:
T217 23535-23640 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 23641-23647 Sentence denotes 2020).
T219 23648-23741 Sentence denotes Our study comes also in contrast with the study in the Netherlands that reported 2.3 (95% CI:
T220 23742-23864 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 23865-23871 Sentence denotes 2020).
T222 23872-24050 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 24052-24082 Sentence denotes 4.4 Strengths and limitations
T224 24083-24242 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 24243-24541 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 24542-24711 Sentence denotes Such high-resolution allows capturing the localised geographical patterns of the pollutants but also ensures adequate confounding and spatial autocorrelation adjustment.
T227 24712-24960 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 24961-25013 Sentence denotes This ensures better generalisability of the results.
T229 25014-25265 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 25266-25407 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 25408-25578 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 25579-25769 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 25770-25845 Sentence denotes The spatial random effect was found to be a crucial component in the model.
T234 25846-26036 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 26037-26073 Sentence denotes Our study has also some limitations.
T236 26074-26152 Sentence denotes The downscaling procedure will likely inflate the reported credible intervals.
T237 26153-26267 Sentence denotes However, this naturally reflects the uncertainty of the place of residence resulted from the downscaling approach.
T238 26268-26425 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 26426-26557 Sentence denotes Case fatality might have been a more appropriate metric for the analysis, since disease spread is accounted for in the denominator.
T240 26558-26772 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 26773-26982 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 26983-27065 Sentence denotes However, part of this clustering was captured in the spatial autocorrelation term.
T243 27066-27202 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 27203-27464 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 27465-27569 Sentence denotes This comprises a population less likely to have moved during the past 5 years (Burgess and Quinio 2020).
T246 27570-27639 Sentence denotes We also could not account for non-residential air-pollution exposure.
T247 27640-27904 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 27906-27925 Sentence denotes 4.5 Interpretation
T249 27926-28130 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 28131-28252 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 28253-28437 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 28438-28698 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 28699-28930 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 28931-29050 Sentence denotes Such susceptibility can reflect immunosuppression, leading to later increases in inflammation (Edoardo Conticini et al.
T255 29051-29205 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 29206-29212 Sentence denotes 2020).
T257 29213-29266 Sentence denotes Our analysis captured strong spatial autocorrelation.
T258 29267-29510 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 29511-29759 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 29760-29930 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 29931-30153 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 30154-30416 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.
T263 30418-30431 Sentence denotes 5 Conclusion
T264 30432-30614 Sentence denotes Overall, this study provides some evidence of an association between averaged exposure during 2014–2018 to NO2 and COVID-19 mortality, while the role of PM2.5 remains more uncertain.
T265 30616-30656 Sentence denotes CRediT authorship contribution statement
T266 30657-30684 Sentence denotes Garyfallos Konstantinoudis:
T267 30685-30818 Sentence denotes Conceptualization, Methodology, Software, Formal analysis, Writing - original draft, Writing - review & editing, Funding acquisition.
T268 30819-30836 Sentence denotes Tullia Padellini:
T269 30837-30904 Sentence denotes Methodology, Software, Formal analysis, Writing - review & editing.
T270 30905-30919 Sentence denotes James Bennett:
T271 30920-30960 Sentence denotes Writing - review & editing, Methodology.
T272 30961-30975 Sentence denotes Bethan Davies:
T273 30976-31003 Sentence denotes Writing - review & editing.
T274 31004-31017 Sentence denotes Majid Ezzati:
T275 31018-31109 Sentence denotes Conceptualization, Project administration, Writing - review & editing, Funding acquisition.
T276 31110-31127 Sentence denotes Marta Blangiardo:
T277 31128-31208 Sentence denotes Conceptualization, Methodology, Writing - review & editing, Funding acquisition.
T278 31210-31243 Sentence denotes Declaration of Competing Interest
T279 31244-31414 Sentence denotes The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.