PMC:7786642 / 4793-16341 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7786642","sourcedb":"PMC","sourceid":"7786642","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7786642","text":"2 Methods\n\n2.1 Study population\nWe included all COVID-19 deaths as reported to Public Health England (PHE) by June 30, 2020. 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. These definitions were consistent during the study period and over the study region. The main outcome of this study was laboratory confirmed deaths. 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. 2020). 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. Population at risk in England was available through Office for National Statistics (ONS). 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).\n\n2.2 Downscaling\nThere were 317 LTLAs in England in 2019 (Supplemental Material Fig. S1). Such a coarse geographical unit is not expected to capture the strong localised spatial patterns of air-pollution. We thus downscaled the LTLA geographical information to the LSOA level. LSOAs are high resolution geographical units in England (32,844 units in 2011, see Supplemental Material Fig. S2). 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). The LTLA boundaries are revised every year, whereas the LSOA ones at census. 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\u003c, 1–4, 5–9, …, 85–90, \u003e90), j-the sex (male or female), k-th ethnic group (White, Mixed, Asian, Black, Other) and l-th LSOA. 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.\n\n2.3 Exposure\nWe considered exposure to NO2 and PM2.5 as indicators of air pollution. We selected these pollutants because: 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). We retrieved NO2 and PM2.5 concentration in England from the Pollution Climate Mapping (PCM; https://uk-air.defra.gov.uk/). The PCM produces annual estimates during 2001–2018 for NO2 and 2002–2018 for PM2.5 at 1x1km resolution for the UK. 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). 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. 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). 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. Let Xgl be the pollutant and Pgl the population in the intersection of the g-th grid cell and l-th LSOA. 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.\nTo calculate P¯gl, we first compute w¯gl=wgl/∑glwgl, where wgl is the area weight per intersection. Then calculate the population per intersection: Pgl'=Pgw¯gl. We then use the Pl (LSOA populations) and obtain P¯gl=vglPl, where vgl is the normalised Pgl', ie vgl= Pgl'/∑glPgl'.\n\n2.4 Confounders\nWe considered confounders related with meteorology, socio-demographics, disease spread, healthcare provision and health related variables (Table 1). 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. We weighted temperature and relative humidity using the population weights calculated for the air-pollution exposure. As socio-demographical confounders we considered age, sex, ethnicity, deprivation, urbanicity, population density and occupation. 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). 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. 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 \u003e1 per week exposed to someone infected, Supplemental Material Text S1.1 and Table S1). 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). Adjustment for the latter factors is expected to attenuate geographical differences generated due to regional differences about the timing on the pandemic curve. For healthcare provision, we used the number of intensive care unit beds per population, in February 2020 per NHS trust, as retrieved from NHS. 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).\nTable 1 Data sources used in the analysis.\nConfounders Source Spatial Resolution Temporal Resolution Type\nTemperature MetOfficehttps://www.metoffice.gov.uk/ 1 km2 March-June 2018 continuous\nRelative humidity MetOfficehttps://www.metoffice.gov.uk/ 1 km2 March-June 2018 continuous\nIndex of Multiple Deprivation Ministry of Housing, Communities and Local Governmenthttps://www.gov.uk/ Lower layer super output area 2019 rank (quintiles)\nUrbanicity Office for National Statisticshttps://www.ons.gov.uk/ Lower layer super output area 2011 urban/rural\nDays since 1st reported case Public Health England Lower tier local authority Until 30th June continuous\nNumber of positive cases Public Health England Lower tier local authority Until 30th June discrete (counts)\nPopulation density Office for National Statisticshttps://www.ons.gov.uk/ Lower layer super output area 2018 continuous (log transformed)\nNumber of intensive care unit beds National Health Servicehttps://www.england.nhs.uk/ National Health Service trust February 2020 continuous (per population)\nSmoking Public Health Englandhttps://fingertips.phe.org.uk/ General practitioner catchment area 2018–2019 continuous (prevalence)\nObesity Public Health Englandhttps://fingertips.phe.org.uk/ General practitioner catchment area 2018–2019 continuous (prevalence)\nHigh Risk Occupation Office for National Statisticshttps://www.ons.gov.uk/ Middle layer super output area 2011 continuous (prevalence)\n\n2.5 Statistical methods\nWe specified Bayesian hierarchical Poisson log-linear models to investigate the association of COVID-19 deaths and NO2 and PM2.5 independently. The LSOA specific standardised mortality ratio is known to be an unstable estimator with high variance when the number of expected deaths is small. 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). 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). We fitted four models including: 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). 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. We do not report results from the joint analysis including both pollutants since they are highly correlated (Supplemental Material Figure S5).\nIn 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. 2020). We performed a complete case analysis since for only 1.1% of the cases information about age, sex and ethnicity is missing. 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. 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.\nThe mathematical formulation of the models and prior specifications are given in the Supplemental Material Text S1.2.\nAll models were fitted in INLA (Rue et al. 2009). Covariate data and code for running the analysis are available at https://github.com/gkonstantinoudis/COVID19AirpollutionEn.\n\n2.6 Sensitivity analyses\nWe performed a series of sensitivity analyses. First, we repeated the main analyses using data at the LTLA level with all exposures and confounding weighted by population. 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. 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). Fourth, we categorised pollutants into quintiles to allow more flexible fits. Fifth, we repeated the analysis including the suspected cases to the outcome. 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. 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. 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