Associations between urban greenspace (normalized difference vegetation index) and SARS-CoV-2 incidence and severity across three Irish cities

Associations between urban greenspace (normalized difference vegetation index) and SARS-CoV-2 incidence and severity across three Irish cities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations between urban greenspace (normalized difference vegetation index) and SARS-CoV-2 incidence and severity across three Irish cities Paul Hynds, Jean O'Dwyer, Martin Boudou, Patricia Garvey, Coilin o'Haiseadha, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5012868/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To date, relatively few epidemiological studies have sought to identify and quantify associations between nature and SARS-CoV-2 infection. Likewise, while multiple studies have focused on the clinical factors pre-empting SARS-CoV-2 (e.g., underlying health conditions, age), no Irish research has examined the effect of the natural environmental on SARS-CoV-2 incidence or severity (hospitalization, ICU admission and mortality). Identifying areas and communities at higher risk due to local environmental factors constitutes a knowledge gap for informing mitigation/intervention strategies prior to future similar public health events. Accordingly, the current study focused on three major Irish cities: Dublin, Cork, and Galway. A Normalized Difference Vegetation Index (NDVI) dataset was constructed using the Google Earth Engine Explorer and Sentinel-2 MSI (Multispectral Instrument) open-access software. NDVI values were geographically linked to Small Area (SA) units across all three cities and associated with their respective SARS-CoV-2 incidence and severity rates from March to November 2020, with demographically and socioeconomically delineated (to account for the confounding) generalised linear modelling subsequently employed to identify relationships between greenspace proportion and SARS-CoV-2. Overall, 22,773 symptomatic laboratory-confirmed and georeferenced cases of SARS-CoV-2 were included for analyses. Greenspace proportion was negatively associated with SARS-CoV-2 incidence rates across all three cities (i.e., increased greenspace conurrent with lower incidence of SARS-CoV-2), with these associations remaining significant when models included potential confounders (aORs 0.101–0.501). Likewise, increased greenspace was typically associated with decreased levels of SARS-CoV-2, however, associations were less pronounced or not present in areas characterised by younger populations and/or increasing affluence. Differing levels of association were found with respect to case gender (male cases typically more “responsive”) and city (less populated cities typically more “responsive”). Findings provide a crucial evidence base for researchers, policymakers and healthcare practitioners to appropriately design non-pharmaceutical interventions and engage with communities to successfully promote appropriate health behaviours. Figures Figure 1 Figure 2 1. Introduction The World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern on January 30th, 2020 (World Health Organization, 2020) as the world battled increasing incidence, rising hospitalisation rates, and discontinuation of socio-economic activities due to ‘lockdown’ measures (Kennelly et al, 2020 ; Onyeaka et al, 2021 ). COVID-19 related mortality continues to increase, and by June 2022, > 7,500 deaths in the Republic of Ireland were attributed to COVID-19, 45.6% of which occurred in counties Cork, Dublin, and Galway (Central Statistics Office, 2021 ; Irish Health Service Executive, 2022). Greenspace is defined as “ open, undeveloped land with natural vegetation ” (Centers for Disease Control, 2013), but may exist in other forms, including urban parks, and public or private open spaces comprising trees and other vegetation (Twohig-Bennett et al., 2018). The concept of using greenspace in urban environments to boost human physical and mental health is not new, however it’s potential as a driver of or buffer against COVID-19 (and other respiratory infections) has understandably gained increased attention since the onset of the pandemic (James et al., 2015 ; Russette et al. 2021 ). Myriad evidence has emerged demonstrating that environmental factors (e.g., poor air quality, lack of access to nature) exacerbate COVID-19 symptoms (Russette et al. 2021 , Isphording et al., 2021, Pan et al., 2021). As COVID-19 is a respiratory disease, air quality is of particular concern for disease severity; exposure and access to greenspace in the urban environment may positively impact human health and, in turn, mitigate COVID-19 severity and mortality via three main mechanisms: improved physical health, reduction of emotional distress, and reduced risk of airborne transmission (Rook, 2013 ; Twohig-Bennett et al., 2018; Russette et al. 2021 ; Vos et al., 2022 ). Previous studies have shown that proximity to greenspaces leads to improved physical health via elevated levels of physical activity, which positively influences heart rate, blood pressure, cardiovascular health, and all-cause mortality (Twohig-Bennett et al., 2018; Vos et al., 2022 ; Barton & Rogerson, 2017 ). Access to vital ecological supports like greenspaces further aids air purification as vegetation mitigate background levels of ozone, particulate matter, nitrogen dioxide, sulphur dioxide, and carbon monoxide (James et al., 2015 ). Greenspaces also offer exposure to environmentally resident micro-organisms including bacteria, protozoa, and helminths, thus playing an essential role in development of the immune system and aiding regulation of inflammatory responses (Rook, 2013 ; Twohig-Bennett et al., 2018). Throughout the COVID-19 pandemic, greenspaces in urban environments have been associated with both physical and psychological health. A 2021 study found probable cases of major depression reported during the outbreak of SARS-CoV-2 in South Korea were significantly associated with reduced access to greenspaces compared to the pre-pandemic period (Heo et al., 2021 ). Likewise, in the United States, regular access to greenspaces and a higher concentration of park area per resident were shown to significantly mitigate the psychological impact of lockdown measures on individuals’ level of emotional distress as well as displaying a significant protective effect on both clinical depression scores and composite mental health scores (Slater et al., 2020 ; Wortzel et al., 2021 ; Larson et al., 2022 ). Greenspaces have also been observed to impact COVID-19 transmission rates - physical transmission modelling studies have reported that the relative risk of airborne transmission due to inhaled particles of viral origin is significantly lower in outdoor settings (i.e., greenspaces) across multiple models compared to indoor settings (Bulfone et al., 2021 ; Rowe et al., 2021 ). Associations between i) decreased COVID-19 incidence and higher park usage, ii) increased urban greenness and reduced disease incidence, and iii) increased exposure to greenspaces and reduced mortality unequivocally highlight the significance of greenspaces as mitigating factors in susceptibility to disease (Russette et al., 2021 ; Johnson et al., 2022; Peng et al., 2022 ). In addition of the direct effect of greenspace on human health, greenspace may also be reflective of (or a proxy for) the general socio-economic characteristics of a locality. Previous research has shown that low-income neighbourhoods are typically characterised by reduced greenspace access and associated with generally lower levels of background population health (Twohig-Bennett et al., 2018; Russette et al., 2021 ). In concurrence with the clinical characteristics of a patient including age, gender or the presence of underlying health conditions, socio-economic features of the local area have been identified as a primary driver of COVID-19 incidence and severity (Hashim et al., 2020 ; Boudou et al., 2021 , Wolff et al., 2021 ). However, to date, few studies have sought to directly assess associations between greenspace exposure, COVID-19 outcomes (incidence, hospitalisation, ICU admission, and mortality), associated characteristics of the living area and clinical features of the patient. Accordingly, the present study sought to fill this knowledge gap via an integrated analysis of georeferenced epidemiological and geo-spatial data to identify and quantify potential relationships between incidence and severity of SARS-CoV-2, presence of greenspace, socio-economic features of the area and population subgroups (age and gender) in three of the primary urban conurbations in the Republic of Ireland (Dublin, Cork and Galway). 2. Methods 2.1 Infection data Anonymized laboratory-confirmed cases of COVID-19 occurring between 29th February and 30th November 2020 (n = 72,654) were obtained from the Computerized Infectious Disease Reporting (CIDR) information system used to compile data on communicable infections in Ireland ( http://www.hpsc.ie/CIDR/ ). Due to evolving testing policies in the early stages of the pandemic, only symptomatic cases (detection of SARS CoV-2 nucleic acid via PCR in a clinical specimen) were included for consideration (n = 47,265). All cases were geographically linked to one of the CSO Census Small Areas (SAs), the smallest administrative spatial delineation currently employed in the Republic of Ireland for national census reporting, using a previously developed multiphase geocoding methodology specific to the Republic of Ireland (Domegan et al., 2021 ). Analyses were based on three of the main urban conurbations in the Republic of Ireland: Dublin (Population = 1,173,179, Cases = 16,664), Cork (Population = 208,669, Cases = 4,429) and Galway (Population = 79,934, Cases = 1,680) (Fig. 1 ). Other Irish cities were not included due to the significantly lower number of cases reported during the study-period. 2.2 NDVI Index (Greenspace) The density of greenspace, (i.e., landcover comprising grass, trees, shrubs, or other vegetation) is traditionally measured via the Normalized Difference Vegetation Index (NDVI) which provides a percentage estimate of the proportionate area characterised as greenspace. The NDVI is measured as the difference between Near-Infrared (strongly reflected by Vegetation) and Red Light (absorbed by vegetation). Quantified NDVI values range from − 1 to 1, with − 1 indicating the presence of waterbodies (i.e., total absence of vegetation) while an index value of 1 indicates an area covered with dense vegetation. For the current study, the NDVI dataset was collated for all three cities via the Google Earth Engine Explorer using the open-access Sentinel-2 MSI: Multispectral Instrument, Level 1-C dataset (Saphores et al. , 2015). Satellites raster files were downloaded for the period March to June 2020, with a cloud filter set to 10% to ensure an optimal representation of greenspace, discarding images with > 10% cloud cover (“Sentinel-2 MSI: MultiSpectral Instrument, 2015; Zhu et al., 2015 ). The NDVI for each image was calculated using a predefined normalization function in JavaScript with a normalized difference of Near-Infrared (NIR) to Red Band (i.e., absorption/reflection). NDVI= \(\:\frac{\text{N}\text{I}\text{R}-\text{R}\text{E}\text{D}}{\text{N}\text{I}\text{R}+\text{R}\text{E}\text{D}}\) In Sentinel-2 imagery, the band corresponding to NIR is ‘B8’ and for RED is ‘B4’ (Zhang et al. , 2017), as follows: NDVI= \(\:\frac{\text{B}8-\text{B}4}{\text{B}8+\text{B}4}\) The mean greenspace value for each Small Area was calculated using the “Zonal Statistic Tool” in ArcMap v.10.7. Table 1 shows the interquartile distribution of the mean NDVI values across Galway, Dublin and Cork cities, respectively, while Fig. 2 illustrates the spatial distribution of their values across the same urban areas. Table 1 Interquartile range of mapped NDVI values per Census Small Areas for Galway, Dublin & Cork cities, March – June 2020 City Min 1st Quartile Median 3rd Quartile Max Galway -0.015 0.352 0.426 0.552 0.717 Dublin -0.063 0.263 0.328 0.39 0.644 Cork -0.051 0.37 0.435 0.579 0.756 2.3 Analyses To identify (and if present, quantify) links between urban greenspace and COVID-19, a series of statistical models were developed. Infection severity data were upsampled based on the minority classification to account for high proportions of “non-severe” outcomes. Both COVID-19 incidence and severity (hospitalization, ICU, mortality) were investigated across all three city areas using generalized linear models (GLM) with a binomial logit link function. Statistical outputs included estimate (coefficient), adjusted odds ratio (aOR), confidence interval (CI) for calculated odds ratios and area under receiver operating curve (AUC) to adjudge model performance. Models were trained and tested using an 80%-20% data partition based on the minority class (i.e., 20% of both training and testing datasets comprised the outcome variable being examined). Estimates for developed models were internally validated using 10x cross validation sampling. All statistical analyses were carried out in R version 4.1.1 using the Caret, pROC, deskTOOLS, dplyr, and glmnet packages. All packages are freely available at http://cran.r-project.org . For COVID-19 incidence, the median crude incidence rate per 100 population for each city was calculated: Galway: 0.90; Dublin: 1.31, Cork: 0.96, with GLMs developed for each SA based on a binary tag i.e., calculated crude incidence rate greater than the regional (city-specific) median as the dependent variable of interest. To account for the likely confounding effects of case age, gender, and local socio-economic profile, a confounder-mediated (i.e, restricted) approach was employed. Data relating to gender and age were extracted from the geo-referenced COVID-19 dataset, while data relating to socio-economic profile and population density for each CSO Small Area was extracted from the HP Pobal Deprivation Index (Haase et al. , 2017). The Pobal Haase-Pratschke (HP) Deprivation Index represents a composite measure of deprivation/affluence derived from 16 components, each of which represents one of the three dimensions of deprivation: demographic profile, social class composition, and labor market situation. The absolute deprivation (HPAbs) score quantifies any changes to the local economy at the SA level between two census periods 2011 and 2016, calculated for each Small Area and ranging from extremely affluent (+ 40) to extremely diadvantaged (-40). Individual models were developed for the following population sub-sets: Age : 0 to 35 years, 36 to 65 years old, 66 years old and above. Gender : Male, Female. Absolute deprivation score (HPAbs) : < -5 (deprived areas), -5 to 3 (intermidiate areas), 3 and above (affluent areas). This classification was delineated based on the interquartile distribution of the HPAbs across the Republic of Ireland. Statistical models developed in the absence (i.e., NDVI only) and presence of likely cofounders for comparison are presented in Supplementary Materials. 3. Results 3.1 Overview Overall, 22,773 symptomatic, laboratory-confirmed cases were included for analyses, corresponding to the total number of cases within the boundaries of the three urban study areas. Study area population, calculated incidence rate and number/percentage of cases associated with severe outcomes are presented in Table 2 . As shown, the highest crude incidence rate (cases/100,000 population) of symptomatic cases was associated with Cork city (2122.5/100,000), followed by Galway (2101.7/100,000) and Dublin (1420.4/100,000). Overall, 1,678 cases (7.4%) were hospitalised, 298 (1.3%) were admitted to ICU and 614 (3%) were associated with mortality. Highest rates of hospitalisation (8%), ICU admission (1.5%) and mortality (3.7%) were reported in Dublin City. The distribution of cases attributed to the three categories of COVID-19 severity used in the current study (i.e., hospitalization, ICU, and death) delineated by each modelled subgroup are presented in Table 3 . The following models failed to converge due to low case numbers: ICU admission among Age group < 35 years, Cork and Galway. ICU Admission among HPAbs 3, Galway. Mortality among Age group < 35, Dublin, Cork and Galway. Mortality among age group 35 to 65, Cork and Galway. Mortality among female cases for Galway. Table 2 Population, number of symptomatic cases, incidence rate, hospitalization, ICU admission and mortality cases of COVID-19 for Dublin, Cork and Galway (March to November 2020) Population Symptomatic cases (% of sympt. cases) Incidence rate per 100k Hospitalization (%) ICU (%) Mortality (%) Dublin 1,173,179 16,664 (35.3) 1420.4 1,336 (8) 246 (1.5) 609 (3.7) Cork 208,669 4,429 (9.4) 2122.5 245 (5.5) 35 (0.8) 54 (1.2) Galway 79,934 1,680 (3.6) 2101.7 97 (5.8) 17 (1) 11 (0.7) Table 3 Case distribution amongst COVID-19 severity levels by age groups, gender and deprivation score (HPAbs) for Dublin, Cork and Galway (March to November 2020) City Severity Gender Age groups HPAbs M F 65 (+ 3) Dublin Hospitalization 766 570 145 481 710 526 436 374 ICU 172 74 13 131 102 79 96 79 Mortality 316 293 < 5 37 569 227 217 165 Cork Hospitalization 137 108 33 96 116 87 95 63 ICU 24 11 < 5 16 18 13 16 6 Mortality 25 29 - < 5 50 31 8 15 Galway Hospitalization 49 48 20 47 30 28 49 20 ICU 12 5 < 5 8 8 < 5 10 < 5 Mortality 7 < 5 - < 5 10 5 - 6 3.2 COVID-19 Incidence As shown (Table 4 ), greenspace proportion was negatively associated with COVID-19 incidence rates across all three cities. The highest model accuracy in the absence of confounders was achieved for Galway (AUC 0.736). Tables S2, S3 & S4 present results of using the cofounder-mediated (restricted) modelling approach. Again, increased greenspace was associated with below-median COVID-19 CIRs (p < 0.05) for 20 of 24 models. AUCs obtained from models based on deprivation score (0.67–0.89) were significantly higher than those obtained for both age and gender restricted GLMs (0.55–0.67). Table 4 Results of generalized linear modelling – Greenspace Vs crude incidence rate per 100 population above the local median β Sig aOR 2.5% 97.5% AUC Galway -2.133 < 0.001 0.118 0.022 0.281 0.736 Cork -2.289 < 0.01 0.101 0.062 0.231 0.597 Dublin -0.596 0.0428 0.551 0.323 0.976 0.612 3.3 Hospitalization Results of generalized linear modelling between greenspace and COVID-19 hospitalization for the three primary cofounders are presented in Tables 5 – 7 . Greenspace proportion was significantly associated with COVID-19 hospitalization in 19 of 24 models, with significant models typically revealing a negative association between NDVI and hospitalization, except for affluent areas (HPAbs > 3), and the lowest age classification (< 35) in Cork City, where hospitalization increased in concurrence with increased greenspace proportion (OR: 2.692, AUC: 0.767; OR: 5.373, AUC: 0.621, respectively). Negative associations ranged from aOR = 0.037 (Galway, Male cases) to aOR = 0.832 (Galway, intermediate deprivation areas). Highest model accuracies (AUC) were obtained for the HP Deprivation Index (0.775 to 0.815) compared to case gender (0.761 to 0.808) and case age (0.572 to 0.662). Table 5 Generalized linear modelling - greenspace Vs hospitalization delineated by case gender Gender City β Sig aOR CI 95% R 2 AUC Male Dublin -1.049 < 0.001 0.350 (0.25, 0.48) 0.320 0.808 Cork -0.243 0.274 0.784 (0.51, 1.21) 0.262 0.761 Galway -3.296 < 0.001 0.037 (0.02, 0.09) 0.523 0.857 Female Dublin -1.313 < 0.001 0.269 (0.19, 0.38) 0.296 0.767 Cork -0.151 0.57 0.860 (0.51, 1.45) 0.359 0.790 Galway 0.528 0.199 1.695 (0.76, 3.80) 0.298 0.783 Table 6 Generalized linear modelling - greenspace Vs hospitalization delineated by small area deprivation score (HPAbs) HPAbs City β P-value aOR CI 95% R 2 AUC > 3 Dublin -0.452 0.02 0.636 (0.43,0.95) 0.315 0.777 Cork 0.990 0.002 2.692 (1.43,5.12) 0.321 0.767 Galway -3.457 < 0.001 0.032 (0.01,0.10) 0.425 0.853 ≥ (-5), ≤3 Dublin -1.475 < 0.001 0.229 (0.15,0.36) 0.271 0.791 Cork 0.311 0.237 1.365 (0.81,2.29) 0.304 0.792 Galway -0.183 0.661 0.832 (0.37,1.89) 0.385 0.871 < (-5) Dublin -1.796 < 0.001 0.166 (0.11,0.25) 0.340 0.813 Cork -2.335 < 0.001 0.097 (0.05,0.18) 0.290 0.784 Galway -2.092 < 0.001 0.123 (0.03,0.44) 0.346 0.815 Table 7 Generalized linear modelling - greenspace Vs hospitalization delineated by case age group Age City β P-value aOR CI 95% R 2 AUC < 35 Dublin -1.261 < 0.001 0.283 (0.18, 0.46) 0.010 0.572 Cork 1.681 < 0.001 5.373 (3.04, 9.54) 0.048 0.621 Galway -2.661 < 0.001 0.070 (0.02, 0.20) 0.065 0.662 ≥ 36, ≤ 65 Dublin -0.599 65 Dublin -2.050 < 0.001 0.129 (0.08, 0.20) 0.037 0.594 Cork -2.364 < 0.001 0.094 (0.04, 0.21) 0.029 0.581 Galway -2.553 < 0.001 0.078 (0.01, 0.33) 0.072 0.623 3.4 ICU admission Restricted GLM results for ICU admissions across all three cities are presented in Tables 8 – 10 . Greenspace proportion exhibited a significant association with COVID-19 related ICU admissions in 17 of 22 developed models. Negative associations between NDVI values and ICU admission were found for cases associated with female patients in all three cities (Dublin, aOR: 0.04; Cork, aOR: 0.027; Galway, aOR: 0.001). A similar observation was found for male cases in Dublin (aOR: 0.463), while male cases were positively associated with greenspace in Galway City (aOR: 13.5). Modelling revealed relatively comparable positive associations between greenspace and ICU admission in Galway (aOR: 2.848) and Cork (aOR: 2.47) for areas characterized by intermediate (-5 to 3) deprivation. A similar association was obtained for Cork in affluent areas (HP > 3, aOR: 4.295). Conversely, negative associations were found in the more socio-economically deprived areas of Dublin (aOR: 0.05) and Cork (aOR: 0.001). Table 8 Generalized linear modelling - greenspace Vs admission in ICU delineated by case gender Gender City β Sig aOR CI 95% R 2 AUC Male Dublin -0.771 < 0.001 0.463 (0.35, 0.61) 0.235 0.735 Cork 0.349 0.172 1.417 (0.86, 2.34) 0.511 0.862 Galway 2.603 < 0.001 13.500 (5.63, 32.77) 0.633 0.883 Female Dublin -3.229 < 0.001 0.040 (0.03, 0.06) 0.248 0.754 Cork -3.596 < 0.001 0.027 (0.02, 0.05) 0.479 0.852 Galway -6.960 3 Dublin -0.004 0.984 0.996 (0.69, 1.43) 0.299 0.789 Cork 1.457 0.003 4.295 (1.62, 11.55) 0.533 0.891 Galway - - - - - - ≥ (-5), ≤3 Dublin -2.440 < 0.001 0.087 (0.06, 0.13) 0.260 0.752 Cork 0.904 < 0.001 2.470 (1.42, 4.30) 0.586 0.874 Galway 1.047 0.03 2.848 (1.10, 7.37) 0.582 0.883 < (-5) Dublin -2.991 < 0.001 0.050 (0.03, 0.07) 0.296 0.764 Cork -8.447 < 0.001 0.001 (0, 0.00049) 0.486 0.861 Galway - - - - - - Table 10 Generalized linear modelling - greenspace Vs admission in ICU delineated by case age group Age City β Sig aOR CI 95% R 2 AUC < 35 Dublin Cork Galway -1.246 - - < 0.001 - - 0.288 - - (0.16, 0.53) - - 0.061 - - 0.647 - - ≥ 36, ≤ 65 Dublin -1.087 < 0.001 0.337 (0.25, 0.45) 0.096 0.649 Cork -3.253 65 Dublin -4.261 < 0.001 0.014 (0.01, 0.02) 0.105 0.653 Cork -1.893 < 0.001 0.151 (0.08, 0.29) 0.131 0.656 Galway -0.779 0.316 0.459 (0.09, 2.00) 0.117 0.706 3.5 Mortality Generalized linear modelling of local greenspace and COVID-19 related mortality delineated by population sub-groups are presented in Tables 11 – 13 . Overall, 13 out of 21 models identified a significant association between local greenspace and COVID-19 mortality rate. Obtained accuracies relating to case age were significantly lower (0.564 to 0.783) than both gender (0.936 to 0.975) and deprivation score (0.925 to 0.991), with converging models revealing a positive association in 12 of 13 cases. For example, increased local greenspace was associated with a significantly increased COVID-19 mortality rate among male cases for both Cork (aOR: 4.138) and Galway (aOR: 7.337) cities, respectively. Conversely, greenspace was negatively associated with mortality in categorically affluent areas of Dublin city (aOR: 0.284). Table 11 Generalized linear modelling- greenspace Vs lethal outcomes delineated by case gender Gender City β Sig aOR CI 95% R 2 AUC Male Dublin -0.333 0.141 0.716 (0.47,1.12) 0.699 0.936 Cork 1.420 < 0.001 * 4.138 (1.94,8.86) 0.777 0.958 Galway 1.993 0.003 * 7.337 (1.95,28.25) 0.749 0.933 Female Dublin 0.774 < 0.001 * 2.168 (1.41,3.33) 0.676 0.918 Cork -0.036 0.941 0.965 (0.37,2.49) 0.050 0.972 Galway - - - - - - Table 12 Generalized linear modelling- greenspace Vs lethal outcomes delineated by Small Area deprivation score (HPAbs) HPAbs City β Sig aOR CI 95% R 2 AUC > 3 Dublin -1.259 < 0.001 0.284 (0.16, 0.52) 0.696 0.930 Cork 6.297 < 0.001 542.781 (143.66, 2179.24) 0.834 0.953 Galway -17.895 0.479 0.0001 (0.001, 1001) 0.938 0.991 ≥ (-5), ≤3 Dublin 1.118 < 0.001 3.060 (1.70,5.52) 0.707 0.925 Cork -2.589 < 0.001 0.075 (0.03,0.19) 0.765 0.967 Galway 1.454 0.111 4.281 (0.74,26.64) 0.788 0.960 < (-5) Dublin 0.970 < 0.001 2.637 (1.65,4.21) 0.668 0.910 Cork -0.796 0.191 0.451 (0.14,1.47) 0.822 0.972 Galway 2.220 0.0414 9.209 (1.05,77.11) 0.874 0.962 Table 13 Generalized linear modelling - greenspace and lethal outcomes delineated by case age group Age City β Sig aOR CI 95% R 2 AUC ≥ 36, ≤ 65 Dublin 0.549 0.084 1.731 (0.93,3.23) 0.017 0.579 Cork - - - - - - Galway - - - - - - Age > 65 Dublin 0.832 < 0.001 2.299 (1.62,3.27) 0.008 0.564 Cork 2.424 < 0.001 11.292 (5.71,22.30) 0.022 0.551 Galway 1.159 0.176 3.186 (0.55,16.16) 0.044 0.753 4. Discussion Multiple recent studies have sought to identify and quantify the association(s) between access/proximity to urban greenspaces and mental health/wellbeing during the global COVID-19 pandemic (Shuvo et al, 2020 ; Burnett et al., 2021 ; Wortzel et al., 2021 ; Larson et al., 2022 ). However, far fewer have focused on the direct and/or indirect physical effects of urban greenspace on COVID-19 transmission and severity. Accordingly, the current study sought to address this knowledge gap via an integrated analysis of georeferenced epidemiological and geo-spatial data to identify and quantify potential relationships between incidence and severity of SARS-CoV-2, presence of greenspace, socio-economic features of the area and population subgroups (age and gender) in three of the primary urban conurbations in the Republic of Ireland (Dublin, Cork and Galway). Study findings will significantly contribute to current understanding on the potential protective effect(s) of greenspace exposure via the Normalised Difference Vegetation Index (NDVI) on COVID-19 incidence and severity. To the authors knowledge, this is the first study within an Irish context to assess the association between greenspace density and COVID-19 susceptibility (hospitalizations, admission to ICU, and mortality). Study findings indicate that increased greenspace (i.e., spatial extent within individuals’ residential area) was associated with reduced COVID-19 incidence rates, with the proportion of greenspace negatively associated with incidence rates of COVID-19 in Cork (aOR: 0.101), Dublin (aOR: 0.551), and Galway (aOR: 0.118). The mitigating impact of access and proximity to greenspace on disease susceptibility has recently been established (e.g., Heo et al., 2021 ; Russette et al., 2021 ; Johnson et al., 2021 ; Peng et al., 2022 ). Previous research has shown that the presence and proximity of greenspace and exposure to biogenic volatile organic compounds (VOCs) can decrease susceptibility to diseases by increasing both NK and T cells while increasing cytotoxic activities, reducing inflammation, and replenishing gut microbiota (Klompmaker et al., 2021 ; Lee et al., 2021 ; Yeoh et al., 2021 ; Yang et al., 2022 ). The presence of greenspace was also associated with COVID-19 related hospitalizations and ICU admissions, with adjusted Odds Ratios (aORs) suggesting that, controlling for other potential confounders, as greenspace increased, recorded hospitalizations and ICU admissions associated to COVID-19 decreased. Interestingly, the heterogeneous intensity of the greenspace-disease relationship across the surveyed urban areas suggests the presence of unaccounted interacting factors. For example, among the lowest age classification (< 35 years old) in Cork city, hospitalizations increased in areas with proportionally higher greenspace density (aOR: 5.373). This association may be attributed to younger generations using urban greenspaces as social gathering places, possibly exposing themselves to an increased transmission risk for SARS-CoV-2. However, this relationship was not observed in the other urban areas; in Dublin a negative association was found among the younger age cohort between greenspace and both hospitalization (aOR: 0.283) and ICU admission (aOR: 0.288), and for hospitalization in Galway (aOR: 0.070). A recent study from De Jalon et al. (2021) reports that communities living closer to greenspaces are more likely to exercise and less likely to be affected by Type-2 Diabetes or obesity i.e., proximity to greenspace may be a potential proxy for background community health. The contradictory association between greenspaces and severity for the sub-population under 35 years old may also be due to varying socio-economic and demographic differences observed across the three urban locations (Wortzel et al., 2021 ). Generational differences in greenspace usage during the COVID-19 pandemic have been reported in the literature (Slater et al., 2020 ; Wortzel et al., 2021 ), potentially accounting for the age-related differences in hospitalization rates across Cork City in the present study. Older individuals have been associated with elevated levels of resourcing (i.e., time, transportation, and/or living arrangements) than younger generations, subsequently being reflected in their greenspace utilisation (Slater et al., 2020 ; Wortzel et al., 2021 ). Older generations have also been shown to typically have higher access to private greenspace, thus limiting contact with both symptomatic and asymptomatic carriers of the COVID-19 virus, in addition to higher quality greenspace (e.g., landscape maintenance, aesthetic values, biodiversity levels) which may impact health (Slater et al., 2020 ; Wortzel et al., 2021 ; Yang et al., 2022 ). The findings thus outline the need for facilitating quality greenspaces for managing and improving public health, and particularly among younger sub-populations. When greenspace increased, COVID-19 related ICU admissions were shown to decrease across all three urban areas for female cases, with this association only being found in Dublin for male cases. In Galway City, it was found that higher greenspace proportion was associated with an increase in COVID-19 related ICU admissions among males (aOR: 13.5). Evidence from the literature suggests that females typically feel more vulnerable than males in green spaces, and particularly in urban environments and without company, which may negatively influence the frequency of greenspace utilization among females (Burnett et al., 2021 ). Accordingly, findings have potential implications for how greenspaces are perceived and utilized across genders and regarding the security aspects of greenspaces. Socio-economic profile (SEP) also has associations with the presence and utilisation of greenspace across communities and risk of ICU admission, morbidity and mortality outcomes (Russette et al., 2021 ; Twohig-Bennett et al., 2018). Firstly, the mitigating effect of greenspace on COVID-19 susceptibility was not always observed among populations residing in relatively affluent areas. Higher income may offset the mitigating impact of greenspace on disease severity due to causally posterior factors like enhanced diets, increased general (background) health, and lower population and household density (Bousquet et al., 2020 ; Merino et al., 2021 ; Russette et al., 2021 ; Wortzel et al., 2021 ). Astell-Burt et al. ( 2014 ) previously reported that the distribution of greenspace in urban settings is frequently ‘clustered’ around areas with a higher proportion of high-income residents, irrespective of population density (Astell-Burt et al., 2014 ). Additionally, multiple studies have shown that low-income neighbourhoods are typically characterised by reduced greenspace access; thus, the legitimacy of “distance-based restrictions”, as employed both nationally and globally during the pandemic, and subsequent impacts on access to greenspaces requires significant examination. For example, a 2-kilometre radius limit was employed across the Republic of Ireland during the first 3–4 months of the pandemic. These restrictions may have serious impacts on community health and should avoided until the unintentional and/or currently poorly understood impacts of diverse socio-economic factors on disease susceptibility vis-à-vis greenspace exposure and access can be examined (Twohig-Bennett et al., 2018; Russette et al., 2021 ). Notwithstanding, when individuals associated with lower socio-economic status do use greenspace, they tend to experience a larger reduction in disease risk (e.g., Alzheimer’s disease, circulatory disease, cardio-metabolic conditions) than individuals from a higher socio-economic profile (Russette et al., 2021 ; Kardan et al., 2017 ). Greenspace density has also been identified as a stronger predictor of poor health outcomes, and particularly in lower income areas (Mitchell & Popham, 2007 ). Conversely, the presence and extent of greenspace (in isolation, i.e., not controlling for potential confounders) has been found to positively impact health outcomes households with higher and lower median income without statistically meaningful differences (Browning & Rigolon, 2018 ). Inferences pertaining to the unmediated impact of greenspace distribution and exposure not accounting for greenspace utilization are not possible based on the current study design (i.e., ecological study). Diet type and quality has also been shown to impact the severity of COVID-19 and should also be considered when examining an individual’s use of greenspace and socio-economic status (Bousquet et al., 2020 ; Merino et al., 2021 ). Socio-economically deprived areas of Cork (aOR: 0.001) and Dublin (aOR: 0.05) were found to have very significantly decreased COVID-19 ICU admissions as the spatial extent of greenspace increased. Burnett et al. ( 2021 ) have recently stated that these associations are likely due to occupational profiles, as individuals with a lower economic standing are more likely to be essential workers and continued to work in manual or service operations during initial stages of the pandemic (i.e., not possible to work from home), thus limiting their access to greenspace and possible virus transmission. Conversely, more affluent and skilled individuals were more likely to shift to online work easily and could have more access to local greenspace during movement restrictions and lockdowns (Burnett et al., 2021 ). Additionally, quality of greenspace and frequency of use are typically higher in high-socioeconomic areas (Leslie et al., 2010 ; Twohig-Bennett et al., 2018). It should also be noted that income-related health inequalities have been observed to be less prevalent within greener neighbourhoods (Mitchell & Popham, 2008). Nonetheless, studies in lower- and middle-income countries (LMIC) systematically and consistently indicate the negative associations between greenspace exposure and the presence of health vulnerabilities and disease susceptibility (Shuvo et al, 2020 ). This suggests that exposure to greenspace, regardless of its interaction with socioeconomic status, likely exhibits a mitigating effect on individuals’ “resistance” to COVID infection and subsequent exacerbations. Future research should seek to further our understanding of the physical/biological greenspace-COVID relationship by investigating intervening factors including occupation, shifting working habits, and greenspace access and use during the COVID-19 pandemic. Mortalities related to COVID-19 also had significant associations with local greenspace density. In the more affluent areas of Dublin, COVID-19-related deaths decreased as greenspace increased (aOR: 0.284). Conversely, in Cork (aOR: 542.781), increased local greenspace was associated with a higher COVID-19 mortality rate. This could be explained by increased presence of the public within those greenspaces, thus increasing potential exposure to and transmission of the disease. Aguilar et al. ( 2022 ) demonstrated that in cities where population flow is concentrated in mobility hotspots, which, in the case of this study, could arguably be greenspace during movement restrictions, transmission and viral loading of SARS-CoV-2 is increased. Similarly, in Oslo, Venter et al. (2020) have shown that urban greenspace utilization, significantly increased during lockdowns and periods with mobility restrictions, being used as a replacement for indoor recreational activities. In Ireland, the first lockdown (in Spring 2020) was marked by abnormally dry and warm conditions (MetEireann, 2020) which might have also encouraged more frequent use of urban greenspaces. Additionally, greenspaces have been shown to foster social interactions, thus influencing mental health, human relationships and potentially reducing stress, anxiety, and depression, and promoting healthy cortisol and vitamin D levels, helping combat Seasonal Affective Disorder (James et al., 2015 ; Twohig-Bennett et al., 2018; Vos et al., 2022 ). This feature of greenspaces may have been particularly important during the COVID-19 pandemic when social interactions in the usual “third spaces” were severely limited (Vos et al., 2022 ). Notwithstanding, 13 out of 21 developed models identified a significant association between local greenspace and COVID-19 mortality rate, and thus, fewer models exhibited a significant relationship than either of the other two examined outcomes (i.e., hospitalization, ICU admission). Accordingly, it can be concluded that greenspace becomes less clear as an outcome pre-cursor as the outcome becomes more severe i.e., outcome more "biological" in nature (age, underlying health conditions, etc.). Previous work by Boudou et al. ( 2021 ) has shown that that the living environment becomes increasingly secondary as an individual or community become increasingly biologically susceptible (Boudou et al., 2021 ). Compared with a significant proportion of studies investigating the health-greenspace relationship, this study utilised the Normalised Difference Vegetation Index (NDVI) as an indicator of urban greenspace density within Cork, Dublin, and Galway in Ireland. NDVI provides quantification of greenspace by measuring the difference between Near-Infrared (strongly reflected by vegetation) and Red Light (absorbed by vegetation), and as such is readily transferable. Another strength of the study was the analysis of four distinct outcomes relating to COVID-19 (COVID-19 incidence, hospitalisation, ICU admission, and mortality). Lastly, the authors consider that findings of this study may be relevant to countries with similar socio-economic status, climatic and geographical features, and population distribution. This approach may be used to investigate the success of state-level policies pertaining to COVID-19 or other respiratory infections in future studies. Despite these strengths, this study comprises some limitations. While greenspace was identified, its utilisation at the individual, household or community level was neither assessed nor included in the present analysis, and as such, no conclusions on greenspace type, quality or utilisation with respect to COVID-19 incidence or severity can be drawn. Further, a paucity of air quality measurements precluded any cross-analyses of the data included in our study. Similarly, this study did not include other factors contributing to transmission, morbidity or mortality, such as the presence of underlying health conditions, pre-existing risk factors, and individual lifestyle choices. Urban and rural dataset comparisons were also not included within the scope of this study, as it is not possible to delineate public and private land in categorically rural areas using the datasets employed. Nonetheless, study findings suggest that greenspace access may currently be overlooked by policymakers as a method to address health inequalities in Ireland and help minimise the impacts of future epidemics/pandemics. This research has significant implications for policymakers, particularly regarding community health and design of equitable non-pharmaceutical interventions. Greenspace encompasses myriad human health benefits, including positively influencing mental health by lowering depression symptoms, fostering social interactions, and improving immunological defence against pathogens and therefore should be incorporated into city and public health planning, disease prevention strategies, and emergency planning. Declarations Ethics approval and consent to participate: N/A Consent for publication: All named authors provided consent for publication Competing interests: None declared Funding: This research was funded by Science Foundation Ireland (Grant No. COVID-19 Rapid Response) Author Contribution SK, MB and PH designed and performed the experiments, derived the models and analysed the data. COH and PG developed and formatted all datasets, SK, MB and PH developed the overarching study concept and approach and acquired all study data. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5012868","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":349902959,"identity":"6fa31161-cc5b-40b6-a4a6-835f5c21ebd4","order_by":0,"name":"Paul Hynds","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYHACxgOMDUDqAPMBBgY2hgQ+ZiAngYAeqBa2xAaQFjYStPAYQrQQcpU5+xmDA4w7DufxnT/z/TFP2eE8NnbeAwwPKnBrsezJAWo5c7hY8kbuxmaec4eL2Zj5EhgSzuDWYnAApKXtcOKGG7wbm3mBjDZmHgOGxDY8Ws6/gWo5f+YhkpZ/eLTcgNlyIIcRSUsDHr/MeFZwILEtPXHmjTTDmXPOpQP9wmNwIOEYbi3m/MkbH3xss07sO3/4wYc3ZdZ5/PxnDB/+qMHjMBCRwNCMKnoAtwaoFgaGOnxqRsEoGAWjYKQDAIE/XBG4mTIQAAAAAElFTkSuQmCC","orcid":"","institution":"Technological University Dublin","correspondingAuthor":true,"prefix":"","firstName":"Paul","middleName":"","lastName":"Hynds","suffix":""},{"id":349902960,"identity":"c737abe8-fdea-4b08-8354-1a4e3e5f4be5","order_by":1,"name":"Jean O'Dwyer","email":"","orcid":"","institution":"University College Cork","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"","lastName":"O'Dwyer","suffix":""},{"id":349902965,"identity":"e0b434f1-f0ac-4bbb-b51b-2e73edd38c8a","order_by":2,"name":"Martin Boudou","email":"","orcid":"","institution":"Technological University Dublin","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Boudou","suffix":""},{"id":349902967,"identity":"29be22e4-99c0-4c38-8bcf-f5c4eb45d650","order_by":3,"name":"Patricia Garvey","email":"","orcid":"","institution":"Health Protection Surveillance Centre","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Garvey","suffix":""},{"id":349902968,"identity":"14f04aad-1e93-47e0-b051-3cc7971b2486","order_by":4,"name":"Coilin o'Haiseadha","email":"","orcid":"","institution":"Health Service Executive","correspondingAuthor":false,"prefix":"","firstName":"Coilin","middleName":"","lastName":"o'Haiseadha","suffix":""},{"id":349902972,"identity":"ce1037b0-bae6-4b6e-8a19-426a1f1a42ca","order_by":5,"name":"Shivam Khandelwal","email":"","orcid":"","institution":"Technological University Dublin","correspondingAuthor":false,"prefix":"","firstName":"Shivam","middleName":"","lastName":"Khandelwal","suffix":""}],"badges":[],"createdAt":"2024-09-01 12:26:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5012868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5012868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67099594,"identity":"a626e286-c53c-466e-a539-3921d2ff4095","added_by":"auto","created_at":"2024-10-21 08:02:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47972,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of Galway, Dublin and Cork cities in the Republic of Ireland\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5012868/v1/315393501a22bd6ba63e254f.png"},{"id":67101060,"identity":"430efd4e-e562-4eb2-abef-35ef15472dd0","added_by":"auto","created_at":"2024-10-21 08:10:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":170049,"visible":true,"origin":"","legend":"\u003cp\u003eMean NDVI values delineated by CSO Small Area (SA) for Galway (A) – Dublin (B) and Cork (C)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5012868/v1/8e04f6f43745d91c9dc94b68.png"},{"id":104775832,"identity":"2bd6062c-c031-4db1-86ea-c8e5169a420d","added_by":"auto","created_at":"2026-03-17 06:42:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1566011,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5012868/v1/f4393811-ae54-4889-a15e-73d723881691.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between urban greenspace (normalized difference vegetation index) and SARS-CoV-2 incidence and severity across three Irish cities","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern on January 30th, 2020 (World Health Organization, 2020) as the world battled increasing incidence, rising hospitalisation rates, and discontinuation of socio-economic activities due to \u0026lsquo;lockdown\u0026rsquo; measures (Kennelly et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Onyeaka et al, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). COVID-19 related mortality continues to increase, and by June 2022, \u0026gt;\u0026thinsp;7,500 deaths in the Republic of Ireland were attributed to COVID-19, 45.6% of which occurred in counties Cork, Dublin, and Galway (Central Statistics Office, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Irish Health Service Executive, 2022).\u003c/p\u003e \u003cp\u003eGreenspace is defined as \u0026ldquo;\u003cem\u003eopen, undeveloped land with natural vegetation\u003c/em\u003e\u0026rdquo; (Centers for Disease Control, 2013), but may exist in other forms, including urban parks, and public or private open spaces comprising trees and other vegetation (Twohig-Bennett et al., 2018). The concept of using greenspace in urban environments to boost human physical and mental health is not new, however it\u0026rsquo;s potential as a driver of or buffer against COVID-19 (and other respiratory infections) has understandably gained increased attention since the onset of the pandemic (James et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Russette et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Myriad evidence has emerged demonstrating that environmental factors (e.g., poor air quality, lack of access to nature) exacerbate COVID-19 symptoms (Russette et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Isphording et al., 2021, Pan et al., 2021). As COVID-19 is a respiratory disease, air quality is of particular concern for disease severity; exposure and access to greenspace in the urban environment may positively impact human health and, in turn, mitigate COVID-19 severity and mortality via three main mechanisms: improved physical health, reduction of emotional distress, and reduced risk of airborne transmission (Rook, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Twohig-Bennett et al., 2018; Russette et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vos et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have shown that proximity to greenspaces leads to improved physical health via elevated levels of physical activity, which positively influences heart rate, blood pressure, cardiovascular health, and all-cause mortality (Twohig-Bennett et al., 2018; Vos et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Barton \u0026amp; Rogerson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Access to vital ecological supports like greenspaces further aids air purification as vegetation mitigate background levels of ozone, particulate matter, nitrogen dioxide, sulphur dioxide, and carbon monoxide (James et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Greenspaces also offer exposure to environmentally resident micro-organisms including bacteria, protozoa, and helminths, thus playing an essential role in development of the immune system and aiding regulation of inflammatory responses (Rook, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Twohig-Bennett et al., 2018).\u003c/p\u003e \u003cp\u003eThroughout the COVID-19 pandemic, greenspaces in urban environments have been associated with both physical and psychological health. A 2021 study found probable cases of major depression reported during the outbreak of SARS-CoV-2 in South Korea were significantly associated with reduced access to greenspaces compared to the pre-pandemic period (Heo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Likewise, in the United States, regular access to greenspaces and a higher concentration of park area per resident were shown to significantly mitigate the psychological impact of lockdown measures on individuals\u0026rsquo; level of emotional distress as well as displaying a significant protective effect on both clinical depression scores and composite mental health scores (Slater et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wortzel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Larson et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGreenspaces have also been observed to impact COVID-19 transmission rates - physical transmission modelling studies have reported that the relative risk of airborne transmission due to inhaled particles of viral origin is significantly lower in outdoor settings (i.e., greenspaces) across multiple models compared to indoor settings (Bulfone et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rowe et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Associations between i) decreased COVID-19 incidence and higher park usage, ii) increased urban greenness and reduced disease incidence, and iii) increased exposure to greenspaces and reduced mortality unequivocally highlight the significance of greenspaces as mitigating factors in susceptibility to disease (Russette et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Johnson et al., 2022; Peng et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition of the direct effect of greenspace on human health, greenspace may also be reflective of (or a proxy for) the general socio-economic characteristics of a locality. Previous research has shown that low-income neighbourhoods are typically characterised by reduced greenspace access and associated with generally lower levels of background population health (Twohig-Bennett et al., 2018; Russette et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In concurrence with the clinical characteristics of a patient including age, gender or the presence of underlying health conditions, socio-economic features of the local area have been identified as a primary driver of COVID-19 incidence and severity (Hashim et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Boudou et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Wolff et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, to date, few studies have sought to directly assess associations between greenspace exposure, COVID-19 outcomes (incidence, hospitalisation, ICU admission, and mortality), associated characteristics of the living area and clinical features of the patient. Accordingly, the present study sought to fill this knowledge gap via an integrated analysis of georeferenced epidemiological and geo-spatial data to identify and quantify potential relationships between incidence and severity of SARS-CoV-2, presence of greenspace, socio-economic features of the area and population subgroups (age and gender) in three of the primary urban conurbations in the Republic of Ireland (Dublin, Cork and Galway).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Infection data\u003c/h2\u003e \u003cp\u003eAnonymized laboratory-confirmed cases of COVID-19 occurring between 29th February and 30th November 2020 (n\u0026thinsp;=\u0026thinsp;72,654) were obtained from the Computerized Infectious Disease Reporting (CIDR) information system used to compile data on communicable infections in Ireland (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hpsc.ie/CIDR/\u003c/span\u003e\u003cspan address=\"http://www.hpsc.ie/CIDR/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Due to evolving testing policies in the early stages of the pandemic, only symptomatic cases (detection of SARS CoV-2 nucleic acid via PCR in a clinical specimen) were included for consideration (n\u0026thinsp;=\u0026thinsp;47,265). All cases were geographically linked to one of the CSO Census Small Areas (SAs), the smallest administrative spatial delineation currently employed in the Republic of Ireland for national census reporting, using a previously developed multiphase geocoding methodology specific to the Republic of Ireland (Domegan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalyses were based on three of the main urban conurbations in the Republic of Ireland: Dublin (Population\u0026thinsp;=\u0026thinsp;1,173,179, Cases\u0026thinsp;=\u0026thinsp;16,664), Cork (Population\u0026thinsp;=\u0026thinsp;208,669, Cases\u0026thinsp;=\u0026thinsp;4,429) and Galway (Population\u0026thinsp;=\u0026thinsp;79,934, Cases\u0026thinsp;=\u0026thinsp;1,680) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Other Irish cities were not included due to the significantly lower number of cases reported during the study-period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 NDVI Index (Greenspace)\u003c/h2\u003e \u003cp\u003eThe density of greenspace, (i.e., landcover comprising grass, trees, shrubs, or other vegetation) is traditionally measured via the Normalized Difference Vegetation Index (NDVI) which provides a percentage estimate of the proportionate area characterised as greenspace. The NDVI is measured as the difference between Near-Infrared (strongly reflected by Vegetation) and Red Light (absorbed by vegetation). Quantified NDVI values range from \u0026minus;\u0026thinsp;1 to 1, with \u0026minus;\u0026thinsp;1 indicating the presence of waterbodies (i.e., total absence of vegetation) while an index value of 1 indicates an area covered with dense vegetation. For the current study, the NDVI dataset was collated for all three cities via the Google Earth Engine Explorer using the open-access Sentinel-2 MSI: Multispectral Instrument, Level 1-C dataset (Saphores \u003cem\u003eet al.\u003c/em\u003e, 2015). Satellites raster files were downloaded for the period March to June 2020, with a cloud filter set to 10% to ensure an optimal representation of greenspace, discarding images with \u0026gt;\u0026thinsp;10% cloud cover (\u0026ldquo;Sentinel-2 MSI: MultiSpectral Instrument, 2015; Zhu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe NDVI for each image was calculated using a predefined normalization function in JavaScript with a normalized difference of Near-Infrared (NIR) to Red Band (i.e., absorption/reflection).\u003c/p\u003e \u003cp\u003eNDVI= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{N}\\text{I}\\text{R}-\\text{R}\\text{E}\\text{D}}{\\text{N}\\text{I}\\text{R}+\\text{R}\\text{E}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIn Sentinel-2 imagery, the band corresponding to NIR is \u0026lsquo;B8\u0026rsquo; and for RED is \u0026lsquo;B4\u0026rsquo; (Zhang \u003cem\u003eet al.\u003c/em\u003e, 2017), as follows:\u003c/p\u003e \u003cp\u003eNDVI= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{B}8-\\text{B}4}{\\text{B}8+\\text{B}4}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe mean greenspace value for each Small Area was calculated using the \u0026ldquo;Zonal Statistic Tool\u0026rdquo; in ArcMap v.10.7. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the interquartile distribution of the mean NDVI values across Galway, Dublin and Cork cities, respectively, while Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the spatial distribution of their values across the same urban areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterquartile range of mapped NDVI values per Census Small Areas for Galway, Dublin \u0026amp; Cork cities, March \u0026ndash; June 2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1st Quartile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3rd Quartile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analyses\u003c/h2\u003e \u003cp\u003eTo identify (and if present, quantify) links between urban greenspace and COVID-19, a series of statistical models were developed. Infection severity data were upsampled based on the minority classification to account for high proportions of \u0026ldquo;non-severe\u0026rdquo; outcomes. Both COVID-19 incidence and severity (hospitalization, ICU, mortality) were investigated across all three city areas using generalized linear models (GLM) with a binomial logit link function. Statistical outputs included estimate (coefficient), adjusted odds ratio (aOR), confidence interval (CI) for calculated odds ratios and area under receiver operating curve (AUC) to adjudge model performance.\u003c/p\u003e \u003cp\u003eModels were trained and tested using an 80%-20% data partition based on the minority class (i.e., 20% of both training and testing datasets comprised the outcome variable being examined). Estimates for developed models were internally validated using 10x cross validation sampling. All statistical analyses were carried out in R version 4.1.1 using the Caret, pROC, deskTOOLS, dplyr, and glmnet packages. All packages are freely available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cran.r-project.org\u003c/span\u003e\u003cspan address=\"http://cran.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor COVID-19 incidence, the median crude incidence rate per 100 population for each city was calculated: Galway: 0.90; Dublin: 1.31, Cork: 0.96, with GLMs developed for each SA based on a binary tag i.e., calculated crude incidence rate greater than the regional (city-specific) median as the dependent variable of interest. To account for the likely confounding effects of case age, gender, and local socio-economic profile, a confounder-mediated (i.e, restricted) approach was employed. Data relating to gender and age were extracted from the geo-referenced COVID-19 dataset, while data relating to socio-economic profile and population density for each CSO Small Area was extracted from the HP Pobal Deprivation Index (Haase \u003cem\u003eet al.\u003c/em\u003e, 2017). The Pobal Haase-Pratschke (HP) Deprivation Index represents a composite measure of deprivation/affluence derived from 16 components, each of which represents one of the three dimensions of deprivation: demographic profile, social class composition, and labor market situation. The absolute deprivation (HPAbs) score quantifies any changes to the local economy at the SA level between two census periods 2011 and 2016, calculated for each Small Area and ranging from extremely affluent (+\u0026thinsp;40) to extremely diadvantaged (-40). Individual models were developed for the following population sub-sets:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAge\u003c/b\u003e: 0 to 35 years, 36 to 65 years old, 66 years old and above.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGender\u003c/b\u003e: Male, Female.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAbsolute deprivation score (HPAbs)\u003c/b\u003e: \u0026lt; -5 (deprived areas), -5 to 3 (intermidiate areas), 3 and above (affluent areas). This classification was delineated based on the interquartile distribution of the HPAbs across the Republic of Ireland.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eStatistical models developed in the absence (i.e., NDVI only) and presence of likely cofounders for comparison are presented in Supplementary Materials.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Overview\u003c/h2\u003e \u003cp\u003eOverall, 22,773 symptomatic, laboratory-confirmed cases were included for analyses, corresponding to the total number of cases within the boundaries of the three urban study areas. Study area population, calculated incidence rate and number/percentage of cases associated with severe outcomes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As shown, the highest crude incidence rate (cases/100,000 population) of symptomatic cases was associated with Cork city (2122.5/100,000), followed by Galway (2101.7/100,000) and Dublin (1420.4/100,000). Overall, 1,678 cases (7.4%) were hospitalised, 298 (1.3%) were admitted to ICU and 614 (3%) were associated with mortality. Highest rates of hospitalisation (8%), ICU admission (1.5%) and mortality (3.7%) were reported in Dublin City.\u003c/p\u003e \u003cp\u003eThe distribution of cases attributed to the three categories of COVID-19 severity used in the current study (i.e., hospitalization, ICU, and death) delineated by each modelled subgroup are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The following models failed to converge due to low case numbers:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eICU admission among Age group\u0026thinsp;\u0026lt;\u0026thinsp;35 years, Cork and Galway.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eICU Admission among HPAbs \u0026lt;-5 and HPAbs\u0026thinsp;\u0026gt;\u0026thinsp;3, Galway.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMortality among Age group\u0026thinsp;\u0026lt;\u0026thinsp;35, Dublin, Cork and Galway.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMortality among age group 35 to 65, Cork and Galway.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMortality among female cases for Galway.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePopulation, number of symptomatic cases, incidence rate, hospitalization, ICU admission and mortality cases of COVID-19 for Dublin, Cork and Galway (March to November 2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymptomatic cases\u003c/p\u003e \u003cp\u003e(% of sympt. cases)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncidence rate per 100k\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHospitalization\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,173,179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16,664 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1420.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,336 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e246 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e609 (3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208,669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,429 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2122.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e245 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79,934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,680 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2101.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11 (0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase distribution amongst COVID-19 severity levels by age groups, gender and deprivation score (HPAbs) for Dublin, Cork and Galway (March to November 2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSeverity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAge groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eHPAbs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;(-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-5) to (+\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026gt; (+\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eDublin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHospitalization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eICU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eCork\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHospitalization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eICU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eGalway\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHospitalization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eICU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 COVID-19 Incidence\u003c/h2\u003e \u003cp\u003eAs shown (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), greenspace proportion was negatively associated with COVID-19 incidence rates across all three cities. The highest model accuracy in the absence of confounders was achieved for Galway (AUC 0.736). Tables S2, S3 \u0026amp; S4 present results of using the cofounder-mediated (restricted) modelling approach. Again, increased greenspace was associated with below-median COVID-19 CIRs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for 20 of 24 models. AUCs obtained from models based on deprivation score (0.67\u0026ndash;0.89) were significantly higher than those obtained for both age and gender restricted GLMs (0.55\u0026ndash;0.67).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of generalized linear modelling \u0026ndash; Greenspace Vs crude incidence rate per 100 population above the local median\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Hospitalization\u003c/h2\u003e \u003cp\u003eResults of generalized linear modelling between greenspace and COVID-19 hospitalization for the three primary cofounders are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Greenspace proportion was significantly associated with COVID-19 hospitalization in 19 of 24 models, with significant models typically revealing a negative association between NDVI and hospitalization, except for affluent areas (HPAbs\u0026thinsp;\u0026gt;\u0026thinsp;3), and the lowest age classification (\u0026lt;\u0026thinsp;35) in Cork City, where hospitalization increased in concurrence with increased greenspace proportion (OR: 2.692, AUC: 0.767; OR: 5.373, AUC: 0.621, respectively). Negative associations ranged from aOR\u0026thinsp;=\u0026thinsp;0.037 (Galway, Male cases) to aOR\u0026thinsp;=\u0026thinsp;0.832 (Galway, intermediate deprivation areas). Highest model accuracies (AUC) were obtained for the HP Deprivation Index (0.775 to 0.815) compared to case gender (0.761 to 0.808) and case age (0.572 to 0.662).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear modelling - greenspace Vs hospitalization delineated by case gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.25, 0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.51, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.02, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.19, 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.51, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.76, 3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear modelling - greenspace Vs hospitalization delineated by small area deprivation score (HPAbs)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPAbs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.43,0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.43,5.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.01,0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026ge; (-5), \u0026le;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.15,0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.81,2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.37,1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt; (-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.11,0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.05,0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.03,0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear modelling - greenspace Vs hospitalization delineated by case age group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.18, 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.04, 9.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.02, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;36, \u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.38, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0491\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.37, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0196\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.15, 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.08, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.04, 0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.01, 0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 ICU admission\u003c/h2\u003e \u003cp\u003eRestricted GLM results for ICU admissions across all three cities are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Greenspace proportion exhibited a significant association with COVID-19 related ICU admissions in 17 of 22 developed models. Negative associations between NDVI values and ICU admission were found for cases associated with female patients in all three cities (Dublin, aOR: 0.04; Cork, aOR: 0.027; Galway, aOR: 0.001). A similar observation was found for male cases in Dublin (aOR: 0.463), while male cases were positively associated with greenspace in Galway City (aOR: 13.5). Modelling revealed relatively comparable positive associations between greenspace and ICU admission in Galway (aOR: 2.848) and Cork (aOR: 2.47) for areas characterized by intermediate (-5 to 3) deprivation. A similar association was obtained for Cork in affluent areas (HP\u0026thinsp;\u0026gt;\u0026thinsp;3, aOR: 4.295). Conversely, negative associations were found in the more socio-economically deprived areas of Dublin (aOR: 0.05) and Cork (aOR: 0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear modelling - greenspace Vs admission in ICU delineated by case gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.35, 0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.86, 2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5.63, 32.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.03, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.02, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.002, 0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear modelling - greenspace Vs admission in ICU delineated by Small Area deprivation score (HPAbs)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPAbs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.69, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.62, 11.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026ge; (-5), \u0026le;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.06, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.42, 4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.10, 7.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt; (-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.03, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0, 0.00049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear modelling - greenspace Vs admission in ICU delineated by case age group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003cp\u003eCork\u003c/p\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.246\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.16, 0.53)\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;36, \u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.25, 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.02, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.66, 5.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.01, 0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.08, 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.09, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Mortality\u003c/h2\u003e \u003cp\u003eGeneralized linear modelling of local greenspace and COVID-19 related mortality delineated by population sub-groups are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e. Overall, 13 out of 21 models identified a significant association between local greenspace and COVID-19 mortality rate. Obtained accuracies relating to case age were significantly lower (0.564 to 0.783) than both gender (0.936 to 0.975) and deprivation score (0.925 to 0.991), with converging models revealing a positive association in 12 of 13 cases. For example, increased local greenspace was associated with a significantly increased COVID-19 mortality rate among male cases for both Cork (aOR: 4.138) and Galway (aOR: 7.337) cities, respectively. Conversely, greenspace was negatively associated with mortality in categorically affluent areas of Dublin city (aOR: 0.284).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear modelling- greenspace Vs lethal outcomes delineated by case gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.47,1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.94,8.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003 *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.95,28.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.41,3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.37,2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear modelling- greenspace Vs lethal outcomes delineated by Small Area deprivation score (HPAbs)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPAbs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.16, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e542.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(143.66, 2179.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-17.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.001, 1001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026ge; (-5), \u0026le;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.70,5.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.03,0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.74,26.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt; (-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.65,4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.14,1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0414\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.05,77.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear modelling - greenspace and lethal outcomes delineated by case age group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;36, \u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.93,3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDublin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.62,3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5.71,22.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.55,16.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMultiple recent studies have sought to identify and quantify the association(s) between access/proximity to urban greenspaces and mental health/wellbeing during the global COVID-19 pandemic (Shuvo et al, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Burnett et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wortzel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Larson et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, far fewer have focused on the direct and/or indirect physical effects of urban greenspace on COVID-19 transmission and severity. Accordingly, the current study sought to address this knowledge gap via an integrated analysis of georeferenced epidemiological and geo-spatial data to identify and quantify potential relationships between incidence and severity of SARS-CoV-2, presence of greenspace, socio-economic features of the area and population subgroups (age and gender) in three of the primary urban conurbations in the Republic of Ireland (Dublin, Cork and Galway). Study findings will significantly contribute to current understanding on the potential protective effect(s) of greenspace exposure via the Normalised Difference Vegetation Index (NDVI) on COVID-19 incidence and severity. To the authors knowledge, this is the first study within an Irish context to assess the association between greenspace density and COVID-19 susceptibility (hospitalizations, admission to ICU, and mortality).\u003c/p\u003e \u003cp\u003eStudy findings indicate that increased greenspace (i.e., spatial extent within individuals\u0026rsquo; residential area) was associated with reduced COVID-19 incidence rates, with the proportion of greenspace negatively associated with incidence rates of COVID-19 in Cork (aOR: 0.101), Dublin (aOR: 0.551), and Galway (aOR: 0.118). The mitigating impact of access and proximity to greenspace on disease susceptibility has recently been established (e.g., Heo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Russette et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Johnson et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous research has shown that the presence and proximity of greenspace and exposure to biogenic volatile organic compounds (VOCs) can decrease susceptibility to diseases by increasing both NK and T cells while increasing cytotoxic activities, reducing inflammation, and replenishing gut microbiota (Klompmaker et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yeoh et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The presence of greenspace was also associated with COVID-19 related hospitalizations and ICU admissions, with adjusted Odds Ratios (aORs) suggesting that, controlling for other potential confounders, as greenspace increased, recorded hospitalizations and ICU admissions associated to COVID-19 decreased. Interestingly, the heterogeneous intensity of the greenspace-disease relationship across the surveyed urban areas suggests the presence of unaccounted interacting factors. For example, among the lowest age classification (\u0026lt;\u0026thinsp;35 years old) in Cork city, hospitalizations increased in areas with proportionally higher greenspace density (aOR: 5.373). This association may be attributed to younger generations using urban greenspaces as social gathering places, possibly exposing themselves to an increased transmission risk for SARS-CoV-2. However, this relationship was not observed in the other urban areas; in Dublin a negative association was found among the younger age cohort between greenspace and both hospitalization (aOR: 0.283) and ICU admission (aOR: 0.288), and for hospitalization in Galway (aOR: 0.070). A recent study from De Jalon et al. (2021) reports that communities living closer to greenspaces are more likely to exercise and less likely to be affected by Type-2 Diabetes or obesity i.e., proximity to greenspace may be a potential proxy for background community health. The contradictory association between greenspaces and severity for the sub-population under 35 years old may also be due to varying socio-economic and demographic differences observed across the three urban locations (Wortzel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerational differences in greenspace usage during the COVID-19 pandemic have been reported in the literature (Slater et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wortzel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), potentially accounting for the age-related differences in hospitalization rates across Cork City in the present study. Older individuals have been associated with elevated levels of resourcing (i.e., time, transportation, and/or living arrangements) than younger generations, subsequently being reflected in their greenspace utilisation (Slater et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wortzel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Older generations have also been shown to typically have higher access to private greenspace, thus limiting contact with both symptomatic and asymptomatic carriers of the COVID-19 virus, in addition to higher quality greenspace (e.g., landscape maintenance, aesthetic values, biodiversity levels) which may impact health (Slater et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wortzel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The findings thus outline the need for facilitating quality greenspaces for managing and improving public health, and particularly among younger sub-populations.\u003c/p\u003e \u003cp\u003eWhen greenspace increased, COVID-19 related ICU admissions were shown to decrease across all three urban areas for female cases, with this association only being found in Dublin for male cases. In Galway City, it was found that higher greenspace proportion was associated with an increase in COVID-19 related ICU admissions among males (aOR: 13.5). Evidence from the literature suggests that females typically feel more vulnerable than males in green spaces, and particularly in urban environments and without company, which may negatively influence the frequency of greenspace utilization among females (Burnett et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Accordingly, findings have potential implications for how greenspaces are perceived and utilized across genders and regarding the security aspects of greenspaces. Socio-economic profile (SEP) also has associations with the presence and utilisation of greenspace across communities and risk of ICU admission, morbidity and mortality outcomes (Russette et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Twohig-Bennett et al., 2018). Firstly, the mitigating effect of greenspace on COVID-19 susceptibility was not always observed among populations residing in relatively affluent areas. Higher income may offset the mitigating impact of greenspace on disease severity due to causally posterior factors like enhanced diets, increased general (background) health, and lower population and household density (Bousquet et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Merino et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Russette et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wortzel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Astell-Burt et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) previously reported that the distribution of greenspace in urban settings is frequently \u0026lsquo;clustered\u0026rsquo; around areas with a higher proportion of high-income residents, irrespective of population density (Astell-Burt et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, multiple studies have shown that low-income neighbourhoods are typically characterised by reduced greenspace access; thus, the legitimacy of \u0026ldquo;distance-based restrictions\u0026rdquo;, as employed both nationally and globally during the pandemic, and subsequent impacts on access to greenspaces requires significant examination. For example, a 2-kilometre radius limit was employed across the Republic of Ireland during the first 3\u0026ndash;4 months of the pandemic. These restrictions may have serious impacts on community health and should avoided until the unintentional and/or currently poorly understood impacts of diverse socio-economic factors on disease susceptibility vis-\u0026agrave;-vis greenspace exposure and access can be examined (Twohig-Bennett et al., 2018; Russette et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notwithstanding, when individuals associated with lower socio-economic status do use greenspace, they tend to experience a larger reduction in disease risk (e.g., Alzheimer\u0026rsquo;s disease, circulatory disease, cardio-metabolic conditions) than individuals from a higher socio-economic profile (Russette et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kardan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Greenspace density has also been identified as a stronger predictor of poor health outcomes, and particularly in lower income areas (Mitchell \u0026amp; Popham, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Conversely, the presence and extent of greenspace (in isolation, i.e., not controlling for potential confounders) has been found to positively impact health outcomes households with higher and lower median income without statistically meaningful differences (Browning \u0026amp; Rigolon, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Inferences pertaining to the unmediated impact of greenspace distribution and exposure not accounting for greenspace utilization are not possible based on the current study design (i.e., ecological study). Diet type and quality has also been shown to impact the severity of COVID-19 and should also be considered when examining an individual\u0026rsquo;s use of greenspace and socio-economic status (Bousquet et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Merino et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Socio-economically deprived areas of Cork (aOR: 0.001) and Dublin (aOR: 0.05) were found to have very significantly decreased COVID-19 ICU admissions as the spatial extent of greenspace increased. Burnett et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have recently stated that these associations are likely due to occupational profiles, as individuals with a lower economic standing are more likely to be essential workers and continued to work in manual or service operations during initial stages of the pandemic (i.e., not possible to work from home), thus limiting their access to greenspace and possible virus transmission. Conversely, more affluent and skilled individuals were more likely to shift to online work easily and could have more access to local greenspace during movement restrictions and lockdowns (Burnett et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, quality of greenspace and frequency of use are typically higher in high-socioeconomic areas (Leslie et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Twohig-Bennett et al., 2018). It should also be noted that income-related health inequalities have been observed to be less prevalent within greener neighbourhoods (Mitchell \u0026amp; Popham, 2008). Nonetheless, studies in lower- and middle-income countries (LMIC) systematically and consistently indicate the negative associations between greenspace exposure and the presence of health vulnerabilities and disease susceptibility (Shuvo et al, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This suggests that exposure to greenspace, regardless of its interaction with socioeconomic status, likely exhibits a mitigating effect on individuals\u0026rsquo; \u0026ldquo;resistance\u0026rdquo; to COVID infection and subsequent exacerbations. Future research should seek to further our understanding of the physical/biological greenspace-COVID relationship by investigating intervening factors including occupation, shifting working habits, and greenspace access and use during the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eMortalities related to COVID-19 also had significant associations with local greenspace density. In the more affluent areas of Dublin, COVID-19-related deaths decreased as greenspace increased (aOR: 0.284). Conversely, in Cork (aOR: 542.781), increased local greenspace was associated with a higher COVID-19 mortality rate. This could be explained by increased presence of the public within those greenspaces, thus increasing potential exposure to and transmission of the disease. Aguilar et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated that in cities where population flow is concentrated in mobility hotspots, which, in the case of this study, could arguably be greenspace during movement restrictions, transmission and viral loading of SARS-CoV-2 is increased. Similarly, in Oslo, Venter \u003cem\u003eet al.\u003c/em\u003e (2020) have shown that urban greenspace utilization, significantly increased during lockdowns and periods with mobility restrictions, being used as a replacement for indoor recreational activities. In Ireland, the first lockdown (in Spring 2020) was marked by abnormally dry and warm conditions (MetEireann, 2020) which might have also encouraged more frequent use of urban greenspaces. Additionally, greenspaces have been shown to foster social interactions, thus influencing mental health, human relationships and potentially reducing stress, anxiety, and depression, and promoting healthy cortisol and vitamin D levels, helping combat Seasonal Affective Disorder (James et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Twohig-Bennett et al., 2018; Vos et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This feature of greenspaces may have been particularly important during the COVID-19 pandemic when social interactions in the usual \u0026ldquo;third spaces\u0026rdquo; were severely limited (Vos et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notwithstanding, 13 out of 21 developed models identified a significant association between local greenspace and COVID-19 mortality rate, and thus, fewer models exhibited a significant relationship than either of the other two examined outcomes (i.e., hospitalization, ICU admission). Accordingly, it can be concluded that greenspace becomes less clear as an outcome pre-cursor as the outcome becomes more severe i.e., outcome more \"biological\" in nature (age, underlying health conditions, etc.). Previous work by Boudou et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) has shown that that the living environment becomes increasingly secondary as an individual or community become increasingly biologically susceptible (Boudou et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompared with a significant proportion of studies investigating the health-greenspace relationship, this study utilised the Normalised Difference Vegetation Index (NDVI) as an indicator of urban greenspace density within Cork, Dublin, and Galway in Ireland. NDVI provides quantification of greenspace by measuring the difference between Near-Infrared (strongly reflected by vegetation) and Red Light (absorbed by vegetation), and as such is readily transferable. Another strength of the study was the analysis of four distinct outcomes relating to COVID-19 (COVID-19 incidence, hospitalisation, ICU admission, and mortality). Lastly, the authors consider that findings of this study may be relevant to countries with similar socio-economic status, climatic and geographical features, and population distribution. This approach may be used to investigate the success of state-level policies pertaining to COVID-19 or other respiratory infections in future studies. Despite these strengths, this study comprises some limitations. While greenspace was identified, its utilisation at the individual, household or community level was neither assessed nor included in the present analysis, and as such, no conclusions on greenspace type, quality or utilisation with respect to COVID-19 incidence or severity can be drawn. Further, a paucity of air quality measurements precluded any cross-analyses of the data included in our study. Similarly, this study did not include other factors contributing to transmission, morbidity or mortality, such as the presence of underlying health conditions, pre-existing risk factors, and individual lifestyle choices. Urban and rural dataset comparisons were also not included within the scope of this study, as it is not possible to delineate public and private land in categorically rural areas using the datasets employed.\u003c/p\u003e \u003cp\u003eNonetheless, study findings suggest that greenspace access may currently be overlooked by policymakers as a method to address health inequalities in Ireland and help minimise the impacts of future epidemics/pandemics. This research has significant implications for policymakers, particularly regarding community health and design of equitable non-pharmaceutical interventions. Greenspace encompasses myriad human health benefits, including positively influencing mental health by lowering depression symptoms, fostering social interactions, and improving immunological defence against pathogens and therefore should be incorporated into city and public health planning, disease prevention strategies, and emergency planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eAll named authors provided consent for publication\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eNone declared\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by Science Foundation Ireland (Grant No. COVID-19 Rapid Response)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSK, MB and PH designed and performed the experiments, derived the models and analysed the data. COH and PG developed and formatted all datasets, SK, MB and PH developed the overarching study concept and approach and acquired all study data. JOD wrote the manuscript in consultation with SK, MB and PH\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eN/A\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData may be provided upon reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguilar, J., Bassolas, A., Ghoshal, G., Hazarie, S., Kirkley, A., Mazzoli, M., ... \u0026amp; Sadilek, A. (2022). Impact of urban structure on infectious disease spreading. \u003cem\u003eScientific reports\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 1-13.\\\u003c/li\u003e\n\u003cli\u003eAstell-Burt, T., Feng, X., Mavoa, S., Badland, H. M., \u0026amp; Giles-Corti, B. (2014). Do low-income neighbourhoods have the least green space? A cross-sectional study of Australia\u0026rsquo;s most populous cities. 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Remote Sensing Environ. 2015, 159, 269\u0026minus;277\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5012868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5012868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo date, relatively few epidemiological studies have sought to identify and quantify associations between nature and SARS-CoV-2 infection. Likewise, while multiple studies have focused on the clinical factors pre-empting SARS-CoV-2 (e.g., underlying health conditions, age), no Irish research has examined the effect of the natural environmental on SARS-CoV-2 incidence or severity (hospitalization, ICU admission and mortality). Identifying areas and communities at higher risk due to local environmental factors constitutes a knowledge gap for informing mitigation/intervention strategies prior to future similar public health events. Accordingly, the current study focused on three major Irish cities: Dublin, Cork, and Galway. A Normalized Difference Vegetation Index (NDVI) dataset was constructed using the Google Earth Engine Explorer and Sentinel-2 MSI (Multispectral Instrument) open-access software. NDVI values were geographically linked to Small Area (SA) units across all three cities and associated with their respective SARS-CoV-2 incidence and severity rates from March to November 2020, with demographically and socioeconomically delineated (to account for the confounding) generalised linear modelling subsequently employed to identify relationships between greenspace proportion and SARS-CoV-2. Overall, 22,773 symptomatic laboratory-confirmed and georeferenced cases of SARS-CoV-2 were included for analyses. Greenspace proportion was negatively associated with SARS-CoV-2 incidence rates across all three cities (i.e., increased greenspace conurrent with lower incidence of SARS-CoV-2), with these associations remaining significant when models included potential confounders (aORs 0.101\u0026ndash;0.501). Likewise, increased greenspace was typically associated with decreased levels of SARS-CoV-2, however, associations were less pronounced or not present in areas characterised by younger populations and/or increasing affluence. Differing levels of association were found with respect to case gender (male cases typically more \u0026ldquo;responsive\u0026rdquo;) and city (less populated cities typically more \u0026ldquo;responsive\u0026rdquo;). Findings provide a crucial evidence base for researchers, policymakers and healthcare practitioners to appropriately design non-pharmaceutical interventions and engage with communities to successfully promote appropriate health behaviours.\u003c/p\u003e","manuscriptTitle":"Associations between urban greenspace (normalized difference vegetation index) and SARS-CoV-2 incidence and severity across three Irish cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-21 08:02:39","doi":"10.21203/rs.3.rs-5012868/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c38f3f3d-dff0-4be1-a54c-88d7bb26b479","owner":[],"postedDate":"October 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-17T06:41:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-21 08:02:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5012868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5012868","identity":"rs-5012868","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}