Fernández‑Martínez et al. BMC Public Health (2022) 22:2316 https://doi.org/10.1186/s12889‑022‑14774‑6 RESEARCH © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Open Access Socioeconomic differences in COVID‑19 infection, hospitalisation and mortality in urban areas in a region in the South of Europe Nicolás F Fernández‑Martínez1,2, Rafael Ruiz‑Montero1,2*, Diana Gómez‑Barroso3,4, Alejandro Rodríguez‑Torronteras1, Nicola Lorusso5, Inmaculada Salcedo‑Leal1,2 and Luis Sordo4,6 Abstract Background: To analyse differences in confirmed cases, hospitalisations and deaths due to COVID‑19 related to cen‑ sus section socioeconomic variables. Methods: Ecological study in the 12 largest municipalities in Andalusia (Spain) during the first three epidemic waves of the COVID‑19 (02/26/20—03/31/21), covering 2,246 census sections (unit of analysis) and 3,027,000 inhabitants. Incidence was calculated, standardised by age and sex, for infection, hospitalisation and deaths based on average gross income per household (AGI) for the census tracts in each urban area. Association studied using a Poisson Bayes‑ ian regression model with random effects for spatial smoothing. Results: There were 140,743 cases of COVID‑19, of which 12,585 were hospitalised and 2,255 died. 95.2% of cases were attributed to the second and third waves, which were jointly analysed. We observed a protective effect of income for infection in 3/12 cities. Almeria had the largest protective effect (smoothed relative risk (SRR) = 0.84 (0.75–0.94 CI 95%). This relationship reappeared with greater magnitude in 10/12 cities for hospitalisation, lowest risk in Algeciras SRR = 0.41 (0.29–0.56). The pattern was repeated for deaths in all urban areas and reached statistical significance in 8 cities. Lowest risk in Dos Hermanas SRR = 0.35 (0.15–0.81). Conclusions: Income inequalities by geographical area were found in the incidence of COVID‑19. The strengths of the association increased when analysing the severe outcomes of hospitalisations and, above all, deaths. Keywords: COVID‑19, Urbanization, Geographical, Mortality, Hospitalisation, Socio‑economic status Background The coronavirus disease of 2019 (COVID-19) has been marked by the lack of prior knowledge of its charac- teristics, an initial underestimation of its impact and delayed decision-making in terms of healthcare measures and public health [1]. This has resulted in very different disease management in different countries, according to the profiles of their inhabitants [2]. Despite these differences, evidence suggests that COVID-19 has had a greater effect on populations with worse socioeconomic characteristics [3, 4], and it has been particularly acute, as with other infectious dis- eases, in urban populations [5, 6]. In urban areas, con- tact between people is more frequent than in rural areas, especially in areas with worse social determinants of COVID-19, such as working and living conditions [7]. Throughout the pandemic, the consequences of these socioeconomic inequalities in the urban context have not *Correspondence: rafaelruizmontero@gmail.com 1 Unidad de Gestión Clínica Medicina Preventiva y Salud Pública, Hospital Universitario Reina Sofía, Córdoba 14004, Spain Full list of author information is available at the end of the article Page 2 of 10Fernández‑Martínez et al. BMC Public Health (2022) 22:2316 been limited to infections. There have also been higher mortality rates [8] and hospitalisation rates [9] in under- privileged urban areas. It should be noted that many studies were conducted at different time points early in the pandemic or were carried out without differentiating between different time points, which might contribute to the inconsistent findings [10, 11]. Hospitalisations and mortality due to COVID-19 are the two issues that have most affected political and public health measures, such as confinements and curfews, and have put the health care infrastructure at risk [11, 12]. In many countries, including Spain, COVID-19 has had an impact during two different time periods with very different characteristics [10]. There are notable differ- ences between the first wave, which began in February of 2020, in which there was scant awareness of the disease compared to the following waves. The first wave in Spain provoked greater excess mortality than in any other Euro- pean country [13]. However, this wave hit certain zones hard, while in others it was much less relevant. In Anda- lusia, for example, confinement took place when there were still very few cases in the region [14]. By mid-July, when new infections started to rise again (with the begin- ning of the second wave), more than 27,000 deaths had been officially registered in Spain as cases of COVID-19 [14, 15]. Only five per cent came from Andalusia, a region with 17.9 per cent of the Spanish population. COVID-19 had a much greater impact in Andalusia during the sec- ond and third waves, accounting for around 30 per cent of cases and deaths in Spain [14]. Despite the existence of research on the association between sociodemographic risk factors and COVID-19, especially concerning urban areas [3, 4, 7, 16, 17], there are few studies that evaluate the risk of infection, hos- pitalisation and death due to the virus in a single study population. Furthermore, in order to accurately interpret the influence of socioeconomic factors on these out- comes, biased data from the first wave (diagnostic bias) should be considered separately [11]. The objective of this study was to analyse socioeco- nomic differences in the 12 primary urban areas in Anda- lusia (Spain) in terms of confirmed cases, hospitalisations and deaths due to COVID-19 during the first three epi- demic waves of the pandemic. Materials and methods We carried out an ecological, retrospective study of the first three epidemic waves of the COVID-19 pandemic in the primary urban areas in Andalusia, Spain. Variables The units of analysis were the census sections (small territorial units inside a neighbourhood encompassing approximately between 1,000 and 2,500 inhabitants) in urban areas (territorial units defined by population vol- ume criteria and by territorial features). The sociode- mographic variables were: the number of inhabitants (aggregated and disaggregated by sex and age) and aver- age annual gross income per household (AGI) in euros. The epidemiological variables were: confirmed cases of COVID-19, as defined by the Spanish Ministry of Health (which mainly considered the laboratory criteria of con- firmed PCR or antigen test to SARS-CoV-2, depending on the period of the pandemic [18]), hospitalisation of confirmed COVID-19 cases, deaths of confirmed cases of COVID-19, and epidemic waves, defined by the accel- eration and deceleration of the number of new cases of COVID-19 (first epidemic wave: February 26 [first con- firmed case in Andalusia] to July 12, 2020; second wave: July 13 to December 27, 2020; third wave: December 28, 2020 to March 31, 2021). Information sources The most recent available information on demographic data (2019) and AGI (2018) came from the National Sta- tistics Institute (NSI) [19]. However, given that AGI in 2018 was available for 99.1 per cent of census sections, information for prior years was used for the rest: 2017 (0.3%), 2016 (0.1%) and 2015 (0.4%). In 0.2 per cent of census sections there were no available data on AGI, thus they were excluded from the analysis. The confirmed COVID-19 cases in terms of diagnosis, hospitalisation and deaths—geocoding (X and Y coordinates)- and the information on institutionalisation in elderly or disabil- ity care centres (these cases were excluded due to the risk of interference with the spatial analysis) were obtained from the Epidemiological Surveillance System of Anda- lusia (SVEA). Finally, in order to establish cut-off points between epidemic waves, information on the evolution of the pandemic in the study area was obtained from the Andalusian Institute of Statistics and Cartography [20]. The dependent variable was calculated using the stand- ardised incidence ratio (SIR) for COVID-19 for three outcomes of interest: cases of infection, hospitalisation and deaths. The independent variables were AGI, age and sex (these were used for the standardisation of rates). Study area This study exclusively analysed the 12 municipalities in Andalusia with a population over 100,000 inhabitants [21]. In total, these urban areas bring together 2,246 cen- sus sections and 3,027,000 inhabitants, which represents 36 per cent of the population of Andalusia and 6 per cent of the total population in Spain (Fig. 1). Page 3 of 10Fernández‑Martínez et al. BMC Public Health (2022) 22:2316 Statistical analysis The first wave was analysed individually, and the second and third waves were analysed jointly. The reason for dividing the period into two-time windows- and for our work, focusing on the second of these windows- is that the data related to the first wave were biased due to the limited accessibility of COVID-19 diagnostic tests [10, 13, 22]. In contrast, despite the existence of differences, the second and third waves are reasonably comparable due to uniformity in the definition of a confirmed case and the general availability of the SARS-CoV-1 antigen tests, thus they were analysed together. The temporal stratification was carried out in terms of the first wave vs the second and third waves. The spatial analysis using the Bayesian model was carried out stratifying by urban area, so each urban area was analysed independently. In summary, three outcomes were studied: the standardised incidence ratio (SIR) of COVID-19 at the case and hos- pitalisation levels and the standardised mortality ratio (SMR) of COVID-19, in 12 urban areas and in two time windows. In order to carry out the statistical analysis, the SIR of COVID-19 was calculated, standardised by sex and age (categorised into 21 quinquennial groups). The SIR was obtained using the quotient of the observed and expected cases, by way of indirect standardisation, setting as ref- erence (SIR = 1) the average ratio across census sections of all municipalities. Later, we employed a Bayesian spa- tial model to investigate spatial risk factors for the three aforementioned COVID-19 outcomes. In order to avoid the effect of small areas, we calculated the smoothed rela- tive risk (SRR), setting as reference (SSR = 1) the average risk across census sections among each urban area. To do so, we used the model proposed by Besag, York and Mollié [23] of spatial smoothing using Poisson regression with random effects, which represents the heterogeneity of each geographic unit and the spatial contiguity, based on an auto-regressive conditional model (CAR). where: λi is the relative risk in area i; Oi the number of cases in area i, α the constant, Ei the expected number of cases, hi the ordinary random-effects component for non-spatial heterogeneity among census sections and bi the spatial term. Finally, the logarithm of AGI for each census section was added to the model as an independent covariable 0i ∼ Po(Ei i) log(i) = α + hi + bi Fig. 1 Location of studied urban areas Page 4 of 10Fernández‑Martínez et al. BMC Public Health (2022) 22:2316 (analysed as a quantitative continuous variable). The sta- tistical analysis was carried out with R software (version 4.0.3), using the spatial analysis package R-INLA. Patient and public involvement Due to the ecological and non-interventional nature of the study we have not involved the public in the design or conduct of our research. Results Figure  1 shows the location of the urban areas studied. The distribution of AGI was non-normal and heterogene- ous in all of them. The AGI ranged from 13,000€ in the census section with the lowest income to 123,000€ in the census section with the highest income, although both of the first two quintiles were under 30,000€ and the third was below 35,000€ (Fig.  2). This asymmetric distribu- tion was similar among the different urban areas, though a greater dispersion was observed in the municipalities with larger populations (Sevilla and less so Málaga and Córdoba) (Supplementary File 1). After excluding 3,952 institutionalised cases (2.4%), 16,547 cases without available geocoding (10.1%) and 1,859 cases geocoded outside the municipalities stud- ied (1.1%), the number of confirmed cases of COVID- 19 analysed was 140,743 (86.3% of the total confirmed cases reported to the SVEA), of which 12,585 were hospitalisations and 2,255 were deaths. Table 1 shows, for each urban area, the distribution of the census sections, the number of inhabitants, the AGI and con- firmed COVID-19 cases (infections, hospitalisations and deaths) during the second time window studied. The confirmed cases of COVID-19 that were declared in the first wave made up only 4.8 per cent of the total number of declared cases during the study period20. As mentioned in the methodology, we mainly report find- ings from the second and third waves (the distribution of confirmed cases during the first wave and their rela- tive contribution to the overall study period is shown in Table 2). COVID‑19 cases In the second and third waves, the urban areas with the greatest number of confirmed cases of COVID-19 per 100,000 inhabitants were Granada (7072.6) and Alm- ería (5906); Huelva had the lowest number of cases (3535.3). The distribution of the incidence of COVID- 19 by urban area is shown in Supplementary File 2. The relationship between AGI and SIR at the case level varied by urban area; however, it only reached statis- tical significance in the municipalities with a nega- tive association, that is to say, there was greater risk in the census sections with lower income (Fig.  3-A and Supplementary File 3). These included Almería (SRR = 0.84, 95% credibility interval [CI] = 0.75–0.94), Granada (SRR = 0.88, 95%CI = 0.78–1.00) and Sevilla (SRR = 0.91, 95%CI 0.85–0.98). Fig. 2 Distribution of average gross income by household in the studied urban areas, by income quintiles Page 5 of 10Fernández‑Martínez et al. BMC Public Health (2022) 22:2316 Ta bl e 1 Po pu la tio n, c en su s se ct io ns , i nc om e an d co nfi rm ed C O VI D ‑1 9 ca se s of h os pi ta lis at io n an d de at h in th e 2n d an d 3r d w av es , b y ur ba n ar ea M U N IC IP A LI TY PO PU LA TI O N 20 20 (N SI ) Ce ns us se ct io ns A G I ( ra ng e) A G I M ed ia n (IQ R) Ca se s of CO VI D ‑1 9 Ca se s /1 00 .0 00 H os pi ta lis ed CO VI D ‑1 9 H os pi ta lis ed /1 00 .0 00 D ea th s CO VI D ‑1 9 D ea th s /1 00 .0 00 Se vi lla 69 1, 39 5 53 1 13 ,3 92 –1 23 ,6 53 33 ,6 12 (2 2, 32 1) 26 ,5 27 38 36 .7 4 23 34 33 7. 58 41 8 60 .4 6 M ál ag a 57 8, 49 0 43 4 13 ,3 92 –1 04 ,6 51 29 ,7 12 (1 3, 04 1) 22 ,8 48 39 49 .5 9 16 91 29 2. 31 28 0 48 .4 Có rd ob a 32 6, 03 9 24 9 13 ,3 92 –1 22 ,6 76 30 ,5 15 (1 5, 53 3) 15 ,0 95 46 29 .8 1 12 08 37 0. 51 16 8 51 .5 3 G ra na da 23 3, 64 8 18 4 13 ,3 92 –6 8, 45 8 35 ,2 32 (1 7, 67 7) 16 ,5 25 70 72 .6 14 22 60 8. 61 30 4 13 0. 11 Je re z 21 3, 10 5 16 5 13 ,6 15 –7 5, 40 3 26 ,7 12 (1 1, 18 5) 87 12 40 88 .1 3 61 8 29 0 15 5 72 .7 3 A lm er ía 20 1, 32 2 13 3 13 ,3 92 –6 9, 60 6 31 ,7 65 (1 3, 35 4) 11 ,8 90 59 05 .9 6 72 3 35 9. 13 13 1 65 .0 7 M ar be lla 14 7, 63 3 77 22 ,9 84 –5 2, 58 2 31 ,8 12 (7 49 6) 67 10 45 45 .0 5 39 7 26 8. 91 88 59 .6 1 H ue lv a 14 3, 83 7 10 9 14 ,9 34 –6 6, 26 1 30 ,5 60 (1 4, 26 4) 50 85 35 35 .2 5 36 3 25 2. 37 56 38 .9 3 D os H er m an as 13 5, 05 0 83 15 ,3 99 –9 4, 18 1 32 ,4 16 (1 4, 05 4) 52 58 38 93 .3 7 35 2 26 0. 64 67 49 .6 1 A lg ec ira s 12 3, 07 8 87 14 ,0 78 –7 4, 99 0 32 ,9 15 (1 3, 38 6) 47 84 38 86 .9 7 36 1 29 3. 31 12 0 97 .5 Cá di z 11 5, 43 9 10 7 20 ,9 61 –8 4, 28 6 33 ,1 03 (1 4, 38 1) 48 53 42 03 .9 5 38 5 33 3. 51 52 45 .0 5 Ja én 11 2, 75 7 87 18 ,4 63 –8 6, 93 3 35 ,0 78 (1 4, 34 3) 57 38 50 88 .8 2 41 2 36 5. 39 70 62 .0 8 Page 6 of 10Fernández‑Martínez et al. BMC Public Health (2022) 22:2316 Ta bl e 2 Co m pa ris on o f t ot al c as es , h os pi ta lis at io ns a nd d ea th s, by ti m e w in do w a nd u rb an a re a M U N IC IP A LI TY 1s t w av e. Ca se s 2n d‑ 3r d w av es . Ca se s To ta lc as es % C as es 1s t w av e 1s t w av e. H os pi ta lis at io ns (% o f c as es ) 2n d‑ 3r d w av es . H os pi ta lis at io ns (% o f c as es ) To ta l ho sp ita lis at io ns % H os pi ta lis at io ns 1s t w av e 1s t w av e. D ea th s (% of c as es ) 2n d‑ 3r d w av es . D ea th s (% of c as es ) To ta l de at hs % D ea th s 1s t w av e Se vi lla 98 8 26 ,5 27 27 ,5 15 3. 59 38 1 (3 8. 6% ) 23 34 (8 .8 % ) 27 15 14 .0 3 48 (4 .9 % ) 41 8 (1 .6 % ) 46 6 10 .3 0 M ál ag a 19 77 22 ,8 48 24 ,8 25 7. 96 64 4 (3 2. 6% ) 16 91 (7 .4 % ) 23 35 27 .5 8 10 5 (5 .3 % ) 28 0 (1 .2 % ) 38 5 27 .2 7 Có rd ob a 69 7 15 ,0 95 15 ,7 92 4. 41 23 5 (3 3. 7% ) 12 08 (8 .0 % ) 14 43 16 .2 8 21 (3 .0 % ) 16 8 (1 .1 % ) 18 9 11 .1 1 G ra na da 11 64 16 ,5 25 17 ,6 89 6. 58 38 5 (3 3. 1% ) 14 22 (8 .6 % ) 18 07 21 .3 0 67 (5 .8 % ) 30 4 (1 .8 % ) 37 1 18 .0 5 Je re z 22 2 87 12 89 34 2. 48 10 9 (4 9. 1% ) 61 8 (7 .1 % ) 72 7 14 .9 9 18 (8 .1 % ) 15 5 (1 .8 % ) 17 3 10 .4 0 A lm er ía 21 1 11 ,8 90 12 ,1 01 1. 74 53 (2 5. 1% ) 72 3 (6 .1 % ) 77 6 6. 82 5 (2 .4 % ) 13 1 (1 .1 % ) 13 6 3. 67 M ar be lla 31 2 67 10 70 22 4. 44 94 (3 0. 1% ) 39 7 (5 .9 % ) 49 1 19 .1 4 14 (4 .5 % ) 88 (1 .3 % ) 10 2 13 .7 2 H ue lv a 17 5 50 85 52 60 3. 32 70 (4 0. 0% ) 36 3 (7 .1 % ) 43 3 16 .1 6 14 (8 .0 % ) 56 (1 .1 % ) 70 20 D os H er m an as 15 5 52 58 54 13 2. 86 59 (3 8. 1% ) 35 2 (6 .7 % ) 41 1 14 .3 5 7 (4 .5 % ) 67 (1 .3 % ) 74 9. 45 A lg ec ira s 16 5 47 84 49 49 3. 33 60 (3 6. 4% ) 36 1 (7 .5 % ) 42 1 14 .2 5 15 (9 .1 % ) 12 0 (2 .5 % ) 13 5 11 .1 1 Cá di z 20 0 48 53 50 53 3. 95 62 (3 1. 0% ) 38 5 (7 .9 % ) 44 7 13 .8 7 10 (5 .0 % ) 52 (1 .1 % ) 62 16 .1 2 Ja én 45 2 57 38 61 90 7. 30 16 7 (3 6. 9% ) 41 2 (7 .2 % ) 57 9 28 .8 4 22 (4 .9 % ) 70 (1 .2 % ) 92 23 .9 1 Page 7 of 10Fernández‑Martínez et al. BMC Public Health (2022) 22:2316 Hospitalised cases In the second and third waves, the number of con- firmed cases of COVID-19 that were hospitalised per 100,000 inhabitants was homogeneous among the urban areas, except for Granada (608.6), with an inci- dence much higher than the next highest (Córdoba, with 370.5). The association between AGI and the SIR of COVID-19 in terms of hospitalisations was negative in all of the municipalities and reached statistical sig- nificance in all except Marbella and Jaén (Fig. 3-B and Supplementary File 3). The municipalities in which a lower AGI at a census section level was associated with a greater relative risk of hospitalisation were Alge- ciras (SRR = 0.41, 95%CI = 0.29–0.56) and Almería (SRR = 0.54, 95%CI = 0.43–0.68). Deaths In the second and third waves, the heterogeneity observed in the number of deaths due to COVID- 19 per 100,000 inhabitants was similar to what was described for the first wave. Granada (130.7) was again the municipality with the greatest number of deaths, followed by Algeciras (97.5), while the munic- ipality with the lowest number of deaths was Huelva (38.9). The association between AGI and the SIR of COVID-19 in terms of deaths was negative in all of the urban areas and reached statistical significance in eight of the twelve (Fig.  3-C and Supplementary File 3). The municipalities in which a lower AGI at a census section level was associated with a greater relative risk of death due to COVID-19 were Dos Hermanas (SRR = 0.35, 95%CI = 0.15–0.81), Alge- ciras (SRR = 0.38, 95%CI = 0.21–0.67), and Málaga (SRR = 0.42, 95%CI = 0.29–0.61). Discussion This study suggests that infections, hospitalisations and deaths related to COVID-19 were greater in underprivi- leged areas. This confirms what has been shown in prior studies both in Spain [10, 24] and internationally [25, 26], though it provides some important nuances: the present study was carried out in an urban area, but the results are based on 12 urban areas (rather than one) with popula- tions between 100,000 and 600,000 inhabitants. Further- more, it is a simultaneous study of cases, hospitalisations and deaths. The association between low socioeconomic level and COVID-19 exists, but it also seems that the magnitude tends to increase along with the severity of the disease. This study was based on the differences within vari- ous urban areas in which there were different socioeco- nomic levels, each one of which had results that were homogeneous, thus showing large income inequalities within each municipality (but not between municipali- ties). The behaviour of the pandemic throughout the second and third waves was much less geographically heterogeneous than the first [14]. Although this study was of the 12 most populated cities in a Spanish region, it is still a region with 8.5 million people and a territo- rial extension larger than Ireland or Austria. This meant that in certain cities such as Granada, the number of cases, hospitalisation and deaths due to COVID-19 was practically double that of Sevilla. These differences could be explained by city characteristics, such as population density, environmental and other climatic factors [24, 27, 28]. However, the fact that census sec- tions with lower income were hit hardest was a similar attribute among all of the areas studied. A 10 per cent increase in risk of infection, 40 per cent increase in risk of hospitalisation, and 100 per cent increase in risk of Fig. 3 Smoothed relative risks for cases of infection (a), hospitalisation (b) and death (c) due to COVID‑19, by household annual gross income, 2nd and 3rd waves Page 8 of 10Fernández‑Martínez et al. BMC Public Health (2022) 22:2316 death was observed for each decrease of one logarith- mic unit in annual AGI (for example, the risk of death with a COVID-19 diagnosis during the study period in a census section with an average AGI of 15,000 € was double that of a census section with 40,000 €). This inverse relationship between socioeconomic level and the entire cascade from COVID-infection to mortality had been pointed out previously in other high-income countries [25, 29, 30]. However, the present study shows a more pronounced relationship between lower level and greater severity of the disease. Referring to cases of infection, the areas with lower income were more affected. This has already been shown in terms of the confirmed cases in other Spanish cities such as Madrid [31] and Barcelona [7, 10] and other areas such as New York [32]. Several reasons have been pointed out to explain the greater infection rate among lower income areas: living in smaller houses that do not allow for social distancing [17]; difficulties of telework for less- qualified jobs [33]- and also along these lines, the eco- nomic component; having lost a source of income would prompt households to search for other options [7]-; and more challenges with the use of private transportation [34]. It has also been pointed out that the information on prevention measures has been received by the population in part based on their cultural level [35], which, in devel- oped countries tends to be associated with income level. One of the most novel aspects of this research is the joint study, in a single population, not only of cases but also of hospitalisations and deaths. The explanations described prior for greater contagion of COVID (crowd- ing, work instability, use of transport) are not useful in the latter cases. The greater rates of hospitalisation and death depend on other mechanisms. There are at least two possible explanations related to this relationship. On the one hand, it could be that these rates were a direct reflection of poorer access to health services of those with low income [36], especially primary care [37]. This could have delayed the early management of symp- toms and increased both the possibilities of hospitalisa- tions and of death. The problem of health access can be approached in two ways. One is “physical” in that greater problems contacting and attending health centres could be aggravated by the collateral impact of COVID-19 on the whole system. The COVID-19 crisis has changed the Spanish National Health System [38]. Operations were suspended, visits were delayed and accessing the system became more complicated. Second, there is also the issue of health culture or the health empowerment of citizens. Not everyone knows how to correctly interpret the signs of severity that should prompt them to seek health ser- vices nor how to provide informal home care. If to this we add the lack of knowledge of the disease, this could have been a notable barrier. During the crisis, it became clear that education should be improved in this sense. On the other hand, the greater impact on those with lower income could be explained, in addition to worse health access, by the life circumstances of these people. A lower socioeconomic level is related to worse health [39]. Also, pre-existing conditions prior to contract- ing COVID-19 have been shown to be related to a more severe development of the disease [40]. Thus, if the characteristics of the environment are rel- evant in contracting COVID, the access to health services and prior levels of health determine the level of affecta- tion. This could explain why inequalities have been espe- cially evident in hospitalisations and deaths. We also cannot rule out that this finding could go beyond the ill- nesses related to COVID-19. Between the beginning of the pandemic and July of 2021, Spain had excess mortal- ity of over 85,000 deaths [15]. We do not know how many are related to the collapse of the health system or whether this could have a greater impact on lower socioeconomic levels, but results such as these should inspire further investigation in this sense. What seems clear, according to this study, is that the pandemic has exacerbated exist- ing structural inequities, though not in a uniform way, over the past year and a half. In terms of study limitations, this is an ecological study, and the mechanisms underlying the associations between living in poor areas and incidence, hospitalisation and mortality during the COVID-19 pandemic can only be hypothesised [22]. Secondly, this study excluded insti- tutionalised people. The reason for this was that it was impossible to link these individuals with their socioeco- nomic level based on their households, because in most cases the geocoding corresponded to the centre where they were institutionalised. As a result, the risk of infor- mation bias was reduced but this may have also caused a selection bias. Furthermore, the data related to the first wave were excluded from the analysis, which reduced the number of cases to study. However, this constitutes more a strength than a limitation, because given the biases in reporting of COVID-19 cases during the first wave- men- tioned earlier- these data are unreliable and could com- promise the internal validity of the study. Thirdly, the AGI was taken from the most recent year available (2018), so we might have overestimated its values; this all the more applies to the census sections in which the AGI between 2015 and 2017 was used (0.7%). However, compared to studies from other European countries, this potential bias might be mitigated by the social measures implemented by the Spanish government in 2020, aimed at protect- ing individuals who became unemployed or partially employed. Fourthly, our results may be only generalisable to large urban areas from high-income countries which Page 9 of 10Fernández‑Martínez et al. BMC Public Health (2022) 22:2316 adopted non-pharmaceutical interventions similar to those adopted in Spain. Finally, although the incidence rates were standardised by sex and age, the proportional- ity between the specific rates per subgroup in each cen- sus section with respect to the rest could not be verified, which may have led to a reduction in the accuracy of the relative risk [41]. Thus, the point estimates yielded by the statistical models should be interpreted with caution. This study also has strengths. The most important is that in a single study, low socioeconomic status was linked not only to COVID-infection, but also to hospi- talisations and deaths. This had not been done in Spain until now. Moreover, our study used more than one urban area and smaller geographical scale than previ- ous studies [22, 31]. Conclusions Our results indicate the existence of income inequali- ties by geographical area in the incidence of cases, hos- pitalisations and deaths due to COVID-19, adjusted by age and sex. They suggest that a lower socioeconomic level is related not only to a greater risk of infection by COVID-19, but also greater related complications. Selective actions are needed to improve the population´s capacity to protect itself from infection and obtain conditions of equal access to health ser- vices. Such a need is not exclusive to COVID-19, as these results show inequalities that existed prior to the onset of the pandemic, which -if unaddressed- might persist and even become worse in the future. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s12889‑ 022‑ 14774‑6. Additional file 1: Supplementary File 1. Distribution of annual average gross income per household, by urban area. Supplementary File 2. Dis‑ tribution of the smoothed standardised incidence ratio (SIR) of infection in the second and third epidemic waves, by urban area. Supplementary File 3. Results of the spatial regression model, by time window and urban area. Acknowledgements We would like to thank all the professionals of the Surveillance Epidemiologi‑ cal System of Andalusia (SVEA) for their contribution. Authors’ contributions NFFM, RRM and LS developed the study. ART, NL and NFFM accessed the data. NFFM, RRM and DGB performed the analyses. All authors interpreted the findings and contributed. NFFM, RRM, ISL and LS to wrote the first draft and all authors contributed to the final submitted version. The author(s) read and approved the final manuscript. Funding This work was supported by the Carlos III Health Institute (ISCIII) under the project Fundación BBVA.DGVI 256/22 "COVID 19 Urban Atlas Spain". Availability of data and materials Data will be made available upon reasonable request by contacting the corre‑ sponding author. Andalusia COVID‑19 data are published up to the municipal level on the website (https:// www. junta deand alucia. es/ insti tutod eesta disti cayca rtogr afia/ salud/ datos Sanit arios. html). Data at census section level, due to its smaller reference population, is not delivered in this format. Declarations Ethics approval and consent to participate Protocol, data collection and study were approved by Dirección General de Salud Pública, Consejería de Salud y Consumo, Junta de Andalucía (Direc‑ torate General for Public Health. Regional Ministry of Health and Consumer Affairs. Andalusian Regional Government) Spain. The Institutional Review Board of the Directorate General of Public Health did not require informed consent due to its ecological, retrospective design using secondary data from the Epidemiological Surveillance System of Andalusia (SVEA). No further administrative permissions were required to access the raw data used in our study, which were anonymised prior to use.Authors confirm that all methods were performed in accordance with the relevant guidelines and regulations. Consent for publication Not applicable. Competing interests Luis Sordo is BMC Public Health associate editor. 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