The Mar Menor Lagoon and the incidence of childhood cancer in the Region of Murcia, Spain (2000-2018): An exploratory analysis

The Mar Menor Lagoon and the incidence of childhood cancer in the Region of Murcia, Spain (2000-2018): An exploratory analysis | 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 The Mar Menor Lagoon and the incidence of childhood cancer in the Region of Murcia, Spain (2000-2018): An exploratory analysis Ojeda-Sánchez Carlos, Ramis-Prieto Rebeca, Fernández-Avilés Gema, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4128975/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Jul, 2024 Read the published version in Discover Public Health → Version 1 posted 16 You are reading this latest preprint version Abstract The Mar Menor, Europe’s largest saltwater lagoon in the Mediterranean basin (Murcia, southeastern Spain), is a ecosystem facing severe pollution, anoxia events, and marine biodiversity loss. his exploratory study examines the association between residential proximity to the Mar Menor and childhood cancer incidence in Murcia from 2000 to 2018. In our case-control study of 594 cancer cases and 3,564 controls, matched by birth year and sex, participants were categorized by proximity to the lagoon (G1 ≥ 20km, G2 = 10-20km, G3 = 2-10km, G4 = ≤ 2km). Odds ratios (ORs) and 95% confidence intervals (95%CI) were adjusted for sociodemographic and environmental covariates. Analysis of distance as a continuous variable indicated a decrease in cancer risk with greater distance (OR = 0.992; 95% CI = 0.987–0.995), while as a categorical variable, it revealed a non-linear pattern. Specifically, the risk did not increase for those living within 2 km of the lagoon (OR = 0.87; 95% CI = 0.52–1.37), whereas increased risks were observed in the 2–20 km range within Campo de Cartagena (G2; OR = 1.32; 95% CI = 1.01–1.73 and G3; OR = 1.43; 95% CI = 1.00-2.02). This suggests modulating factors near the lagoon may counterbalance risks from broader environmental contamination. These findings highlight the complexity of environmental health dynamics and the importance of detailed research to guide public health strategies and urban planning for environmental and child health protection. childhood cancer environmental health leukemia blue spaces epidemiology Figures Figure 1 Figure 2 Figure 3 1. Introduction Blue spaces, encompassing natural water (rivers, lakes, and oceans) as well as urban aquatic (swimming pools and water parks) have garnered increasing attention in scientific research due to their impact on human health and well-being [ 1 ]. Green and blue spaces have been observed to have beneficial effects on both physical and mental health [ 2 ]. Specifically, there is emerging interest in whether exposure to blue spaces may be associated with a reduced risk of certain health outcomes [ 3 ], including childhood cancer incidence [ 4 ]. Over the last few years, the focus has been on the footprint that human beings can produce on the resources on which a large part of their economy is based and, therefore, on their survival. Among the most important resources for survival, one stands out above all, the water resource [ 5 ]. This resource has been affected over time by human-driven pressures from industrial development and agriculture. These activities have led to an increase in the levels of compounds such as nitrate, sulfides, and other elements, as well as chemical products, which have produced changes in water’s composition, consequently arising in the ecosystems they house (Schwarzenbach et al., 2010) [ 6 ]. An example of this activity is the case of the Mar Menor Lagoon in the Region of Murcia, Spain. It is the Spanish lagoon with the largest extension of salt water in the Mediterranean Sea [ 7 ]. This coastal lagoon is regarded as one of the most ecologically rich ecosystems in the Mediterranean Basin [ 8 ]. Additionally, it is among the most significant environmental sites in the Mediterranean area, where many economic and industrial activities meet [ 9 ]. Its unique weather conditions and abundant natural resources have attracted tourism, recreational, and fishing uses, without forgetting the relevance of farming to the local economy [ 10 ]. Nevertheless, during the last decades, it has been seriously affected by the practice of intensive irrigation and the high pressure of construction in the area. This has led to an increase in nitrate levels in its waters, causing a process of eutrophication, affecting both its quality and the biodiversity it hosts [ 11 , 12 ]. The situation, far from improving, has deteriorated [ 10 ]. The potential capacity for biological damage that this alteration can cause in the population near this lagoon is currently unknown. Children are among the most vulnerable to this phenomenon of contamination of its water. A growing body of evidence suggests that exposure to natural environments, such as urban green space, may have a beneficial role in the prevention or survival cancer, but the evidence is still inconclusive (Ojeda Sánchez et al., 2021; Porcherie et al., 2021) [ 13 , 14 ]. But it is still more limited evidence regarding the effects of exposure to blue spaces and health [ 4 ]. A child's development and growth stage is exposed to continuous changes during its process. Within this process are involved from genetic factors to environmental factors. Some of them have been described in the literature as risk factors for the appearance of oncological pathologies [ 15 ]. Among these pathologies, acute leukemia stands out, being the one with the highest incidence with a third of the tumors diagnosed in this stage of life, followed by lymphomas and tumors of the central nervous system [ 16 ]. Although childhood cancer is one of the leading cause of death in this population subset, to date, unfortunately, only approximately 10% of its etiology is known. Childhood cancer is closely linked to genetic alterations and prenatal exposures such as tobacco or alcohol consumption [ 17 , 18 ]. Nevertheless, in recent years, attempts have been made to explore whether certain environmental factors such as agricultural land, industrial facilities, or home exposure to traffic may be related to an increased incidence of cancer [ 19 – 21 ]. Additionally, it is unknown if the contamination of water spaces, as is the case of the Mar Menor, can be a source of increased incidence. Therefore, this study aims to explore the possible relationship between the distance of exposure to this blue space and childhood cancer in the Region of Murcia. 2. Materials and Methods 2.1 Study Location The Region of Murcia is a Mediterranean region located in the southeast of Spain (Fig. 1 ). It has a population of approximately 1.5 million inhabitants spread over 45 municipalities, of which Murcia, Cartagena, Lorca, and Molina de Segura comprise more than 55% of the total population. There are currently 298,468 children under the age of 18 in the territory. The Region of Murcia is the second region with the lowest average income in Spain. The Mar Menor is a coastal lagoon in Murcia (South-East Spain), adjacent to the Campo de Cartagena area, where intensive agricultural activity has taken place since the nineteen eighties. The total lagoon surface is 135 km 2 , covers a coastline of 73 km, and contains five volcanic islands [ 10 ]. Moreover, it is considered a shallow lagoon, with an average depth of 4.4 m being a maximum depth of around 7 m [ 10 ]. It is separated from the open sea by a 22-km-long sand bar (La Manga), although one natural inlet and two artificial channels allow some water renewal. The Campo de Cartagena occupies an area of 1609 km2, of which the majority has discharged their runoff into the Mar Menor. There is a regular flux of groundwater, feeding these watercourses, and it receives agricultural runoff, treated urban effluents, runoff from old mining areas, and brackish water effluents. The Mar Menor becomes a bucket where heavy metals and organic chemical contaminants settle. 2.2 Sample Selection A population-based case-control study on childhood leukemia in the Region of Murcia, Spain, has been designed. The cases of this study have been provided by the Pediatric Environmental Health Unit (PESHU) of the Virgen de la Arrixaca University Clinical Hospital in Murcia. The subjects of analysis were pediatric cancer cases diagnosed in the Region of Murcia (RM) between January 2000 and December 2018 by the MACAPEMUR (Environment and Pediatric Cancer in the Region of Murcia) project. MACAPEMUR is a project for the compilation of Pediatric Environmental History in newly diagnosed cancer patients since 1998 in this region. The project was approved by the ethics research committee of Clinical University Hospital Virgen de la Arrixaca [ 22 ]. The controls were extracted from the Birth Registry of the National Institute of Statistics (INE) [ 23 ]. Different stages were used for the geolocation process of the cases and controls. The georeferencing of the cases was carried out based on the address coordinates at the time of diagnosis. The georeferencing of the controls was carried out from the coordinates of the maternal address, provided by the National Institute of Statistics (INE) with a random error of 30 meters to preserve anonymity. The coordinates of the cases were initially found in the cartographic reference system (CRS): EPSG 4326. Subsequently, they were re-projected to CRS: 25830, the system in which the controls were originally projected. Then, a random sampling was performed, and controls were individually matched to cases by year of birth and sex in a 6:1 ratio. For the selection, sampling, and descriptive analysis processes, the QGIS and R® programs were used. 2.3 Measurement of exposure to the Mar Menor After the geolocation of the cases and the controls selected for this study, we extracted the spatial polygon representing the Mar Menor from the cartographic bases of the National Institute of Geography (NIG). Subsequently, we have calculated the minimum distance in kilometers for each of the subjects to said space, and we have categorized said distance into five groups: G1 (reference) = ≥ 20 km, G2 = 10–20 km, G3 = 2–10 km, and G4 = ≤ 2 km (Fig. 2 ). 2.4 Exposure to urban green and blue spaces As covariates of the environment of the subjects of this study, we have calculated the exposure to urban green and blue spaces. We have used the available databases of the Information System on Land Occupation in Spain (SIOSE). This national database, provided by the National Institute of Geography [ 24 ], divides the land into polygons of different sizes (0.5-2ha) and categorizes them through a system of cover levels in which the percentage of the floor of the said polygon that is covered by each specific cover. For this study, we decided to use the bases of 2005, 2011, and 2014. After extracting the green and blue urban spaces from these cartographic bases, a buffer of 500m distance from the subjects' homes was built. Then, we proceeded to dichotomize these variables (presence-absence of the land). Furthermore, we used the previously created exposure buffer and assigned the mean value of the normalized difference vegetation index (NDVI) to each subject. This index is based on the combination of spectral bands recorded by satellites whose function is to enhance the vegetation based on its spectral response and attenuate the details of other elements, such as the ground. It is mainly used to estimate the quantity, quality, and development of the vegetation and to observe the variations of the vegetation cover throughout the seasonal periods. The value range of this index is from − 1 to + 1. The positive value is the one that represents the greenest surface [ 25 ]. We searched for the Landsat TM images with the lowest percentage of clouds and the highest possible NDVI value (spring-summer) for the same years as the SIOSES databases used (2005, 2011, and 2014). These images were extracted from the National Aeronautics and Space Administration's Earth Observing System Data and Information System [ 26 ]. 2.5 Data Analysis Firstly, a descriptive analysis was carried out. This was divided into two parts: the first consists of a general descriptive analysis of the study subjects, and the second deals with the application of a statistical model of spatial point processes to evaluate the spatial distribution of the cases diagnosed each year and their relationship with the Mar Menor. Secondly, the odds ratio (OR) and its 95% confidence intervals (95% CI) associated with exposure to the Mar Menor were estimated to estimate the potential effect associated with exposure. To perform this, various unconditional multivariate logistic regression models were created where the variables sex, year of birth, exposure to urban green and blue spaces within a radius of 500m, as well as the mean value of NDVI in said space were included in all of them for fitting the measurement association. To explore how this natural space could be linked with childhood cancer incidence, the distance to the sea was treated in the two previously explained ways: as a continuous variable and a categorized variable. In all the models, G1 was taken as the reference exposure group. The statistical programs Microsoft Excel 2021®, R® version 4.1.1, STATA® version 16, and the geographic information system QGIS® version 3.18.2 were used. The tests conducted in this paper were found to be statistically significant if the p-value was less than 0.05. 3. Results 3.1 Descriptive Analysis The analysis included 594 cases and 3,564 controls. Table 1 shows the characteristics of the children in the study. The main type of tumor diagnosed among all cases was childhood leukemia, which represented almost a third of the cases (30.5%), closely followed by tumors of the central nervous system (23.4%). The average age of diagnosis of the cases was around five years. Regarding the distance to the Mar Menor of the cases, the average was around 41 km, observing that the vast majority of them, 75.9%, were at a distance greater than 20 km. In the initial sensitivity analysis focusing on children diagnosed with childhood leukemia, there were no statistically significant differences when comparing their characteristics with the main group of cases. Subsequently, in the second sensitivity analysis, we identified a third of the cases (33.83%) with the same address at birth and at the time of diagnosis. Their characteristics were not significantly different from those observed overall. Table 1 Characteristics of Cases, Controls, and Sensitivity Groups. Variables Control (n = 3564) Cases (n = 594) Leukemia (n = 181) p-valueª Same Address (n = 201) p-valueª Sex Male 1914(53.7%) 319 (53.7%) 100 (55.2%) 105 (52.2%) Female 1650(46.3%) 275 (46.3%) 81 (44.8%) 0.715* 96 (47.8%) 0.719* Age of Diagnostic media (DS) - 5.1 (4.0) 5.2 (3.7) 0.843* 4.7 (4) 0.190# Type of Tumor Leukemia - 181 (30.5%) - 50 (24.9%) CNS - 139 (23.4%) - 53 (26.4%) NBs - 62 (10.4%) - 27 (13.4%) lymphomas - 53 (8.9%) - 19 (9.5%) Sarcomas - 39 (6.6%) - 16 (8.0%) Renal - 33 (5.6%) - 7 (3.5%) Other - 92 (15.5%) - - 29 (14.4%) 0.262* Presence of urban green spaces 500m Yes 3439 (96.5%) 576 (97.0%) 173 (95.6%) 201 (100%) No 125 (3.5%) 18 (3.0%) 8 (4.4%) 0.363* 0 0.010* Presence of blue spaces 500m Yes 512 (14.4%) 76 (12.8%) 21 (11.6%) 22 (11.0%) No 3052 (85.6%) 518 (87.2%) 160 (88.4%) 0.671* 179 (89.0%) 0.491* NDVI 500m buffer, mediana (RIQ) -0.05 (0.13) -0.05 (0.15) -0.04 (0.15) 0.669¥ -0.06 (0.09) 0.012¥ Distance from mar Menor (km) 45.6 (24.6) 41.0 (25.0) 40.6 (26.9) 0.864# 40.2 (24.1) 0.678# Distance from mar Menor zones G1 2802(78.6%) 451 (75.9%) 134 (74.4%) 151 (75.1%) G2 397 (11.1%) 80 (13.5%) 24 (13.3%) 29 (14.4%) G3 192 (5.4%) 43 (7.3%) 14 (7.8%) 15 (7.5%) G4 155 (4.3%) 20 (3.4%) 8 (4.4%) 0.912* 6 (3.0%) 0.980* Note: CNS = Central Nervous System; NBs = Neuroblastomas; G1 ≥ 20kms; G2 zone = 10-20kms; G3 = 2–10 kms; G4 (≤ 2km). ª P-value from main group of cases compared to sensitivity group, # Student’s t-test, ¥ Kruskal-Wallis test, * Chi-square test. 3.2 Analysis of specific processes The analysis of a punctual pattern encompasses a series of techniques that allow us to study the distribution of a set of events. The interest in the use of these techniques arises in order to explore whether the diagnosed cases of any type of childhood tumor occurring in the Region of Murcia present an aggregation pattern or not. Figures 3 a and 3 b illustrate the spatial distribution of all cases diagnosed yearly in the Region of Murcia. The main foci of cases diagnosed throughout all the years of the study occurred mainly in the municipalities near the coast and, therefore, the Mar Menor. Among these areas with a higher concentration of cases, the areas of the municipalities of Murcia and Cartagena stand out. A migration from the northwest/central areas to the Campo de Cartagena/Mar Menor is observed. Additionally, it can be seen that the years with the highest intensity occur from 2006 onwards. 3.3 Modelling of Mar Menor's effect. The logistic regression model allows us to predict the probability of a subject belonging to one group or another (response variable) from predictor variables as well as being able to explore the association of one or more predictor variables with the response variable, adjusting for the rest of the variables that are included to create the regression model. In our study, we focused on exploring how the distance variable could impact, as a continuous variable or categorized variable (taking G1 as the reference group) on the response variable (being case or not). The logistic regression model built was as follows: (𝑌=𝑘 |𝑋=𝑥) = 11+𝑒−(𝛽0+𝛽1𝑆𝑒𝑥𝑜+𝛽2( Birth year )+𝛽3 𝐷𝑖𝑠𝑡𝑎𝑛𝑐e +𝛽4𝑁𝐷𝑉𝐼+𝛽5( 𝑃𝑟𝑒𝑠𝑒𝑛ce of green urban )+𝛽6( Presence of Blue Space )) Tables 2 and 3 show the ORs obtained for all the cases, the cases diagnosed with childhood leukemia, and the cases whose address was the same at birth and at the time of diagnosis of his oncological pathology. The estimates obtained in the models worked with the distance variable continuously (Table 2 ) show that, in any of the three groups of the analysis, the increase of one kilometer with respect to the Mar Menor leads to a decrease in the incidence of childhood tumors, being for cases of childhood leukemia this association greater (OR = 0.990; CI 95%=0.983–0.997). Regarding the comparative models (Table 3 ), we can observe how the incidence of childhood cancer is concentrated in the areas between 10 and 20 km (G2; OR = 1.32; 95% CI = 1.01–1.73) and 2 to 10 km away from the Mar Menor (G3; OR = 1.43; 95% CI = 1.00-2.02). Table 2 Analysis of the distance to the Mar Menor as a continuous variable. Simple models and models adjusted for sex, date of birth and covariates of exposure to urban green and blue spaces in a buffer of 500 meters for the group of childhood tumors, childhood leukemias and subjects with unchanged address. Distance from Mar Menor General Infant Leukemia Same address Simple model OR (IC 95%) 0.992 (0.989–0.995) 0.990 (0.984–0.996) 0.991 (0.985–0.997) Adjusted model OR (IC 95%) 0.992 (0.987–0.995) 0.990 (0.983–0.997) 0.989 (0.983–0.996) Table 3 Analysis of the distance to the Mar Menor as a categorized variable. Simple models and models adjusted for sex, date of birth and covariates of exposure to urban green and blue spaces in a buffer of 500 meters for the group of childhood tumors, childhood leukemias and subjects with unchanged address. Distance from mar Menor All cases Infant Leukemia Same address Simple Model OR (IC 95%) G1 (reference)* G2 (10-20kms) 1.22 (0.95–1.59) 1.26 (0.79–1.94) 1.33 (0.86–1.97) G3 (2-10kms) 1.35 (0.95–1.89) 1.42 (0.77–2.44) 1.41 (0.78–2.36) G4 (≤ 2km) 0.78 (0.47–1.23) 1.04 (0.46–2.04) 0.70 (0.27–1.47) Adjusted Model OR (IC 95%) G1 (reference)* G2 (10-20kms) 1.32 (1.01–1.73) * 1.29 (0.80–2.03) 1.35 (0.86–2.04) G3 (2-10kms) 1.43 (1.00-2.02) * 1.52 (0.81–2.65) 1.43 (0.79–2.45) G4 (≤ 2km) 0.87 (0.52–1.37) 1.15 (0.50–2.27) 0.70 (0.27–1.48) *G1 = reference group (subjects at a distance greater than or equal to 20 km from the Mar Menor); 4. Discussion In this study, we cautiously initiate a mapping of the influence of Mar Menor pollution on childhood cancer incidence in the Murcia region. Our findings tentatively indicate an association between childhood cancer and proximity to this particular blue space, requiring a careful interpretation due to the pioneering nature of the research area. The limited existing literature on the health effects of blue spaces [ 3 , 4 ], is expanded upon by our methodology, which includes analysis of distance as both a continuous and a categorical variable. This approach revealed a decrease in cancer incidence with increased distance from the Mar Menor. However, the relationship is not linear when distance is categorized; notably, an increased risk is not apparent in the area closest to the sea (less than 2 km). Particularly, areas G2 and G3, within Campo de Cartagena, showed higher incidence rates, reflecting the intense agricultural transformation and ecological costs of the region, a stark representation of environmental injustice. Yet, paradoxically, near-coastal areas (G4) indicate possible protective environmental factors against pollution. This complexity, intertwined with socio-economic disparities, underlines the multifaceted nature of health determinants [ 27 , 28 ]. Murcia, Cartagena, La Manga, and San Javier, among others, are very close to the Mar Menor. These municipalities have grown in population in recent decades due to strong investment in construction and especially agriculture in the area. While the crude incidence rate across that entire period 1998–2020 in the Region of Murcia was 149.6 per 1 million, there was an increase over that time in the incidence. The areas with a higher standardized incidence ratio have shifted from the northwest (1998–2003) to the southeast (2016–2020) region [ 29 ]. Urbanization has been linked to increased risks of certain childhood cancers [ 30 ]. Our residual analysis from fitted models suggests a spatial distribution of cases that merits a deeper investigation into urbanization's role (Appendix A). Future research should include a detailed analysis of these spatial trends, taking into account that current studies have yet to fully explore all potential factors. The direct and indirect consequences of Mar Menor`s pollution are critical, with areas G2 and G3 within Campo de Cartagena, characterized by intense agricultural industrialization, and presenting the highest childhood cancer rates. This region's ecological decline due to intensive agriculture has resulted in significant environmental costs, contributing to poverty and social exclusion, the highest being in Campo de Cartagena/Mar Menor [ 27 ]. This environmental degradation has exacerbated flooding and pollution in Mar Menor, leading to biodiversity loss and anoxia [ 28 , 31 ], and illustrating a cycle of poverty-induced environmental damage [ 32 ], highlighting a clear case of environmental injustice, Legislative responses to this injustice include the Mar Menor laws, targeting ecological restoration in the affected areas [ 28 ]. Community advocacy, notably from children, has been instrumental in these changes, culminating in Law 19/2022, which declares Mar Menor a 'legal person'—a landmark for European environmental law [ 33 ], aligning with international trends in granting legal rights to natural entities, such as in Ecuador, Colombia, India, and New Zealand's recognition of the Whanganui River. For decades, the Mar Menor Lagoon has been significantly impacted by anthropogenic activities, with its current compromised state being a cumulative consequence of various mining, intensive agriculture, and industrial processes. These activities have precipitated a fundamental shift in the lagoon's ecological balance [ 9 ]. Research has increasingly demonstrated the connection between water pollution and the rise of oncological diseases, implicating the toxicological effects of waterborne contaminants on human genetics [ 34 – 36 ]. In addition, some of its possible indirect impacts on health could be related to recreational activities in its surroundings, leading to greater sun exposure and an increased risk of skin cancer [ 37 ]. Our study sets a precedent for other blue spaces facing similar challenges, protecting and promoting both Nature and Child rights. Significantly, the Act on Guardians of Mar Menor must recognize and empower the vital role of children, whose natural and innate, and often passionate, interest that children show in preserving the environment makes them a dynamic and powerful force in protecting the Mar Menor. On the other hand, blue spaces like the Mar Menor could potentially mitigate the effects of pollution in terrestrial areas, serving as a protective component [ 38 ]. There is growing evidence that spending time in or around water bodies or ‘blue spaces’ can lead to improved human health [ 39 – 42 ]. In particular, the sea breeze and marine environment have been shown to enhance air quality by dispersing pollutants, thereby reducing exposure for coastal populations [ 38 ]. Given these potential benefits, safeguarding blue spaces like the Mar Menor is essential for global health initiatives, particularly when aiming to reduce the impacts of pollution on children, one of the most vulnerable groups. Regarding childhood cancer, there hasn’t been research linking it to water pollution. To adequately determine the presence of toxic metabolites or not in children, exhaustive monitoring of them would be necessary, which entails a high cost. However, other environmental factors have been related to an increase in the incidence of childhood cancer, such as exposure to pesticides, industries, or exposure to traffic. All of them without being able to analytically objectify toxic compounds in organisms or even clarify a plausible etiopathogenic mechanism [ 19 , 21 , 43 ]. Likewise, other factors, such as those related to the environment and the parents' habits, also play a fundamental role in the appearance of childhood cancer during the initial phases of children's development. Among them, we can highlight how the consumption of drugs, tobacco, or parental exposure to industries or tobacco during pregnancy has been related to the appearance of childhood cancer [ 44 – 47 ]. To date, these exposures are more complex to measure because a national collection system for children's habits and immediate environment has not been developed. However, this collection of such specific information has begun to be developed by the Pediatric Environmental Health Units [ 48 ]. For future studies, this could have a very important value when investigating other environmental factors possibly related to childhood cancer. However, environmental factors have recently been studied and could reduce childhood cancer incidence, particularly in urban green spaces and blue spaces. After having developed a methodology that has allowed us to explore these spaces, we decided to apply it in this study [ 4 , 13 ]. To date, there is no consensus on the possible optimal distances to measure the effects of these spaces [ 49 , 50 ], so, based on our previous results, we decided to use an exposure buffer of 500 meters. Regarding the determined sensitivity groups, cases diagnosed with childhood leukemia, and cases whose address was the same at birth and diagnosed time, their characteristics and the results obtained from the logistic regression models do not present significant differences with the general group of cases. This could be explained by these subgroups' great weight concerning the general set of cases. However, for future studies, it would be interesting to break down the characteristics of the rest of the tumor types and/or determine other possible sensitivity groups, such as those children whose age at the time of diagnosis is less than 12 months. 5. Conclusion To the best of our understanding, this investigation represents the inaugural effort to elucidate the connection between the Mar Menor Lagoon, a significant blue space, and the incidence of childhood cancer. Our findings preliminarily suggest a relationship, indicated by a lower incidence of cancer with increasing distance from this natural habitat. Interestingly, blue spaces might also serve as buffers to the adverse health outcomes associated with environmental pollutants in nearby agricultural and urban coastal areas. However, due to substantial limitations—such as the absence of individual-level subject data and the exclusion of various environmental variables from our models—our conclusions must be drawn with prudence. Further research is essential to deepen our comprehension of these associations. The insights gained hold the potential to inform urban and public health planning substantially, facilitating the development of strategies that protect both the environment and the welfare of children. Declarations Ethics approval and consent to participate The MACAPEMUR (Environment and Pediatric Cancer in the RM) project was approvals from the ethics research committee of Clinical University Hospital Virgen de la Arrixaca. Informed consent forms signed by all parents and children over 12 were collected. Availability of data and materials The datasets generated during and/or analyzed in the current study are not publicly available due to the fact that the geographical coordinates of the study subjects are considered private information. These coordinates are subject to privacy restrictions. Therefore, it is not possible to publicly share the data containing this confidential information. However, the data are available from the corresponding author upon reasonable request. Authors’ Contributions Conceptualization: COS and JAOG; Data curation: COS; Formal analysis: COS; Funding acquisition: JAOG and RRP; Investigation: COS; Methodology: COS, JAOG, RRP and GFA; Project administration: RRP and GFA; Resources: JAOG and RRP; Software: COS; Supervision: RRP and GFA; Validation: RRP, JAOG and GFA; Visualization: COS; Roles/Writing - original draft: COS and JAOG; Writing - review & editing: JAOG, GFA, RRP, EOP. Competing Interests The authors declare that they have no competing interests. Funding This research was supported by the Environmental Health Profile for Children in the Murcia Region Project in the Foundation for Formation and Research (FFIS) funded by Sociedad Pediatria Sureste of Spain (SPSE.ES) (FFIS-DF-2022-36) References White MP, Elliott LR, Gascon M, Roberts B, Fleming LE. 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Pobreza y exclusion social en la Región de Murcia, 2019. URL https://eapnmurcia.org/datos-pobreza/ (accessed 31 jan 2024). López-Ballesteros A, Trolle D, Srinivasan R, Senent-Aparicio J. Assessing the effectiveness of potential best management practices for science-informed decision support at the watershed scale: The case of the Mar Menor coastal lagoon, Spain. Sci Total Environ. 2023; 859, 160144. https://doi.org/10.1016/j.scitotenv.2022.160144 Cabrera-Rivera LT, Sweetser B, Fuster-Soler JL, Ramis R, López-Hernández FA, Pérez-Martínez A, Ortega-García JA. Looking Towards 2030: Strengthening the Environmental Health in Childhood-Adolescent Cancer Survivor Programs. Int J Environ Res Public Health. 2022;20:443. doi: 10.3390/ijerph20010443. Malagoli C, Malavolti M, Costanzini S, Fabbri S, Tezzi S, Palazzi G, Arcolin E, Vinceti M. Increased incidence of childhood leukemia in urban areas: a population-based case-control study. Epidemiol Prev. 2015; 39, 102–107. Martínez-Gómez C, Valdehita A, Vethaak AD, Navas JM, León VM. Toxicity characterization of surface sediments from a Mediterranean coastal lagoon. Chemosphere. 2020; 253:126710. https://doi.org/10.1016/j.chemosphere.2020.126710 McGranahan G, Songsore J, Kjellén M. Sustainability, poverty and urban environmental transitions, in: The Earthscan Reader in Sustainable Cities. Routledge, 2021; 107–133. Krämer L. Rights of Nature in Europe: The Spanish Lagoon Mar Menor Becomes a Legal Person. J. Eur. Environ. Plan. Law. 2023; 20, 5–23. https://doi.org/10.1163/18760104-20010003 Cantor KP. Drinking water and cancer. Cancer Causes & Control 1997; 8, 292–308. https://doi.org/10.1023/A:1018444902486 Castro-Jiménez J, Berrojalbiz N, Wollgast J, Dachs J. Polycyclic aromatic hydrocarbons (PAHs) in the Mediterranean Sea: Atmospheric occurrence, deposition and decoupling with settling fluxes in the water column. Environmental Pollution. 2012; 166, 40–47. https://doi.org/10.1016/j.envpol.2012.03.003 Gruber ES, Stadlbauer V, Pichler V, Resch-Fauster K, Todorovic A, Meisel TC, Trawoeger S, Hollóczki O, Turner SD, Wadsak W, Vethaak AD, Kenner L. To Waste or Not to Waste: Questioning Potential Health Risks of Micro- and Nanoplastics with a Focus on Their Ingestion and Potential Carcinogenicity. Expo Health. 2023;15:33-51. doi: https://doi.org/10.1007/s12403-022-00470-8 Moehrle M. Outdoor sports and skin cancer. Clin Dermatol. 2008;26:12-5. https://doi.org/10.1016/j.clindermatol.2007.10.001 Blackburn S, Pelling M, Marques C. Megacities and the Coast: Global Context and Scope for Transformation, in: Coasts and Estuaries. 2019; Elsevier, pp. 661–669. https://doi.org/10.1016/B978-0-12-814003-1.00038-1 Díaz-Martínez F, Sánchez-Sauco MF, Cabrera-Rivera LT, Sánchez CO, Hidalgo-Albadalejo MD, Claudio L, Ortega-García JA. Systematic Review: Neurodevelopmental Benefits of Active/Passive School Exposure to Green and/or Blue Spaces in Children and Adolescents. IJERPH. 2023: 20, 3958. https://doi.org/10.3390/ijerph20053958 Garrett JK, White MP, Huang J, Ng S, Hui Z, Leung C, Tse LA, Fung F, Elliott LR, Depledge MH, Wong MCS. Urban blue space and health and wellbeing in Hong Kong: Results from a survey of older adults. Health & Place. 2019; 55, 100–110. https://doi.org/10.1016/j.healthplace.2018.11.003 Gascon M, Triguero-Mas M, Martínez D, Dadvand P, Forns J, Plasència A, Nieuwenhuijsen MJ. Mental health benefits of long-term exposure to residential green and blue spaces: a systematic review. Int J Environ Res Public Health. 2015; 12(4):4354-79. doi: https://doi.org/10.3390/ijerph120404354 Georgiou M, Morison G, Smith N, Tieges Z, Chastin S. Mechanisms of Impact of Blue Spaces on Human Health: A Systematic Literature Review and Meta-Analysis. Int J Environ Res Public Health. 2021;18(5):2486. doi: https://doi.org/10.3390/ijerph18052486 Valbuena-Garcia AM, Rodriguez-Villamizar LA, Uribe-Pérez CJ, Moreno-Corzo FE, Ortiz-Martinez RG. A spatial analysis of childhood cancer and industrial air pollution in a metropolitan area of Colombia. Pediatr Blood Cancer. 2020;67(9):e28353. doi:https://doi.org/10.1002/pbc.28353 Auger N, Goudie C, Low N, Healy-Profitós J, Lo E, Luu TM. Maternal use of illicit drugs, tobacco or alcohol and the risk of childhood cancer before 6 years of age. Drug and Alcohol Dependence. 2019; 200, 133–138. https://doi.org/10.1016/j.drugalcdep.2019.03.008 Lavigne É, Bélair MA, Do MT, Stieb DM, Hystad P, van Donkelaar A, Martin RV, Crouse DL, Crighton E, Chen H, Brook JR, Burnett RT, Weichenthal S, Villeneuve PJ, To T, Cakmak S, Johnson M, Yasseen AS 3rd, Johnson KC, Ofner M, Xie L, Walker M. Maternal exposure to ambient air pollution and risk of early childhood cancers: A population-based study in Ontario, Canada. Environ Int. 2017; 100, 139–147. https://doi.org/10.1016/j.envint.2017.01.004 Liu R, Zhang L, McHale CM, Hammond SK. Paternal Smoking and Risk of Childhood Acute Lymphoblastic Leukemia: Systematic Review and Meta-Analysis. Journal of Oncology. 2011; 2011, 1–16. https://doi.org/10.1155/2011/854584 Ma X, Buffler PA, Gunier RB, Dahl G, Smith MT, Reinier K, Reynolds P. Critical windows of exposure to household pesticides and risk of childhood leukemia. Environ Health Perspect. 2002;110(9):955-60. https://doi.org/10.1289/ehp.02110955 Cárceles-Álvarez A, Ortega-García JA, López-Hernández FA, Orozco-Llamas M, Espinosa-López B, Tobarra-Sánchez E, Alvarez L. Spatial clustering of childhood leukaemia with the integration of the Paediatric Environmental History. Environ Res. 2017;156:605-612. doi: https://doi.org/10.1016/j.envres.2017.04.019 Dzhambov AM, Browning MHEM, Markevych I, Hartig T, Lercher P. Analytical approaches to testing pathways linking greenspace to health: A scoping review of the empirical literature. Environ Res. 2020;186:109613. https://doi.org/10.1016/j.envres.2020.109613 Markevych I, Schoierer J, Hartig T, Chudnovsky A, Hystad P, Dzhambov AM, de Vries S, Triguero-Mas M, Brauer M, et al.Exploring pathways linking greenspace to health: Theoretical and methodological guidance. Environmental Research. 2017; 158, 301–317. https://doi.org/10.1016/j.envres.2017.06.028 Additional Declarations No competing interests reported. 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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-4128975","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288281219,"identity":"9132fa04-8c5d-4a8c-9260-664c159909fe","order_by":0,"name":"Ojeda-Sánchez Carlos","email":"","orcid":"","institution":"Hospital Universitario de Guadalajara","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ojeda-Sánchez","middleName":"","lastName":"Carlos","suffix":""},{"id":288281220,"identity":"7d63ee59-4255-4289-93da-04aa5dec03f7","order_by":1,"name":"Ramis-Prieto Rebeca","email":"","orcid":"","institution":"Instituto de Salud Carlos III","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ramis-Prieto","middleName":"","lastName":"Rebeca","suffix":""},{"id":288281221,"identity":"bb8943f2-68af-478e-b6ce-42b017a7b4e4","order_by":2,"name":"Fernández-Avilés Gema","email":"","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fernández-Avilés","middleName":"","lastName":"Gema","suffix":""},{"id":288281222,"identity":"4f0b8b44-acaf-43f2-884d-503003fc8a95","order_by":3,"name":"Díaz-Martínez Francisco","email":"","orcid":"","institution":"Instituto Murciano de Investigación Biosanitaria","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Díaz-Martínez","middleName":"","lastName":"Francisco","suffix":""},{"id":288281223,"identity":"c2dbbd18-27a9-4808-b815-6e04fd96b19b","order_by":4,"name":"Orenes-Piñero Esteban","email":"","orcid":"","institution":"Instituto Murciano de Investigación Biosanitaria","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Orenes-Piñero","middleName":"","lastName":"Esteban","suffix":""},{"id":288281224,"identity":"03f0e7d2-8aa0-45b0-abd6-b8b5e60b134c","order_by":5,"name":"Ortega-García Juan A","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYPACGwYGCeJVM4OINNK1HCZBC7/0+YMPv9Scz+OXbn8mwVBxz66BkBbJvmRmY5ljt4sl55wxk2A4U5xMUIvBGWY2aQm224kbbuSwSTC2JSQTdBhQC/tviX/nEvffSH9GtBY2xo9tBxI3SCSYgbTYEdQi2cNsLM3Yl1wscSPH2CLhTEICQS38PIwPP/74ZpfHPyP94Y0PFQn2BLWAADMPAwPEcCCZ2ECMFsYfMC1AQJwto2AUjIJRMKIAAA/COWdomKX1AAAAAElFTkSuQmCC","orcid":"","institution":"University of Murcia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ortega-García","middleName":"Juan","lastName":"A","suffix":""}],"badges":[],"createdAt":"2024-03-19 09:20:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4128975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4128975/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12982-024-00157-2","type":"published","date":"2024-07-26T16:16:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54317956,"identity":"ea91dfdf-0995-4616-8038-86ab8f425edb","added_by":"auto","created_at":"2024-04-08 18:28:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":359075,"visible":true,"origin":"","legend":"\u003cp\u003eTopographic map of the area selected for the study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4128975/v1/b3c7f3c0a01692270aeed820.png"},{"id":54317958,"identity":"8fa9143e-8f53-43c4-86dd-e566d5a1d974","added_by":"auto","created_at":"2024-04-08 18:28:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":656606,"visible":true,"origin":"","legend":"\u003cp\u003eExposure zones were created around the perimeter of the Mar Menor.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4128975/v1/77802104fcc6d691c29b6cb7.png"},{"id":54317960,"identity":"6f443d6b-1fec-4225-978e-bf8eb8ed5091","added_by":"auto","created_at":"2024-04-08 18:28:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4057327,"visible":true,"origin":"","legend":"\u003cp\u003ea. Estimation of the intensity of childhood cancer cases diagnosed in the Region of Murcia, years 2000-2011.\u003c/p\u003e\n\u003cp\u003eb. Estimation of the intensity of childhood cancer cases diagnosed in the Region of Murcia, years 2012-2018.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4128975/v1/014f6cf3463862d069fc05ef.png"},{"id":61596707,"identity":"0fcc6ab5-bd57-4549-a2bf-7e2ee46ad724","added_by":"auto","created_at":"2024-08-01 17:29:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8995264,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4128975/v1/3d173a20-d519-4775-bf61-ed144518e783.pdf"},{"id":54317959,"identity":"9165133c-33d4-4614-87d5-52d8613edda4","added_by":"auto","created_at":"2024-04-08 18:28:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":88680,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-4128975/v1/bc34907644cd5bdb7f5065ea.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Mar Menor Lagoon and the incidence of childhood cancer in the Region of Murcia, Spain (2000-2018): An exploratory analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBlue spaces, encompassing natural water (rivers, lakes, and oceans) as well as urban aquatic (swimming pools and water parks) have garnered increasing attention in scientific research due to their impact on human health and well-being [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Green and blue spaces have been observed to have beneficial effects on both physical and mental health [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Specifically, there is emerging interest in whether exposure to blue spaces may be associated with a reduced risk of certain health outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], including childhood cancer incidence [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver the last few years, the focus has been on the footprint that human beings can produce on the resources on which a large part of their economy is based and, therefore, on their survival. Among the most important resources for survival, one stands out above all, the water resource [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This resource has been affected over time by human-driven pressures from industrial development and agriculture. These activities have led to an increase in the levels of compounds such as nitrate, sulfides, and other elements, as well as chemical products, which have produced changes in water\u0026rsquo;s composition, consequently arising in the ecosystems they house (Schwarzenbach et al., 2010) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn example of this activity is the case of the Mar Menor Lagoon in the Region of Murcia, Spain. It is the Spanish lagoon with the largest extension of salt water in the Mediterranean Sea [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This coastal lagoon is regarded as one of the most ecologically rich ecosystems in the Mediterranean Basin [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, it is among the most significant environmental sites in the Mediterranean area, where many economic and industrial activities meet [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Its unique weather conditions and abundant natural resources have attracted tourism, recreational, and fishing uses, without forgetting the relevance of farming to the local economy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, during the last decades, it has been seriously affected by the practice of intensive irrigation and the high pressure of construction in the area. This has led to an increase in nitrate levels in its waters, causing a process of eutrophication, affecting both its quality and the biodiversity it hosts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The situation, far from improving, has deteriorated [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The potential capacity for biological damage that this alteration can cause in the population near this lagoon is currently unknown. Children are among the most vulnerable to this phenomenon of contamination of its water.\u003c/p\u003e \u003cp\u003eA growing body of evidence suggests that exposure to natural environments, such as urban green space, may have a beneficial role in the prevention or survival cancer, but the evidence is still inconclusive (Ojeda S\u0026aacute;nchez et al., 2021; Porcherie et al., 2021) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. But it is still more limited evidence regarding the effects of exposure to blue spaces and health [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA child's development and growth stage is exposed to continuous changes during its process. Within this process are involved from genetic factors to environmental factors. Some of them have been described in the literature as risk factors for the appearance of oncological pathologies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Among these pathologies, acute leukemia stands out, being the one with the highest incidence with a third of the tumors diagnosed in this stage of life, followed by lymphomas and tumors of the central nervous system [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough childhood cancer is one of the leading cause of death in this population subset, to date, unfortunately, only approximately 10% of its etiology is known. Childhood cancer is closely linked to genetic alterations and prenatal exposures such as tobacco or alcohol consumption [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Nevertheless, in recent years, attempts have been made to explore whether certain environmental factors such as agricultural land, industrial facilities, or home exposure to traffic may be related to an increased incidence of cancer [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, it is unknown if the contamination of water spaces, as is the case of the Mar Menor, can be a source of increased incidence. Therefore, this study aims to explore the possible relationship between the distance of exposure to this blue space and childhood cancer in the Region of Murcia.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Location\u003c/h2\u003e \u003cp\u003eThe Region of Murcia is a Mediterranean region located in the southeast of Spain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt has a population of approximately 1.5\u0026nbsp;million inhabitants spread over 45 municipalities, of which Murcia, Cartagena, Lorca, and Molina de Segura comprise more than 55% of the total population. There are currently 298,468 children under the age of 18 in the territory. The Region of Murcia is the second region with the lowest average income in Spain. The Mar Menor is a coastal lagoon in Murcia (South-East Spain), adjacent to the Campo de Cartagena area, where intensive agricultural activity has taken place since the nineteen eighties. The total lagoon surface is 135 km\u003csup\u003e2\u003c/sup\u003e, covers a coastline of 73 km, and contains five volcanic islands [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, it is considered a shallow lagoon, with an average depth of 4.4 m being a maximum depth of around 7 m [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is separated from the open sea by a 22-km-long sand bar (La Manga), although one natural inlet and two artificial channels allow some water renewal. The Campo de Cartagena occupies an area of 1609 km2, of which the majority has discharged their runoff into the Mar Menor. There is a regular flux of groundwater, feeding these watercourses, and it receives agricultural runoff, treated urban effluents, runoff from old mining areas, and brackish water effluents. The Mar Menor becomes a bucket where heavy metals and organic chemical contaminants settle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample Selection\u003c/h2\u003e \u003cp\u003eA population-based case-control study on childhood leukemia in the Region of Murcia, Spain, has been designed. The cases of this study have been provided by the Pediatric Environmental Health Unit (PESHU) of the Virgen de la Arrixaca University Clinical Hospital in Murcia. The subjects of analysis were pediatric cancer cases diagnosed in the Region of Murcia (RM) between January 2000 and December 2018 by the MACAPEMUR (Environment and Pediatric Cancer in the Region of Murcia) project. MACAPEMUR is a project for the compilation of Pediatric Environmental History in newly diagnosed cancer patients since 1998 in this region. The project was approved by the ethics research committee of Clinical University Hospital Virgen de la Arrixaca [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The controls were extracted from the Birth Registry of the National Institute of Statistics (INE) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifferent stages were used for the geolocation process of the cases and controls. The georeferencing of the cases was carried out based on the address coordinates at the time of diagnosis. The georeferencing of the controls was carried out from the coordinates of the maternal address, provided by the National Institute of Statistics (INE) with a random error of 30 meters to preserve anonymity. The coordinates of the cases were initially found in the cartographic reference system (CRS): EPSG 4326. Subsequently, they were re-projected to CRS: 25830, the system in which the controls were originally projected. Then, a random sampling was performed, and controls were individually matched to cases by year of birth and sex in a 6:1 ratio. For the selection, sampling, and descriptive analysis processes, the QGIS and R\u0026reg; programs were used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measurement of exposure to the Mar Menor\u003c/h2\u003e \u003cp\u003eAfter the geolocation of the cases and the controls selected for this study, we extracted the spatial polygon representing the Mar Menor from the cartographic bases of the National Institute of Geography (NIG). Subsequently, we have calculated the minimum distance in kilometers for each of the subjects to said space, and we have categorized said distance into five groups: G1 (reference)\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;20 km, G2\u0026thinsp;=\u0026thinsp;10\u0026ndash;20 km, G3\u0026thinsp;=\u0026thinsp;2\u0026ndash;10 km, and G4\u0026thinsp;=\u0026thinsp;\u0026le;\u0026thinsp;2 km (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Exposure to urban green and blue spaces\u003c/h2\u003e \u003cp\u003eAs covariates of the environment of the subjects of this study, we have calculated the exposure to urban green and blue spaces. We have used the available databases of the Information System on Land Occupation in Spain (SIOSE). This national database, provided by the National Institute of Geography [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], divides the land into polygons of different sizes (0.5-2ha) and categorizes them through a system of cover levels in which the percentage of the floor of the said polygon that is covered by each specific cover. For this study, we decided to use the bases of 2005, 2011, and 2014.\u003c/p\u003e \u003cp\u003eAfter extracting the green and blue urban spaces from these cartographic bases, a buffer of 500m distance from the subjects' homes was built. Then, we proceeded to dichotomize these variables (presence-absence of the land).\u003c/p\u003e \u003cp\u003eFurthermore, we used the previously created exposure buffer and assigned the mean value of the normalized difference vegetation index (NDVI) to each subject. This index is based on the combination of spectral bands recorded by satellites whose function is to enhance the vegetation based on its spectral response and attenuate the details of other elements, such as the ground. It is mainly used to estimate the quantity, quality, and development of the vegetation and to observe the variations of the vegetation cover throughout the seasonal periods. The value range of this index is from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1. The positive value is the one that represents the greenest surface [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. We searched for the Landsat TM images with the lowest percentage of clouds and the highest possible NDVI value (spring-summer) for the same years as the SIOSES databases used (2005, 2011, and 2014). These images were extracted from the National Aeronautics and Space Administration's Earth Observing System Data and Information System [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Analysis\u003c/h2\u003e \u003cp\u003eFirstly, a descriptive analysis was carried out. This was divided into two parts: the first consists of a general descriptive analysis of the study subjects, and the second deals with the application of a statistical model of spatial point processes to evaluate the spatial distribution of the cases diagnosed each year and their relationship with the Mar Menor.\u003c/p\u003e \u003cp\u003eSecondly, the odds ratio (OR) and its 95% confidence intervals (95% CI) associated with exposure to the Mar Menor were estimated to estimate the potential effect associated with exposure. To perform this, various unconditional multivariate logistic regression models were created where the variables sex, year of birth, exposure to urban green and blue spaces within a radius of 500m, as well as the mean value of NDVI in said space were included in all of them for fitting the measurement association. To explore how this natural space could be linked with childhood cancer incidence, the distance to the sea was treated in the two previously explained ways: as a continuous variable and a categorized variable. In all the models, G1 was taken as the reference exposure group.\u003c/p\u003e \u003cp\u003eThe statistical programs Microsoft Excel 2021\u0026reg;, R\u0026reg; version 4.1.1, STATA\u0026reg; version 16, and the geographic information system QGIS\u0026reg; version 3.18.2 were used. The tests conducted in this paper were found to be statistically significant if the p-value was less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive Analysis\u003c/h2\u003e \u003cp\u003eThe analysis included 594 cases and 3,564 controls. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the characteristics of the children in the study. The main type of tumor diagnosed among all cases was childhood leukemia, which represented almost a third of the cases (30.5%), closely followed by tumors of the central nervous system (23.4%). The average age of diagnosis of the cases was around five years. Regarding the distance to the Mar Menor of the cases, the average was around 41 km, observing that the vast majority of them, 75.9%, were at a distance greater than 20 km.\u003c/p\u003e \u003cp\u003eIn the initial sensitivity analysis focusing on children diagnosed with childhood leukemia, there were no statistically significant differences when comparing their characteristics with the main group of cases.\u003c/p\u003e \u003cp\u003eSubsequently, in the second sensitivity analysis, we identified a third of the cases (33.83%) with the same address at birth and at the time of diagnosis. Their characteristics were not significantly different from those observed overall.\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\u003eCharacteristics of Cases, Controls, and Sensitivity Groups.\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3564)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;594)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeukemia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;181)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-value\u0026ordf;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSame Address\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;201)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep-value\u0026ordf;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1914(53.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319 (53.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e105 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1650(46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e275 (46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.715*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.719*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge of Diagnostic\u003c/em\u003e\u003c/p\u003e \u003cp\u003emedia (DS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.843*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.7 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.190#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eType of Tumor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181 (30.5%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (24.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (23.4%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNBs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (10.4%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27 (13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elymphomas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (8.9%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcomas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (6.6%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (5.6%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (15.5%)\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\u003e29 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.262*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ePresence of urban green spaces 500m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3439 (96.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e576 (97.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173 (95.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e201 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.363*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ePresence of blue spaces 500m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e512 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3052 (85.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e518 (87.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160 (88.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.671*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e179 (89.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.491*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNDVI\u003c/em\u003e 500m buffer, mediana (RIQ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.05 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.669\u0026yen;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.06 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u0026yen;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDistance from mar Menor (km)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.6 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.0 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.6 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.864#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.2 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.678#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDistance from mar Menor zones\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2802(78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e451 (75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e151 (75.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e397 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.912*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.980*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: CNS\u0026thinsp;=\u0026thinsp;Central Nervous System; NBs\u0026thinsp;=\u0026thinsp;Neuroblastomas; G1\u0026thinsp;\u0026ge;\u0026thinsp;20kms; G2 zone\u0026thinsp;=\u0026thinsp;10-20kms; G3\u0026thinsp;=\u0026thinsp;2\u0026ndash;10 kms; G4 (\u0026le;\u0026thinsp;2km). \u0026ordf; P-value from main group of cases compared to sensitivity group, # Student\u0026rsquo;s t-test, \u0026yen; Kruskal-Wallis test, * Chi-square test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Analysis of specific processes\u003c/h2\u003e \u003cp\u003eThe analysis of a punctual pattern encompasses a series of techniques that allow us to study the distribution of a set of events. The interest in the use of these techniques arises in order to explore whether the diagnosed cases of any type of childhood tumor occurring in the Region of Murcia present an aggregation pattern or not.\u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb illustrate the spatial distribution of all cases diagnosed yearly in the Region of Murcia. The main foci of cases diagnosed throughout all the years of the study occurred mainly in the municipalities near the coast and, therefore, the Mar Menor. Among these areas with a higher concentration of cases, the areas of the municipalities of Murcia and Cartagena stand out. A migration from the northwest/central areas to the Campo de Cartagena/Mar Menor is observed. Additionally, it can be seen that the years with the highest intensity occur from 2006 onwards.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Modelling of Mar Menor's effect.\u003c/h2\u003e \u003cp\u003eThe logistic regression model allows us to predict the probability of a subject belonging to one group or another (response variable) from predictor variables as well as being able to explore the association of one or more predictor variables with the response variable, adjusting for the rest of the variables that are included to create the regression model.\u003c/p\u003e \u003cp\u003eIn our study, we focused on exploring how the distance variable could impact, as a continuous variable or categorized variable (taking G1 as the reference group) on the response variable (being case or not). The logistic regression model built was as follows:\u003c/p\u003e \u003cp\u003e(\u0026#119884;=\u0026#119896; |\u0026#119883;=\u0026#119909;)\u0026thinsp;=\u0026thinsp;11+\u0026#119890;\u0026minus;(\u0026#120573;0+\u0026#120573;1\u0026#119878;\u0026#119890;\u0026#119909;\u0026#119900;+\u0026#120573;2(\u003cem\u003eBirth year\u003c/em\u003e)+\u0026#120573;3\u003cem\u003e\u0026#119863;\u0026#119894;\u0026#119904;\u0026#119905;\u0026#119886;\u0026#119899;\u0026#119888;e\u003c/em\u003e+\u0026#120573;4\u0026#119873;\u0026#119863;\u0026#119881;\u0026#119868;+\u0026#120573;5(\u003cem\u003e\u0026#119875;\u0026#119903;\u0026#119890;\u0026#119904;\u0026#119890;\u0026#119899;ce of green urban\u003c/em\u003e)+\u0026#120573;6(\u003cem\u003ePresence of Blue Space\u003c/em\u003e))\u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show the ORs obtained for all the cases, the cases diagnosed with childhood leukemia, and the cases whose address was the same at birth and at the time of diagnosis of his oncological pathology. The estimates obtained in the models worked with the distance variable continuously (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) show that, in any of the three groups of the analysis, the increase of one kilometer with respect to the Mar Menor leads to a decrease in the incidence of childhood tumors, being for cases of childhood leukemia this association greater (OR\u0026thinsp;=\u0026thinsp;0.990; CI 95%=0.983\u0026ndash;0.997).\u003c/p\u003e \u003cp\u003eRegarding the comparative models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), we can observe how the incidence of childhood cancer is concentrated in the areas between 10 and 20 km (G2; OR\u0026thinsp;=\u0026thinsp;1.32; 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.73) and 2 to 10 km away from the Mar Menor (G3; OR\u0026thinsp;=\u0026thinsp;1.43; 95% CI\u0026thinsp;=\u0026thinsp;1.00-2.02).\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\u003eAnalysis of the distance to the Mar Menor as a continuous variable. Simple models and models adjusted for sex, date of birth and covariates of exposure to urban green and blue spaces in a buffer of 500 meters for the group of childhood tumors, childhood leukemias and subjects with unchanged address.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from Mar Menor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInfant Leukemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSame address\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimple model OR (IC 95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992 (0.989\u0026ndash;0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990 (0.984\u0026ndash;0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.991 (0.985\u0026ndash;0.997)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted model OR (IC 95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992 (0.987\u0026ndash;0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990 (0.983\u0026ndash;0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.989 (0.983\u0026ndash;0.996)\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\u003eAnalysis of the distance to the Mar Menor as a categorized variable. Simple models and models adjusted for sex, date of birth and covariates of exposure to urban green and blue spaces in a buffer of 500 meters for the group of childhood tumors, childhood leukemias and subjects with unchanged address.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from mar Menor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInfant Leukemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSame address\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSimple Model OR (IC 95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eG1 (reference)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2 (10-20kms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (0.95\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (0.79\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33 (0.86\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3 (2-10kms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35 (0.95\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (0.77\u0026ndash;2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41 (0.78\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4 (\u0026le;\u0026thinsp;2km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78 (0.47\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.46\u0026ndash;2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.27\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAdjusted Model OR (IC 95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eG1 (reference)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2 (10-20kms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.32 (1.01\u0026ndash;1.73) *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29 (0.80\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35 (0.86\u0026ndash;2.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3 (2-10kms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.43 (1.00-2.02) *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52 (0.81\u0026ndash;2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43 (0.79\u0026ndash;2.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4 (\u0026le;\u0026thinsp;2km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.52\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 (0.50\u0026ndash;2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.27\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*G1\u0026thinsp;=\u0026thinsp;reference group (subjects at a distance greater than or equal to 20 km from the Mar Menor);\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we cautiously initiate a mapping of the influence of Mar Menor pollution on childhood cancer incidence in the Murcia region. Our findings tentatively indicate an association between childhood cancer and proximity to this particular blue space, requiring a careful interpretation due to the pioneering nature of the research area. The limited existing literature on the health effects of blue spaces [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], is expanded upon by our methodology, which includes analysis of distance as both a continuous and a categorical variable. This approach revealed a decrease in cancer incidence with increased distance from the Mar Menor. However, the relationship is not linear when distance is categorized; notably, an increased risk is not apparent in the area closest to the sea (less than 2 km).\u003c/p\u003e \u003cp\u003eParticularly, areas G2 and G3, within Campo de Cartagena, showed higher incidence rates, reflecting the intense agricultural transformation and ecological costs of the region, a stark representation of environmental injustice. Yet, paradoxically, near-coastal areas (G4) indicate possible protective environmental factors against pollution. This complexity, intertwined with socio-economic disparities, underlines the multifaceted nature of health determinants [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMurcia, Cartagena, La Manga, and San Javier, among others, are very close to the Mar Menor. These municipalities have grown in population in recent decades due to strong investment in construction and especially agriculture in the area. While the crude incidence rate across that entire period 1998\u0026ndash;2020 in the Region of Murcia was 149.6 per 1\u0026nbsp;million, there was an increase over that time in the incidence. The areas with a higher standardized incidence ratio have shifted from the northwest (1998\u0026ndash;2003) to the southeast (2016\u0026ndash;2020) region [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUrbanization has been linked to increased risks of certain childhood cancers [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our residual analysis from fitted models suggests a spatial distribution of cases that merits a deeper investigation into urbanization's role (Appendix A). Future research should include a detailed analysis of these spatial trends, taking into account that current studies have yet to fully explore all potential factors.\u003c/p\u003e \u003cp\u003eThe direct and indirect consequences of Mar Menor`s pollution are critical, with areas G2 and G3 within Campo de Cartagena, characterized by intense agricultural industrialization, and presenting the highest childhood cancer rates. This region's ecological decline due to intensive agriculture has resulted in significant environmental costs, contributing to poverty and social exclusion, the highest being in Campo de Cartagena/Mar Menor [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This environmental degradation has exacerbated flooding and pollution in Mar Menor, leading to biodiversity loss and anoxia [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and illustrating a cycle of poverty-induced environmental damage [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], highlighting a clear case of environmental injustice,\u003c/p\u003e \u003cp\u003eLegislative responses to this injustice include the Mar Menor laws, targeting ecological restoration in the affected areas [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Community advocacy, notably from children, has been instrumental in these changes, culminating in Law 19/2022, which declares Mar Menor a 'legal person'\u0026mdash;a landmark for European environmental law [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], aligning with international trends in granting legal rights to natural entities, such as in Ecuador, Colombia, India, and New Zealand's recognition of the Whanganui River.\u003c/p\u003e \u003cp\u003eFor decades, the Mar Menor Lagoon has been significantly impacted by anthropogenic activities, with its current compromised state being a cumulative consequence of various mining, intensive agriculture, and industrial processes. These activities have precipitated a fundamental shift in the lagoon's ecological balance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Research has increasingly demonstrated the connection between water pollution and the rise of oncological diseases, implicating the toxicological effects of waterborne contaminants on human genetics [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition, some of its possible indirect impacts on health could be related to recreational activities in its surroundings, leading to greater sun exposure and an increased risk of skin cancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our study sets a precedent for other blue spaces facing similar challenges, protecting and promoting both Nature and Child rights. Significantly, the Act on Guardians of Mar Menor must recognize and empower the vital role of children, whose natural and innate, and often passionate, interest that children show in preserving the environment makes them a dynamic and powerful force in protecting the Mar Menor.\u003c/p\u003e \u003cp\u003eOn the other hand, blue spaces like the Mar Menor could potentially mitigate the effects of pollution in terrestrial areas, serving as a protective component [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. There is growing evidence that spending time in or around water bodies or \u0026lsquo;blue spaces\u0026rsquo; can lead to improved human health [\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In particular, the sea breeze and marine environment have been shown to enhance air quality by dispersing pollutants, thereby reducing exposure for coastal populations [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Given these potential benefits, safeguarding blue spaces like the Mar Menor is essential for global health initiatives, particularly when aiming to reduce the impacts of pollution on children, one of the most vulnerable groups.\u003c/p\u003e \u003cp\u003eRegarding childhood cancer, there hasn\u0026rsquo;t been research linking it to water pollution. To adequately determine the presence of toxic metabolites or not in children, exhaustive monitoring of them would be necessary, which entails a high cost. However, other environmental factors have been related to an increase in the incidence of childhood cancer, such as exposure to pesticides, industries, or exposure to traffic. All of them without being able to analytically objectify toxic compounds in organisms or even clarify a plausible etiopathogenic mechanism [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLikewise, other factors, such as those related to the environment and the parents' habits, also play a fundamental role in the appearance of childhood cancer during the initial phases of children's development. Among them, we can highlight how the consumption of drugs, tobacco, or parental exposure to industries or tobacco during pregnancy has been related to the appearance of childhood cancer [\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. To date, these exposures are more complex to measure because a national collection system for children's habits and immediate environment has not been developed. However, this collection of such specific information has begun to be developed by the Pediatric Environmental Health Units [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For future studies, this could have a very important value when investigating other environmental factors possibly related to childhood cancer.\u003c/p\u003e \u003cp\u003eHowever, environmental factors have recently been studied and could reduce childhood cancer incidence, particularly in urban green spaces and blue spaces. After having developed a methodology that has allowed us to explore these spaces, we decided to apply it in this study [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To date, there is no consensus on the possible optimal distances to measure the effects of these spaces [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], so, based on our previous results, we decided to use an exposure buffer of 500 meters.\u003c/p\u003e \u003cp\u003eRegarding the determined sensitivity groups, cases diagnosed with childhood leukemia, and cases whose address was the same at birth and diagnosed time, their characteristics and the results obtained from the logistic regression models do not present significant differences with the general group of cases. This could be explained by these subgroups' great weight concerning the general set of cases. However, for future studies, it would be interesting to break down the characteristics of the rest of the tumor types and/or determine other possible sensitivity groups, such as those children whose age at the time of diagnosis is less than 12 months.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eTo the best of our understanding, this investigation represents the inaugural effort to elucidate the connection between the Mar Menor Lagoon, a significant blue space, and the incidence of childhood cancer. Our findings preliminarily suggest a relationship, indicated by a lower incidence of cancer with increasing distance from this natural habitat. Interestingly, blue spaces might also serve as buffers to the adverse health outcomes associated with environmental pollutants in nearby agricultural and urban coastal areas. However, due to substantial limitations\u0026mdash;such as the absence of individual-level subject data and the exclusion of various environmental variables from our models\u0026mdash;our conclusions must be drawn with prudence. Further research is essential to deepen our comprehension of these associations. The insights gained hold the potential to inform urban and public health planning substantially, facilitating the development of strategies that protect both the environment and the welfare of children.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MACAPEMUR (Environment and Pediatric Cancer in the RM) project was approvals\u0026nbsp;from the ethics research committee of Clinical University Hospital Virgen de la Arrixaca. Informed consent forms signed by all parents and children over 12 were collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed in the current study are not publicly available due to the fact that the geographical coordinates of the study subjects are considered private information. These coordinates are subject to privacy restrictions. Therefore, it is not possible to publicly share the data containing this confidential information. However, the data are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization: COS and JAOG; Data curation: COS; Formal analysis: COS; Funding acquisition: JAOG and RRP; Investigation: COS; Methodology: COS, JAOG, RRP and GFA; Project administration: RRP and GFA; Resources: JAOG and RRP; Software: COS; Supervision: RRP and GFA; Validation: RRP, JAOG and GFA; Visualization: COS; Roles/Writing - original draft: COS and JAOG; Writing - review \u0026amp; editing: JAOG, GFA, RRP, EOP.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Environmental Health Profile for Children in the Murcia Region Project in the Foundation for Formation and Research (FFIS) funded by Sociedad Pediatria Sureste of Spain (SPSE.ES) (FFIS-DF-2022-36)\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWhite MP, Elliott LR, Gascon M, Roberts B, Fleming LE. 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Analytical approaches to testing pathways linking greenspace to health: A scoping review of the empirical literature. Environ Res. 2020;186:109613. https://doi.org/10.1016/j.envres.2020.109613\u003c/li\u003e\n\u003cli\u003eMarkevych I, Schoierer J, Hartig T, Chudnovsky A, Hystad P, Dzhambov AM, de Vries S, Triguero-Mas M, Brauer M, et al.Exploring pathways linking greenspace to health: Theoretical and methodological guidance. Environmental Research. 2017; 158, 301\u0026ndash;317. https://doi.org/10.1016/j.envres.2017.06.028\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"childhood cancer, environmental health, leukemia, blue spaces, epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-4128975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4128975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Mar Menor, Europe\u0026rsquo;s largest saltwater lagoon in the Mediterranean basin (Murcia, southeastern Spain), is a ecosystem facing severe pollution, anoxia events, and marine biodiversity loss. his exploratory study examines the association between residential proximity to the Mar Menor and childhood cancer incidence in Murcia from 2000 to 2018. In our case-control study of 594 cancer cases and 3,564 controls, matched by birth year and sex, participants were categorized by proximity to the lagoon (G1\u0026thinsp;\u0026ge;\u0026thinsp;20km, G2\u0026thinsp;=\u0026thinsp;10-20km, G3\u0026thinsp;=\u0026thinsp;2-10km, G4\u0026thinsp;=\u0026thinsp;\u0026le;\u0026thinsp;2km). Odds ratios (ORs) and 95% confidence intervals (95%CI) were adjusted for sociodemographic and environmental covariates. Analysis of distance as a continuous variable indicated a decrease in cancer risk with greater distance (OR\u0026thinsp;=\u0026thinsp;0.992; 95% CI\u0026thinsp;=\u0026thinsp;0.987\u0026ndash;0.995), while as a categorical variable, it revealed a non-linear pattern. Specifically, the risk did not increase for those living within 2 km of the lagoon (OR\u0026thinsp;=\u0026thinsp;0.87; 95% CI\u0026thinsp;=\u0026thinsp;0.52\u0026ndash;1.37), whereas increased risks were observed in the 2\u0026ndash;20 km range within Campo de Cartagena (G2; OR\u0026thinsp;=\u0026thinsp;1.32; 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.73 and G3; OR\u0026thinsp;=\u0026thinsp;1.43; 95% CI\u0026thinsp;=\u0026thinsp;1.00-2.02). This suggests modulating factors near the lagoon may counterbalance risks from broader environmental contamination. These findings highlight the complexity of environmental health dynamics and the importance of detailed research to guide public health strategies and urban planning for environmental and child health protection.\u003c/p\u003e","manuscriptTitle":"The Mar Menor Lagoon and the incidence of childhood cancer in the Region of Murcia, Spain (2000-2018): An exploratory analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 18:28:54","doi":"10.21203/rs.3.rs-4128975/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-24T10:13:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-24T10:12:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-24T10:11:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-24T06:35:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4f8eb7b5-3305-498a-be02-9e02d0b1d98f","date":"2024-04-22T07:29:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-21T23:23:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-18T21:51:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ac062008-25fb-4b81-84af-466ff7cade8f","date":"2024-04-17T04:47:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2879a698-ae90-4373-b4a0-9ece9fc7e951","date":"2024-04-15T18:06:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"02463e4c-f017-4f71-81a7-264b5bebbff1","date":"2024-04-14T22:01:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-11T10:17:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1c3ef12a-227d-4656-9ce3-180bcb159c17","date":"2024-04-11T09:28:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-08T16:40:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-03T07:22:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-03T07:22:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2024-03-19T09:19:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ddb207f4-a557-4562-b6ea-2888497f6da3","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-01T17:13:14+00:00","versionOfRecord":{"articleIdentity":"rs-4128975","link":"https://doi.org/10.1186/s12982-024-00157-2","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2024-07-26 16:16:52","publishedOnDateReadable":"July 26th, 2024"},"versionCreatedAt":"2024-04-08 18:28:54","video":"","vorDoi":"10.1186/s12982-024-00157-2","vorDoiUrl":"https://doi.org/10.1186/s12982-024-00157-2","workflowStages":[]},"version":"v1","identity":"rs-4128975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4128975","identity":"rs-4128975","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}