Urban green spaces and childhood leukemia incidence: A population-based case-control study in Madrid.

BACKGROUND: Childhood leukemia is the most common childhood cancer. To date, few risk factors related to predisposition have been identified; therefore, new hypotheses should be considered. OBJECTIVE: To explore the possible relationship of residential proximity to urban green spaces on childhood leukemia. METHODS: We conducted a population-based case control study in the metropolitan area of Madrid from 2000 to 2015. It included 383 incident cases and 1935 controls, individually matched by birth year, sex and area of residence. Using the geographical coordinates of the participants' home residences, we built a proxy for exposure with four distances (250 m, 500 m, 750 m and 1 km) to urban parks (UPs) and urban wooded areas (UWAs). We employed logistic regression models to determinate the effect of them on childhood leukemia adjusting for environmental and socio-demographic covariates. RESULTS: we found a reduction in childhood leukemia incidence at a distance of 250 m from UPs (OR = 0.78; 95%CI = 0.62-0.98), as well as a reduction of the incidence in the Q3 and Q4 quintiles for exposure to UWAs, in the 250 m and 500 m buffers respectively (Q3 (250 m): OR = 0.69; 95%CI = 0.48-1.00; and, Q4 (500 m): OR = 0.69; 95%CI = 0.48-0.99). CONCLUSIONS: Our results suggest a possible association between lower incidence of childhood leukemia and proximity to different forms of urban green space. This study is a first approach to the possible urban green space effects on childhood leukemia so is necessary to continue studying this spaces taking into account more individual data and other environmental risk factors.

Lockdown measures and relative changes in the age-specific incidence of SARS-CoV-2 in Spain.

During the first months of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) epidemic in 2020, Spain implemented an initial lockdown period on 15 March followed by a strengthened lockdown period on 30 March when only essential workers continued to commute to work. However, little is known about the epidemic dynamics in different age groups during these periods.We used the daily number of coronavirus 2019 cases (by date of symptom onset) reported to the National Epidemiological Surveillance Network among individuals aged 15-19 years through 65-69 years. For each age group g, we computed the proportion PrE(g) of individuals in age group g among all reported cases aged 15-69 years during the pre-lockdown period (1-10 March 2020) and the corresponding proportion PrL(g) during two lockdown periods (initial: 25 March-3 April; strengthened: 8-17 April 2020). For each lockdown period, we computed the proportion ratios PR(g) = PrL(g)/PrE(g). For each pair of age groups g1, g2, PR(g1)>PR(g2) implies a relative increase in the incidence of detected SARS-CoV-2 infection in the age group g1 compared with g2 for the lockdown period vs. the pre-lockdown period.For the initial lockdown period, the highest PR values were in age groups 50-54 years (PR = 1.21; 95% CI: 1.12,1.30) and 55-59 years (PR = 1.19; 1.11,1.27). For the second lockdown period, the highest PR values were in age groups 15-19 years (PR = 1.26; 0.95,1.68) and 50-54 years (PR = 1.20; 1.09,1.31).Our results suggest that different outbreak control measures led to different changes in the relative incidence by age group. During the initial lockdown period, when non-essential work was allowed, individuals aged 40-64 years, particularly those aged 50-59 years, had a higher relative incidence compared with the pre-lockdown period. Younger adults/older adolescents had an increased relative incidence during the later, strengthened lockdown. The role of different age groups during the epidemic should be considered when implementing future mitigation efforts.

Determinants of the current and future distribution of the West Nile virus mosquito vector Culex pipiens in Spain.

Changes in environmental conditions, whether related or not to human activities, are continuously modifying the geographic distribution of vectors, which in turn affects the dynamics and distribution of vector-borne infectious diseases. Determining the main ecological drivers of vector distribution and how predicted changes in these drivers may alter their future distributions is therefore of major importance. However, the drivers of vector populations are largely specific to each vector species and region. Here, we identify the most important human-activity-related and bioclimatic predictors affecting the current distribution and habitat suitability of the mosquito Culex pipiens and potential future changes in its distribution in Spain. We determined the niche of occurrence (NOO) of the species, which considers only those areas lying within the range of suitable environmental conditions using presence data. Although almost ubiquitous, the distribution of Cx. pipiens is mostly explained by elevation and the degree of urbanization but also, to a lesser extent, by mean temperatures during the wettest season and temperature seasonality. The combination of these predictors highlights the existence of a heterogeneous pattern of habitat suitability, with most suitable areas located in the southern and northeastern coastal areas of Spain, and unsuitable areas located at higher altitude and in colder regions. Future climatic predictions indicate a net decrease in distribution of up to 29.55%, probably due to warming and greater temperature oscillations. Despite these predicted changes in vector distribution, their effects on the incidence of infectious diseases are, however, difficult to forecast since different processes such as local adaptation to temperature, vector-pathogen interactions, and human-derived changes in landscape may play important roles in shaping the future dynamics of pathogen transmission.

Geographical spread of influenza incidence in Spain during the 2009 A(H1N1) pandemic wave and the two succeeding influenza seasons.

The aim of this study was to monitor the spatio-temporal spread of influenza incidence in Spain during the 2009 pandemic and the following two influenza seasons 2010-2011 and 2011-2012 using a Bayesian Poisson mixed regression model; and implement this model of geographical analysis in the Spanish Influenza Surveillance System to obtain maps of influenza incidence for every week. In the pandemic wave the maps showed influenza activity spreading from west to east. The 2010-2011 influenza epidemic wave plotted a north-west/south-east pattern of spread. During the 2011-2012 season the spread of influenza was geographically heterogeneous. The most important source of variability in the model is the temporal term. The model of spatio-temporal spread of influenza incidence is a supplementary tool of influenza surveillance in Spain.

Space-time pattern of hepatitis A in Spain, 1997-2007.

In Spain hepatitis A is a compulsory notifiable disease and individual cases are reported to the national epidemiological surveillance network. Incidence rates show variations in different regions. The aim of this study was to analyse the space-time pattern of hepatitis A risk at municipal level in Spain and at global and local levels during the period 1997-2007. At global level we used two estimates of risk: the standardized incidence ratio (SIR) and the posterior probability that the smoothed relative risk is >1 (PP). At local level we used the scan statistic method to analyse the space-time clusters. The SIR and significant PP (>0·8) showed the highest risk concentrated in areas of the Mediterranean coast. The most likely cluster gave a relative risk of 53·530. These spatial statistics methodologies can be complementary tools in the epidemiological surveillance of infectious diseases.

Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data.

Designing public health responses to outbreaks requires close monitoring of population-level health indicators in real-time. Thus, an accurate estimation of the epidemic curve is critical. We propose an approach to reconstruct epidemic curves in near real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We address two data collection problems that affected the reliability of the available real-time epidemiological data, namely, the frequent missing information documenting when a patient first experienced symptoms, and the frequent retrospective revision of historical information (including right censoring). This is done by using a novel back-calculating procedure based on imputing patients' dates of symptom onset from reported cases, according to a dynamically-estimated "backward" reporting delay conditional distribution, and adjusting for right censoring using an existing package, NobBS , to estimate in real time (nowcast) cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number ( R t ) in real-time. At each step, we evaluate how different assumptions affect the recovered epidemiological events and compare the proposed approach to the alternative procedure of merely using curves of case counts, by report day, to characterize the time-evolution of the outbreak. Finally, we assess how these real-time estimates compare with subsequently documented epidemiological information that is considered more reliable and complete that became available later in time. Our approach may help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health surveillance systems in other locations.

Spatio-temporal analysis of tuberculosis in Spain, 2008-2010.

BACKGROUND: The characteristics of respiratory tuberculosis (TB) favour the appearance of clusters of cases in space and time. It is important for public health authorities to know which clusters occur randomly and which merit further investigation. OBJECTIVE: To detect spatial and spatio-temporal clusters of respiratory TB in Spain during the period from 1 January 2008 to 31 December 2010. MATERIALS AND METHODS: Retrospective spatio-temporal study of respiratory TB cases reported to Spain's National Epidemiological Surveillance Network from 2008 to 2010, at a municipal level. We used the purely spatial and space-time Scan statistic estimators. All analyses were adjusted for age and sex. RESULTS: The spatial cluster analysis detected 28 significant clusters and the spatio-temporal cluster analysis detected 20 significant clusters. The most likely spatial cluster comprised seven municipalities in the Greater Barcelona Area. Most space-time clusters were situated in the same area, and were detected between 1 April 2008 and 31 March 2009. CONCLUSION: The distribution of TB clusters as shown by the proposed models furnishes a spatial pattern of the distribution of the disease. The two methods used can be a useful tool for analysing the distribution of respiratory TB in Spain.