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Background: Mammographic density (MD) phenotypes, including percent density (PMD), area of dense tissue (DA), and area of non-dense tissue (NDA), are associated with breast cancer risk. Twin studies suggest that MD phenotypes are highly heritable. However, only a small proportion of their variance is explained by identified genetic variants. Methods: We conducted a genome-wide association study, as well as a transcriptome-wide association study (TWAS), of age- and BMI-adjusted DA, NDA, and PMD in up to 27,900 European-ancestry women from the MODE/BCAC consortia. Results: We identified 28 genome-wide significant loci for MD phenotypes, including nine novel signals (5q11.2, 5q14.1, 5q31.1, 5q33.3, 5q35.1, 7p11.2, 8q24.13, 12p11.2, 16q12.2). Further, 45% of all known breast cancer SNPs were associated with at least one MD phenotype at p

El 'Atlas of Cancer Mortality in Portugal and Spain 2003–2012', muestra la distribución espacial de la mortalidad municipal por distintos tipos cáncer para el periodo 2003-2012. Ha sido desarrollado por la Unidad de Epidemiología del Cáncer y Ambiental del Centro Nacional de Epidemiología del ISCIII, que forma parte del CIBERESP, y por el Departamento de Epidemiología del Instituto Nacional De Saúde Doutor Ricardo Jorge de Portugal. El estudio de la distribución geográfica del riesgo de fallecer por cáncer es una de las herramientas que se usan en epidemiología para generar hipótesis sobre la posible implicación de factores ambientales en el origen de los tumores. This project is partially supported by research grant from the Spanish Health Research Fund (FIS) of the National Institute of Health Carlos III (ISCIII), Spain (Project PI17CIII/00040: “Spatial distribution of municipal cancer mortality in Spain (SICAMSA)” (“Distribución Espacial de la Mortalidad municipal por CÁncer en ESpaña (DEMOCAES)”). Introduction. Methods. Results: Oesophagus (ICD-10 C15) Stomach (ICD-10 C16) Colorectal (ICD-10 C18–C21) Pancreas (ICD-10 C25) Larynx (ICD-10 C32) Lung (ICD-10 C33–C34) Female Breast (ICD-10 C50) Prostate (ICD-10 C61) Bladder (ICD-10 C67) Leukaemia (ICD-10 C91–C95) References. Annexes: Annex I and Annex II No

Comunicación presentada en las II Jornada del Centro Nacional de Epidemiología - 2021. Se exponen los sistemas de información epidemiológica para vigilancia de las Enfermedades Crónicas del Centro Nacional de Epidemiología. son: Raziel, Ariadna, Informes, Atlas, tablas y diferentes Apps. El Sistema de Información Epidemiológica sobre Mortalidad de las Principales Enfermedades Crónicas en España (SIEMEC) y el Sistema de Información Epidemiológica del Cáncer en España (SIEC).

Objectives To characterize asymptomatic SARS-CoV-2 infections and develop a symptom-based risk score useful in primary healthcare. Study design and setting 61,092 community-dwelling participants in a nationwide population-based serosurvey completed a questionnaire on COVID-19 symptoms and received an immunoassay for SARS-CoV-2 IgG antibodies between April 27-June 22, 2020. Standardized prevalence ratios for asymptomatic infection were estimated across participant characteristics. We constructed a symptom-based risk score and evaluated its ability to predict SARS-CoV-2 infection. Results 28.7% of infections were asymptomatic (95% CI 26.1-31.4%). Standardized asymptomatic prevalence ratios were 1.19 (1.02-1.40) for men versus women, 1.82 (1.33-2.50) and 1.45 (0.96-2.18) for individuals, Graphical Abstract Image, graphical abstract

Background: The analysis of the evolution of the COVID-19 epidemic can provide evidence of the impact of measures implemented to reduce its progression. Our aim was to describe the evolution of the pandemic in the different Spanish regions and to examine the effect of the non-pharmaceutical public health interventions during the first epidemic wave on these trends. Methods: Daily incidence rates of cases were calculated at national and regional level between 31th of January and 10th of May 2020. Epidemic curves, important dates of interventions and effective reproduction number (Rt) were plotted and transmissibility parameters were calculated. To summarize the geographical heterogeneity in the evolution, regional epidemic curves have been classified into homogeneous groups using a clustering procedure. Findings: The incidence rate reached 5 cases per 100,000 on March 1 and peaked at March 20. The Rt gradually decreased after the national lockdown falling below 1 on March 24. Two homogeneous groups of epidemic curves were identified among regions, mainly differentiated by the magnitude of the daily incidence rate and the evolution of the Rt in the period prior to lockdown. However, irrespectively of the previous trend, the lockdown was followed by a steep decrease in the number of cases starting 6 days after its implementation. Interpretation: Our results confirm that the restrictive national lockdown efficiently reduced the progression of the epidemic in Spain during the first wave. This effect was similar in the two regional clusters, independent of the previous dynamics of the epidemic. Funding Statement: The study was supported by Instituto de Salud Carlos III, Spain (ISCIII) grant number COV20-008 Declaration of Interests: All authors declare no competing interests.

Miguel Angel Luque-Fernandez,1– 3 Daniel Redondo-Sánchez,1,2 Miguel Rodríguez-Barranco,1,2,4 Yoe-Ling Chang-Chan,1,4 Elena Salamanca-Fernández,1,2 Olivier Núñez,2,5 Pablo Fernandez-Navarro,2,5 Marina Pollán,2,5 María-José Sánchez1,2,4,6 1Instituto de Investigación Biosanitaria de Granada, Non-Communicable Disease and Cancer Epidemiology Group, ibs.GRANADA, University of Granada, Granada, Spain; 2Biomedical Network Research Centers of Epidemiology and Public Health (CIBERESP), Madrid, Spain; 3London School of Hygiene and Tropical Medicine, Non-Communicable Disease Epidemiology, London, UK; 4Andalusian School of Public Health, Granada, Spain; 5National Centre of Epidemiology, Health Institute Carlos III (CNE-ISCIII), Madrid, Spain; 6Department of Preventive Medicine and Public Health, University of Granada, Granada, SpainCorrespondence: Miguel Angel Luque-FernandezAndalusian School of Public Health, Cuesta Del Observatorio, 4, Granada 18080, SpainEmail miguel-angel.luque@lshtm.ac.ukBackground: Colorectal cancer (CRC) is the most frequently diagnosed cancer in Spain. Socioeconomic inequalities in cancer survival are not documented in Spain. We aim to study the association of socioeconomic inequalities with overall mortality and survival among CRC patients in southern Spain.Methods: We conducted a multilevel population-based cohort study, including CRC cases for the period 2011– 2013. The study time-to-event outcome was death, and the primary exposure was CRC patients’ socioeconomic status assessed by the Spanish deprivation index at the census tract level. We used a mixed-effects flexible hazard model, including census tract as a random intercept, to derive overall survival estimates by deprivation.Results: Among 3589 CRC patients and 12,148 person-years at risk (pyr), 964 patients died before the end of the follow-up. Mortality by deprivation showed the highest mortality rate for the most deprived group (96.2 per 1000 pyr, 95% CI: 84.0– 110.2). After adjusting for sex, age, cancer stage, and the area of residence, the most deprived had a 60% higher excess mortality risk than the less deprived group (excess mortality risk ratio: 1.6, 95% CI: 1.1– 2.3).Conclusions: We found a consistent association between deprivation and CRC excess mortality and survival. The reasons behind these inequalities need further investigation in order to improve equality cancer outcomes in all social groups.Keywords: socioeconomic inequalities, colorectal cancer, survival, population-based epidemiology, epidemiological methods, multilevel

We present a novel approach for the analysis of multivariate case-control georeferenced data using Bayesian inference in the context of disease mapping, where the spatial distribution of different types of cancers is analyzed. Extending other methodology in point pattern analysis, we propose a log-Gaussian Cox process for point pattern of cases and the controls, which accounts for risk factors, such as exposure to pollution sources, and includes a term to measure spatial residual variation. For each disease, its intensity is modeled on a baseline spatial effect (estimated from both controls and cases), a disease-specific spatial term and the effects on covariates that account for risk factors. By fitting these models the effect of the covariates on the set of cases can be assessed, and the residual spatial terms can be easily compared to detect areas of high risk not explained by the covariates. Three different types of effects to model exposure to pollution sources are considered. First of all, a fixed effect on the distance to the source. Next, smooth terms on the distance are used to model non-linear effects by means of a discrete random walk of order one and a Gaussian process in one dimension with a Mat\'ern covariance. Models are fit using the integrated nested Laplace approximation (INLA) so that the spatial terms are approximated using an approach based on solving Stochastic Partial Differential Equations (SPDE). Finally, this new framework is applied to a dataset of three different types of cancer and a set of controls from Alcal\'a de Henares (Madrid, Spain). Covariates available include the distance to several polluting industries and socioeconomic indicators. Our findings point to a possible risk increase due to the proximity to some of these industries.

Prostate cancer (PCa) is the second most common cancer among men worldwide. Its etiology remains largely unknown compared to other common cancers. We have developed a risk stratification model combining environmental factors with family history and genetic susceptibility. 818 PCa cases and 1,006 healthy controls were compared. Subjects were interviewed on major lifestyle factors and family history. Fifty-six PCa susceptibility SNPs were genotyped. Risk models based on logistic regression were developed to combine environmental factors, family history and a genetic risk score. In the whole model, compared with subjects with low risk (reference category, decile 1), those carrying an intermediate risk (decile 5) had a 265% increase in PCa risk (OR = 3.65, 95% CI 2.26 to 5.91). The genetic risk score had an area under the ROC curve (AUROC) of 0.66 (95% CI 0.63 to 0.68). When adding the environmental score and family history to the genetic risk score, the AUROC increased by 0.05, reaching 0.71 (95% CI 0.69 to 0.74). Genetic susceptibility has a stronger risk value of the prediction that modifiable risk factors. While the added value of each SNP is small, the combination of 56 SNPs adds to the predictive ability of the risk model. Biological samples were stored at the biobanks supported by Instituto de Salud Carlos III- FEDER: Parc de Salut MAR Biobank (MARBiobanc) (), ‘Biobanco La Fe’ () and FISABIO Biobank (), as well as at the Public Health Laboratory of Gipuzkoa, the Basque Biobank, the ICOBIOBANC (sponsored by the Catalan Institute of Oncology), the IUOPA Biobank of the University of Oviedo, and the ISCIII Biobank. SNP genotyping services were provided by the Spanish ‘Centro Nacional de Genotipado’ (CEGEN-ISCIII). We thank all the subjects who participated in the study and all MCC-Spain collaborators. This work was supported by the ‘Acción Transversal del Cancer’, approved by the Spanish Ministry Council on the 11th October 2007, by the Instituto de Salud Carlos III, co-founded by FEDER funds –‘a way to build Europe’ (grants , , , , , , , , , , ,, , , , , , , , , and ). Support was also provided by the Fundación Marqués de Valdecilla (grant ); the Junta de Castilla y León (grant ); the Consejería de Salud of the Junta de Andalucía (); the Conselleria de Sanitat of the Generalitat Valenciana (grant ); the Recercaixa (grant ); the Regional Government of the Basque Country; the Consejería de Sanidad de la Región de Murcia; European Commission grants ; the Spanish Association Against Cancer (AECC) Scientific Foundation; the Catalan Government DURSI (grant ); the Fundación Caja de Ahorros de Asturias; the University of Oviedo; Societat Catalana de Digestologia; and COST action Eucolongene. The authors thank the “Bioinformatics and Research Group in Genetic and Environmental Epidemiology” (BRG-GEE) of the Cancer and Environmental Epidemiology Unit in III Institute of Health (ISCIII), Spain, for their technical scientific support (Group consisting of Pablo Fernández-Navarro (group leader), Mario González-Sánchez (bioinformatic), Javier González-Palacios (bioinformatic)). Sí