Spatio-temporal trends of mortality in small areas of Southern Spain

Background: Most mortality atlases show static maps from count data aggregated over time. This procedure has several methodological problems and serious limitations for decision making in Public Health. The evaluation of health outcomes, including mortality, should be approached from a dynamic time perspective that is specific for each gender and age group. At the moment, researches in Spain do not provide a dynamic image of the population’s mortality status from a spatio-temporal point of view. The aim of this paper is to describe the spatial distribution of mortality from all causes in small areas of Andalusia (Southern Spain) and evolution over time from 1981 to 2006. Methods: A small-area ecological study was devised using the municipality as the unit for analysis. Two spatiotemporal hierarchical Bayesian models were estimated for each age group and gender. One of these was used to estimate the specific mortality rate, together with its time trends, and the other to estimate the specific rate ratio for each municipality compared with Spain as a whole. Results: More than 97% of the municipalities showed a diminishing or flat mortality trend in all gender and age groups. In 2006, over 95% of municipalities showed male and female mortality specific rates similar or significantly lower than Spanish rates for all age groups below 65. Systematically, municipalities in Western Andalusia showed significant male and female mortality excess from 1981 to 2006 only in age groups over 65. Conclusions: The study shows a dynamic geographical distribution of mortality, with a different pattern for each year, gender and age group. This information will contribute towards a reflection on the past, present and future of mortality in Andalusia.

Atlas interactivo de mortalidad de Andalucía (AIMA)

Until now, mortality atlases have been static. Most of them describe the geographical distribution of mortality using count data aggregated over time and standardized mortality rates. However, this methodology has several limitations. Count data aggregated over time produce a bias in the estimation of death rates. Moreover, this practice difficult the study of temporal changes in geographical distribution of mortality. On the other hand, using standardized mortality hamper to check differences in mortality among groups. The Interactive Mortality Atlas in Andalusia (AIMA) is an alternative to conventional static atlases. It is a dynamic Geographical Information System that allows visualizing in web-site more than 12.000 maps and 338.00 graphics related to the spatio-temporal distribution of the main death causes in Andalusia by age and sex groups from 1981. The objective of this paper is to describe the methods used for AIMA development, to show technical specifications and to present their interactivity. The system is available from the link products in www.demap.es. AIMA is the first interactive GIS that have been developed in Spain with these characteristics. Spatio-temporal Hierarchical Bayesian Models were used for statistical data analysis. The results were integrated into web-site using a PHP environment and a dynamic cartography in Flash. Thematic maps in AIMA demonstrate that the geographical distribution of mortality is dynamic, with differences among year, age and sex groups. The information nowadays provided by AIMA and the future updating will contribute to reflect on the past, the present and the future of population health in Andalusia.

Sources of pre-analytical variations in yield of DNA extracted from blood samples: analysis of 50,000 DNA samples in EPIC.

The European Prospective Investigation into Cancer and nutrition (EPIC) is a long-term, multi-centric prospective study in Europe investigating the relationships between cancer and nutrition. This study has served as a basis for a number of Genome-Wide Association Studies (GWAS) and other types of genetic analyses. Over a period of 5 years, 52,256 EPIC DNA samples have been extracted using an automated DNA extraction platform. Here we have evaluated the pre-analytical factors affecting DNA yield, including anthropometric, epidemiological and technical factors such as center of subject recruitment, age, gender, body-mass index, disease case or control status, tobacco consumption, number of aliquots of buffy coat used for DNA extraction, extraction machine or procedure, DNA quantification method, degree of haemolysis and variations in the timing of sample processing. We show that the largest significant variations in DNA yield were observed with degree of haemolysis and with center of subject recruitment. Age, gender, body-mass index, cancer case or control status and tobacco consumption also significantly impacted DNA yield. Feedback from laboratories which have analyzed DNA with different SNP genotyping technologies demonstrate that the vast majority of samples (approximately 88%) performed adequately in different types of assays. To our knowledge this study is the largest to date to evaluate the sources of pre-analytical variations in DNA extracted from peripheral leucocytes. The results provide a strong evidence-based rationale for standardized recommendations on blood collection and processing protocols for large-scale genetic studies.

Genetic variations in drug-induced liver injury (DILI): resolving the puzzle.

Despite stringent requirements for drug development imposed by regulatory agencies, drug-induced liver injury (DILI) is an increasing health problem and a significant cause for failure to approve drugs, market withdrawal of commercialized medications, and adoption of regulatory measures. The pathogenesis is yet undefined, though the rare occurrence of idiosyncratic DILI (1/100,000–1/10,000) and the fact that hepatotoxicity often recurs after re-exposure to the culprit drug under different environmental conditions strongly points toward a major role for genetic variations in the underlying mechanism and susceptibility. Pharmacogenetic studies in DILI have to a large extent focused on genes involved in drug metabolism, as polymorphisms in these genes may generate increased plasma drug concentrations as well as lower clearance rates when treated with standard medication doses. A range of studies have identified a number of genetic variants in drug metabolism Phase I, II, and III genes, including cytochrome P450 (CYP) 2E1, N-acetyltransferase 2, UDP-glucuronosyltransferase 2B7, glutathione S-transferase M1/T1, ABCB11, and ABCC2, that enhance DILI susceptibility (Andrade et al., 2009; Agundez et al., 2011). Several metabolic gene variants, such as CYP2E1c1 and NAT2 slow, have been associated with DILI induced by specific drugs based on individual drug metabolism information. Others, such as GSTM1 and T1 null alleles have been associated with enhanced risk of DILI development induced by a large range of drugs. Hence, these variants appear to have a more general role in DILI susceptibility due to their role in reducing the cell's antioxidative capacity (Lucena et al., 2008). Mitochondrial superoxide dismutase (SOD2) and glutathione peroxidase 1 (GPX1) are two additional enzymes involved in combating oxidative stress, with specific genetic variants shown to enhance the risk of developing DILI