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.

European cancer mortality predictions for the year 2013.

Background Estimated cancer mortality statistics were published for the years 2011 and 2012 for the European Union (EU) and its six more populous countries. Patients and methods Using logarithmic Poisson count data joinpoint models and the World Health Organization mortality and population database, we estimated numbers of deaths and age-standardized (world) mortality rates (ASRs) in 2013 from all cancers and selected cancers. Results The 2013 predicted number of cancer deaths in the EU is 1 314 296 (737 747 men and 576 489 women). Between 2009 and 2013, all cancer ASRs are predicted to fall by 6% to 140.1/100 000 in men, and by 4% to 85.3/100 000 in women. The ASRs per 100 000 are 6.6 men and 2.9 women for stomach, 16.7 men and 9.5 women for intestines, 8.0 men and 5.5 women for pancreas, 37.1 men and 13.9 women for lung, 10.5 men for prostate, 14.6 women for breast, and 4.7 for uterine cancer, and 4.2 and 2.6 for leukaemia. Recent trends are favourable except for pancreatic cancer and lung cancer in women. Conclusions Favourable trends will continue in 2013. Pancreatic cancer has become the fourth cause of cancer death in both sexes, while in a few years lung cancer will likely become the first cause of cancer mortality in women as well, overtaking breast cancer.

Bayesian tools for zero counts in compositional data

The log-ratio methodology makes available powerful tools for analyzing compositionaldata. Nevertheless, the use of this methodology is only possible for those data setswithout null values. Consequently, in those data sets where the zeros are present, aprevious treatment becomes necessary. Last advances in the treatment of compositionalzeros have been centered especially in the zeros of structural nature and in the roundedzeros. These tools do not contemplate the particular case of count compositional datasets with null values. In this work we deal with \count zeros" and we introduce atreatment based on a mixed Bayesian-multiplicative estimation. We use the Dirichletprobability distribution as a prior and we estimate the posterior probabilities. Then weapply a multiplicative modi¯cation for the non-zero values. We present a case studywhere this new methodology is applied.Key words: count data, multiplicative replacement, composition, log-ratio analysis

The Geographical Scope of Industrial Location Determinants: An Alternative Approach

This paper considers the estimation of the geographical scope of industrial location determinants. While previous studies impose strong assumptions on the weighting scheme of the spatial neighbour matrix, we propose a exible parametrisation that allows for di fferent (distance-based) de finitions of neighbourhood and di fferent weights to the neighbours. In particular, we estimate how far can reach indirect marginal e ffects and discuss how to report them. We also show that the use of smooth transition functions provides tools for policy analysis that are not available in the traditional threshold modelling. Keywords: count data models, industrial location, smooth transition functions, threshold models. JEL-Codes: C25, C52, R11, R30.

Distributional equivalence and subcompositional coherence in the analysis of contingency tables, ratio-scale measurements and compositional data

We consider two fundamental properties in the analysis of two-way tables of positive data: the principle of distributional equivalence, one of the cornerstones of correspondence analysis of contingency tables, and the principle of subcompositional coherence, which forms the basis of compositional data analysis. For an analysis to be subcompositionally coherent, it suffices to analyse the ratios of the data values. The usual approach to dimension reduction in compositional data analysis is to perform principal component analysis on the logarithms of ratios, but this method does not obey the principle of distributional equivalence. We show that by introducing weights for the rows and columns, the method achieves this desirable property. This weighted log-ratio analysis is theoretically equivalent to spectral mapping , a multivariate method developed almost 30 years ago for displaying ratio-scale data from biological activity spectra. The close relationship between spectral mapping and correspondence analysis is also explained, as well as their connection with association modelling. The weighted log-ratio methodology is applied here to frequency data in linguistics and to chemical compositional data in archaeology.

An appropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data.

Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders.

Very high-resolution environmental predictors in species distribution models: moving beyond topography?

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species' micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions - and therefore local management - compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

Integrating species distribution models (SDMs) and phylogeography for two species of Alpine Primula.

The major intention of the present study was to investigate whether an approach combining the use of niche-based palaeodistribution modeling and phylo-geography would support or modify hypotheses about the Quaternary distributional history derived from phylogeographic methods alone. Our study system comprised two closely related species of Alpine Primula. We used species distribution models based on the extant distribution of the species and last glacial maximum (LGM) climate models to predict the distribution of the two species during the LGM. Phylogeographic data were generated using amplified fragment length polymorphisms (AFLPs). In Primula hirsuta, models of past distribution and phylogeographic data are partly congruent and support the hypothesis of widespread nunatak survival in the Central Alps. Species distribution models (SDMs) allowed us to differentiate between alpine regions that harbor potential nunatak areas and regions that have been colonized from other areas. SDMs revealed that diversity is a good indicator for nunataks, while rarity is a good indicator for peripheral relict populations that were not source for the recolonization of the inner Alps. In P. daonensis, palaeo-distribution models and phylogeographic data are incongruent. Besides the uncertainty inherent to this type of modeling approach (e.g., relatively coarse 1-km grain size), disagreement of models and data may partly be caused by shifts of ecological niche in both species. Nevertheless, we demonstrate that the combination of palaeo-distribution modeling with phylogeographical approaches provides a more differentiated picture of the distributional history of species and partly supports (P. hirsuta) and partly modifies (P. daonensis and P. hirsuta) hypotheses of Quaternary distributional history. Some of the refugial area indicated by palaeodistribution models could not have been identified with phylogeographic data.

European cancer mortality predictions for the year 2012.

BACKGROUND: Estimating current cancer mortality figures is important for defining priorities for prevention and treatment.Materials and methods:Using logarithmic Poisson count data joinpoint models on mortality and population data from the World Health Organization database, we estimated numbers of deaths and age-standardized rates in 2012 from all cancers and selected cancer sites for the whole European Union (EU) and its six more populated countries. RESULTS: Cancer deaths in the EU in 2012 are estimated to be 1 283 101 (717 398 men and 565 703 women) corresponding to standardized overall cancer death rates of 139/100 000 men and 85/100 000 women. The fall from 2007 was 10% in men and 7% in women. In men, declines are predicted for stomach (-20%), leukemias (-11%), lung and prostate (-10%) and colorectal (-7%) cancers, and for stomach (-23%), leukemias (-12%), uterus and colorectum (-11%) and breast (-9%) in women. Almost stable rates are expected for pancreatic cancer (+2-3%) and increases for female lung cancer (+7%). Younger women show the greatest falls in breast cancer mortality rates in the EU (-17%), and declines are expected in all individual countries, except Poland. CONCLUSION: Apart for lung cancer in women and pancreatic cancer, continuing falls are expected in mortality from major cancers in the EU.

The Determinants of Exit in Argentina: Core and Peripheral Regions

This paper analyses the regional determinants of exit in Argentina. We find evidence of a dynamic revolving door by which past entrants increase current exits, particularly in the peripheral regions. In the central regions, current and past incumbents cause an analogous displacement effect. Also, exit shows a U-shaped relationship with respect to the informal economy, although the positive effect is weaker in the central regions. These findings point to the existence of a core-periphery structure in the spatial distribution of exits. Key words: firm exit, count data models, Argentina JEL: R12; R30; C33

Predictors for hospitalization and outpatient visits in patients with inflammatory bowel disease: results from the Swiss Inflammatory Bowel Disease Cohort Study.

OBJECTIVES: Patients with inflammatory bowel disease (IBD) have a high resource consumption, with considerable costs for the healthcare system. In a system with sparse resources, treatment is influenced not only by clinical judgement but also by resource consumption. We aimed to determine the resource consumption of IBD patients and to identify its significant predictors. MATERIALS AND METHODS: Data from the prospective Swiss Inflammatory Bowel Disease Cohort Study were analysed for the resource consumption endpoints hospitalization and outpatient consultations at enrolment [1187 patients; 41.1% ulcerative colitis (UC), 58.9% Crohn's disease (CD)] and at 1-year follow-up (794 patients). Predictors of interest were chosen through an expert panel and a review of the relevant literature. Logistic regressions were used for binary endpoints, and negative binomial regressions and zero-inflated Poisson regressions were used for count data. RESULTS: For CD, fistula, use of biologics and disease activity were significant predictors for hospitalization days (all P-values <0.001); age, sex, steroid therapy and biologics were significant predictors for the number of outpatient visits (P=0.0368, 0.023, 0.0002, 0.0003, respectively). For UC, biologics, C-reactive protein, smoke quitters, age and sex were significantly predictive for hospitalization days (P=0.0167, 0.0003, 0.0003, 0.0076 and 0.0175 respectively); disease activity and immunosuppressive therapy predicted the number of outpatient visits (P=0.0009 and 0.0017, respectively). The results of multivariate regressions are shown in detail. CONCLUSION: Several highly significant clinical predictors for resource consumption in IBD were identified that might be considered in medical decision-making. In terms of resource consumption and its predictors, CD and UC show a different behaviour.

Regional determinants of firm entry in a developing country

We analyse the determinants of firm entry in developing countries using Argentina as an illustrative case. Our main finding is that although most of the regional determinants used in previous studies analysing developed countries are also relevant here, there is a need for additional explanatory variables that proxy for the specificities of developing economies (e.g., poverty, informal economy and idle capacity).We also find evidence of a core-periphery pattern in the spatial structure of entry that seems to be mostly driven by differences in agglomeration economies. Since regional policies aiming to attract new firms are largely based on evidence from developed countries, our results raise doubts about the usefulness of such policies when applied to developing economies. JEL classification: R12, R30, C33. Key words: Firm entry, Argentina, count data models.

Programa MicroEconometria. Paquet estadístic i economètric

MicroEconometria és un paquet estadístic i economètric que contempla l’estimació de models uniequacionals: 1- Regressió simple i múltiple: anàlisi de residus, influència i atipicitat, diagnòstics de multicol·linealitat, estimació robusta, predicció, diagnòstics d’estabilitat, bootstrap. 2- Regressió en panell: efectes fixes, efectes aleatoris i efectes combinats. 3- Regressió lògit i probit. 4- Regressió censurada: tobit i model de selecció de Heckman. 5- Regressió multinomial. 6- Regressió poisson: model ‘count data’. 7- Índexs amb variables renda i riquesa i impostos transferències. Genera un informe per a cada una de les possibilitats contemplades que conté la presentació dels resultats de les estimacions, incloent les sortides gràfiques pertinents. L’input del programa és qualsevol base de dades, en la que es pugui identificar la variable endògena i les variables exògenes del model utilitzat, continguda en un llibre d’EXCEL de Microsoft.