Carlos R. García-Alonso

The Impact of Socio-Economic Status on Self-Rated Health: Study of 29 Countries Using European Social Surveys (2002-2008)

Studies show that the association between socio-economic status (SES) and self-rated health (SRH) varies in different countries, however there are not many country-comparisons that examine this relationship over time. The objective of the present study is to determine the effect of three SES measures on SRH in 29 countries according to findings in European Social Surveys (2002–2008), in order to study how socio-economic inequalities can vary our subjective state of health. In line with previous studies, income inequalities seem to be greater not only in Anglo-Saxon and Scandinavian countries, but especially in Eastern European countries. The impact of education is greater in Southern countries, and this effect is similar in Eastern and Scandinavian countries, although occupational status does not produce significant differences in southern countries. This study shows the general relevance of socio-educational factors on SRH. Individual economic conditions are obviously a basic factor contributing to a good state of health, but education could be even more relevant to preserve it. In this sense, policies should not only aim at reducing income inequalities, but should also further the education of people who are in risk of social exclusion.

Meso-level Comparison of Mental Health Service Availability and Use in Chile and Spain

OBJECTIVES There is a demand for international comparisons of mental health care in Latin America. The purpose of this study was to describe mental health care in catchment health areas in Chile and Spain in order to complement information reported at the macro-level (countries or regions). METHODS Availability and utilization of services for the adult population were assessed in two urban areas in Chile and in three urban areas in Spain by using the European Service Mapping Schedule (meso-level data). Indicators from a previous data envelopment analysis (DEA) model of basic community care were applied to this analysis. RESULTS For the two countries, local data on beds and staff differed from data provided at the national level. In Chile meso-level data indicated more available beds and more psychologists per capita than did macro-level data. Quantitative indicators of community care were described, and the main gaps in Chiles urban areas were identified, particularly in day care and nonhospital residential care. There was nearly a tenfold difference in use of residential and day care between the benchmark area in Spain and the areas explored in Chile. In Chiles catchment areas there was no availability of nonacute hospital services, any work-related services for persons with mental disorders, or 24-hour mobile or nonmobile emergency psychiatric care. The meso-level data indicated that delivery and use of care in Chile was more similar to the pattern found in the poorer area in southern Spain than macro-level data would indicate. CONCLUSIONS The European Service Mapping Schedule was useful for describing mental health care outside of Europe and allowed for an international comparison between Chile and Spain. The meso-level description gathered in this study adds to the macro-level information on the mental health care system that has been provided in other reports. The gap between mental health treatment needed and mental health treatment received in Chile may be lower than expected.

Spatial analysis to identify hotspots of prevalence of schizophrenia

IntroductionThe geographical distribution of mental health disorders is useful information for epidemiological research and health services planning.ObjectiveTo determine the existence of geographical hotspots with a high prevalence of schizophrenia in a mental health area in Spain.MethodThe study included 774 patients with schizophrenia who were users of the community mental health care service in the area of South Granada. Spatial analysis (Kernel estimation) and Bayesian relative risks were used to locate potential hotspots. Availability and accessibility were both rated in each zone and spatial algebra was applied to identify hotspots in a particular zone.ResultsThe age-corrected prevalence rate of schizophrenia was 2.86 per 1,000 population in the South Granada area. Bayesian analysis showed a relative risk varying from 0.43 to 2.33. The area analysed had a non-uniform spatial distribution of schizophrenia, with one main hotspot (zone S2). This zone had poor accessibility to and availability of mental health services.ConclusionA municipality-based variation exists in the prevalence of schizophrenia and related disorders in the study area. Spatial analysis techniques are useful tools to analyse the heterogeneous distribution of a variable and to explain genetic/environmental factors in hotspots related with a lack of easy availability of and accessibility to adequate health care services.

Identification and location of hot and cold spots of treated prevalence of depression in Catalonia (Spain)

BackgroundSpatial analysis is a relevant set of tools for studying the geographical distribution of diseases, although its methods and techniques for analysis may yield very different results. A new hybrid approach has been applied to the spatial analysis of treated prevalence of depression in Catalonia (Spain) according to the following descriptive hypotheses: 1) spatial clusters of treated prevalence of depression (hot and cold spots) exist and, 2) these clusters are related to the administrative divisions of mental health care (catchment areas) in this region.MethodsIn this ecological study, morbidity data per municipality have been extracted from the regional outpatient mental health database (CMBD-SMA) for the year 2009. The second level of analysis mapped small mental health catchment areas or groups of municipalities covered by a single mental health community centre. Spatial analysis has been performed using a Multi-Objective Evolutionary Algorithm (MOEA) which identified geographical clusters (hot spots and cold spots) of depression through the optimization of its treated prevalence. Catchment areas, where hot and cold spots are located, have been described by four domains: urbanicity, availability, accessibility and adequacy of provision of mental health care.ResultsMOEA has identified 6 hot spots and 4 cold spots of depression in Catalonia. Our results show a clear spatial pattern where one cold spot contributed to define the exact location, shape and borders of three hot spots. Analysing the corresponding domain values for the identified hot and cold spots no common pattern has been detected.ConclusionsMOEA has effectively identified hot/cold spots of depression in Catalonia. However these hot/cold spots comprised municipalities from different catchment areas and we could not relate them to the administrative distribution of mental care in the region. By combining the analysis of hot/cold spots, a better statistical and operational-based visual representation of the geographical distribution is obtained. This technology may be incorporated into Decision Support Systems to enhance local evidence-informed policy in health system research.

Determination of relative agrarian technical efficiency by a dynamic over-sampling procedure guided by minimum sensitivity

In this paper, a dynamic over-sampling procedure is proposed to improve the classification of imbalanced datasets with more than two classes. This procedure is incorporated into a Hybrid algorithm (HA) that optimizes Multi Layer Perceptron Neural Networks (MLPs). To handle class imbalance, the training dataset is resampled in two stages. In the first stage, an over-sampling procedure is applied to the minority class to partially balance the size of the classes. In the second, the HA is run and the dataset is over-sampled in different generations of the evolution, generating new patterns in the minimum sensitivity class (the class with the worst accuracy for the best MLP of the population). To evaluate the efficiency of our technique, we pose a complex problem, the classification of 1617 real farms into three classes (efficient, intermediate and inefficient) according to the Relative Technical Efficiency (RTE) obtained by the Monte Carlo Data Envelopment Analysis (MC-DEA). The multi-classification model, named Dynamic Smote Hybrid Multi Layer Perceptron (DSHMLP) is compared to other standard classification methods with an over-sampling procedure in the preprocessing stage and to the threshold-moving method where the output threshold is moved toward inexpensive classes. The results show that our proposal is able to improve minimum sensitivity in the generalization set (35.00%) and obtains a high accuracy level (72.63%).

Income prediction in the agrarian sector using product unit neural networks

European Union financial subsidies in the agrarian sector are directly related to maintaining a sustainable farm income, so its determination using, for example, the farm gross margin is a basic element in agrarian programs for sustainable development. Using this tool, it is possible the identification of the agrarian structures that need financial support and to what extent it is needed. However, the process of farm gross margin determination is complicated and expensive because it is necessary to find the value of all the inputs consumed and outputs produced. Considering the circumstances mentioned, the objectives of this research were to: (1) select a representative and reduced set of easy-to-collect descriptive variables to estimate the gross margin of a group of olive-tree farms in Andalusia; (2) investigate if artificial neural network models (ANN) with two different types of basis functions (sigmoidal and product-units) could effectively predict the gross margin of olive-tree farms; (3) compare the effectiveness of multiple linear, quadratic and robust regression models versus ANN; and (4) validate the best mathematical model obtained for gross margin prediction by analysing realistic farm and farmer scenarios. Results from ANN models, specially the product-unit ones, have provided the most accurate gross margin predictions.

Changes in socioeconomic determinants of health: comparing the effect of social and economic indicators through European welfare state regimes

AimThis study is aimed at comparing the effect of different measures of socioeconomic status on self-rated health throughout European welfare state regimes during the period 2002–2008, in order to study how diverse socioeconomic inequalities can vary our health over time.Subjects and methodsThis study uses the European Social Survey to compare the impact of three specific socioeconomic measures (income, education and occupational status) on self-rated health.ResultsThe main finding to be highlighted is that the importance of education-related inequalities surpasses differences in income and occupational status, especially in southern and eastern countries. The relationship between income and self-rated health is stronger in liberal and social-democratic regimes, where labour market regulation is characterized by its flexibility and high liberalization. The impact of occupational status is moderate among liberal, social-democratic and conservative regimes, but lower in southern and eastern ones.ConclusionThese findings support the existence of a contextual effect among welfare states that varies the impact of social and economic indicators in self-rated health over time.

Development of a new spatial analysis tool in mental health: Identification of highly autocorrelated areas (hot-spots) of schizophrenia using a Multiobjective Evolutionary Algorithm model (MOEA/HS)

AIMS This study had two objectives: (1) to design and develop a computer-based tool, called Multi-Objective Evolutionary Algorithm/Hot-Spots (MOEA/HS), to identify and geographically locate highly autocorrelated zones or hot-spots and which merges different methods, and (2) to carry out a demonstration study in a geographical area where previous information about the distribution of schizophrenia prevalence is available and which can therefore be compared. METHODS Local Indicators of Spatial Aggregation (LISA) models as well as the Bayesian Conditional Autoregressive Model (CAR) were used as objectives in a multicriteria framework when highly autocorrelated zones (hot-spots) need to be identified and geographically located. A Multi-Objective Evolutionary Algorithm (MOEA) model was designed and used to identify highly autocorrelated areas of the prevalence of schizophrenia in Andalusia. Hot-spots were statistically identified using exponential-based QQ-Plots (statistics of extremes). RESULTS Efficient solutions (Pareto set) from MOEA/HS were analysed statistically and one main hot-spot was identified and spatially located. Our model can be used to identify and locate geographical hot-spots of schizophrenia prevalence in a large and complicated region. CONCLUSIONS MOEA/HS enables a compromise to be achieved between different econometric methods by highlighting very special zones in complex areas where schizophrenia shows a high autocorrelation.

Standard comparison of local mental health care systems in eight European countries

Aims. There is a need of more quantitative standardised data to compare local Mental Health Systems (MHSs) across international jurisdictions. Problems related to terminological variability and commensurability in the evaluation of services hamper like-with-like comparisons and hinder the development of work in this area. This study was aimed to provide standard assessment and comparison of MHS in selected local areas in Europe, contributing to a better understanding of MHS and related allocation of resources at local level and to lessen the scarcity in standard service comparison in Europe. This study is part of the Seventh Framework programme REFINEMENT (Research on Financing Systems’ Effect on the Quality of Mental Health Care in Europe) project. Methods. A total of eight study areas from European countries with different systems of care (Austria, England, Finland, France, Italy, Norway, Romania, Spain) were analysed using a standard open-access classification system (Description and Evaluation of Services for Long Term Care in Europe, DESDE-LTC). All publicly funded services universally accessible to adults (≥18 years) with a psychiatric disorder were coded. Care availability, diversity and capacity were compared across these eight local MHS. Results. The comparison of MHS revealed more community-oriented delivery systems in the areas of England (Hampshire) and Southern European countries (Verona – Italy and Girona – Spain). Community-oriented systems with a higher proportion of hospital care were identified in Austria (Industrieviertel) and Scandinavian countries (Sør-Trøndelag in Norway and Helsinki-Uusimaa in Finland), while Loiret (France) was considered as a predominantly hospital-based system. The MHS in Suceava (Romania) was still in transition to community care. Conclusions. There is a significant variation in care availability and capacity across MHS of local areas in Europe. This information is relevant for understanding the process of implementation of community-oriented mental health care in local areas. Standard comparison of care provision in local areas is important for context analysis and policy planning.

A macro-economic model to forecast remittances based on Monte-Carlo simulation and artificial intelligence

A computer system based on Monte-Carlo simulation and fuzzy logic has been designed, developed and tested to: (i) identify covariates that influence remittances received in a specific country and (ii) explain their behavior throughout the time span involved. The resulting remittance model was designed theoretically, identifying the variables which determined remittances and their dependence relationships, and then developed into a computer cluster. This model aims to be global and is useful for assessing the long term evolution of remittances in scenarios where a rich country is the host (United States of America) while a poor country is the where the migrant is from (El Salvador). By changing the socio-economic characteristics of the countries involved, experts can analyze new socio-economic frameworks to obtain useful conclusions for decision-making processes involving development and sustainability.