Time trends in socio-economic inequalities in the lack of access to dental services among children in Spain 1987-2011.

INTRODUCTION: Adult oral health is predicted by oral health in childhood. Prevention improves oral health in childhood and, consequently in adulthood, so substantial cost savings can be derived from prevention. The burden of oral disease is particularly high for disadvantaged and poor population groups in both developing and developed countries. Therefore, an appropriate and egalitarian access to dental care becomes a desirable objective if children's dental health is to be promoted irrespective of socioeconomic status. The aim of this research is to analyse inequalities in the lack of access to dental care services for children in the Spanish National Health System by socio-economic group over the period 1987-2011. METHODS: Pooled data from eight editions of the Spanish National Health Survey for the years 1987-2011, as well as contextual data on state dental programmes are used. Logistic regressions are used to examine the related factors to the probability of not having ever visited the dentist among children between 6 and 14 years old. Our lack of access variable pays particular attention to the socioeconomic level of children's household. RESULTS: The mean probability of having never been to the dentist falls considerably from 49.5% in 1987 to 8.4% in 2011. Analysis by socioeconomic level indicates that, in 1987, the probability of not having ever gone to the dentist is more than two times higher for children in the unskilled manual social class than for those in the upper non-manual social class (odds ratio 2.35). And this difference is not reduced significantly throughout the period analysed, rather it increases as in 1993 (odds of 2.39) and 2006 (odds of 3.03) to end in 2011 slightly below than in 1987 (odds ratio 1.80). CONCLUSION: There has been a reduction in children's lack of access to dentists in Spain over the period 1987-2011. However, this reduction has not corrected the socioeconomic inequalities in children's access to dentists in Spain.

Equity in specialist waiting times by socioeconomic groups: evidence from Spain.

In countries with publicly financed health care systems, waiting time--rather than price--is the rationing mechanism for access to health care services. The normative statement underlying such a rationing device is that patients should wait according to need and irrespective of socioeconomic status or other non-need characteristics. The aim of this paper is to test empirically that waiting times for publicly funded specialist care do not depend on patients' socioeconomic status. Waiting times for specialist care can vary according to the type of medical specialty, type of consultation (review or diagnosis) and the region where patients' reside. In order to take into account such variability, we use Bayesian random parameter models to explain waiting times for specialist care in terms of need and non-need variables. We find that individuals with lower education and income levels wait significantly more time than their counterparts.

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.

Spatial analysis to identify hotspots of prevalence of schizophrenia.

INTRODUCTION: The geographical distribution of mental health disorders is useful information for epidemiological research and health services planning. OBJECTIVE: To determine the existence of geographical hotspots with a high prevalence of schizophrenia in a mental health area in Spain. METHOD: The 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. RESULTS: The 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. CONCLUSION: A 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.