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Abstract Background Spain has gone from a surplus to a shortage of medical doctors in very few years. Medium and long-term planning for health professionals has become a high priority for health authorities. Methods We created a supply and demand/need simulation model for 43 medical specialties using system dynamics. The model includes demographic, education and labour market variables. Several scenarios were defined. Variables controllable by health planners can be set as parameters to simulate different scenarios. The model calculates the supply and the deficit or surplus. Experts set the ratio of specialists needed per 1000 inhabitants with a Delphi method. Results In the scenario of the baseline model with moderate population growth, the deficit of medical specialists will grow from 2% at present (2800 specialists) to 14.3% in 2025 (almost 21 000). The specialties with the greatest medium-term shortages are Anesthesiology, Orthopedic and Traumatic Surgery, Pediatric Surgery, Plastic Aesthetic and Reparatory Surgery, Family and Community Medicine, Pediatrics, Radiology, and Urology. Conclusions The model suggests the need to increase the number of students admitted to medical school. Training itineraries should be redesigned to facilitate mobility among specialties. In the meantime, the need to make more flexible the supply in the short term is being filled by the immigration of physicians from new members of the European Union and from Latin America.

Abstract Background Although health is one of the main determinants of the welfare of societies, few studies have evaluated health related quality of life in representative samples of the population of a region or a country. Our aim is to describe the health-related quality of life of the inhabitants of two quite different Spanish regions (Canary Islands and Catalonia) and to compare the prevalence of health problems between age-sex groups. Methods We use data obtained from the 2006 Health Survey of Catalonia and the 2004 Canary Islands Health Survey. With an ordinal composite variable measuring HRQOL we identify the association of characteristics of individuals with self-reported quality of life and test for differences between the regions. Results The prevalence of problems in the five EQ-5 D dimensions increases with age and is generally higher for women than for men. The dimension with the highest prevalence of problems is "anxiety/depression", and there is noteworthy the extent of discomfort and pain among Canary Island women. Education, especially among the elderly, has an important effect on health-related quality of life. Conclusions There are substantial structural and compositional differences between the two regions. Regional context is a significant factor, independent of the compositional differences, and the effects of context are manifest above all in women. The findings show the importance of disease prevention and the need for improving the educational level of the population in order to reduce health inequalities.

Abstract Background The importance of Small Area Variation Analysis for policy-making contrasts with the scarcity of work on the validity of the statistics used in these studies. Our study aims at 1) determining whether variation in utilization rates between health areas is higher than would be expected by chance, 2) estimating the statistical power of the variation statistics; and 3) evaluating the ability of different statistics to compare the variability among different procedures regardless of their rates. Methods Parametric bootstrap techniques were used to derive the empirical distribution for each statistic under the hypothesis of homogeneity across areas. Non-parametric procedures were used to analyze the empirical distribution for the observed statistics and compare the results in six situations (low/medium/high utilization rates and low/high variability). A small scale simulation study was conducted to assess the capacity of each statistic to discriminate between different scenarios with different degrees of variation. Results Bootstrap techniques proved to be good at quantifying the difference between the null hypothesis and the variation observed in each situation, and to construct reliable tests and confidence intervals for each of the variation statistics analyzed. Although the good performance of Systematic Component of Variation (SCV), Empirical Bayes (EB) statistic shows better behaviour under the null hypothesis, it is able to detect variability if present, it is not influenced by the procedure rate and it is best able to discriminate between different degrees of heterogeneity. Conclusion The EB statistics seems to be a good alternative to more conventional statistics used in small-area variation analysis in health service research because of its robustness.