Sicras-Mainar et al Table 4 General and unit distribution of the model of costs per patient per year (in euros), in the absence or presence of obesity Nonobese (n = 30.843) Obese subjects (n = 11.594) Concepts Total costs Mean (SD) Corrected Mean (SD) Corrected Mean total cost p* model* model* per patient Total cost in PC 27,696,430 638 (1.245) 694 [679–709] 981 (1.467) 811 [787–835] 735 (1.320) 0.0001 Fixed/semiﬁxed cost 7,511,580 (27%) 182 (170) 192 [190–194] 242 (196) 212 [209–215] 199 (180) 0.0001 Variable cost 20,184,850 (73%) 455 (1.182) 502 [488–516] 739 (1.395) 599 [575–622] 535 (1.253) 0.0001 Cost of referrals 2,211,296 (8%) 54 (76) 56 [55–57] 70 (86) 64 [62–65] 59 (79) 0.0001 Pharmaceutical cost 16,460,466 (59%) 363 (673) 407 [400–414] 624 (807) 497 [485–510] 437 (723) 0.0001 Notes: Values are given as percentage or mean; costs in euros ( ); *adjusted p level; each F-test contrasts the simple effect of obesity in each combination of levels of the rest of effects shown.These contrasts are based on comparisons by pairs (linearly independent), between the estimated marginal means.The co-variables appearing in the model are evaluated in the following values: Charlson Index = 0.35, mean age = 52.2. Method: Bonferroni correction. Abbreviations: SD, standard deviation and 95% conﬁdence interval; PC, Primary Care. In studies of the use of psychoactive drugs, and in coincidence with our own observations, a larger proportion of treatments were seen to correspond to women – this possibly being due to the greater tendency among women to visit Primary Care centers (Ustun 1999; Vedia et al 2005). If the behavioral pattern among males is changing, then this discrep- ancy between the costs of males and females could decrease over time. It therefore would be interesting to conduct similar studies in the future, to conﬁrm this possibility. A relevant observation in our study is that patients show increased health care resource consumption at all levels, regardless of age, sex or morbidity – thus resulting in an increased cost per patient per year. The largest difference in this sense cor- responds to pharmacological expenditure. This cost could be due to the greater number of drug substances consumed, though it cannot be ruled out that part of the difference may be attributable to a greater purchasing cost for psychoactive medicines, or to the possibility that patients of this kind are prescribed latest-generation (and more expensive) drugs for the treatment of other mental problems. The second most important cost component corresponded to medical visits in the Primary Care setting. This coincides with the literature, where in general psychiatric patients are seen to seek care more often, and do so more in Primary Care centers than in psychiatric clinics (Kokkinos et al 2005). In this context, in a study of the epidemiology and use of health care resources related to mental health in Primary Care, reported that these patients present higher consulting rates than those individu- als without mental disease (Vázquez et al 1997). The highest consulting rates were likewise associated with the demo- graphic variables of patient sex (female) and age. The results obtained can be explained not only by the different geographical composition of the study sample 224 (a factor clearly associated to patient age), but also by other differences implicated in the studies consulted, and which could be explained in part by different health or dietary habits (Katerndahl 1995; Gómez et al 2005). Particularly as relates to obesity, the question emerges as to what other factors could be playing a relevant role in the fact that metabolic syndrome is more prevalent among these patients. In this context, unhealthy living habits could play an important role. In this sense, the role of diet seems to be conﬁrmed, in view of the observed high prevalence of obesity, dyslipidemia, and hypertriglyceridemia. These observations, attributable to diet, lifestyle, or other con- ditioning factors, appear to coincide with the tendency of schizophrenic patients to accumulate fat and intraabdominal adipose tissue (Hagg et al 2006). Regardless of the effect of lifestyle and health-damaging habits, attention should also focus on the role of the APDs used as treatment – some of which have been shown to exert a direct inﬂuence upon weight gain and lipid and carbohydrate metabolic disorders (Ryan et al 2002; Barnett et al 2006). Although controversy exists surrounding this, such substances could be making an important contribution to the appearance of metabolic disorders with an increased risk of cardiovascular disease. Other possible causes, such as certain alterations of the hypothalamic-pituitary-adrenal neuroendocrine axis inducing increased blood cortisol levels with genotypic expression in the form of abdominal obesity among patients receiving APDs, or the possible effects upon hippocampal volume, are aspects requiring further study and conﬁrmation as potential cardiovascular risk factors in patients of this kind (McIntyre et al 2001). Other limitations require caution in generalizing the results, including the inherent observational design of the Neuropsychiatric Disease and Treatment 2008:4(1)