Patient needs four years after first psychiatric hospitalization in a Brazilian cohort

Knowledge about the needs of psychiatric patients is essential for mental health care planning. However, research on met and unmet needs is still scarce, particularly in low- and middle-income countries. This study aimed to describe the patients' needs (met and unmet) at least four years after their first psychiatric hospitalization and to verify the role of demographic and clinical features as possible predictors of these needs. Patients who had their first psychiatric admission between January 1, 2006 and December 31, 2007 at an inpatient unit in the city of Ribeirão Preto, Brazil, were eligible to participate in the study. Patients were contacted and face-to-face interviews were conducted by psychologists using the Camberwell Assessment of Need. Data were analyzed using zero-inflated negative binomial regression model. Of 933 eligible patients, 333 were interviewed. The highest level of needs was related to welfare benefits (32.4%, unmet=25.5%), followed by household skills (30.3%, unmet=3.0%), psychotic symptoms (29.4%, unmet=9.0%), psychological distress (27.6%, unmet=8.4%), physical health (24.3%, unmet=5.4%), daytime activities (19.5%, unmet=16.5%), and money (16.8%, unmet=9.0%). Fewer years of schooling, living with relatives, and unemployment at the moment of the first admission were significantly associated with a higher number of both met and unmet needs in the follow-up. Unmet needs were also more often reported by patients living alone. In conclusion, socioeconomic indicators were the best predictors of needs. The unmet needs related to welfare benefits point to the need for specific social and health policies.

The scientific production in health and biological sciences of the top 20 Brazilian universities

Brazilian scientific output exhibited a 4-fold increase in the last two decades because of the stability of the investment in research and development activities and of changes in the policies of the main funding agencies. Most of this production is concentrated in public universities and research institutes located in the richest part of the country. Among all areas of knowledge, the most productive are Health and Biological Sciences. During the 1998-2002 period these areas presented heterogeneous growth ranging from 4.5 percent (Pharmacology) to 191 percent (Psychiatry), with a median growth rate of 47.2 percent. In order to identify and rank the 20 most prolific institutions in these areas, searches were made in three databases (DataCAPES, ISI and MEDLINE) which permitted the identification of 109,507 original articles produced by the 592 Graduate Programs in Health and Biological Sciences offered by 118 public universities and research institutes. The 20 most productive centers, ranked according to the total number of ISI-indexed articles published during the 1998-2003 period, produced 78.7 percent of the papers in these areas and are strongly concentrated in the Southern part of the country, mainly in São Paulo State.

Decision support system for the diagnosis of schizophrenia disorders

Clinical decision support systems are useful tools for assisting physicians to diagnose complex illnesses. Schizophrenia is a complex, heterogeneous and incapacitating mental disorder that should be detected as early as possible to avoid a most serious outcome. These artificial intelligence systems might be useful in the early detection of schizophrenia disorder. The objective of the present study was to describe the development of such a clinical decision support system for the diagnosis of schizophrenia spectrum disorders (SADDESQ). The development of this system is described in four stages: knowledge acquisition, knowledge organization, the development of a computer-assisted model, and the evaluation of the system's performance. The knowledge was extracted from an expert through open interviews. These interviews aimed to explore the expert's diagnostic decision-making process for the diagnosis of schizophrenia. A graph methodology was employed to identify the elements involved in the reasoning process. Knowledge was first organized and modeled by means of algorithms and then transferred to a computational model created by the covering approach. The performance assessment involved the comparison of the diagnoses of 38 clinical vignettes between an expert and the SADDESQ. The results showed a relatively low rate of misclassification (18-34%) and a good performance by SADDESQ in the diagnosis of schizophrenia, with an accuracy of 66-82%. The accuracy was higher when schizophreniform disorder was considered as the presence of schizophrenia disorder. Although these results are preliminary, the SADDESQ has exhibited a satisfactory performance, which needs to be further evaluated within a clinical setting.