Socio-Economic Determinants of the Need for Dental Care in Adults.

BACKGROUND: Oral health has improved in France. However, there are still inequalities related to the socio-economic status. OBJECTIVES: The aim of this study was to measure the prevalence of dental care needs in an adult population and to identify the demographic, socio-economic and behavioral variables that may explain variations in this parameter. METHODS: A cross-sectional analysis of the French SIRS cohort (n = 2,997 adults from the Paris region; 2010 data) was carried out to determine the prevalence of self-reported dental care needs relative to demographic, socio-economic and behavioral variables. A logistic regression model was used to identify the variables that were most strongly associated with the level of need. RESULTS: In 2010, the prevalence of the need for dental care in the SIRS cohort was 35.0% (95% CI [32.3-37.8]). It was lower in people with higher education levels (31.3% [27.9-34.6]), without immigrant background (31.3% [28.0-34.6]) and with comprehensive health insurance (social security + complementary health cover; 32.8% [30.2-35.4]). It decreased as the socio-economic status increased, but without following a strict linear change. It was also lower among individuals who had a dental check-up visit in the previous two years. In multivariate analyses, the socioeconomic variables most strongly associated with the need for dental care were: educational attainment (OR = 1.21 [1.02-1.44]), income level (OR = 1.66 [1.92-2.12]) and national origin (OR = 1.53 [1.26-1.86]). CONCLUSION: These results confirm that the prevalence of dental care needs is higher among adults with low socio-economic status. Education level, income level and also national origin were more strongly associated with the need for dental care than insurance cover level.

Oral health status and treatment needs among psychiatric inpatients in Rennes, France: a cross-sectional study.

BACKGROUND: Severe mental disorders have a chronic course associated with a high risk for co-morbid somatic illnesses and premature mortality and oral health is critical for overall systemic health. But general health care needs in this population are often neglected. Some studies have aimed at determining the oral health status of psychiatric in-patients but to date, no emphasis has been placed on oral health of psychiatric patients in France. The goal of this study was to assess the oral health and treatment needs of institutionalized patients in a large psychiatric hospital, where a dental service was available and free, to compare it with the average population, with psychiatric in-patients in other countries and to provide recommendations for psychiatrists and care-giving staff. METHODS: The dental status (DMFT), the oral hygiene (OHIS: Simplified Oral Hygiene Index), the saliva flow rate were recorded on a randomized patient sample. Demographic and medical data were retrieved from the institutional clinical files. RESULTS: Among the 161 examined patients, 95 (59.0%) were men and 66 (41.0%) were women. The mean age was 46.9 ± 17.5 years. The majority was diagnosed schizophrenia (36.6%) or mood disorders (21.1%). The mean OHIS was 1.7 ± 1.1. Among the 147 patients who agreed to carry out the salivary examination, the average saliva flow rate was 0.3 g ± 0.3 g/min. Saliva flow under the average rest saliva flow (0.52 mg/min) was found for 80.3% of the patient. The mean DMFT was 15.8 ± 8.8 (D = 3.7 ± 4.4, M = 7.3 ± 9.4, F = 4.7 ± 4.9) and significantly increased with age (p < 0.001) and degree of disability (p = 0.003) (stepwise linear regression). Eighteen patients (11.2%) were edentulous. CONCLUSIONS: The DMFT was similar to low income French population but psychiatric patients had almost 4 times more decayed teeth, slightly less missing teeth and 1.5 times less filled teeth. Oral health appeared to be better than in most other countries. But compared to general population, the still unmet dental and prosthetic needs indicated the major need of enhanced access to dental care and specific preventive programs.

Ontology driven decision support systems for medical diagnosis - an interactive form for consultation in patients with plasma cell disease.

Multiple myeloma (MM) is a malignant disorder characterized by the monoclonal proliferation of B cell derived plasma cells in the bone marrow. The diagnosis depends on the identification of abnormal monoclonal marrow plasma cells, monoclonal protein in the serum or urine, evidence of end-organ damage, and a clinical picture consistent with MM. The distinction between MM stages- monoclonal gammopathy of undetermined significance or indolent myeloma-is critical in guiding therapy. This paper describes how to produce ontology-driven semiological rules base (SRB) and a consultation form to aid in the diagnosis of plasma cells diseases. We have extracted the MM sub-ontology from the NCI Thesaurus. Using Protégé 3.4.2 and owl1, criteria in the literature for the diagnosis and staging of MM have been added to the ontology. All quantitative parameters have been transformed to a qualitative format. A formal description of MM variants and stages has been given. The obtained ontology has been checked by a reasoner and instantiated to obtain a SRB. The form created has been tested and evaluated utilizing 63 clinical medical reports. The likelihood for a disease being the correct diagnosis is determined by computing a ratio. The resulting tool is relevant for MM diagnosis and staging.

Ontology and medical diagnosis.

Ontology and associated generic tools are appropriate for knowledge modeling and reasoning, but most of the time, disease definitions in existing description logic (DL) ontology are not sufficient to classify patient's characteristics under a particular disease because they do not formalize operational definitions of diseases (association of signs and symptoms=diagnostic criteria). The main objective of this study is to propose an ontological representation which takes into account the diagnostic criteria on which specific patient conditions may be classified under a specific disease. This method needs as a prerequisite a clear list of necessary and sufficient diagnostic criteria as defined for lots of diseases by learned societies. It does not include probability/uncertainty which Web Ontology Language (OWL 2.0) cannot handle. We illustrate it with spondyloarthritis (SpA). Ontology has been designed in Protégé 4.1 OWL-DL2.0. Several kinds of criteria were formalized: (1) mandatory criteria, (2) picking two criteria among several diagnostic criteria, (3) numeric criteria. Thirty real patient cases were successfully classified with the reasoner. This study shows that it is possible to represent operational definitions of diseases with OWL and successfully classify real patient cases. Representing diagnostic criteria as descriptive knowledge (instead of rules in Semantic Web Rule Language or Prolog) allows us to take advantage of tools already available for OWL. While we focused on Assessment of SpondyloArthritis international Society SpA criteria, we believe that many of the representation issues addressed here are relevant to using OWL-DL for operational definition of other diseases in ontology.

Creating an ontology driven rules base for an expert system for medical diagnosis.

Expert systems of the 1980s have failed on the difficulties of maintaining large rule bases. The current work proposes a method to achieve and maintain rule bases grounded on ontologies (like NCIT). The process described here for an expert system on plasma cell disorder encompasses extraction of a sub-ontology and automatic and comprehensive generation of production rules. The creation of rules is not based directly on classes, but on individuals (instances). Instances can be considered as prototypes of diseases formally defined by "destrictions" in the ontology. Thus, it is possible to use this process to make diagnoses of diseases. The perspectives of this work are considered: the process described with an ontology formalized in OWL1 can be extended by using an ontology in OWL2 and allow reasoning about numerical data in addition to symbolic data.