Poor Oral Health in Patients with Schizophrenia: a Meta-Analysis of Case-Control Studies

Oral health of people with emotional disorders: A systematic review and meta-analysis

OBJECTIVES

This study aims to determine the association between severe mental disorders and oral health among individuals over 18 years of age.

METHODS

An electronic search was conducted in six electronic databases and gray literature. Qualitative and quantitative analyses were performed on studies that met the inclusion criteria. The methodology of the included studies was assessed using the Joanna Briggs Institute Critical Appraisal tool. A meta-analysis of proportions with a random effect was carried out. The certainty of evidence was evaluated using the GRADE tool.

RESULTS

After searching the databases, 5,734 references were retrieved, and twenty articles were selected for synthesis. Considering the DMFT index between the groups with mental disorders and the control group, the values of the DMFT index were higher among individuals with schizophrenia [MD = 5.27; 95% CI = 4.13 - 6.42; I2 = 35%] and bipolar disorder [MD = 1.90; 95% CI = 0.87 - 2.93]. Values were lower among individuals with obsessive-compulsive disorder [MD = -0.85; 95% CI = -1.46-0.24]. The risk of bias was considered low for 16 studies, and four were classified with a moderate risk of bias. The certainty of evidence was very low.

CONCLUSION

Patients with schizophrenia and bipolar disorder exhibit increased frequency in the number of decayed, missing, or filled teeth. There was no effect in relation to periodontal probing depth, plaque index, and TMD, but the evidence is still uncertain for this outcome.

CLINICAL RELEVANCE

These findings underscore the need for a comprehensive health approach.

Classifying Schizophrenia Cases by Artificial Neural Network Using Japanese Web-Based Survey Data: Case-Control Study

BACKGROUND

In Japan, challenges were reported in accurately estimating the prevalence of schizophrenia among the general population. Retrieving previous studies, we investigated that patients with schizophrenia were more likely to experience poor subjective well-being and various physical, psychiatric, and social comorbidities. These factors might have great potential for precisely classifying schizophrenia cases in order to estimate the prevalence. Machine learning has shown a positive impact on many fields, including epidemiology, due to its high-precision modeling capability. It has been applied in research on mental disorders. However, few studies have applied machine learning technology to the precise classification of schizophrenia cases by variables of demographic and health-related backgrounds, especially using large-scale web-based surveys.

OBJECTIVE

The aim of the study is to construct an artificial neural network (ANN) model that can accurately classify schizophrenia cases from large-scale Japanese web-based survey data and to verify the generalizability of the model.

METHODS

Data were obtained from a large Japanese internet research pooled panel (Rakuten Insight, Inc) in 2021. A total of 223 individuals, aged 20-75 years, having schizophrenia, and 1776 healthy controls were included. Answers to the questions in a web-based survey were formatted as 1 response variable (self-report diagnosed with schizophrenia) and multiple feature variables (demographic, health-related backgrounds, physical comorbidities, psychiatric comorbidities, and social comorbidities). An ANN was applied to construct a model for classifying schizophrenia cases. Logistic regression (LR) was used as a reference. The performances of the models and algorithms were then compared.

RESULTS

The model trained by the ANN performed better than LR in terms of area under the receiver operating characteristic curve (0.86 vs 0.78), accuracy (0.93 vs 0.91), and specificity (0.96 vs 0.94), while the model trained by LR showed better sensitivity (0.63 vs 0.56). Comparing the performances of the ANN and LR, the ANN was better in terms of area under the receiver operating characteristic curve (bootstrapping: 0.847 vs 0.773 and cross-validation: 0.81 vs 0.72), while LR performed better in terms of accuracy (0.894 vs 0.856). Sleep medication use, age, household income, and employment type were the top 4 variables in terms of importance.

CONCLUSIONS

This study constructed an ANN model to classify schizophrenia cases using web-based survey data. Our model showed a high internal validity. The findings are expected to provide evidence for estimating the prevalence of schizophrenia in the Japanese population and informing future epidemiological studies.

Oropharyngeal microbiome composition as a possible diagnostic marker for true psychosis in a forensic psychiatric setting: A narrative literature review and an opinion

The malingered psychosis has increasingly occurred over the past few years due to the tendency towards care in the community and the closures of long-stay psychiatric institutions. Thus, it is required to identify malingered psychosis to reach accurate forensic assessments and inhibit misuse of restricted healthcare resources and miscarriages of justice. Despite the fact that some practical psychometric tools and strategies have been proposed for diagnosing true psychosis over the past decades, the differentiation between true psychosis and malingered psychosis is still sometimes challenging. Accordingly, it seems crucial to identify innovative and reliable diagnostic alternatives. Hence, the present article summarizes a collection of evidence that can be used by researchers to improve future assessment of oropharyngeal microbiome composition as a feasible diagnostic marker for true psychosis in a forensic psychiatric setting.

Management of Dental Patients With Mental Health Problems in Special Care Dentistry: A Practical Algorithm

Introduction Individuals with mental health problems have poor oral health affecting their quality of life with an increased burden on their well-being. Dentists find treating them challenging due to a lack of training and awareness in modifications of dental care delivery in special needs patients. Also, polypharmacy is common in psychiatric care, further complicating dental care while prescribing routine medications as potential drug interactions must be considered.  Methods Due to a lack of clinical practice guidelines in the literature and the absence of guidelines issued by dental governing bodies, we attempted to consolidate the existing challenges and propose a model for managing psychiatric special needs patients. Results and discussion Based on the current evidence, we hereby recommend 'psychiatric dental consultation liaison' (PDCL) services as the acceptable framework for the management of dental patients with mental health problems in special care dentistry. Conclusion PDCL services will favour both dentists and patients as it includes psychiatric consultation and interventions that will result in the positive execution of comprehensive dental treatment care.

Recent findings on subjective well-being and physical, psychiatric, and social comorbidities in individuals with schizophrenia: A literature review

AIM

Care for people with schizophrenia is shifting the locus from long-stay mental hospitals to nonspecialized community-based settings. Knowledge on the care is not a sole property of psychiatric specialists. Community healthcare workers who do not specialize in psychiatry are recommended to learn more about schizophrenia. This review aimed to summarize recent findings on subjective well-being and physical, psychiatric, and social comorbidities in individuals with schizophrenia.

METHODS

A literature review was conducted. We retrieved findings from existing systematic reviews and meta-analyses as our preferred method. When data were not available, we referred to other types of studies.

RESULTS

As per our review, individuals with schizophrenia demonstrated poor subjective well-being, happiness, and life satisfaction despite individual differences. Pharmacotherapy caused weight gain and constipation, whereas race and hospitalization might affect weight reduction. Individuals with schizophrenia demonstrated poor oral health, a high prevalence of noncommunicable diseases, and unique eating behaviors. Depression, sleep disorders, smoking, and alcohol and drug consumption were frequently found in the individuals. Research findings regarding problematic internet and smartphone use and stress perception were limited. Low health literacy and neglect of preventable behaviors were frequently seen in individuals with schizophrenia. They tended to be less educated, poor, unemployed, unmarried/unattached, and had poor social cognition, resulting in little social support and a small social network.

CONCLUSION

Retrieving recent data, we confirmed that individuals with schizophrenia had poor subjective well-being and suffer from various physical, psychiatric, and social comorbidities.

Is There a Link between Oropharyngeal Microbiome and Schizophrenia? A Narrative Review

The study aimed to examine the impact of the oropharyngeal microbiome in the pathophysiology of schizophrenia and to clarify whether there might be a bidirectional link between the oral microbiota and the brain in a context of dysbiosis-related neuroinflammation. We selected nine articles including three systemic reviews with several articles from the same research team. Different themes emerged, which we grouped into 5 distinct parts concerning the oropharyngeal phageome, the oropharyngeal microbiome, the salivary microbiome and periodontal disease potentially associated with schizophrenia, and the impact of drugs on the microbiome and schizophrenia. We pointed out the presence of phageoma in patients suffering from schizophrenia and that periodontal disease reinforces the role of inflammation in the pathophysiology of schizophrenia. Moreover, saliva could be an interesting substrate to characterize the different stages of schizophrenia. However, the few studies we have on the subject are limited in scope, and some of them are the work of a single team. At this stage of knowledge, it is difficult to conclude on the existence of a bidirectional link between the brain and the oral microbiome. Future studies on the subject will clarify these questions that for the moment remain unresolved.