The impact of oral health on depression: A systematic review
Bibliographic record
Abstract
INTRODUCTION: As of 2020, about 21% of adults in the United States have a diagnosable mental health disorder, excluding substance use and developmental disorders. Depression, predicted by the WHO to be the leading cause of disease burden by 2030, is linked to various systemic conditions and has been associated with poor oral health. Both behavioral factors, like poor dental hygiene and irregular visits, and biological mechanisms, such as changes in salivary immunity, contribute to this connection, which impacts overall well-being and quality of life. This systematic review aims include: (1) Does tooth loss affect depression? (2) Does oral pain, such as that experienced during chewing and speaking, impact depression? (3) Does oral functionality, including chewing and speaking, influence depression? (4) Does overall oral health affect depression? METHODS: We conducted a systematic search of PubMed, EBSCO host, Medline, and Google Scholar databases from January 2000 to June 2024 using relevant keywords. Studies examining the impact of oral health parameters (tooth loss, oral pain, oral functionality, overall oral health) on depression were included. Articles were included if (1) full text manuscripts in English were available, (2) the study described the association of oral health and depression, and (3) the independent value was an oral related factor and the dependent value was depression. The following were excluded from our analysis: (1) any articles where oral factors were not the independent value, (2) systematic reviews, (3) case reports, and (4) duplicate studies among our databases. Thirty-one studies met the inclusion criteria. RESULTS: Tooth loss, oral pain, and impaired oral functionality were consistently associated with increased depressive symptoms across the included studies. Greater tooth loss was linked to higher odds of both onset and progression of depression. Oral pain exacerbated depressive symptoms, while difficulties in chewing or speaking were associated with elevated risks of depression. CONCLUSION: There is a bidirectional relationship between oral health and depression, highlighting the urgent need for comprehensive public health initiatives. Integrating oral health assessments into routine medical care, and developing targeted interventions are crucial steps to mitigate the impact of poor oral health on mental health outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".