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Record W7105999472 · doi:10.7939/83188

Periodontal Status and Risk Factors of a Vulnerable Low-Income Community in Edmonton

2025· dissertation· en· W7105999472 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsnot available
Fundersnot available
KeywordsMissing dataImputation (statistics)CohortCollinearityLasso (programming language)Socioeconomic statusTooth lossPopulationPeriodontal diseaseConfounding

Abstract

fetched live from OpenAlex

Background: Periodontal disease is one of the most prevalent non-communicable diseases worldwide and a leading cause of tooth loss. It is strongly influenced by modifiable risk factors including smoking, substance use, and social determinants of health such as income, education, and access to care. While previous studies have established this link globally, Canadian data remains sparse, particularly regarding vulnerable inner-city populations. This study aimed to address this knowledge gap by assessing the periodontal health of individuals in Edmonton’s Boyle McCauley Street community, a population characterized by socioeconomic vulnerability. Methods: This was a population-based, cross-sectional study involving 322 adults recruited from four community organizations serving vulnerable Edmontonians. Data was collected via structured questionnaires and clinical oral examinations. Descriptive statistics showcased the population’s socio-demographic, behavioral, as well as general and oral health parameters. Given the dataset’s complexity, additional statistical methods were employed to identify factors associated with periodontal status, as defined by average probing depth and average interproximal probing depths of the six index Ramfjord teeth. To manage missing data and reduce modeling bias, multiple imputation using the Predictive Mean Matching (PMM) was performed. Variable filtering and collinearity checks were conducted to reduce dimensionality, followed by variable selection using LASSO (Least Absolute Shrinkage and Selection Operator) regression via the HierNet algorithm. Separate models were constructed for total average probing depth and interproximal depth to account for site-specific periodontal involvement. Final variable selection used backward elimination with pooled estimates from the multiple imputations. Results: The cohort had a mean age of 49 years, was predominantly male (71%), and overwhelmingly unemployed (89%), with only 24% earning over $12,000 annually. High-risk behaviors were widespread: 69% smoked, 53% consumed alcohol regularly, and 56% used recreational drugs. Only 36% had seen a dentist in the past year, and 9% were fully edentulous. Mucosal inflammation and soft tissue lesions were identified in over half of the participants, and the average DMFT (Decayed, Missing, and Filled Teeth) index was 13.3, indicating a high caries experience. Multivariate regression identified several statistically significant associations with increased periodontal probing depths. First, individuals presenting with pink or white mucosal lesions demonstrated significantly altered probing depths suggesting a potential link between periodontal inflammation and mucosal pathology. Second, participants who reported esthetic or functional oral limitations (e.g., impaired speech or appearance concerns) had worse periodontal outcomes, indicating that subjective perceptions may correlate with underlying disease. Third, individuals self-identifying as “Other” ethnicity (non-White, non-Indigenous) showed slightly but significantly higher probing depths than other ethnic groups, suggesting potential racial variations in periodontal health. Contrary to expectations, no statistically significant associations were observed with traditionally recognized risk factors such as smoking status, income, or education. This is likely attributable to the cohort’s overall homogeneity in low socioeconomic status and high-risk behaviors, which may have limited variability and statistical contrast. Conclusion: This study provided a detailed snapshot of the periodontal and oral health landscape within one of Edmonton’s most underserved communities. It highlights the overwhelming burden of disease and unmet need in a population where access to care is severely limited and risk factors are widespread. The results emphasize the importance of integrating oral health with broader community health initiatives, particularly in vulnerable communities. From a research perspective, the study demonstrates the utility of advanced modeling strategies including multiple imputation, dimensionality reduction, and penalized regression in maximizing the value of complex, imperfect real-world datasets. Clinically, it suggests that patient-reported oral health concerns and visible mucosal changes may potentially serve as screening indicators for periodontal disease in resource-limited settings. Future studies should explore these associations in more diverse and representative populations, and ideally adopt longitudinal designs to examine causality and disease progression. The implementation of programs like the Canadian Dental Care Plan (CDCP) provides a unique opportunity to revisit these communities in future years and assess the impact of expanded public dental coverage on periodontal outcomes. In sum, this research contributes critical insight to the Canadian literature on oral health equity and offers a foundation for targeted interventions in high-risk populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.009
GPT teacher head0.230
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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