Factors associated with the dental service utilization by enrollees on the Lagos State health insurance scheme, Nigeria
Bibliographic record
Abstract
BACKGROUND: Despite assumptions that insurance coverage would boost oral healthcare utilization in Nigeria, there is insufficient evidence supporting this claim. This study investigates the associations between residential location, awareness of the oral health insurance scheme, history of dental service utilization, and acceptance of oral health insurance among individuals benefiting from the Ilera Eko Scheme; a scheme that integrates preventive and curative oral health care into the state health insurance scheme. METHODS: A cross-sectional survey was conducted from July to November 2023 recruiting from a database of 1520 enrollees aged of 18 and 72-years-old who had been on the scheme for at least three months. An interviewer-administered questionnaire was used to collect the data from participants living in five regions of Lagos State. The dependent variable was dental service utilization. The independent variables were awareness about Ilera Eko health insurance scheme, history of oral health problem, residential location of the respondents (Lagos Island, Badagry, Epe, Ikorodu and Ikeja), and perception about the scheme. The confounding variables were the age at last birthday, sex at birth (male or female), educational level (no education, primary, secondary, and tertiary education), level of income (< 50,000, 50,000-10000, 150,000-200,000, > 200,000), employment status (employed, self-employed and unemployed), marital status (single, married, divorced. widow/widower) and duration on the scheme (< 6 months, 6-12 months, > 12 months). A binary logistic regression analysis was conducted to determine the associations between the dependent and independent variables, controlling for confounders. RESULTS: The study recruited 485 participants of which 31 (6.4%) had used the oral health care services. Respondents with oral health problems had higher odds of using the scheme (AOR:21.065; p < 0.001). Residents in Ikeja had significantly lower odds of using the scheme when compared with residents in Lagos Island (AOR: 0.174; p = 0.005). CONCLUSION: Respondents with oral health problems had higher odds of using the oral health insurance scheme. Innovative approaches are needed to drive the utilization of free dental service packages on health insurance schemes in Lagos State, especially for preventive care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".