Evidence map›Paper›PMID 38783219›Full record

ArticleBMC public health2024

Socio-demographic predictors of not having private dental insurance coverage: machine-learning algorithms may help identify the disadvantaged.

Venkata R Duvvuri, Mona Abdelrehim, Sonica Singhal

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Venkata R DuvvuriPublic Health Ontario, Toronto, ON, Canada.
Mona AbdelrehimDental Public Health, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada. mona.abdelrehim@mail.utoronto.ca.
Sonica SinghalPublic Health Ontario, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFor accessing dental care in Canada, approximately 62% of the population has employment-based insurance, 6% have some publicly funded coverage, and 32% have to pay out-of pocket. Those with no insurance or public coverage find dental care more unaffordable compared to those with private insurance. To support the development of more comprehensive publicly funded dental care programs, it is important to understand the socio-demographic attributes of all those, who find dental care unaffordable.

methodsThis study is a secondary analysis of the data collected from Ontarians during the latest available cycle of the Canadian Community Health Survey (2017-18), a cross-sectional survey that collects information on health status, health care utilization, and health determinants for the Canadian population. First, bivariate analysis was conducted to determine the characteristics of Ontarians who lack dental insurance. Afterwards, we employed machine learning (ML) to analyze data and identify risk indicators for not having private dental insurance. Specifically, we trained several supervised ML models and utilized Shapley additive explanations (SHAP) to determine the relative feature importance for not having private dental insurance from the best ML model [the gradient boosting (GBM)].

resultsApproximately one-third of Ontarians do not have private insurance coverage for dental care. Individuals with an income below $20,000, those unemployed or working part-time, seniors aged above 70, and those unable to afford to have their own housing are more at risk of not having private dental insurance, leading to financial barriers in accessing dental care.

conclusionIn the future, government-funded programs can incorporate these identified risk indicators when determining eligible populations for publicly funded dental programs. Understanding these attributes is critical for developing targeted and effective interventions, ensuring equitable access to dental care for Canadians.

Indexed as

Insurance, DentalMachine LearningAdolescentAdultAgedAlgorithmsCanadaCross-Sectional StudiesFemaleHealth Services AccessibilityHumansInsurance CoverageMaleMiddle AgedOntarioSociodemographic FactorsAccess to dental careBarriers to dental careDental care utilizationDental insurance

Identifiers

PMID38783219
PMCPMC11112852

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.