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Record W4293787290 · doi:10.1371/journal.pone.0268899

Comparative inequalities in child dental caries across four countries: Examination of international birth cohorts and implications for oral health policy

2022· article· en· W4293787290 on OpenAlexafffundabout
Sharon Goldfeld, Kate Francis, Elodie O’Connor, Johnny Ludvigsson, Tomas Faresjö, Béatrice Nikièma, Lise Gauvin, Junwen Yang‐Huang, Yara Abu Awad, Jennifer J. McGrath, Jeremy D. Goldhaber‐Fiebert, Åshild Faresjö, Hein Raat, Lea Kragt, Fiona Mensah

Bibliographic record

VenuePLoS ONE · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de MontréalCree Board of Health and Social Services of James Bay
FundersErasmus Universitair Medisch Centrum RotterdamNational Health and Medical Research CouncilFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchFonds de Recherche du Québec-Société et CultureSocial Sciences and Humanities Research Council of CanadaAustralian Institute of Family StudiesCentre hospitalier universitaire Sainte-JustineKnut och Alice Wallenbergs StiftelseNederlandse Organisatie voor Wetenschappelijk OnderzoekErasmus Medisch CentrumInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du TravailForskningsrådet för Arbetsliv och SocialvetenskapErasmus Universiteit RotterdamZonMwMinistère de l'Éducation et de l'Enseignement supérieurDepartment of Social Services, Australian GovernmentState Government of VictoriaVetenskapsrådetAustralian GovernmentUniversité du Québec à MontréalForskningsrådet i Sydöstra SverigeMinistère de la Santé et des Services sociauxBarndiabetesfondenMinisterie van Volksgezondheid, Welzijn en SportJuvenile Diabetes Research Foundation Canada
KeywordsDemographyMedicinePopulationOral healthInequalityEnvironmental healthDentistrySociology

Abstract

fetched live from OpenAlex

Child dental caries (i.e., cavities) are a major preventable health problem in most high-income countries. The aim of this study was to compare the extent of inequalities in child dental caries across four high-income countries alongside their child oral health policies. Coordinated analyses of data were conducted across four prospective population-based birth cohorts (Australia, n = 4085, born 2004; Québec, Canada, n = 1253, born 1997; Rotterdam, the Netherlands, n = 6690, born 2002; Southeast Sweden, n = 7445, born 1997), which enabled a high degree of harmonization. Risk ratios (adjusted) and slope indexes of inequality were estimated to quantify social gradients in child dental caries according to maternal education and household income. Children in the least advantaged quintile for income were at greater risk of caries, compared to the most advantaged quintile: Australia: AdjRR = 1.18, 95%CI = 1.04-1.34; Québec: AdjRR = 1.69, 95%CI = 1.36-2.10; Rotterdam: AdjRR = 1.67, 95%CI = 1.36-2.04; Southeast Sweden: AdjRR = 1.37, 95%CI = 1.10-1.71). There was a higher risk of caries for children of mothers with the lowest level of education, compared to the highest: Australia: AdjRR = 1.18, 95%CI = 1.01-1.38; Southeast Sweden: AdjRR = 2.31, 95%CI = 1.81-2.96; Rotterdam: AdjRR = 1.98, 95%CI = 1.71-2.30; Québec: AdjRR = 1.16, 95%CI = 0.98-1.37. The extent of inequalities varied in line with jurisdictional policies for provision of child oral health services and preventive public health measures. Clear gradients of social inequalities in child dental caries are evident in high-income countries. Policy related mechanisms may contribute to the differences in the extent of these inequalities. Lesser gradients in settings with combinations of universal dental coverage and/or fluoridation suggest these provisions may ameliorate inequalities through additional benefits for socio-economically disadvantaged groups of children.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.095
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.097
GPT teacher head0.384
Teacher spread0.287 · 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".

Quick stats

Citations15
Published2022
Admission routes3
Has abstractyes

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Same venuePLoS ONESame topicDental Health and Care UtilizationFrench-language works237,207