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Small-area contextual effects on children’s dental caries in Alberta: a multilevel analysis

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Abstract

Objectives

The objective of this study was to examine the presence of small-area-level effects on children’s dental caries in Alberta, Canada, where dental public health programming is targeted in nature, based on an area-level measure of socio-economic circumstances.

Methods

This cross-sectional study included data on tooth decay (from an intra-oral examination conducted by dental hygienists at school) and socio-demographic and behavioural information (from a parent questionnaire) from 5677 grade 1 and 2 schoolchildren attending schools in public or Catholic school systems in Calgary and Edmonton in 2013/2014. Area-level socio-economic circumstances were quantified using the Pampalon Material Deprivation Index derived from census data, applied to the dissemination area (DA) of the child’s school. The outcome variable was presence (vs. absence) of tooth decay (cavitation). Data were analyzed using multilevel modeling with two levels: individual level (level 1) and school dissemination area (DA) (level 2).

Results

We observed a small but statistically significant area-level effect on children’s caries experience, above and beyond individual-level characteristics.

Conclusion

Study findings are relevant to dental public health programming in Alberta and other jurisdictions that use targeted strategies. Multilevel interventions, including universal approaches, are necessary to reduce inequities in children’s dental caries.

Résumé

Objectifs

Examiner la présence d’effets de petite région sur les caries dentaires des enfants en Alberta, au Canada, où les programmes publics de santé dentaire sont ciblés de nature, d’après un indicateur régional de la situation socioéconomique.

Méthode

Cette étude transversale a inclus des données sur la carie dentaire (venant d’un examen intra-buccal mené par des hygiénistes dentaires dans les écoles) et des informations sociodémographiques et comportementales (venant d’un questionnaire auprès des parents) concernant 5 677 enfants d’âge scolaire de 1e et de 2e année fréquentant les écoles du système public ou du système catholique de Calgary et d’Edmonton en 2013-2014. La situation socioéconomique régionale a été chiffrée à l’aide de l’indice de défavorisation matérielle de Pampalon dérivé des données du Recensement, lesquelles ont été appliquées à l’aire de diffusion (AD) des écoles des enfants. Le résultat a été la présence (c. l’absence) de carie dentaire (cavitation). Les données ont été analysées par modélisation multiniveaux selon deux niveaux : la personne (niveau 1) et l’AD de l’école (niveau 2).

Résultats

Nous avons observé un effet régional léger mais significatif sur l’expérience de caries des enfants, au-delà des caractéristiques individuelles.

Conclusion

Les constatations de l’étude sont pertinentes pour les programmes de santé dentaire en Alberta et dans d’autres administrations qui utilisent des stratégies ciblées. Des interventions multiniveaux, y compris des approches universelles, sont nécessaires pour réduire les iniquités dans les caries dentaires des enfants.

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Notes

  1. In Calgary and Edmonton, respectively: overall school-level response rates were 57.3% and 54.1%; overall student-level response rates within participating schools were 49.1% and 47.0%. These student-level response rates reflect those with both oral exam and questionnaire data. The response rate for those with questionnaire data only was slightly higher (54% overall).

  2. Briefly, the primary population of interest for the larger study (McLaren et al. 2017) was grade 2 students, but a smaller number of grade 1 students were also sampled to permit comparison with the Canadian Health Measures Survey oral health component, which included 6 year olds (approximately grade 1).

References

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Funding

This research was funded by an Operating Grant from CIHR (Funding Reference Number: GIR – 127083) and an Applied Public Health Chair Award from CIHR, the Public Health Agency of Canada, and Alberta Innovates – Health Solutions (CIHR Funding Reference Number: CPP – 137907) held by Lindsay McLaren.

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Authors and Affiliations

Authors

Contributions

CS and LM conceptualized the study. All authors contributed to data collection, analysis, or interpretation. CS and LM led the writing of the work and FT, SP, PF, and LM revised it critically for important intellectual content. All authors approve the final submitted version and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Lindsay McLaren.

Ethics declarations

Ethics approval

This study received approval from the Conjoint Health Research Ethics Board at the University of Calgary (ID REB17-1918).

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Please refer to “Ethics approval”.

Consent for publication

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Conflict of interest

The authors declare that they have no conflicts of interest.

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Appendix. Calculation of variance partition coefficient (VPC) in multilevel logistic regression

Appendix. Calculation of variance partition coefficient (VPC) in multilevel logistic regression

In a multilevel linear model, both the between-subject (the first level) and the between-cluster (the second level) variations can be derived directly from the fitted model and the VPC is intuitively easy to understand:

$$ \mathrm{VPC}=\frac{\mathrm{between}-\mathrm{cluster}\ \mathrm{variation}}{\mathrm{between}-\mathrm{subject}\ \mathrm{variation}+\mathrm{between}-\mathrm{cluster}\ \mathrm{variation}} $$

However, VPC is less straightforward to understand and calculate in multilevel regression for outcome measures that are not continuous. For binary outcomes (presence of dental caries), the VPC was calculated based on the latent response formulation, as it is the most widely adopted approach in applied work. This formulation assumes that a latent continuous response underlies the observed binary response and it is on the basis of this latent continuous response that the VPC is calculated and interpreted (Li et al. 2008) (Merlo et al. 2005). The VPC for a binary outcome is computed as:

$$ \mathrm{VPC}=\frac{\mathrm{between}-\mathrm{cluster}\ \mathrm{variation}}{\mathrm{between}-\mathrm{cluster}\ \mathrm{variation}+{\pi}^2/3}, $$

where π2/3 denotes the variance of a standard logistic distribution.

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Shi, C., Aparicio-Ting, F., Faris, P. et al. Small-area contextual effects on children’s dental caries in Alberta: a multilevel analysis. Can J Public Health 112, 773–781 (2021). https://doi.org/10.17269/s41997-021-00485-9

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