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



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.


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).


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


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.



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.


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).


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


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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  1. 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. 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).


  1. Aida, J., Ando, Y., Oosaka, M., Niimi, K., & Morita, M. (2008). Contributions of social context to inequality in dental caries: a multilevel analysis of Japanese 3-year-old children. Community Dentistry and Oral Epidemiology.

  2. Alberta Health Services. (2016). Presentation and evaluation of the 2011 Pampalon deprivation index for Alberta.

  3. Antunes, J. L., Peres, M. A., de Campos Mello, T. R., & Waldman, E. A. (2006). Multilevel assessment of determinants of dental caries experience in Brazil. Community Dent Oral Epidemiol, 34(2), 146–152.

    Article  PubMed  Google Scholar 

  4. Canadian Academy of Health Sciences. (2014). Improving access to oral health care for vulnerable people living in Canada. Resource document.

  5. Diez Roux, A. V. (2001). Investigating neighborhood and area effects on health. American Journal of Public Health, 91(11), 1783–1789.

    CAS  Article  Google Scholar 

  6. Figueiredo, R., de Graaff, C., Rabie, H., Baran, S., Huber, C., & Patterson, S. (2016). Oral health action plan. Research document. Alberta Health Services.

  7. Fisher-Owens, S. A., Gansky, S. A., Platt, L. J., Weintraub, J. A., Soobader, M. J., Bramlett, M. D., & Newacheck, P. W. (2007). Influences on children’s oral health: a conceptual model. Pediatrics, 120(3), e510–e520.

    Article  PubMed  Google Scholar 

  8. Gillcrist, J. A., Brumley, D. E., & Blackford, J. U. (2001). Community socioeconomic status and children’s dental health. Journal of the American Dental Association.

  9. Graham, H. (2004). Tackling inequalities in health in England: remedying health disadvantages, narrowing health gaps or reducing health gradients? Journal of Social Policy, 33(1), 115–131.

    Article  Google Scholar 

  10. Hudson, K., Stockard, J., & Ramberg, Z. (2007). The impact of socioeconomic status and race-ethnicity on dental health. In Sociological Perspectives.

  11. Kavanagh, A. M., Goller, J. L., King, T., Jolley, D., Crawford, D., & Turrell, G. (2005). Urban area disadvantage and physical activity: a multilevel study in Melbourne, Australia. Journal of Epidemiology and Community Health.

  12. Li, J., Gray, B. R., & Bates, D. M. (2008). An empirical study of statistical properties of variance partition coefficients for multi-level logistic regression models. In Communications in Statistics: Simulation and Computation.

    Google Scholar 

  13. Marinacci, C., Spadea, T., Biggeri, A., Demaria, M., Caiazzo, A., & Costa, G. (2004). The role of individual and contextual socioeconomic circumstances on mortality: analysis of time variations in a city of north west Italy. Journal of Epidemiology and Community Health.

  14. McLaren, L., Patterson, S., Thawer, S., Faris, P., McNeil, D., Potestio, M. L., & Shwart, L. (2017). Exploring the short-term impact of community water fluoridation cessation on children’s dental caries: a natural experiment in Alberta, Canada. Public Health.

  15. Merlo, J. (2003). Multilevel analytical approaches in social epidemiology: measures of health variation compared with traditional measures of association. In Journal of Epidemiology and Community Health. London: BMJ Publishing Group Ltd.

    Google Scholar 

  16. Merlo, J., Chaix, B., Yang, M., Lynch, J., & Råstam, L. (2005). A brief conceptual tutorial on multilevel analysis in social epidemiology: interpreting neighbourhood differences and the effect of neighbourhood characteristics on individual health. Journal of Epidemiology & Community Health, 59(12), 1022–1029.

    Article  Google Scholar 

  17. Mobley, C., Marshall, T. A., Milgrom, P., & Coldwell, S. E. (2009). The contribution of dietary factors to dental caries and disparities in caries. In Academic Pediatrics. Amsterdam: Elsevier.

    Google Scholar 

  18. Muirhead, V., & Marcenes, W. (2004). An ecological study of caries experience, school performance and material deprivation in 5-year-old state primary school children. Community Dent Oral Epidemiol, 32(4), 265–270.

    Article  PubMed  Google Scholar 

  19. O’Campo, P. (2003). Invited commentary: advancing theory and methods for multilevel models of residential neighborhoods and health. American Journal of Epidemiology, 157(1), 9–13.

    Article  Google Scholar 

  20. Pampalon, R., Hamel, D., Gamache, P., & Raymond, G. (2009). A deprivation index for health planning in Canada. Chronic Diseases in Canada.

  21. Public Health Agency of Canada. (2004). What is the population health approach? Research document.

  22. Rose, G. (1985). Sick individuals and sick populations. International Journal of Epidemiology.

  23. Shaw, J. L., & Farmer, J. W. (2015). An environmental scan of publicly financed dental care in Canada: 2015 update. Resource document.

  24. Sheiham, A. (2006). Dental caries affects body weight, growth and quality of life in pre-school children. Br Dent J, 201(10), 625–626.

    CAS  Article  PubMed  Google Scholar 

  25. Shi, C. (2019). Assessment of the magnitude of geographic variation and socioeconomic contextual effects on children’s dental caries: a multilevel cross-sectional analysis of a population based sample. MSc thesis, University of Calgary (Available at: [The Vault: Electronic Theses and Dissertations]).

  26. Shi, C., Faris, P., McNeil, D. A., Patterson, S., Potestio, M. L., Thawer, S., & McLaren, L. (2018). Ethnic disparities in children’s oral health: findings from a population-based survey of grade 1 and 2 schoolchildren in Alberta. Canada. BMC Oral Health, 18(1), 1.

    Article  PubMed  Google Scholar 

  27. Statistics Canada. (2013). Postal Code Conversion File (PCCF), Reference Guide. Research document.

  28. Tellez, M., Sohn, W., Burt, B. A., & Ismail, A. I. (2006). Assessment of the relationship between neighborhood characteristics and dental caries severity among low-income African-Americans: a multilevel approach. Journal of Public Health Dentistry.

  29. Warren, J. J., Levy, S. M., & Kanellis, M. J. (2002). Dental caries in the primary dentition: assessing prevalence of cavitated and noncavitated lesions. Journal of Public Health Dentistry, 62(2), 109–114.

    Article  Google Scholar 

  30. Weijs, C., Gobrail, S., Lucas, J., Zwicker, J., & McLaren, L. (2019). Identifying and critically examining government legislation relevant to children’s dental caries in Calgary, Alberta, Canada: a health inequities lens. Journal of Public Health Dentistry.

  31. Zulman, D. M., Vijan, S., Omenn, G. S., & Hayward, R. A. (2008). The relative merits of population-based and targeted prevention strategies. Milbank Quarterly.

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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.

Author information




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.

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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 (2021).

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  • Small-area effects
  • Contextual
  • Dental caries
  • Children


  • Effets de petite région
  • contextuel
  • caries dentaires
  • enfant