Citation

Vafaei A, Pickett W, Alvarado BE. BMJ Open 2014; 4(7): e004919.

Affiliation

Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada.

Copyright

(Copyright © 2014, BMJ Publishing Group)

DOI

10.1136/bmjopen-2014-004919

PMID

24993755

Abstract

OBJECTIVES: Social sorting mechanisms or analogous selection processes may impose confounding effects in the study of aetiological relationships. Such processes are referred to as structural confounding. If present, certain strata of social factors could hypothetically never be exposed to specific risk factors. This prohibits exchangeability across groups that is needed for meaningful causal inference. The objectives of this study were to: (1) develop and test the reliability and validity of composite scales for the measurement of social capital (SC), socioeconomic status (SES) and built environment (BE) and (2) to explore the possible roles of community level SC, SES and BE factors in studies of the aetiology of youth injury. SETTING/PARTICIPANTS: A nationally representative sample of over 26 000 Canadian students aged 11-15 years. MEASURES/ANALYSIS: Scales describing these key factors were developed and validated via exploratory and confirmatory factor analyses. We then used tabular analyses to explore structural confounding in our population.

RESULTS: The proposed scales all demonstrated good psychometric properties. Despite variations in the number of adolescents across social and environmental strata, no evidence for the presence of structural confounding was detected in our data.

CONCLUSIONS: Relationships between social capital and the occurrence of injuries in Canadian youth aged 11-16 can potentially be studied without consideration of structural confounding biases. Canada is a suitable place to disentangle the effects of different neighbourhood social and environmental exposures on occurrence of injuries and other outcomes in adolescent populations. Exchangeability is possible across exposure strata and therefore a meaningful multilevel regression analysis is feasible. However, more studies are needed to test the consistency of our findings in other populations and for different outcomes.

Language: en