Navigating non-positivity in neighbourhood studies: an analysis of collective efficacy and violence.
BACKGROUND
In multilevel studies, strong correlations of neighbourhood exposures with individual and neighbourhood confounders may generate problems with non-positivity (ie, inferences that are 'off-support'). The authors used propensity restriction and matching to (1) assess the utility of propensity restriction to ensure analyses are 'on-support' and (2) examine the relation between collective efficacy and violence in a previously unstudied city.


METHODS
Associations between neighbourhood collective efficacy and violent victimisation were estimated in data from New York City in 2005 (n=4000) using marginal models and propensity matching.


RESULTS
In marginal models adjusted for individual confounders and limited to observations 'on-support', under conditions of high collective efficacy, the estimated prevalence of violent victimisation was 3.5/100, while under conditions of low collective efficacy, it was 7.5/100, resulting in a difference of 4.0/100 (95% CI 2.6 to 5.8). In propensity-matched analysis, the comparable difference was 4.0/100 (95% CI 2.1 to 5.9). In analyses adjusted for individual and neighbourhood confounders and limited to observations 'on-support', the difference in violent victimisation associated with collective efficacy was 3.1/100 (95% CI 1.2 to 5.2) in marginal models and 2.4/100 (95% CI 0.2 to 4.5) in propensity-matched analysis. Analyses without support restrictions produced surprisingly similar results.


CONCLUSIONS
Under conditions of high collective efficacy, there was about half the prevalence of violence compared with low collective efficacy. The results contribute to a growing body of evidence that suggests collective efficacy may shape violence, and illustrate how careful techniques can be used to disentangle exposures from highly correlated confounders without relying on model extrapolation.
Division of Epidemiology, Berkeley School of Public Health, University of California, Berkeley, CA, USA. jahern@berkeley.edu
Jennifer
J
Ahern
Magdalena
M
Cerdá
Sheri A
SA
Lippman
Kenneth J
KJ
Tardiff
David
D
Vlahov
Sandro
S
Galea
D000293:Adolescent; D000328:Adult; D000368:Aged; D019548:Crime Victims / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D055361:Multilevel Analysis; D009519:New York City; D057216:Propensity Score; D012111:Residence Characteristics; D012307:Risk Factors; D020377:Self Efficacy; D012924:Social Conditions; D012931:Social Environment; D012945:Social Values; D012959:Socioeconomic Factors; D014754:Violence / Q000706:statistics & numerical data*; D055815:Young Adult