An ecometric analysis of neighbourhood cohesion

Abstract

Background: It is widely believed that the social environment has an important influence on health, but there is less certainty about how to measure specific factors within the social environment that could link the neighbourhood of residence to a health outcome. The objectives of the study were to examine the underlying constructs captured by an adapted version of Buckner's neighbourhood cohesion scale, and to assess the reliability of the scale at the small-area-level by combining ecometric methodology with ordinal modelling of a five-point scale.

Methods: Data were analysed from 11,078 participants in the Caerphilly Health and Social Needs Study, who were sampled from within 325 UK census enumeration districts in Caerphilly county borough, Wales, UK. The responses of interest came from 15 question items designed to capture different facets of neighbourhood cohesion. Factor analysis was used to identify constructs underlying the neighbourhood cohesion item responses. Using a multilevel ecometric model, the variability present in these ordinal responses was decomposed into contextual, compositional, item-level and residual components.

Results: Two constructs labelled neighbourhood belonging and social cohesion were identified, and variability in both constructs was modelled at each level of the multilevel structure. The intra-neighbourhood correlations were 6.4% and 1.0% for the neighbourhood belonging and social cohesion subscales, respectively. Given the large sample size, contextual neighbourhood cohesion scores can be estimated reliably. The wide variation in the observed frequency of occurence of the scale item activities suggests that the two subscales have desirable ecometric properties. Further, the majority of between-neighbourhood variation is not explained by the socio-demographic characteristics of the individual respondents.

Conclusion: Assessment of the properties of the adapted neighbourhood cohesion scale using factor analysis and ecometric analysis extended to an ordinal scale has shown that the items allow fine discrimination between individuals. However, large sample sizes are needed in order to accurately estimate contextual neighbourhood cohesion. The scale is therefore appropriate for use in the measurement of neighbourhood cohesion at small-area-level in future studies of neighbourhoods and health.

Figure 1

Histogram of responses by question item. In each instance '1' corresponds to the lowest response category of neighbourhood cohesion, and '5' to the highest category of neighbourhood cohesion. The abbreviations n'hood and n'bour(s) denote neighbourhood and neighbour(s), respectively.

Figure 2

Scree plot from Principal Components Analysis. The eigenvalues – that is, the proportion of variance explained by each component – plotted against component number and ordered by decreasing eigenvalue.

Figure 3

Posterior estimated neighbourhood belonging random effects, at the item-level. Estimates are on the log-odds scale, with large positive values corresponding to distinctive behaviour patterns. A score of 0.1, for example, corresponds to the odds of not continuing to a higher category, as opposed to continuing, being increased by a factor of exp(0.1) ≈ 1.11, or around 11%.

Figure 4

Posterior estimated social cohesion random effects, at the item-level. Estimates are on the log-odds scale, with large positive values corresponding to distinctive behaviour patterns. A score of 0.1, for example, corresponds to the odds of not continuing to a higher category, as opposed to continuing, being increased by a factor of exp(0.1) ≈ 1.11, or around 11%.

Figure 5

Estimated reliability for Neighbourhood Belonging and Social Cohesion subscales. Histograms of the reliabilities for the two neighbourhood cohesion subscales, accounting for uncertainty in both the number of individuals in an ED and the variability in the estimates of the variance parameters.