Hierarchical clustering of English local authorities by sub-domains of the English Indices of Multiple Deprivation
PHE ePoster Library. Senior S. Apr 9, 2019; 259597; 15556
Dr. Steven Senior
Dr. Steven Senior
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Abstract
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Abstract Background

The English Indices of Multiple Deprivation (IMD) is widely used as a measure of deprivation of geographic areas in analyses of health inequalities between places. However, similarly ranked areas can differ substantially in the underlying sub-domains and indicators that are used to calculate the IMD score. These sub-domains and indicators contain a richer set of data that might be useful for classifying local authorities. Clustering methods offer a set of techniques to identify groups of areas with similar patterns of deprivation using the full set of sub-domain scores. This could offer insights into areas that face similar challenges.

Methods:

Hierarchical agglomerative (i.e. bottom-up) clustering methods were applied to sub-domain scores for 152 upper-tier local authorities. Recent advances in statistical testing allow clusters to be identified that are unlikely to have arisen from random partitioning of a homogeneous group. Results are visualised to show patterns in types of deprivation and geographic distribution of clusters.

Results

Five statistically significant clusters of local authorities were identified. These clusters represented local authorities that were: (i) Most deprived, predominantly urban;(ii) Least deprived, predominantly rural;(iii) Less deprived, rural;(iv) Deprived, high crime, high barriers to housing; and(v) Deprived, low education, poor employment, poor health.
Conclusion

Hierarchical clustering methods can be used to identify distinct clusters of English local authorities. Two of these clusters were similar in overall deprivation score, but markedly different in their sub-domain scores, suggesting they face different challenges. This work could help to identify areas that may benefit from similar interventions, or from sharing effective practice. Funding This work was not funded.
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