The ACE Index: mapping childhood adversity in England

Abstract

Background Studies of adults show that adverse childhood experiences (ACEs) are associated with health and social problems and are more common among people living in deprived areas. However, there is limited information about the geographical pattern of contemporary ACEs. Methods We used data from the police, social services, schools and vital statistics in England to calculate population rates of events that represent childhood adversity. We constructed an ‘ACE Index’ that summarizes the relative frequency of ACEs at local authority level, informed by the methods of the Index of Multiple Deprivation. We explored associations between the ACE Index and local characteristics in cross-sectional ecological analysis. Results The ACE Index was strongly associated with the proportion of children that live in income-deprived households (child poverty). In addition, the ACE Index was independently associated with higher population density and was higher in certain regions, particularly the north-east. Conclusions The association between ACEs and child poverty provides evidence of a process in which deprivation increases the risk of adverse experiences in childhood. The ACE Index can inform allocation of resources for prevention and mitigation of ACEs.

The impact of adverse childhood experiences and recent life events on anxiety and quality of life in university students

“…Where precise comparisons could be made with the general population or other universities, the UEL sample had higher rates of childhood adversities, particularly for sexual abuse, verbal/emotional abuse and living with domestic violence in childhood (Davies et al, 2021 ). These differences are consistent with previous findings that ACE scores are highly correlated with poverty (Nurius et al, 2016 ) and deprived geographical areas (Lewer et al, 2019 ), where most of UEL’s students live.…”

Bayesian spatial modelling of early childhood development in Australian regions

“…[14] and [66]) we use a single summary measure of the socio-economic condition of a specific local area, since it is simple and interpretable, as well as model efficient because adding more variables introduces instability in the small area estimates [67]. Our findings using socio-economic disadvantage to characterize the spatial heterogeneity of geographical areas mirror those found in the United Kingdom [68], the United States [69], Norway [70], amongst other countries. Although the AEDC collects information from roughly 309,000 children representing 96% of the population of children in their first year of full-time education, there are a number of sparsely populated areas where the data is not reasonably large enough to provide reliable estimates of the relatively small prevalence of child vulnerability.…”

Mapping the “hard edges” of disadvantage in England: Adults involved in homelessness, substance misuse, and offending

“…It is also confirmed by correlations at the level of LADs (OASys and NDTMS 0.84; SP and OASys 0.69; SP and NDTMS 0.67). It is also quite clear that the measures from all three sources concur in showing a strong relationship with poverty as measured by the standard IMD low‐income score (Bramley et al., 2015, p. 25), with an overall correlation of 0.80 with the contemporaneous measure, and similarly high with the overall IMD score (0.83) as well as with a recent index of “ACE” incidence (Lewer et al, 2019). However, correlations are lower with survey or proxy‐based measures of relative low income, suggesting that there is a stronger link with poverty associated with worklessness and benefit reliance than with poverty associated with lower earnings.…”