Relationship between Socioeconomic Status and child abuse and neglect in South Australia

Background Neighbourhood characteristics such as socio-economic status (SES) have been shown to correlate with poorer health outcomes, mortality rates, childhood development, and education Previous studies suggested that the SES of a neighbourhood may be related to rates of childhood abuse and neglect These did not answer our questions Communities with less disadvantage may provide more social support and networks to support families. Few studies have quantified the association between SES and rates of childhood abuse and neglect using recent data and particularly in south australia.

CP Data from 3 one-year periods (2006/07 to 2008/09) This Study Exploration of the relationship between SES and rates of childhood abuse and neglect in South Australia (SA) CP Data from 3 one-year periods (2006/07 to 2008/09) Incidence data at population level (Statistical Local Area (SLA)) Data aggregated to SLA level Data on childhood abuse and neglect is held by the Dept of Families and Communities in SA. This data records all reported allegations and confirmed incidences across SA. Other research has identified links between SES and childhood abuse and neglect in relation to individual children or families rather than on a population wide-basis.

This Study Research questions: What is the relationship between socio-economic status and rates of childhood abuse and neglect in SA? What areas of SA have observed rates of abuse and neglect which are above or below those expected based on their SES? Which measurements of disadvantage indicate a relationship with rates of child abuse and neglect? We are interest in how much of the variance in rate of childhood abuse and neglect can be explained by SES

SES represented by Socio-Economic Indexes for Areas (SEIFA) SEFIA produced by the ABS from 2006 Census data Index of relative disadvantage used 17 items from Census indicate the level of disadvantage in the SLA SES is represented by Socio-Economic Indexes for Area or SEIFA. SEIFA contains for indexes, which are produced by the ABS. Data item from the 2006 Census data are used to calculate the SEIFA indexes. This presentation is focused on the Index of relative disadvantage.

Data from 121 SLA’s across SA included Population of areas Data from 121 SLA’s across SA included Large variation in number of children living in these SLA’s Population aged 0 to 17 years (from 2006 Census) Total population 0 to 17 = 341,561 Average = 2,823 (SD 2,119) Range = 29 to 8,641

Rates of childhood abuse and neglect Childhood abuse and neglect data have been averaged over the three data periods and reported as per year Represents the incidence of children who have contact with the child protection system across SA per year CP data aggregated by SLA of where a child was living when the incident occurred The aggregated number of children subject to a notification or to experience a substantiation within each of three data periods was extracted for all 121 SLAs. These aggregated numbers were averaged over the three study periods. This allowed an average number of and rate per year to be estimated. Three study periods were used to reduce the influence of one-off spikes or falls or random variation in the child protection data within SLAs.

Statistical analysis Associations between SEIFA and rates of childhood abuse and neglect were estimated using Negative Binomial regression models Can account for a number of issues when analysing count data at a population level Models can estimate the relative difference in rates between and within predictors These issue include Negative Binominal regression can account for: Count data, which has discrete integers of 0 or above; Areas with difference populations at risk; Skew distributions, that is Over-dispersion from some much higher rates; ….

Interpretation of results Cross-sectional study Can identify relationships between level of disadvantage characteristics and rates of childhood abuse and neglect However, causal links can not be established

Level of disadvantage may vary within a SLA Limitations Population at risk over the three data years take from 2006 Census (ie no population growth) Level of disadvantage may vary within a SLA As childhood abuse and neglect may not be independent of the family group.

Distribution of rate of children subject to a notification across SLA’s Rate of 39.3 per 1,000 children per year across all SLAs Note the skew distribution from some SLA with high rates 305 = unincorp West Coast, 264 = Unincorp Whyalla Highest metro SLAs – 127 = Playford – Elizabeth, 119 = Playford – West Central, 102 = Onkaparinga – North Coast The median rate was 29.9 per 1000 children per year

Distribution of rate of children to experience a substantiation across SLA’s Rate of 5.2 per 1,000 children per year across all SLAs Again notice the skew distribution from some SLA with high rates. 112 = unincorp West Coast, 52 = Unincorp Far North Highest rates were mainly in regional SLAs, including Cobber Pedy, Port August, Ceduna and Berri & Barmera – Berri Highest metro included – Playford – West Central, Playford – Elizabeth, Onkaparinga – North Coast The median rate was 3.3 per 1000 children per year

Rate of children subject to a notification Analysis estimated that the rate of children subject to a notification for children living in a SLA with a given decile is approx 23% higher compared to children living in a SLA is one unit decile decrease in disadvantage

Rate of children subject to a notification by primary type The index of disadvantage had the strongest association with the rate of children subject to a neglect notifications and rate of children subject to an emotional notifications. Approx increase in rate per unit increase in level of disadvantage was: Sexual 19% Physical 19% Neglect 29% Emotional 21% Non-incident 25%

Rate of children to experience a substantiation Analysis estimated that the rate of children to experience a substantiation for children living in a SLA with a given decile is approx 30% higher compared to children living in a SLA is one unit decile decrease in disadvantage

Rate of children to experience a substantiation by primary type Approx increase in rate per unit increase in level of disadvantage was: Sexual 18% Physical 28% Neglect 35% Emotional 29% Other 30%

Indigenous children were more likely to be subject to a notification Cultural background Indigenous children were more likely to be subject to a notification 228 per 1000 Indigenous children per year 34 per 1000 non-Indigenous children per year And experience a substantiation 48 per 1000 Indigenous children per year 3.9 per 1000 non-Indigenous children per year The rate of children subject to a notification per year was about 6.6 times higher for Indigenous children The of children to experience a substantiation per year was about 12 times higher for Indigenous children Similar results shown for the type of notification and substantation

Cultural background Comparison of the association of the level of disadvantage and rate of childhood abuse and neglect within SLAs between Aboriginal and/or Torres Strait Islander children and non-Indigenous was explored The population of Aboriginal and/or Torres Strait Islander children taken from the 2006 Census.

Rate of children subject to a notification by cultural background Indigenous children had a higher rate across all levels of disadvantage deciles as shown in this Figure. Indigenous children living in SLAs with a lower level of disadvantage (Decile 7 to 9) had a higher rate than non-Indigenous children living in the most disadvantaged SLA’s (Decile 1 and 2). A limitation in these result are issues on the identification of Indigenous children in both the SA child protection system and the Census. However, these issue are unlikely to be the driver of these results shown here.

Rate of children to experience a substantiation by cultural background Indigenous children had a higher rate across all levels of disadvantage deciles as shown in this Figure. Indigenous children living in SLAs with a lower level of disadvantage (Decile 7 to 9) had a higher rate than non-Indigenous children living in the most disadvantaged SLA’s (Decile 1 and 2).

Expected rates based on SES The models can calculate expected rates of children subject to a notification or substantiation based on the level of disadvantage of where they live Comparison of the observed and expected rates can indicate the how well a rate for a SLA can be explained by the level of disadvantage within the SLA

Rate of children subject to a notification (per 1000 per year) The expected rate of children subject to a notification for a SLA based on the level of disadvantage of within the SLA was estimated using negative binomial regression models. The SEIFA index of disadvantage score was included in these model as the only predictor. This is shown in the x-axis in this Figure. A log transformation has been applied to the rate to make it easier to display the skewed distribution. The y-axis shows the deviance residual. The deviance residual indicates that goodness of the model fit and is a measure of each SLA’s contribution to the total deviance or error in the model. The size of the population is accounted for by the deviance residuals. A deviance residual of 0 indicates that the expended rate for a SLA was equal to its observed rate. A deviance residual >0 indicates that SLA has the observed rate greater than its expected rate. A deviance residual <0 indicates that SLA has the observed rate less than its expected rate. This Figure indicate a few SLA’s where Index of disadvantage does not explain the observed rates. These include some unincorporated SLA and Robe in the South East and Kimba on the Eyre Peninsula. SLAs with an observed rate lower than their expected rate were mainly rural SLAs. Metro SLAs with a lower observed rate compared to their expected rate included: Port Adelaide Enfield – Park, Burnside – North–East, and Walkerville. Metro SLAs with a higher observed rate compared to their expected rate included: Adelaide, Onkaparinga – North Coast, Onkaparinga – South Coast, Onkaparinga – Hackham and Playford – East Central These results can be expanded to include types of notifications. For example, Unincorp West Coast has a higher observed rates than their expected rate for neglect, emotional and non-incidents notifications, while were similar for sexual and physical notification.

Rate of children to experience a substantiation (per 1000 per year) A log transformation has been applied to the rate to make it easier to display the skewed distribution. This Figure indicate a few SLA’s where Index of disadvantage does not explain the observed rates. These include some unincorporated SLA and Robe in the South East and Berri & Barmera - Berri in the Riverland. As with notifications, SLAs with an observed rate lower than their expected rate were mainly rural SLAs. This includes SLAs in the South East and Eyre Peninsula. Metro SLAs with a lower observed rate compared to their expected rate includes: Port Adelaide Enfield – Park, Onkaparinga – Hills, Playford - Elizabeth. SLAs with an observed rate higher than their expected rate were mainly regional centres. This includes Berri, Ceduna, Port Augusta, Roxby Downs, Loxton Waikerie and Cobber Pedy. Metro SLAs with a lower observed rate compared to their expected rate includes: Adelaide, Murray Bridge and Marion – South. Metro SLAs with a higher observed rate compared to their expected rate included: Adelaide, Onkaparinga – North Coast, Onkaparinga – South Coast, Onkaparinga – Hackham and Playford – East Central The overall fit of the models was better for the incidence of children subject to a notification than for children to experience a substantiation.

The Index of Disadvantage contains 17 items SEIFA items The Index of Disadvantage contains 17 items Data on 16 of these items were collected for each SLA at population level % of dwellings requiring one or more extra bedrooms not available The next section explored the association of these items individually with rates childhood abuse and neglect Data for SEIFA items were taken from the ABS website and Tablebuilder.

SEIFA items Income low % people with stated annual household equivalised income between $13,000 and $20,799 No Qual % people aged 15 years and over with no post-school qualifications No School % people aged 15 years and over who did not go to school Unemployed % people (in the labour force) unemployed Occ Labour % employed people classified as Labourers Occ Drivers % employed people classified as Machinery Operators and Drivers Occ Service L % employed people classified as Low skill community and Personal service workers Rent Social % households renting from a Government or Community organisation

SEIFA items Low Rent % households paying rent who pay less than $120 per week One Parent % families that are one parent families with dependent offspring only No Car % occupied private dwelling with no car Divorced % people aged 15 years and over who are separated or divorced Indigenous % people who identified themselves as being of Aboriginal and/or Torres Strait Islander origin English Poor % people who do not speak English well No Net % occupied private dwellings with no Internet connection Disability U70 % people aged under 70 who need assistance with core activities A natural log transformation was applied to some of these items in the analysis to reduce the influence of possible outliers and to identify linear trends.

While, no significant association with Associations with rate of children subject to a notification (121 SLAs) The following items were significantly associated with increased rates of children subject to a notification per year: Income low, No Qual, Unemployed, Occ labour, Occ driver, Occ service L, Rent social, Low rent, One parent, No car, Divorced, Indigenous, English poor, No net, Disability U70 While, no significant association with No school 15 of the 16 items were significantly associated with higher rates of children subject to notification per year.

% Indigenous by rate of children subject to a notification Size of circle represents size of population aged 0-17 This figure shown % of Indigenous people in a SLA by rate of children subject to a notification. Notice the curved shape and positive association. The blue bubble represent the population of children aged 0 to 17 years in the SLA. The possible outliers are small bubbles and indicates these have a small population at risk. Similar graphs of SEIFA items can be included.

While, no significant association with Associations with rate of children to experience a substantiation (121 SLAs) The following items were significantly associated with increased rates of children to experience a substantiation per year: No Qual, Occ labour, Occ driver, Occ service L, Rent social, Low rent, No car, Divorced, Indigenous, English poor, No net, Disability U70 While, no significant association with Income low, No school, Unemployment, One parent 12 of the 16 items were significantly associated with higher rates of children to experience a substantiation per year.

% Indigenous by rate of children to experience a substantiation Size of circle represents size of population aged 0-17 This figure shown % of Indigenous people in a SLA by rate of children to experience a substantiation. Notice the curved shape and positive association.

Summary Strong positive relationship between the level of disadvantage where children live and the reported rates of childhood abuse and neglect in that community at a population level Stronger relationships for emotional abuse and neglect (both alleged and confirmed abuse) Higher rates for Indigenous children regardless of level of disadvantage Relationship stronger for rate of children subject to a notification compared to children to experience a substantiation.