Table 2 summarizes state and territory IRSAD scores, along with average approved budgets and average utilization rates in June 2020 and June 2021. IRSAD scores ranged from 697 to 1131, with higher scores indicating lower levels. higher relative advantage. In 2020, the average national approved NDIS budget was $75,047 (SD $29,932), and on average only 57.14% of budgets were utilized (SD 8.70). Average budgets decreased slightly in 2021 ($73,538, SD $21,399) and average usage increased slightly (61.87%, SD 6.22). The Northern Territory had scores indicating the greatest disadvantage across the country, as well as the highest average approved NDIS budget. The Northern Territory has the highest proportion of people living in remote and very remote communities, as well as the highest proportion of Aboriginal and Torres Strait Islander people – two factors associated with greater need for health care and social support. The ability to access appropriate NDIS services appears to be lacking in the Northern Territory – the average utilization rate for all disabilities for the Territory was 42% in 2020; increasing to 54% in 2021. In contrast, the Australian Capital Territory (ACT) is the smallest territory in the country and had the highest average relative advantage scores across the country. It also had significantly higher average utilization rates for all disabilities (66% in 2020, 68% in 2021), while having a lower average approved budget of $62,000, compared to the national average of 75,047 $.62. However, the average approved budget for the ACT service area has increased significantly in 2021, doubling to $124,000. ACT is counted as one NDIS service area, while other states and territories include at least 4 service areas where budgets are averaged, but this substantial increase in approved funding is worth noting and investigating more in-depth if tackling socio-economic inequalities is a concern for the NDIS Programme.
Hypothesis 1: Higher average approved plan budgets would be associated with higher levels of socioeconomic benefit.
The results of the linear regression are presented in Table 3. The linear regression showed a significant negative relationship between IRSAD scores and approved budgets, where higher IRSAD scores, or higher levels of relative advantage, predicted lower average approved budgets in 2020 (β = -0.325, p< 0.001, R2 = 0.106) and 2021 (β = -0.227, p= 0.043, R2= 0.051) (see Figs. 1 and 2, Table 3). While most average plan budgets were between $50,000 and $100,000, a number of higher approved budgets were outliers, particularly in service districts with higher levels of socioeconomic disadvantage. .
Hypothesis 2: Higher average rates of plan use would be associated with higher levels of socioeconomic advantage.
However, when looking at the relationship between relative advantage and disadvantage and fund utilization rate, a significantly different relationship was observed (see Figures 3 and 4). Higher levels of advantage (β = 0.530, p< 0.001, R2= 0.281) predicted higher average utilization rates for “all” disability types (excluding SIL and SDA clients), explaining 28% of model variance in 2020 (see Table 3) . Similarly, higher levels of socioeconomic advantage (β = 0.550, p< 0.001, R2= 0.302) predicted higher average plan utilization rates in 2021. That is, for an area such as ACT, with the highest average IRSAD in the country (1089), clients with an NDIS fund also use, on average, a greater proportion of their allocated budget (66% in 2020, 68% in 2021). In contrast, a state like Tasmania, which has an average IRSAD score (929.55) compared to the national average, predicts that customers on average spend less on their budget (59% in 2020, 62.75% in 2021) . As Figs. 3 and 4 show, the relationship between socioeconomic advantage and higher rates of plan use showed a clearer linear relationship, where participants living in areas of higher socioeconomic disadvantage used a higher proportion. low of their approved plans. Both of these analyzes indicate that even though clients living in lower socio-economic areas are approved for higher individual funds, they use a smaller proportion of their funds compared to clients living in more advantaged areas.
Hypothesis 2a: Higher rates of plan utilization and higher levels of socio-economic benefit would remain significant once the plan’s average approved budget is controlled.
When forecasting utilization rates that excluded SIL and SDA (SILSDA), the average approved budget amount had a significantly negative contribution to the model above what was already predicted by the average IRSAD scores in 2020 (β = – 0.221, p= 0.025). This relationship was not significant in 2021 (β = 0.026, p= 0.792, see Table 4). This suggests that the amount of the approved budget can contribute significantly to predicting the degree to which funds will be used for all types of disabilities, for services that are not focused on assisted living or accommodation for people with disabilities. However, the relationship is not consistent across the two years of data and requires further investigation.
When looking at the average fund utilization rate that includes clients using funding for SILSDA, the average socio-economic benefit score was still a significant predictor, so living in more favored predicted higher fund utilization rates in 2020 (β = 0.416, p< 0.001), however, explained less variance of the model (17.3%) compared to the prediction of use when SILSDA clients were excluded. This relationship between socioeconomic benefit and use of funds was stronger in 2021 (β = 0.464, p< 0.001), explaining 21.5% of the model variance. This suggests that socioeconomic advantage may be a less explanatory factor of utilization for clients who receive support through SILSDA, but this relationship is still significant. The average approved budget amount did not significantly predict utilization rates in 2020 (β = -0.005, p= 0.963), nor did it contribute significantly to the amount of variance explained by the regression model. In 2021, the average approved budget amount explained the additional variance for the use of funds beyond the socio-economic benefit (β = 0.231, p= 0.024). This provides early evidence that in 2021 having a higher approved budget can contribute to higher utilization levels. However, this inconsistent relationship requires further exploration rather than implying that there is a consistent trend toward higher approved budgets linked to higher utilization rates beyond the level of socioeconomic disadvantage.
Hypothesis 2b: The relationship between plan utilization rates and higher levels of socioeconomic advantage would not vary significantly across types of disability support categories.
Linear regressions, modeling the relationship between average utilization rate and average ISRAD scores for service districts, were conducted on three types of disability support classes – basic, capacity building and capital – and were calculated when the use included SILSDA plans. In the June 2020 and June 2021 data, there was a significant positive relationship between utilization rates and higher levels of socioeconomic advantage (see Table 5). All types of disability support classes were significantly associated with socio-economic benefit, with the exception of capital support in June 2020. Beta values were consistently high for capacity building activities (β ranging from 0.550 in June 2021 excluding SILSDA plans, at 0.592 when SILSDA plans were included), versus core support and capital activities.