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3.2  Approach and data

Insights replicates the content of SII, but extends the original 2013 data to the 2014 and 2015 years, and allows the data to be visualised in different ways. As discussed above, the population is defined slightly differently. This resulted in a slightly smaller population of children aged 0 to 5 (359,070 instead of 362,832), a slightly larger population of children aged 6 to 14 (517,900 instead of 510,348), a slightly larger population of youth aged 15 to 19 (292,764 instead of 289,539), and an almost identical population of youth aged 20 to 24 (292,314 instead of 292,764).

The calculation of some risk indicators has also been improved through minor refinements. For example, parental education was taken from MSD benefit records in the 2015/16 work. This has now been supplemented by direct educational qualification information for the birth mother, where that information is available.

Overall, the population of children considered to be at risk is estimated to be slightly larger using the new approach. In December 2013, 15.3 percent of children aged 0 to 5 and 14.5 percent of children aged 6 to 14 were estimated to have 2 or more risk indicators. This compared with 14.6 percent and 13.4 percent respectively in the earlier analysis. Consistent with this, young people aged 15 to 19 were also slightly more likely to be considered to be at risk with the new data (14.0 percent, up from 12.6 percent). The number of youth aged 20 to 24 considered to be at risk was the same under both the old and new approaches (9.6 percent).

These approaches represent just one way of identifying populations of children and youth at risk, taking advantage of data held in the IDI. There are many other approaches that could be used, either based on IDI data or on alternative data sources. Further investigation of such approaches is warranted, and could provide more useful results for some applications. Nevertheless, the approaches used in this study are probably of general applicability, and show the way administrative data can be used to inform policies and service provision.

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