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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Bowden, Nicholas Gibb, Sheree Thabrew, Hiran Kokaua, Jesse Audas, Richard Merry, Sally Taylor, Barry Hetrick, Sarah E |
| Abstract | Background In a novel endeavour we aimed to develop a clinically relevant case identification method for use in research about the mental health of children and young people in New Zealand using the Integrated Data Infrastructure (IDI). The IDI is a linked individual-level database containing New Zealand government and survey microdata. Methods We drew on diagnostic and pharmaceutical information contained within five secondary care service use and medication dispensing datasets to identify probable cases of mental health and related problems. A systematic classification and refinement of codes, including restrictions by age, was undertaken to assign cases into 13 different mental health problem categories. This process was carried out by a panel of eight specialists covering a diverse range of mental health disciplines (a clinical psychologist, four child and adolescent psychiatrists and three academic researchers in child and adolescent mental health). The case identification method was applied to the New Zealand youth estimated resident population for the 2014/15 fiscal year. Results Over 82,000 unique individuals aged 0–24 with at least one specified mental health or related problem were identified using the case identification method for the 2014/15 fiscal year. The most prevalent mental health problem subgroups were emotional problems (31,266 individuals), substance problems (16,314), and disruptive behaviours (13,758). Overall, the pharmaceutical collection was the largest source of case identification data (59,862). Conclusion This study demonstrates the value of utilising IDI data for mental health research. Although the method is yet to be fully validated, it moves beyond incidence rates based on single data sources, and provides directions for future use, including further linkage of data to the IDI. |
| Related Links | https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-020-1057-8.pdf |
| Ending Page | 13 |
| Page Count | 13 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14726947 |
| DOI | 10.1186/s12911-020-1057-8 |
| Journal | BMC Medical Informatics and Decision Making |
| Issue Number | 1 |
| Volume Number | 20 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2020-02-27 |
| Access Restriction | Open |
| Subject Keyword | Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Integrated data infrastructure Administrative data Big data Mental health Case identification |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Informatics Computer Science Applications Health Policy |
| Journal Impact Factor | 3.3/2023 |
| 5-Year Journal Impact Factor | 3.9/2023 |
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