Nicholas Bowden1,2, Sheree Gibb3,4, Hiran Thabrew3,5, Jesse Kokaua3,6, Richard Audas3,7, Sally Merry3,5, Barry Taylor3,8, Sarah E Hetrick3,5. 1. A Better Start National Science Challenge, Auckland, New Zealand. Nick.Bowden@otago.ac.nz. 2. Department of Women's and Children's Health, University of Otago, 201 Great King St, Dunedin, 9016, New Zealand. Nick.Bowden@otago.ac.nz. 3. A Better Start National Science Challenge, Auckland, New Zealand. 4. Department of Public Health, University of Otago Wellington, 23 Mein St, Newtown, Wellington, 6021, New Zealand. 5. Department of Psychological Medicine, University of Auckland, 22-30 Park Ave Grafton, Auckland, 1023, New Zealand. 6. Centre for Pacific Health, Va'a O Tautai, Health Sciences Division, University of Otago, 71 Frederick St, Dunedin, 9016, New Zealand. 7. Department of Women's and Children's Health, University of Otago, 201 Great King St, Dunedin, 9016, New Zealand. 8. Dean of the Otago Medical School, University of Otago, 290 Great King St, Dunedin, 9016, New Zealand.
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.
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.
Entities:
Keywords:
Administrative data; Big data; Case identification; Integrated data infrastructure; Mental health
Authors: Nicholas Bowden; Sheree Gibb; Richard Audas; Sally Clendon; Joanne Dacombe; Jesse Kokaua; Barry J Milne; Himang Mujoo; Samuel William Murray; Kirsten Smiler; Hilary Stace; Larah van der Meer; Barry James Taylor Journal: JAMA Pediatr Date: 2022-07-01 Impact factor: 26.796
Authors: Sarah Fortune; Sarah Hetrick; Vartika Sharma; Gabrielle McDonald; Kate M Scott; Roger T Mulder; Linda Hobbs Journal: BMJ Open Date: 2022-05-25 Impact factor: 3.006