ArticleBMJ open2022
Premature mortality in early-intervention mental health services: a data linkage study protocol to examine mortality and morbidity outcomes in a cohort of help-seeking young people.
Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionUnderstanding the risk of premature death from suicide, accident and injury and other physical health conditions in people seeking healthcare for mental disorders is essential for delivering targeted clinical interventions and secondary prevention strategies. It is not clear whether morbidity and mortality outcomes in hospital-based adult cohorts are applicable to young people presenting to early-intervention services. METHODS AND ANALYSIS: The current data linkage project will establish the Brain and Mind Patient Research Register-Mortality and Morbidity (BPRR-M&M) database. The existing Brain and Mind Research Institute Patient Research Register (BPRR) is a cohort of 6743 young people who have accessed primary care-based early-intervention services; subsets of the BPRR contain rich longitudinal clinical, neurobiological, social and functional data. The BPRR will be linked with the routinely collected health data from emergency department (ED), hospital admission and mortality databases in New South Wales from January 2010 to November 2020. Mortality will be the primary outcome of interest, while hospital presentations will be a secondary outcome. The established BPRR-M&M database will be used to establish mortality rates and rates of ED presentations and hospital admissions. Survival analysis will determine how time to death or hospital presentation varies by identified social, demographic and clinical variables. Bayesian modelling will be used to identify predictors of these morbidity and mortality outcomes. ETHICS AND DISSEMINATION: The study has been reviewed and approved by the human research ethics committee of the Sydney Local Health District (2019/ETH00469). All data will be non-identifiable, and research findings will be disseminated through peer-reviewed journals and scientific conference presentations.
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