Data Integration and spatio temporal statistics can quantify relative risk of medico-legal reforms: the example of police emergency mental health responses in Queensland (Australia)

This study examined the spatial-temporal dynamics of Emergency Examination Order or Authority (EE-O/A) admissions in Far Northern Queensland (FNQ) from 2009 to 2020, using 13,035 unique police records aggregated across 83 postcodes. A two-stage modelling framework was used: Lasso was used to identify a parsimonious set of socio economic and health-service covariates, and a Conditional Autoregressive (CAR) model incorporated these predictors with structured spatial and temporal random effects. This research demonstrates that socio-economic disadvantage and service accessibility drive EE-O/A incidence, underscoring the need for targeted mental-health interventions and resource allocation in impoverished FNQ communities. Limitations include reliance on cross-sectional census data for covariates and potential ecological bias from data fusion.

Publication Details

Journal
ArXiv.org
Published
2025-10-13
Primary Topic
Data-Driven Disease Surveillance
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article
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article

Data Integration and spatio temporal statistics can quantify relative risk of medico-legal reforms: the example of police emergency mental health responses in Queensland (Australia)

ArXiv.org
Data-Driven Disease Surveillance
article

Data Integration and spatio temporal statistics can quantify relative risk of medico-legal reforms: the example of police emergency mental health responses in Queensland (Australia)

article en

Abstract

This study examined the spatial-temporal dynamics of Emergency Examination Order or Authority (EE-O/A) admissions in Far Northern Queensland (FNQ) from 2009 to 2020, using 13,035 unique police records aggregated across 83 postcodes. A two-stage modelling framework was used: Lasso was used to identify a parsimonious set of socio economic and health-service covariates, and a Conditional Autoregressive (CAR) model incorporated these predictors with structured spatial and temporal random effects. This research demonstrates that socio-economic disadvantage and service accessibility drive EE-O/A incidence, underscoring the need for targeted mental-health interventions and resource allocation in impoverished FNQ communities. Limitations include reliance on cross-sectional census data for covariates and potential ecological bias from data fusion.

ArXiv.org
Openalex Percentile: Top 37%
Data-Driven Disease Surveillance
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Data Integration and spatio temporal statistics can quantify relative risk of medico-legal reforms: the example of police emergency mental health responses in Queensland (Australia) · ArXiv.org (2025) | TGRS Research Map | TGRS