标题： 数据整合与时空统计可以量化医疗法律改革的相对风险：昆士兰（澳大利亚）警察紧急心理健康应对的示例 显示英文标题

标题： 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)

摘要： 本研究考察了2009年至2020年Far Northern Queensland (FNQ)地区紧急检查命令或授权（EE-O/A）入院的空间-时间动态，使用了83个邮政编码汇总的13,035条独特的警察记录。采用了一个两阶段建模框架：Lasso用于识别简约的社会经济和卫生服务协变量集，条件自回归（CAR）模型将这些预测因子与结构化的空间和时间随机效应结合。这项研究显示，社会经济劣势和服务可及性推动了EE-O/A的发生率，强调了在贫困FNQ社区中需要有针对性的心理健康干预和资源分配。局限性包括依赖于横断面人口普查数据作为协变量，以及数据融合可能带来的生态偏差。 显示英文摘要

摘要： 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.