Byrne Declan, Conway Richard, Cournane Seán, O'Riordan Deirdre, Silke Bernard
Department of Internal Medicine, St. James's Hospital, Dublin 8, Ireland.
Department of Medical Physics, St. Vincent's University Hospital, Dublin 4, Ireland.
Ir J Med Sci. 2019 Feb;188(1):303-310. doi: 10.1007/s11845-018-1815-0. Epub 2018 Apr 24.
Classical deprivation instruments use a factor analytical approach relying on a smaller number of dimensions, factors or components. Multi-dimensional deprivation models attempt classification in fine detail-even down to street level.
Single-centre retrospective cohort study using routinely collected aggregated and anonymised data on emergency medical admissions (96,526 episodes in 50,731 patients; 2002-2016). We calculated admission/readmission rate incidences for the 74 small areas within the hospital catchment area. We compared a classical Small Area Health Research Unit (SAHRU) to the multi-dimensional POBAL Haase and Pratschke Deprivation Index for Small Areas (POBAL) deprivation instrument and their deprivation ranks for two Irish censuses (2006/ 2011).
There was poor agreement between the instruments of the Deprivation Ranks by Quintile-with agreement in 46 and 42% of small areas for the respective 2006 and 2011 censuses. The classical model (SAHRU) suggested more areas with severe deprivation (Q5 66 and 55%) compared with POBAL (Q5 32 and 24%) from the respective censuses. SAHRU classical instrument had a higher prediction level incidence rate ratio (IRR) 1.48 (95% CI 1.47, 1.49)) compared with POBAL IRR 1.28 (95% CI 1.27, 1.28) and systematically lower estimates of hospital admission and readmission rate incidences. Earlier Census data modelled more powerfully, suggesting a long latency between social circumstances and the ultimate expression of the emergency medical admission.
Deprivation influences hospital incidence rates for emergency medical admissions and readmissions; instruments focusing at the very small area (individual or street level) have a utility but appear inferior in terms of representing the population risk of environmental/socio-economic factors which seem best approximated at a larger scale.
传统的贫困测量工具采用因子分析方法，依赖较少的维度、因素或成分。多维贫困模型试图进行细致的分类，甚至细化到街道层面。
采用单中心回顾性队列研究，使用常规收集的关于急诊入院的汇总匿名数据（50,731例患者中的96,526次住院；2002 - 2016年）。我们计算了医院服务区域内74个小区域的入院/再入院率发病率。我们将传统的小区域健康研究单位（SAHRU）与多维的小区域POBAL哈泽和普拉施克贫困指数（POBAL）贫困测量工具进行了比较，并比较了它们在两次爱尔兰人口普查（2006/2011年）中的贫困排名。
贫困排名五分位数的测量工具之间一致性较差——在2006年和2011年人口普查中，分别有46%和42%的小区域一致性。传统模型（SAHRU）表明，与相应人口普查中的POBAL（2006年为32%，2011年为24%）相比，有更多严重贫困区域（2006年为66%，2011年为55%）。与POBAL发病率比（IRR）1.28（95%置信区间1.27, 1.28）相比，SAHRU传统测量工具的预测水平发病率比更高，为1.48（95%置信区间1.47, 1.49），且对医院入院和再入院率发病率的估计系统性较低。较早的人口普查数据建模效果更强，表明社会环境与急诊入院的最终表现之间存在较长的延迟。
贫困影响急诊入院和再入院的医院发病率；关注非常小区域（个人或街道层面）的测量工具具有实用性，但在代表环境/社会经济因素的人群风险方面似乎较差，而这些因素在更大规模上似乎能得到更好的近似。
