NIHR School for Primary Care Research, Centre for Primary Care, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK
Manchester Academic Health Sciences Centre (MAHSC), Manchester, UK.
BMJ Open. 2020 Sep 9;10(9):e036046. doi: 10.1136/bmjopen-2019-036046.
We aimed to spatially describe hospital admissions for ambulatory care sensitive conditions (ACSC) in England at small-area geographical level and assess whether recorded practice performance under one of the world's largest primary care pay-for-performance schemes led to reductions in these potentially avoidable hospitalisations for chronic conditions incentivised in the scheme.
We obtained numbers of ACSC hospital admissions from the Hospital Episode Statistics database and information on recorded practice performance from the Quality and Outcomes Framework (QOF) administrative dataset for 2015/2016. We fitted three sets of negative binomial models to examine ecological associations between incentivised ACSC admissions, general practice performance, deprivation, urbanity and other sociodemographic characteristics.
Hospital admissions for QOF incentivised ACSCs varied within and between regions, with clusters of high numbers of hospital admissions for incentivised ACSCs identified across England. Our models indicated a very small effect of the QOF on reducing admissions for incentivised ACSCs (0.993, 95% CI 0.990 to 0.995), however, other factors, such as deprivation (1.021, 95% CI 1.020 to 1.021) and urbanicity (0.875, 95% CI 0.862 to 0.887), were far more important in explaining variations in admissions for ACSCs. People in deprived areas had a higher risk of being admitted in hospital for an incentivised ACSC condition.
Spatial analysis based on routinely collected data can be used to identify areas with high rates of potentially avoidable hospital admissions, providing valuable information for targeting resources and evaluating public health interventions. Our findings suggest that the QOF had a very small effect on reducing avoidable hospitalisation for incentivised conditions. Material deprivation and urbanicity were the strongest predictors of the variation in ACSC rates for all QOF incentivised conditions across England.
本研究旨在以小地域地理层面来描述英格兰门诊保健敏感条件（ACSC）的住院情况，并评估在世界上最大的初级保健按绩效付费计划之一下记录的实践表现是否导致了该计划中激励的慢性病的这些潜在可避免住院的减少。
我们从医院入院统计数据库中获取了 ACSC 住院人数的数据，并从 2015/2016 年的质量和结果框架（QOF）行政数据集获取了记录的实践表现信息。我们拟合了三组负二项式模型，以检查激励性 ACSC 入院、全科医生表现、贫困、城市化和其他社会人口统计学特征之间的生态关联。
QOF 激励的 ACSC 住院人数在各地区内和地区间存在差异，在英格兰各地都发现了激励性 ACSC 住院人数较多的集群。我们的模型表明，QOF 对减少激励性 ACSC 入院的影响非常小（0.993，95%置信区间 0.990 至 0.995），然而，其他因素，如贫困（1.021，95%置信区间 1.020 至 1.021）和城市化（0.875，95%置信区间 0.862 至 0.887），对于解释 ACSC 入院的变化更为重要。贫困地区的人因激励性 ACSC 状况住院的风险更高。
基于常规收集的数据进行空间分析可以用于识别高潜在可避免住院率的地区，为资源定位和评估公共卫生干预措施提供有价值的信息。我们的发现表明，QOF 对减少激励性条件的可避免住院的影响非常小。物质贫困和城市化是英格兰所有 QOF 激励条件下 ACSC 率变化的最强预测因素。
