Smith Nadia, Livina Valerie, Byford Rachel, Ferreira Filipa, Yonova Ivelina, de Lusignan Simon
Data Science Department, National Physical Laboratory, UK.
Department of Clinical and Experimental Medicine, University of Surrey, UK.
Stud Health Technol Inform. 2018;247:151-155.
Identifying incident (first or new) episodes of illness is critical in sentinel networks to inform about the seasonal onset of diseases and to give early warning of epidemics, as well as differentiating change in health service utilization from change in pattern of disease. The most reliable way of differentiating incident from prevalent cases is through the clinician assigning episode type to the patient's computerized medical record (CMR). However, episode type assignment is often made inconsistently. The objective of this collaborative study between the Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC), University of Surrey and the National Physical Laboratory (NPL) is to develop a methodology to reconstruct missing or miscoded episode types. The data, gathered from the RCGP RSC network of over 230 practices, are analyzed and poor episode typing reconstructed by disease type. The methodology is tested in practices with good episode type data quality. This method could be used to improve prediction of epidemics, and to improve the quality of historical rates retrospectively.
在哨点监测网络中，识别新发（首次或新出现）疾病发作对于了解疾病的季节性发病情况、发出疫情早期预警以及区分卫生服务利用的变化与疾病模式的变化至关重要。区分新发病例和现患病例的最可靠方法是通过临床医生将发作类型分配到患者的电子病历（CMR）中。然而，发作类型的分配往往不一致。英国皇家全科医师学院研究与监测中心（RCGP RSC）、萨里大学和国家物理实验室（NPL）之间的这项合作研究的目的是开发一种方法来重建缺失或编码错误的发作类型。从RCGP RSC超过230家诊所的网络收集的数据进行了分析，并按疾病类型重建了不良的发作类型分类。该方法在发作类型数据质量良好的诊所中进行了测试。此方法可用于改善疫情预测，并回顾性地提高历史发病率的质量。
