Bartholomeeusen Stefaan, Kim Chang-Yeon, Mertens Raf, Faes Christel, Buntinx Frank
Department of General Practice, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium.
Fam Pract. 2005 Aug;22(4):442-7. doi: 10.1093/fampra/cmi054. Epub 2005 Jun 17.
To determine the denominator or the 'population at risk' is a problem which has long been encountered in general practice-based epidemiological research. It is important for calculating epidemiological figures.
The aim of this article is to demonstrate how in the absence of a patient list, a reliable denominator can be calculated, starting from the number of patients who contacted their GP in the period of one year. Therefore a brief overview will be given from known approaches, then the new approach will be illustrated on a database named Intego, with data from 43 general practices in Belgium.
The Intego database contains information about patient contacts, diagnoses, laboratory results and drug prescriptions, extracted from the participants' structured electronic medical record system. The number of patients who contacted the practice in a year can be calculated from the Intego data. On the other hand, the percentage of the population that consults a GP during a particular period was obtained from the reimbursement claims data available from the sickness funds. By combining these two datasets, stratified by age, gender and district, a correction factor was calculated. An estimate of the real size of the Intego practice populations was obtained by extrapolating the yearly contact group by this factor.
In 2003 according the Intego-register, 64,161 patients contacted their family practice and this correlated with an estimated practice population of 80,094 patients. The absence of the socio-economic status in the estimation is irrelevant in our model of estimating the practice population.
The availability of a denominator in general practice-based research is essential to calculate epidemiological figures. This method using a correction factor makes it possible to calculate a reliable practice population. A similar approach will probably also be applicable in other European countries.
确定分母或“风险人群”是基于全科医疗的流行病学研究中长期遇到的一个问题。这对于计算流行病学数据很重要。
本文旨在说明在没有患者名单的情况下，如何从一年内联系其全科医生的患者数量出发计算出可靠的分母。因此，将简要概述已知方法，然后在名为Intego的数据库上说明新方法，该数据库包含比利时43家全科医疗的数据。
Intego数据库包含从参与者的结构化电子病历系统中提取的有关患者就诊、诊断、实验室检查结果和药物处方的信息。可以从Intego数据中计算出一年内联系该医疗机构的患者数量。另一方面，特定时期内咨询全科医生的人群百分比可从疾病基金提供的报销申请数据中获得。通过合并这两个按年龄、性别和地区分层的数据集，计算出一个校正因子。通过用这个因子外推年度就诊人群来估计Intego医疗机构实际人群规模。
根据Intego登记册，2003年有64,161名患者联系了他们的家庭医生，这与估计的80,094名患者的医疗机构人群相关。在我们估计医疗机构人群的模型中，估计中缺少社会经济地位无关紧要。
在基于全科医疗的研究中，分母的可用性对于计算流行病学数据至关重要。这种使用校正因子的方法使得计算可靠的医疗机构人群成为可能。类似的方法可能在其他欧洲国家也适用。
