Brameld Kate J, Holman C D'arcy J, Lawrence David M, Hobbs Michael S T
Department of Public Health, The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, Australia.
Int J Epidemiol. 2003 Aug;32(4):617-24. doi: 10.1093/ije/dyg191.
Linked hospital morbidity data can be used to estimate the incidence of serious chronic disease. However, incidence rates calculated from first-time hospital admissions tend to be overestimated as a result of the erroneous inclusion of prevalent cases that have had previous hospital admissions prior to the study observation period. To address this problem, we have developed the backcasting method.
A retrograde survival model was implemented to calculate the level of over-ascertainment of incidence according to the number of years of linked data on which the estimates were based and corresponding correction factors were calculated. The method is illustrated using the example of linked hospital morbidity data on diabetes mellitus and then acute myocardial infarction, which was validated against the Perth MONICA database for cardiovascular disease.
Corrected estimates of the incidence of diabetes and acute myocardial infarction were produced. The incidence of diabetes was shown to be lower than in North America in accordance with prevalence estimates, whereas the incidence of acute myocardial infarction was overestimated by approximately 10%.
A new method is presented for estimating incidence trends in disease from linked hospital morbidity data. The advantages of this method are its ease of use with routinely collected data and the relatively low cost of applying it in comparison with community surveys or maintaining formal disease registers. The method has other applications using linked data, such as the study of trends in first-time health care procedures and pharmaceutical prescriptions.
关联的医院发病率数据可用于估计严重慢性病的发病率。然而，由于在研究观察期之前曾有过住院治疗的现患病例被错误纳入，首次住院病例计算出的发病率往往被高估。为解决这一问题，我们开发了回溯法。
实施了一种逆向生存模型，以根据估计所基于的关联数据年份数量计算发病率的过度确定水平，并计算相应的校正因子。以糖尿病的关联医院发病率数据为例进行说明，然后以急性心肌梗死为例，该模型已针对珀斯心血管疾病监测、流行病学和结果（MONICA）数据库进行了验证。
得出了糖尿病和急性心肌梗死发病率的校正估计值。根据患病率估计，糖尿病的发病率低于北美地区，而急性心肌梗死的发病率被高估了约10%。
提出了一种从关联的医院发病率数据估计疾病发病率趋势的新方法。该方法的优点是易于使用常规收集的数据，并且与社区调查或维护正式疾病登记册相比，应用成本相对较低。该方法在使用关联数据方面还有其他应用，例如首次医疗程序和药物处方趋势的研究。
