Goldacre M, Kurina L, Yeates D, Seagroatt V, Gill L
Unit of Health-Care Epidemiology, Institute of Health Sciences, University of Oxford, Oxford, UK.
QJM. 2000 Oct;93(10):669-75. doi: 10.1093/qjmed/93.10.669.
We describe the use of a dataset of statistical medical records, the Oxford Record Linkage Study (ORLS), to identify diseases which occur together more commonly (association), or less commonly (dissociation), than their individual frequencies in the population would predict. We investigated some conditions known or suspected to enhance the subsequent risk of cancer, some conditions thought to be linked with schizophrenia, and some associations between conditions with a known autoimmune component. Diseases may occur in combination more often (or less often) than expected by chance because one predisposes to (or protects against) another or because they share environmental and/or genetic mechanisms in common. The investigation of such associations can yield important information for clinicians interested in potential disease sequelae, for epidemiologists trying to understand disease aetiology, and for geneticists attempting to determine the genetic basis of variation in disease course among individuals. We suggest that, through the use of datasets like the ORLS, it will be possible to 'map' comprehensively the phenomic expression of co-occurring diseases.
我们描述了如何使用一个统计医学记录数据集——牛津记录链接研究（ORLS），来识别那些共同出现的频率高于（关联）或低于（解离）其在人群中的个体频率所预测的疾病。我们研究了一些已知或疑似会增加后续癌症风险的病症、一些被认为与精神分裂症有关的病症，以及一些具有已知自身免疫成分的病症之间的关联。疾病可能比偶然预期的更频繁（或更不频繁）地同时出现，原因可能是一种疾病易引发（或预防）另一种疾病，或者是因为它们共享共同的环境和/或遗传机制。对于关注潜在疾病后遗症的临床医生、试图理解疾病病因的流行病学家以及试图确定个体疾病进程变异遗传基础的遗传学家而言，对这种关联的研究可以产生重要信息。我们认为，通过使用像ORLS这样的数据集，将有可能全面“绘制”同时出现的疾病的表型表达。
