Use of large medical databases to study associations between diseases
Use of large medical databases to study associations between diseases
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DOI：
10.1093/qjmed/93.10.669
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发表时间：
2000-10-01
期刊：
影响因子：
--
通讯作者：
Gill, L
中科院分区：
文献类型：
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作者：
Goldacre, M;Kurina, L;Gill, L
我们描述了统计医疗记录数据集的使用，牛津记录关联研究（ORLS），以确定疾病更常见（关联）或更不常见（分离），而不是他们在人群中的个体频率预测。我们调查了一些已知或怀疑会增加随后患癌症风险的疾病，一些被认为与精神分裂症有关的疾病，以及一些与已知自身免疫成分有关的疾病之间的关联。疾病在组合运动中发生的频率可能比预期的要高（或低），因为一个人易患（或预防）另一个人，或者因为他们有共同的环境和/或遗传机制。对这些关联的调查可以为对潜在疾病后遗症感兴趣的临床医生、试图了解疾病病原学的流行病学家和试图确定个体之间疾病过程变异的遗传基础的遗传学家提供重要信息。我们建议，通过使用像ORLS这样的数据集，将有可能全面“绘制”共同发生疾病的表型表达。
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 move 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.