Diagnostic boundaries, reasoning and depressive disorder, I. Development of a probabilistic morbidity model for public health psychiatry
Diagnostic boundaries, reasoning and depressive disorder, I. Development of a probabilistic morbidity model for public health psychiatry
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诊断界限、推理和抑郁症，I. 公共卫生精神病学概率发病率模型的开发
作者：
N. Wainwright;P. Surtees;W. Gilks
背景资料。近年来，精神病学的诊断实践已经变得越来越有条理，试图使疾病的定义标准化，并提高可靠性。与此同时，越来越多的人认识到有必要考虑到诊断决策过程中的不确定性。在大多数情况下，诊断仍然是由两个结果表示的，尽管这已知需要大量的信息损失。许多诊断方案在一定程度上涉及对症状列表中所需症状的数量设定阈值。方法：研究方法。这里提出了一个模型，使用从潜在类分析中获得的思想，通过将精神病学病例状态的二进制度量转变为概率度量，并用平滑的过渡取代阈值，从而允许从这些方案中进行泛化。结果。结果度量是在不改变原始度量的含义的情况下用概率来表示无序状态的度量。患病率估计(使用ICD-10抑郁发作标准)更稳定，可以更精确地给出。结论。以这种方式表达的障碍状态保留了更多的诊断信息，并在查看流行率和风险因素估计时提供了对传统二进制分析的有用扩展。
Background. In recent years diagnostic practice in psychiatry has become increasingly structured in an attempt to standardize definitions of disorders and improve reliability. At the same time there has been an increasing recognition of the need to take account of uncertainty in the process of diagnostic decision making. For the most part, diagnosis is still represented by a binary outcome while this is known to entail a substantial loss of information. Many diagnostic schemes involve, in part, taking thresholds on the numbers of symptoms required from symptom lists. Methods. A model is proposed here, using ideas derived from latent class analysis to permit generalization from these schemes through moving from a binary to a probabilistic measure of psychiatric case status and replacing thresholds with smoothed transitions. Results. An outcome measure is produced where disorder status is expressed in terms of probabilities without changing the meaning of the original measure. Prevalence estimates (using ICD-10 Depressive Episode criteria) are more stable and can be given with increased precision. Conclusions. Disorder status when expressed in this way retains more diagnostic information and provides a useful extension to traditional binary analyses when looking at prevalence and risk factor estimation.