Zaveri Sanskruti, Nambiar Tarun, Thornley Simon, Selak Vanessa, Sundborn Gerhard, Roskvist Rachel, Morris Arthur J
Section of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand.
Department of Pacific Health, School of Population Health, Faculty of Medical and Health Sciences, the University of Auckland, Auckland, New Zealand.
J Paediatr Child Health. 2025 Aug;61(8):1216-1225. doi: 10.1111/jpc.70100. Epub 2025 May 29.
To develop and test a clinical prediction model based on demography and symptoms to assist in screening for scabies diagnosis in primary care.
Data from a scabies prevalence survey conducted in Auckland, New Zealand (NZ) were analysed using logistic regression to predict diagnosis, defined by either clinical criteria (International Alliance for the Control of Scabies, IACS) or quantitative polymerase chain reaction (qPCR) for scabies derived from a skin swab. Best subsets regression was used to select predictors in the final models, using Akaike's information criterion. Model performance was assessed using internal validation with bootstrap resampling.
From a survey of 181 children aged between 8 months and 14 years, 105 were available for analysis with complete questionnaires. Age and symptoms of itch (in the child and their close contacts) were retained as predictors of the model where clinical scabies diagnosis was the outcome. In addition to these predictors, whether household members were affected by insect bites, skin sores or blisters was also retained as a predictor of qPCR-based diagnosis. Accuracy was good for both models, with discrimination and calibration indices favourable (Harrell's concordance index 0.96 [IACS] and 0.85 [qPCR]; calibration slope 0.82 [IACS] and 0.77 [qPCR]).
Simple logistic models based on common symptoms of scabies, especially itch in the child and 'simultaneous itch in the family', accurately predict scabies status. Such tools have potential use in supporting screening and improving diagnosis of scabies in community and primary care settings.
开发并测试一种基于人口统计学和症状的临床预测模型，以协助在初级保健中筛查疥疮诊断。
对在新西兰奥克兰进行的一项疥疮患病率调查的数据进行逻辑回归分析，以预测诊断，诊断依据为临床标准（国际疥疮控制联盟，IACS）或来自皮肤拭子的疥疮定量聚合酶链反应（qPCR）。使用赤池信息准则，通过最佳子集回归在最终模型中选择预测因子。使用自助重采样进行内部验证来评估模型性能。
在对181名年龄在8个月至14岁之间的儿童进行的调查中，有105名儿童的问卷完整，可用于分析。年龄和瘙痒症状（儿童及其密切接触者）被保留为以临床疥疮诊断为结果的模型的预测因子。除了这些预测因子外，家庭成员是否受到昆虫叮咬、皮肤溃疡或水泡影响也被保留为基于qPCR诊断的预测因子。两个模型的准确性都很好，判别和校准指标都很理想（哈雷尔一致性指数：IACS为0.96，qPCR为0.85；校准斜率：IACS为0.82，qPCR为0.77）。
基于疥疮常见症状，特别是儿童瘙痒和“家庭同时瘙痒”的简单逻辑模型能够准确预测疥疮状况。此类工具在支持社区和初级保健环境中的疥疮筛查及改善诊断方面具有潜在用途。
