Department of Epidemiology and Cancer Registry, CancerCare Manitoba, Winnipeg, Canada.
Department of Paediatrics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.
BMC Med Res Methodol. 2021 May 16;21(1):105. doi: 10.1186/s12874-021-01296-9.
Previous research has shown that chronic disease case definitions constructed using population-based administrative health data may have low accuracy for ascertaining cases of episodic diseases such as rheumatoid arthritis, which are characterized by periods of good health followed by periods of illness. No studies have considered a dynamic approach that uses statistical (i.e., probability) models for repeated measures data to classify individuals into disease, non-disease, and indeterminate categories as an alternative to deterministic (i.e., non-probability) methods that use summary data for case ascertainment. The research objectives were to validate a model-based dynamic classification approach for ascertaining cases of juvenile arthritis (JA) from administrative data, and compare its performance with a deterministic approach for case ascertainment.
The study cohort was comprised of JA cases and non-JA controls 16 years or younger identified from a pediatric clinical registry in the Canadian province of Manitoba and born between 1980 and 2002. Registry data were linked to hospital records and physician billing claims up to 2018. Longitudinal discriminant analysis (LoDA) models and dynamic classification were applied to annual healthcare utilization measures. The deterministic case definition was based on JA diagnoses in healthcare use data anytime between birth and age 16 years; it required one hospitalization ever or two physician visits. Case definitions based on model-based dynamic classification and deterministic approaches were assessed on sensitivity, specificity, and positive and negative predictive values (PPV, NPV). Mean time to classification was also measured for the former.
The cohort included 797 individuals; 386 (48.4 %) were JA cases. A model-based dynamic classification approach using an annual measure of any JA-related healthcare contact had sensitivity = 0.70 and PPV = 0.82. Mean classification time was 9.21 years. The deterministic case definition had sensitivity = 0.91 and PPV = 0.92.
A model-based dynamic classification approach had lower accuracy for ascertaining JA cases than a deterministic approach. However, the dynamic approach required a shorter duration of time to produce a case definition with acceptable PPV. The choice of methods to construct case definitions and their performance may depend on the characteristics of the chronic disease under investigation.
先前的研究表明，使用基于人群的行政健康数据构建的慢性病病例定义对于确定类风湿关节炎等发作性疾病的病例可能准确性较低，这些疾病的特点是健康期和疾病期交替出现。目前尚无研究考虑使用重复测量数据的统计（即概率）模型将个体分类为疾病、非疾病和不确定类别的动态方法，而不是用于确定病例的确定性（即非概率）方法，后者使用摘要数据进行病例确定。研究目的是验证一种基于模型的动态分类方法，用于从行政数据中确定青少年关节炎 (JA) 病例，并将其性能与用于确定病例的确定性方法进行比较。
研究队列由加拿大马尼托巴省儿科临床登记处确定的 16 岁或以下的 JA 病例和非 JA 对照组成，他们出生于 1980 年至 2002 年之间。登记处的数据与 2018 年之前的医院记录和医生计费索赔相关联。应用纵向判别分析 (LoDA) 模型和动态分类对年度医疗保健利用指标进行分析。确定性病例定义基于医疗保健使用数据中任何时候从出生到 16 岁之间的 JA 诊断；它需要一次住院治疗或两次就诊。基于基于模型的动态分类和确定性方法的病例定义在敏感性、特异性和阳性和阴性预测值 (PPV、NPV) 方面进行了评估。还测量了前者的分类平均时间。
队列包括 797 人；386 人（48.4%）为 JA 病例。使用任何 JA 相关医疗保健接触的年度测量值的基于模型的动态分类方法的敏感性为 0.70，PPV 为 0.82。平均分类时间为 9.21 年。确定性病例定义的敏感性为 0.91，PPV 为 0.92。
与确定性方法相比，基于模型的动态分类方法确定 JA 病例的准确性较低。然而，动态方法需要较短的时间来生成具有可接受 PPV 的病例定义。用于构建病例定义的方法及其性能的选择可能取决于所研究的慢性病的特征。
