Classifying Young Children with Attention-Deficit/Hyperactivity Disorder Based on Child, Parent, and Family Characteristics: A Cross-Validation Study.

Classifying Young Children with Attention-Deficit/Hyperactivity Disorder Based on Child, Parent, and Family Characteristics: A Cross-Validation Study.
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DOI:
10.3390/ijerph19159195
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发表时间:
2022-07-27
影响因子:
--
通讯作者:
Sheridan, Margaret
Sheridan, Margaret
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Law, Evelyn;Sideridis, Georgios;Alkhadim, Ghadah;Snyder, Jenna;Sheridan, Margaret

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我们旨在确定具有不同ADHD风险的幼儿亚组,并通过独立的儿童样本对这些亚组进行交叉验证。研究1中的所有儿童(N = 120)在7岁前接受了心理评估并被诊断为多动症。潜类分析(LCA)将儿童分为危险亚组。研究2 (N = 168)包括7岁以下儿童的独立样本。研究1的预测模型应用于研究2。研究1的潜在分类分析表明,3类溶液的偏好(BIC = 3807.70, p < 0.001)。与儿童因素相比,母亲教育程度、收入需求比和家族精神病理史对班级成员的影响更大。研究2中复制了与研究1几乎相同的LCA结构(BIC = 5108.01, p < 0.001)。敏感性指数(0.913,95% C.I. 0.814-0.964)和特异性指数(0.788,95% C.I. 0.692-0.861)在所有研究中都很高。结论是,这些分类代表了儿童、父母和家庭特征的有效组合,可以预测幼儿多动症。
We aimed to identify subgroups of young children with differential risks for ADHD, and cross-validate these subgroups with an independent sample of children. All children in Study 1 (N = 120) underwent psychological assessments and were diagnosed with ADHD before age 7. Latent class analysis (LCA) classified children into risk subgroups. Study 2 (N = 168) included an independent sample of children under age 7. A predictive model from Study 1 was applied to Study 2. The latent class analyses in Study 1 indicated preference of a 3-class solution (BIC = 3807.70, p < 0.001). Maternal education, income-to-needs ratio, and family history of psychopathology, defined class membership more strongly than child factors. An almost identical LCA structure from Study 1 was replicated in Study 2 (BIC = 5108.01, p < 0.001). Indices of sensitivity (0.913, 95% C.I. 0.814–0.964) and specificity (0.788, 95% C.I. 0.692–0.861) were high across studies. It is concluded that the classifications represent valid combinations of child, parent, and family characteristics that are predictive of ADHD in young children.
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影响因子: 8
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