Michelle S. Franklin, PhD, FNP-BC, PMHNP-BC, CNS, is Postdoctoral Research Associate, Duke University Duke-Margolis Center for Health Policy, Durham, North Carolina. Susan G. Silva, PhD, is Associate Research Professor, Duke University School of Nursing, Durham, North Carolina. Gary R. Maslow, MD, MPH, is Assistant Professor, Duke University School of Medicine, and Assistant Professor, Duke University School of Nursing, Durham, North Carolina. Carolyn T. Halpern, PhD, is Professor, University of North Carolina at Chapel Hill Gillings School of Global Public Health. Elizabeth I. Merwin, PhD, RN, FAAN, is Dean, The University of Texas at Arlington College of Nursing and Health Innovation. Sharron L. Docherty, PhD, PNP-BC, FAAN, is Associate Professor, Duke University School of Nursing, Durham, North Carolina.
Nurs Res. 2020 Nov/Dec;69(6):436-447. doi: 10.1097/NNR.0000000000000469.
Much remains unknown about the longitudinal health and well-being of individuals with intellectual disability (ID); thus, new methods to identify those with ID within nationally representative population studies are critical for harnessing these data sets to generate new knowledge.
Our objective was to describe the development of a new method for identifying individuals with ID within large, population-level studies not targeted on ID.
We used a secondary analysis of the de-identified, restricted-use National Longitudinal Study of Adolescent to Adult Health (Add Health) database representing 20,745 adolescents to develop a method for identifying individuals who meet the criteria of ID. The three criteria of ID (intellectual functioning, adaptive functioning, and disability originating during the developmental period) were derived from the definitions of ID used by the American Psychiatric Association and the American Association on Intellectual and Developmental Disabilities. The ID Indicator was developed from the variables indicative of intellectual and adaptive functioning limitations included in the Add Health database from Waves I to III.
This method identified 441 adolescents who met criteria of ID and had sampling weights. At Wave I, the mean age of this subsample of adolescents with ID was 16.1 years. About half of the adolescents were male and from minority racial groups. Their parents were predominately female, were married, had less than a high school education, and had a median age of 41.62 years. The adolescents' mean maximum abridged Peabody Picture Vocabulary Test standardized score was 69.6, and all demonstrated at least one adaptive functioning limitation.
This study demonstrates the development of a data-driven method to identify individuals with ID using commonly available data elements in nationally representative population data sets. By utilizing this method, researchers can leverage existing rich data sets holding potential for answering research questions, guiding policy, and informing interventions to improve the health of the ID population.
个体的智力障碍（ID）的纵向健康和幸福感仍有许多未知之处；因此，在针对 ID 人群的全国代表性研究中，新的方法来识别 ID 人群对于利用这些数据集来生成新知识至关重要。
我们的目的是描述一种新方法的开发，该方法用于在非针对 ID 的大型人群研究中识别 ID 个体。
我们使用了经过去识别的、受限使用的全国青少年到成人健康纵向研究（Add Health）数据库的二次分析，该数据库代表了 20745 名青少年，以开发一种识别符合 ID 标准的个体的方法。ID 的三个标准（智力功能、适应功能和发育期间起源的残疾）来自美国精神病学协会和美国智力和发育障碍协会使用的 ID 定义。ID 指标是从 Add Health 数据库的 I 到 III 波中包含的智力和适应功能限制的指示变量中得出的。
这种方法确定了 441 名符合 ID 标准并有抽样权重的青少年。在第一波，这个 ID 青少年子样本的平均年龄为 16.1 岁。大约一半的青少年是男性，来自少数族裔群体。他们的父母主要是女性，已婚，受教育程度低于高中，平均年龄为 41.62 岁。青少年的平均最大简明 Peabody 图片词汇测试标准分数为 69.6，所有青少年都表现出至少一种适应功能限制。
这项研究展示了一种使用全国代表性人群数据集的常用数据元素来识别 ID 个体的数据驱动方法的开发。通过利用这种方法，研究人员可以利用现有的丰富数据集，为回答研究问题、指导政策和为改善 ID 人群的健康提供信息干预提供潜力。
