Interdisciplinary Centre for Health & Society, University of Toronto Scarborough, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Women's College Research Institute, Women's College Hospital, Toronto, Canada.
Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Disabil Health J. 2020 Jul;13(3):100909. doi: 10.1016/j.dhjo.2020.100909. Epub 2020 Feb 27.
Women with disabilities experience significant health disparities. A barrier to progress in addressing these disparities is the lack of population-based data on their health outcomes, which are needed to plan health care delivery systems. Administrative health data are increasingly being used to measure the health of entire populations, but these data may only capture impairment and not activity and participation restrictions.
We conducted a systematic review to identify and appraise existing literature on the development and validation of algorithms to identify reproductive-aged women with physical and sensory disabilities in administrative health data.
We searched Medline, EMBASE, CINAHL, PsycINFO, and Scopus from inception to April 2019 for studies of the development and/or validation of algorithms using diagnostic, procedural, or prescription codes to identify physical and sensory disabilities in administrative health data. Study and algorithm characteristics were extracted and quality was assessed using standardized instruments.
Of 14,073 articles initially identified, we reviewed 6 articles representing 2 unique algorithms. One algorithm aimed to correlate diagnoses, procedure codes, and prescriptions with ability to access routine care as an indicator of functional limitation. The other algorithm used diagnostic and procedure codes to identify use of mobility-assistive devices to measure functional limitation. Only one algorithm was validated against self-reported disability.
Our findings underscore the need to strengthen current methods to identify disability in administrative health data, including linkage with other sources of information on functional limitations, so that population-based data can be used to optimize health care for women with disabilities.
残疾女性的健康差距较大。在解决这些差距方面，一个障碍是缺乏针对其健康结果的基于人群的数据，而这些数据是规划医疗保健提供系统所必需的。行政健康数据越来越多地被用于衡量整个人群的健康状况，但这些数据可能仅捕捉到损伤，而无法捕捉到活动和参与受限的情况。
我们进行了一项系统综述，以确定和评估现有文献，这些文献涉及在行政健康数据中开发和验证用于识别处于生育年龄的身体和感官残疾女性的算法。
我们从建库开始到 2019 年 4 月，在 Medline、EMBASE、CINAHL、PsycINFO 和 Scopus 中搜索了关于使用诊断、程序或处方代码在行政健康数据中识别身体和感官残疾的算法的开发和/或验证的研究。提取研究和算法特征，并使用标准化工具评估质量。
在最初确定的 14073 篇文章中，我们审查了 6 篇代表 2 个独特算法的文章。一个算法旨在将诊断、程序代码和处方与常规护理的获取能力相关联，作为功能限制的指标。另一个算法使用诊断和程序代码来识别使用移动辅助设备以衡量功能限制。只有一个算法针对自我报告的残疾进行了验证。
我们的研究结果强调需要加强当前在行政健康数据中识别残疾的方法，包括与其他功能限制信息源的链接，以便能够使用基于人群的数据来优化残疾女性的医疗保健。
