Enhancing data quality for cross-national harmonization: Assessment of cognitive function in the CHARLS HCAP by language, literacy, and visual impairment

阿尔茨海默病和相关痴呆症（ADRD）是一项重大的全球公共卫生和政策挑战。的 NIA通过资助协调认知评估，投资于解决全球ADRD负担 美国健康和退休及其国际合作伙伴研究的HCAP子研究。HCAP 是一项创新，旨在首次对老年人的认知功能进行可比性测量。 世界各地的成年人。中国健康与退休纵向研究（CHARLS）中的HCAP是第一个 在2018年CHARLS第4波中部署到年龄≥60岁的受试者中。中国是一个多元化的大国， 至少有九种语言和七种方言的普通话之间的人口。文盲（35.3%） 患病率）和未矫正视力障碍（患病率18.3%）在老年人口中也很常见。 然而，CHARLS HCAP的访谈材料是在假设流利的情况下准备和管理的。 口语和书写标准的普通话，没有系统地适应文盲或视觉 损伤如果在认知测试项目上的表现受到这些无关特征的影响， 被调查者的真实认知水平，那么差异项目功能（DIF）被认为是目前的认知 供试品。虽然DIF可能导致认知得分有偏差，但我们有工具可以在给定假设的情况下进行调整， 这种DIF改善认知测量。本补充申请的目的是净化 CHARLS HCAP认知测试组合中由于口语或方言、书面语 读写能力和视觉能力从真正的认知功能的潜在水平。母体R 01（R 01 AG 070953）旨在 在统计上协调HCAP措施，并在五个不同国家的国家一级测试DIF (US墨西哥、英国、南非和印度）。本补充申请是本申请的扩展。 父母补助金的统计机制和科学范围。我们建议：1）识别和调整潜在的 DIF在CHARLS HCAP中由于语言或方言、识字和视觉而导致的认知功能测量中的作用 能力; 2）识别和调整CHARLS先前波中认知功能测量中的潜在DIF （波1，2，3），由语言或方言，识字，和视觉能力。进行DIF分析并调整后 根据DIF所需的认知评分，我们将检查年龄，性别，教育， 和城市/农村居住与DIF调整的认知得分将不同于非DIF调整的得分的方式 这表明，在DIF调整的患者中，由于语言或方言、文盲和视力障碍而导致的偏见减少， 成绩.如能顺利完成这项拟议的补充计划，将有助改善现有的“儿童及青少年学习计划”的质素 认知数据，并将产生的内容，管理和面试官培训的建议， 在未来计划的CHARLS HCAP浪潮中进行认知测试组合。我们预计，这项工作将使我们能够 建议收集更多信息以及认知措施，以提高数据质量并帮助 解释，如语言，识字和视觉能力的客观测量。

Alzheimer’s disease and related dementias (ADRD) are a major global public health and policy challenge. The NIA has invested in addressing the global ADRD burden through funding the Harmonized Cognitive Assessment Protocol (HCAP) sub-studies of the US Health and Retirement and its International Partner Studies. The HCAP is an innovation that aims to allow, for the first time, comparable measurement of cognitive function among older adults around the world. The HCAP in the China Health and Retirement Longitudinal Study (CHARLS) was first deployed in Wave 4 of CHARLS in 2018 to participants aged ≥60 years. China is a large, diverse country with at least nine languages and seven dialects of Mandarin spoken amongst its population. Illiteracy (35.3% prevalence) and uncorrected visual impairment (18.3% prevalence) are also common in the older population. However, interview materials for the CHARLS HCAP were prepared and administered assuming fluency in spoken and written standard Mandarin and were not systematically adapted to account for illiteracy or visual impairment. If performance on a cognitive test item is affected by these extraneous features rather than a respondent’s true level of cognition, then differential item functioning (DIF) is said to be present for that cognitive test item. While DIF can lead to biased cognitive scores, we have tools that can, given assumptions, adjust for such DIF to improve measurement of cognition. The goal of this supplemental application is to decontaminate measurement differences in the CHARLS HCAP cognitive test battery due to spoken language or dialect, written literacy, and visual ability from true underlying levels of cognitive function. The parent R01 (R01AG070953) aims to statistically harmonize HCAP measures and test for DIF at the country-level across five different countries (US, Mexico, England, South Africa, and India). The present supplemental application is an extension of the statistical machinery and scientific scope of the parent grant. We propose to 1) identify and adjust for potential DIF in measurement of cognitive function in CHARLS HCAP due to language or dialect, literacy, and visual ability; and 2) Identify and adjust for potential DIF in measurement of cognitive function in prior waves of CHARLS (Waves 1, 2, 3), by language or dialect, literacy, and visual ability. After performing DIF analysis and adjusting cognitive scores as needed for DIF, we will examine whether the associations of each of age, sex, education, and urban/rural residence with DIF-adjusted cognitive scores will differ from the non-DIF-adjusted scores in ways that suggest reduction of bias due to language or dialect, illiteracy, and vision impairment in the DIF-adjusted scores. Successful completion of this proposed supplement will help improve the quality of existing CHARLS cognitive data, and will produce recommendations for the content, administration, and interviewer training for the cognitive test battery in future planned waves of CHARLS HCAP. We anticipate that this work will enable us to recommend additional information to collect alongside cognitive measures to enhance data quality and assist interpretations, such as objective measurement of language, literacy, and visual ability.