Development of a Longitudinal Dataset of Persons With Dementia and Their Caregivers Through End-of-Life: A Statistical Analysis System Algorithm for Joining National Health and Aging Trends Study/National Study of Caregiving.

Abstract

Background: Alzheimer’s disease and related dementias (AD/ADRD) are terminal conditions impacting families and caregivers, particularly at end-of-life. Longitudinal, secondary data analyses present opportunities for insight into dementia caregiving and decision-making over time; however, joining complex datasets and preparing them for analysis poses many challenges. Objectives: To describe an approach to linking national survey data of older adults with their primary caregivers to build a prospective, longitudinal dataset, and to share the Statistical Analysis System (SAS) coding statement algorithms with other researchers. Methods: The National Health and Aging Trends Study (NHATS) and National Study of Caregiving (NSOC) are joined using a series of algorithms based on conceptual and operational definitions of dementia, primary caregivers, and the occurrence of death. A series of SAS algorithms resulting in the final longitudinal dataset was created. Results: NHATS/NSOC participants were linked using three preliminary data files (n = 12 427) and one final data join (n = 3305) over nine rounds of data collection. Presence of dementia was defined based on the indicator in the year preceding the last month-of-life (LML) interview. Primary caregivers were defined as the person providing the most frequent care over time. Additional flag variables (LML interview, dementia classification, and cohort (2011 vs 2015)) were created. The SAS algorithms are presented herein. Discussion: The SAS coding statement algorithms provide an opportunity to conduct longitudinal analysis of care for both members of the dyad in the context of dementia and end-of-life. Future research using the proposed dataset can further explore care and caregiving in these populations.