How does patient quality of life impact caregiving burden? Often this is determined using caregiver surveys. However, a paper by Jiao et al. (2025) aims to use a data based approach to help estimate caregiver burden. They
The authors use data from the 2010-2018 Health and Retirement Survey (HRS). HRS data was selected because there is information on functional status including activities of daily living (ADLs;
e.g., getting across a room, dressing, bathing, toileting, eating, and getting in/out of bed) and instrumental activities of daily living (IADLs; e.g., managing money, taking medications, shopping for groceries, preparing meals, using a map, and using the phone). They use 2000 data to map ADLs and IADLs to Health Utilities Index [HUI] Mark 3; and 2002 data to map ADLs and IADLs to visual analog scales (VAS). Then, using this mapping, ADLs/IADLs are used to estimate HUI utilities.
Then, the authors conducted regression analyses in order to examine the relationship between informal caregiving time and the HrQoL of care recipients. The outcome variable, informal caregiving time. Informal caregivers included not only family members, but also friends, or other nonprofessional caregivers. For each respondent and survey year, the authors calculated the total monthly hours of informal caregiving, summing hours provided by all caregivers. More specifically, 6 models were developed depending on how utilities were measured: (1) HUI alone, (2) HUI with ADL, (3) HUI with ADL and IADL, (4) VAS alone, (5) VAS with ADL, and (6) VAS with ADL and IADL.
If you read the full paper, you will be able to see that you can predict caregiving time as long as you have the following information:
Utility (HUI/VAS, optionally ADL/IADL)AgeSexRaceEducationWealth quintileUrban/Rural statusWhether the patient received formal caregiving
Perhaps most helpful, the paper has a table describing how to implement this algorithm into cost effectiveness analyses or other modelling approaches.
You can read the full paper here.
