Abstract
Objectives
The purpose of this study was to examine whether frailty could explain variability
in healthcare expenditure beyond multimorbidity and disability among Chinese older
adults.
Design
Cross-sectional.
Setting and Participants
Participants were 5300 community-dwelling adults age at least 60 years from the China
Health and Retirement Longitudinal Study.
Methods
Frailty was identified by the physical frailty phenotype approach that has been created
and validated among Chinese older adults. Five criteria were used: slowness, weakness,
exhaustion, inactivity, and shrinking. Persons were classified as “nonfrail” (0 criteria),
“prefrail” (1‒2 criteria), or “frail” (3‒5 criteria). Healthcare expenditure was measured
based on participants’ self-report and was classified into 3 types: outpatient expenditure,
inpatient expenditure, and self-treatment expenditure. The association of frailty
and healthcare expenditure was analyzed using a 2-part regression model to account
for excessive zero expenditures.
Results
Frailty was associated with higher odds of incurring outpatient, inpatient, and self-treatment
expenditure. Among persons with non-zero expenditure, prefrail and frail persons,
on average, had US $30.62 [95% confidence interval (CI) 8.41, 52.82] and US $60.60
(95% CI 5.84, 115.36) higher outpatient expenditure than the nonfrail, adjusting for
sociodemographics, multimorbidity, and disability. After adjustment for all covariates,
prefrail persons, on average, had US $3.34 (95% CI 0.54, 6.13) higher self-treatment
expenditure than the nonfrail.
Conclusions and Implications
Frailty is an independent predictor of higher healthcare expenditure among older adults.
These findings suggest that timely screening and recognition of frailty are important
to reduce healthcare expenditure among older adults.
Keywords
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Article info
Publication history
Published online: April 21, 2020
Footnotes
This work was supported by Suzhou Municipal Science and Technology Bureau (SS2019069).
C. W. provides paid consultant services to HealthKeeperS, a data analytics company in China. The other authors declare no conflicts of interest and no financial associations to disclose.
Identification
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© 2020 AMDA - The Society for Post-Acute and Long-Term Care Medicine.
