TY  - JOUR
A1  - Schroyen, Fred
T1  - The importance of correcting for health-related survey non-response when estimating health expectancies: Evidence from The HUNT Study
Y1  - 2024/04/05
JF  - Demographic Research
JO  - Demographic Research
SN  - 1435-9871
SP  - 667
EP  - 732
DO  - 10.4054/DemRes.2024.50.25
VL  - 50
IS  - 25
UR  - https://www.demographic-research.org/volumes/vol50/25/
L1  - https://www.demographic-research.org/volumes/vol50/25/50-25.pdf
L2  - https://www.demographic-research.org/volumes/vol50/25/50-25.pdf
L3  - https://www.demographic-research.org/volumes/vol50/25/files/readme.50-25.txt
L3  - https://www.demographic-research.org/volumes/vol50/25/files/demographic-research.50-25.zip
N2  - Background: Most studies on health expectancies rely on self-reported health from surveys to measure the prevalence of disabilities or ill health in a population. At best, such studies only correct for sample selection based on a limited number of characteristics observed on the invitees. 

Objective: Using longitudinal data from the Trøndelag Health Study (HUNT), I investigate the extent to which adjustments for a health-related sample selection affect the age profiles for the prevalence of functional impairment (FI) and the associated disability-free life expectancy (DFLE). 

Methods: I estimate a probit model with sample selection under the identifying restriction that the strength of the health-related selection is of similar order to the strength of the selection on observable characteristics. I then compute the selection-adjusted FI prevalence rates and trace out the implications for DFLE using the Sullivan method. 

Results: The analysis confirms that poor health measured at younger ages correlates with nonresponse behaviour in later waves of the survey, and that even for a conservative lower bound for the assumed degree of health-related selection, the estimated age profiles for DFLE lie systematically below the corresponding profiles when controlling only for selection on observable characteristics. 

Conclusions: Health related non-response downwardly biases the raw sample prevalence rates for FI obtained from survey data and contributes to overestimating the expansion in DFLE. 

Contribution: I present a statistical framework for taking health-related survey non-responses into account when estimating the prevalence rate of FI. The framework can be used to gauge the sensitivity of estimated (changes in) DFLE to health-related sample selection. 

ER  -