Description/Abstract
This study develops and estimates a model of measurement error in self-reported health conditions. The model allows self-reports of a health condition to differ from a contemporaneous medical examination, prior medical records, or both. The model is estimated using a two-sample strategy, which combines survey data linked medical examination results and survey data linked to prior medical records. The study finds substantial inconsistencies between self-reported health, the medical record, and prior medical records. The study proposes alternative estimators for the prevalence of diagnosed and undiagnosed conditions and estimates the bias that arises when using self-reported health conditions as explanatory variables.
Document Type
Working Paper
Date
Summer 8-2016
Keywords
Measurement Error, Disease Prevalence, Diabetes, Hypertension, Medical Examination, Self-Reported, Health Condition
Language
English
Series
Working Papers Series
Disciplines
Health Policy | Medicine and Health | Public Affairs, Public Policy and Public Administration | Social and Behavioral Sciences
ISSN
1525-3066
Recommended Citation
Li, Ling and Singleton, Perry, "A Framework for Measurement Error in Self-Reported Health Conditions" (2016). Center for Policy Research. 223.
https://surface.syr.edu/cpr/223
Accessible PDF version
Source
Local Input
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Additional Information
Working paper no. 191
The authors would like to thank John Cawley, Gary Engelhardt, Alfonso Flores-Lagunes, Bruce Meyer, Jeffrey Kubik, Yoonseok Lee, and seminar participants at Cornell University for valuable comments.