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The Influence of Respondent Characteristics on the Validity of Self‐Reported Survey Responses
Author(s) -
Guerard Barbara,
Omachonu Vincent,
Harvey Raymond A.,
Hernandez S. Robert,
Sen Bisakha
Publication year - 2016
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/1475-6773.12356
Subject(s) - concordance , respondent , medical prescription , medicine , descriptive statistics , logistic regression , family medicine , health care , statistics , nursing , mathematics , political science , law , economics , economic growth
Objective To examine concordance between member self‐reports and the organization's administrative claims data for two key health factors: number of chronic conditions, and number of prescription drugs. Data Medicare Advantage plan claims data and member survey data from 2011 to 2012. Design Mailed surveys to 15,000 members, enrolled minimum 6 months, drawn from a random sample of primary care physician practices with at least 200 members. Methods Descriptive statistics were generated for extent of concordance. Multivariable logistic regressions were used to analyze the association of selected respondent characteristics with likelihood of concordance. Findings Concordance for number of chronic conditions was 58.4 percent, with 27.3 percent under‐reporting, 14.2 percent over‐reporting. Concordance for number of prescription drugs was 56.6 percent with 38.9 percent under‐reporting, 4.5 percent over‐reporting. Number of prescriptions and assistance in survey completion were associated with higher likelihood of concordance for chronic conditions. Assistance in survey completion and number of chronic conditions were associated with higher concordance, and age and number of prescriptions were associated with lower concordance, for prescription drugs. Conclusions Self‐reported number of chronic conditions and prescription medications are not in high concordance with claims data. Health care researchers and policy makers using patient self‐reported data should be aware of these potential biases.