Participation Bias in a Survey of Community Patients With Heart Failure

社区心力衰竭患者调查中的参与偏差

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Abstract

OBJECTIVE: To identify differences between participants and nonparticipants in a survey of physical and psychosocial aspects of health among a population-based sample of patients with heart failure (HF). PATIENTS AND METHODS: Residents from 11 Minnesota counties with a first-ever code for HF (International Classification of Diseases, Ninth Revision 428 and Tenth Revision I50) between January 1, 2013, and December 31, 2016, were identified. Participants completed a questionnaire by mail or telephone. Characteristics and outcomes were extracted from medical records and compared between participants and nonparticipants. Response rate was calculated using guidelines of the American Association for Public Opinion Research. The association between nonparticipation and outcomes was examined using Cox proportional hazards regression for death and Andersen-Gill modeling for hospitalizations. RESULTS: Among 7911 patients, 3438 responded to the survey (American Association for Public Opinion Research response rate calculated using formula 2 = 43%). Clinical and demographic differences between participants and nonparticipants were noted, particularly for education, marital status, and neuropsychiatric conditions. After a mean ± SD of 1.5±1.0 years after survey administration, 1575 deaths and 5857 hospitalizations occurred. Nonparticipation was associated with a 2-fold increased risk for death (hazard ratio, 2.29; 95% CI, 2.05-2.56) and 11% increased risk for hospitalization (hazard ratio, 1.11; 95% CI, 1.02-1.22) after adjusting for age, sex, time from HF diagnosis to index date, marital status, coronary disease, arrhythmia, hyperlipidemia, diabetes, cancer, chronic kidney disease, arthritis, osteoporosis, depression, and anxiety. CONCLUSION: In a large survey of patients with HF, participation was associated with notable differences in clinical and demographic characteristics and outcomes. Examining the impact of participation is critical to draw inference from studies of patient-reported measures.

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