OBJECTIVES: To extend earlier work (Beebe et al, Med Care. 2007;45:959-965) that demonstrated Health Insurance Portability and Accountability Act authorization form (HAF) introduced potential nonresponse bias (toward healthier respondents). RESEARCH DESIGN: The sample frame from the earlier experiment was linked to administrative medical record data, enabling the comparison of background and clinical characteristics of each set of respondents (HAF and No HAF) to the sample frame. SUBJECTS: A total of 6939 individuals residing in Olmsted County, Minnesota who were mailed a survey in September 2005 assessing recent gastrointestinal symptoms with an embedded HAF experiment comprised the study population. MEASURES: The outcomes of interest were response status (survey returned vs. not) by HAF condition (randomized to receive HAF or not). Sociodemographic indicators included gender, age, and race. Health status was measured using the severity-weighted Charlson Score and utilization was measured using emergency room visits, hospital admissions, clinic office visits, and procedures. RESULTS: Younger and nonwhite residents were under-represented and those with more clinical office visits were over-represented in both conditions. Those responding to the survey in the HAF condition were significantly more likely to be in poor health compared with the population (27.3% with 2+ comorbidities vs. 24.6%, P=0.02). CONCLUSIONS: The HAF did not influence the demographic composition of the respondents. However, in contrast to earlier findings based on self-reported health status (Beebe et al, Med Care. 2007;45:959-965), responders in the HAF condition were slightly sicker than in the non-HAF condition. The HAF may introduce a small amount of measurement error by suppressing reports of poor health. Furthermore, researchers should consider the effect of the HAF on resultant precision, respondent burden, and available financial resources.
RCT Entities:
OBJECTIVES: To extend earlier work (Beebe et al, Med Care. 2007;45:959-965) that demonstrated Health Insurance Portability and Accountability Act authorization form (HAF) introduced potential nonresponse bias (toward healthier respondents). RESEARCH DESIGN: The sample frame from the earlier experiment was linked to administrative medical record data, enabling the comparison of background and clinical characteristics of each set of respondents (HAF and No HAF) to the sample frame. SUBJECTS: A total of 6939 individuals residing in Olmsted County, Minnesota who were mailed a survey in September 2005 assessing recent gastrointestinal symptoms with an embedded HAF experiment comprised the study population. MEASURES: The outcomes of interest were response status (survey returned vs. not) by HAF condition (randomized to receive HAF or not). Sociodemographic indicators included gender, age, and race. Health status was measured using the severity-weighted Charlson Score and utilization was measured using emergency room visits, hospital admissions, clinic office visits, and procedures. RESULTS: Younger and nonwhite residents were under-represented and those with more clinical office visits were over-represented in both conditions. Those responding to the survey in the HAF condition were significantly more likely to be in poor health compared with the population (27.3% with 2+ comorbidities vs. 24.6%, P=0.02). CONCLUSIONS: The HAF did not influence the demographic composition of the respondents. However, in contrast to earlier findings based on self-reported health status (Beebe et al, Med Care. 2007;45:959-965), responders in the HAF condition were slightly sicker than in the non-HAF condition. The HAF may introduce a small amount of measurement error by suppressing reports of poor health. Furthermore, researchers should consider the effect of the HAF on resultant precision, respondent burden, and available financial resources.
Authors: Timothy J Beebe; Nicholas J Talley; Michael Camilleri; Sarah M Jenkins; Kari J Anderson; G Richard Locke Journal: Med Care Date: 2007-10 Impact factor: 2.983
Authors: David Armstrong; Eva Kline-Rogers; Sandeep M Jani; Edward B Goldman; Jianming Fang; Debabrata Mukherjee; Brahmajee K Nallamothu; Kim A Eagle Journal: Arch Intern Med Date: 2005-05-23
Authors: Timothy J Beebe; Jeanette Y Ziegenfuss; Sarah M Jenkins; Lindsey R Haas; Michael E Davern Journal: Ann Epidemiol Date: 2011-04-16 Impact factor: 3.797
Authors: William R Carpenter; Anne-Marie Meyer; Amy P Abernethy; Til Stürmer; Michael R Kosorok Journal: J Clin Epidemiol Date: 2012-11 Impact factor: 6.437
Authors: Jennifer L St Sauver; Brandon R Grossardt; Barbara P Yawn; L Joseph Melton; Walter A Rocca Journal: Am J Epidemiol Date: 2011-03-23 Impact factor: 4.897
Authors: Timothy J Beebe; Donna D McAlpine; Jeanette Y Ziegenfuss; Sarah Jenkins; Lindsey Haas; Michael E Davern Journal: Health Serv Res Date: 2012-01-17 Impact factor: 3.402
Authors: David T Eton; Timothy J Beebe; Philip T Hagen; Michele Y Halyard; Victor M Montori; James M Naessens; Jeff A Sloan; Carrie A Thompson; Douglas L Wood Journal: Patient Relat Outcome Meas Date: 2014-02-10