Literature DB >> 7934275

Assessment and control of nonresponse bias in a survey of medicine use by the elderly.

K M Grotzinger1, B C Stuart, F Ahern.   

Abstract

Health services research based on survey data is subject to potentially serious selection bias because observations are typically available only for survey respondents. This study describes a method of assessing and controlling for selection bias in the context of a survey of prescription and over-the-counter drug use by the elderly. A random sample of 6,500 Pennsylvania Medicare enrollees was sent a questionnaire regarding medicine use, insurance coverage, and health status in 1990. Applying a two-stage, limited dependent variable selection model developed by Heckman to baseline Medicare enrollment and utilization data for both respondents (70%) and nonrespondents (30%) allowed us to detect and control for negative and significant nonresponse bias in estimates of prescription drug use. Purchase of over-the-counter medication was free of such bias. The report describes how the Heckman method can be applied in other cases where health services survey samples are generated from program or organizational files that contain person-level data on all members of the sample frame.

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Year:  1994        PMID: 7934275     DOI: 10.1097/00005650-199410000-00002

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  12 in total

1.  Exploring the demand for a voluntary Medicare prescription drug benefit.

Authors:  Richard R Cline; David A Mott
Journal:  AAPS PharmSci       Date:  2003

2.  Patterns of unit and item nonresponse in the CAHPS Hospital Survey.

Authors:  Marc N Elliott; Carol Edwards; January Angeles; Katrin Hambarsoomians; Ron D Hays
Journal:  Health Serv Res       Date:  2005-12       Impact factor: 3.402

3.  Investigating response bias in an information technology survey of physicians.

Authors:  Nir Menachemi; Neset Hikmet; Mary Stutzman; Robert G Brooks
Journal:  J Med Syst       Date:  2006-08       Impact factor: 4.460

Review 4.  Effectiveness of multidisciplinary rehabilitation services in postacute care: state-of-the-science. A review.

Authors:  Janet A Prvu Bettger; Margaret G Stineman
Journal:  Arch Phys Med Rehabil       Date:  2007-11       Impact factor: 3.966

Review 5.  Men: good health and high mortality. Sex differences in health and aging.

Authors:  Anna Oksuzyan; Knud Juel; James W Vaupel; Kaare Christensen
Journal:  Aging Clin Exp Res       Date:  2008-04       Impact factor: 3.636

6.  Estimation of non-response bias in the Medicare FFS HOS.

Authors:  Nancy McCall; Galina Khatutsky; Kevin Smith; Gregory C Pope
Journal:  Health Care Financ Rev       Date:  2004

7.  Comparison of a French pediatric type 1 diabetes cohort's responders and non-responders to an environmental questionnaire.

Authors:  Sophie Le Fur; Pierre Bougnères; Alain-Jacques Valleron
Journal:  BMC Public Health       Date:  2014-12-03       Impact factor: 3.295

8.  Official statistics and claims data records indicate non-response and recall bias within survey-based estimates of health care utilization in the older population.

Authors:  Matthias Hunger; Larissa Schwarzkopf; Margit Heier; Annette Peters; Rolf Holle
Journal:  BMC Health Serv Res       Date:  2013-01-03       Impact factor: 2.655

9.  Assessing response bias from missing quality of life data: the Heckman method.

Authors:  Anne E Sales; Mary E Plomondon; David J Magid; John A Spertus; John S Rumsfeld
Journal:  Health Qual Life Outcomes       Date:  2004-09-16       Impact factor: 3.186

10.  The cumulative incidence of conventional risk factors of cardiovascular disease and their population attributable risk in an Iranian population: The Isfahan Cohort Study.

Authors:  Masoumeh Sadeghi; Mohammad Talaei; Shahram Oveisgharan; Katayoun Rabiei; Minoo Dianatkhah; Ahmad Bahonar; Nizal Sarrafzadegan
Journal:  Adv Biomed Res       Date:  2014-11-29
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