The importance of estimating selection bias on prevalence estimates shortly after a disaster.

2.50
Hdl Handle:
http://hdl.handle.net/10029/6657
Title:
The importance of estimating selection bias on prevalence estimates shortly after a disaster.
Authors:
Grievink, Linda; Velden, Peter G van der; Yzermans, C Joris; Roorda, Jan; Stellato, Rebecca K
Abstract:
PURPOSE: The aim was to study selective participation and its effect on prevalence estimates in a health survey of affected residents 3 weeks after a man-made disaster in The Netherlands (May 13, 2000). METHODS: All affected adult residents were invited to participate. Survey (questionnaire) data were combined with electronic medical records of residents' general practitioners (GPs). Data for demographics, relocation, utilization, and morbidity 1 year predisaster and 1 year postdisaster were used. RESULTS: The survey participation rate was 26% (N = 1171). Women (odds ratio [OR], 1.46; 95% confidence interval [CI], 1.28-1.67), those living with a partner (OR, 2.00; 95% CI, 1.72-2.33), those aged 45 to 64 years (OR, 2.00; 95% CI, 1.59-2.52), and immigrants (OR, 1.50; 95% CI, 1.30-1.74) were more likely to participate. Participation rate was not affected by relocation because of the disaster. Participants in the survey consulted their GPs for health problems in the year before and after the disaster more often than nonparticipants. Although there was selective participation, multiple imputation barely affected prevalence estimates of health problems in the survey 3 weeks postdisaster. CONCLUSIONS: Estimating actual selection bias in disaster studies gives better information about the study representativeness. This is important for policy making and providing effective health care.
Citation:
Ann Epidemiol 2006, 16(10):782-8
Issue Date:
1-Oct-2006
URI:
http://hdl.handle.net/10029/6657
DOI:
10.1016/j.annepidem.2006.04.008
PubMed ID:
16882468
Type:
Article
Language:
en
ISSN:
1047-2797
Appears in Collections:
Miscellaneous

Full metadata record

DC FieldValue Language
dc.contributor.authorGrievink, Linda-
dc.contributor.authorVelden, Peter G van der-
dc.contributor.authorYzermans, C Joris-
dc.contributor.authorRoorda, Jan-
dc.contributor.authorStellato, Rebecca K-
dc.date.accessioned2006-12-19T12:11:20Z-
dc.date.available2006-12-19T12:11:20Z-
dc.date.issued2006-10-01-
dc.identifier.citationAnn Epidemiol 2006, 16(10):782-8en
dc.identifier.issn1047-2797-
dc.identifier.pmid16882468-
dc.identifier.doi10.1016/j.annepidem.2006.04.008-
dc.identifier.urihttp://hdl.handle.net/10029/6657-
dc.description.abstractPURPOSE: The aim was to study selective participation and its effect on prevalence estimates in a health survey of affected residents 3 weeks after a man-made disaster in The Netherlands (May 13, 2000). METHODS: All affected adult residents were invited to participate. Survey (questionnaire) data were combined with electronic medical records of residents' general practitioners (GPs). Data for demographics, relocation, utilization, and morbidity 1 year predisaster and 1 year postdisaster were used. RESULTS: The survey participation rate was 26% (N = 1171). Women (odds ratio [OR], 1.46; 95% confidence interval [CI], 1.28-1.67), those living with a partner (OR, 2.00; 95% CI, 1.72-2.33), those aged 45 to 64 years (OR, 2.00; 95% CI, 1.59-2.52), and immigrants (OR, 1.50; 95% CI, 1.30-1.74) were more likely to participate. Participation rate was not affected by relocation because of the disaster. Participants in the survey consulted their GPs for health problems in the year before and after the disaster more often than nonparticipants. Although there was selective participation, multiple imputation barely affected prevalence estimates of health problems in the survey 3 weeks postdisaster. CONCLUSIONS: Estimating actual selection bias in disaster studies gives better information about the study representativeness. This is important for policy making and providing effective health care.en
dc.format.extent170071 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.titleThe importance of estimating selection bias on prevalence estimates shortly after a disaster.en
dc.typeArticleen
dc.format.digYES-

Related articles on PubMed

All Items in WARP are protected by copyright, with all rights reserved, unless otherwise indicated.