Literature DB >> 8978880

The effect of nonresponse on prevalence estimates for a referent population: insights from a population-based cohort study. Atherosclerosis Risk in Communities (ARIC) Study Investigators.

E Shahar1, A R Folsom, R Jackson.   

Abstract

Characterization of nonrespondents, with the aim of detecting nonresponse bias, is a crucial component of prospective studies. This study was undertaken to investigate the demographic and health characteristics of nonrespondents to a population-based cohort study of cardiovascular disease, to determine whether early-stage nonrespondents differ from late-stage nonrespondents, and to estimate the bias in prevalence estimates for the source population. Sixty-seven percent of eligible subjects completed all phases of the cohort recruitment. Compared to respondents, nonrespondents were less likely to be married, less likely to be employed, and less likely to be well educated. Nonrespondents tended to describe their general health in less favorable terms and were more likely to be smokers. Their reported disease profile, however, was not dissimilar to that of respondents. For several demographic and health characteristics, including marital status, education, and smoking, early-stage nonrespondents differed from respondents more than did late-stage nonrespondents. For example, 42% of early nonrespondents were smokers compared to 37% of late nonrespondents and 22% of respondents. Overall, the bias in prevalence estimates related to nonresponse was small (< 5%) for most of the measured characteristics. Although nonresponse to health surveys is associated with typical attributes, early nonrespondents differ from respondents more than do late-stage nonrespondents. With few exceptions, however, a 33% nonresponse rate did not appear to introduce substantial bias into prevalence estimates for the source community.

Entities:  

Mesh:

Year:  1996        PMID: 8978880     DOI: 10.1016/s1047-2797(96)00104-4

Source DB:  PubMed          Journal:  Ann Epidemiol        ISSN: 1047-2797            Impact factor:   3.797


  41 in total

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2.  A prospective study of household smoking bans and subsequent cessation related behaviour: the role of stage of change.

Authors:  B A Pizacani; D P Martin; M J Stark; T D Koepsell; B Thompson; P Diehr
Journal:  Tob Control       Date:  2004-03       Impact factor: 7.552

3.  Effect on trend estimates of the difference between survey respondents and non-respondents: results from 27 populations in the WHO MONICA Project.

Authors:  Hanna Tolonen; Annette Dobson; Sangita Kulathinal
Journal:  Eur J Epidemiol       Date:  2005       Impact factor: 8.082

4.  Smoking in 6 diverse Chicago communities--a population study.

Authors:  Jade L Dell; Steven Whitman; Ami M Shah; Abigail Silva; David Ansell
Journal:  Am J Public Health       Date:  2005-06       Impact factor: 9.308

5.  Variations in the health conditions of 6 Chicago community areas: a case for local-level data.

Authors:  Ami M Shah; Steven Whitman; Abigail Silva
Journal:  Am J Public Health       Date:  2006-06-29       Impact factor: 9.308

6.  Selection by socioeconomic factors into the Danish National Birth Cohort.

Authors:  Tine Neermann Jacobsen; Ellen Aagaard Nohr; Morten Frydenberg
Journal:  Eur J Epidemiol       Date:  2010-03-27       Impact factor: 8.082

7.  25-year trends and socio-demographic differences in response rates: Finnish adult health behaviour survey.

Authors:  Hanna Tolonen; Satu Helakorpi; Kirsi Talala; Ville Helasoja; Tuija Martelin; Ritva Prättälä
Journal:  Eur J Epidemiol       Date:  2006-06-28       Impact factor: 8.082

8.  Assessment of potential bias from non-participation in a dynamic clinical cohort of long-term childhood cancer survivors: results from the St. Jude Lifetime Cohort Study.

Authors:  Rohit P Ojha; S Cristina Oancea; Kirsten K Ness; Jennifer Q Lanctot; D Kumar Srivastava; Leslie L Robison; Melissa M Hudson; James G Gurney
Journal:  Pediatr Blood Cancer       Date:  2012-09-28       Impact factor: 3.167

9.  Prevalence of obesity among children in six Chicago communities: findings from a health survey.

Authors:  Helen Margellos-Anast; Ami M Shah; Steve Whitman
Journal:  Public Health Rep       Date:  2008 Mar-Apr       Impact factor: 2.792

10.  Effects of self-reported health conditions and pesticide exposures on probability of follow-up in a prospective cohort study.

Authors:  Martha P Montgomery; Freya Kamel; Jane A Hoppin; Laura E Beane Freeman; Michael C R Alavanja; Dale P Sandler
Journal:  Am J Ind Med       Date:  2010-05       Impact factor: 2.214

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