Literature DB >> 26223176

Response to Fergusson & Boden (2015): The importance of considering the impacts of survey non-participation.

Anne Illemann Christensen1, Ola Ekholm2, Linsay Gray3, Charlotte Glümer4, Knud Juel2.   

Abstract

Entities:  

Keywords:  Bias; health behaviour; morbidity; mortality; non-response

Mesh:

Year:  2015        PMID: 26223176      PMCID: PMC4568297          DOI: 10.1111/add.13016

Source DB:  PubMed          Journal:  Addiction        ISSN: 0965-2140            Impact factor:   6.526


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We thank Fergusson & Boden for their interest in our paper, as detailed in their commentary 1. They point to some interesting issues which, for the main part, have also been debated during the preparation of the paper. The first issue raised is that with the large sample size, the likelihood of finding significant group differences is high. We acknowledge that this should be considered, but as the hazard ratios in the present study are relatively large and confidence limits in most cases relatively narrow, we consider the large sample size to be more a strength than a drawback. Ferguson & Boden also point to the fact that the low baseline rates of morbidity and mortality will generate relatively large hazard ratios, even with relatively small differences in the absolute number of events between respondents and non‐respondents. We acknowledge that looking only at relative differences can be somewhat misleading, and we have therefore also provided the absolute number of events and the rates for each group in our paper 2. The most careful way of interpreting results is often to look at both relative and absolute differences. However, as pointed out by Ferguson & Boden, the use of respondent‐only data can cause biased estimates even when the absolute difference in the number of events among respondents and non‐respondents is small. It is suggested that the future sample sizes could be reduced and the saved cost should be used on contacting the non‐contacts. This is an interesting reflection. Consideration of strategies to raise the response rate among specific groups of non‐respondents is indeed warranted, and different strategies to improve response rate among different types on non‐response groups are presented in our paper 2. However, the present study used pooled data from two health surveys. Hence, the sample size in each of the surveys is not as large as it might appear in the commentary by Fergusson & Boden. Furthermore, both surveys are designed to provide county and regional representative data, respectively, and hence a minimum sample size is required in each county/region. The number of non‐contacts is very small in the present study, thanks to a notable effort to establish contact with all invited individuals, and we think that it would be very difficult to establish contact with all invited individuals even if the resources are used differently. However, their suggestion will be considered when planning future surveys. Lastly, Ferguson & Boden argue that non‐response bias may have less of an impact when examining exposure–outcome associations compared to studies of prevalence estimates. This is often true 3, 4, 5. There are, however, exceptions, and thus the impact of non‐response bias in studies of associations cannot be assumed to be negligible 6. In summary, Ferguson & Boden raise some interesting issues, which we agree need to be taken into account when both analysing and interpreting results on non‐response in surveys, while the impact of survey non‐participation should not be overlooked.

Declaration of interest

None.
  7 in total

1.  Exploring issues arising from survey non-response.

Authors:  David M Fergusson; Joseph M Boden
Journal:  Addiction       Date:  2015-09       Impact factor: 6.526

Review 2.  Participation rates in epidemiologic studies.

Authors:  Sandro Galea; Melissa Tracy
Journal:  Ann Epidemiol       Date:  2007-06-06       Impact factor: 3.797

3.  Survey error in measuring socio-economic risk factors of health status: a comparison of a survey and a census.

Authors:  Vincent Lorant; Stefaan Demarest; Pieter-Jan Miermans; Herman Van Oyen
Journal:  Int J Epidemiol       Date:  2007-09-26       Impact factor: 7.196

4.  Non-response in a survey of cardiovascular risk factors in the Dutch population: determinants and resulting biases.

Authors:  H C Boshuizen; A L Viet; H S J Picavet; A Botterweck; A J M van Loon
Journal:  Public Health       Date:  2005-12-20       Impact factor: 2.427

5.  What is wrong with non-respondents? Alcohol-, drug- and smoking-related mortality and morbidity in a 12-year follow-up study of respondents and non-respondents in the Danish Health and Morbidity Survey.

Authors:  Anne Illemann Christensen; Ola Ekholm; Linsay Gray; Charlotte Glümer; Knud Juel
Journal:  Addiction       Date:  2015-06-02       Impact factor: 6.526

6.  Survey non-response in the Netherlands: effects on prevalence estimates and associations.

Authors:  A Jeanne M Van Loon; Marja Tijhuis; H Susan J Picavet; Paul G Surtees; Johan Ormel
Journal:  Ann Epidemiol       Date:  2003-02       Impact factor: 3.797

7.  Response to Fergusson & Boden (2015): The importance of considering the impacts of survey non-participation.

Authors:  Anne Illemann Christensen; Ola Ekholm; Linsay Gray; Charlotte Glümer; Knud Juel
Journal:  Addiction       Date:  2015-09       Impact factor: 6.526

  7 in total
  2 in total

1.  Response to Fergusson & Boden (2015): The importance of considering the impacts of survey non-participation.

Authors:  Anne Illemann Christensen; Ola Ekholm; Linsay Gray; Charlotte Glümer; Knud Juel
Journal:  Addiction       Date:  2015-09       Impact factor: 6.526

2.  Correcting for non-participation bias in health surveys using record-linkage, synthetic observations and pattern mixture modelling.

Authors:  Linsay Gray; Emma Gorman; Ian R White; S Vittal Katikireddi; Gerry McCartney; Lisa Rutherford; Alastair H Leyland
Journal:  Stat Methods Med Res       Date:  2019-06-11       Impact factor: 2.494

  2 in total

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