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Design effects associated with dietary nutrient intakes from a clustered design of 1 to 14-year-old children

European Journal of Clinical Nutrition volume 61, pages 1064–1071 (2007)Cite this article

Abstract

Objective:

To calculate intra-cluster and intra-household design effects and intra-class correlation coefficients for dietary nutrients obtained from a 24 h record-assisted recall.

Design:

Children were recruited using clustered probability sampling. Randomly selected starting-point addresses were obtained with probability proportional to mesh block size.

Setting:

Children aged 1–14 years in New Zealand.

Subjects:

There were 125 children in 50 clusters, giving an average of 2.498 children per cluster. In 15 homes, there were two children for the calculation of intra-household statistics.

Results:

Intra-cluster design effects ranged from 1.0 for cholesterol, β-carotene, vitamin A, vitamin D, vitamin E, selenium, fructose and both carbohydrate and protein expressed as their contribution to total energy intakes to 1.552 for saturated fat, with a median design effect of 1.148. Their corresponding intra-cluster correlations ranged from 0 to 0.37, respectively. Intra-household design effects ranged from 1.0 for height to 1.839 for vitamin B6, corresponding to intra-household correlations of 0 and 0.839. The median intra-household design effect was 1.550. Using a sampling design of two to three households per cluster for estimating dietary nutrient intakes would need, on average, a 15% increase in sample size compared with simple random sampling with a maximum increase of 55% to cover all nutrients.

Conclusions:

These data enable sample sizes for dietary nutrients to be estimated for both cluster and non-cluster sampling for children aged 1–14 years. The larger design effects found within households suggest that little extra information may be obtained by sampling more than one child per household.

Sponsorship:

The New Zealand Ministry of Health contracted this study.

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References

  • Armitage P, Berry G (1994). Statistical methods in medical research, 3rd ed. Blackwell Scientific Publications: Oxford.

    Google Scholar 

  • Campbell MK, Grimshaw JM (1998). Cluster randomised trials: time for improvement. The implications of adopting a cluster design are still largely being ignored. BMJ 317, 1171–1172.

    Article  CAS  Google Scholar 

  • Connelly LB (2003). Balancing the number and size of sites: an economic approach to the optimal design of cluster samples. Control Clin Trials 24, 544–559.

    Article  Google Scholar 

  • Donner A, Brown KS, Brasher P (1990). A methodological review of non-therapeutic intervention trials employing cluster randomisation, 1979–1989. Int J Epidemiol 19, 795–800.

    Article  CAS  Google Scholar 

  • Donner A, Klahr N (2000). Design and analysis of cluster randomization trials in health research. Arnold: London.

    Google Scholar 

  • Food Files (2004). Data Files of the New Zealand Food Composition Database. Palmerston North, New Zealand: New Zealand Institute of Crop & Food Research.

  • Gulliford MC, Ukoumunne OC, Chinn S (1999). Components of variance and intraclass correlations for the design of community-based surveys and intervention studies: data from the Health Survey for England 1994. Am J Epidemiol 149, 876–883.

    Article  CAS  Google Scholar 

  • Hampel FR (1974). The influence curve and its role in robust estimation. J Am Stat Assoc 69, 383–393.

    Article  Google Scholar 

  • Harville DA (1977). Maximum likelihood approaches to variance component estimations and to related problems. J Am Stat Assoc 72, 320–338.

    Article  Google Scholar 

  • Kerry SM, Bland JM (1998). The intracluster correlation coefficient in cluster randomisation. BMJ 316, 1455–1460.

    Article  CAS  Google Scholar 

  • Koepsell TD, Wagner EH, Cheadle AC, Patrick DL, Martin DC, Diehr PH et al. (1992). Selected methodological issues in evaluating community-based health promotion and disease prevention programs. Annu Rev Public Health 13, 31–57.

    Article  CAS  Google Scholar 

  • Metcalf PA, Scragg RK, Sharpe S, Fitzgerald ED, Schaff D, Watts C (2003). Short-term repeatability of a food frequency questionnaire in New Zealand children aged 1–14 years. Eur J Clin Nutr 57, 1498–1503.

    Article  CAS  Google Scholar 

  • Reading R, Harvey I, Mclean M (2000). Cluster randomised trials in maternal and child health: implications for power and sample size. Arch Dis Child 82, 79–83.

    Article  CAS  Google Scholar 

  • Rice N, Leyland A (1996). Multilevel models: applications to health data. J Health Services Res Policy 1, 154–164.

    Article  CAS  Google Scholar 

  • SAS Institute Inc (2004). SAS/STAT User's Guide Version 91. SAS Institute Inc.: Cary, NC.

Download references

Acknowledgements

Other collaborators on the Children's Nutrition Pilot Survey were Professor Boyd Swinburn, Dr Cameron Grant and Dr David Schaaf from the University of Auckland, Professor Mason Durie and Eljon Fitzgerald (Massey University, Palmerston North), Dr Elaine Rush (Auckland University of Technology) and Dr Clare Wall, Kate Sladden and Patsy Watson (Massey University, Albany). Dr Patricia Metcalf was supported by the Health Research Council of New Zealand.

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Authors and Affiliations

  1. Department of Statistics, University of Auckland, Auckland, 1, New Zealand

    P A Metcalf & A J Scott

  2. Department of Epidemiology and Biostatistics, University of Auckland, Auckland, 1, New Zealand

    P A Metcalf, R K R Scragg & A W Stewart

Authors
  1. P A Metcalf
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  2. R K R Scragg
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  3. A W Stewart
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  4. A J Scott
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Corresponding author

Correspondence to P A Metcalf.

Additional information

Guarantor: PA Metcalf.

Contributors: RKRS, AWS and PAM contributed to the design of this study. PAM, AJS and AWS contributed to the statistical analysis and interpretation of the data. All authors contributed to the conduct of the study, data collection and writing of this manuscript.

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Metcalf, P., Scragg, R., Stewart, A. et al. Design effects associated with dietary nutrient intakes from a clustered design of 1 to 14-year-old children. Eur J Clin Nutr 61, 1064–1071 (2007). https://doi.org/10.1038/sj.ejcn.1602618

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