Clustering and meso-level variables in cross-sectional surveys: an example of food aid during the Bosnian crisis

BMC Health Serv Res. 2011 Dec 21;11 Suppl 2(Suppl 2):S15. doi: 10.1186/1472-6963-11-S2-S15.


Background: Focus groups, rapid assessment procedures, key informant interviews and institutional reviews of local health services provide valuable insights on health service resources and performance. A long-standing challenge of health planning is to combine this sort of qualitative evidence in a unified analysis with quantitative evidence from household surveys. A particular challenge in this regard is to take account of the neighbourhood or clustering effects, recognising that these can be informative or incidental.

Methods: An example of food aid and food sufficiency from the Bosnian emergency (1995-96) illustrates two Lamothe cluster-adjustments of the Mantel Haenszel (MH) procedure, one assuming a fixed odds ratio and the other allowing for informative clustering by not assuming a fixed odds ratio. We compared these with conventional generalised estimating equations and a generalised linear mixed (GLMM) model, using a Laplace adjustment.

Results: The MH adjustment assuming incidental clustering generated a final model very similar to GEE. The adjustment that does not assume a fixed odds ratio produced a final multivariate model and effect sizes very similar to GLMM.

Discussion: In medium or large data sets with stratified last stage random sampling, the cluster adjusted MH is substantially more conservative than the naïve MH computation. In the example of food aid in the Bosnian crisis, the cluster adjusted MH that does not assume a fixed odds ratio produced similar results to the GLMM, which identified informative clustering.

MeSH terms

  • Altruism*
  • Bosnia and Herzegovina
  • Causality
  • Cluster Analysis
  • Cross-Sectional Studies
  • Data Collection / methods*
  • Food Supply / statistics & numerical data*
  • Humans
  • Linear Models
  • Multivariate Analysis
  • Odds Ratio
  • Risk Factors