Article Text

Download PDFPDF

Sampling variability of the Kunst-Mackenbach relative index of inequality
  1. L J Hayes1,
  2. G Berry2
  1. 1Department of Family and Community Nursing, Faculty of Nursing, University of Sydney, New South Wales, Australia
  2. 2School of Public Health, Faculty of Medicine, University of Sydney, New South Wales, Australia
  1. Correspondence to:
 Dr L Hayes Department of Family and Community Nursing, Faculty of Nursing, University of Sydney, New South Wales 2006, Australia;
 lhayes{at}nursing.usyd.edu.au

Abstract

Study objective: To derive methods of calculating confidence limits for the relative index of inequality, defined by Kunst and Mackenbach as a measure of the influence of socioeconomic status on an adverse health index, such as mortality rate. The methods may be used for a health outcome recorded on a continuous scale, as a Poisson count or as a binomial variable.

Results and Conclusion: The confidence limits depend on the sampling variability of both the mean mortality rate and the slope of the regression line of mortality on the socioeconomic status scale variable. The best method for a continuous health outcome is based on Fieller’s theorem but a good approximation is obtained by substituting the confidence limits for the slope of the regression line into the formula for the calculation of the index, or by using the variance of the logarithmic transform of the index. The last method is the most appropriate for the construction of significance tests comparing indices. The mortality rates may show statistically significant departure from linearity, while not suggesting that a linear relation is inappropriate, and the main decision is whether to base the confidence limits on the conventional standard error of the slope derived from the regression analysis or whether to use the standard deviation of the estimates of mortality rates.

  • socioeconomic factors
  • small area analysis
  • confidence intervals
  • health status indicators
View Full Text

Statistics from Altmetric.com

Footnotes

  • Funding: this work was funded, in part, by a Public Health and Research Development Committee research scholarship from the National Health and Medical Research Council awarded to Lillian Hayes.

  • Conflicts of interest: none.

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Linked Articles