Comparisons of the sizes of health inequalities must be based on measures that are unaffected by the prevalence of an outcome
James Scanlan, James P. Scanlan, Attorney at Law
22 September 2010
Jackson et al.[1] explore some complex issues concerning the possibility that differences in the administrative structures of populations may confound cross-country comparisons of geographic health inequalities. But the authors overlook a fundamental problem with standard comparisons of health inequalities that exists irrespective of the issues they raise – specifically, that the measures underlying those comparisons tend to be affected by the overall prevalence of an outcome. Most notably, for reasons inherent in the shapes of distributions of factors associated with experiencing an outcome, the rarer the outcome, the greater tends to be the relative difference in experiencing it and the smaller tends to be the relative difference in avoiding it.[2-7] Thus, other things being equal, relative differences in mortality will tend to be larger in countries with low mortality, while relative differences in survival will tend to be smaller in such countries. Absolute differences and odds ratios tend also to be affected by the overall prevalence of an outcome, though in a more complicated way. The measures the authors discuss late in their article are all variations on relative and absolute differences and hence are in some manner affected by the overall prevalence of the outcome being examined.
The authors rely on the Gini coefficient. But when used to measure inequalities involving dichotomies, the Gini coefficient is also affected by the overall prevalence of an outcome. As illustrated in Table 1 of reference 5, the Gini coefficient exhibits a correlation with overall prevalence similar to that exhibited by relative differences. Thus, inequality measured by the Gini coefficient tends to be greater in settings where the outcome examined is rarer, while inequality in the opposite outcome tends to be smaller in such settings.
The distributionally-driven forces will not predominate in every case, since observed patterns are also functions of the differences among the distributions in the settings examined.[3-5] But the existence of such forces undermines the utility of standard measures of differences between outcome rates for comparing inequalities in different settings (and even for determining whether an inequality should be considered large or small). Valid appraisals of the size of inequalities must be based on measures that are unaffected by the overall prevalence of an outcome, such as that discussed in references 5 to 8. Studies of the role of potential confounders must likewise be based on such measures.
References:
1. Jackson AL, Davis CA, Leyland AH. Do differences in the administrative structure of populations confound comparisons of geographic health equalities? BMC Medical Research Methodology 2010: http://www.biomedcentral.com/1471-2288/10/74 (Accessed Sept. 20, 2010.)
2. Scanlan JP. Can we actually measure health disparities? Chance 2006:19(2):47-51: http://www.jpscanlan.com/images/Can_We_Actually_Measure_Health_Disparities.pdf (Accessed Sept. 20, 2010.)
3. Scanlan JP. Race and mortality. Society 2000;37(2):19-35: http://www.jpscanlan.com/images/Race_and_Mortality.pdf (Accessed Sept. 20, 2010.)
4. The Misinterpretation of Health Inequalities in the United Kingdom, presented at the British Society for Populations Studies Conference 2006, Southampton, England, Sept. 18-20, 2006: http://www.jpscanlan.com/images/BSPS_2006_Complete_Paper.pdf (Accessed Sept. 20, 2010.)
5. Scanlan JP. Understanding the forces driving cross-national variations in relative differences in outcome rates. Eur J Pub Health Jan. 25, 2009 (responding to Huijts T, Eikemo TA. Causality, social selectivity or artefacts? Why socioeconomic inequalities in health are not smallest in the Nordic countries. Eur J Pub Health 2009;19:452-53): http://eurpub.oxfordjournals.org/cgi/eletters/19/5/452 (Accessed Sept. 20, 2010.)
6. Scanlan JP. Comparing the size of inequalities in dichotomous measures in light of the standard correlations between such measures and the prevalence of an outcome. Journal Review Jan. 14, 2008 (responding to Boström G, Rosén M. Measuring social inequalities in health – politics or science? Scan J Public Health 2003;31:211-215): http://journalreview.org/v2/articles/view/12850975.html (Accessed Sept. 20, 2010.)
7. Scanlan JP. Measuring Health Inequalities by an Approach Unaffected by the Overall Prevalence of the Outcomes at Issue, presented at the Royal Statistical Society Conference 2009, Edinburgh, Scotland, Sept. 7-11, 2009: http://www.jpscanlan.com/images/Scanlan_RSS_2009_Presentation.ppt (Accessed Sept. 20, 2010.)
8. Solutions sub-page of Measuring Health Disparities page of jpscanlan.com: http://www.jpscanlan.com/measuringhealthdisp/solutions.html (Accessed Sept. 20, 2010.)
Comparisons of the sizes of health inequalities must be based on measures that are unaffected by the prevalence of an outcome
22 September 2010
Jackson et al.[1] explore some complex issues concerning the possibility that differences in the administrative structures of populations may confound cross-country comparisons of geographic health inequalities. But the authors overlook a fundamental problem with standard comparisons of health inequalities that exists irrespective of the issues they raise – specifically, that the measures underlying those comparisons tend to be affected by the overall prevalence of an outcome. Most notably, for reasons inherent in the shapes of distributions of factors associated with experiencing an outcome, the rarer the outcome, the greater tends to be the relative difference in experiencing it and the smaller tends to be the relative difference in avoiding it.[2-7] Thus, other things being equal, relative differences in mortality will tend to be larger in countries with low mortality, while relative differences in survival will tend to be smaller in such countries. Absolute differences and odds ratios tend also to be affected by the overall prevalence of an outcome, though in a more complicated way. The measures the authors discuss late in their article are all variations on relative and absolute differences and hence are in some manner affected by the overall prevalence of the outcome being examined.
The authors rely on the Gini coefficient. But when used to measure inequalities involving dichotomies, the Gini coefficient is also affected by the overall prevalence of an outcome. As illustrated in Table 1 of reference 5, the Gini coefficient exhibits a correlation with overall prevalence similar to that exhibited by relative differences. Thus, inequality measured by the Gini coefficient tends to be greater in settings where the outcome examined is rarer, while inequality in the opposite outcome tends to be smaller in such settings.
The distributionally-driven forces will not predominate in every case, since observed patterns are also functions of the differences among the distributions in the settings examined.[3-5] But the existence of such forces undermines the utility of standard measures of differences between outcome rates for comparing inequalities in different settings (and even for determining whether an inequality should be considered large or small). Valid appraisals of the size of inequalities must be based on measures that are unaffected by the overall prevalence of an outcome, such as that discussed in references 5 to 8. Studies of the role of potential confounders must likewise be based on such measures.
References:
1. Jackson AL, Davis CA, Leyland AH. Do differences in the administrative structure of populations confound comparisons of geographic health equalities? BMC Medical Research Methodology 2010: http://www.biomedcentral.com/1471-2288/10/74 (Accessed Sept. 20, 2010.)
2. Scanlan JP. Can we actually measure health disparities? Chance 2006:19(2):47-51: http://www.jpscanlan.com/images/Can_We_Actually_Measure_Health_Disparities.pdf (Accessed Sept. 20, 2010.)
3. Scanlan JP. Race and mortality. Society 2000;37(2):19-35: http://www.jpscanlan.com/images/Race_and_Mortality.pdf (Accessed Sept. 20, 2010.)
4. The Misinterpretation of Health Inequalities in the United Kingdom, presented at the British Society for Populations Studies Conference 2006, Southampton, England, Sept. 18-20, 2006:
http://www.jpscanlan.com/images/BSPS_2006_Complete_Paper.pdf (Accessed Sept. 20, 2010.)
5. Scanlan JP. Understanding the forces driving cross-national variations in relative differences in outcome rates. Eur J Pub Health Jan. 25, 2009 (responding to Huijts T, Eikemo TA. Causality, social selectivity or artefacts? Why socioeconomic inequalities in health are not smallest in the Nordic countries. Eur J Pub Health 2009;19:452-53): http://eurpub.oxfordjournals.org/cgi/eletters/19/5/452 (Accessed Sept. 20, 2010.)
6. Scanlan JP. Comparing the size of inequalities in dichotomous measures in light of the standard correlations between such measures and the prevalence of an outcome. Journal Review Jan. 14, 2008 (responding to Boström G, Rosén M. Measuring social inequalities in health – politics or science? Scan J Public Health 2003;31:211-215):
http://journalreview.org/v2/articles/view/12850975.html (Accessed Sept. 20, 2010.)
7. Scanlan JP. Measuring Health Inequalities by an Approach Unaffected by the Overall Prevalence of the Outcomes at Issue, presented at the Royal Statistical Society Conference 2009, Edinburgh, Scotland, Sept. 7-11, 2009: http://www.jpscanlan.com/images/Scanlan_RSS_2009_Presentation.ppt (Accessed Sept. 20, 2010.)
8. Solutions sub-page of Measuring Health Disparities page of jpscanlan.com:
http://www.jpscanlan.com/measuringhealthdisp/solutions.html (Accessed Sept. 20, 2010.)
Competing interests
None declared