GLOSSARY Measures of health inequalities : part 2

Absolute difference in frequencies This measure can be calculated as the difference between the observed frequency of the health event in each category of the socioeconomic variable and the reference category, based on data in simple contingency tables, or by using the estimated frequency based on log-linear regression models. As is the case with frequency ratios, when we obtain a single estimate it can be called a summary measure of socioeconomic inequality in health. It has the same advantages and limitations noted for the frequency ratios in comparing and monitoring socioeconomic inequality in health. An additional consideration is that the size of the absolute difference may vary even when the relative difference remains constant. Or the absolute difference may decrease, as frequently occurs when the frequency of the health event decreases and, at the same time, the relative difference may increase (table 1). There is some evidence showing that ranking countries based on the size of the socioeconomic inequality in health may vary depending on whether absolute or relative differences are used. 2 However, there is no clear criterion as to whether a relative or absolute difference in frequencies is more appropriate to reflect the size of socioeconomic inequality in health. A frequency ratio of 1.5 between two categories of a socioeconomic variable may be very important if the frequency of the health problem is 20% and 10%, but much less so if the frequency is 2% and 1%, respectively. Some authors support the use of the absolute difference to evaluate the effect of public policies on health inequality as their objective is to reduce the number of cases of a health problem. However, relative differences are also appropriate to evaluate the strength of the relation between an intervention and the reduced frequency of the health problem. It may be that the measures of association used to make comparisons of socioeconomic inequality in health should include both estimates: the frequency ratio and the difference in frequencies. Regression coefficient The regression coefficient represents the increase (or decrease) in the absolute magnitude of the dependent variable for each unit of increase in the socioeconomic variable. For example, in the equation Y =39–2.1X, where Y represents the body mass index (BMI) in kg/m, and X represents monthly income in thousands of euros, the regression coefficient shows that BMI decreases by 2.1 kg/m for each additional thousand euros of income. In calculating this statistic, both the dependent variable representing the health event and the independent variable representing the socioeconomic characteristic should be measured on an interval scale. Sometimes, however, it has been used with social class defined on an ordinal scale. The regression coefficient can be transformed into a measure of relative difference. It is necessary to previously perform a log transformation of the dependent variable. In this case, the exponent of the regression coefficient minus 1 represents the proportion (or percentage if it is multiplied by 100) of increase in the dependent variable for each unit of increase in the socioeconomic variable. The regression coefficient is a summary measure of health inequality as it is a single estimate and, therefore, can easily be used to compare various populations. Its disadvantage is that it is not appropriate when the adjustment of the regression function shows deviations from linearity.