Methods for measuring health inequalities (Part II)
Maria Cristina Schneider, Carlos Castillo-Salgado, Jorge Bacallao, Enrique Loyola, Oscar J. Mujica, Manuel Vidaurre, and Anne Roca.
Most Common Indicators for Measuring Health Inequalities
Where applicable, depending on the type of indicator, the same example is used to facilitate interpretation and comparison among indicators: infant mortality (IM) in the Andean area, calculated and interpreted according to different methods.
The health variable used in the examples is the infant mortality rate (IMR) per 1,000 live births (LB), obtained from PAHO’s basic indicators for 1997.6 The other demographic indicators used come from the same source.6 The socioeconomic variable was gross national product (GNP) per capita, adjusted for purchasing power parity (PPP), obtained from the World Bank7 and also published in PAHO’s basic indicators.6
Table 1 shows the basic procedures for calculating any of the indicators included in this article on the basis of secondary data.
Rate ratio and rate difference
Two groups in extreme situations are compared, for example social class V (or V + IV) and social class I (or I + II), or two geographic units with extreme socioeconomic indicators. However, it is recommended that the groups are not so extreme that the summary measure masks most of the existing health inequalities nor so broad that the summary measure conceals the real extent of the inequities in the population.3
The interpretation is based on the ratio of, or the difference between, the mortality or morbidity rates of the lowest versus the highest socioeconomic group: the higher the value of the ratio or the difference, the greater the inequality. When percentiles are used, the terms of the ratio or the difference are the lowest and highest quintiles. The most well-known work that has used this indicator is the Black Report,9 published in the 1980s, which analyzed mortality data by social class in England and which, together with other later publications that employed the same procedure, gave rise to the methodological debate over how to measure inequalities in health.
Effect index
Some measures of effect, such as rate ratio and rate difference, take into account only inequalities between the two socioeconomic groups being compared, ignoring those that exist between groups excluded from the comparison. The effect index does not have this limitation because it describes the differences between all population groups through the parameters of a regression model in which the dependent variable tends to be a mortality or morbidity rate and the independent variable is generally an indicator of socioeconomic status. If the relationship between these variables is linear, the slope of the regression line is the absolute effect index and is interpreted as the change that occurs in the dependent variable when the independent variable is modified by one unit (for example, a thousand dollars of GNP).
The biggest drawback to this index is the risk of using inappropriate regression models or estimation methods, such as when the relationship is not linear or the groups are of very different size. In the first case, a linear model cannot be applied, and in the second, ordinary least squares cannot be used as an estimation procedure. To use linear regression it is recommended to confirm, first, that the basic assumptions of the regression are met and, second, that linearity is present.10 Other models, such as Poisson regression or logistic regression, may be more appropriate.
Population attributable risk (PAR)
Population attributable risk is one of the best-known indicators of total impact in the field of health. Also known as the etiologic fraction, it is much used in epidemiology. It is defined as the difference between the general rate and the rate of the highest socioeconomic group, expressed as a percentage of the general rate; the more this diverges from zero, the greater the inequality and the greater potential for reduction of inequality. This makes it possible to estimate the proportion of the general rate of morbidity or mortality that it would be possible to eliminate if all the groups had the same or lower rates of mortality or morbidity as the highest socioeconomic group. In the publication of Kunst and Mackenbach5 on socioeconomic inequalities in the field of health, the reference group is the one with the highest socioeconomic status, that does not always coincide with the group with the lowest rate. Depending on the objective of the study, it may be of interest to measure inequality with respect to the lowest observed rate, so that the reference group for calculation of PAR could be the group with the lowest observed value.
PAR can also be calculated through a regression in which the dependent variable (y) is mortality or morbidity and the independent variable (x) is socioeconomic status. In this case the value used for the rate of the highest socioeconomic group is the value estimated through regression, instead of the observed value of the rate. It is necessary to choose the regression model with the best fit, which normally implies choosing among simple linear regression, logistic regression, and Poisson regression. The latter is especially appropriate for modeling the relationship with rates for very infrequent events.5
Using PAR one can also calculate the size of the reduction needed in each group to reach full equality, an indicator that is useful for decision-making bodies because it makes it possible to estimate goals for reduction.
Index of dissimilarity
This index can be interpreted as the percentage of all cases that would have to be redistributed in order to have the same rate for the indicator in all the socioeconomic groups. In other words, it expresses the extent to which the distribution of the health event studied in the population approximates the situation in which everyone has the same socioeconomic level.5 The index of dissimilarity is large when a large part of the population are in low and high socioeconomic groups and there are few people in intermediate groups.5
This indicator can be applied to variables related to health services, such as the number of physicians that would be necessary to redistribute among municipalities to achieve equity. Its application is doubtful for analyzing inequalities in mortality, morbidity, or other indicators of health status because speaking about redistributing deaths or disease does not make practical and ethical sense. For this reason, in this case we do not use the example of IM.
Slope index of inequality (SII) and relative index of inequality (RII)
Other measures of total impact in health, including the SII and the RII, can be obtained through regression analysis.
These indices are obtained through a regression analysis of a dependent health variable on an indicator of the cumulative relative position of each group with respect to a socioeconomic variable, taking into account both the socioeconomic status of the groups and the size of the population. The groups are ordered by decreasing socioeconomic status. Each group is characterized by a value (ridit) that corresponds to the average cumulative frequency of the group, ordered with respect to the socioeconomic variable. The morbidity or mortality rate of each country is the dependent variable (y).
The slope of the regression line (b) is estimated by the weighted least squares method and represents the change in mortality when the position of the group changes by one unit, or, in other words, the difference between the end points of the scale with respect to the health variable, since the respective positions of these points (their ridits) are 0 and 1 (or 0 and 100%). This slope is known as the SII. If it is negative, the two variables (x and y) vary in opposed directions. That is, when socioeconomic status worsens, mortality increases. Just as in the case of other indices based on linear regression, the relation between the two variables should fulfill the basic assumptions for regression and linearity.
To obtain the relative version of this index (the RII), Mackenbach and Kunst3 suggest first obtaining the quotient of b divided by the estimated value of the health variable (mortality) for the higher socioeconomic status (x = 1; the highest point in the ridit scale). This value then represents the ratio of the rate of the lower socioeconomic group to that of the highest socioeconomic group. To express the result as a rate ratio 1 is added to this value, giving the modified RII. The greater this value, the greater the difference among the groups.
This index should be used preferably when the criterion for grouping preserves a total order for the full set, so that any individual in group i has a better socioeconomic status than any individual of group j (if j < i). When data are grouped by geopolitical units, ordered in relation to a socioeconomic indicator, it is not the case that all individuals in a group that have higher average socioeconomic status are better off than all those in a group with lower average socioeconomic status. In test studies conducted with the SII and RII using aggregate data by geopolitical units, these indicators did not appear very stable.1 The basic requirements for regression and linearity are, as always, conditions for application of these indices based on regression models.
References (Part II)
1. Greenland S, Morgenstern H. Ecological bias, confounding and effect modification. Int J Epidemiol 1989;18:269–274.
3. Mackenbach JP, Kunst AE. Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe. Soc Sci Med 1997;44:757–771.
5. Kunst AE, Mackenbach JP. Measuring socioeconomic inequalities in health. WHO Regional Office for Europae, 1994 (document EUR/ICP/RPD 416). Se acceso el 12 noviembre 2002 en la siguiente dirección web: http://www.who.dk/Document/PAE/Measrpd416.pdf
6. Organización Panamericana de la Salud, División de Salud y Desarrollo Humano, Programa de Análisis de la Situación de Salud. Situación de salud en las Américas. Indicadores básicos, 1998. Washington, DC: OPS; 1998. (OPS/HDP/HAD/98.01).
7. World Bank. 1998 World Development Indicators. Washington, DC: World Bank; 1988.
8. Organización Panamericana de la Salud, Análisis de la Situación de Salud. Situación de salud en las Américas. Indicadores básicos — Glosario. Washington, DC: OPS; 1998.
9. Townsed P, Davidson N. The Black Report. En: Townsend P, Davidson N, Whitehead M, eds. Inequalities in health: The Black report and the health divide. London: Penguin Books; 1988.
10. Daniel WW. Bioestadística. México, D.F.: Noruega Limusa; 1991.
The references respect the order of the original article.
Source:
Originally published with the title “Métodos de medición de las desigualdades de salud” in Pan American Journal of Public Health 12(6), 2002.
