[Data aggregation in measuring inequalities and inequities in the health of populations].
OBJECTIVE
To compare how different degrees of data aggregation influence the measurement of health inequalities and health inequities within a population, and to assess the appropriateness of those different degrees of data aggregation in performing studies on inequalities and inequities.


METHODS
As an example, we used data on the infant mortality rate in Costa Rica in 1973 and in 1984 and calculated measurements that are frequently used to quantify inequalities and inequities.


RESULTS
According to our results, the inequality measures presented (except for those that were derived using regression models) are not sensitive to data aggregation by socioeconomic groups. However, when geographic areas are compared, more disaggregation of the data results in the measures indicating greater inequality.


CONCLUSIONS
Our results show that some measures can vary widely depending on the level of data aggregation. It is thus crucial to know how to select these measures and also how to aggregate the data in a way that is consistent with the objectives of each study.
Metzger
Xavier
X
Programa Especial de Análisis de Situación de Salud (SHA), Organización Panamericana de Salud, 525 23rd St., NW, Washington, DC 20037 USA. sha@paho.org
D003364:Costa Rica; D006304:Health Status; D006801:Humans; D007223:Infant; D007226:Infant Mortality / Q000639:trends*; D012044:Regression Analysis; D012935:Social Justice / Q000706:statistics & numerical data*; D012959:Socioeconomic Factors
La agregación de datos en la medición de desigualdades e inequidades en la salud de las poblaciones.