visual display of the data can offer a dramatic and succinct representation
of the socioeconomic disparities in your data. Often, graphical
representations provide a means of communicating key features
of the data, and can enhance summary presentations of data in
tabular form. For example, as part of our project, we produced
a series of booklets – one for each health outcome, at each
level of geography. Each page of the booklet summarized the standardized
incidence rates, rate ratio, rate differences, relative index
of inequality, and population attributable fraction, for each
ABSM, and was supplemented by a visual display of the incidence
rates in each category of the ABSM, and the population distribution
of the ABSM. Figure 1 below shows a sample booklet page summarizing
the analysis of all cause mortality in Suffolk County, Massachusetts,
by CT poverty. (This is the same analysis presented in our case
example). Similar pages could be constructed to summarize
all cause mortality by % working class, % less than high school
Figure 1. Booklet page
of Census derived ABSMs can also give a dramatic visual representation
of how socioeconomic conditions are distributed geographically.
In the figure below, we mapped CT level poverty in Suffolk County,
MA, using ArcView/ArcGIS.
Map of Suffolk County, MA poverty
of disease rates at the census tract level can present complications,
however, because rates for small areas are often unstable due
to small numbers. For this reason, and because our focus was on
area-based socioeconomic disparities in health across all of Massachusetts
and Rhode Island, rather than within specific census tracts, we
explicitly chose not to map disease rates as part of this project.
way of displaying the data is shown in Figure
2 from the Introduction. In
these graphs, which we newly apply to routinely collected U.S.
state health department data1-3,
the width of each bar is proportional to the size of the population
in the specified socioeconomic statum4.
We created these graphs in S-plus. Here's
an example of this mode of graphing applied to our case example.
Graph of Suffolk County, MA poverty by
N, Chen JT, Waterman PD, Soobader M-J, Subramanian SV, Carson R. Geocoding
and monitoring US socioeconomic inequalities in mortality and cancer
incidence: does choice of area-based measure and geographic level
matter?—The Public Health Disparities Geocoding Project. Am
J Epidemiol 2002;156:471-82.
N, Chen JT, Waterman PD, Soobader M-J, Subramanian SV, Carson R. Choosing
area-based socioeconomic measures to monitor social inequalities in
low birthweight and childhood lead poisoning –The Public Health
Disparities Geocoding Project (US). J Epidemiol Community Health 2003;57:186-99.
N, Chen JT, Waterman PD, Soobader M-J, Subramanian SV. Monitoring
socioeconomic inequalities in sexually transmitted infections, tuberculosis,
and violence: geocoding and choice of area-based socioeconomic measures—The
Public Health Disparities Geocoding Project (US). Public Health Reports
A, Paci P, van Doorslaer E. On the measurement of inequalities in
health. Soc Sci Med 1991;33:545-57.