is well known that there are substantial area variations in mortality
rates in the U.S. However, the presence of area differences in
mortality does not necessarily mean that area matters. Area variations
in mortality can be observed due to a number of reasons some of
which may be due to characteristics that relate to areas and others
that relate to the characteristics of the individuals who live
in these areas. Disentangling the two sources of variation (e.g.:
individual and area) in mortality is therefore vital to distinguishing
area differences from the difference that area makes. Such an
approach to examining area variations in mortality, consequently,
entails describing the patterning and causes in mortality variations,
which in turn, requires answering the following empirical questions
preferably in a sequential manner.
we outline the questions, it is worth asking what role could places
or areas play in influencing mortality (and indeed other health
outcomes). Pure locational attributes of an area (e.g., altitude,
proximity to coast) or environmental aspects of an area (e.g.,
levels of air pollution) or structural attributes of an area (e.g.,
residential segregation, labor markets, population density) or
collective social aspects of an area (e.g., proportion of poor
in an area, proportion population that has less than high school
education) are some concrete elements along which area variations
in mortality may get patterned. Indeed, the different examples
mentioned above need not be mutually exclusive. Thus, an examination
of area variations and area-based explanations to these variations
could be addressed by answering the following questions:
how does the total variation in mortality get partitioned across
the individual and area levels?
how much of the variation in mortality that is attributable
to areas is influenced by the characteristics of individual
residents who live in these areas?
does the magnitude of variation in mortality that is attributable
to areas differ for different population groups? For instance,
is the area-attributable variation in mortality greater for
blacks as compared to whites?
to what extent do area-based characteristics account for the
area-attributable variation in mortality, in whites and blacks,
what is the systematic relationship between area-based characteristics
and mortality, and does this relationship systematically differ
across different population sub-groups?
these types of questions requires adopting a multilevel statistical
modeling approach (also known as hierarchical, mixed and random-effects,
covariance components or random-coefficient regression). These
techniques have provided researchers one possible framework for
incorporating and understanding the role of areas and context
while studying mortality variations. The key advantage of this
approach is, therefore, in analyzing, “why some areas are
more likely to experience higher levels of mortality, while taking
into account of why some individuals (independent of which area
they live) are more likely to die”.
use of multilevel statistical techniques is especially pertinent
under the following circumstances.
first is when the individual health outcome measure (or group-specific
prevalence) are anticipated to be clustered with the source
of clustering being a geographic area, such as block-groups
or/and census-tracts and the interest is in ascertaining the
relative importance of the different levels for the outcome.
This is particularly relevant for public health departments
as they provide a clue about the level at which actions occur.
The assessment of what level matters the most for the outcomes
can be done unconditionally (not adjusting for covariates) and
conditionally (adjusted for covariates).
second situation that necessitates the use of multilevel methods
is when the exposure is measured at multiple levels and the
interest is in evaluating the relative importance of a same
ABSM at different levels (e.g.: establishing whether the block-group
poverty has a larger effect than the census-tract poverty).
multilevel methods offer a bridge between statistical modeling
and descriptive map-based presentations. Since the specific
census-tracts and block-groups identifiers are intrinsic to
the analytical design, it is possible to develop conditional
statistical maps showing how different places are doing on a
particular health outcome and importantly whether the “geography
of health” differs for different population sub-groups.
This provides a useful means to monitor health inequalities
that is conditional on a range of important socioeconomic characteristics.
Technical benefits also flow from utilizing this perspective.
There of course are serious substantive issues (such as “naming”
and “shaming” places) as well technical issues (such
as instability in intrinsically small areas with less population;
mismatch of outcome measure with the denominator information)
that need to be considered given the immediate appeal of maps.
While strategies drawing upon “empirically bayes”
modeling (utilized widely within the multilevel models) or smoothing
may bring certain technical solutions, issues of mapping for
small areas in particular are complex and substantive.
this approach is gaining usage in public health research, given
the relative complexity of these modeling strategies it is yet
to become a part of the mainstream public health surveillance
and monitoring. At the same time, the reasons to empirically
evaluate the above questions are compelling. For instance, patterns
of all cause mortality are likely to be shaped by a complex
constellation of compositional and contextual factors that may
conceivably vary for different population subgroups, as suggested,
for example, by different leading causes of death for different
racial/ethnic groups. An investigation of the racial/ethnic
heterogeneity in geographic variation in mortality can give
insight into the relative importance of compositional and contextual
effects to mortality experienced by different racial/ethnic
populations. For example, if the geographic variation in mortality
rates for a specific group is large, this suggests that geographically
varying contextual factors may be of particular importance in
shaping mortality risk for this population. Conversely, if the
geographic variation in mortality rates is low for a particular
group, it suggests that contextual factors are of relatively
less importance in shaping overall mortality risk for that population.
subject of modeling area-related effects – through measuring
the area-attributable variation and through identifying area-based
characteristics – is intrinsically multilevel and this
note outlined the sort of questions and motivations that could
underlie investigations of variation in health and mortality.
models may now be implemented using a variety of software packages
including SAS, STATA, R and MLwiN. The Center for Multilevel
Modeling website provides a list of these software packages
fundamental texts, see:
H. Multilevel statistical models. 2nd ed. London: Arnold, 1995.
N. Random coefficient models. Oxford: Clarendon Press, 1993.
S, Bryk A. Hierarchical linear models: applications and data
analysis methods. Thousand Oaks: Sage, 2002.
applied introductions to multilevel statistical models, see:
J. Multilevel analysis: techniques and applications. Mahwah,
NJ: Lawrence Erlbaum Associates, 2002.
AH, Goldstein H. Multilevel modelling of health statistics.
Wiley Series in Probability and Statistics. Chichester: John
Wiley & Sons Ltd., 2001.
T, Bosker R. Multilevel analysis: an introduction to basic and
advanced multilevel modeling. London: Sage Publications, 1999.
SV, Jones K, Duncan C, 2003, Multilevel methods for public health
research, in Kawachi I, Berkman L. Eds. Neighborhoods and Health,
New York: Oxford University Press, 65-111.
hands-on tutorial, see:
WJ. MCMC estimation in MLwiN. London: Centre for Multilevel
Modelling, Institute of Education, 2002.
J, Browne W, Goldstein H, Yang M, Plewis I, Healy M, Woodhouse
G, Draper D, Langford I, Lewis T. A user's guide to MLwiN, Version
2.1. London: Multilevel Models Project, Institute of Education,
University of London, 2000.
issues related to mapping see:
P, Wakefield J, Best N, Briggs D (eds). Spatial Epidemiology:
Methods and Applications. Oxford: Oxford University Press, 2000.
J. Mapping environmental injustices: pitfalls and potential
of geographic information systems in assessing environmental
health and equity. Environ Health Perspect 2002; 110 (suppl
M. How to Lie with Maps. 2nd ed. Chicago: University of Chicago
M. Cartographies of Danger: Mapping Hazards in America. Chicago:
University of Chicago Press, 1997.
DA, Carpenter TE. Spatial analytical methods and geographic
information systems: use in health research and epidemiology.
Epidemiol Rev 1999 21:143-161.
TB, Croner CM, Rushton G, Brown CK, Fowler L. Geographic information
systems and public health: mapping the future. Public Health
Rep 1999; 114:359-373.