1
Population vulnerability and broad environmental
climatic exposure
Short version1 for CD-ROM edition of the paper prepared for presentation at the
Open Meeting of the Global Environmental Change Research Community, Rio de
Janeiro, October 6-8 2001
Pedro Reginaldo Prata2, MD, MSc, PhD, FRGS.
ABSTRACT
This paper comprises a geographical study of the spatial distribution of mortality
patterns at population level, in Brazil, with a particular analysis of possible
explanatory links with local and broad environmental dimensions in contrast with
socio-economic conditions. The dependent variable was represented by patterns of
ancient and modern mortality causes according to the epidemiological transition
theory, as applied to the Brazilian setting. The independent explanatory variables
were represented by a general socio-economic, and by local and broad
environmental dimensions. The local one was represented by housing conditions
water supply and sanitation, and the broad one by climatic characteristics
(temperature, UV-B exposure and precipitation). The main method applied in this
study was principal components analysis, which revealed associations of interrelated mortality causes and underlying explanatory conditions. The larger
geographical areas showed that mortality patterns were associated with two
independent underlying explanatory determinants. By contrast, the minor
geographical areas, represented by highly urbanised populations, showed the same
common mortality patterns but associated with only one underlying explanatory
dimension. Multiple regression showed that socio-economic development, despite
been important, was not the only dimension explaining the geography of mortality
patterns in Brazil. At a local environmental level, housing conditions were found
to be a more important environmental explanatory dimension than water supply
and sanitation. At a broad environmental level the climatic dimension represented
by temperature and UV-B exposure was present in several explanatory models for
patterns of modern mortality causes of a chronic sort and for patterns of ancient
mortality causes of an infective nature (either as a protective or as a risk factor).
The same association with conditions where infections are involved was found
with a second climatic dimension, represented by precipitation. The author argues
in favour of a general vulnerability dimension, acting spatially upon populations,
regardless of specific risk factors to specific diseases.
1
The full version will be available on paper to be published elsewhere. For further info contact the
author e.mail: pedrorp@ufba.br
2
Senior Lecturer at the Instituto de Saude Coletiva, UFBA, Brazil.
2
Introduction
Consistent or common patterns of apparently unrelated causes are often seen in
the mortality characteristics of populations (Mackenbach, 1994). Explanation for
these patterns may be needed, inasmuch as they indicate that some vulnerability
dimension may predispose to illness at population level. Than a web of risk
factors may follow precipitating poor health at individual level leading to disease,
and enhancing morbidity and mortality at population level.
Hence, the study of the ever-changing disease patterns of populations may
contribute to increased knowledge about their determinants at the population level
(Evans et al, 1994). This is particularly true when the dimensions of space is taken
into account to analyse the geography of mortality patterns in populations. This
contrasts with the traditional view of single diseases being explained by single
factors or multiple risk factors at individual level.
Differentials in disease pattern distribution spatially may be driven by socioeconomic development and local or broad environmental determinants. Indeed the
dynamic of environmental changes, which may affect population health are
calling the attention of both the lay and scientific communities (McMichael,
1993a, 1993b and 1995; Bentham, 1992, 1993a, 1994).
This paper argues that common patterns of mortality causes do occur in the
Brazilian context and verify if any broad dimension at population level can
explain these patterns in a consistent way. Brazil, due to its continental size and
contrasting environments, is a valuable setting for exploring whether these
patterns are driven primarily by socio-economic development or whether there is
also a clear local / or broad environmental dimension involved.
Moreover, this approach may contribute by proxy to the understanding of the
human dimension effects of global environmental change especially those related
to climate change as will be seen.
3
Methodology
The findings discussed here are based on an ecological epidemiological study
characterising a study of aggregates (Susser, 1994.; Rose, 1985 and Mayer, 1983).
The setting
Brazilian larger geographical areas including its 26 Federal States and Brazilian;
27 minor highly urbanised geographical areas (including 26 Capital cities plus 1)
have been included in a cross-sectional study covering a 23 years period (19791992). This period has been defined as a cross-section to enhance data stability.
The data
Eleven million death certificate records covering that 23 years period (available in
CD-ROM) were used to build up a SPSS data-base where the place of residence
and cause of death were selected. The population for the period was estimated
from the 1980 and 1991 census for each of the geographical areas. Census data
(1970, 1980 and 1991) for the socio-economic and local environmental variables
were used to derive the indicators needed for this study (see below). The broad
environmental variables were derived from a 15 years climatic data (cloud cover,
temperature, humidity and rainfall) for the studied geographical areas. Population
UV-B radiation exposure has been estimated according to latitude and cloud cover
(Prata, 2000).
The analysis
The analytical method used was at first principal components analysis in order to
disclose mortality patterns spatially (the dependent variables), and to summarise
explanatory dimensions (the independent variables). Principal components
analysis is a method used to find combinations of variables, called components,
that explain the overall variation, thus reducing the complexity of the data.
Moreover, the method permits an empirical approach without requiring in
principle any previous assumption about the underlying structure, which might
explain the distribution of the variables, as pointed out by Haynes (1986).
Secondly, multiple regression was used to disclose the best-fit model to explain
the geographical differences of mortality patterns.
4
The independent variables
The principal components analysis allowed data reduction from seven original
socio-economic variables3 to one general indicator of socio-economic level
representing 78% of the geographical variability and one independent dimension
represented by inequality (representing 17% of the spatial variability).
Similarly, the method allowed data reduction from four original local
environmental variables4 to only two local environmental indicators each
representing almost half of the geographical variability: one expressing water and
sanitation and the other housing conditions. Crowding conditions showed related
to the sanitary dimension for the capital cities and to housing conditions at larger
areas (such as the Federal States).
In the same way, the method allowed data reduction from four original broad
(global) environmental variables5 to only two broad climatic indicators: the main
one expressing UV-B radiation exposure and temperature, representing 44% of
the geographical variability and second one expressing rainfall and humidity,
representing 36% of the spatial variability.
The dependent variables
The principal components analysis allowed data reduction from 47 causes of
mortality to six for the Federal States and three for the urban capital cities (see
tables 1 and 2). This expressed in summary two patterns of mortality: one called
modern and another one called ancient. The modern pattern included some
chronic diseases (cardiovascular and chronic respiratory conditions; ischaemic
heart disease and multiple sclerosis, and cancer sites such as: breast, lung,
prostate, lymphomas) and the ancient pattern included infections, TB, cancer of
the liver, cervix and penis. It is important to highlight that at the State
geographical level the method disclosed that each of these patterns (the modern
and ancient) were related to two independent explanatory dimensions represented
by each of the principal components (PC1 and PC2). On the other hand, at the
3
Family income, low income (%), poverty threshold gap, inequality, illiteracy, schooling in years,
and childhood conditions (children at school, child labour).
4
Adequate water supply, adequate sanitation, adequate housing and over crowding.
5
urban capital cities geographical level, each pattern was related to the same
explanatory dimensions represented by the first component (PC1).
Table 1 PCs, mortality causes groups, States geographical level
Dependent
components
PC1
(modern
pattern)
Associated
Main causes
Neoplasms
Circulatory diseases
Respiratory diseases
Musculoskeletal diseases
Nervous system diseases
Congenital diseases
Skin diseases
Endocrine diseases
Mental disorders
Associated
Specific causes
Ischaemic heart diseases
Chronic respiratory
Multiple sclerosis
Stroke
Asthma
Associated
Cancer Sites
Breast
Bladder
Leukaemias
Ovarian
Kidney
Lung
Lymphomas
Hodgkin's disease
Myeloma multiple
Melanoma
Colon and rectum
Central nervous system
Oesophagus
Prostate
TB
Appendicites
Rheumatic heart diseases
Cervical
Penis
Liver
Ill-defined conditions (-)
PC2
(ancient
pattern)
Infections diseases
Pregnancy complications
Blood diseases
Table 2 PC1, mortality causes groups, Capital geographical level
Dependent
PC 1
Positive
Loadings
(modern
pattern)
Negative
Loadings
(ancient
pattern)
Associated
Main causes
Neoplasms
Circulatory
Respiratory
Musculoskeletal
Nervous system
Congenital
Ill-defined (-)
Pregnancy (-)
Infections
Associated
Specific causes
Ischaemic heart diseases
Chronic respiratory dis.
Multiple sclerosis
TB (-)
Appendicites (-)
Associated
Cancer Sites
Breast
Bladder
Leukaemias
Ovarian
Kidney
Lung
Lymphomas
Hodgkin disease
Myeloma multiple
Melanoma
Colon and rectum
Central nervous system
Oesophagus
Prostate
Cervical (-)
Penis (-)
Liver (-)
The model fit
5
UV-B exposure (annual), average temperature, rainfall and relative humidity.
6
Tables 3 and 4 can be better summarised by the square value of the
correlation coefficient (R2) that discloses what proportion of the dependent
variable geographical variability is explained by the independent ones as
they enter the model. The detailed discussion of the model will be found
on the full version of this paper.
Table 3 Multiple regression, explanatory variables model-fit according to
mortality pattern, States (negative correlations in italic)
Models
Chr
SocEcoCon
SocEcoCon + Hous
SocEcoCon + Hous + Ineq
SocEcoCon + Hous + Ineq + WaSa
SocEcoCon + Hous + Ineq + WaSa + Clim-TempUV
SocEcoCon + Hous + Ineq + WaSa + Clim-TempUV + Clim-Prec
SpMod
Clim-TempUV
Clim-TempUV + SocEcoCon
Clim-TempUV + SocEcoCon + Hous
Clim-TempUV + SocEcoCon + Hous + Ineq
Clim-TempUV + SocEcoCon + Hous + Ineq + Clim-Prec
Clim-TempUV + SocEcoCon + Hous + Ineq + Clim-Prec + WaSa
CaMod
SocEcoCon
SocEcoCon + Hous
SocEcoCon + Hous + Clim-TempUV
SocEcoCon + Hous + Clim-TempUV + Clim-Prec
SocEcoCon + Hous + Clim-TempUV+ Clim-Prec + WaSa
SocEcoCon + Hous + Clim-TempUV + Clim-Prec + WaSa + Ineq
InfPreg
Clim-Prec
Clim-Prec + SocEcoCon
Clim-Prec + SocEcoCon + Hous
Clim-Prec + SocEcoCon + Hous + Ineq
Clim-Prec + SocEcoCon + Hous + Ineq + WaSa
Clim-Prec + SocEcoCon + Hous + Ineq + WaSa + Clim-TempUV
SpAnc
SocEcoCon
SocEcoCon + Hous
SocEcoCon + Hous + Ineq
SocEcoCon + Hous + Ineq + Clim-Prec
SocEcoCon + Hous + Ineq + Clim-Prec + WaSa
SocEcoCon + Hous + Ineq + Clim-Prec + WaSa + Clim-TempUV
CaAnc
SocEcoCon
SocEcoCon + Hous
SocEcoCon + Hous + Ineq
SocEcoCon + Hous + Ineq + Clim-TempUV
SocEcoCon + Hous + Ineq + Clim-TempUV + Clim-Prec
SocEcoCon + Hous + Ineq + Clim-TempUV + Clim-Prec + WaSa
Adjusted
R2
0.859
0.898
0.626
0.931
0.936
0.937
R2
%
86 %
91 %
94 %
94 %
95 %
95 %
0.718
0.793
0.834
0.875
0.877
0.872
73 %
81 %
85 %
90 %
90 %
90 %
0.759
0.862
0.897
0.897
0.897
0.893
77 %
87 %
91 %
91 %
92 %
92 %
0.044
0.023
0.004
0.004
0.053
0.107
8%
10 %
12 %
16 %
16 %
16 %
0.127
0.257
0.411
0.499
0.481
0.455
16 %
32 %
48 %
58 %
58 %
58 %
0.364
0.363
0.355
0.342
0.339
0.320
41 %
41 %
43 %
45 %
47 %
48 %
7
Table 4 Multiple regression, explanatory variables model-fit according to
mortality pattern, Capitals (negative correlations in italic)
Models
MainGr
SocEcoCon
SocEcoCon + Ineq
SocEcoCon + Ineq + Clim-TempUV
SocEcoCon + Ineq + Clim-TempUV + Hous
SocEcoCon + Ineq + Clim-TempUV + Hous + Clim-Prec
SocEcoCon + Ineq + Clim-TempUV + Hous + Clim-Prec + WaSa
SpGr
Clim-TempUV
Clim-TempUV + Clim-Prec
Clim-TempUV + Clim-Prec + Ineq
Clim-TempUV + Clim-Prec + Ineq + WaSa
Clim-TempUV + Clim-Prec + Ineq + WaSa + Hous
Clim-TempUV + Clim-Prec + Ineq + WaSa + Hous + SocEcoCon
CaGr
SocEcoCon
SocEcoCon + Hous
SocEcoCon + Hous + Clim-Prec
SocEcoCon + Hous + Clim-Prec + Clim-TempUV
SocEcoCon + Hous + Clim-Prec + Clim-TempUV + Ineq
SocEcoCon + Hous + Clim-Prec + Clim-TempUV + Ineq +WaSa
Adjusted
R2
0.676
0.675
0.673
0.665
0.649
0.631
R2
%
69 %
70 %
71 %
72 %
72 %
72 %
0.541
0.650
0.667
0.673
0.681
0.690
56 %
71 %
72 %
72 %
73 %
73 %
0.850
0.874
0.880
0.901
0.910
0.915
86 %
88 %
89 %
92 %
93 %
93 %
The best fit model
All the relevant findings related to the explanatory power of the
independent variables, in the goodness of fit of all possible models can be
enhanced by looking at the best-fit models with the significant (at the 5%
level, p 0.05) explanatory underlying variables, for each of the mortality
pattern groups according to geographical area, as seen on Tables 5 and 6.
Once more, the detailed discussion of the best-fit model will be found on
the full version of this paper.
Table 5 Stepwise multiple regression, explanatory variables best-fit model
according to mortality pattern, Capital
Explanatory
variables
MainGr
SocEcoCon
SpGr
Clim-TempUV
Clim-Prec
CaGr
SocEcoCon
Hous
b
s.e.
rp
p
0.83
0.11
0.83
0.00
-0.75
-0.39
0.11
0.11
-0.81
-0.59
0.00
0.00
0.83
0.19
0.08
0.08
0.91
0.44
0.00
0.02
8
Table 6 Stepwise multiple regression, explanatory variables best-fit model
according to mortality pattern, States
Explanatory
variables
Chr
SocEcoCon
Hous
Ineq
SpMod
Clim-TempUV
SocEcoCon
Hous
Ineq
CaMod
SocEcoCon
Hous
Clim-TempUV
SpAnc
SocEcoCon
Hous
Ineq
Clim-Prec
CaAnc
SocEcoCon
b
s.e.
rp
p
0.73
0.36
-0.19
0.07
0.07
0.06
0.92
0.71
-0.55
0.00
0.00
0.01
-0.26
0.31
0.53
-0.28
0.13
0.10
0.13
0.10
-0.40
0.57
0.67
-0.53
0.05
0.00
0.00
0.01
0.54
0.27
-0.28
0.09
0.09
0.10
0.80
0.56
-0.53
0.00
0.00
0.01
0.88
-0.70
0.58
0.35
0.18
0.19
0.17
0.16
0.73
-0.62
0.60
0.44
0.00
0.00
0.00
0.04
0.70
0.19
0.61
0.00
Conclusions
Principal components analysis revealed mortality patterns (one ancient and one
modern) associated with underlying explanatory dimensions. The larger
geographical areas showed that these patterns were associated with two
independent underlying explanatory dimensions. Highly urbanised populations
showed these patterns associated with only one underlying explanatory dimension.
Multiple regression showed that socio-economic level, despite been important,
was not the only dimension explaining the geographical variability of mortality
patterns. At a local environmental level, housing conditions were found to be a
more important environmental explanatory dimension than water supply and
sanitation. At a broad environmental level the climatic dimension represented by
temperature and UV-B exposure was present in several explanatory models for
patterns of modern mortality causes of a chronic sort and for patterns of ancient
mortality causes of an infective nature (either as a protective or as a risk factor).
The same association with conditions where infections are involved was found
9
with a second climatic dimension, represented by precipitation at larger
geographical areas.
In regard to the first climatic dimension, the direction of the relationship showed
by the regression model, points out to the UV-B exposure effect. The role of solar
UV-B exposure on health has been described as having both a protective and a
damaging effect depending on the outcome under study. Two summarising papers
about these effects are De Gruijl, 1997 and Longstreth et al, 1998. The biological
plausibility of the UV-B effect has been described as: direct genetic damage (risk
factor for skin cancers), down regulation of the immune system (protective factor
for Multiple Sclerosis and immune disorders and risk factor for infectious), acting
upon metabolic pathways: Vit-D, Na and Ca equilibrium, hormonal regulation
(protective factor for cardiovascular diseases and for some modern cancers).
Furthermore, a few hypothesis can be derived or enhanced by this study:

Basic and common processes present in the onset of many cancers sites
suggests that UV-B can be a protective factor for several modern cancers apart
from skin cancers.

Basic and common processes present in the onset of many cancers sites
suggests that UV-B can be a risk factor for several ancient cancers where an
infective agent may be involved (liver, penile and cervical).

UV-B exposure can be a protective factor for several modern mortality causes,
such as multiple sclerosis, stroke and ischaemic heart disease.

UV-B can be a risk factor for ancient mortality causes of an infective nature.

Rainfall can be a risk factor for ancient mortality causes of an infective nature
in the interior.
The findings, which require further studies, strengths the importance of climatic
variation upon population health. Moreover, these findings suggest a general
vulnerability dimension, acting spatially upon populations, regardless of specific
risk factors to specific diseases.
10
As can be seen, this study is based on a conceptual framework that considers on
one hand individual susceptibility and on the other population vulnerability.
For the author individual susceptibility to disease (related to the genome, to prenatal and to life course determinants) has a relationship with specific risk factors
operating at individual level, which are associated with specific diseases that
requires exposed and not exposed individuals. Differently and complementary, the
author considers that population vulnerability to disease (related to the genetic
population pool6, to socio-economic development and inequality, to local and
broad or global environmental exposure7) has a relationship with disease patterns.
Moreover, appendix A below, presents by map figures (with the aid of the
geographical information system software ArcView) the spatial distribution of the
summarised variables indexes, in principal components scores, for the Brazilian
States geographical level. This is for illustration purpose only. The full version of
the paper will also show the figures for the Capital Cities. Notice that the higher
positive scores or the darker colour shades indicates higher socio-economic
development, higher inequality, better sanitation, better housing conditions, higher
UV-B radiation & Temperature or higher levels of rainfall and humidity.
Similarly, the higher positive scores or the darker colour shades indicates the
higher prevalence of the modern or ancient pattern of mortality8.
References
Bentham G (1993a). Depletion of the ozone layer: consequences for noninfectious human diseases. Parasitology; 106(S): 39-46.
Bentham G (1993b). Depletion of the Ozone Layer and Change in Incidence of
Disease. CSERGE Working Paper; pp 25. UEA. Norwich.
Bentham G (1994). Global Environmental Change and Health. In: Phillips D and
Verhasselt Y (eds). Health and Development: 33-49. Routledge. London.
6
As a result of natural selection at long evolutionary terms and selective survival at short
generations terms
7
In this case it operates at population level, and refers to widespread degrees of exposure where
there is no clear cut discrimination between those exposed and those not exposed.
8
Before deriving the PC scores all rates were adjusted for the age distribution of the period
population.
11
De Gruijl FR (1997). Health effects from solar UV radiation. Radiation Protection
Dosimetry; 72 (3-4): 177-96.
Evans RG, Barer ML and Marmor TR (1994). Why are some people health and
others not? The determinants of health of populations. Aldine de Gruyter. New
York.
Haynes R (1986). Cancer Mortality and Urbanisation in China. International
Journal of Epidemiology; 15 (2): 268-71.
Longstreth J, Gruijl F, Kripk M, Abseck S, Arnold F, Slaper H, Velders G,
Takizawa Y and Leun J (1998). Health risks. Journal of Photochemistry and
Photobiology B: Biology; 46: 20-39.
Mackenbach JP (1994). The epidemiologic transition theory. Journal of
Epidemiology and Community Health; 48: 329-32.
Mayer J (1983). The role of spatial analysis and geographic data in the detection
of disease causation. Social Science and Medicine; 17 (16):1213-21
McMichael A (1993a). Global Environmental Change and Human Population
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Journal of Epidemiology; 22(1): 1-8.
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health of the human species. Cambridge University Press. Cambridge.
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epidemiology comes full circle. Epidemiology; 6 (6): 633-36.
Prata PR (2000). Epidemiological Transition and the Geography of Mortality in
Brazil. PhD Thesis; School of Environmental Sciences, University of East
Anglia, UK.
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12
Appendix
Map 1. Socio-economic development index (PC1) spatial distribution,
Brazilian, States, 1970, 81,91
Socio-economic PC1, States
Key: PC Scores
-1.613 - -1.408
-1.408 - -0.657
-0.657 - 0.067
0.067 - 0.663
0.663 - 2.401
N
W
700
0
700
1400
E
2100 Miles
S
Map 2. Inequality index (PC2) spatial distribution, Brazilian, States, 1970, 81,91
Socio-economic PC2, States
Key:- PC Scores
-1.757 - -1.604
-1.604 - -0.4
-0.4 - -0.101
-0.101 - 0.362
0.362 - 1.751
N
W
1000
0
1000
E
2000 Miles
S
13
Map 3. Water supply & sanitation index (PC1), spatial distribution, Brazilian,
States, 1970, 81,91
Basic- Environmental, PC1 States
Key: PC Scores
-1.277 - -1.059
-1.059 - -0.69
-0.69 - -0.327
-0.327 - 0.014
0.014 - 2.072
N
W
1000
0
1000
E
2000 Miles
S
Map 4. Housing conditions index (PC2) spatial distribution, Brazilian, States,
1970, 81,91
Basic-Environmental, PC2, States
Key: PC Scores
-1.972 - -1.698
-1.698 - -0.844
-0.844 - 0.054
0.054 - 0.659
0.659 - 1.559
N
W
1000
0
1000
E
2000 Miles
S
14
Map 5. Climatic PC1 (UV-B & Temp index) spatial distribution, Brazilian,
States.
Climatic Environmental, PC1, States
Key: PC Scores
-2.225 - -1.837
-1.837 - -0.724
-0.724 - -0.201
-0.201 - 0.674
0.674 - 1.467
N
W
1000
0
1000
E
2000 Miles
S
Map 6. Climatic PC2 (Rainfall & humidity index) spatial distribution, States
Climatic Environmental, PC 2, States
Key: PC Scores
-1.761 - -1.081
-1.081 - -0.463
-0.463 - 0.309
0.309 - 1.438
1.438 - 2.437
N
W
1000
0
1000
E
2000 Miles
S
15
Map 7. PC1 Scores geographical modern pattern, main causes of mortality,
States, 1979-1992
Main mortality causes, PC1, States
Key:- PC Scores
-1.439 - -1.128
-1.128 - -0.639
-0.639 - -0.05
-0.05 - 1.095
1.095 - 2.018
N
E
W
1000
1000
0
2000 Miles
S
Map 8. PC2 Scores geographical ancient pattern, main causes of mortality,
States, 1979-1992
Main mortality causes, PC2, States
Key: PC Scores
-1.534 - -1.038
-1.038 - -0.439
-0.439 - -0.032
-0.032 - 0.748
0.748 - 2.897
N
W
1000
0
1000
E
2000 Miles
S
16
Map 9. PC1 Scores geographical modern pattern, other specific causes of
mortality, States, 1979-1992
Other Specific mortality causes, PC1, States
Key: PC Scores
-1.107 - -0.932
-0.932 - -0.476
-0.476 - 0.222
0.222 - 0.909
0.909 - 1.814
N
E
W
1000
0
1000
2000 Miles
S
Map 10. PC2 Scores geographical ancient pattern, other specific causes of
mortality, States, 1979-1992
Other Specific mortality causes, PC2, States
Key: PC Scores
-1.257 - -0.946
-0.946 - -0.366
-0.366 - 0.352
0.352 - 1.076
1.076 - 2.763
N
W
1000
0
1000
E
2000 Miles
S
17
Map 11. PC1 Scores geographical modern pattern, cancers sites causes of
mortality, States, 1979-1992
Cancers sites, PC1, States
Key: PC Scores
-1.236 - -0.992
-0.992 - -0.637
-0.637 - -0.115
-0.115 - 0.504
0.504 - 2.285
N
W
1000
0
1000
E
2000 Miles
S
Map 12. PC2 Scores geographical ancient pattern, cancers sites causes of
mortality, States, 1979-1992
Cancers sites, PC2, States
Key: PC Scores
-1.269 - -0.882
-0.882 - -0.272
-0.272 - 0.225
0.225 - 1.02
1.02 - 3.441
N
W
1000
0
1000
E
2000 Miles
S