The health status of southern children: a neglected regional disparity.

Abstract

Great variations exist in child health outcomes among states in the United States, with southern states consistently ranked among the lowest in the country. Investigation of the geographical distribution of children's health status and the regional factors contributing to these outcomes has been neglected. We attempted to identify the degree to which region of residence may be linked to health outcomes for children with the specific aim of determining whether living in the southern region of the United States is adversely associated with children's health status.
A child health index (CHI) that ranked each state in the United States was computed by using state-specific composite scores generated from outcome measures for a number of indicators of child health. Five indicators for physical health were chosen (percent low birth weight infants, infant mortality rate, child death rate, teen death rate, and teen birth rates) based on their historic and routine use to define health outcomes in children. Indicators were calculated as rates or percentages. Standard scores were calculated for each state for each health indicator by subtracting the mean of the measures for all states from the observed measure for each state. Indicators related to social and economic status were considered to be variables that impact physical health, as opposed to indicators of physical health, and therefore were not used to generate the composite child health score. These variables were subsequently examined in this study as potential confounding variables. Mapping was used to redefine regional groupings of states, and parametric tests (2-sample t test, analysis of means, and analysis-of-variance F tests) were used to compare the means of the CHI scores for the regional groupings and test for statistical significance. Multiple-regression analysis computed the relationship of region, social and economic indicators, and race to the CHI. Simple linear-regression analyses were used to assess the individual effect of each indicator.
A geographic region of contiguous states, characterized by their poor child health outcomes relative to other states and regions of the United States, exists within the "Deep South" (Mississippi, Louisiana, Arkansas, Tennessee, Alabama, Georgia, North Carolina, South Carolina, and Florida). This Deep-South region is statistically different in CHI scores from the US Census Bureau-defined grouping of states in the South. The mean of CHI scores for the Deep-South region was >1 SD below the mean of CHI scores for all states. In contrast, the CHI score means for each of the other 3 regions were all above the overall mean of CHI scores for all states. Regression analysis showed that living in the Deep-South region is a stronger predictor of poor child health outcomes than other consistently collected and reported variables commonly used to predict children's health.
The findings of this study indicate that region of residence in the United States is statistically related to important measures of children's health and may be among the most powerful predictors of child health outcomes and disparities. This clarification of the poorer health status of children living in the Deep South through spatial analysis is an essential first step for developing a better understanding of variations in the health of children. Similar to early epidemiology work linking geographic boundaries to disease, discovering the mechanisms/pathways/causes by which region influences health outcomes is a critical step in addressing disparities and inequities in child health and one that is an important and fertile area for future research. The reasons for these disparities may be complex and synergistically related to various economic, political, social, cultural, and perhaps even environmental (physical) factors in the region. This research will require the use and development of new approaches and applications of spatial analysis to develop insights into the societal, environmental, and historical determinants of child health that have been neglected in previous child health outcomes and policy research. The public policy implications of the findings in this study are substantial. Few, if any, policies identify these children as a high-risk group on the basis of their region of residence. A better understanding of the depth and breadth of disparities in health, education, and other social outcomes among and within regions of the United States is necessary for the generation of policies that enable policy makers to address and mitigate the factors that influence these disparities. Defining and clarifying the regional boundaries is also necessary to better inform public policy decisions related to resource allocation and the prevention and/or mitigation of the effects of region on child health. The identification of the Deep South as a clearly defined subregion of the Census Bureau's regional definition of the South suggests the need to use more culturally and socially relevant boundaries than the Census Bureau regions when analyzing regional data for policy development.

Full text

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DOI: 10.1542/peds.2005-0366
published online Nov 1, 2005;
Pediatrics
David Wood, Graham Watts and William Livingood
Jeffrey Goldhagen, Radley Remo, Thomas Bryant, Peter Wludyka, Amy Dailey,

The Health Status of Southern Children: A Neglected Regional Disparity
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The Health Status of Southern Children: A Neglected Regional Disparity
Jeffrey Goldhagen, MD, MPH*‡; Radley Remo, MPH§; Thomas Bryant, III, MSW§; Peter Wludyka, PhD?;
Amy Dailey, MPH§; David Wood, MD, MPH§‡; Graham Watts, PhD§; and William Livingood, PhD§‡
ABSTRACT.
health outcomes among states in the United States, with
southern states consistently ranked among the lowest in
the country. Investigation of the geographical distribu-
tion of children’s health status and the regional factors
contributing to these outcomes has been neglected. We
attempted to identify the degree to which region of res-
idence may be linked to health outcomes for children
with the specific aim of determining whether living in
the southern region of the United States is adversely
associated with children’s health status.
Methods.
A child health index (CHI) that ranked each
state in the United States was computed by using state-
specific composite scores generated from outcome mea-
sures for a number of indicators of child health. Five
indicators for physical health were chosen (percent low
birth weight infants, infant mortality rate, child death
rate, teen death rate, and teen birth rates) based on their
historic and routine use to define health outcomes in
children. Indicators were calculated as rates or percent-
ages. Standard scores were calculated for each state for
each health indicator by subtracting the mean of the
measures for all states from the observed measure for
each state. Indicators related to social and economic sta-
tus were considered to be variables that impact physical
health, as opposed to indicators of physical health, and
therefore were not used to generate the composite child
health score. These variables were subsequently exam-
ined in this study as potential confounding variables.
Mapping was used to redefine regional groupings of
states, and parametric tests (2-sample t test, analysis of
means, and analysis-of-variance F tests) were used to
compare the means of the CHI scores for the regional
groupings and test for statistical significance. Multiple-
regression analysis computed the relationship of region,
social and economic indicators, and race to the CHI.
Simple linear-regression analyses were used to assess the
individual effect of each indicator.
Results.
A geographic region of contiguous states,
characterized by their poor child health outcomes rela-
tive to other states and regions of the United States, exists
within the “Deep South” (Mississippi, Louisiana, Arkan-
sas, Tennessee, Alabama, Georgia, North Carolina, South
Purpose.
Great variations exist in child
Carolina, and Florida). This Deep-South region is statis-
tically different in CHI scores from the US Census Bu-
reau–defined grouping of states in the South. The mean
of CHI scores for the Deep-South region was >1 SD
below the mean of CHI scores for all states. In contrast,
the CHI score means for each of the other 3 regions were
all above the overall mean of CHI scores for all states.
Regression analysis showed that living in the Deep-
South region is a stronger predictor of poor child health
outcomes than other consistently collected and reported
variables commonly used to predict children’s health.
Conclusions.
The findings of this study indicate that
region of residence in the United States is statistically
related to important measures of children’s health and
may be among the most powerful predictors of child
health outcomes and disparities. This clarification of the
poorer health status of children living in the Deep South
through spatial analysis is an essential first step for de-
veloping a better understanding of variations in the
health of children. Similar to early epidemiology work
linking geographic boundaries to disease, discovering
the mechanisms/pathways/causes by which region influ-
ences health outcomes is a critical step in addressing
disparities and inequities in child health and one that is
an important and fertile area for future research. The
reasons for these disparities may be complex and syner-
gistically related to various economic, political, social,
cultural, and perhaps even environmental (physical) fac-
tors in the region. This research will require the use and
development of new approaches and applications of spa-
tial analysis to develop insights into the societal, envi-
ronmental, and historical determinants of child health
that have been neglected in previous child health out-
comes and policy research. The public policy implica-
tions of the findings in this study are substantial. Few,
if any, policies identify these children as a high-risk
group on the basis of their region of residence. A better
understanding of the depth and breadth of disparities
in health, education, and other social outcomes among
and within regions of the United States is necessary for
the generation of policies that enable policy makers to
address and mitigate the factors that influence these
disparities. Defining and clarifying the regional bound-
aries is also necessary to better inform public policy
decisions related to resource allocation and the preven-
tion and/or mitigation of the effects of region on child
health. The identification of the Deep South as a clearly
defined subregion of the Census Bureau’s regional defi-
nition of the South suggests the need to use more cul-
turally and socially relevant boundaries than the Census
Bureau regions when analyzing regional data for policy
development. Pediatrics 2005;116:e1–e0. URL: www.
pediatrics.org/cgi/doi/10.1542/peds.2005-0366; child
health status, health disparity, spatial analysis, epidemi-
ologic methods, geographic location.
From the *Duval County Health Department, Jacksonville, Florida; ‡De-
partment of Pediatrics, University of Florida, Jacksonville, Florida; §Insti-
tute for Health, Policy and Evaluation Research, Duval County Health
Department, Jacksonville, Florida; and ?University of Florida, College of
Medicine, Jacksonville, Florida.
Accepted for publication Jun 6, 2005.
doi:10.1542/peds.2005-0366
No conflict of interest declared.
Address correspondence to William C. Livingood, PhD, Institute for
Health, Policy and Evaluation Research, Duval County Health Department,
900 University Blvd, Suite 604, Jacksonville, FL 32211. E-mail: william?
livingood@doh.state.fl.us
PEDIATRICS (ISSN 0031 4005). Copyright © 2005 by the American Acad-
emy of Pediatrics.
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ABBREVIATIONS. CHI, child health index; AECF, Annie E. Casey
Foundation; ANOM, analysis of means.
L
and characterized in numerous reports and stud-
ies,2–4and national calls have been made for inter-
ventions.5–7Although health disparities and inequi-
tiesareincreasinglydocumented
children, their etiologies remain poorly defined. Cur-
rent research focuses primarily on identifying dis-
parities in access to child health services related to
race, socioeconomic status, insurance coverage, ge-
ography, etc6,8–10despite the lack of clear evidence
that access to health care is directly related to health
status.6Research into causation is limited and has
targeted poverty and the “intuitive” social, eco-
nomic, and environmental factors associated with
living in poverty.11,12However, after decades of
study, the pathways through which access to health
services and poverty impact health remain unclear.
Although variations in the geographic distribution
of health outcomes have been noted, the relevance of
the spatial characteristics of children’s health have
tended to be overlooked.13Despite the stark differ-
ences in the health status of children living in differ-
ent regions of the United States, children in the
southern states have consistently poorer outcomes
for most indicators of children’s health and well-
being.1Only a few studies have included uni- and
multifactorial analyses related to the impact of re-
gion on access and utilization of child health servic-
es.12,14–16Even fewer studies have attempted to de-
fine the contribution of region as an independent
variable to explain variations in child health status.13
If region is an independent predictor of child
health outcomes, critical questions arise as to how
this effect is mediated. These questions will require a
review of previous studies and new and/or ex-
panded use of currently used research methods to be
answered. Future epidemiologic research could in-
clude consideration of the impact of region as a
strategy to better define the relationship between
region and child health outcomes and disparities.
Moreover, new and expanded interdisciplinary and
nontraditional research methods could be consid-
ered to examine and describe regional health ecolo-
gies and their impact on children’s well-being. These
findings could lead to new interdisciplinary inter-
vention strategies and regional and national health
policies to improve children’s health and decrease
health disparities.
This study addresses 3 questions that seek to better
define the relationship between region of residence
and the physical health and social well-being of chil-
dren.
arge disparities exist in the health status and
social well-being of children in the United
States.1These disparities have been cataloged
among US
1. Is region in the United States a variable that has a
statistically significant relationship to the health of
children?
2. Can a geographic region composed of states in the
South be characterized and defined based on the
region’s poorer health outcomes for children as
compared with other regions of the country?
3. Is living in the “Deep South” a predictor of child
health outcomes?
METHODS
Sequential spatial analyses were used to answer the research
questions. These analytic processes included (1) computation of a
composite child health index (CHI) for states based on commonly
used statistical indices, (2) use of mapping to define regional
groupings of states homogeneous for child health outcomes, and
(3) statistical analyses using parametric tests for differences and
associations.
Development of a Composite Child Health Score
A single aggregated composite child health index (CHI) score
for the health of children in each state was developed by using
Annie E. Casey Foundation (AECF) indicator data from their Kids
Count Data Book.1Five indicators for physical health were chosen
(percent low birth weight infants, infant mortality rate, child death
rate, teen death rate, and teen birth rates) based on their historic
and routine use to define health outcomes in children1,3,17,18and
their inclusion as objectives for Healthy People 2010.5These data are
provided by federal agencies and “reflect the best available state-
level data for tracking yearly changes in each indicator.1” Indica-
tors related to social and economic status were considered to be
variables that impact physical health, as opposed to indicators of
physical health, and therefore were not used to generate the
composite CHI score. These variables were subsequently exam-
ined in this study as potential confounding variables, as described
in a following section.
The purpose of the aggregation was to construct a composite
health index that would facilitate statistical analysis by state for
overall physical health, because each AECF indicator reflects only
a single health dimension. The 5 physical health indicators were
standardized by using the AECF methodology for ranking states
for various issues including health.1,19First, these physical health
indicators were calculated as rates or percentages. Standard scores
were calculated for each state for each health indicator by sub-
tracting the mean of the measures for all states from the observed
measure for each state. The resulting measure was then divided by
the standard deviation (SD) and multiplied by ?1. All measures
were given the same weight in calculating the overall standard
score. The range of scores fell between -3 and 3. Scores closer to 3
represent better health, and those closer to ?3 represent poorer
health. For each state, the standard scores for each physical health
indicator were added together and then standardized by using the
same process to obtain the composite score. Finally, states were
ranked in order of best to worst (1–50) on the basis of the com-
posite score (Table 1).
Specifically, 5 indicators of health were selected, identifying the
measurements by Iij(there are 5 indicators [i] and 50 states [j], ie,
Iijis the ith indicator for state j). Then, for each indicator, the mean
and SD were calculated and yielded:
Ii? ?Iij/50
and
Si? ???Iij? Ii?2
(1)
49
. (2)
Then, the (negative) standardized scores for the indicators were
constructed, defined as
zj
i? ?Iij? Ii
Si
.(3)
The CHI was then calculated for each state,
HIj? ?
i?1
5
zj
i
for j ? 1, . . . , 50 states, (4)
Mapping Analyses
Mapping techniques using ArcGIS 3.1 (ESRI, Redlands, CA)
were then used to assess the individual and composite health
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status of each state in the US Census Bureau–defined South, as
well as geographically contiguous states that could be linked and
included in a region as the South. Analysis included descriptive
maps created for each of the 5 standardized scores for the physical
health indicators and a map based on the composite health scores
(Figs 1 and 2 illustrate variations by state in infant mortality and
low birth weight). Based on this analysis, a Deep-South region was
identified for additional analyses.
Statistical Comparison of Reconfigured Regions by
Health Indicators
A 3-step hierarchical analysis was used to (1) compare health
indices by Census Bureau–determined regions, (2) explore the
impact of combining states into new regions that maximize the
regional effect on child health outcomes, and (3) compare the
effect of these redefined regions on predicting health outcomes
with and without adjustment for other probable confounding
variables related to child health. Commonly used parametric sta-
tistics were used to compare means and examine the relative
associations of multiple variables. The 2-sample t test, analysis of
means (ANOM), and analysis-of-variance F tests were used to
compare the means of groups. Dunnett’s multiple-comparison test
was used to test for differences in the regional means for state
CHIs between the Deep South and the other regions (because
differences between the other regions [ie, West and Midwest] were
not of concern to this study). Multiple regression was used to
determine which variables contribute to the outcomes (CHI
scores). Because multiple-regression coefficients in the model are
meaningful only in the presence of all the variables currently in
the model (and because mild multicollinearity existed in the mul-
tiple-regression model), simple linear-regression models were
used to assess the individual effect of each indicator.
RESULTS
Health Status of Children
Using the CHI ranking scores generated for each
state, 12 of the 16 worst states were from the Census
Bureau–defined South. The results show that chil-
dren in many southern states have poorer overall
physical health outcomes than children in other re-
TABLE 1.
State Composite Score Rank for CHI
Rank StateScore*
1
2
3
4
5
6
7
8
9
New Hampshire
Massachusetts
Vermont
Maine
Washington
Minnesota
Rhode Island
Connecticut
Utah
Oregon
Hawaii
Iowa
California
New Jersey
North Dakota
New York
Alaska
Wisconsin
Nebraska
Virginia
Pennsylvania
Ohio
Michigan
Kansas
Montana
Colorado
Idaho
Delaware
South Dakota
Arizona
Nevada
West Virginia
Texas
Illinois
Florida
Kentucky
Missouri
Maryland
Indiana
Oklahoma
North Carolina
Wyoming
New Mexico
Georgia
Tennessee
Arkansas
South Carolina
Louisiana
Alabama
Mississippi
1.62
1.61
1.35
1.25
1.20
1.10
1.10
1.08
1.06
1.05
1.02
1.02
0.91
0.87
0.74
0.70
0.54
0.54
0.40
0.33
0.22
0.10
0.09
0.03
?0.05
?0.06
?0.17
?0.21
?0.23
?0.24
?0.25
?0.30
?0.31
?0.32
?0.34
?0.35
?0.39
?0.44
?0.45
?0.87
?0.94
?0.96
?1.01
?1.05
?1.19
?1.35
?1.76
?1.85
?2.12
?2.69
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
* Standard scores range from ?3 to 3, 3 SD below and above the
mean of 0.
Fig 1. Infant mortality rate by state, 1999. Source of data: Annie E.
Casey Foundation. 2002 Kids Count Data Book: State Profiles of
Well-Being. Baltimore, MD: Annie E. Casey Foundation; 2002.
Fig 2. Low birth weight rate by state, 1999. Source of data: Annie
E. Casey Foundation. 2002 Kids Count Data Book: State Profiles of
Well-Being. Baltimore, MD: Annie E. Casey Foundation; 2002.
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gions of the country (Table 1). The Census Bureau–
defined West and Midwest regions each had 2 states
and the Northeast had none in the bottom-16 rank-
ing. Overall, the states in the Census Bureau–defined
South have poorer child physical health outcomes
than the other Census Bureau–defined areas (North-
east, Midwest, and West) of the country.
Defining the South
States within the South were then examined to
determine if the current Census Bureau definition of
the South (Delaware, Maryland, Virginia, West Vir-
ginia, Kentucky, Arkansas, Oklahoma, Texas, Loui-
siana, Mississippi, Tennessee, Alabama, Georgia,
North Carolina, South Carolina, and Florida) uni-
formly reflects/predicts health outcomes for chil-
dren.
A Deep-South region was defined based on the
health index for the purpose of comparison to the
states included in the Census Bureau’s Southern re-
gion. The states included in the Deep South (North
Carolina, South Carolina, Georgia, Florida, Alabama,
Mississippi, Louisiana, Arkansas, and Tennessee) are
both geographically contiguous and inclusive of the
states with the worst composite scores for overall
child health. Delaware, Maryland, Virginia, West
Virginia, Kentucky, Oklahoma, and Texas are not
included in the Deep-South region in this analysis.
Of the 9 Deep-South states, 7 are at the very bottom
of the composite rank list (44th–50th). The remaining
Deep-South states (Florida and North Carolina) were
ranked 35th and 41st. Delaware, Maryland, Virginia,
West Virginia, Kentucky, Oklahoma, and Texas were
ranked 28th, 38th, 20th, 32nd, 36th, 40th, and 33rd,
respectively.
The use of choropleth (area shaded) maps illus-
trates the differences in state rates for the composite
scores (Fig 3). Areas with darker shading indicate
worse rates, most of which were located in the Deep
South. The t test was then used to compare compos-
ite scores for states in the redefined Deep-South re-
gion to the remaining states in the Census Bureau–
defined South. With equal variances assumed, the
results indicate that the composite score for child
health in the Deep-South region is significantly dif-
ferent (lower) (P ? .0001) than the other states in-
cluded in the Census Bureau–defined South (Table
2).
Comparison of Regions in the United States
Analysis of differences between the redefined
Deep-South region and the other Census Bureau–
defined regions (North, Midwest, and West) was
conducted after reconfiguring the regions by placing
the remaining Census Bureau–defined Southern
states (other than the Deep-South states) in other
contiguous regions. Texas, Oklahoma, West Virginia,
and Kentucky were included in the Midwest region,
and Delaware, Maryland, and Virginia were in-
cluded in the Northeast region. The ANOM decision
chart (see Fig 4) indicates that at the .001 level of
significance, the average CHI score for the Deep
South (mean: ?1.48) is significantly below the overall
mean (0) for the 50 states. The average score for the
Northeast (mean: 0.79) is significantly higher than
the overall mean, and the means for 2 other regions
are higher than the overall mean but were not statis-
tically significant variations from the overall mean.
The ANOM has the same assumptions and approx-
imately the same power as the analysis-of-variance F
test (which yielded P ? .001 for these data) but has
the advantage of providing a decision chart to aid in
interpretation.20
To control the type I error rate at 5% for the set of
3 pairwise comparisons, Dunnett’s multiple-compar-
ison test was run to compare the Deep South with
each of the other 3 regions. Because direction was
important (we anticipated that states in the Deep
South had a worse outcome than the other regions),
a 1-sided test was run. The Deep-South region was
statistically different from each of the 3 other regions.
This test confirmed that children in this Deep-South
region have significantly worse health outcomes
than children in other regions of the country (Table
3). Figure 5 illustrates the differences between the
Deep South and other combined regions for each of
the health indicators.
Multivariate Analyses
A number of commonly used social, economic,
and geographic variables for child well-being (per-
cent of teens who are high school dropouts, percent
of children living in poverty, percent of children
living in families in which no parent has full-time
employment, percent of families with children
Fig 3. Composite health score by state. Source of data: Annie E.
Casey Foundation. 2002 Kids Count Data Book: State Profiles of
Well-Being. Baltimore, MD: Annie E. Casey Foundation; 2002.
TABLE 2.
Deep South to the Remaining States in the Census Bureau–De-
fined South
Comparison of Composite Health Index Scores of
RegionNo. of
States
Health
Score
Mean
95% CI
t Score
P
Deep South
Rest of South
9
7
?1.4774 ?1.9378, ?1.0170 ?3.991 .0001
?0.3089
?.5714, ?.0464
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headed by a single parent, percent of teens not at-
tending school and not working, percent of those
who are black in the population, and region of resi-
dence) were examined to assess their relationship to
the composite index of physical health (Table 1).
Multiple models that included different variables
were tested to explain the statistical relationship
among the variables using forward and stepwise
selection and backward elimination regression meth-
ods. The model accounting for the greatest propor-
tion of the variance (r2? .785; P ? .0001) included 2
regions, the percent of teens not attending school and
not working, the percent of children living with par-
ents who do not have full-time, year-round employ-
ment, and percent of the population who are black.
Table 4 illustrates the values for each of the variables
in the final model for which 5 variables were associ-
ated with the composite health index (all variables
?.05).
Bivariate analyses were then performed for each
variable that was identified through the multiple-
regression analysis to determine their associations
with the computed health index. Bivariate regression
analysis for percent of teen dropouts, percent of sin-
gle parents, percent in poverty, living in the North-
east, and living in the West was not performed be-
cause they fell out of the previously described
multiple-regression model because of a lack of sta-
tistical significance. Living in the South was the best
predictor for poor health outcomes in children. Per-
cent of black people in the population and the per-
cent of teens not attending school or not working
follow living in the Deep South as predictors of poor
health outcomes (Table 5).
DISCUSSION
The findings in this study confirm the existence of
a region in the United States of contiguous states in
the Deep South that can be defined by its poor health
outcomes for children, as compared with other states
in the Census Bureau–defined South and other re-
gions of the United States. Living in this region is a
powerful predictor of poor child health outcomes.
These findings raise important research questions
and have implications for public policy. Future re-
search efforts related to the epidemiology of child
health need to take into account the impact of region
of residence of the children being studied. By defin-
ing and comparing the variables that are associated
with the poor health status of children in specific
regions of the country, more proximal ecological fac-
tors that contribute directly to child health outcomes
and/or mediate the impact of poverty, race, gender,
etc can perhaps be isolated. This would have an
enormous impact on our ability to implement effec-
tive region-specific intervention strategies and de-
velop public policies that consider the impact of
region in the context of resource allocation and pro-
gram strategies. Consideration should be given to
focusing initial research endeavors in the Deep
South, where poor health outcomes and disparities
are the most pronounced. Consideration should also
be given to configuring regions based on current
social, cultural, and geopolitical factors rather than
Census Bureau–defined regions, which may not
have relevance to the type of analysis being con-
ducted. This would facilitate the generation of more
relevant region-specific health data.
Spatial Analysis
The methodologies used in this study to address
the research questions include an adaptation of spa-
tial analysis. Spatial analysis, the linking of diseases
to geographic areas, is a fundamental epidemiologic
tool dating back to the earliest days of epidemiology
when John Snow linked a London cholera epidemic
to a contaminated well.21–23It has continued to be
used as an essential tool in defining the epidemiol-
ogy of a wide range of infectious diseases24–28and is
evolving as an important approach to environmental
and other areas of epidemiology.29–37Its relevance to
a broad range of health issues such as diabetes,38
childhood lead poisoning,39pediatric burn injuries,40
fertility,41cancer screening,42general chronic disease
prevention,43and health services research44is also
unfolding.
Spatial analysis is an important first step in devel-
oping a better understanding into variations in the
health of children. Similar to Snow’s early epidemi-
ology work, discovering the mechanisms/path-
ways/causes by which region influences health out-
comes is the next step in addressing disparities and
inequities in child health and is one that is an impor-
tant and fertile area for future research. Although
much of the emphasis on spatial epidemiology has
been recently related to the challenges faced in the
study of rare diseases, such as specific forms of can-
cer,45the challenges posed by research questions
concerning the impact of region on the health of
children relate more to simpler forms of spatial anal-
ysis associated with large-scale mortality mapping
than to those related to the study of rare diseases.
Sophisticated approaches to spatial analyses that are
Fig 4. ANOM decision chart for deviation of regional means from
overall the mean (0). Level of significance: .001. Adapted from SAS
ANOM chart using SAS 9.1 (SAS Institute, Cary, NC). - - - indi-
cates the decision limit (defines statistical significance, similar to
the function of confidence interval; limits vary for each region
because of the number of states in each region [minimum n: 9;
maximum n: 16]).
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necessary for the study of rare diseases (eg, Bayesian
[maximum-likelihood] techniques, parametric boot-
strapping, and penalized quasi-likelihood models)
are not necessary for the relatively simple ap-
proaches to mapping and the analysis of data for
comparisons and associations that are used in this
study. These simpler forms of spatial analysis, de-
signed to identify locations in which health policy
changes are needed and explore patterns to “gener-
ate etiologic clues for further study”22will continue
to have utility.
Public Policy
The public policy implications of the findings in
this study are substantial. The identification of the
Deep South as a clearly defined subregion of the
Census Bureau’s regional definition of the South sug-
gests the need to use more culturally and socially
relevant boundaries than the Census Bureau–de-
fined regions when analyzing regional data for pol-
icy development. The historical basis for this group-
ing of states46has lost its relevance in the context of
contemporary political, social, economic, demo-
graphic, etc factors that currently characterize this
region. The links between Maryland and Mississippi
and the links between Delaware and Alabama that
may have had some foundations in pre–Civil War
regional definitions do not seem to be sound foun-
dations for categorizing regions today. Regional
comparisons of health, social, economic, etc issues
using the current Census Bureau–defined regions
will greatly underestimate the important differences
between the Deep South and other states included in
this region. It will preclude the opportunity to iden-
tify differences among states and clarify causation of
these differences.
Improved clarity of the variation among states and
regions in the United States with respect to health
outcomes and determinants could inform public pol-
icy decisions related to resource allocation and op-
portunities to prevent and/or mitigate the effects of
region on child health. A better understanding of the
depth and breadth of disparities in health, education
and other social outcomes among and within regions
of the United States is necessary for the generation of
policy that ensures the equitable distribution of re-
sources. Without an in-depth understanding of the
etiology of these disparities, the ability to target re-
sources and interventions will be limited. The iden-
tification of subregions as the unit of inquiry and the
reorganization of Census Bureau regions into units
that reflect both the uniqueness and commonality of
member states are required to optimize the transla-
tion of research findings into evidence-based public
policy.
The disparities in health outcomes between chil-
dren in southern states in comparison to children in
other regions of the country is, in part, a result of the
lack of public policies directed at their unique needs.
Few, if any, policies identify these children as a high-
risk group on the basis of their region of residence.
The nation’s primary health planning document,
Healthy People 2010,19makes no reference and estab-
lishes no special consideration for these children.
Other national studies examining health disparities,
such as the Institute of Medicine report6Unequal
Treatment: Confronting Racial and Ethnic Disparities in
Health Care, neglect these glaring regional disparities.
Limitations
This was a retrospective study based on existing
data, with all of the limitations associated with such
investigations. The selected variables were chosen on
the basis of their national availability and compara-
bility and their use by other recognized authorities
for documenting the health of children. Data were
obtained from multiple sources (ie, mortality data
were from the National Center for Health Statistics,
Division of Vital Statistics, whereas poverty data
were from the Census Bureau, Current Population
Survey). One state, Florida, was included among the
states in the Deep South despite its higher ranking
for overall child health (35th) compared with other
Deep-South states. This was because northern Flor-
ida relates historically and demographically to the
other contiguous Deep-South states, and its child
health outcome statistics also are similar. Although
not including Florida in the Deep South would have
strengthened the statistical basis for defining the
Deep South as a region linked to poor health out-
comes for children, Florida was retained because of
its contiguous relationship to the Deep South and
because it could not logically be placed in another
contiguous region.
Another limitation of note relates to multivariate
analyses. Specifically related to the multivariate anal-
ysis, the final regression model was selected on the
basis of main effect variables that were significant
TABLE 3.
Pairwise Comparison of the Deep South to Each of the Other Regions
ControlMean Difference SE SignificanceUpper Bound
Northeast and Mid-Atlantic
Midwest
West
Deep South
Deep South
Deep South
2.2669*
1.5267*
1.7108*
0.2988
0.2824
0.2939
2.1626 ? 10?5
2.4315 ? 10?5
2.2233 ? 10?5
2.8910
2.1164
2.3245
Dunnett t tests treat 1 group as a control and compare all other groups against it.
* The mean difference is significant at the .05 level.
Fig 5. Standardized scores by health indicator. Source of data:
2002 Kids Count Report. Prepared by the Institute for Health,
Policy and Evaluation Research, Duval County Health Depart-
ment, January 2004.
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and the model that had the highest aggregated r2
value (?0.750). Models that were considered were
based on a logical fit, without attempting to exhaust
all possible combinations of variables. Models that
did not show strong associations were discarded. It
is presumed that other variables, to be identified
through future research, may be as or more directly
linked to these poor health outcomes and have stron-
ger predictive associations with poor child health
status. This study only included those potential con-
founding variables that have been commonly used as
indicators of the social or economic well-being of
children.
The potential for inconsistency in data collection
among states is also a limitation, because data are
collected independently at the state or local level.
Although these data typically have standard proce-
dures for collection and recording, variations are
possible. Concerning possible alternative measures,
many other hypothetical measures could be more
sensitive or more accurate reflections of child health
and well-being, but these hypothetical measures are
typically not collected and reported with any consis-
tency across states. The measures used in this study
were selected because of their widespread recogni-
tion, acceptance, and use as indicators of children’s
health and well-being. Using data consistently col-
lected across the states is critical to be able to make
comparisons reflected in this study and the annual
reports on children’s health.1,17,18
CONCLUSIONS
The findings of this study indicate that region of
residence in the United States is statistically related
to important measures of child health and may be
among the most powerful predictors of child health
outcomes and disparities. This clarification of the
poorer health status of children living in the Deep
South is an essential first step to addressing these
disparities. The reasons for these disparities may be
complex and synergistically related to various eco-
nomic, political, social, cultural, and perhaps even
environmental (physical) factors in the region. Fu-
ture research related to the reasons that children in
the South have such poor health outcomes in com-
parison to children in other US regions is likely to
require interdisciplinary research methods drawn
from the social and biomedical sciences. It will re-
quire the collaboration of interdisciplinary profes-
sionals from medicine, public health, economics, the
social sciences, etc and new tools and hypothetical
models of the ecology of disease and health causa-
tion. We cannot continue to ignore an obvious re-
gional disparity in child health, because research and
surveillance efforts focused on these regional differ-
ences have the potential to provide the insights that
could improve outcomes and reduce disparities.
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TABLE 4.
Variables With Statistical Association to CHI Through Multiple-Regression Analysis
Term
? Coefficient*SE
P
95% CI of Coefficient
Intercept
Not attending school or working
No parent working full-time
Deep South
New Midwest
Percent black
0.4541
?0.6053
0.2376
?1.0299
?0.7903
?0.3358
0.1005
0.0900
0.0822
0.2624
0.1538
0.0968
?.0001
?.0001
.0059
.0003
?.0001
.0012
0.2515, 0.6566
?0.7865, ?0.4240
0.0720, 0.4032
?1.5586, ?0.5011
?1.1002, ?0.4804
?0.5310, ?0.1407
* Positive ? scores equal relative good health, and negative ? scores equal relative poor health.
TABLE 5.
Through Independent Bivariate Analysis
Variables With Statistical Associations Revealed
Variables
R
Adjusted
R2
Significance
Living in the South
Percent of population who
are black
Percent of teens not attending
school and not working
Percent of children living
with parents who do not
have full-time, year-round
employment
Living in the New Midwest
0.6914
0.6870
0.478
0.461
.0001
.0001
0.57710.333.0001
0.14140.020 .164
0.0316
?.020 .828
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DOI: 10.1542/peds.2005-0366
published online Nov 1, 2005;
Pediatrics
David Wood, Graham Watts and William Livingood
Jeffrey Goldhagen, Radley Remo, Thomas Bryant, Peter Wludyka, Amy Dailey,

The Health Status of Southern Children: A Neglected Regional Disparity
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