Introduction to the 2006 Eastern North Carolina Atlas of Mortality

A tombstone in an eastern North Carolina church cemetery is inscribed:

In Memory of

James Bonner Foreman

Who was born

The 1st of December 1785

And died

The 22nd of December 1807

Aged 22 Years and 21 Days

Come view my Tomb as you pass by,

As you are now so once was I;

As I am now so must you be,

Therefore prepare to follow me.

Death is a personal event that we will all eventually experience. It is also

something fundamentally empirical, recordable, and therefore measurable. The

tradition and culture of recordkeeping varies throughout the world and in the west

some countries have been compiling data on peoples’ lives for centuries either

for ecclesiastical or secular purposes. One extremely important secular purpose

is the amassing of individual records over time and place into part of North

Carolina’s vital statistics collection. Eventually, every North Carolina resident

shows up in the vital statistics registry “ book” as a single data record, an

abstraction, of a life once lived. Unlike Mr. Foreman’s epigraph two centuries

ago, more data and information pertaining to the circumstances of the mortal

event are recorded. In addition to date of birth and death ( i. e., age at death),

these include the decedent’s location at death, cause of death, race, sex, and

residence. The data recording the circumstances surrounding people’s deaths

can be formed into a picture about the conditions of living in their period of time

and their society when aggregated at various scales and dimension. The atlas

format is an appropriate means of display and description of vital events such as

mortality.

The present chapter is an introduction to the approach and concepts used in the

current edition of the Eastern North Carolina Atlas of Mortality. Specifically

addressed topics can be found using the following linked headings.

Overview of the Atlas

Portraying Geographic Data

Data Sources

Mapping with GIS Software

Maps in the Atlas

Time Series Charts in the Atlas

Overview of the Atlas

The 2007 edition of the Eastern North Carolina Atlas of Mortality is a narrated

collection of such statistical pictures that describe the spatial and temporal

facets— the descriptive geography-- of death in the eastern- most 41 counties of

North Carolina ( ENC). Over the last three decades, this region has seen

thousands of individuals dying in excess of what would be expected or

experienced in other parts of the country. The underlying motivation for this work

is to bring this ongoing tragedy to light and to show health professionals and

policy makers where and on what problems need their attention. The information

presented in this atlas will allow the reader to form a coherent image in his or her

mind of the history and future of mortality in Eastern North Carolina. It is hoped

that these statistical images will lead to not only an increased awareness of the

conditions of life-- and death-- in ENC but that it will also stimulate thinking about

hypotheses, research questions, policy, and strategies for making life better in

our region.

In this work, the geographical distributions of mortality from leading causes are

aggregated and portrayed for the years 2000 to 2004 ( 5 years) and chronicled

over a 26- year time series beginning in the year 1979 an ending in 2004. From

2004, rate projections ( linear best fit lines) are included. Figure 1.1 portrays the

100 counties of North Carolina and delineates the major regions used in this

Atlas. The regional focus is the eastern- most 41 counties whose western

boundary is approximated by I- 95 and extends to the coastline. ENC 41 also

corresponds to the physiographic province of the Coastal Plain. The 41- county

region is further divided into two sub- regions: ENC 29, comprised of the

northeastern- most 29 counties of ENC 41, and a remaining southern 12- county

region. ENC 29 corresponds spatially to the county service area of University

Health Systems of Eastern Carolina. ENC 41 possesses North Carolina’s

greatest levels of poverty and ethnic diversity, while population and economic

growth lags behind the remaining western 59 counties. To contrast and compare

mortality rates with the rest of the state, the remaining 59 counties are grouped

into two regions corresponding to the Piedmont ( PNC) and the western mountain

region ( WNC). Over the last 30 to 40 years, PNC and WNC have experienced

rather different population and economic trajectories than the east and this is

reflected in their more favorable mortality outcomes.

The Atlas traces the spatial and temporal domains of ENC’s mortality experience

with the use of maps, tables, and time series charts. These three components of

the Atlas are built on measures that summarize the population’s mortality

experience. Summary measures like mortality rates are calculated from several

of the descriptive elements of the individual death record. The resulting rate

calculations are then tabulated by county, region, and time period. In contrast to

the simple table, maps are a 2- dimensional spatial ordering of mortality rates that

describe a place’s mortality experience and burden. Time series charts portray

the temporal order of mortality rates for regions, counties, and their constituent

population groups. These charts show general parallel, convergent, or divergent

2

trends among regions and population groups. Relative and absolute mortality

rate comparisons can be made from the maps, tables, and charts to determine

progress toward the elimination of rate disparities and mortality burden over

space and time.

Portraying Geographic Data

Maps are the most important feature of a geographical atlas. Along with other

graphical means of communication, a wide range of topical literature has evolved

that discuss the nature of maps and the geographic information and meaning that

they portray from a variety of technical and philosophical both within and without

the discipline of geography. A good discussion of the foregoing, which also

includes Information Theory, can be found in Poore and Chrisman’s Order from

Noise: Toward a Social Theory of Geographic Information ( Poore & Chrisman,

2006). The more salient and general points concerning maps and time series

data found in this work are discussed below. For a more technical treatment of

charts, with a strong emphasis on the proper construction of graphics that convey

meaningful information from quantitative data, the reader is directed to the works

of Tufte ( Tufte, 1995; Tufte, 1997; Tufte, 2001; Tufte, 2006). Pragmatically,

different aspects of various techniques and perspectives necessarily come

together in the development of any atlas and how they come together may

distinguish one atlas’s approach from another. In this Atlas, our approach is one

of description and chronicling in such a way that the reader can make meaningful

geographical comparisons of the regional mortality experience.

One functional definition of geography considers both space and time as

referential systems. Borrowing terminology from Werlen ( Werlen, 1993), a space

can be defined as a three dimensional container. This type of space orders

events ( an occurrence or areas with given attributes like mortality rates) by

measuring their positional relationships ( the x and y axes) and their sizes or

magnitudes ( the z axis). Another dimension can be added that orders those

events temporally and therefore, sequentially. The 2- dimensional or 3-

dimensional static map can be stacked or sequenced along a temporal axis to

form a time series of maps. As long ago as 1964, Berry ( Berry, 1964) described

and operationalized a very similar concept as the geographical data matrix,

where the matrix is the container of geographically referenced data—

attributes/ characteristics ( or mortality rates) that are linked to places or areas.

With some modifications, this prosaic and functional conceptualization describes

how spatially referenced data are managed in modern Geographic Information

Systems ( GISs). With a GIS, these data can be stacked or sequenced in

temporal order very quickly to create a moving picture of a geographic process.

Because of space constraints, only the most current 5- year maps of mortality

rates are provided in this Atlas, but they are accompanied by charts that show

temporal trends among regions and population groups.

3

Geographical referencing and the binding together of attribute data over points in

time or sequence of time periods are a means to the comparative study of trends

in mortality processes. In both spatial and temporal referential systems, there is

a well- known tendency for objects within the system that are nearer to one

another to be more alike than those more distant or, as stated in Waldo Tobler’s

first law of geography, “… everything is related to everything else, but near things

are more related than distant things.” ( Tobler, 1970) This notion of propinquity

and similarity is important for understanding relationships among demographic,

social, biological, and physical attributes of places. For example, a group of

neighboring counties such as those found in Eastern North Carolina will tend to

have similar age, race, and sex structures because they have had similar

economic and demographic histories or, more generally, have experienced

similar social relations and processes as well as live within similar spatial

structures ( Gregory & Urry, 1985). Since age is the greatest risk factor for

mortality we would also expect a group of neighboring counties that share a

similar age structure to have similar mortality rates. In varying degrees, these

same counties may also have similarities in other known risk factors such as

certain occupations, race, housing, and poverty. Within the spatial analytical line

of inquiry, this well known propensity in geography is extremely useful for

constructing hypotheses, modeling, and theory testing.

Maps can be thought of as models of real- world patterns and processes at a

given point in time. They reduce reality to a set of graphical and geometric

objects that have an a priori common meaning, which is necessary for

interpretation and communication. This reality is not produced, reproduced, or

experienced in exactly the same way by any two persons or reflected in

individual death records but collectively similarities and patterns can emerge and

be traced for population aggregates. A map as a representation allows a way for

the user to apprehend a myriad of facts about places and order them both

spatially and temporally into one coherent mental picture. Once geographic data

have been integrated into a suitable level of coherency, assessment and

analyses can begin with a certain set of well- grounded assumptions. These

assumptions might include Tobler’s first law of geography ( the closer, the more

similar) or considered in conjunction with certain risk factors such as age or

diabetes with certain mortality outcomes. However, it should always be borne in

the mind of the map user or analyst that these newly acquired understandings

and cognitive models are ultimately based on a reduced reality— that is, in the

time- worn phrase: the map is not the territory.

Finally, maps can be used either as arguments to make a case for further study

into the etiology of the causes of mortality and morbidity or they can be used as

propositions ( or hypotheses) addressing potential causes of observed mortality

and morbidity patterns ( Koch, 2005). To illustrate, given the range of social and

structural inequalities that exist among certain demographic groups in the US

and particularly in the South, the Atlas provides evidence for the argument that

differences in the underlying social fabric will manifest themselves in the

4

observed patterns of mortality for Whites and Non- whites in eastern North

Carolina and for all Eastern North Carolinians versus the rest of the state. The

case can be made by employing maps, tables, and charts that permit

comparisons among the race- sex groups at county, regional, and national scales.

Maps of related demographic and socio- economic variables are either included

or referenced in the Atlas as propositions about relationships underlying the

observed mortality patterns. As a tool for integrating disparate data, either as

argument or proposition, the Atlas can assist in developing research questions

for topics on health disparities, health resources, and economic development.

Representational data used in the construction of maps are of two distinct

classes. The first data class is made up of a limited set of geometrical objects

that are used to represent a large range of real- world features on a map. The

most basic of these data is the geometric point that is located on a geometric

plane. The point can represent an event, institution, or place, for example. On

this same plane an additional point will define a line and a series of lines can

represent features such as road networks, stream systems, or social

relationships and connections. Three or more points will define a polygon and

can represent real- world entities such as counties or urban areas. In some maps

polyhedra or solids defined by four or more polygons can be constructed to

represent specific types of features. These geometrical representations ( or

features) have some measurable quality or attribute assigned to them, which

provides the basis for making comparisons and discerning patterns.

Points, lines, and polygons can be assigned an attribute, quality, or quantity that

describes map features. This second class of data can be partitioned into three

categories: nominal, ordinal, and interval/ ratio ( Earickson & Harlin, 1994).

Nominal data refers to the binary presence/ absence of a quality or one or more

types of a given feature, such as vegetation cover or soil. Ordinal data are

ranked in ascending or descending order and can be used to describe a

hierarchical system of, for example, health states or levels of care quality

measured as poor, fair, good, or excellent. Finally, interval/ ratio scale ( or metric

scale) data measure quantities like mortality rates, dentist to population ratios, or

disease prevalence. For interval data the difference between any pair of values

is always the same no matter where they are located along the metric scale.

There is a small but important distinction when considering either interval or ratio

data. Interval data can include values that are less than an arbitrarily defined

zero, such as temperature or elevation. However, unlike elevation, one cannot

speak of a temperature being twice as cold or hot as another. These data are

strictly interval in nature. Ratio data are interval data that can be compared

meaningfully. For example, one could make the statement that the mortality rate

for female breast cancer in county A is 33% greater than the rate in county B.

Interval data can be evaluated as “ twice as much,” “ half as great,” or as some

percent or proportion of one value in relation to another.

5

Data Sources

The predominant types of data employed in this Atlas are polygons bound or

joined to interval/ ratio data attributes. Polygons are used to represent counties,

which are the basic units of analysis and are the building blocks for larger multi-county

regions. County- level polygon data ( i. e., boundary files) are obtainable

from the geography page of the US Census website. These data are available in

several formats and are ready for use with most GIS packages. Because

boundary files have unique county identifiers, they are also ready to “ join” or link

to attribute data.

A wide variety of county- level attribute data are employed in this work.

Demographic and socio- economic data can be obtained from the American

FactFinder section of the US Census website and the NC State Data Center. In

the Atlas, mortality rates by leading causes of death are calculated from two

sources. The North Carolina source is located at the University of North

Carolina’s Odum Institute, which provides the most up to date vital statistics for

the state. Mortality data for the nation and other areas of the county are

calculated from data found in the Compressed Mortality File ( CMF) series

produced by the National Center for Health Statistics. These data tend to be 3 to

4 years behind the latest year for North Carolina.

Mapping with GIS Software

Today, nearly all data required for GIS and mapping exist in a digital form. Many

printed tabular data sources, collected in more remote periods of time, have been

archived either on paper or microforms. These data sources can be scanned or

imaged into formats suitable for optical character recognition ( OCR) programs or

other software tools that will transform the printed character or numeral into a

digital rendition. Once obtained, the data need to be stored in some type of

database. Storage can be in a large relational enterprise level database such as

MS- SQL ® or Oracle ® with member tables distributed according to function

anywhere on the globe or data storage can simply be in a spreadsheet

“ database” residing on a desktop PC. In Microsoft’s Excel ® , one or more data

ranges ( i. e., columns × rows) described in a worksheet can behave as individual

database tables within a workbook. These data ranges and tables loosely

correspond to Berry’s geographic data matrices. ( Berry, 1964)

Using a small set of basic database functions in Excel, it is possible to link and

match records ( table rows) in a way similar to what is done in a true relational

database. In order to match records, there must be a field serving as an index.

An index field contains rows of unique identifiers and is common to all tables that

will be linked or joined. In this Atlas, we use either the unique county name

within the state or the Federal Information Processing Standard ( FIPS) code that

uniquely identifies any county among the more than 3,000 counties in the US.

These same identifiers are used to match attribute data to county polygons prior

to mapping in a GIS.

6

Map- making today is largely done using GIS software that integrates a wide

variety of disparate data sources and data types. The construction of maps is

actually one of many functions a modern GIS can perform. Other functions

include spatial querying, spatial analyses, modeling, as well as layering and

combining spatial objects and their attribute data to develop new data. For the

purposes of descriptive spatial epidemiology and ultimately the comparisons that

will be made, the Atlas here employs the primary and more basic functions of a

GIS which manage geographically referenced data and quickly generate map

layers with accompanying cartographic elements.

Cartographic elements include the legend or map key derived from data and

feature classification and symbology. Data in an atlas of mortality are typically

rates and percentages ( interval/ ratio data). A GIS is able to partition and classify

a data distribution with a choice of automated default methods ( e. g. quantile,

equal interval, natural breaks, or statistical) or the user can classify the data

manually. The choice of method is based on the purpose of the map ( e. g.,

statistical description, proposition, or argument) and the intended audience of

map readers ( Wilson & Buescher, 2002). The GIS also provides color palettes

for selecting a hue for each theme. A hue can be further divided into a series of

graded shades with hue saturation corresponding logically to category ranges.

Analysis proceeds by examining the resulting patterns of categorized rates

represented as shades: do counties with more saturated shades tend to cluster

together? Or are they more dispersed, demonstrating no real comprehensible

pattern? Such basic analyses can yield ideas for the development of hypotheses

or intervention strategies if something is known about the processes that created

them. Different ideas about presentation of map data and experimentation with

categories can proceed quickly with a GIS. What took several days to produce

by hand as recently as twenty years ago today only takes several minutes. The

maps in this Atlas were created in ESRI’s ArcGIS 9.1 and 9.2.

Maps in the Atlas

The Atlas is organized in a way that invites the assessment of patterns in both

the spatial and temporal domains. Maps show the distribution of categorized

county rates of mortality for the years 2000 to 2004. Mortality rates are, in effect,

measures of density. They measure the density of events ( deaths by selected

causes) in relation to the population producing those events. Both crude and

age- adjusted rates are employed for making regional comparisons in those maps

depicting total deaths by cause, while only age- adjusted rates are used for

making county and regional comparison by race- sex groups. Crude rates are

constructed by dividing the number of events ( or case mortality by cause) in a

county by that county’s total population, and then multiplying the result by

100,000, which has the effect of reducing in a certain time period the number of

decimal places and thereby making the rate more easily understood. A crude

rate is the actual rate and is useful for measuring the burden of disease mortality

7

in an area and time period. However, making comparisons among counties with

crude rates is problematic because the differences in their respective age-structures

can confound interpretation. For example, knowing that increased age

is the greatest risk factor for dying in a given time period, a county with a larger

proportion of elderly ( e. g., retirees) will naturally produce a greater crude rate

than a county where there are larger proportions of college- age students or

individuals stationed on military bases.

To make meaningful comparisons, a county’s age structure ( the numbers of

people in each previously defined age group) must be adjusted. Essentially this

adjustment is a re- weighting of a county’s population that produces an expected,

as opposed to actual or observed, number of deaths for that population. The

weights are based on an external or synthetic population structure known as the

standard million population. Age- specific death rates based on the weights are

calculated for each group in the age structure and then summed to produce an

age- adjusted rate ( Buescher, 1998). An age- adjusted county rate is the rate a

county would have if it had the same age structure as the external or standard

million population and renders this county’s rate comparable to any other county

using the same standard million population. It should be emphasized that age-adjusted

rates used in making comparisons are not the actual observed rates but

are the rates that would be expected if each county and region had the same age

structure. The external population used in this work is the US Standard Million

for the year 2000. Knowing which standard million population is used is

extremely important when comparing rates calculated from mortality data from

different states and time periods, otherwise the rates are simply not comparable.

Time Series Charts in the Atlas

Time series graphs for the years 1979 to 2004 provide a synoptic view of

mortality trends for regions and race- sex groups. Age- adjusted rates are used to

make comparisons among the 41 counties of ENC, the remaining 59 counties of

the state ( RNC), North Carolina, and the US ( 1979 to 2002). Time series plots

for four ENC race- sex groups ( male and female Whites and Nonwhites) are

provided on an additional graph. Best- fit lines are incorporated into the time

series plots for both regional and population charts so that the user can assess

differences and trends. How well the trend and projection line fits the data is

described by the coefficient of determination, R2. ( R2 is a statistic with values 0.0

to 1.0; the closer to 1.0, the better the fit.) For some leading causes of death

there are Healthy People 2010 goals, which are age- adjusted target rates for the

year 2010 ( U. S. Department of Health and Human Services, 2000). Where

applicable, target values are included in the chart and can be used in conjunction

with the projected trend lines. This permits the user to make comparisons

among regions and population groups in terms of the amount of progress that is

being made against a nationally recognized standard.

8

Over the course of many years, mortality rates will ebb and flow with small

annual perturbations deviating from the general trend. A larger view over many

decades may show gradually decreasing ( the ebb) or increasing ( the flow) trends

for chronic diseases and intermittent spiking for epidemics during that period of

time when communicable and infectious diseases were predominant causes of

death ( see figure 1.2). Long term directional changes and pattern shifts in

mortality rates are known as secular trends.∗ These trends are both responding

and contributing to the underlying long term shifts in demographic, socio-economic,

and environmental processes. One of the best examples is the nearly

complete decline of mortality due to infectious diseases in the early part of the

twentieth century. Infectious diseases tend to carry off larger proportions of

susceptible young as well as those in the older age groups. Socio- economic and

environmental processes such as improved access to better food and nutrition,

improved sanitation, and generally better living conditions resulted in fewer

deaths of the young as a result of contagion. In turn, a gradual shift in

demographics occurred: more children survived into adulthood and into later life.

This demographic shift— the result of more individuals now surviving into the

older age groups-- is a major influence on the rise of the crude mortality rate from

cardiovascular disease ( with the exception of stroke) in the early- mid twentieth

century. These kinds of changes are described in Omran’s work on the

epidemiologic transition ( Omran, 2005).

The long term mortality trends resulting from different causes of death may not all

be the same. Generally, mortality rates over the long term trace curvilinear

patterns. As these patterns are examined more closely, parts of the curve begin

to take on a more linear form. To simplify and give a general snapshot of recent

trends, the mortality time series depicted in this Atlas models the data linearly.

The benefit to this is that it provides easily understandable summary measures of

mortality events occurring over three decades. However, the reader is cautioned

to examine the general pattern of the entire series, giving more weight to events

that have occurred later in the series than earlier.

The maps, tables, and charts found in the Eastern North Carolina Atlas of

Mortality form an armamentarium for understanding, integration, and synthesis of

the region’s mortality burden and experience. Singly, an individual’s death, like

the one found in an obscure corner of a church cemetery may appear to be a

random event. However, when lone events like these are amassed into

numerators and then rates, meaningful pictures about the conditions of life in a

place can be created. In the end, it should always be kept in mind when gazing

upon the abstract representation of the mortality map that ultimately it was the

lives rather than the deaths of people that generated the observed patterns.

∗ The term secular as used here refers to a characteristic pattern for a given age or time period in

population history. For example, until the First World War in the United States infectious and

communicable diseases had a much more prominent role in observed mortality patterns than they

do today. The last several decades of the twentieth century has seen a gradual decline for

certain chronic diseases like those of the heart and some cancers.

9

The next chapter addresses general mortality. In this chapter, the leading

causes of death for ENC 41 are delineated for the 5- year period, 2000 to 2004.

Discussion of the spatial and temporal distributions of mortality from all causes

( i. e., general mortality) follows, including a more in- depth treatment of rates and

measures in light of the observed data. Subsequent chapters address the 10

leading causes of death for the region and will generally follow the pattern of

discussion found in the chapter on general mortality.

References

Berry, B. J. L. ( 1964). Approaches to regional analysis: A synthesis. Annals of

the Association of American Geographers, 54( 1), 2- 11.

Buescher, P. A. ( 1998). Age- adjusted death rates. Raleigh, North Carolina: North

Carolina Center for Health Statistics.

Earickson, R., & Harlin, J. M. ( 1994). Geographic measurement and quantitative

analysis. New York : Macmillan ; Toronto; New York: Maxwell Macmillan

Canada; Maxwell Macmillan International.

Gregory, D., & Urry, J. ( 1985). Social relations and spatial structures. New York:

St. Martin's Press.

Koch, T. ( 2005). Cartographies of disease : Maps, mapping, and medicine ( 1st

ed.). Redlands, Calif.: ESRI Press.

OMRAN, A. R. ( 2005). The epidemiologic transition: A theory of the epidemiology

of population change. Milbank Quarterly, 83( 4), 731- 757.

Poore, B. S., & Chrisman, N. R. ( 2006). Order from noise: Toward a social theory

of geographic information. Annals of the Association of American

Geographers, 96( 3), 508- 523.

Tobler, W. R. ( 1970). A computer movie simulating urban growth in the detroit

region. Economic Geography, 46( Supplement: Proceedings. International

Geographical Union. Commission on Quantitative Methods), 234- 240.

Tufte, E. R. ( 2006). Beautiful evidence. Cheshire, Conn.: Graphics Press.

Tufte, E. R. ( 2001). The visual display of quantitative information ( 2nd ed.).

Cheshire, Conn.: Graphics Press.

10

11

Tufte, E. R. ( 1997). Visual explanations : Images and quantities, evidence and

narrative. Cheshire, Conn.: Graphics Press.

Tufte, E. R. ( 1995). Envisioning information ( 5th printing, August 1995 ed.).

Cheshire, Conn.: Graphics Press.

Werlen, B. ( 1993). Society action and space : An alternative human geography

[ Gesellschaft, Handlung und Raum.] . London ; New York: Routledge.

Wilson, J. L., & Buescher, P. A. ( 2002). Mapping mortality and morbidity rates.

Raleigh, North Carolina: North Carolina Center for Health Statistics.

Pitt

Wake

Hyde

Duplin

Bladen

Bertie

Pender

Wilkes

Moore

Onslow

Union

Surry

Ashe

Beaufort

Craven

Halifax

Robeson

Nash

Sampson

Iredell

Columbus

Swain

Carteret

Burke

Brunswick

Johnston

Anson

Guilford

Randolph

Harnett Wayne

Jones

Chatham

Macon

Rowan

Hoke

Martin

Tyrrell

Dare

Lee

Stokes

Stanly Lenoir

Franklin

Buncombe

Warren

Granville

Davidson

Jackson

Haywood

Gates

Person

Caldwell

Wilson

Forsyth

Polk

Caswell

Cumberland

Orange

Pamlico

Rutherford

Madison

Yadkin

Gaston

Clay

Cherokee

Richmond

Cleveland

Catawba

Davie

Rockingham

McDowell

Hertford

Alamance

Vance

Avery

Yancey

Mecklenburg

Northampton

Edgecombe

Montgomery

Durham

Graham

Scotland

Greene

Watauga

Henderson

Washington

Transylvania

Mitchell

Alleghany

Currituck

Camden

Chowan

Perquimans

Pasquotank

New Hanover

Lincoln

Cabarrus

Alexander

Western ( WNC)

Piedmont ( PNC)

Remaining 59- County Region ( RNC 59)

Eastern North Carolina 29- County Sub- region ( ENC 29)

Eastern North Carolina 12- County Sub- region

Eastern North Carolina 41- County Region ( ENC 41)

North Carolina County and Regional Locations

Center for Health Services Research and Development

East Carolina University

Greenville, NC

ECU, Center for Health Services Research and Development, 2007

Figure 1.1

Six Leading Causes of Mortality in the US 1900 to 2001

0

100

200

300

400

500

600

700

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Year

Heart Disease

Cancer ( All Types)

Pneumonia & Influenza

Tuberculosis ( All Forms) Diarrhea & Enteritis

Three Infectious/ Communicable and Three Chronic Diseases

Deaths per 100,000 Population*

Sources: Leading Causes of Death, 1900- 1998

http:// www. cdc. gov/ datawh/ statab/ unpubd/ mortabs/ hist- tab. htm

( Last accessed Dec. 29, 2005)

Data for 1999- 2001 from NCHS’s Compressed Mortality Files

Stroke

* Rates are not age- adjusted

Center for Health Services Research and Development

East Carolina University

Greenville, NC

ECU, Center for Health Services Research and Development, 2007

Figure 1.2

General Mortality in Eastern North Carolina 2000 to 2004

The chapter on general mortality is divided into several topics related to mortality

from all causes for eastern North Carolina. They can be accessed directly with

the following links.

Introduction

The Spatial Distribution of Crude and Age- Adjusted General Mortality Rates

The Temporal Distribution of Age- Adjusted General Mortality Rates

Mortality Burden

The Spatial Distribution of Premature Mortality from All Causes

The Temporal Distribution of Premature Mortality from All Causes

From Empiricism to Explanation: General Mortality Disparities

Conclusion

Introduction

General mortality includes all causes of death over a specified time interval.

Causes of death are further defined and classified into internationally recognized

series of grouped codes, such as the International Statistical Classification of

Diseases and Disorders and Related Problems, 10th Revision or ICD- 10 ( World

Health Organization, 2004). ( For the most recent revision of codes, see the

electronic version at the World Health Organization’s website ( World Health

Organization, 2006).) Periodically, revisions are made to incorporate changes in

medical knowledge and to incorporate and facilitate improved coding rules ( see

U. S. Department of Health and Human Services, Centers for Disease Control

and Prevention, & National Center for Health Statistics, 2006). Standardized

coding, in conjunction with using standard populations for age- adjustment,

permits comparability of rates among different time periods and geographical

units. Once the cause of death has been coded, each record is accumulated into

a time and place- specific total number of deaths. The accumulated totals are

then used to determine the relative ranking and importance of leading causes of

death for a county or a region. Figure 2.1 portrays the resulting 5- year totals for

the ten leading causes of death proportionally to the total number of deaths in

ENC from 2000 to 2004.

In this figure, two general classes of mortality causes dominate the mortality

experience of ENC: Total Cardiovascular Disease ( TCVD) and Malignant

Neoplasms ( All Cancers). From 2000 to 2004, more than 59% or 65,442 deaths

have occurred due to these two disease categories. The remaining eight leading

causes of death account for just 21.4% or 23,605 deaths during this same period.

The number one leading cause of death in ENC for the study period is TCVD,

which accounts for 37.0% ( 40,820) of the region’s 110,390 deaths. ( The TCVD

category is based on the definitions proposed by the American Heart Association

( American Heart Association, 2005) and includes mortality due to stroke.) Death

from malignant neoplasms is the second of the ten leading causes of death and

2

accounts for 22.3% ( 24,622) of all regional mortality. A distant third leading

cause is attributed to Chronic Obstructive Pulmonary Disease and Chronic Lower

Respiratory Disease COPD/ CLRD with 4.9% ( 5,384) of all deaths from this

cause. Mortality from Diabetes Mellitus follows with 3.5% ( 3,904) of all ENC

deaths. In fifth place, death from Unintentional Motor Vehicle Injuries ( UMVI),

accounts for 2.8% ( 3,047) of the region’s deaths. Septicemia is the tenth ranking

cause of death claiming 1.7% of all deaths. The ten leading causes of death are

followed by a single category, All Other, which accounts for 19.3% ( 21,343) of

General Mortality. Within this final category, 1,378 people have committed

suicide, 1,193 people have died from chronic liver disease and cirrhosis, 1,104

people have been murdered, and 776 people have died from AIDS due to HIV

( Human Immuno- deficiency Virus). Regionally, deaths from specific causes in

the All Other category make up very small percentages within general mortality.

Nevertheless, when counties are examined separately, the seemingly

insignificant causes of death at the regional scale can be important causes of

death at the more local county level scale. It is therefore important to monitor at

the “ basement” level so that emerging mortality trends at regional and local

scales can be detected.

The present chapter is organized around three general topics. The first two

topics describe patterns of mortality from all causes, but using two different

approaches in its portrayal. The first approach examines the spatial and

temporal patterns of two density measures: crude and age- adjusted general

mortality rates. These two measures describe mortality quantities in relation to

population sizes and their distributions in space and time. However, density

measures do not provide information about what part of a population is being

affected. Mortality, whether from specific or general causes, can affect

populations in a differential manner across spatial and temporal dimensions.

Measuring the cumulative differences of age- at- death of individuals that occur

before an accepted standard age- at- death ( say, 75 years) produces information

about the level of premature mortality. Larger amounts of years of potential life

lost in a population signify greater levels of mortality burden being shouldered by

that population. The second topic covered in this chapter addresses the

distributions of premature mortality in eastern North Carolina and the state.

Finally, we move from empirical descriptions to a brief discussion of how patterns

of general mortality can be explained by their relationships to other factors.

The Spatial Distribution of Crude and Age- Adjusted General Mortality Rates

A map of crude mortality rates will draw the map- reader’s attention to those

areas that are experiencing the highest numbers of deaths relative to their local

populations. The crude mortality rate measures the density of resident deaths

occurring in an area in relation to the population of that area. It is a summary

measure representing the proportion of a population that has died over a given

interval of time. Because this proportion is frequently a very small value, it is

multiplied by a larger number ( of persons) like 1,000, 10,000, 100,000, or even

3

1,000,000 for extremely rare causes of death. ( This atlas will employ the multiple

of 100,000 persons when discussing and comparing density measures.)

Because age is the greatest risk factor for dying, the map of crude mortality rates

is also, to some degree, a map of the underlying spatial distribution of population

age structures. Controlling for the effects of age variation will permit the map

reader to make comparisons of mortality rates among different areas on the map.

This is accomplished through the technique of age- adjustment, which adjusts the

observed number of deaths to an expected number of deaths if the population

under study had the same age structure as some external reference population

( Buescher, 1998). In this atlas, the US Standard Million for the year 2000 is

employed ( Anderson & Rosenberg, 1998). It is extremely important that the

standard population used in each case is the same when comparing age-adjusted

maps from one period of time to another, or when comparing maps of

age- adjusted rates from other states. Different model or standard populations

will generate different age- adjusted rates even when the actual or observed

distribution of deaths across the population age distribution remains constant.

Figure 2.2 shows the mapped distribution of crude mortality rates from all causes

for counties in the contiguous US from 2001 to 2003. The category classification

is based on the extension of the classification scheme used in the North Carolina

mortality maps discussed later in this chapter.∗ Higher rates of general mortality

in this map are concentrated in the central part of the nation, which includes the

Great Plains and Midwest, the South, and the outlying high rate counties in the

Far West. There is also a significant cluster of counties centered in mountainous

West Virginia and eastern Kentucky. Recall that age is the greatest risk for

dying. Figure 2.3 is a map that shows the distribution of county level proportions

of people 60 years of age and older for the 2000 US Census year. The

distribution of the proportions of elderly is similar to the distribution of the higher

crude mortality rates seen in the previous figure. Statistically, the relationship,

measured as a correlation, results in an r- value of 0.81 and an R2 of 0.66, which

means that 66% of the variation in county crude rates of mortality is explained by

just the proportions of individuals greater than 60 years of age. Although the

∗ The North Carolina rates ( crude and age- adjusted) are based on more current numbers from the

State Center for Health Statistics and State Data Center and use a five- year period such as 2000

to 2004. Because numbers for the entire US are usually available three years behind the state’s

and that there are a significant number of US counties that experience small numbers of mortality

events, US county rates ( crude and age- adjusted) in this work are based on three- year

aggregations ( 2001 and 2003) from the National Center for Health Statistics’ Compressed

Mortality File ( CMF) 1999 to 2003. The center point year or fulcrum year for the US county rate

maps is 2002. That same year is the fulcrum for the period 2000 to 2004, which is the period

used in the state and regional discussions throughout the Atlas. However, in the state and

regional comparisons, the US value for one year is 2002, because their numbers are sufficiently

large not to warrant aggregation. It should also be noted that the rates generated for North

Carolina counties in the three- year US map will be slightly different than those generated for the

five- year NC maps seen elsewhere in the Atlas. This is the result of using different numbers of

data points ( three and five) and slight differences found in the denominators ( i. e., county

populations) between the US ( CMF 1999- 2003) and NC ( state demographic estimates) data

sources.

4

crude mortality rate map depicts where mortality is occurring in relation to

population age structure, this map cannot be used to make meaningful

comparisons among individual counties because their respective age structures

are different. Figure 2.4 shows the effect that age- adjustment has on the county

mortality pattern using an external standard population ( US 2000 Standard

Million). The high rate counties shift and concentrate their spatial distribution to

the Ozarks, Lower Mississippi Valley, the southern Coastal Plain, and the south-central

Appalachian region of West Virginia and eastern Kentucky. A few

outlying high rate counties are found scattered throughout the west, which

generally correspond to Indian reservations. The inset map in figure 2.4 shows

that ENC is a northern extension of the high rates of age- adjusted mortality found

in the southern Coastal Plain. To contrast, the national age- adjusted map also

shows that most of the remaining counties on North Carolina ( RNC) are part of

the southern extension of the much more favorable mortality conditions of the

Northeast.

Maps showing the spatial distribution of crude and age- adjusted mortality rates

from all causes in both NC and ENC for the years 2000 to 2004 are found in

figure 2.5. Individual county and regional mortality rates are listed in table 2.1

and their locations can be found using the map in appendix A of this chapter.

The state map for crude rates shows that the greatest mortality burden is

experienced at both ends of the state. Western North Carolina ( WNC) has the

highest crude general mortality rate of 1,080 deaths per 100,000 people. The

next highest crude mortality rate is found in the northeast 29- county region of

North Carolina ( ENC 29) with a rate of 967.7-- 7% higher than the 41- county ENC

regional rate of 905.4. The highest county rates in ENC cluster together along a

northwest- southeast axis. In this cluster, Hertford County experiences the

highest rate of 1,330.6 and the second highest rate is found in its southern

neighboring county, Bertie, at 1,313.3. Onslow County’s rate is the lowest in the

region ( 518.4), experiencing mortality at just 39% of Hertford’s level. The

greatest local impact of mortality from all causes is felt in the northern county

cluster of ENC which also possesses an older aging- in- place population. This

contrasts starkly to Onslow County where a significant portion of its population is

made up of young, transient, military service- age people.

When the county rates are age- adjusted ( figure 2.5b), the high mortality

categories become more concentrated in the east and less so in the state’s

mountainous west-- where the relative older ages of those county populations

played a role in that region’s observed higher crude rates. The effect of age

adjustment on relatively youthful county populations with low crude mortality

rates can be quite dramatic. For example, when the crude rates for Cumberland

and Onslow-- two counties with large military aged populations-- are age- adjusted,

there is an apparent jump in mortality rates of 51% and 84%, respectively. At the

regional level, age- adjustment widens the disparity for general mortality between

ENC and RNC to more than 12% from the crude rate difference of 7%. Within

the ENC region, counties with the highest age- adjusted rates form two centers,

5

one in the northeastern 29- county sub- region of ENC and the other in the

remaining southern 12- county sub- region. In the 29- county sub- region,

Edgecombe County possesses the highest rate ( 1125.6) among the eight county

cluster found there. Other counties in this cluster include Halifax ( 1023.9),

Northampton ( 1007.0), Hertford ( 1124.5), Gates ( 1061.1), Bertie ( 1111.2), Martin

( 1091.0), and Washington ( 1027.8).∗ The highest age- adjusted general mortality

rate is found in the southern sub- region. Robeson County with 1133.3 age-adjusted

deaths per 100,000 has the highest general mortality rate in the state

and forms the core of the southern- center- high- rate- county- cluster. This county

cluster includes Scotland ( 1063.6), Hoke ( 1015.5), Bladen ( 1101.7), and

Columbus ( 1069.9) counties. A north- northeast linear series of adjacent high

rate counties ( Sampson, Wayne, and Lenoir) continues from the northeastern

border of Bladen County. Immediately adjacent to the east of the southern

cluster is a three county cluster of the lowest age- adjusted general mortality rates

found in the entire 41 county region. The counties in this cluster include New

Hanover ( 832.7), Brunswick ( 842.8), and Pender ( 830.1). All three of these

counties have rates that are more favorable than the RNC 59 county rate of

866.1 age- adjusted deaths per 100,000. The examination and comparison of

crude and age- adjusted general mortality in North Carolina yields two

conclusions. First, the higher crude rates found in the east like in the western

counties, can partly be explained as a function of greater proportions of elderly.

Second, when general mortality rates are adjusted for age, 16 of the 20 highest

rate counties are found in ENC 41, which clearly demarcates this region as one

experiencing a greater mortality burden in both an absolute and relative sense.

Many of the counties in this discussion will be seen again in later sections of the

Atlas when the geographic patterns of mortality from specific causes are

explored.

Figure 2.6 shows the contributions that race- sex specific age- adjusted general

mortality make to the overall pattern of general mortality in North Carolina seen in

the age- adjusted map ( fig. 2.5b). Applying the same age- adjusted rate category

classification found in figure 2.5b to the rate distributions of each of the four

demographic groups in figure 2.6 produces four distinct map patterns. Males of

both races have higher rates ( i. e. they occupy the highest rate category: 1,004.3

to 2,107.8) of general mortality throughout the state. White males ( fig. 2.6c) have

a state rate of 1,034.0 and a regional ( ENC 41) rate of 1,100.2 compared to

nonwhite males whose rates are 1,336.2 and 1,406.0, respectively. Counties

with larger proportions of retirement age populations, found within each of the

state’s three physiographic provinces, as well as the larger metropolitan counties

of the Piedmont have lower rates of death from all causes for white males. For

nonwhite males, 95 of the state’s 100 counties are in the highest rate category.

The remaining five counties are found in the westernmost portion of the state,

and their lower rates for nonwhite males are probably the result of fewer people

being in this race- sex group in the western region. The age- adjusted death rates

∗ In the three year average ( 2001 to 2003) for the 3,100 plus counties of the US, Martin County

raked 15th highest in the nation at 1313 age- adjusted deaths per 100,000.

6

for females of either race are significantly less than their male counterparts.

White females ( fig. 2.6b) have a state rate of 720.4 and a regional ( ENC 41) rate

of 773.5 compared to nonwhite females ( fig. 2.6d) whose rates are 857.3 and

891.6, respectively. White females have rates in the lowest map category

throughout the state. Eight out of eleven of this group’s highest rate counties are

found in ENC. Nonwhite females have the most complex spatial distribution of

mortality. A wide range of rates are observed throughout the state with the

largest concentration of high rate counties found in ENC. Another large

concentration of higher rate counties can be found along a north- south axis in the

central Piedmont. In some counties, these high rates may be attributed to the

smaller representation of this demographic group and thereby the potential

effects of random variation of rates due to small numbers. Overall, there is very

little geographic effect on nonwhite males with respect to the age- adjusted

general mortality map patterns. White males, and females of both racial groups

appear to shape or delimit the regional distribution of mortality from all causes,

while the relatively greater proportion of nonwhite males in ENC further

accentuates the high general mortality rates found in that region.

When general mortality rates for North Carolina are age- adjusted for the years

2000 to 2004, 35 of ENC’s 41 counties ( 85%) emerge with rates above the state

rate of 896.5, while 26 of RNC’s 59 counties ( 44%) do so. Partitioning the

general mortality map for the total population into four separate maps based on

race and sex reveals how the distribution of rates for the total population is

weighted and shaped by its constituent sub- populations. Later chapters of the

atlas will show the impacts of specific leading causes of death on these sub-populations

and their subsequent contribution to the observed spatial patterns of

general mortality. The age- adjusted general mortality map of NC and ENC

represents the integration of the patterns produced by component leading causes

of death. It is also the culmination of many different mortality processes that

have been operating at their own characteristic scales, tempos, and modes.

The next section discusses how some of these processes have affected the

observed pattern of mortality in ENC over time.

The Temporal Distribution of Age- Adjusted General Mortality Rates

The following two figures ( 2.7 and 2.8) show how mortality has evolved over the

26- year time period from 1979 to 2004. The last five data points ( the years 2000

through 2004) in the ENC 41, RNC 59, and NC time series illustrate the amount

of variation in annual rates that are subsumed into the single age- adjusted five-year

( 2000- 2004) rate seen in the preceding table and maps. A trend line, shown

by dashes in the figure, is fitted for each of the time series and extended to the

year 2010. The trend line is calculated based on information from the entire

series of data points ( i. e., annual rates). Additional information about the trend

line is also provided below the figure. This information includes the percent

change in rates from the initial year to the latest year in the time series. The R2

value is a measure of how well the fitted trend line corresponds to the observed

7

series. The equation of the line, also shown, generates the trend line that allows

the investigator to calculate an expected value for a given point in time. Time

series trend lines can diverge, converge, or run parallel to one another. To make

analysis easier, linearity of the observed data is assumed for the 26- year period

in these time series graphs. However, broader temporal scales of observation

show that mortality from any number of causes is generally non- linear ( see

figure 1.2).

With the simplifying assumption of linearity, it is possible to calculate an

approximate time when two series will have the same rate ( convergence) or

when two series began to separate ( divergence) from each other by setting the

two equations of the line equal to one another. However this should be done

only when R2 values are high ( i. e., approaching 1.00) and when making

projections into the near future or more recent past. Making projections too far

into the future, or past, over- extends the more limiting and linear perspective of

recent mortality trends, resulting in the danger of making spurious conclusions

about long- term and, most, likely non- linear processes. For example, using the

equations- of- the- line in the trends description section found in figure 2.7, the

age- adjusted general mortality rate for ENC 41 and RNC 59 will not be equal or

converge until the year 2154! Clearly, the use of linear trend lines should only be

used short term prognostication. Their utility lies in permitting the researcher to

make summary assessments and examine potentially meaningful trends,

emerging differences or improvements in rate disparities.

Figure 2.7 illustrates four solid trends in regional declines of general mortality.

The goodness- of- fit lines are all above 0.90, indicating that from 1979 to 2004

there are very tight fits to the modeled trend line and that predictions for the next

several years could be reasonably and confidently made. Over this 26- year

study period, age- adjusted mortality rates have declined by 16 and 17 percent for

all four regions. The greatest decreasing coefficient belongs to the US (- 7.75)

and the least to RNC 59 (- 6.34). This translates into an average growing

disparity of age- adjusted general mortality rates of about 1.4 age- adjusted deaths

per 100,000 per year over the course of the last 26 years. Although all trends

are certainly favorable in absolute terms, the ENC 41 trend line stands out well

above the others with the line equations demonstrating persistent relative

disparity in mortality rate trends between this region and RNC 59.

A closer look at the mortality experience of ENC 41 reveals substantial

differences by race and gender. Figure 2.8 shows relatively flat trends ( from

negligible to 7% decrease) for females, with only a slight growth in disparity by

race ( see trend descriptions) over the 26- year period. White males have had the

greatest amount of rate decline with a 28% decrease from 1979 to 2004. The

trend for white males is very consistent over time and can probably be used

reliably as an indicator of mortality scenarios in the near future. Nonwhite males

follow with a more modest 16% decrease and a less confident trend line than

their white counterparts. Although the trend lines for males from either racial

8

group are decreasing, the relative rate disparity between them, as measured by

the equations- of- the- line, increases from 17% in 1979 to 37% in 2004. Since

1979, age- adjusted general mortality has been improving for all males in the

region, while rates have remained relatively flat or changing little for regional

females.

The female pattern suggests that mortality rates may reach an asymptotic level

for a period of time. One reason for this flattening out might be that all benefits

from current health technologies, innovations, knowledge concerning care and

behaviors have been nearly realized for that group over the last two to three

decades. There may also be a certain amount of intra- regional “ balancing out” or

counteracting of high and low rates among counties in different parts of ENC 41.

The trend lines for males are converging on the trend lines for females— with

white males approximating the mortality rates for nonwhite females some time

around the year 2014 or 2015. It will be interesting to see if white males, and

probably much later for nonwhite males, begin to approach a similar mortality

asymptote as has been the case for females. It is likely that the reasons for the

relatively low rates for females have yet to be completely realized for males, but

the rates show that they are still in the process of responding to or adopting

mortality reducing behaviors and technologies. Certainly the pattern between

both female groups indicates that differential mortality remains even when rates

are low and relatively stable. What accounts for this persistent differential forms

the bulk of health disparities literature today.

The above discussion and description of the patterns of crude and age- adjusted

mortality reveals that a geographic disparity exists between the 41 county ( and

29 county) region of NC and the remaining counties of the state, with the east

experiencing significantly higher rates than RNC. Within ENC, age- adjusted

general mortality rates have been declining over the past three decades for the

major demographic groups discussed in this chapter. For females of both racial

groups the decline is relatively minimal, but for males the decline has been more

dramatic, with nonwhite males having the sharpest decrease. Nonwhite males,

although experiencing a larger decrease in general mortality rates have begun

their downward trek at a much higher beginning rate so that the relative rate

disparities between them and the other demographic groups will remain high for

the foreseeable future.

As previously mentioned, density measures tend to mask other types of

information that can be derived from mortality records. The next section focuses

on the concept of mortality burden and its measurement. Understanding the

impact of premature mortality on county populations can assist in discriminating

where disparities of mortality burden are occurring.

9

Mortality Burden

Mortality burden can be viewed at different scales of impact. Within a family

there is the obvious psychological, social, and economic impact of a member’s

death. The decedent’s stage in the life cycle, occupation, resources, and

position in society also has relevance in broader local and community scales of

social relationships. Implicit in any decedent’s age at death is the tangible and

intangible cost, benefit, and potential contribution of that individual’s life to both

family and friends, and to the larger extended communities to which he or she

belonged. Collectively these mortality experiences can be summarized into one

point value: crude mortality rate. This density measure indicates the direct

arithmetic impact or burden actual deaths can have on a population. However, a

population with an older age structure will naturally have more individuals at risk

of dying as they enter the latter stages of their life cycle during a given time

interval and so that population may appear to be experiencing a higher burden of

mortality. Another way to look at mortality burden is to look at how much

potential life is lost, which is a comparison of an observed age- at- death against

some expected or standard age at death. Instead of one point value, two point

values are used, with greater differences between corresponding to increases in

mortality burden.

Age- at- death can be used to measure the amount of life lost prematurely from a

standard number of years of life that an individual can be expected to live in the

population of interest. The typical standard age used in current research is 75

years, which is close to 77.5 years, the life expectancy at birth ( e0) for the US in

2003 ( Arias, 2006) and nearly identical to the mean age of death in North

Carolina. The number of deaths and their ages of occurrence before the age of

75 can be accumulated, age- adjusted, and normalized by the underlying

population. Greater differences mean greater years of life lost, when calculated in

this manner, and indicates a greater level of mortality burden being experienced

prematurely.

The meaning of a premature mortality rate or years of life lost rate as described

above is qualitatively different than for the more commonly used density

measures. To illustrate, the age- adjusted mortality rate in North Carolina for

female breast cancer was 25.6 per 100,000 and for prostate cancer in males it

was 29.1 per 100,000 in 2004. A comparison of these two rates would lead one

to the conclusion that prostate was a slightly bigger killer of men than breast

cancer is in women. However, when the premature mortality rates∗ for these

two causes of death are compared, the number of years of life lost before age 75

is 33.9 years per 10,000 for female breast cancer and 6.4 years for prostat

e

∗ Currently premature mortality is typically measured by the number of years of life lost ( YLL)

before age 75 per 10,000 people. Each death is aggregated into an age category and the total

number of deaths in that category is multiplied by the difference between the age category mean

age at death and age 75. The resulting age category YLLs are then summed, divided by the

population, and then multiplied by 10,000 to make interpretation easier. The YLL- 75 ( premature

mortality) measure can either be crude or age- adjusted.

10

cancer. These values indicate that males tend to die at much later ages from

prostate cancer and not prematurely relative to the age of 75. Females tend to

die from breast cancer at earlier ages, suffering a greater mortality burden than

their male counterparts for a sex- specific disease, with perhaps a greater impact

on families and communities.

The Spatial Distribution of Premature Mortality from All Causes

The national age- adjusted premature mortality rate for the year 2002 is 751 years

of life lost per 10,000 people. The lowest state premature mortality rate in this

same year is found in Vermont at 568, while the worst state rate belongs to

Mississippi at 1088. If the District of Columbia is added as a state it would fall

behind Mississippi ranking a distant 51st with a rate of 1323. Within the state

rankings, North Carolina is 39th with a rate of 833, and with the exception of

Florida and Virginia, has the lowest premature mortality of the remaining

southern states. If the 41 county region of ENC is entered into the state

rankings, it would rank 47th at 959, with Arkansas, Alabama, Louisiana,

Mississippi, and the District of Columbia trailing in the lowest ranks.† The 29-

county region of ENC would rank 48th at 975, ousting Tennessee, which moves

up to 47th. The Piedmont region compares more favorably as a state with a

premature mortality rate of 774 placing it 29th among the states. The Western

NC region has a more intermediate premature mortality rate of 805 and ranks

34th. Figure 2.9 is a map of the United States that shows the age- adjusted

premature mortality rates for the states with North Carolina’s three regions

mapped as ” states”. From this national context we now move to a more specific

in- depth discussion of how premature mortality varies by sub- region and county

within North Carolina.

Figure 2.10 portrays both crude ( fig. 10a) and age- adjusted ( fig 2.10b)

premature mortality rates measured as years of life lost before the age of 75

years ( YLL- 75). The maps in this figure describe the distribution of mortality

burden for counties. Unlike the maps in figure 2.5, age- adjusting the rates ( i. e.,

the expected number of deaths) has very little effect on the map pattern of

premature mortality. The ENC 41- county region stands out distinctly relative to

the other regions of the state with its large number of high premature mortality

counties. Table 2.2 bears this out with the age- adjusted rate for premature

mortality 22% higher than RNC, and when compared to PNC and WNC, the

region is 23% and 17% higher, respectively. Finally, the age- adjusted premature

mortality rate for ENC is 27% higher than the rate for the nation, which for the

year 2002, is 751.0.

When premature mortality is compared on a national and regional level, the

counties of North Carolina and ENC do not fare well. Only 14 NC counties in the

state have premature mortality rates less than the US 2002 rate, with New

† ENC 41 and 29 county regional rates, as well as other NC regional rates, are calculated using

the National Center for Health Statistics’ Compressed Mortality File series data for the year 2002.

11

Hanover, at 705.6 years of life lost per 10,000, being the only county in the east

to do so. Regionally, 36 of the 41 counties in ENC ( 84%) have rates above the

North Carolina rate, while 27 of the 59 counties of the remaining NC counties

( 46%) have rates greater than the state. In terms of population exposed to risk of

dying prematurely at a rate higher than the state, the difference between the two

regions becomes even more dramatic. In ENC, 84% of the region’s population

who are under the age of 75 years live in those counties that have higher rates

than the state, while 27% of RNC’s population under 75 live in counties with a

higher rate than the state. Moving to the individual county comparisons, Wake

County experiences the least years of life lost in the state for the 2000 to 2004

period with a rate of 564.8 years per 10,000, which is 32% lower than the state

rate. Robeson County has the least favorable rate for this study period at

1,234.7 years of life lost, 119% greater than the rate for Wake County.

When the age- adjusted map for premature mortality for all causes is

decomposed into maps focusing on the four demographic groups, differences in

their contributions to the overall rates emerge ( see figure 2.11). The greatest

contribution to the overall rate is made by nonwhite males ( fig. 2.11b). Like age-adjusted

mortality rates, high county rates for this group are a ubiquitous feature

throughout North Carolina, with the exception of a few counties in the western

part of the state. ( For county locations, see the map in appendix A.) Duplin

County, in southern ENC had the highest premature mortality rate in the state at

2,133.4 years of life lost per 10,000 ( see table 2.2). To contrast, white females

( fig. 2.11b) have ubiquitously low county rates with the highest state- wide county

rate found in an ENC county, Northampton, at 803.4. Regionally, the lowest rate

for white females is found in New Hanover County at 412.4, slightly more than

half of the Northampton rate. Overlaying these two contrasting map patterns, are

the rate distributions of white males ( fig. 2.11a) and nonwhite females ( fig.

2.11d). Both of these map patterns are more variegated than the previous two.

The mapped distribution for white males, though heterogeneous, is weighted

more by the higher rate categories concentrated primarily in ENC, but also found

distributed throughout the peripheral non- metropolitan counties of the Piedmont,

and the western counties. The highest rate for white males, 1,563.0 years of life

lost, is found in Robeson County located in southern ENC on the South Carolina

border. Like white males, the distribution of high rate categories for nonwhite

female culminates in the east, while high rate counties are found scattered to the

west of the region. For this group, the highest rate-- 1,517.3 years of life lost-- is

found in Perquimans County. While the highest rates for each of the four

demographic groups are found in ENC, the lowest rates for any of these groups

are located outside of ENC. For white males and females ( fig. 2.11a— b), the

lowest premature mortality rates are found in Wake County at rates of 578.1 and

352.9, respectively. The lowest meaningful rates ( i. e., rates calculated from

deaths numbering 20 and more) for their nonwhite counterparts are found in

McDowell County with nonwhite males at 977.8 years and nonwhite females at

494.0 years in Wilkes County. Both of these counties are found in the western

portion of the state. To conclude, there is a discernable geographic difference in

12

mortality burden between ENC and RNC that is driven by the mortality

experience of white males and nonwhite females

The Temporal Distribution of Premature Mortality from All Causes

Figure 2.12 is a comparison of premature mortality trends among regions from

the years 1979 to 2004 ( 2002 for the US). Premature mortality for all four

regions is declining at approximately the same rates. The relationships among

the trends, in terms of their relative ranking in years of life lost rates, remains

constant throughout the time series study period. ENC consistently experiences

the highest rates of age- adjusted premature mortality but the trend line indicates

an approximate 27% decrease from the beginning of the study period in 1979 to

2004, slightly less than the other regions. All regions show a similar pattern of

decline, including the gentle oscillations of observed values about their

respective trend lines. For the first five or six years, the decline in trends is

steeper than any other interval in the series. Thereafter, the observed regional

rates decline less steeply and fluctuate very little from their respective trend lines.

This suggests that there may be emerging countervailing trends in premature

mortality from specific causes, which either balance each other out or have

become more stable over time.

When ENC’s observed premature general mortality rate series is decomposed

into four separate premature mortality series corresponding to each of the four

demographic sub- groups, several distinct patterns emerge ( figure 2.13). The

greatest decline in premature mortality is experienced by white males at 34%.

Although nonwhite males have the largest negative coefficient (- 30.35), indicating

the steepest rate of decline, they begin the series with an expected or modeled

premature mortality rate ( the intercept) at a level some 72% greater than their

white counterparts. The pattern of decline for this group is very similar to the one

observed for regions and it may be that the nonwhite male experience is what is

driving the patterns seen in the previous figure. The trend line for white males is

decreasing more than twice as fast as the trend for nonwhite females and

overtakes the latter sometime around the year 2002. The observed rate patterns

suggest a convergence— a convergence that has been evident for the 10 years

prior to 2002. For the last two or three years of the series the rates appear to be

diverging but it is probably not indicative of a reversal in trends. The last

demographic group, white females, shows the least amount of decrease ( 17%)

over the 26- year period and like the age- adjusted mortality rates for this group

they appear to approaching a rate asymptote. If present trends continue for the

four demographic groups, the next convergence of premature mortality rates will

occur between white males and females around the year 2030. As with age-adjusted

mortality, decomposing the general premature mortality rate by

demographic groups reveals differences and potential disparities among them.

The shift in the county distributions from crude premature mortality rates to age-adjusted

premature mortality rates is minimal when compared to west- to- east

13

shift in distributions of the density measures. One reason for this difference in

pattern shifting is that ages at death close to 75 years have a small negative

impact on the premature mortality outcome measure and a zero impact when

deaths occur after that age. Larger numbers of deaths occurring at ages several

years prior to 75 indicate a population experiencing a greater share of mortality

burden as an outcome. For example, the accumulation of years of life lost due to

high infant mortality rates, and earlier ages at death from cardiovascular

diseases and cancer can reflect inherent problems with access to appropriate

healthcare. ( Density measures essentially treat all deaths as equal in impact and

cannot be used to measure the depth of mortality burden.) Regionally, this

suggests that although the western region of the state possesses populations

with higher relative proportions of elderly, their respective mortality burdens are

not greater than expected. This contrasts to what the measure portrays for the

eastern 41 counties of the state— a region that not only has a high proportion of

elderly population with its attendant mortality but it is also a region that has a

disproportionate number of its population dying prematurely.

From Empiricism to Explanation: General Mortality Disparities

The numerical evidence tells us that mortality is not experienced equally between

ENC and the 59 remaining counties of North Carolina ( RNC). From 2000 to

2004, 110,390 deaths occurred in ENC and 249,278 deaths occurred in RNC.

The latter region’s population is larger with a 5- year population- at- risk of

29,349,691 individuals compared to ENC’s 12,192,418. Proportionally, the

expected number of deaths for ENC numbers would be 103,555. Subtracting the

proportionalized mortality from the observed value of 110,390 yields an excess of

6,835 deaths ( 6.6% more) carried by ENC-- a crude measure of a geographic

disparity for general mortality between the two regions. However, this does not

account for the probable regional differences in age structure. ( Recall that age is

the greatest risk factor for any individual dying during a specified time interval.) If

age structure is controlled for the two regions, the difference in the number of

deaths between the two regions grows to 12,924 ( 12.2% more), nearly twice the

observed value and further exacerbating the apparent geographic disparity

between the two regions. ENC experiences a greater burden of mortality-- almost

2,600 more deaths per year than would be expected given its population size and

its age- structure.

Characteristics other than population size and age may affect the observed and

adjusted mortality disparity between the two regions. We can hypothesize ( or

speculate) that there may be other factors or covariates at work with mortality

rates that are also geographically distributed. For illustrative purposes,

explanatory variables might include underlying racial and ethnic diversity,

poverty, and rurality. The rationale or assumption for the choices of these

variables is that income distribution ( related to racial/ ethnic diversity) and

measurable financial and physical ( distance) access to health care have some

discernable effect or relationship to mortality. However, one can further

14

speculate that these covariates are associated with many other measurable

variables such as educational attainment, occupation and associated social

relations ( including peer pressure), risky or health promoting behaviors, the value

and awareness of health as a personal and social good, and so on. The first

three covariates introduced can be thought of as surrogate measures— they are

meant to capture and simplify a complex series of relationships among a

spectrum of factors that are operating at different scales. Surrogate measures

are used to assist the students of public health and mortality in focusing on those

relationships with the most explanatory power and in the construction of the most

parsimonious ecological model of mortality.

Racial/ ethnic diversity, poverty, and rurality can be measured like age- adjusted

mortality at the county and county- based regional level. For example, ENC’s

county populations are more racially and ethnically diverse when compared to

the counties of the rest of the state ( RNC). According to the US Census atlas,

Mapping Census 2000: the Geography of U. S. Diversity ( see page 22 in Brewer

& Suchan, 2001), 26 of ENC’s 41 counties have diversity index values at or

above the US value of 0.49, with a regional index of 0.52. ( The diversity index is

a measure of the probability that any two random people chosen from a county’s

population will be of a different race.) Only 13 of RNC’s 59 counties are more

diverse than the US, with a regional index of 0.39. From the US Census year

2000 ( 1999) data, a little more than 16.0% of ENC’s population is below the

poverty line for that census year, which is almost 50% greater than the 10.7%

reported for RNC’s population. Rurality is another attribute that distinguishes

ENC from RNC. Slightly less than 49% of ENC’s population is classified as rural

by the US Census Bureau, which contrasts to slightly more than 36% of RNC’s

population being rural. The next step is to determine what influence or how well

these proposed variables explain the county distribution of general mortality.

To assess the relationships and associations between any two of these

variables, we employ a methodology similar to that used in studying the temporal

trends of general mortality. The following discussion will describe the linear

relationships between the dependent ( age- adjusted mortality from all deaths)

variable and each of the independent variables: the diversity index, poverty, and

the proportion of rural population.. The interrelationships among the independent

variables will also be examined. Exploring the strengths and weaknesses of

association among variables is fundamental to hypotheses testing and the

development of explanatory models.

The correlation coefficient between mortality and the diversity index is 0.61. The

adjusted R2 value is 0.365, which translates into more than 36% of the variation

in mortality is explained by the variation found in the diversity index alone. The

correlation coefficient between mortality and poverty is 0.63 and has an R2 of

0.385. More than 38% of the variation in mortality is explained by poverty

alone— about 2% more than the diversity index. The least amount of explanation

( 0.0%) can be attributed to the measure of rurality. The correlation coefficient is

15

only 0.046, which produces the negligible R2 of 0.002. These simple analyses

show that ethnic/ racial diversity and poverty have a substantial and direct effect

on mortality. The next step would be to determine if there was any direct

relationship between diversity and poverty and whether at some indirect level,

rurality having some effect. A relationship among these variables would indicate

that their effects on mortality were not independent.

To get a handle on the amount of interaction between diversity and poverty

( collinearity) we can apply the same method used in the preceding example

Lower R2 values will suggest smaller amounts of collinearity, less association,

and more independence among the independent variables. For rurality and

poverty the R is 0.344 and the R2 is 0.118, which means there is a small level of

rurality and poverty associated with each other at the county level. Next, the

diversity index and poverty measure yield an R of 0.531, with an R2 of 0.27,

which means that racial/ ethnic diversity is more related to poverty than the

degree of county rurality. How related is a county’s racial and ethnic diversity to

its level of rurality? The R for this comparison is 0.186 with an R2 value of just

0.025. Recall that poverty, in this simple example, offers the greatest

explanation of mortality. We now know that while rurality has some effect on

poverty, diversity has an even greater effect on this variable. In more elaborate

models of explanation, the rurality measure ( as devised here) would not

contribute much to explanation and could probably be excluded.

The foregoing discussion is meant as a simple example of how empirical

descriptions of mortality can provide a basis for research questions and the

building statistically oriented explanatory models. However, numerical and

graphical descriptions of mortality can also stimulate further research or thinking

in non- statistical ways. For example, thoughtfully publicized rate increases in

mortality due to automobile accidents or diabetes will raise the awareness of

policy makers and citizenry and help promote interventions, funding, and other

ameliorative measures. Empirical description and explanatory models each have

their own place and can be useful adjuncts to each other in the presentation and

understanding of public health and demographic problems.

Conclusion

Geographically, different ways of measuring and describing general mortality

demonstrates that the eastern 41 counties of North Carolina experience both

higher comparative levels of death from all causes and a disproportionate share

of mortality burden in regional and national contexts. When general mortality

rates for ENC 41 are decomposed into four major demographic groups, rate

differentials ( or disparities) emerge. The distributions of age- adjusted general

mortality rates also have unique characteristics for each of the race- sex sub-populations.

Time series depictions ( 1979 to 2004) for both regions and race-sex

sub- populations also show that there has been progress, but relatively large

gaps or “ disparities” continue to exist. For sub- populations, males of both racial

16

groups have greater relative declines in their rates compared to their female

counterparts. All measures, spatially and temporally, indicate that although

absolute differences in general mortality has been declining among regions and

sub- populations, relative disparities will continue for some time to come. A

description and examination of general mortality, which reveals the great

disparities observed in our region of interest, naturally leads to further questions

about how and why such disparities exist. With this in mind, we enter into the

realm of explanation and can begin to consider the relationships and

associations of covariates and mortality. Explanatory models are valuable aids

for determining where changes can be effected and where healthcare resources

can best be allocated.

General mortality encompasses a myriad of causes of death, all which have been

classified and coded. In this regional atlas of mortality, the subsequent chapters

will address the ten leading causes of death as shown in figure 2.1. These ten

leading causes of death account for more than 80% of deaths occurring in ENC

41 during the years 2000 through 2004. It is our hope that a consideration of

each of these will lead to an increased understanding in the exceptional

character of the region’s mortality experience.

References

American Heart Association. ( 2005). Heart disease and stroke statistics-- 2005

update. Dallas, Texas: American Heart Association.

Anderson, R. N., & Rosenberg, H. M. ( 1998). Age standardization of death rates:

Implementation of the year 2000 standard. National Vital Statistics Reports,

47( 3)

Arias, E. ( 2006). United states life tables, 2003. National Vital Statistics Reports,

54( 14)

Brewer, C. A., & Suchan, T. A. ( 2001). Mapping census 2000: The geography of

U. S. diversity. Washington, D. C.: U. S. Government Printing Office.

17

Buescher, P. A. ( 1998). Age- adjusted death rates ( 13th ed.). Raleigh, North

Carolina: North Carolina Center for Health Statistics.

U. S. Department of Health and Human Services, Centers for Disease Control

and Prevention & National Center for Health Statistics. ( 2006). International

classification of diseases, tenth revision ( ICD- 10). Retrieved 10/ 20, 2006,

from http:// www. cdc. gov/ nchs/ about/ major/ dvs/ icd10des. htm

World Health Organization. ( 2006). International statistical classification of

diseases and disorders and related problems 10th revision for 2006.

Retrieved 10/ 20, 2006, from

http:// www. who. int/ classifications/ apps/ icd/ icd10online/

World Health Organization. ( 2004). International statistical classification of

diseases and related health problems ( 10th revision, 2nd ed.). Geneva:

World Health Organization.

Total Cardiovascular Disease 37.0%

Malignant Neoplasms 22.3%

COPD/ CLRD1 4.9%

Diabetes Mellitus 3.5%

UMVI2 2.8%

AOUIAD3 2.5%

Pneu/ Infl4 2.3%


NNN5 2.0%

Alzheimer’s 1.7%

Septicemia 1.7%

All Other 19.3%

1Chronic Obstructive Pulmonary Diseases and Allied Conditions/ Chronic Lower Respiratory Disease

2Unintentional Motor Vehicle Injuries

3All Other Unintentional Injuries and Adverse Effects

4Pneumonia and Influenza

5Nephritis, Nephrotic Syndrome, and Nephrosis

Figure 2.1: General Mortality in Eastern North Carolina 2000 to 2004

Percent Contributions from the

Top Ten Leading Causes of Death to the

5- year Total Number of Deaths: 110,390

ECU, Center for Health Services Research and Development, 2007

0.0 to 856.8

856.8 to 974.2

974.2 to 1064.9

1064.9 to 1188.7

1188.6 to 2018.3

Figure 2.2

US Crude General Mortality Rates1 2001 to 2003

Per 100,000 Population

ENC 41 Counties

ECU, Center for Health Services Research and Development, 2007

1Data from Compressed Mortality Files 1999 to 2003

0.03 to 0.15

0.15 to 0.18

0.18 to 0.20

0.20 to 0.23

0.23 to 0.42

Figure 2.3

US County Population Proportions 60 Years and Older1 2000

County Proportion

GTE 60 Years

ENC 41 Counties

ECU, Center for Health Services Research and Development, 2007

1Data from US Census 2000

0.0 to 843.0

843.0 to 896.8

896.8 to 944.8

944.8 to 1004.3

1004.3 to 2018.3

Per 100,000 Population

Figure 2.4

US Age- Adjusted General Mortality Rates1 2001 to 2003

ENC 41 Counties

ECU, Center for Health Services Research and Development, 2007

1Data from Compressed Mortality Files 1999 to 2003

and the 2000 Standard Million Population for the US

Per 100,000 Population

a. Crude

b. Age- Adjusted1

Data Source: Odum Institute, UNC— Chapel Hill

Per 100,000 Population

503.6 to 856.8

856.8 to 974.2

974.2 to 1064.9

1064.9 to 1188.6

1188.6 to 1480.9

752.3 to 843.1

843.1 to 896.8

896.8 to 944.8

944.8 to 1004.3

1004.3 to 1133.3

ECU, Center for Health Services Research and Development, 2007

1 Five- Year Average, Age- Adjusted Rates Standardized to US 2000 SM

Mortality Rates from All Causes of Death:

North Carolina and Eastern North Carolina Total Population, 2000- 2004

Figure 2.5

ECU, Center for Health Services Research and Development, 2007

Age- Adjusted1 Mortality Rates from All Causes of Death:

North Carolina and Eastern North Carolina Race- Sex Specific, 2000- 2004

a. White Males b. White Females

c. Non- White d. Non- White

Per 100,000 Population

1 Five- Year Average, Age- Adjusted Rates Standardized to US 2000 SM

2 in Mitchell County, there were no non- white female deaths

Data Source: NC State Center for Health Statistics

Figure 2.6

0.02 to 843.1

843.1 to 896.8

896.8 to 944.8

944.8 to 1004.3

1004.3 to 2107.8 ( NWM)

Males Females

ECU, Center for Health Services Research and Development, 2007

1 Age- Adjusted Rates Standardized to US 2000 SM

Figure 2.7

North Carolina: Comparisons among Regions2, 1979 to 2004

Trend Descriptions

Age- Adjusted1 Mortality Rate Trends from All Causes of Death

ENC 41

16% decrease

R2 = 0.92

Y = - 7.04x + 1146

RNC 59

16% decrease

R2 = 0.92

Y = - 6.34x + 1023

NC

16% decrease

R2 = 0.93

Y = - 6.57x + 1059

US

17% decrease

R2 = 0.96

Y = - 7.75x + 1034

800

850

900

950

1000

1050

1100

1150

79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Age- adjusted mortality rate per 100,000 population

ENC 41

RNC 59

NC

US

Years

2 NC, ENC 41, and RNC 59 1979- 2004 mortality data from NC SCHS & US 1979- 2002 mortality data from NCHS’s Compressed Mortality File

600

700

800

900

1000

1100

1200

1300

1400

1500

1600

1700

1800

79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Age- adjusted mortality rate per 100,000 population

NWM

WM

NWF

WF

ECU, Center for Health Services Research and Development, 2007

1 Age- Adjusted Rates Standardized to US 2000 SM

Trend Descriptions

WM

28% decrease

R2 = 0.97

y = - 16.35x + 1497

WF

7% decrease

R2 = 0.45

y = - 2.24x + 813

NWM

16% decrease

R2 = 0.53

y = - 10.80x + 1751

NWF

------

R2 = 0.09

Y = - 1.16x + 943

Years

Figure 2.8

Eastern North Carolina: Comparisons among Race- Sex Groups2, 1979 to 2004

Age- Adjusted1 Mortality Rate Trends from All Causes of Death

2 ENC 41mortality data from NC SCHS

567.6 to 630.9

630.9 to 707.3

707.3 to 788.1

788.1 to 911.7

911.7 to 1088.0

Age- Adjusted1 Years of

Potential Life Lost before Age 752

Per 10,000 Population

Natural Breaks

Regional Variation of

Years of Potential Life

Lost in North Carolina

ECU, Center for Health Services Research and Development, 2007

Figure 2.9 Premature Mortality in the United States 2002 with Selected Rankings

Not Shown:

AK: 36th

DC: 52nd

HI: 5th

VT: 1st

NH: 2nd

MN: 3rd

IA: 4th

NC: 37th

AR: 48th

AL: 49th

LA: 50th

MS: 51st

ENC 41: 47th

PNC: 29th

WNC: 34th

VA: 22nd

SC: 45th

US ( 751.0)

2 ENC 41, PNC, WNC, and US 1979- 2002 mortality data from NCHS’s Compressed Mortality File

1 Age- Adjusted Rates Standardized to US 2000 SM

DC ( 1323.0)

Years of Life Lost

Per 10,000 Population

a. Crude

b. Age- Adjusted1

Data Source: Odum Institute, UNC— Chapel Hill

541.0 to 806.7

806.7 to 878.2

878.2 to 958.4

958.4 to 1073.2

1073.2 to 1273.6

Years of Life Lost

Per 10,000 Population

564.8 to 775.2

775.2 to 835.2

835.2 to 924.5

924.5 to 1036.6

1036.6 to 1234.7

ECU, Center for Health Services Research and Development, 2007

1 Five- Year Average, Age- Adjusted Rates Standardized to US 2000 SM

Premature Mortality Rates from All Causes of Death:

North Carolina and Eastern North Carolina Total Population, 2000- 2004

Figure 2.10

ECU, Center for Health Services Research and Development, 2007

Age- Adjusted1 Premature Mortality Rates from All Causes of Death:

North Carolina and Eastern North Carolina Race- Sex Specific, 2000- 2004

Years of Life Lost

Per 10,000 Population

1 Five- Year Average, Age- Adjusted Rates Standardized to US 2000 SM

2 in Mitchell County, there were no non- white female deaths

Data Source: NC State Center for Health Statistics

Figure 2.11

0.02 to 775.2

775.2 to 835.2

835.2 to 924.5

924.5 to 1036.6

1036.6 to 2174.3 ( NWM)

a. White Males b. White Females

c. Non- White d. Non- White

Males Females

ECU, Center for Health Services Research and Development, 2007

600

700

800

900

1000

1100

1200

1300

1400

79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Age- adjusted years of life lost per 10,000 population < 75 years of age

ENC 41

RNC 59

NC

US

Trend Descriptions

ENC 41

27% decrease

R2 = 0.94

Y = - 12.97x + 1265

RNC 59

30% decrease

R2 = 0.94

Y = - 12.43x + 1083

NC

29% decrease

R2 = 0.94

Y = - 12.72x + 1139

US

30% decrease

R2 = 0.96

Y = - 13.01x + 1053

Years

1 Age- Adjusted Rates Standardized to US 2000 SM

Figure 2.12

North Carolina: Comparisons among Regions2, 1979 to 2004

Age- Adjusted1 Mortality Rate Trends from All Causes of Death

2 NC, ENC 41, and RNC 59 1979- 2004 mortality data from NC SCHS & US 1979- 2002 mortality data from NCHS’s Compressed Mortality File

ECU, Center for Health Services Research and Development, 2007

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Age- adjusted years of life lost per 10,000 population < 75 years of age

NWM

WM

NWF

WF

WM

34% decrease

R2 = 0.91

y = - 18.79x + 1424

WF

17% decrease

R2 = 0.75

y = - 4.30x + 670

NWM

32% decrease

R2 = 0.81

y = - 30.35x + 2445

NWF

18% decrease

R2 = 0.65

Y = - 8.13x + 1168

Trend Descriptions

Years

1 Age- Adjusted Rates Standardized to US 2000 SM

Figure 2.13

Eastern North Carolina: Comparisons among Race- Sex Groups2, 1979 to 2004

Age- Adjusted1 Mortality Rate Trends from All Causes of Death

2 ENC 41mortality data from NC SCHS

ECU, Center for Health Services Research and Development, 2007

Table 2.1 Mortality from All Causes:

Eastern North Carolina, 2000- 2004

County Deaths Crude Adjusted Deaths Rate Deaths Rate Deaths Rate Deaths Rate

Beaufort 2,692 1185.2 988.2 925 1159.1 975 770.0 382 1418.9 410 929.5

Bertie 1,292 1313.3 1111.2 249 1170.4 300 907.4 373 1541.3 370 899.4

Bladen 1,983 1216.3 1101.7 586 1238.8 614 875.0 390 1551.1 393 971.0

Brunswick 3,806 962.7 842.8 1,741 927.3 1,533 717.3 278 1289.0 254 789.0

Camden 323 856.8 844.1 131 966.5 126 763.4 39 1122.7 27 589.5

Carteret 3,452 1142.1 932.7 1,667 1108.4 1,575 785.2 102 1251.7 108 812.7

Chowan 925 1275.0 949.1 296 1058.6 308 719.4 169 1620.3 152 785.2

Columbus 3,169 1157.3 1069.9 1,063 1316.3 1,066 824.8 531 1488.5 509 927.7

Craven 4,275 931.7 938.0 1,534 975.0 1,578 769.0 567 1448.4 596 974.8

Cumberland 10,093 663.7 1004.3 3,140 1175.3 3,080 832.5 1,965 1274.8 1,908 879.0

Currituck 833 835.9 911.8 367 964.8 365 804.8 48 1453.3 53 985.3

Dare 1,317 822.6 859.7 683 964.3 587 733.9 23 1141.7 24 1045.9

Duplin 2,525 999.3 983.9 807 1095.1 844 768.3 440 1534.7 434 888.7

Edgecombe 3,045 1107.6 1125.6 707 1336.7 778 881.2 772 1563.1 788 924.7

Gates 614 1149.1 1061.1 185 1251.1 190 906.9 121 1331.2 118 890.2

Greene 897 918.4 941.9 290 1256.9 277 722.9 169 1334.1 161 737.8

Halifax 3,310 1167.0 1023.9 785 1184.1 891 769.2 830 1413.4 804 868.8

Harnett 3,807 787.8 944.8 1,448 1136.9 1,477 760.6 459 1375.2 423 815.3

Hertford 1,499 1330.6 1124.5 317 1404.6 336 828.0 432 1621.4 414 903.1

Hoke 1,256 691.0 1015.5 330 1144.7 282 765.5 323 1323.8 321 922.1

Hyde 340 1197.6 905.1 109 1171.5 123 771.9 58 1233.8 50 679.4

Johnston 4,985 752.5 917.7 2,117 1128.0 2,014 739.2 462 1344.1 392 759.1

Jones 587 1138.6 980.2 189 1198.3 171 738.8 98 1260.5 129 928.2

Lenoir 3,546 1201.9 1078.8 1,058 1275.8 1,084 834.1 702 1637.1 702 910.5

Martin 1,605 1275.2 1091.0 444 1388.0 498 881.0 299 1324.0 364 953.6

Nash 4,449 998.1 1003.6 1,414 1122.0 1,591 814.7 721 1516.9 723 900.4

New Hanover 7,084 850.9 832.7 2,732 917.5 2,907 672.9 646 1310.6 799 954.8

Northampton 1,432 1309.9 1007.0 297 1040.5 317 759.2 448 1653.7 370 779.8

Onslow 3,879 518.4 956.7 1,648 1171.1 1,442 789.0 381 1193.9 408 852.2

Pamlico 723 1124.7 815.1 263 931.9 280 678.8 89 1210.4 91 758.0

Pasquotank 1,820 1020.5 925.0 538 1072.7 615 735.0 322 1308.6 345 843.3

Pender 1,877 873.8 830.1 730 945.2 632 687.2 251 1214.7 264 753.9

Perquimans 743 1285.3 929.0 264 998.7 250 731.6 112 1543.4 117 893.9

Pitt 5,269 768.2 955.8 1,510 1058.1 1,700 740.4 1,005 1458.0 1,054 924.6

Robeson 5,904 943.9 1133.3 1,292 1326.2 1,258 876.7 1,674 1464.5 1,680 977.7

Sampson 3,158 1028.3 1013.0 1,073 1253.1 1,006 764.4 549 1410.8 530 895.4

Scotland 1,800 1000.8 1063.6 478 1237.1 551 859.8 384 1622.9 387 903.3

Tyrrell 221 1064.9 864.3 72 1016.5 72 742.0 45 1184.7 32 667.6

Washington 829 1226.9 1027.8 217 1074.5 249 794.5 174 1364.6 189 1000.6

Wayne 5,307 936.0 1046.6 1,686 1199.2 1,742 850.2 898 1394.9 981 974.6

Wilson 3,719 992.3 976.7 1,120 1114.1 1,239 769.2 674 1394.8 686 879.9

ENC 29 61,468 967.7 983.2 19,772 1109.8 20,503 783.8 10,493 1439.7 10,700 892.4

ENC 41 110,390 905.4 972.2 36,502 1100.2 36,923 773.5 18,405 1406.0 18,560 891.6

RNC 59 249,278 849.3 866.1 99,131 1011.2 105,502 703.2 22,467 1284.6 22,178 831.6

PNC 190,449 796.7 871.7 71,626 1009.0 77,309 706.3 20,889 1287.9 20,625 833.9

WNC 58,829 1080.4 854.1 27,505 1032.0 28,193 700.1 1,578 1252.7 1,553 810.3

NC 359,668 865.8 896.5 135,633 1034.0 142,425 720.4 40,872 1336.2 40,738 857.3

US, 2002 2,443,030 847.2 845.5 1,024,966 993.1 1,077,337 701.5 174,016 1118.2 166,711 773.1

White Males White Females

5- Year Race- Sex Specific Age- Adjusted Death Rates

Rates

5- Year Totals

Non- White Males Non- White Females

Center for Health Services Research and Development

East Carolina University

Source NC Data: Odum Institute-- UNC, Chapel Hill

US Data: NCHS

ECU, Center for Health Services Research and Development, 2007

Table 2.2

Premature Mortality from All Causes:

Years of Life Lost before Age 75 in Eastern North Carolina, 2000- 2004

County Deaths Crude Adjusted Deaths Rate Deaths Rate Deaths Rate Deaths Rate

Beaufort 1,265 1119.7 1048.2 526 1181.0 294 595.3 260 1966.2 185 1102.0

Bertie 612 1219.9 1172.9 127 1142.3 94 639.8 242 1931.5 149 821.9

Bladen 996 1205.6 1143.7 334 1239.2 207 642.4 269 2008.4 186 996.9

Brunswick 2,002 930.0 859.5 1,052 1082.3 638 548.0 183 1580.4 129 780.8

Camden 163 801.8 755.6 75 826.5 46 529.4 26 1758.3 16 541.0

Carteret 1,594 1003.2 931.1 880 1184.7 596 673.6 68 1264.7 50 778.6

Chowan 387 977.5 924.5 137 906.1 89 527.9 102 1903.1 59 760.0

Columbus 1,655 1219.4 1166.5 648 1360.1 390 681.4 371 1997.8 246 1020.6

Craven 1,992 843.9 833.5 818 893.9 535 510.8 352 1457.3 287 961.5

Cumberland 5,971 881.3 935.5 2,005 989.4 1,383 627.3 1,471 1380.1 1,112 857.2

Currituck 425 843.6 816.5 217 924.8 165 660.7 28 1397.2 15 654.7

Dare 677 889.2 858.1 409 1098.2 236 588.6 19 1679.8 13 702.8

Duplin 1,236 1024.0 1004.9 469 1043.4 278 576.4 301 2133.6 188 912.7

Edgecombe 1,583 1165.4 1141.8 414 1148.4 263 628.2 521 1866.6 385 893.0

Gates 257 958.4 936.9 91 1000.1 59 745.4 62 1433.7 45 662.7

Greene 458 1021.5 1006.2 170 1131.5 96 603.0 112 1331.4 80 1081.8

Halifax 1,618 1181.7 1155.6 401 1226.5 278 601.8 574 1759.3 365 980.8

Harnett 1,978 844.2 878.7 874 1025.7 560 581.9 331 1464.1 213 821.1

Hertford 691 1241.5 1203.7 153 1350.5 92 535.7 267 1865.1 179 932.6

Hoke 774 1004.0 1069.0 229 1174.3 122 668.9 242 1487.8 181 929.0

Hyde 145 931.0 884.6 57 1015.5 38 614.4 27 1109.8 23 949.6

Johnston 2,620 830.4 833.7 1,298 1003.7 781 525.0 337 1609.1 204 813.7

Jones 276 1012.2 964.2 114 1269.2 53 494.5 58 1330.3 51 977.3

Lenoir 1,779 1273.6 1225.9 595 1307.7 385 758.5 477 2047.6 322 1149.2

Martin 775 1203.4 1143.9 239 1243.3 156 650.7 204 1777.9 176 1120.2

Nash 2,113 970.7 949.9 774 1028.4 507 570.9 484 1572.3 348 961.7

New Hanover 3,169 733.7 705.6 1,423 765.3 947 412.4 432 1614.4 367 938.4

Northampton 679 1266.4 1209.6 153 1138.9 107 803.4 259 1851.0 160 948.7

Onslow 2,315 719.4 816.9 1,091 898.0 693 629.1 283 1156.1 248 801.8

Pamlico 321 887.0 826.6 145 924.9 86 503.4 47 1155.2 43 1165.9

Pasquotank 784 828.0 821.2 251 822.3 184 508.3 189 1289.6 160 862.8

Pender 968 901.3 856.0 429 991.9 259 567.6 161 1557.2 119 709.1

Perquimans 333 1110.1 1055.5 135 1052.2 86 783.7 69 1530.9 43 1517.3

Pitt 2,671 857.1 913.4 844 843.9 586 541.2 711 1692.7 530 1008.8

Robeson 3,290 1219.0 1234.7 820 1563.0 444 728.3 1,184 1649.3 842 962.8

Sampson 1,553 1096.3 1081.3 616 1243.2 342 665.5 347 1725.6 248 1017.3

Scotland 944 1087.9 1077.7 281 1119.3 207 687.2 257 1715.1 199 964.3

Tyrrell 101 1041.3 1007.1 36 909.8 26 765.7 25 1564.1 14 1100.5

Washington 376 1079.7 1036.6 114 1137.4 67 480.6 113 1592.5 82 1072.1

Wayne 2,752 1010.0 1000.0 1,001 1023.5 617 628.7 636 1628.7 498 1061.7

Wilson 1,776 1008.9 983.6 608 1019.7 394 549.3 446 1716.2 328 932.0

ENC 29 30,154 976.4 964.5 11,044 1002.0 7,106 598.1 6,962 1648.4 5,042 965.0

ENC 41 56,074 957.8 951.6 21,053 1014.9 13,386 584.2 12,547 1608.2 9,088 936.7

RNC 59 113,504 794.0 780.8 52,209 885.4 34,378 513.2 15,669 1420.1 11,248 827.1

PNC 88,764 777.5 773.9 38,159 848.3 25,450 499.6 14,602 1420.4 10,553 825.5

WNC 24,740 868.2 814.9 14,050 1026.1 8,928 565.6 1,067 1419.0 695 863.0

NC 169,578 842.2 830.6 73,262 918.9 47,764 531.1 28,216 1495.5 20,336 870.7

US, 2002 1,054,300 755.3 751.0 509,168 878.6 337,327 511.3 120,404 1276.0 87,401 761.1

White Males White Females

5- Year Race- Sex Specific Age- Adjusted Death Rates

Rates

5- Year Totals

Non- White Males Non- White Females

Center for Health Services Research and Development

East Carolina University

Source NC Data: Odum Institute-- UNC, Chapel Hill

US Data: NCHS

Pitt

Wake

Hyde

Duplin

Bladen

Bertie

Pender

Wilkes

Moore

Onslow

Union

Surry

Ashe

Beaufort

Craven

Halifax

Robeson

Nash

Sampson

Iredell

Columbus

Swain

Carteret

Burke

Brunswick

Johnston

Anson

Guilford

Randolph

Harnett Wayne

Jones

Chatham

Macon

Rowan

Hoke

Martin

Tyrrell

Dare

Lee

Stokes

Stanly Lenoir

Franklin

Buncombe

Warren

Granville

Davidson

Jackson

Haywood

Gates

Person

Caldwell

Wilson

Forsyth

Polk

Caswell

Cumberland

Orange

Pamlico

Rutherford

Madison

Yadkin

Gaston

Clay

Cherokee

Richmond

Cleveland

Catawba

Davie

Rockingham

McDowell

Hertford

Alamance

Vance

Avery

Yancey

Mecklenburg

Northampton

Edgecombe

Montgomery

Durham

Graham

Scotland

Greene

Watauga

Henderson

Washington

Transylvania

Mitchell

Alleghany

Currituck

Camden

Chowan

Perquimans

Pasquotank

New Hanover

Lincoln

Cabarrus

Alexander

Western ( WNC)

Piedmont ( PNC)

Remaining 59- County Region ( RNC 59)

Eastern North Carolina 29- County Sub- region ( ENC 29)

Eastern North Carolina 12- County Sub- region

Eastern North Carolina 41- County Region ( ENC 41)

North Carolina County and Regional Locations

Center for Health Services Research and Development

East Carolina University

Greenville, NC

ECU, Center for Health Services Research and Development, 2007

Appendix A

ECU, Center for Health Services Research and Development, 2007

CARDIOVASCULAR DISEASE MORTALITY

The biggest cause of death in both the United States and North Carolina

continues to be from diseases of the circulatory system, commonly referred to

collectively as cardiovascular disease. Cardiovascular disease ( CVD) includes

high blood pressure ( hypertension), coronary heart disease, congestive heart

failure, atherosclerosis, and stroke, conditions which often occur in combination.

An estimate for the year 2004 indicates that 79 million adult Americans, about 1

of every 3, have one or more types of CVD and mortality from CVD comprises a

little more than 36% of the 2.4 million deaths that occurred in the United States

( Writing Group Members et al., 2006). In 2004, CVD in North Carolina accounts

for almost 34% of the 72,000 resident deaths that year and in Eastern North

Carolina more than 35% of its 22,000 deaths have been attributable to CVD.

The impact and burden of CVD is so great that if all its forms were to be

eliminated, life expectancy in the United States would rise by almost 7 years. For

Americans born today, there is nearly a 50- 50 chance that their eventual death

will be due to CVD ( Anderson, 1999).

In the present chapter, CVD mortality includes deaths due to heart disease ( HD),

coronary heart disease ( CHD), and stroke, in addition to several other less

prominent causes of the circulatory system. 1 The largest CVD mortality

component is heart disease, which includes rheumatic heart disease, irregular

heart rhythms, and diseases of the linings, valves, and vessels of the heart. The

latter- most group generally pertains to blockages and constriction of the vessels

that supply the heart and can lead to diseases like infarction and ischemia.

Mortality from this group is a significant part of HD mortality and is considered

separately as CHD. Stroke mortality is a distinct category within CVD that

includes intracranial blockages ( resulting in infarctions) and hemorrhages, and

other cerebrovascular diseases. Figure 3.1 summarizes the relationships of the

TCVD mortality categories for the 41 counties of ENC during the period 2000 to

2004. For this 5- year period, heart disease and stroke comprise nearly 92% of

all mortality attributed to TCVD, while CHD alone contributes slightly more than

half of all CVD deaths. The less prominent CVD mortality category ( All Other) is

not considered in this chapter. A complete listing of ICD10 codes organized by

the categories used here can be found in the appendix for this section.

1 ICD9 Codes 390- 459; ICD10 Codes I00- I99

Cardiovascular Disease Mortality 1

ECU, Center for Health Services Research and Development, 2007

CVD mortality and its three major component diseases discussed in this chapter

can be accessed below.

CARDIOVASCULAR DISEASE MORTALITY

Spatial Distribution of Cardiovascular Disease Mortality

Temporal Distribution of Cardiovascular Disease Mortality

HEART DISEASE MORTALITY

Spatial Distribution of Heart Disease Mortality

Temporal Distribution of Heart Disease Mortality

CORONARY HEART DISEASE MORTALITY

Progress towards Coronary Heart Disease Mortality Reduction

Spatial Distribution of Coronary Heart Disease Mortality

Temporal Distribution of Coronary Heart Disease Mortality

STROKE MORTALITY

Progress towards Stroke Mortality Reduction

Spatial Distribution of Stroke Mortality

Temporal Distribution of Stroke Mortality

SUMMARY

References

As can be seen from the chart Six Leading Causes of Mortality in the US 1900 to

2001 ( figure 1.2), heart disease has emerged as the nation’s leading cause of

death in the 1920s and continues to be the leading cause into the early 21st

century. The chart also shows how the decline of infectious and communicable

diseases in the first several decades of the twentieth century paved the way for

this emergence. If both stroke mortality and HD mortality rates depicted in figure

1.2 were combined, then the combined rate would account for the largest share

of general mortality since the turn of the 20th century ( with the exception of the

influenza pandemic of 1918). The diminishing effect of infectious and

communicable diseases on the mortality experience of the first half of the 20th

century in the United States has given way to the rising prominence of death

from heart disease in the latter half.

The Epidemiologic Transition ( Omran, 1977) discussed in chapter one

( introduction) describes the secular decline of infectious/ communicable diseases

and the concomitant rise of chronic disease mortality and its demographic

consequences. The increase seen in HD mortality is more than likely the result

of the rise in the proportion of people surviving the onslaughts of communicable

diseases. Communicable diseases have their impact on both ends of the age

Cardiovascular Disease Mortality 2

ECU, Center for Health Services Research and Development, 2007

spectrum. Over time, survivors of childhood diseases swell older age groups

which have increasing susceptibility to HD and other cardiovascular problems.

This pattern is repeated wherever infectious/ communicable diseases are brought

under control with various public health measures and interventions. However,

the demographic responses and outcomes can vary geographically and

culturally. It is interesting to note that the states with the lowest rates, Minnesota,

Alaska, and New Mexico are quite different in regard to their demographic

attributes; investigation of the role of culture is suggested.

The US Department of Health and Human Service’s document, Healthy People

2010 ( U. S. Department of Health and Human Services, 2000) provides target

rates for the two major mortality categories of CVD: coronary heart disease, and

stroke. Objective maps are included in this chapter for these two causes of

death. Time series charts ( 1979 to 2004) are also included for each CVD

mortality category ( including total CVD). For the coronary heart disease and

stroke mortality time series charts, the HP 2010 targets are indicated.

Spatial Distribution of Cardiovascular Disease Mortality

The 2002 age- adjusted mortality rate for CVD ( ICD- 10: I00- I99) for the United

States is 319 deaths per 100,000 population but there is remarkable geographic

variation across the nation. State rankings2 ( including the District of Columbia)

place Minnesota, Alaska, and New Mexico first, second, and third with the lowest

respective age- adjusted rates per 100,000 of 237.7, 242.1, and 255.9 per

100,000, respectively. The highest rates are found for Tennessee, Oklahoma,

and Mississippi, ( rates of 380.8, 398.8, and 420.7, which placed 50th, 51st, and

52nd respectively). The rate for North Carolina in 2002 was 327.0, ranking it 33rd

in the nation. The 2002 average age- adjusted rate for the 41- county region

within Eastern North Carolina ( ENC) is 366.6. If this region were treated as a

state, it would rank 45th. For the 5- year period 2000- 2004, seven counties in

ENC ranked worse than the state of Mississippi in 2002.3

The maps at the top of figure 3.2 shows the spatial distribution of CVD crude

mortality rates for the 100 counties of North Carolina and the 41- county ENC

region. CVD crude mortality has its greatest impact in the northeastern part of

the state in those counties that comprise the 29- county hospital service area and

sub- region. ( For county locations and names, see appendix A.) From Table 3.1,

three counties-- Chowan, Perquimans, and Washington— have 5- year ( 2000-

2004) crude rates above 500 per 100,000. This translates to an average of 5

CVD deaths per 1,000 people per year living in those counties. Many counties

2 These rankings are based on calculations made at East Carolina University’s Center for Health

Services Research and Development. The data for combined state and regional comparisons

are from the National Center for Health Statistics Compressed Mortality Files ( 1999- 2002).

3 Calculations for county comparisons use primary data from North Carolina’s State Center for

Health Statistics via University of North Carolina— Chapel Hill’s Odum Institute.

Cardiovascular Disease Mortality 3

ECU, Center for Health Services Research and Development, 2007

with relatively high observed crude rates also have relatively small numbers of

people and may be proportionally older, which naturally leads to their increased

susceptibility to more chronic conditions like CVD. Crude mortality rates are a

kind of density measure— the number of deaths normalized ( or divided by) the

population of interest and do not account for age structure. Their depiction on

maps is for the purpose of focusing the reader to areas where the mortality

burden is greatest ( see chapter 1 for more discussion). Maps of crude rates are

useful in the development of policy, intervention measures, and determining the

allocation of health care resources.

The age- adjusted mortality rate maps found at the bottom of figure 3.2 permit

comparisons among counties and population groups which may have different

age structures ( see chapter 1). The state map shows a sharper distinction in the

disparity of county age- adjusted rates between the state’s eastern 41 counties

and the remaining counties to the west. As regions, 41- county ENC’s age-adjusted

rate of 367.3 is 19% greater that the 59- county region of NC at 308.0

deaths per 100,000 ( see table 3.1). In 2002, the age- adjusted rate for the US

was 319.0, less than 2% of the 2000- 2004 rate for the state and less than 13% of

ENC’s rate. From another perspective, if ENC 41 had the same mortality rate as

RNC 59 during the years 2000 to 2004, 6,590 lives would have been spared from

death due to CVD.

Figure 3.3 shows age- adjusted mortality by race and sex using the same rate

classification cut points found in the age- adjusted map in figure 3.2. These

maps provide a visual sense of group contributions to the overall CVD mortality

rate and distribution. For white males, the heaviest concentration of high rate

counties is found in the east, while some metropolitan counties to the west and a

chain of mountain counties tend towards lower rates. Within ENC, the county

with the highest rate for white males is Hertford at 576.3 and 131 observed

deaths ( table 3.1). High rates are ubiquitous throughout the state for non- white

males with the highest found in the ENC county of Currituck at 644.3 and 21

deaths. The highest rates for white females are found scattered throughout ENC

with Washington County having the highest rate in this region at 374.6 ( 124

deaths). Currituck County also had the highest rate for non- white females at

528.2 ( 29 deaths). Statewide, ENC is home to the largest concentrations of high

rate counties for these four demographic groups. For males of both races there

appears to be little difference between ENC and the rest of the state. ENC

becomes distinct as a high rate region because of the influence of regional white

and non- white female rates.

Temporal Distribution of Cardiovascular Disease Mortality

The decline in CVD is hinted at in figure 1.2 using the large proportional effects

( 72.1%) of HD mortality as a surrogate. This figure depicts the secular trend in

heart disease ( HD) mortality reaching its peak in the 1960s and soon after, crude

stroke mortality rates begin to decline. ( Together, these two diseases currently

Cardiovascular Disease Mortality 4

ECU, Center for Health Services Research and Development, 2007

comprise more than 90% [ see figure 3.1] of CVD mortality and so gives a good

approximation of the patterns of burden and progress made with respect to this

disease.) Figure 3.4 is a closer, comparative look at how ENC has been faring

over time with respect to CVD mortality over the last two decades of the 20th

century and the early years of the 21st. It charts the continuing decline in age-adjusted

CVD mortality rates for ENC, the remaining 59 counties of North

Carolina ( RNC), North Carolina, and the United States, from 1979 to 2004 ( US:

1979 to 2002). Within the 26- year period, ENC’s annual rates are the highest,

followed by the state, the nation, and the remaining 59- county region, each

showing very similar patterns of decline. ( The state values are a weighted

average between ENC and RNC and will always have intermediate values.) The

negative coefficients found in the equations of the lines, listed in the chart ( figure

3.4), show that ENC’s rate of decline is slightly greater than RNC’s rate with the

relative gap between the regions’ fitted rates growing from 9% in 1979 to 13% in

2004. This represents a relative 44% increase in regional disparity for CVD

mortality. In absolute terms, these same line equations show that the expected

or fitted rate differences in age- adjusted death rates declined from 51 deaths per

100,000 in 1979 to 41 deaths per 100,000, which translates into a 24% decrease

in regional disparity.

Figure 3.5 depicts the 26- year trend of CVD mortality among the four major

demographic groups in ENC. It is immediately apparent that the age- adjusted

rates are declining for all groups. ENC white males show the greatest

decreasing trend-- a decrease of 52%, which on average saves 16.7 lives per

annum. This compares favorably to the 42% decline for white females; a saving

of 8 lives per year. With R2 values around 0.90 one can make projection into the

not- too- distant future with a fair amount of confidence. If the same trends

continue, the age- adjusted CVD rates for white males and white females will

converge around the year 2015 with an age- adjusted rate of approximately 184

per 100,000. The age- adjusted rates for both non- white men and non- white

women are also converging but with their age- adjusted rate trends not projected

to converge until sometime around the year 2030, when both non- white sexes

attain the rate of approximately 188. In this scenario, it takes non- whites almost

15 years longer to achieve a projected rate similar to that of whites. Recall that

the calculations are based on simplifying assumptions concerning the behavior of

rates over time and any projections will have an increasing range of error as they

move more distant in time from the last observed rate year. However such

exercises can be viewed as another way of describing disparities and the amount

of relative effort that would be required to achieve parity measured over time.

Although mortality due to CVD is declining, its greatest impact is on the county

populations of ENC. White males appear to do better in the large metropolitan

counties of the Piedmont. However, these lower rates are comparable to the

highest rates found in white female population. The highest rates for this latter

group are concentrated in the counties of ENC. High rates of mortality for non-white

males are nearly ubiquitous within the state, with low rates interspersed in

Cardiovascular Disease Mortality 5

ECU, Center for Health Services Research and Development, 2007

the mountain counties. ( Low rates here are probably due to the small numbers

of non- whites in this region.) For non- white females, high rates are concentrated

in ENC, as well as the south- central portion of the state.

Trend analysis covering the period 1979 to 2004 show a dramatic 45% decrease

in regional rates for CVD mortality ( figure 3.4). The decrease in the age-adjusted

rate for ENC roughly parallels the declining rates for the other regions,

but there is a relative increase in regional disparity during this time— an artifact

that results from using decreasing bases. When the CVD time series trend line

for ENC is broken down into four race- sex trend lines, two patterns emerge:

divergence in mortality rates between the two racial groups and convergence

between the sexes for each racial group.

HEART DISEASE MORTALITY

Proportionally, heart disease ( HD) comprises more than 70% of all TCVD deaths

for the period 2000 to 2004 ( see figure 3.1). The spatial and temporal patterns

of HD mortality, therefore, should correlate strongly to those patterns observed

for CVD. Any observable differences in these patterns will probably be due to

the effects of stroke mortality, the next largest category outside of HD accounting

for almost 20% of all CVD mortality. The ICD- 10 definitions for HD can be found

at the end of this section in appendix B.

Spatial Distribution of Heart Disease Mortality

A comparison of the crude and age- adjusted maps for HD ( figure 3.6) and CVD

( figure 3.2) mortality does show strong similarities in patterns of mortality. ( Note

that the cut- points of HD mortality rate categories in the legends for both crude

and age- adjusted maps are approximately 70% of the ranges observed for CVD

mortality.) The crude map of HD mortality shows concentrations of higher rates

in the extreme northeastern and western portions of the state, with smaller

concentrations in the southeast and south. Age- adjustment produces a larger

concentration of high rates in ENC, de- emphasizing HD mortality rates in the

western region of the state.

Comparisons of regional age- adjusted HD mortality rates illustrate the continuing

presence of geographic disparities. From table 3.2, ENC’s 2000- 2004 age-adjusted

rate ( 263.5) is 13% higher than the US rate ( 240.8) and 19% greater

than the rate for RNC ( 221.9). The coastal counties of Dare and Pamlico

possess the lowest rates at 187.9 ( 286 deaths) and 190.4 ( 174 deaths),

respectively. ( For county locations and names, see appendix A) These counties

compare favorably to RNC’s rate for the same period. Moving inland, the highest

age- adjusted HD rates are found in two county clusters. The first cluster is found

in the southern part of the 41- county ENC region. Here, the counties of Bladen

( 319.2), Columbus ( 347.5), Robeson ( 315.6), and Scotland ( 310.4) experience

Cardiovascular Disease Mortality 6

ECU, Center for Health Services Research and Development, 2007

12.7% of ENC’s mortality attributable to HD while 10.1% of the region’s

aggregated estimated population from 2000 to 2004 lives in those counties. The

proportional disparity grows when we move to the next high rate cluster of

counties found in the northern part of the region. The high rates for Beaufort

( 309.1), Edgecombe ( 305.5), Martin ( 311.0), and Washington ( 314.8) counties

comprise 8.2% of the region’s HD deaths, but comprise only 5.7% of the region’s

population. Given their respective populations sizes, these two county clusters

have a disproportionate share of ENC’s HD mortality. 4

Figure 3.7 depicts the spatial distribution of age- adjusted mortality rates for HD

( 2000- 2004) broken down into four race- sex groups. The observed spatial

patterns closely resemble those for CVD ( figure 3.3) and indicate similar regional

effects among the four groups: higher rates for females of both racial groups are

again more concentrated in the eastern portion of the state, while high white

male rates are found throughout the state with the exception of the Piedmont’s

metropolitan counties, and non- white male rates are ubiquitously high with the

exception of several counties in the west. From table 3.2, the highest regional

age- adjusted county rate for white males is Columbus at 424.1 with 335 dying

from HD over five years. For the same period, Washington County is the

deadliest for white females who experience 96 HD deaths and an age- adjusted

rate of 294.0 per 100,000. Non- white males experience their highest rate of age-adjusted

HD mortality in Currituck County at 480.7 per 100,000 but this is the

result of only 16 individuals dying during that period— Perquimans County has

the next highest rate at 441.1 and a more statistically stable death count of 32.

In Columbus County, 189 non- white females died from HD producing the highest

age- adjusted county rate of 339.1 during the years 2000 to 2004. The total age-adjusted

HD mortality rate Columbus County is weighted largely by deaths

contributed from females of both racial groups, although white males also make a

significant contribution. The high CVD rate experienced by non- white males in

Edgecombe County appears to be heavily influenced by the HD component for

this race- sex group. Within ENC, the lowest statistically reliable age- adjusted

rate for any race- sex group is that found for white females in Greene County at

165.0.

Temporal Distribution of Heart Disease Mortality

Figure 3.8 shows trend lines for age- adjusted HD mortality among the four

regions for the period 1979 to 2004. The slope of the lines all follow the same

pattern of decline observed in figure 3.4 for CVD. Closer observation shows,

however, that with the exception of the ENC trend line, the relative positions of

the other three regions have shifted slightly. For CVD ( figure 3.4), North

Carolina has been consistently above the US rate, but for HD the state emerges

4 Because age- adjusted rates can be used for making comparisons, they can be helpful in targeting areas where

problems might exist. In this case, two county clusters have been identified and their count data are used to create

proportions, which can be used to calculate the relative amount of mortality burden.

Cardiovascular Disease Mortality 7

ECU, Center for Health Services Research and Development, 2007

with rates slightly less than the nation. ( This is probably due to the impact of

stroke mortality in ENC, which tends to be higher and has a significant additive

effect to the state rate for CVD.) Rates for RNC have been consistently below

the declining trend for the US, whereas for CVD the trend lines closely matched

one another. The impact of HD mortality on RNC’s population is less than it is for

the nation as a whole. ENC’s age- adjusted mortality rates for HD are clearly

higher throughout the 26- year time series with a slightly greater rate of decrease

among all the regions. The pattern of HD mortality decline witnessed here is a

good example of the secular trend in HD mortality burden observed during the

20th century ( see figure 1.2).

Both observed and modeled trend lines for race- sex groups ( figure 3.9) show

patterns of decline similar to CVD ( figure 3.5). What emerges in the pr