Volume 5: No. 3, July 2008
Use of Peer Groupings to
Assess County Public Health Status
Norma Kanarek, PhD, Ron Bialek, MPP, Jennifer Stanley, MA
Suggested citation for this article: Kanarek N,
Bialek R, Stanley J. Use of peer groupings to assess county public health
status. Prev Chronic Dis 2008;5(3).
jul/07_0145.htm. Accessed [date].
The Community Health Status Indicators Project was undertaken to produce
county-specific reports assessing the status of community health for local
jurisdictions throughout the United States. To accomplish this assessment, the
Community Health Status Indicators Project team selected peer groupings of
counties to monitor and analyze the health of local communities relative to peer
To identify peer counties, the project team used 5 categorical county
demographic variables, a specified order for applying criteria, and a
predetermined target for peer grouping size to subdivide counties into
homogeneous subgroups called peer groupings.
Eighty-eight peer groupings were developed with 14–58 counties
in each. The average size of each peer grouping was 35 counties. All peer
groupings included counties representing at least 6 states.
Peer groupings are very useful for community health assessment. They convey the
range of health status indicator values for similar counties, serve as a basis
for expected numbers of reportable diseases, and provide a method for comparing
communities with peer and U.S. medians. To maintain their usefulness, peer
groupings must be updated periodically.
Back to top
Use of a comparison group to assess
community health is as old as epidemiology itself and is the basis for
calculating the expected values and relative risks of public health
interventions and for assessing what levels of success those interventions are
likely to achieve. The first step in prevention and control of chronic disease
is to assess a community’s standing with respect to chronic disease outcomes.
Such an assessment can identify the community’s needs and serve as the basis for
gathering support for new or revitalized interventions to address those needs.
The Community Health Status Indicators (CHSI) Project was undertaken to produce
county-specific reports assessing community health status relative to peer
counties across the United States.
The CHSI Project sought to identify
the appropriate comparison population for assessing a county’s standing with
regard to the incidence of chronic diseases. To accomplish this, the CHSI
Project needed to assign each county a set of peer counties (1). We used the Health Resources and Services Administration’s Area Resource File (ARF) (2)
to define county aggregates. ARF defines these aggregates as a borough, a
parish, a city, the state of Alaska, or an otherwise defined “local” area. In
this article, we use “county” interchangeably with these other local
aggregations. The project identified peer groups from
among like counties and county aggregates throughout the United States (3).
The CHSI Project is ongoing and
issued its first set of peer counties in 2000 (CHSI 2000). An updated set of
peer groupings was issued in September 2007 (CHSI 2007).
Previous efforts to implement the
concept of peer communities have taken 1 of 3 approaches: 1) subjective
selection of 2 or 3 counties without regard for actual jurisdictional
characteristics, often made out of convenience (4); 2) selection of other
counties in a defined geographic area (e.g., in the same state) (5,6); or 3)
selection of mathematical neighbors, those determined to be the shortest
statistical distance on the basis of a weighted summary of several variables (7,8).
Peers selected out of convenience are frequently in the same state or taken from
communities in a common project, such as the Big Cities Inventory (9). Peers
from the state to which the county belongs are the usual choice for state health
departments, their vital statistics departments, or statewide Web-based data
warehouses and are an example of peers from the same geographical area. States
like Maryland, for instance, report mortality rates for its 23 counties and
the city of Baltimore only (6); other states’ counties are not reported.
Mathematical neighbors are determined by a weighted combination of several
variables. The weighted combinations having the smallest size (i.e.,
shortest distance) are identified as neighbors (i.e., peers). Thus, peers may be
selected to be within a specified “distance” from the index county or to be of a
specified number of one’s mathematically closest neighbors. This approach may be
viewed as a “gold standard”; however, such models are used less often because
either the calculations are not evident to the user or the variable(s) used are
not those the user would choose (10).
Designated peer groupings allow
explicit comparisons of counties and may include rankings and statistical
testing of differences observed between the county and its peers. More often,
however, comparisons are implicitly made by the reader. In such cases, an
alphabetical listing of counties and their health indicators is provided with no
attempt to indicate whether the number (e.g., a mortality rate) represents a
county’s “good” or “poor” health. In a survey of hospital-initiated
assessments of local health, Fielding and colleagues noted that they looked
for a designation of whether the community was better or worse, health-wise,
than the chosen comparison group, but found that that information was rarely
The CHSI Project compared each county
it assessed to its peers. During the development and evaluation phases of the
project, user feedback consistently noted that the feature of peer counties
from throughout the United States was a value-added utility of the CHSI Project
county report. Thus, peer comparisons became a cornerstone of the CHSI Project
reports. This paper will describe the strategy used to provide peer comparisons,
address constraints in developing peer groupings in the CHSI Project, and detail
the CHSI Project’s peer-grouping algorithm. We discuss our experience in
determining health status indicators for U.S. counties and make recommendations
for future peer-grouping strategies.
In 1998, the CHSI Project assembled
an advisory panel of academic public health services researchers and local,
state, and federal public health representatives who guided the development of
the project’s local county reports (1). Member representatives of the
National Association of County and City Health Officials (NACCHO) and the
Association of State and Territorial Health Officers met independently to poll
their colleagues to obtain their recommendations for approaching peer
comparisons and other report content. NACCHO had developed needs assessment
tools (e.g., Mobilizing for Action through Planning and Partnerships [MAPP] 
and Assessment Protocol for Excellence in Public Health [APEX-PH] ), which
assist local health departments in establishing priorities based on local health
information. Because of NACCHO’s close involvement in the CHSI Project, the
project (14) and MAPP (12) specified many of the same indicators. For example,
CHSI 2000 provided indicators such as low birth weight percentage, all-cause
mortality, county population characteristics and other measures that are
suggested in the MAPP “core indicators list.” The CHSI Project advisory group
and staff determined the format, size, and range of the report contents.
It was determined early on in the
project design that county-specific reports would be brief but would represent
as broad a perspective on public health as space and data would allow.
Ultimately, the report template was 16 pages, including title and back pages.
Much background material was published in a companion document (14) so that
readers interested in detail about methods, such as International Classification
of Diseases codes for cause-specific mortality, could access this information.
Three goals guided the development of
peer groupings. First, peer groupings needed to be transparent and immediately
understandable in order to easily communicate a county’s standing among its
peers. A second goal for the CHSI Project reports was to explicitly compare
counties with their peers. The third goal was to use the wisdom of practitioners
and academicians to ground peer-grouping formation.
Criteria for peer groupings
Peer groupings were constructed with
the 3 goals in mind. The first goal in creating peer groupings was to make the groupings a
manageable size, 30–40 counties each, so that all members of any peer grouping
could be listed in the county report. Because the entire CHSI 2000 Report was
only 16 half-pages long, we arbitrarily constrained space for
listing peer counties to 1 page. Nevertheless, the CHSI Project provided a
relatively large set of peers for each grouping. This approach enabled the
listing of counties in the CHSI Project report on 1 page and resulted in peer
groupings with more than a few peers.
A second goal for the CHSI Project
reports was to explicitly compare counties with their peers. Thus, the reports
incorporated calculations such as expected numbers of reportable diseases based
on peer experience, the peer grouping range for other indicators, and a symbol
as to whether the county’s data for a specific indicator was above the median of
its peers or at or below the median of its peers. Expected numbers were obtained by
calculating a rate for the peer group as a whole and comparing it with each peer
grouping member’s population. For each indicator and peer grouping, the range of
values for 80% of the counties was represented by the 10th and 90th percentile,
which excluded the highest 10% and the lowest 10% of values exhibited by
counties in the peer grouping. When a county’s rate was worse than the peer
grouping’s median value, a magnifying glass was printed alongside the county
rates. When a county’s rate was better than or equal to the peer grouping’s
median value, an apple was printed.
A third goal in generating peer
groupings was use of wisdom and conventions from public health practice. From
the experience and advice of our advisors, 5 criteria for grouping counties
as peers were obtained and then applied to the creation of peer groupings. Thus,
because NACCHO, in its periodic survey of local health departments, used
particular population size categories to describe a community served by a local
health department (15), CHSI Project peer groupings included the same county
population size categories of ≥1,000,000; 500,000–999,999;
250,000–499,999; 100,000–249,999; 50,000–99,999; 25,000–49,999; and <25,000
(15). The second criterion adopted was community-level poverty, which was
thought to represent a number of health issues — access to primary health care,
having resources such as health insurance, and having a usual source of health
care (16). Age (i.e., being older than 65 years or younger than 18 years), like
poverty, is a common determinant of health services use and thus was included as
another peer grouping criterion (17). Researchers and practitioners on the
advisory group noted that the urban-rural continuum was a factor in relation to
mortality and disease development (16); thus, frontier designation (population
density <7 people per square mile) (18) and population density were deemed to be
additional peer grouping variables (19).
Selecting peer groups
The CHSI Project’s steering
committee provided guidance on the order in which these variables would be
applied, based on their knowledge of how health services are organized
(i.e., by frontier status, urban and rural population density factors) (20)
and on informal use of population size (as in the Big Cities Inventory )
to designate peers. The criteria were applied in the following order:
frontier status (yes/no), population size (7 categories), poverty (by
quartile of U.S. counties), age distribution (deciles), and population
density (half deciles). These criteria were applied until optimal size peer
groupings were reached. At times only 2 criteria
(nonfrontier status and population size) were used.
shows the 34 nonfrontier U.S. counties with populations of at least
1,000,000. In the case of this peer grouping, no further criteria were used.
The second and third peer groupings
that CHSI 2000 created are distinguished from each other by poverty (the median proportion of all county populations living in poverty was ≤10.5%).
Both peer groupings were made up of nonfrontier counties having a population
size of 500,000–999,999, and included either low (≤10.5%) or high poverty levels (>10.5%). For most counties, all criteria were applied, and thus peer groupings
were defined by frontier status, population size, poverty level, age
distribution, and population density (18).
provides an example of a second peer grouping created using 4 criteria.
Disease data aggregation
By design, the CHSI Project
provided the most recent data to counties while assuring stable measures
(i.e., sufficient sample size). Data were aggregated by peer grouping across
10, 5, or 3 years because peer groupings were made up of counties having
similar population size. In summary, counties were subdivided into
relatively homogeneous county subgroups using up to 5 categorical variables, a
specified order for the criteria, and a predetermined target for peer
From the 3082 counties in the United
States, 88 peer groupings were designated, with sizes ranging from 14–58
counties and an average size of 35
One peer grouping comprised 14 counties, and 3 comprised 58 counties each.
County peer groupings had low numbers or high numbers of counties when there
were not enough counties in the predetermined categories, as was the case with
frontier counties with populations >25,000, or when there were no criteria left
by which to further divide subdivide the group.
Every peer grouping contained
counties from multiple states
4). Counties classified in a
single peer grouping represented 6–25 states. Diversity of states in peer
groupings was greatest in counties with populations of 100,000–249,999. In
counties of this size, the modal number of states represented in peer groupings
was 24. Peer groupings representing the smallest counties (<25,000 population)
contained jurisdictions from fewer states; the modal number of states in peer
groupings of counties with populations <25,000 was 11. No peer grouping had
fewer than 6 states represented. Maps are another means of characterizing the
diversity of states represented among peer groups (21).
Aggregation of data during a 3-, 5-, or
10-year period depended on the size of counties. Most counties (59%) were
provided indicators aggregated during a 5-year period
5), and nearly a quarter of
counties (24%) were provided data in 10-year aggregates. Only about one-sixth of
counties (17%) were eligible for 3-year rates because they had populations of
Use of peer groupings within the
CHSI Project report
County and peer county
The CHSI Project report provided
population size and density, percentage of residents living in poverty,
race/ethnicity, and age distributions. The report section presented the minimum
and maximum values among the peers (1,14).
County status relative to the
County health status indicator values
were assessed as being above, equal to, or below the median value within the
county’s peer grouping. County indicators showing an outcome better than or
equal to the median were noted with an apple symbol. Values for counties below
the median were noted with a magnifying glass (1,14).
Peer groupings range
The range of values in a peer
grouping was indicated by the 10th and 90th percentiles of county outcomes
Peer grouping expected values
Peer counties’ disease counts and
populations were totaled and an overall rate generated for each peer group by
dividing total cases by total population for the period. Expected number of
cases for each disease (rounded to the nearest whole number) was obtained by
multiplying the peer grouping rate by the county population (1).
Aggregation of data years
Indicators for natality and mortality
were aggregated during varying numbers of years to balance the issues of using
the most recent data available and providing an estimate that was relatively stable.
The span of years presented is the same for all counties in any 1 peer
grouping. Three-, 5-, and 10-year annual averages were calculated for
populations ≥100,000, 25,000–99,999, and <25,000.
Indicators other than natality and
mortality were presented throughout all counties only for a single year or 1
multiple-year period depending on the source of data involved. For example,
toxic release substances were reported for 1 year while quality of life and life
expectancy were reported for a single 5-year period. Suppression rules were
applied to data to assure stability among the indicators presented (1,14).
CHSI 2000 incorporated peer groupings
into the community health assessment assembled for each U.S. county, 3082 in
all. Creation of peer groupings facilitated decisions about the number of data
years to aggregate and allowed several states to be represented among a county’s
peers. Peer findings were integrated into the reports by indicating the number
of cases of disease expected in a peer group, the range of the number of cases
within the peer group, and whether a county was better than the median of its
peers or of the United States.
The CHSI Project’s approach to
designating peers created 88 strata, based on the following hierarchically
applied factors: frontier status, population size, poverty, age distribution, and
population density. The approach yielded a peer grouping average size of 35 counties but did not avoid the creation of very small and very large peer
Diversity within peer groupings is greatest among those groupings of moderate size, no doubt because of the
diversity that is present in states themselves. Few states have sparsely
The peer grouping approach is readily
transparent, easy to put into operation, and is consonant with local health
departments, neighborhood planners, advocates, and citizenry who have an
interest in local health (22). After we conducted this analysis, the Internet relaunch of the CHSI Project in
June 2008 (CHSI 2008) used the same peer
groupings but with the reassignment of Virginia cities and counties and the
inclusion of Alaska as boroughs or counties. It is likely that counties will
need to be reassigned in the future because of changes in their characteristics.
A peer grouping should not be thought of as a static entity but rather as one in
which members transition in and out because of changes in county population
size and density, demographics, or boundaries. Even though membership in peer
groupings will be updated, peer groupings remain a value-added characteristic of
the CHSI Project report and a means for assessing just how well communities are
faring relative to similar jurisdictions (1).
Practice-based alternative peer
groupings should be examined with feedback from users of the CHSI Project
reports. It may be that additional data necessary for determining new peer
groupings are not yet available (e.g., county public health expenditures). In CHSI 2000, provision of county information immediately generated requests for
neighborhood-level data and peers, data that are not available routinely yet.
Sub-county areas such as neighborhoods may display the heterogeneity that is
present in the county-level measures because counties may be quite large (22).
Conversely, it is possible that counties identify with regions rather
than with the entire country, illustrated by fewer states being represented in
some of the peer groupings. Future iterations of the CHSI Project may allow
the user an option of selecting a predefined peer grouping (e.g., multiple
geographical groupings) or of specifying a peer grouping based on the user’s own
Peer groupings have much utility for
community health assessment, including conveying the range of health status
indicator values for similar counties, a basis for expected numbers of
reportable diseases, and a method for a median comparison. To maintain their
utility, peer groupings must be updated periodically. Peer grouping criteria,
such as population size and density and age composition, are components
influencing county health outcomes (23) and will contribute to future research,
particularly in the emerging field of public health services and public health
services research (24). There are few examples of using like counties to
benchmark a county’s progress toward improvement in health outcomes, but CHSI 2000 was a first attempt to provide a comparison group of more than
3 peers for every U.S. county. In addition to being updated periodically, peer groupings may be continuously improved as our understanding
of what constitutes a peer county grows and as the user community becomes more
accomplished in using peers for comparison or benchmarking.
Back to top
The authors thank the CHSI Project Advisory Committee, which first met in
1998, for its advice throughout the development of the county reports; the Health
Resources and Services Administration staff for their administrative guidance
and financial support; and others dedicated to the concept and pursuit of
community health assessment. Norma Kanarek has an Interagency Personnel Agreement with the Centers for Disease
Control and Prevention.
Back to top
Corresponding Author: Norma Kanarek, PhD, Johns Hopkins Bloomberg School of
Public Health, Department of Environmental Health Sciences, 615 N Wolfe St,
Room e7038, Baltimore, MD 21205. Telephone: (410) 955-3758. Email: firstname.lastname@example.org.
Author Affiliations: Ron Bialek, Jennifer Stanley, Public Health
Foundation, Washington, District of Columbia.
Back to top
- Community Health Status
Indicators (CHSI) Project Team. Community health status report: data
sources, definitions, and notes. Washington (DC): Health
Resourcesand Services Administration; 2000.
- Area Resource File (ARF). Rockville
(MD): U.S. Department of Health and Human Services, Health Resources and
Services Administration, Bureau of Health Professions; 2000.
- Teutsch SM, Churchill RE. Principles and practice of public health
surveillance. 2nd ed. New York (NY): Oxford University Press; 2000.
- Studnicki J, Hevner AR, Berndt DJ, Luther SL.
Comparing alternative methods for composing community peer groups: a
data warehouse application. J Public Health Manag Pract
- Stafford-Alewine S, Land GH.
The Missouri experience in providing tools and resources to promote
community assessment. J Public Health Manag Pract 2002;8(4):14-9.
- Maryland vital statistics annual report, 2004. Baltimore (MD):
Maryland Department of Health and Mental Hygiene; 2006.
- Apparicio P, Cloutier MS, Shearmur R.
The case of Montreal’s missing food deserts: evaluation of accessibility
to food supermarkets. Int J Health Geogr 2007;6:4.
- Rothenburg R, Muth SQ, Malone S, Potterat JJ, Woodhouse DE.
Social and geographic distance in HIV risk. Sex Transm Dis
- Benbow N, editor. Big cities inventory 2003. Washington (DC):
National Association of County and City Health Officials; 2003. http://www.kcmo.org/health/pdf/2003_BCHI_color_FINAL.pdf. Accessed May 23, 2007.
- Hillemeier MM, Lynch J, Harper S, Casper M.
Measuring contextual characteristics for community health. Health
Serv Res 2003;38(6 Pt 2):1645-717.
- Fielding JE, Sutherland CE, Halfon N.
Community health report cards. Results of a national survey. Am J
Prev Med 1999;17(1):79-86.
- Mobilizing for action through planning and parterships handbook.
Washington (DC): National Association of County and City Health
Officials; 2004. http://mapp.naccho.org/mapp_introduction.asp.
Accessed September 5, 2007.
- Assessment protocol for excellence in public health workbook.
Washington (DC): National Association of County and City Health
- Community Health Status Indicators (CHSI) Project Team. Community
Health Status Indicators report. Washington (DC): Health Services and
Resources Administration; 2000.
- NACCHO and Centers for Disease Control and Prevention. 1992-93
national profile of local health departments. Washington (DC): National
Association of County and City Health Officials; 1995.
- Centers for Disease Control and Prevention.
Community indicators of health-related quality of life—United States,
1993-1997. MMWR Morb Mortal Wkly Rep 2000;49(13):281-5.
- Dever GEA. Community health analysis: global awareness at the local
level. 2nd ed. Gaithersburg (MD): Aspen Publishers, Inc.;
- Compendium of recommendations by the National Advisory Committee on
Rural Health. Rockville (MD): National Advisory Committee on Rural
Health. http://ruralcommittee.hrsa.gov/nac_comp.htm. Accessed May 21,
- Hall SA, Kaufman JS, Ricketts TC.
Defining urban and rural areas in U.S. epidemiologic studies. J
Urban Health 2006;83(2):162-75.
- Wilkinson RG, Pickett KE.
The problems of relative deprivation: why some societies do better than
others. Soc Sci Med 2007;65(9):1965-78.
- Heitgerd JL, Dent AL, Holt JB, Elmore KA, Melfi K, Stanley JM, et
al. Community health status indicators: adding a geospatial component.
Prev Chronic Dis 2007;5(3).
- Hajat A. Evaluation of the Community Health Status Indicators
project. Washington (DC): National Association of County and City Health
- Kanarek N, Stanley J, Bialek R.
Local public health agency performance and community health status.
J Public Health Manag Pract 2006;12(6):522-7.
- Scutchfield FD, Marks JS, Perez DJ, Mays GP.
Public health services and systems research. Am J Prev Med
Back to top