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Volume
2:
Special Issue, November 2005
TOOLS & TECHNIQUES
Geocoding and Social Marketing in Alabama’s Cancer Prevention Programs
Julianna W. Miner, MPH, Arica White, MPH, Anne E. Lubenow, MPH, Sally
Palmer
Suggested citation for this article: Miner JW, White
A, Lubenow AE, Palmer S. Geocoding and social marketing in Alabama’s cancer prevention
programs. Prev Chronic Dis [serial online] 2005 Nov [date cited].
Available from: URL: http://www.cdc.gov/pcd/issues/2005/
nov/05_0073.htm.
Abstract
The Alabama Department of Public Health (ADPH) is collaborating with the National
Cancer Institute to develop detailed profiles of underserved Alabama
communities most at risk for cancer. These profiles will be combined with
geocoded data to create a pilot project, Cancer Prevention for Alabama’s Underserved Populations:
A Focused Approach. The project's objectives are to provide the ADPH's cancer prevention programs with a more accurate and cost-effective means of
planning, implementing, and evaluating its prevention activities in an
outcomes-oriented and population-appropriate manner.
The project links geocoded
data from the Alabama Statewide Cancer Registry with profiles generated by the
National Cancer Institute’s cancer profiling system, Consumer Health Profiles.
These profiles have been successfully applied to market-focused cancer
prevention messages across the United States.
The ADPH and the
National Cancer Institute will evaluate the efficacy of using geocoded data
and lifestyle segmentation information in strategy development and program
implementation. Alabama is the first state in the nation not only to link geocoded cancer registry data with lifestyle segmentation data but also
to use the National Cancer Institute’s profiles and methodology in combination
with actual state data.
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Introduction
The Alabama Department of Public Health (ADPH) estimates that in 2005, more
than 24,000 people in Alabama will be diagnosed with
cancer, and approximately 10,000 people will die of the disease (1). Health
disparities compound this problem and create additional challenges for the
public health infrastructure. Cancer incidence and mortality are affected by a
wide variety of socioeconomic, behavioral, and other environmental factors,
including poverty, race, access to and quality of care, education, obesity,
nutrition, and tobacco use, among others (2). The ADPH’s cancer prevention
efforts are aimed at lowering incidence and mortality for all Alabamians; however,
the program’s main focus is ameliorating health disparities and reaching
underserved populations. In Alabama and across the United States, African
Americans bear a higher cancer burden than their white counterparts (3). The socioeconomically disadvantaged are also more likely to have cancer than the
general population (4). Reaching poor, rural populations with screening and
prevention messages and improving access to treatment and services create additional challenges for public health (5).
The ADPH is working to reduce health disparities by
implementing several comprehensive programs offering outreach, education, and
cancer screenings to low-income and uninsured populations. Since 1996, the ADPH
has provided free breast and cervical cancer screenings to more than 18,000
low-income women (6). Other publicly and privately funded programs, many in
partnership with the ADPH, are working concurrently to reach medically
underserved communities (7,8).
Eliminating health disparities is one of Healthy
People 2010’s two overarching goals (9). In addition, the American Cancer
Society (ACS) has identified reducing the burden of cancer on the poor and
underserved as one of its advocacy priorities (4). However, public health
efforts to reduce disparities are challenged by a lack of socioeconomic data
that can
be linked with data on health behavior and health care use within a relatively
small geographic area. In the American Journal of Epidemiology, Krieger et al of
Harvard’s Public Health Disparities Geocoding Project state, “Despite growing recognition of the magnitude and persistence of socioeconomic
inequalities in health and the need to address them, few or no socioeconomic
data exist in most U.S. public health surveillance databases” (10).
Geocoding technology offers a way to link area-based
socioeconomic data and public health surveillance (10). Geocoding is a process
of mapping each record in a data set based on a street address and assigning
it to a census block group, the smallest geographic unit for which U.S. census
data are available. Data from census block groups can be compared with and
linked to other data sets. Healthy People 2010’s objective 23-3 is to “increase
the proportion of all major national, State, and local health data systems
that use geocoding to promote nationwide use of geographic information systems
(GIS) at all levels” (11). The target is 90% of all public health data
systems (11).
Health communications, education, and outreach are
increasingly expected to be data-driven and outcomes-oriented (12-14), but
these expectations can be difficult to meet for smaller programs, county
health departments, or activities targeting rural communities or smaller
geographic areas. Simply providing preintervention and postintervention
statistics on incidence, screening, and health behaviors can be difficult.
Reliable national data sources such as the Behavioral Risk Factor Surveillance
System (BRFSS) and the National Health and Nutrition Examination Survey (NHANES)
are excellent resources, but when used at the county level (or below) they
become less reliable because of small sample sizes (11).
In the actual practice of health promotion, it is not
always realistic to expect small or underfunded programs to conduct surveys
and focus groups to set baselines for planning and evaluation (15,16). Many
state public health agencies function in a limited-resource environment, and
often this means prioritizing among many program elements (17,18). In such
environments, funds are expected to be allocated for direct services (free
screenings, visits with outreach workers or caseworkers, hours of education provided);
materials (posters, pamphlets, educational materials); or direct media (radio,
television, print and outdoor advertising). Funding sources often limit the
amount a program may spend on administrative costs (19,20). In our experience,
despite the need to make health promotions more data-driven, limited resources
hamper our ability to translate theory into practice.
Alabama’s recognition of these problems led to the development of a unique
solution, Cancer Prevention for Alabama’s Underserved Populations: A
Focused Approach. This project involves linking geocoded data with other
public health databases to plan social marketing activities that will reach
communities most at risk for various types of cancer. The ADPH is the first state health department in the United
States to license commercial planning and marketing software for this purpose. The ADPH’s Bureau of Health Promotion and
Chronic Disease began to use this software in 2003. These efforts are being
coordinated through the Bureau of Health Promotion and Chronic Disease’s Social Marketing
Branch.
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Project Goals
Cancer Prevention for Alabama’s Underserved Populations: A
Focused Approach has the primary objective of improving the overall efficacy
and cost-effectiveness of cancer prevention messages targeting underserved
communities through a pilot project to be conducted from 2004 through 2006.
Project goals include 1) the development of profiles of poor and underserved
Alabama communities most at risk for various types of cancer; 2) the
development of the most effective and cost-efficient ways to reach those
communities with prevention messages; 3) the ability to plan, implement, and
evaluate cancer prevention activities using valid and reliable data at
different geographic levels; and 4) assessment of the value and validity of
profiles based on cancer incidence compared with profiles developed from self-reported
national health behavior surveys.
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Project Background
Geocoding Alabama statewide cancer registry data
Gaining access to integrated commercial planning and marketing software was
not a quick or an inexpensive process. More than a year was
spent working with various programs within the ADPH to discuss the value and
usefulness of such an investment. Concerns such as costs, compliance with the
Health Insurance Portability and Accountability Act of 1996 (HIPAA), ease of
use, and training were addressed. Ultimately, three programs decided to
underwrite the cost of the software contract for the first year; during the first
year, three additional programs signed on. This
arrangement allowed all participating programs to bear a smaller burden of the
cost and made the information more widely available. We found that
diffusing the cost of the contract across multiple program areas
eliminated the cost barrier for most programs that wanted to
participate.
We selected a specific vendor for two primary reasons. First, the vendor
had several national public health clients, including the Centers for Disease
Control and Prevention (CDC), the Centers for Medicare & Medicaid Services
(CMS), the ACS, the National Cancer Institute (NCI),
and the National Heart, Lung, and Blood Institute (NHLBI). Both the CDC and
the NCI had been sharing this type of data with the ADPH programs for several
years, and we saw value in having unlimited, direct access to the
data source.
Second, this commercial vendor was the only one to link
data from the BRFSS, the U.S. census, and several other national health
surveys with its proprietary health care use survey (an annual health behavior survey
of 100,000 households) and with lifestyle segmentation clusters. The cluster
methodology organizes the U.S. population into 66 segments based on several dozen demographic,
geographic, and lifestyle variables as well as consumer-purchase records and
media-preference data.
One of the programs to sign on during the first year
was the Alabama Statewide Cancer Registry (ASCR). After using the data for
several
months, the ADPH began the process of geocoding its state cancer registry data in
May 2004 with the intention of linking the geocoded cancer data to
the various health behavior and socioeconomic databases included in the
software, including the lifestyle segmentation clusters.
Cluster data are linked to a variety of other data within the software,
including market research data. These data provide detailed information about
each cluster’s media preferences (e.g.,
television shows, newspapers, radio programs), Internet access,
and other types of consumer information (e.g., brands of cigarettes smoked,
chain restaurants preferred, vehicles purchased). We believed that linking
consumer market research, socioeconomic, and health behavior data with
7 years of Alabama state cancer data would
offer an unprecedented understanding of who was becoming ill and how best to
reach them.
The NCI’s Consumer Health Profiles
The vendor brought to our attention that one of its clients, the NCI, had
developed a series of cluster-based Consumer Health Profiles (CHPs) to help
focus cancer prevention outreach to underserved populations. CHPs are designed to profile audiences most in need of cancer education and outreach by potential
cancer site (e.g., breast, lung, prostate) based on health behavior and
lifestyle information. CHPs incorporate geodemographic, health status, and
health care use data to allow the demographic, access-to-care, and behavioral components of
prevention and treatment to be better understood. Moreover, CHPs provide lifestyle segmentation data
that can be used to
design focused outreach to communities based on lifestyle variables such as
media preferences, consumer behavior, and the manner in which consumers choose
to access information.
The NCI’s profiles have been successfully applied nationally to
market-focused cancer prevention and screening messages and have
been used extensively at the local and regional levels through the NCI’s
Cancer Information Service (CIS). For the past 7 years, the CIS Partnership
Program staff has used CHPs data to identify underserved and
minority populations and to plan and evaluate successful cancer
education programs for these groups across the country.
Collaboration between the ADPH and
the NCI
Through the vendor, the ADPH and the NCI decided to collaborate on the
project to share expertise and data. We learned that the NCI’s profiles were
based on self-reported survey data and national data sets. Our
data would be specific to the geographic area where implementation would occur
and would be based on cancer incidence rates rather than self-reported
screening and behavioral data.
The NCI and the ADPH each identified
individuals within their organizations to work on the project. Within the ADPH,
representatives from the Social Marketing Branch and the ASCR participated.
From the NCI, representatives from various
groups within the Office of Communications participated.
A series of conference calls between project partners over the summer of 2004
resulted in a preliminary program plan and a Memorandum of Understanding that
would allow for the free sharing of data between the organizations while
preserving confidentiality.
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Steps to Completion
We have identified the following seven steps to completion of the
project:
1. Geocode 7 years (1996–2002) of data from the ASCR and
develop a custom software application to allow for various types of data
analysis. (This step was completed in November 2004.)
2. Assess the geocoded cancer data to discern trends in
incidence and to link incidence data to information on socioeconomic status,
access to care, screening behavior, and media or outreach preferences. The
analysis will focus on several cancer sites: breast, cervix, colorectal,
prostate, lung, and all cancer sites combined. (This step is currently
underway.)
3. Link Alabama’s findings with the NCI’s CHPs for further examination, validation, and
strategy development.
4. Collaboratively develop CHPs specific to Alabama for
various cancer sites for underserved populations. This project phase will
include recommendations on how best to reach profiled communities based on
their media or outreach preferences and health behaviors.
5. Select profiles in most urgent need of prevention
messages, and conduct additional planning and baseline data collection
around the communities where the intervention will be focused.
6. Implement a focused cancer prevention outreach, education,
or media campaign.
7. Evaluate and report on the efficacy of the campaign.
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Discussion
We are currently in the process of analyzing the geocoded cancer incidence
data and linking the data to information on socioeconomic status, access to care,
screening behavior, and media or outreach preferences. Although we are still in
the early stages of the project, we have identified several important
findings. First, there is a need for ongoing process evaluation. Fortunately,
monthly conference calls with all the project partners have served as an
excellent way to share suggestions, changes, and ideas on how to improve
the project. The commercial vendor has become a partner through this
process. This increased involvement has been especially helpful partly because unanticipated alterations to the software application
were needed. Because the project has no direct funding and is underwritten by participating
programs at the ADPH, the vendor’s time, support, and good will have been
invaluable.
Second, cancer staging data should be included along with incidence data to
ascertain cancer burden. To simplify the process, we did not include staging
data in our initial upload of the cancer registry information to the vendor
for geocoding. However, such data would have increased
opportunities for analysis. For example,
by linking this data set with mortality data, we could calculate 5-year
survival rates and identify populations with the highest cancer
burdens.
Third, there are strengths and weaknesses in using a cluster-based model.
Clusters are useful because so much information is already
associated with them. However, such clusters are based on national statistics.
For example, Alabama’s general population is 26% African American, compared
with 12.3% of the general U.S. population (21). Therefore, the demographics of
several key clusters in our analysis do not match national statistics. It
has been necessary to rerun the demographics for each cluster in Alabama at
the block-group level to account for these differences. However, we have confidence in the cluster methodology and its applicability to
Alabama’s population, as does the NCI. Their CHPs, which rely on national
data, have been used successfully in regional programs across the country.
This Alabama population analysis strengthens the composition and use of the
profiles and will help to further validate the project.
Fourth, we need to identify additional uses for data outside of the scope
of this project. We are currently working to identify cancer
prevention projects in Alabama’s Black Belt region that 1) focus on the
cancer sites we are assessing, 2) have had interventions occur during 1999 or 2000, and 3) are
still in progress. That timeline will allow us to provide
these programs with at least 2 years of preintervention and postintervention
data to assist them in evaluating their efforts. We hope that this information
will assist them with managing their programs and increasing their
competitiveness in securing funds.
Providing ongoing projects with this information would be useful for us
because it would give us additional experience in applying our methodology and
would allow us to examine outcomes data months ahead of what we had
anticipated. It would also give us the opportunity to further disseminate this
information, thus making the best possible use of our
investment in the software and furthering the mission of public health by
providing support to grassroots cancer prevention efforts.
Fifth and finally, there is a need to conduct literature
searches on cancer incidence, cancer sites, socioeconomic status, geocoding in
public health data systems, and a variety of other issues related to the
project. We have found that it was useful to place both the process and the
preliminary findings in a broader context. This has also yielded the
opportunity to speak with public health professionals across the country
engaged in similar research who have offered valuable advice and feedback.
The findings of this project are preliminary, and no outcome data are yet available. We hope to have such
evaluative information in the next 12 to 18 months. By describing the project
and the reasons for its inception, we hope to articulate some of the issues
facing public health communications and cancer prevention programs and to
outline one of the solutions the ADPH Bureau of Health Promotion and Chronic
Disease has adopted to address them. While our solution may not be appropriate
for our counterparts in state government across the country, we believe there
is value in documenting our experience thus far and hope that it may provide
some ideas on how to address the challenging environment in which we all
function.
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Acknowledgments
The authors acknowledge Ranjeeta Pal, MPH, for her invaluable
assistance in the research and editing of this article.
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Author Information
Corresponding Author: Julianna W. Miner, MPH, Alabama Department of Public
Health, Social Marketing Branch, 201 Monroe St, Suite 990, Montgomery, AL 36106.
Telephone: 334-206-6416. E-mail: jminer@adph.state.al.us.
Author Affiliations: Arica White, MPH, Alabama Statewide Cancer Registry,
and Sally Palmer, Social Marketing Branch, Alabama
Department of Public Health, Montgomery, Ala; Anne E. Lubenow,
MPH, Office of Communications, National Cancer Institute, Bethesda, Md.
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