1. The Geography of Colorectal Cancer Progression in Iowa: 1999 to 2010
I would like to thank Resa and the Department for this opportunity to tell you about myself and my research interests.
For those of you who have not yet seen my CV, I call myself a Health Geographer and geographic information systems specialist… I make maps!
I am currently a PhD candidate in the department of geography at the University of Iowa, but I spent most of life in Virginia.
I also have considerable experience as a health geographer in the Family Medicine Departments… particularly the University of Virginia working with Norm Oliver in mapping prostate cancer and currently with Dr. Barcey Levy mapping colorectal cancer.
Part of the reason that these Family medicine investigators are attracted to geographical perspective is their understanding that effective care for patients requires an understanding of the patient’s community and family context.
Researchers interested in access to services, clusters of disease, distribution of the physician workforce, and issues of social disparity require specific tools and techniques that enable this geographic perspective.
I would like to share some of the research that I have been working on.
I will bring this geographic perspective to this post-doctoral fellowship.
Kevin Matthews
PhD Candidate
University of Iowa
Department of Geographical and Sustainability Sciences
Presentation to the Virginia Commonwealth University
Department of Family Medicine and Population Health
January 24, 2014

2. Cancer maps are tools for a variety of audiences and cancer control purposes
Public engagement
Investigators can seek local knowledge leading to improved research hypotheses
Influence members of the public to engage in health behaviors and preventive care
Characterize places with high disease burdens
to understand the unique characteristics of the place that led to the
Provide evidence for decision-making such as resource allocation or targeted interventions
Hypothesis generation
Tthe purposes that motivate my current research are:
Public engagement: Seeking input from local community members in high-risk areas can help the investigator ask better research questions
AND can serve as an impetus for people in those communities to modify their behavior. In the case of CRC, seek preventive screening or early detection
I would like to briefly discuss two recent projects. Maps of persistently high disease rates and the Geographic Dynamics of a Progressive Chronic Disease

3. Two Recent Projects
Identifying Areas with Persistently High Rates of Colorectal Cancer
The Geographic Dynamics of a Progressive Chronic Disease: Colorectal Cancer in Iowa

4. This is an example of the type of map I will be discussing during this talk.
This is from the Iowa Cancer Maps project
This is a map of indirectly standardized late-stage colorectal cancer incidence in Iowa between 2000 and 2005.
The areas shown in red have rates that were higher than the state rate during the 6 year time period. Of course, then, the areas in blue were below the statewide rate.
This map is made using data from the Iowa Cancer Registry, a population-based SEER registry…
I will briefly discuss the method for making this map, but the reason I chose to map the rates as a surface of disease risk, rather than into more common maps made at the county level, is that they provide a high level of geographic detail.
This detail helps public health officials in identifying communities at risk and allocating scarce resources.
It also helps in engaging with the public, especially in the high risk areas. Its human nature for people to look at a map and focus on their community. If you couple strong public health messages tailored to high-risk communities, I believe can potentially change the behavior of those community-members
This particular map was made by my predecessors in 2008, but when the Iowa Cancer Consortium approached me to update these maps, we decided to take a new approach…
NEXT SLIDE

5. These next maps are from the Iowa Cancer Maps 2.0 project
Anecdotally, one of the cancer control specialists I work with told me that when he see’s a cancer map, his first instinct is to devise plans to intervene in those areas… to take some action.
This is a series of maps showing the indirectly age-sex standardized rate of late-stage CRC incidence, over three time periods (1999 to 2002, 2003 to 2006, 2007 to 2010).
As you can see the geographic patterns of the late-stage measure changed dramatically over time.
So, maps made for a single time period, even the most recent time period, have limited use if they are to be used for resource allocation, targeted interventions or public engagement.

6. Iowa Cancer Maps 2.0 Our solution is what we call a “persistence map”
Here we see maps showing where incidence, late-stage incidence or mortality were high in all three time periods
In the upper left is a map of incidence
The upper right is late-stage incidence, and
The lower map is mortality
Areas shown in red are places where the disease rates are always high.
Rather, red shows us which areas had persistently high incidence rates… or high late-stage rates… or high mortality rates… over the 12-year span
These maps provide the cancer control specialist with some confidence that these places with persistently high disease rates will still have high rates when making resource allocation decisions or designing targeted interventions.

7. Major gap in the disease mapping literature
The Geographic Dynamics of a Progressive Chronic Disease: Colorectal Cancer in Iowa
Major gap in the disease mapping literature
Progression: Incidence  Late-Stage incidence  Mortality
Colorectal cancer is a progressive disease with measurable phases that are related
However, we have little knowledge about how these measures co-vary over geographic space:
In the disease mapping literature, measures of incidence, late-stage incidence, and mortality in a population are treated as independent measures
Here, I suggest that these measures represent distinct and measurable phases of a progressive disease.
Considering that they

8. Colorectal Cancer (CRC)
Colorectal cancer is the second leading cause of cancer-related deaths*
The third most common cancer in men and in women.
In the United States in 2009
136,717 people were diagnosed
51,848 died
What point do you want to make?
This is a cancer that we screen for because it is largely preventable and survivable if detected early
Source: Centers for Disease Control
* Of cancers that affect both men and women

9. The possible progressions of CRC at an individual level*
*This is the natural history of the diagnosis, not the disease

10. Geographic Trajectories: The Predominant progression among individuals in a local area

11. Stage: Delayed diagnosis
Healthy
Individual
Preventable
Phase
Develops
Pre-cancerous Polyp
Pre-clinical Phase
What do I mean
by colorectal cancer
progression?
Progresses to Cancer
Incidence
What point do you want to make?
Stage: Delayed diagnosis
Late-Stage Incidence
Death
(or survival)
Mortality

12. Commonly mapped disease outcome measures
Incidence
Commonly mapped disease outcome measures
Proportion of cases diagnosed at a late-stage rates most common measure in academic disease mapping
What point do you want to make?
Late-Stage Incidence
Mortality

13. Why are we interested in the geographic pattern of how a disease progresses?
Measures should be interpreted in the context of other measures.
Unexpected patterns may require closer scrutiny.
For example, we expect a high correlation between late-stage rates and mortality rates
However, there are many places in Iowa where this expectation does not hold.*
What point do you want to make?
*examples will follow…

14. A Brief Interlude for Data and Methods
What point do you want to make?

15. Data and Methods Data Description: Spatially Adaptive Filters
Colorectal cancer cases between 1999 and 2010 in Iowa from the Iowa Cancer Registry
Mortality data from the Iowa Department of Public Health
Spatially Adaptive Filters
Establish a finely spaced set of sampling locations
Create a set of circles centered over the samples
Expand the radius to some threshold
(in this case 150 expected cases)
What point do you want to make?

16. 21,994 people were diagnosed 7,799 diagnosed late
We obtained CRC incidence data from the Iowa Cancer Registry (ICR), a population-based Surveillance Epidemiology and End Results (SEER) registry.
The ICR provided zipcode-level data for all in situ and invasive CRCs newly diagnosed in Iowa residents between 1999 and 2010;
For each diagnosis, we had their age at diagnosis (in years), year of diagnosis, sex, SEER summary stage at diagnosis (in situ, local, regional, distant, or unstaged), and a five-digit zipcode of residence at diagnosis.
We defined a death as an individual whose underlying cause of death was CRC between 1999 and 2010.
The Iowa Department of Public Health (IDPH) allowed the ICR to match death certificate information to CRC incidence data.
21,994
people were
diagnosed
7,799 diagnosed late
7,639 CRC-specific deaths

17. Adaptive Spatial FilteringC
The map of the state of Iowa, on the left, shows the distribution of 2,913 sampling locations.
For each sampling location, I calculated indirectly age-sex standardized incidence, late-stage incidence, and mortality ratios using 12-years of intercensal data between 1999 and 2010 as the standard population.
If I were to use counties or zipcodes or some other unit of geography, each unit would have a widely varying number of cases because of varying population densities. The result is that the statistical reliability of the map is not constant over the entire geographic area.
This may not matter, UNLESS it is a very rare disease and/or a very sparsely populated place. If this is the case, the observed rate would likely be extremely high, but unreliable…
Instead, I use a unit of geography called the adaptive spatial filter to overcome the “small-numbers” problem and to ensure the statistical reliability of the map is constant over the entire geographic area.
This procedure creates a set of geographic units of analysis consisting of overlapping circles of varying sizes. The rate is calculated using the cases and the population that fall within each circle
Not shown on this figure is that we have normalized our underlying population and case-level data to the zipcode level. So, these “filter areas” are really a conglomeration of zipcodes.
The radius of each circle expands until a pre-determined number of cases is reached.
In this case, each filter has approximately 150 cases.
As shown on the figure on the right, the size of the spatial filter is inversely proportional to the population within it
The area in yellow is Johnson County, Iowa which is where the University of Iowa and Iowa City are located.
At the sampling location label as “A”, we see a very small filter area. This sampling location is in the middle of Iowa City where population densities are relatively high, thus the filter area does not need to be as large as its rural counterparts
However, the sampling point labelled as “D” has a filter in a rural area. Rural filters need to expand further to ensure the threshold number of cases has been captured B D
A single set of filters, established with respect to 150 expected cases incidence in the 12-year period, were used to calculate the incidence, late-stage incidence and mortality rates in each time period

18. Note:
The rates on all the following maps are the standardized incidence (or mortality) rate
This is the ratio of the number of observed cases divided the number of expected cases
The interpretation is that ratios above 1 have rates that were higher than expected given the age-sex distribution of the people living in the filter area

19. Why Not Just Use Counties?
Arbitrary boundaries
Modifiable areal unit problem (MAUP)
Rearranging the configuration of units and boundaries can yield different spatial patterns
e.g. talk to your local gerrymanderer
Disease patterns are not confined in these borders
What point do you want to make?

20. Again, areas shown in red have incidence rates higher than expected
Blue areas have rates that are lower than expected…
White is somewhere in between
However, this map communicates that disease patterns drop preciptiously at county borders when we know that disease patterns do not observe these arbitrary administrative units

21. Instead, we use the spatial filtering procedure to create highly detailed maps of disease rate that have a constant level of statistical (because we have chosen filters containing approximately the same number of people.

22. Getting back to this idea of mapping a chronic progressive disease, here are some examples…
What point do you want to make?

23. Example 1: Late-stage rates are out of context
Incidence is declining nationally and within Iowa due to screening and prevention efforts
The interpretation of late-stage incidence maps over time is dependent on the interpretation of the incidence maps
What point do you want to make?

24. that are relative to the statewide rate for the entire 12-year period
Here we see the colorectal cancer incidence rate over the three, four year, time periods
Because we used 12-years of intercensal data as the standard, each rate is interpreted relative to the state as whole and to the entire time period.
This set of maps demonstrates that colorectal cancer incidence rates have decreased over time.
However…
Age-sex standardized incidence rates in each filter, and within a 4 year period,
that are relative to the statewide rate for the entire 12-year period
White = 1: Filter has an incidence rate that mirrors that state-rates
Red = areas of excess incidence
Blue = areas of decreased incidence

25. But, if we did not know that incidence had decreased…
However, it appears that the late-stage rate has increased over time.
A caveat: This is actually the proportion of cases diagnosed at a late-stage NOT late-stage incidence (the rate among the total population at-risk). The disease mapping literature calls these proportions late-stage incidence rates.
Nonetheless, it appears that, by the 2007 to 2010 time period, the late-stage incidence has increased
…we may conclude that prevention efforts have failed

26. The real story The proportion of late-stage cases is increasing
This would should lead to further questions about potential barriers to screening or other preventive care
That is, if overall incidence is decreasing in an area, but the late-stage rate is increasing… To me, this trend suggests that certain segments of the population in those areas that have trouble accessing CRC preventions? Otherwise trends in incidence and late-stage incidence should mirror one another
The proportion of late-stage cases is increasing
in areas where incidence decreased

27. Example 2: Unexpected relationships between late-stage incidence and mortality
Stage at diagnosis is a predictor of mortality.
Incidence-based mortality, or the case-fatality rate, is a proportion of the number of CRC-specific deaths to the total number diagnosed within the study period.
In this case, the deaths are a subset of all cases.
Note: This example is based on a slightly different study that used to 2005
and Incidence-based mortality

28. Late-stage incidence and mortality at 2,913 sample locations in Iowa: 2000 to 2005
This scatterplot shows:
High late stage  High mortality
Low late stage  Low mortality
But…
What about these areas with:
Low late stage  High mortality
High late stage  Low mortality
Recall the 2,913 spatial filters previously described
On this scatterplot, there are 2,913 points
That is, I calculated the late-stage incidence and mortality rate for each filter location
So, each point on this scatterplot corresponds to a rate for a spatial filter.

29. Late-stage incidence and mortality measures at 2,913 sample locations in Iowa: 2000 to 2005
This scatterplot shows:
High late stage  High mortality
Low late stage  Low mortality
But,
What about these areas with:
Low late stage  High mortality
High late stage  Low mortality
What point do you want to make?

30. Late-stage incidence and mortality measures at 2,913 sample locations in Iowa: 2000 to 2005
The extremes in
each quadrant…
What I have done here is to remove the points on the scatterplot with rates between 0.95 and 1.05
Note the colors on the four quadrants… this is actually the legend for the next map where we will see a map with black areas in the upper left hand corner, and so on

31. What point do you want to make?

32. Low Late-Stage  Low Mortality
Successful local screening programs, concentrations of healthful individuals, high access to diagnostic resources.

33. High Late-Stage  High Mortality
Geographic concentrations of genetic or behavioral predispositions, poor access to screening, or physicians are not communicating recommended screening

34. High Late-Stage  Low Mortality
Geographic concentration of protective factors, access to quality treatment.

35. Low Late-Stage  High Mortality
Geographic concentration of behavioral risk factors, limited access to treatment, or some genetic factor that increases the lethality of the disease

36. Concluding Remarks
To improve our understanding of spatial disease patterns, we should:
examine the measures of disease burden as a continuum of disease progression with
multiple biological, social, and economic dimensions
The unexpected patterns my represent
concentrations of genetic or behavioral risk/protective factors
Disparities in level of access
Preventive Care
Screening
Treatment
Places that do not follow this expected relationship may increased scrutiny and more tailored interventions
What point do you want to make?

37. My research goals for this fellowship?
Make colorectal cancer maps and use them for public engagement
Evidence for decision-making,
Generating hypotheses, and
Characterize the places with high (or low) disease burdens
And this is the reason I am interested in this particular fellowship.
I have developed a method to detect where patterns of disease are persistent over time and
Shown that some places have disease patterns that are not expected given our understanding of the natural progression of the disease
What I have not attempted during my doctoral program or dissertation work is to perform any of these research motivations that I mentioned at the beginning of my talk.
My goal for this Fellowship is to continue my expertise in geographic informations and disease mapping. I am interested in using these maps for public engagement purposes, or for characterizing places with high disease burdens…
or low burdens because we would want to know the characteristics of those places as well
And, I would like to test some of the hypotheses related to areas with unexpected relationships… such as areas with low late-stage rates, but high mortality.

38. Acknowledgements
Gerry Rushton, PhD: University of Iowa (UI) -- Department of Geography Barcey Levy MD/PhD: UIHC -- Department of Family Medicine Anne Gaglioti, MD: UIHC -- Department of Family Medicine Charles Lynch MD/PhD: UI -- Department of Epidemiology Tina Devery, Iowa Cancer Consortium This project was supported by The National Cancer Institute (1RC4 CA ) PI: Barcey Levy MD/PhD: UIHC -- Department of Family Medicine
I would like to acknowledge the following people who have enabled this line of inquiry and who have supported me every step of the way…

39. Questions? Kevin-matthews@uiowa.edu Kevin Matthews PhD Candidate
University of Iowa
Department of Geographical and Sustainability Sciences
Presentation to the Virginia Commonwealth University
Department of Family Medicine and Population Health
January 24, 2014

40. The Geography of Colorectal Cancer Progression in Iowa: 1999 to 2010
Kevin Matthews
University of Iowa
Department of Geography

41. Definitions
Progression: The way in which cancer invades an individual’s body and the resulting outcomes
Disease Measures in a Population
Incidence
Late-Stage Incidence
Mortality
Trajectory: The predominant progression path in a local area

42. standardized incidence rate
12-year Statewide
standardized incidence rate
Note: For 1999 to 2010
14,443 late-stage cases
22,832 cases
In 1999 to 2002
5,367 late-stage
8,467 cases

43. standardized incidence rate
12-year Statewide
standardized incidence rate
Note: For 1999 to 2010
14,443 late-stage
22,832 cases
In 2003 to 2006
4,700 late-stage
7,568 cases

44. standardized incidence rate
12-year Statewide
standardized incidence rate
Note: For 1999 to 2010
14,443 late-stage
22,832 cases
In 2007 to 2010
4,376 late-stage
6,797 cases

45. Data Description Reference Population: Cases:
Census 2000
Census 2010, and
intercensal population estimates (1999, 2001 to 2009)
Cases:
Definitions: All persons aged 40 and older diagnosed with colorectal cancer cases diagnosed between 1999 and 2010
Source: Iowa Cancer Registry – a population based cancer registry
Staging: AJCC Staging Manual
Deaths:
Definitions: All persons aged 40 and older who died of CRC between 1999 and 2010
Source: Iowa Department of Public Health