Access to Mammography Facilities and Detection of Breast Cancer by Screening Mammography: A GIS...

Access to Mammography Facilities and Detection of BreastCancer by Screening Mammography: A GIS Approach

Selina Rahman1,2, James H. Price2, Mark Dignan4, Saleh Rahman*,5, Peter S. Lindquist3,and Timothy R. Jordan21 Environmental Sciences Institute, Florida A and M University2 Department of Health and Human Services, University of Toledo3 Department of Geography and Planning, University of Toledo4 Prevention Research Center, University of Kentuckey5 Institute of Public Health, Florida A and M University

AbstractObjectives: The objective of the study was to examine the association between access tomammography facilities and utilization of screening mammography in an urban population.

Methods: Data on female breast cancer cases were obtained from an extensive mammographysurveillance project. Distance to mammography facilities was measured by using GIS, which wasfollowed by measuring geographical access to mammography facilities using Floating CatchmentArea (FCA) method (considering all available facilities within an arbitrary radius from thewoman's residence by using Arc GIS 9.0 software).

Results: Of 2,024 women, 91.4% were Caucasian; age ranged from 25 to 98 years; most (95%)were non-Hispanic in origin. Logistic regression found age, family history, hormone replacementtherapy, physician recommendation, and breast cancer stage at diagnosis to be significantpredictors of having had a previous mammogram. Women having higher access to mammographyfacilities were less likely to have had a previous mammogram compared to women who had lowaccess, considering all the facilities within 10 miles (OR=0.41, CI=0.22-0.76), 30 miles (OR=0.52,CI=0.29-0.91) and 40 miles (OR=0.51, CI=0.28-0.92) radiuses.

Conclusions: Physical distance to mammography facilities does not necessarily predictutilization of mammogram and greater access does not assure greater utilizations, due toconstraints imposed by socio economic and cultural barriers. Future studies should focus onmeasuring access to mammography facilities capturing a broader dimension of access consideringqualitative aspect of facilities, as well as other travel impedances.

KeywordsMammography; GIS; access; distance; breast cancer

© 2008 Nova Science Publishers, Inc.* Corresponding author: MBBS, PhD, MPH, Assistant Professor, Behavioral Sciences and Health Education, Institute of PublicHealth, Florida A and M University, College of Pharmacy and Pharmaceutical Sciences, 209-A FSH Science Research Center,Tallahassee, FL 32307. Ph: 850-599-8840. Fax: 850-599-8830. [email protected].

NIH Public AccessAuthor ManuscriptInt J Canc Prev. Author manuscript; available in PMC 2010 July 12.

Published in final edited form as:Int J Canc Prev. 2009 ; 2(6): 403–413.

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IntroductionBreast cancer is one of the leading causes of death among women in the United States. TheAmerican Cancer Society estimated that 178,480 new cases and 40,460 deaths from breastcancer occurred among women in the United States in 2007 (American Cancer Society[ACS], 2007). Due to a lack of primary prevention of breast cancer, breast cancer mortalityand morbidity reduction depends on secondary prevention, chiefly through screeningmammography. Several randomized trials as well as population-based screening evaluationshave indicated that early detection of breast cancer through screening mammographyimproves treatment options, the likelihood of successful treatment, and improved survival(William, Holladay, and Sheikh, 2003; Taber et al., 2003; Humphrey, Helfand, Chan, andWoolf, 2002; Duffy, Tabar, and Chen, 2002). A rise in mammography utilization issuggested by the observed trends (1987-1999) of an increase in breast cancer incidenceconfined to early stage breast cancer (Howe, et al., 2001; Edwards, et al., 2002; Blanchard,et al., 2004). A significant and substantial reduction in female breast cancer mortality hasbeen observed in recent years because of screening mammography (Smith, et al., 2003;Duffy et al., 2006). However, the mortality rate from breast cancer is still too high, eventhough screening rates have increased and mortality decreased somewhat. The HealthyPeople 2010 target is 22.3 deaths per 100,000 women, but according to the American CancerSociety data the death rate is 26 per 100,000 women in 2007 (ACS, 2007).

Several researchers have explored barriers to obtaining mammograms, including thephysical distance to mammography facilities and other barriers (Ann, Ronald, Raymond,and Gilligan, 2001; Jilda, Hyndman, and Holman, 2000a; Jilda, Hyndman, and Holman,2000b). Understanding the geographical and social connections between the utilization ofmammography and the locations of mammography facilities is critically important fordeveloping effective programs to reduce breast cancer mortality. Health EducationPromotion programs designed to increase mammography screening and produce subsequentreduction in breast cancer mortality may have opportunities to improve their effectiveness ifthey are able take barriers such as geographic distance to screening services intoconsideration. Health care decisions are strongly influenced by the type and quality ofservices available in the local area and the distance, time, cost, and ease of traveling to reachthose services (Goodman, Fisher, Stukel, and Chang, 1997; Haynes, Bentham, Lovett, andGale, 1999; Joseph and Phillips, 1984; Croner, Sperling, and Broome, 1996; Fortney, Rost,and Warren, 2003). The term ‘spatial accessibility’ is gaining more and more attention in thehealth care geography literature (Khan and Bhardwaj, 1994; Luo, 2004; Luo and Wang,2003), which is a combination of dimensions of accessibility (travel impedance betweenpatients and service points, that is measured in units of distance or travel time), andavailability (refers to the number of local service locations from which a patient can choose).In this study, we focused on measuring access to mammogram facility by using GIS,considering both accessibility and availability dimensions. We also examined whetheraccess to mammography facilities and other demographic variables influence utilization ofmammography.

MethodsData Collection

The data for this study were obtained from the Colorado Mammography Project (CMAP).CMAP was a National Cancer Institute funded project that was in operation from1994-2004. CMAP was one member of a seven-site consortium, and obtained data onmammograms from approximately half of all mammography facilities in the six-countyDenver metropolitan area of Colorado. For this study, information on mammograms forwomen from 1999-2001 was analyzed. The CMAP database included demographic data

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(age, race/ethnicity, education, and insurance status), data on mammogram results, previousmammogram history, family history, use of hormone replacement therapy, physicianrecommendation, and the zip codes of women's residences. Addresses of mammographyfacilities participating in CMAP were obtained for this study from the Colorado Departmentof Public Health and Environment. There were 46 facilities on the list that were operatingduring the time period (1999-2001) and were considered as the possible facilities thatwomen might use to obtain a mammogram.

Calculation of Access to Mammography FacilityWe used the “Floating catchment area” (FCA) method by Luo and Wang (2004) to calculateaccess, that considered all available facilities within an arbitrary radius around a woman'saddress. Forty-six mammography facilities were geocoded using the ArcGIS System andplaced in a separate file. Zip code centroids were obtained from a Zip code polygon file andcompared to the database of patients. All Zip code centroids that had no patients from thesample were discarded, and then the numbers of patients were summed for each Zip codecentroid and placed in a separate file. Mammography facility points and Zip code centroidpoints were connected to the regional street and highway network. Point-to-Point mileageswere computed in a separate shortest path utility embedded within the GIS. The mileageswere outputted in the form of a distance matrix. The distance matrix between Zip codecentroids and mammography facilities was then imported into an Excel spreadsheet.Minimum distance that a woman would be willing to travel to get to a mammogram wasconsidered 10, 20, 30, 40, or, 50 miles and following operations were performed for each ofthese arbitrary radius. For each specified radius, the number of women among all Zip codeswithin the specified radius was summed for each of the 46 mammography facilitiesidentified within the study area. Then the inverse of these sums were computed to calculatethe availability of that facility. Now, for each woman's Zip code within a specified radius,the availability for all facilities was summed to obtain the FCA index, representing heraccess to mammogram facility. Finally, indices for five different arbitrary radii 10, 20, 30,40, and 50 miles were computed to calculate access to mammography facility.

Statistical Analysis to Examine Relationship between the VariablesTo further explore the association between the variables, logistic regression was performed.The dependent variable entered into the logistic model was whether the woman has had aprevious mammogram or not (coded as yes=1 and No=0). Women who had a previousrecord of mammogram in the CMAP database or answered, “yes” on their patientinformation form when asked about their previous mammogram history at their indexexamination were considered as having had a previous mammogram (Figure 1 displays thedistribution of the study population that did not have a previous mammogram in the sixcounty areas).

A series of categorical variables were created and entered into the logistic model such as,age, race/ethnicity, education, insurance status, family history, hormone replacementtherapy, physician recommendation, and breast cancer stage at diagnosis along with accessto mammography facilities. Among the independent variables, the ‘physicianrecommendation’ variable was divided into two broad categories: ‘diagnostic’ that includedall the diagnostic procedures (such as, biopsy, needle localization, and ultrasound) and‘evaluative’ that included the rest of the categories, such as, follow up, physicalexamination, surgical consultation etc. Breast cancer stage at diagnosis was also condensedinto two categories: non-advanced breast cancer stage at diagnosis included carcinoma insitu, and localized tumors, which are malignant and invasive but confined to the organ oforigin; and advanced stage of breast cancer at diagnosis included regional neoplasm thathave extended beyond the organ of origin into surrounding tissues, involving regional lymph

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https://www.researchgate.net/publication/8992063_Using_a_GIS-based_floating_catchment_method_to_assess_areas_with_shortage_of_physicians?el=1_x_8&enrichId=rgreq-43313777-33ae-48ab-9b2f-95a2b9adc6db&enrichSource=Y292ZXJQYWdlOzQ1MTg1Nzk1O0FTOjEwNDEzNTMyNDA3ODA4NEAxNDAxODM5MjA1MDQ0

nodes, or both, and distant tumors that have spread to remote parts of the body from theprimary site.

With the access ratio for five different radii (such as, 10 miles, 20 miles, 30 miles, 40 miles,and 50 miles) five different logistic regression models were developed. Both univariate andmultivariate analyses were conducted and on the basis of analysis of maximum likelihoodestimates, significant interaction terms were identified and there was no significantinteraction between the variables.

ResultsDemographic Characteristics

The data from the Colorado Cancer Registry included 2042 individuals diagnosed withbreast cancer during the period of 1999 to 2001. Descriptive statistics for the studypopulation are summarized in Table 1.

The breast cancer cases ranged in age from 25 to 98 years with 30% being 50-59 years ofage and nearly all were Caucasian (91%). Twenty one percent reported having Medicaidand/or Medicare and 78% also had private insurance.

Among those with data on family history, 17% had a positive family history of breastcancer. Among those with data on hormone replacement therapy, 42% were on hormonereplacement therapy at the time of the initial mammogram. Nearly all (91%) of the womenin the database had a previous mammogram.

Table 2 presents the odds ratios for the factors influencing having had a previousmammogram. Access to mammography facilities was negatively associated with having hada previous mammogram in the adjusted model developed for 10 miles, 30 miles, and 40miles radius. Women who had greater access to mammography facilities were 59% lesslikely and women who had medium access to such facilities were 58% less likelyrespectively of having had a previous mammogram, compared to women who had lowaccess to mammography facilities; and these findings were significant in both the crude andadjusted models for the 10 miles radius measure.

For the 30 miles radius access measure, women who had high access to mammographyfacilities was 48% less likely of having had a previous mammogram when compared towomen who had less access to mammography facilities (OR = 0.52, 95% CI = 0.29-0.91).

The odds of having had a previous mammogram for women who had high access tomammography facilities were 0.51(95% CI= 0.28-0.92) times compared to women who hadless access to mammography facilities and these findings were significant for both the crudeand adjusted models developed for the 40 miles radius access measure. The 50 miles radiusaccess measure finding was not statistically significant in any model.

In Table 2, after adjustment for all other variables, women in the age group below 40 yearswere negatively associated with having had a previous mammogram when compared towomen in the age group 40-49 years, which was statistically significant (adjusted OR =0.11, 95% CI = 0.06-0.22). After adjustment for other variables, neither race nor ethnicityremained significantly associated with having had a previous mammogram when comparedwith White women. Women's educational attainment level and insurance status were notstatistically significantly associated with having had a previous mammogram in the adjustedmodel. Not having a family history of breast cancer appeared as a negative predictor ofhaving had a previous mammogram in the adjusted model, as it had in the univariate model,and remained statistically significant (adjusted OR = 0.37, 95% CI = 0.19-0.69). The odds of

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having had a previous mammogram for women who did not have a family history of breastcancer were about one-third as likely as women who had a positive family history of breastcancer. Hormone replacement therapy remained negatively associated with having had aprevious mammogram after controlling for all other variables in the adjusted model(adjusted OR = 0.15, 95% CI = 0.08-0.27) and the finding was statistically significant. In theadjusted model after controlling for all other variables, the evaluative recommendation byphysicians was found to be a significant predictor of having had a previous mammogram(OR = 2.00, 95% CI = 1.24-3.23).

DiscussionThe gravity model, a combined measure of accessibility and availability was used toevaluate the potential spatial interaction between any woman's location and all alternativemammography facilities within a reasonable distance. The relationship of geographicalaccess and utilization of mammogram is noteworthy. In Denver metropolitan area most ofmammography facilities are located close to the downtown where accessibility is higher.Women diagnosed with breast cancer without a previous mammogram also higher in thisarea (Figure 1). In another study we found women diagnosed with advanced stage of breastcancer are also higher in these areas (Rahman, et al., 2007). Several issues contribute indetermining which mammography facility to be used to get a mammogram, or morespecifically, why a woman would not use the nearest mammography facility or just onefacility to obtain her mammograms. Factors such as the type of health insurance and theirpolicies regarding reimbursement may have determined which mammography facility awoman must use to get a mammogram. A common physician practice is to recommend theirpatients to a specific mammography facility. Some women may prefer to go to amammography facility that is closer to their work place rather than from their residence.Moreover, it is crucial to specify one mammogram facility that the woman used to measurethe straight-line distance from her residence. Typically for a diagnosis of breast cancer awoman will have one or two mammograms and then an ultrasound, which will be followedby a biopsy and all of these examinations usually do not occur within the same clinic or onthe same day. Both access and distance are equally important in considering barriers toovercome for screening mammogram and diagnostic testings for breast cancer. Beinghindered in either way would likely result in a later stage of breast cancer at diagnosis.Taking into account all the above issues it seems more appropriate that we measure access tomammogram facility considering all the available facilities within an arbitrary radius, ratherthan the distance from the woman's residence to nearest facility or one specific facility.

Again, in the literature the arbitrary radius is usually considered as 30 miles for FCAmethod; however most of these studies are about primary care rather than preventive care.Assuming that the minimum distance a woman would be willing to travel to get a preventiveservice, such as, mammogram would be different, access ratio for several different radii (10,20, 30, 40, and 50 miles) were measured and compared. While comparing access measuresof different arbitrary radiuses in the FCA method, as the radius increased from 10 miles to50 miles, the standard deviation of access measures decreased and also the range fromminimum to maximum decreased (Table 3).

This indicates that the access measure with a higher radius had less variance, which led tostronger spatial smoothing, which is a manifestation of MAUP (modifiable areal unitproblem). Access scores tended to increase with increasing radius, as one would have moreaccess if she were permitted or capable of traveling further. As the radius increased from 10miles to 50 miles, the population with high access also increased (Figure 2, Figure 3 andFigure 4).

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However, if we look at the mean access measure for the population, it remains the same forall the measures with different radiuses (Table 3) as because increasing radius does notnecessarily mean increasing access. Access depends on the distribution of supply of anddemand for mammograms. In the method of calculating access to mammography facilities inthe current study, the availability of the facility was considered only by the total number ofwomen sharing that facility, which meant that all the mammography facilities were viewedas having equal capacity. When the radius increased, the number of women within thatarbitrary radius increased as well, which acted to decrease the ultimate access to amammography facility as more women shared that facility. To overcome this limitation,future research is needed that will consider the qualitative aspects of the mammographyfacilities, such as, the size, number of staff members, amount of equipment and other detailsthat might have affected the capacity of a facility.

Several other limitations that were related to the access measure of the current study are asfollows: First, the population data were geocoded by using women's Zip codes, as exactaddresses were not available because of a requirement to maintain confidentiality. By usingZip codes, women were assigned to an area rather than assigned to a single point. Thistechnique might have decreased the level of precision for the measure of access tomammography facility. Second, the current study was limited to only six county areas. Aknown limitation of the FCA method in measuring access is that people within a catchmentarea have equal access to all providers within that same catchment area, and all providersbeyond the radius of the catchment area are inaccessible, regardless of any differences indistances (Luo, 2004; Luo and Wang, 2004;). Finally, absence of individual level data onincome or socio-economic status and missing data on health insurance, education, hormonereplacement therapy, family history, physician recommendation and previous mammogramwere also a major limitation of the current study.

AcknowledgmentsDr. Fahui Wang, Department of Geography, Northern Illinois University.

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Rahman, S.; Price, JM.; Dignan, M.; Rahman, SM.; Lindquist, PS.; Jordan, TM. Access tomammography facilities and breast cancer stage at diagnosis: A GIS approach; Paper accepted fororal presentation at the AACE Annual Meeting 2007 (Oct 11-13): Cancer Education in Minorityand Underserved Populations; UAB Comprehensive Cancer Center: Alabama; 2007.

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Figure 1.Distribution of Population Who did not have had a Previous Mammogram (by Zip Code).

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Figure 2.Accessibility to Mammogram Facilities within 10 Miles Radius (by Zip Code).

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Table 1

Characteristics of the Study Population

Factors Number (N=2042) Percentage (%)

Age

Below 40 years 121 6

40-49 503 24

50-59 609 30

60-69 364 18

70 years and above 445 22

Race/Ethnicity

White 1811 91

Black 45 2

Asian 17 <1

American Indians and others 5 <1

Hispanic 104 5

Ethnicity

Hispanic 104 5

Non Hispanic 1898 95

Education

Less than High School Graduate 46 6

High School Graduate 198 26

Some College 251 32

College, or Post Graduate 280 36

Insurance Status

Medicaid and/or Medicare 392 21

No Insurance 15 <1

Other (Private insurance, managed care and others) 1417 78

Stage of Breast Cancer

In situ 320 16

Localized 1080 56

Regional 498 26

Direct 32 2

Previous mammogram

Yes 1560 91

No 155 9

Family History

Yes 190 17

No 953 83

Hormone replacement therapy

Yes 440 42


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Factors Number (N=2042) Percentage (%)

No 608 58

Physician Recommendation

Diagnostic 497 46

Evaluative 585 54

Note. Totals do not add to 2042 because of missing values.

Percentage calculated based on the non-missing values.


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in 5

0 m

iles r

adiu

sH

igh

acce

ss0.

910.

50-1

.63

0.82

0.41

-1.6

1

Med

ium

acc

ess

0.81

0.46

-1.4

40.

780.

40-1

.51

Low

acc

ess

1.00

1.00

Not

e. O

R =

Odd

s rat

io; C

I = C

onfid

ence

inte

rval

(Adj

uste

d O

dds r

atio

for a

ll th

e in

depe

nden

t var

iabl

es a

re ta

ken

from

the

logi

stic

mod

el fo

r 30

mile

radi

us)


NIH


NIH


NIH


Rahman et al. * =

stat

istic

ally

sign

ifica

nt.


NIH


NIH


NIH


Rahman et al.

Tabl

e 3

Com

paris

on o

f Acc

essi

bilit

y M

easu

res

Rad

ius

Tot

al n

umbe

rM

inim

umM

axim

umM

ean

Std.

Dev

iatio

n

10 M

ile17

45.0

0000

00.1

0759

26.0

2636

10.0

2611

28

20 M

ile17

45.0

0000

00.0

7684

12.0

2636

10.0

1364

16

30 M

ile17

45.0

0000

00.0

5361

46.0

2636

10.0

0840

47

40 M

ile17

45.0

0000

00.0

3770

56.0

2636

10.0

0565

72

50 M

ile17

45.0

0115

24.0

3147

64.0

2636

10.0

0423

59

N=

2042

.

Freq

uenc

y m

issi

ng 2

97.


Access to Mammography Facilities and Detection of Breast Cancer by Screening Mammography: A GIS...

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