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Geographic access to mammography and its relationship to breast cancer screening and stage at diagnosis: a systematic review

. Author manuscript; available in PMC: 2016 Sep 1.

A review was conducted to summarize the current evidence and gaps in the literature on geographic access to mammography and its relationship to breast cancer-related outcomes.

Ovid Medline and PubMed were searched for articles published between January 1, 2000 and April 1, 2013 using Medical Subject Headings and key terms representing geographic accessibility and breast cancer-related outcomes. Due to a paucity of breast cancer treatment and mortality outcomes meeting the criteria (N=6), outcomes were restricted to breast cancer screening and stage at diagnosis. Studies included one or more of the following types of geographic accessibility measures: capacity, density, distance and travel time. Study findings were grouped by outcome and type of geographic measure.

Twenty-one articles met inclusion criteria. Fourteen articles included stage at diagnosis as an outcome, five included mammography utilization, and two included both. Geographic measures of mammography accessibility varied widely across studies. Findings also varied, but most articles found either increased geographic access to mammography associated with increased utilization and decreased late-stage at diagnosis or no statistically significant association.

The gaps and methodologic heterogeneity in the literature to date limit definitive conclusions about an underlying association between geographic mammography access and breast cancer-related outcomes. Future studies should focus on the development and application of more precise and consistent measures of geographic access to mammography.

Breast cancer is the most frequently diagnosed cancer among women and the second leading cause of cancer death among women in the U.S. (Siegel et al., 2014). Early breast cancer detection through regular mammography screening is an important factor in breast cancer survival, as screen-detected cancers are more likely to be diagnosed with more favorable prognostic factors than symptom-detected cancers (Burke et al., 2008; Chiarelli et al., 2012; Dillon et al., 2004). Numerous studies have documented persistent disparities in mammography utilization and in late-stage diagnosis across age, race/ethnicity, and socioeconomic status.

Disparities in utilization may be due to a number of potential barriers in accessing mammography services, including poor geographic access to services. If only a few mammography facilities are located in a large geographic area or in an area serving a large population this may result in limited availability of mammography appointments and longer wait lists to be seen. This can present as a barrier to women seeking regular screening mammography or timely diagnosis of abnormal mammograms. Long travel distance/times to a mammography facility may also be a barrier that hinders women from seeking screening mammography on a recommended schedule. Longer times between screenings due to this barrier may result in a later stage at diagnosis.

There are numerous ways to define access to healthcare services. Aday and Anderson define access, specifically geographic accessibility, as a “function of time and physical space that must be traversed to receive care” which is aside from the mere existence of care (Aday & Andersen, 1974). Penchansky and Thomas define five specific dimensions of access: availability, accessibility, accommodation, affordability, and acceptability. Within this concept, availability refers to the adequacy of supply of physicians or facilities, whereas accessibility refers to the supply location in relation to demand (Penchansky & Thomas, 1981). With respect to healthcare facility accessibility, capacity (number of facilities that exist per number of individuals served in a pre-defined area) and density (number of facilities in a pre-defined area) and are two measures that have been previously reported. Other geographic measures, such as distance, travel time or some combination of both measures have also been utilized. Yang, Goerge, and Mullner found that the combination of measures, such as distance to nearest facility, and demand for services comprise the best geographic accessibility measures. Examples of such measures include the two-step catchment area (2SFCA) method and the kernel density (KD) method (Yang, Goerge, & Mullner, 2006). Geographic information systems (GIS), computer systems used to manage and display spatially referenced data on maps, can be used to describe the distribution of health services or disease patterns over space and time (Higgs, 2004) and can be used to calculate spatial accessibility measures such as the 2SFCA and KD method (Yang et al., 2006).

Studies have evaluated the relationship between geographic access and mammography utilization and/or stage at diagnosis, and have shown mixed results with regards to direction of the association and statistical significance. The purpose of this review is to synthesize the existing literature examining the relationship between geographic access measures (capacity, density, travel distance and travel time) and both mammography utilization and breast cancer stage at diagnosis. The goal is to better understand the scope of the literature on this topic, assess the direction of these relationships across studies, and identify future research needs.

Searches were conducted in Ovid Medline and PubMed using the following Medical Subject Headings and key terms in the title/abstract: (“Geographic Information Systems”[MeSH] OR “Geographic information systems” OR “geographic distance” OR “Spatial Analysis”[MeSH] OR “spatial analysis” OR “Geographic Mapping”[MeSH] OR “geographic mapping” OR “geographic locations” OR GIS OR “kernel density” OR “service density” OR “geographic density” OR “Health services accessibility” OR “accessibility” OR “travel time”)) AND (“Breast Neoplasms”[MeSH] OR “Mammography/utilization”[MeSH] OR “mammography utilization” OR “breast neoplasms” OR (“breast cancer” AND (incidence OR screening OR survival OR “drug therapy” OR therapy OR “follow-up time” OR “time-to-treatment”))). The search was limited to English language articles that were published between January 1, 2000 and April 1, 2013. Articles were limited to those published since 2000 due to technologic advancements in GIS and the development of the 2SFCA in 2000 (Radke & Mu, 2000).

After excluding duplicates (N=553), the initial query returned 798 articles of which the title and abstract were screened by two reviewers (JLE and JAK; Figure 1). Studies were excluded if conducted outside the U.S. due to differences in healthcare systems that may affect accessibility to mammography. Articles were restricted to those including a specific set of geographic measures of mammography access (i.e., capacity, density, travel distance, and travel time; Table 1) and breast cancer related outcomes (i.e., mammography screening or diagnostic utilization, stage at diagnosis, timeliness or type of treatment received and breast cancer mortality). Capacity measures are calculated as the number of facilities per number of individuals served in a pre-defined geographic area, whereas density measures include those calculated as the number of facilities in a pre-defined geographic area. Travel distance and travel time measures include road network-based or Euclidean (“as the crow flies”) measures.

Flowchart of selection of articles for review

Descriptions of included geographic spatial accessibility measures

Four articles included treatment outcomes(Lipscomb et al., 2012; Punglia et al., 2006; Schroen et al., 2005; Voti et al., 2006), only two of which used comparable treatment outcome measures. Two articles included breast cancer mortality outcomes (Russell et al. 2011; Tian et al., 2012), but used different geographic measures (capacity vs. travel time/distance). Due to a paucity of articles found on treatment received or breast cancer mortality and limited ability to group these articles by geographic measures or outcomes for comparison, the authors decided to focus the review only on access to screening mammography facilities and its relationship with mammography utilization and stage at breast cancer diagnosis. Ultimately, seventeen articles met our inclusion criteria. The bibliographic references listed in relevant papers were manually searched, through which 4 additional references were identified, resulting in a total of twenty-one articles for review. Data were extracted from these articles by JLE and JAK into an excel spreadsheet which included columns for data source for both study population and mammography facilities, population, location, descriptions and categorization of the geographic measure(s) of access, descriptions and categorizations of outcomes, additional covariates included in the analysis, and primary results. Any articles for which inclusion was questionable were discussed by all authors until consensus was reached. Specifically, the authors decided to keep one article in which the outcome was tumor size (Schroen & Lohr, 2009) since it is one of the main components of breast cancer TNM staging (AJCC Cancer Staging Manual, 2010). Descriptions of included articles are presented in two tables separated by outcome (mammography utilization and stage of diagnosis). Tables are organized by type of access measure: capacity, density, distance, and travel time.

Table 2 describes the articles (n=7) examining the relationship between geographic access and mammography utilization, while Table 3 includes articles (n=16) examining the association with stage at diagnosis. Many articles reported results on more than one access measure and are therefore listed multiple times.

Summaries of articles measuring geographic access to mammography and its relation to breast cancer screening

Articles measuring geographic access to mammography and its relation to breast cancer stage at diagnosis

Seven articles analyzed geographic access to mammography in relation to screening (Table 2)(Coughlin et al., 2008; Elkin et al., 2010; Elting et al., 2009; Engelman et al., 2002; Marchick & Henson, 2005; Meersman et al.,, 2009; Rahman et al., 2009). Most authors used FDA-certified mammography facility locations and defined mammography utilization as having had a mammogram within the previous two years. Five included measures of capacity (Coughlin et al., 2008; Elkin et al., 2010; Elting et al., 2009; Marchick & Henson, 2005; Rahman et al., 2009), two included a density measure (Marchick & Henson, 2005; Meersman et al., 2009), two included distance measures (Engelman et al., 2002; Meersman et al., 2009), and none included travel time. While the outcome definition was fairly consistent across articles, there was variability in the methods and categorizations of geographic measures.

Sample sizes varied from 2,042 (Rahman et al., 2009) to 891,929 (Elkin et al., 2010), and geographic areas ranged from smaller areas, such as cities and counties, to nationwide coverage. Rahman et al. was the only article that reported mammography utilization among a population of women who had been diagnosed with breast cancer, whereas the other articles included a screening population (Rahman et al., 2009). Studies by Rahman et al. and Meersman et al. were conducted in metropolitan areas (Denver metropolitan area and Los Angeles County, respectively), whereas the other five studies were conducted with state or national level data (Meersman et al., 2009; Rahman et al., 2009). Data sources included state cancer registries (Elting et al., 2009; Rahman et al., 2009), California Health Interview Survey (CHIS) (Meersman et al., 2009), Behavioral Risk Factor Surveillance System (BRFSS) (Coughlin et al., 2008; Elkin et al., 2010; Elting et al., 2009), Medicare (Elkin et al., 2010; Engelman et al., 2002), and Surveillance Epidemiology and End Results (SEER) (Marchick & Henson, 2005). Marchick et al. was the only ecologic study among the group with states used for the unit of analysis (Marchick & Henson, 2005).

Geographic measures of capacity varied in complexity from the presence of at least one mammography facility in a county (Elting et al., 2009), to the number of mammography screening centers or machines per X number of women in a county (Coughlin et al., 2008; Elkin et al., 2010; Marchick & Henson, 2005), to the more complex 2SFCA at the zip code level (Rahman et al., 2009). Density was described as both the number of mammography facilities within a 2 mile buffer of residence (Meersman et al., 2009) and the number of mammography facilities per 1,000 square miles (Marchick & Henson, 2005). Distance based on the road network was used for one study (Engelman et al., 2002), while Euclidean distance was measured in another (Meersman et al., 2009).

Both Elting et al. and Elkin et al. found that increased mammography capacity was positively associated with having had a mammogram within the last 2 years, whereas Marchick et al. found no association between mammography use within the last year and capacity (Elkin et al., 2010; Elting et al., 2009; Marchick & Henson, 2005). Coughlin et al. found slight, but statistically significant, differences in the percentage of women who had a mammogram within the last 2 years by the number of mammography facilities per 100,000 women in bivariate analysis (Coughlin et al., 2008). Mammography use was lowest for 3 or more facilities (73.7%) and increased from <1 (76.1%), 1–2 (77.3%), to 2–3 (77.9%) facilities; however, no association was found in multivariate analysis (Coughlin et al., 2008). Diverging from the other studies, Rahman et al. found an inverse association such that women with greater accessibility scores were less likely to report having had a previous mammogram (Rahman et al., 2009). Both Meersman et al. and Marchick et al. found increased mammography use associated with increased density of mammography facilities (Marchick & Henson, 2005; Meersman et al., 2009). While Engleman et al. found an inverse association between distance and having had a mammogram (Engelman et al., 2002), Meersman et al. did not find an association between mammography use and distance from residence to nearest mammography facility (Meersman et al., 2009).

There appeared to be trends in results by size of the geographic area. Conclusions varied within the state and smaller area studies (Elting et al., 2009; Meersman et al., 2009; Rahman et al., 2009), whereas more consistent positive associations between access and mammography use were found in larger national studies (Elkin et al., 2010; Engelman et al., 2002; Marchick & Henson, 2005).

Sixteen articles analyzed geographic access to mammography in relation to stage at breast cancer diagnosis (Table 3)(Celaya et al., 2010; Dai, 2010; Elting et al., 2009; Goovaerts, 2010; Gumpertz, Pickle, Miller, & Bell, 2006; Henry et al., 2011; Henry et al., 2013; Huang, Dignan, Han, & Johnson, 2009; Lian, Struthers, & Schootman, 2012; L. Luo, McLafferty, & Wang, 2010; Marchick & Henson, 2005; McLafferty & Wang, 2009; Onega et al., 2011; Schroen & Lohr, 2009; Tarlov et al., 2009; Wang et al., 2008). Authors either used FDA-certified mammography facilities, did not report the source of mammography facility locations, or used study-specific Group Health radiology facilities (Onega et al., 2011). Four articles included measures of capacity (Dai, 2010; Elting et al., 2009; Lian et al., 2012; Marchick & Henson, 2005), three included a density measure (Henry et al., 2013; Lian et al., 2012; Marchick & Henson, 2005), seven measured distance (Celaya et al., 2010; Goovaerts, 2010; Gumpertz et al., 2006; Huang et al., 2009; L. Luo et al., 2010; Schroen & Lohr, 2009; Tarlov et al., 2009), and seven measured travel time (Celaya et al., 2010; Henry et al., 2011; Henry et al., 2013; Lian et al., 2012; McLafferty & Wang, 2009; Onega et al., 2011; Wang et al., 2008). The majority of articles used AJCC or SEER staging systems and categorized the outcome into advanced or late stage disease at diagnosis versus early stage. One article used AJCC tumor size categories as their outcome (Anneke T. Schroen & Lohr, 2009) and was included in this section since staging is dependent on tumor size.

Sample sizes varied from 1,306 (Onega et al., 2011) to 161,619 women (Henry et al., 2013) and geographic areas ranged from smaller metropolitan areas to larger nationwide coverage. The majority of the studies included women 40 and older diagnosed with breast cancer. Marchick et al. was an ecologic study using the percent of advanced stage diagnoses at the county level as an outcome (Marchick & Henson, 2005). Data sources included state cancer registries (Celaya et al., 2010; Dai, 2010; Elting et al., 2009; Goovaerts, 2010; Henry et al., 2011; Henry et al., 2013; Huang et al., 2009; Lian et al., 2012; L. Luo et al., 2010; McLafferty & Wang, 2009; Schroen & Lohr, 2009; Tarlov et al., 2009; Wang et al., 2008), SEER (Gumpertz et al., 2006; Marchick & Henson, 2005) and enrollees in the Group Health integrated health care delivery system (Onega et al., 2011).

Capacity measures included the presence of mammography facilities in a county (Elting et al., 2009), number of mammography facilities per 10,000 women (Marchick & Henson, 2005) and 2SFCA scores (Dai, 2010; Lian et al., 2012). Lian et al. applied six different levels of weighting to the 2SFCA and analyzed each type of weighted accessibility score (Lian et al., 2012). Density was measured as the number of mammography machines at facilities reached within a 30 minute drive time (Lian et al., 2012), number of mammography facilities within 1,000 square miles (Marchick & Henson, 2005), and an accessibility score that considered both the number of mammography facilities and drive times (Henry et al., 2013). Distances were measured from the patient’s address (Celaya et al., 2010; Goovaerts, 2010; Huang et al., 2009; Schroen & Lohr, 2009; Tarlov et al., 2009), or zip code (L. Luo et al., 2010) or census tract centroid (Gumpertz et al., 2006; L. Luo et al., 2010) to the nearest mammography facility using road network (Celaya et al., 2010; Huang et al., 2009; L. Luo et al., 2010; Schroen & Lohr, 2009; Tarlov et al., 2009) and Euclidean measurements (Goovaerts, 2010). Travel time was calculated from the patient’s address (Celaya et al., 2010; Henry et al., 2011), zip code (McLafferty & Wang, 2009; Wang et al., 2008), census tract or block centroid (Henry et al., 2013; Lian et al., 2012; Onega et al., 2011) to the nearest mammography facility (Celaya et al., 2010; Henry et al., 2013; Lian et al., 2012; McLafferty & Wang, 2009; Wang et al., 2008) or the patient’s diagnostic facility (Henry et al., 2011) based on the road network (Celaya et al., 2010; Henry et al., 2013; Lian et al., 2012; McLafferty & Wang, 2009; Onega et al., 2011; Wang et al., 2008) or Euclidean distance (Henry et al., 2011).

Overall, findings were mixed and largely not statistically significant. There did not appear to be any trends in results by size of the geographic area. Conclusions varied within the national level studies, as well as within state and smaller area studies. Three articles found increased odds of late stage diagnosis with decreased mammography facility capacity (Dai, 2010; Elting et al., 2009; Lian et al., 2012), while one study found no association (Marchick & Henson, 2005). Of the three articles with density measures, one found less accessibility associated with late stage at diagnosis (Henry et al., 2013) another found no association (Lian et al., 2012), and the third found a positive correlation between access and late stage diagnosis among white women and a negative association among black women (Marchick & Henson, 2005). Four of the seven distance measure articles found no association between distance and late stage diagnosis (Celaya et al., 2010; Goovaerts, 2010; Schroen & Lohr, 2009; Tarlov et al., 2009). Gumpertz et al. and Huang et al. found increased odds of late stage diagnosis with increased distance from mammography (Gumpertz et al., 2006; Huang et al., 2009). Luo et al. also found a negative association, but only at by zip code level and only in one of the two counties included in the study (L. Luo et al., 2010). Henry et al. found an unexpected result that women living greater than a 40 minute travel time to their diagnosing facility were less likely to be diagnosed at a later stage compared to women living less than 40 minutes from their diagnosing facility (Henry et al., 2011). None of the other articles examining travel time found significant associations.

Acknowledgement of system-level factors in the role of health has brought attention to issues such as access to mammography. Additionally, advancements in GIS methods and increasingly available large electronic datasets have made the evaluation of geographic access more feasible than it has been in the past. However, the most appropriate GIS methods and measures of mammography accessibility have not yet been established and this review demonstrates that there is great variation in current approaches. Improvements have been made by researchers such as W. Luo, F. Wang, M. McGrail, and P. Delamater, although their methods have not been adopted widely in the public health literature (Delamater, 2013; W. Luo & Qi, 2009; W. Luo & Wang, 2003; McGrail, 2012). The purpose of this review was to assemble and describe the literature examining geographic access and breast cancer-related outcomes as well as identify gaps in this area of the literature. The variation in measures used and results from these studies were described. While some studies examined access to treatment facilities and other outcomes, such as receipt of treatment and mortality, this area of the literature is sparse and does not allow for sufficient comparison. This is revealed as a gap in the literature requiring further attention. Although unable to provide firm conclusions, this review does serve to introduce future researchers to the state of the science and gaps in the literature.

Overall, there was great variability in measures of geographic access to mammography. Outcomes also varied, but the majority found either increased geographic access to mammography was associated with increased utilization and decreased late-stage at diagnosis or no statistically significant association. Only three articles found an unexpected reverse association (Henry et al., 2011; Marchick & Henson, 2005; Rahman et al., 2009). Few articles distinguished between rural/urban populations inhibiting comparisons between the two. The majority of analyses using distance and travel time measures found no statistically significant results between geographic access and mammography use or late-stage at diagnosis, whereas the majority of results from capacity and density found statistically significant associations. This suggests that distance and travel time alone may not be sufficient measures of geographic access to care since neither method accounts for size of the geographic area being examined or the underlying population composition (i.e., service demand) (Eberth et al., 2014; Gumpertz et al., 2006), however it is difficult to draw conclusions based on inconsistencies across studies in methods of measurement within each type of geographic measure.

There are several limitations including the potential accuracy and wide variability in measures that creates difficulty in drawing conclusions on the effect of access to mammography facilities on patient outcomes. The majority of studies examined access to the nearest mammography facility rather than actual facilities used by women for screening. For example, it could be possible that the facility nearest to a woman’s home may not accurately represent the facility she visits (Liu et al,, 2008; Tai, Porell, & Adams, 2004). Some women may travel further for screening out of preference or due to insurance factors (Liu et al., 2008; Tai et al., 2004). The nearest facility approach is especially biased in urban areas, where women have more choices of mammography facilities to choose from (McGrail, 2012). Women may also seek care closer to their workplace since appointments may occur during work hours. Actual travel patterns may be more strongly tied to patient outcomes than measures of potential accessibility (Massarweh et al., 2014; Schroen et al., 2005); however, clinical and public health datasets rarely collect data on women’s place of employment or origin for care-seeking. Henry et al. examined both actual travel time to each woman’s diagnosing facility as well as their nearest mammography facility among women from 10 U.S. states (Henry et al., 2011). On average, the travel time to a woman’s actual diagnosing facility was longer than to the nearest mammography facility. Interestingly, the study found women with longer travel times to their diagnosing facility had lower odds of late stage diagnosis than those with shorter travel times. No association was found between travel time to the nearest mammography facility and stage at diagnosis. These unexpected and differing results between actual and potential access suggest a need to further explore the actual geographic patterns of seeking care rather than access to the nearest facilities.

The great variability in measures of geographic access to mammography hinders the ability to draw definitive conclusions from this literature. Even examined separately by type of access measure (capacity, density, travel distance, travel time), there was major heterogeneity in measurement. For example, some studies used a geographic centroid to represent patient residence, while others used actual patient address. Furthermore, most articles used one method of measuring access which did not allow for making comparisons across methods. Lian et al., however, sought to compare several measures of mammography accessibility including two measures of travel time to the nearest facility, a density measure, and six variations of the 2SFCA in metropolitan St. Louis, in which 337,966 women of screening age live (Lian et al., 2012). The study found low correlation and low agreement between accessibility measures. Only one of the measures, the most precise 6-time zone weighted 2SFCA, showed increased accessibility associated with decreased odds of late stage diagnosis, whereas all other measures were not statistically significant (Lian et al., 2012). Given the low agreement across measures, as well as mixed findings between measures in Lian et al’s study, it proves difficult to draw definitive conclusions across this literature. However, it is important to note that Lian et al’s study was confined to a two adjacent counties and did not use actual facilities visited or specific patient addresses, rather they used the nearest facilities and census block centroids. Providing a sensitivity analysis across measures does represent a significant advancement in how studies have been conducted to date.

This review suggests that additional research, using sophisticated measures of geographic accessibility, is needed to provide more conclusive evidence of the effect of geographic access to mammography on breast cancer related outcomes. These studies can provide valuable information about regionally-specific relationships between access to care and patient outcomes, which can in turn help inform targeted interventions to increase geographic access to care in specific areas or among subgroups of the population.

To draw clearer conclusions about the role of geographic access to mammography and mammography use and cancer outcomes, more accurate representations of geographic access to care are needed. In addition to improved measures, future studies should explore actual travel patterns in patient care-seeking (e.g., utilized accessibility).

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