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. Author manuscript; available in PMC: 2010 Aug 11.
Published in final edited form as: Prev Med. 2008 Jan 16;46(6):479–488. doi: 10.1016/j.ypmed.2008.01.001

Lower adherence to screening mammography guidelines among ethnic minority women in America: A meta-analytic review

Rebecca J Purc-Stephenson a,*, Kevin M Gorey b
PMCID: PMC2920292  CAMSID: CAMS1429  PMID: 18295872

Abstract

Objective

This study investigates the association between ethnic minority status and receiving a screening mammogram within the past 2 years among American women over 50.

Method

The findings from 33 studies identified from interdisciplinary research databases (1980 to 2006) were synthesized. Separate pooled analyses compared white non-Hispanics to African Americans (28 outcomes), Hispanics (18 outcomes), and Asian/Pacific Islanders (10 outcomes).

Results

Using the random effects model, results showed that African Americans were screened less than white non-Hispanics at a marginal level (OR 0.87, 95% CI 0.75, 1.00). Larger and significant discrepancies were observed for Hispanics (OR 0.65, 95% CI 0.50, 0.85) and Asian/Pacific Islanders (OR 0.63, 95% CI 0.39, 0.99) compared to white non-Hispanics. However, among studies controlling for socioeconomic status, ethnic differences in mammography screening were no longer significant for African Americans (OR 1.05, 95% CI 0.71, 1.76), Hispanics (OR 1.08, 95% CI 0.64, 1.93), or Asian/Pacific Islanders (OR 1.08, 95% CI 0.64, 1.93). Subgroup analyses further showed that geographical region, sampling method, and data collection strategy significantly impacted results.

Conclusions

This study found evidence that ethnic minority-screening mammography differences exist but were impacted by socioeconomic status. Implications for interpreting existing knowledge and future research needs are discussed.

Keywords: Mammography, Breast cancer, Prevention, Screening, Meta-analysis, Ethnic minorities, Race

Introduction

Next to skin cancer, breast cancer is the most commonly diagnosed cancer affecting women in the United States (American Cancer Society [ACS], 2005). In 2007, an estimated 240,510 new cases of breast cancer will be diagnosed in the United States, and approximately 40,460 women will die from the disease (ACS, 2007). Mortality rates from breast cancer have substantially declined in the past decade, which is attributed to improvements in treatments and to increases in the use of screening mammography leading to earlier detection (Berry et al., 2005).

The burden of breast cancer is not distributed equally across all women (Peek and Han, 2004; Gotzsche and Olsen, 2000; Weir et al., 2003). Data from the ACS (2005) shows that from 1998 to 2002, the average annual female breast cancer prevalence rate was highest among white non-Hispanics (141.1 cases per 100,000 females), followed by African Americans (119.4), Asian Americans (96.6), Hispanics (89.9), and Native Americans (54.8). Although ethnic minority women have a lower overall prevalence of breast cancer, research also shows that they experience later stage at diagnosis, greater prevalence for multiple cancer sites, greater mortality and morbidity than their white non-Hispanic counterparts (Ashing-Giwa et al., 2004; Heeden and White, 1999; Lannin et al., 1998; Polite and Olopade, 2005; Randolph et al., 2002; Weir et al., 2003).

Although some research suggests that screening mammography rates are similar for white non-Hispanics and African Americans (Rajaram and Rashidi, 1998), some observers maintain that ethnic minority women remain under-users (Bastani et al., 1995; Friedman et al., 1995; Pearlman et al., 1996; Peek and Han, 2004; Siegler and Costa, 1994). For example, after adjusting for age, income and education, national studies report that Hispanics are less likely than African Americans and white non-Hispanics to be screened in the past year (Meyerowitz et al., 1998). In fact, Polite and Olopade (2005) suggest that one reason ethnic minorities present at a later stage of breast cancer is that they do not receive the same level of screening as white non-Hispanics.

There are a several comprehensive reviews exploring race/ethnicity and issues related to breast cancer screening (Austin et al., 2002; Consedine et al., 2004; Katapodi et al., 2004; Raja-Jones 1999; Vernon et al., 1990; Wells and Roetzheim, 2007; Wu et al., 2004). However, these reviews do not systematically compare white non-Hispanics with different ethnic minority groups. Instead, heterogeneous ethnicities are collapsed into a single “non-white” group preventing comparisons across minority populations. Moreover, some reviews (e.g., Wells and Roetzheim, 2007; Wu et al., 2004) examine breast cancer screening patterns among a single ethnic minority group with no comparisons to white non-Hispanics. This lack of information is unfortunate given the rapidly increasing ethnic diversity within the United States and the high cancer rates within some ethnic groups (Meyerowitz et al., 1998). Furthermore, various methodological artifacts confound their conclusions, as these reviews do not take into account the methods of their primary studies. For example, research using data from insurance claims may inflate estimates of mammography use as these sources tend to combine mammography for screening and diagnostic purposes (Kagay et al., 2006).

Research examining mammography use among various ethnic groups has increased over the past two decades and the availability of such data offers the opportunity to examine whether ethnic minority women undergo screening mammography at a similar rate as white non-Hispanics or whether ethnic disparities in screening persist. Therefore, this review empirically investigates the relationship between screening mammography and ethnicity by integrating the findings of existing studies using meta-analytic techniques. The following specific research questions were developed:

Research question 1: Are ethnic minority women at risk for receiving fewer mammograms than white non-Hispanic women?

Research question 2: Are there significant socioeconomic status factors that impact reports of screening mammography?

We also plan to explore if any other significant demographic and methodological factors impact reports of screening mammography.

Method

Literature search

In February 2007, the following peer-reviewed research literature databases were searched: ERIC, PsychINFO, Medline, Social Services Abstracts, and Sociological Abstracts (1975 to 2006). The search strategy included the keywords: (ethnic*, minorit*, race) and (‘breast cancer’, ‘breast neoplasm’, ‘breast carcinoma’) and (screen*, preventi*, mammogra*). This search was augmented by bibliographic reviews of retrieved manuscripts and previous published reviews.

Inclusion criteria

As the focus of the review was the use of screening mammography among white non-Hispanic and ethnic minority women, studies had to meet the following criteria: (a) include a white non-Hispanic comparison group; (b) be conducted in the United States or Canada; (c) focus on women aged 50 years and older; (d) include women without a history of breast cancer; and (e) present their findings with sufficient detail so that effect sizes were calculable. Nonempirical manuscripts (i.e., theoretical, reviews, and qualitative studies) were excluded. Studies that focused on mammography for diagnostic purposes or combined screening and diagnostic mammograms were excluded because these studies could inflate mammography rates. Ethnic minority status was defined as African American, Hispanic, Asian/Pacific Islander, or Native American/Alaskan Native. A global definition of the dependent variable, namely having a screening mammography within the past 2 years allowed inclusion of a greater number of studies and encompassed the screening mammography guidelines outlined by several government and healthcare organizations (e.g., ACS, 2003; Canadian Cancer Institute [CCI], 2007; National Cancer Institute [NCI], 2007; United States Preventive Services Task Force [USPSTF], 2002).

Statistical analysis

Data gathered from the primary studies were classified into comparisons between white non-Hispanic women and African American women (meta-analysis 1), between white non-Hispanic women and Hispanic women (meta-analysis 2), and between white non-Hispanic women and Asian/Pacific Islander women (meta-analysis 3). Only one study that examined Native American/Alaskan Natives with white non-Hispanics met our inclusion criteria, therefore this comparison was excluded.

The odds ratio (OR), which is an estimate of the relative odds of an ethnic minority woman having a mammography versus white non-Hispanic woman having a mammography, was selected as this study’s central meta-analytic statistic (Cooper, 1998; Greenland, 1987). When interpreting an OR, it is helpful to look at how much it deviates from 1. In this study, for example, an OR of 0.75 would be interpreted as the ethnic minority group being 25% less likely to receive a screening mammogram compared to the non-Hispanic white group, whereas an OR of 1.33 would be interpreted as the ethnic minority group being 33% more likely. Effect size estimates were adjusted for sample size and the 95% confidence intervals were calculated to assess the statistical significance of average effect sizes. As research suggests that the random effects model is preferable to the fixed effects model (Hunter and Schmidt, 2000), the DerSimonian–Laird random effects model is reported. The random effects model takes into account sampling variation of the estimates and variation in the underlying parameter over the studies, and has the effect of widening the confidence limits around the pooled effect. A one-tailed Fail-safe N at p<.05 was calculated for each significant overall meta-analysis as a control for publication bias (Rosenthal, 1979). Although there are no firm guidelines about the appropriate size of N, if the Fail-safe N is relatively large in comparison to the number of studies in the meta-analysis, researchers can be more confident in the stability of their results (Carson et al., 1990).

A test of heterogeneity was computed for each research outcome using Cochran’s Q statistic (Fleiss, 1981). This test evaluates dispersion between studies and average effect sizes to determine if this is more than what would be expected by chance (Hedges and Orkin, 1985). If significant heterogeneity was observed (p<.05), possible sources heterogeneity was explored through subgroup analysis.

Subgroup analysis

Grouping variables were introduced to both describe the studies included in the meta-analysis and to evaluate the potential impact on the research outcomes. As subgroup analysis can potentially generate spurious findings (Higgins et al., 2003), only a small number of subgroup analyses were undertaken and were determined a priori, with the alpha criteria set at p<.05. Grouping variables were generated from a review of the literature and were pragmatically dependent upon available information within the studies. Each study was coded for:

  1. socioeconomic status

  2. year data were collected

  3. geographic region where data were collected and

  4. residence.

In addition to contextual variables, methodological variables that could potentially impact the outcomes were coded:

  1. research design

  2. sampling method and

  3. data collection strategy.

Grouping studies by research design (i.e., non-experimental descriptive versus control group in an experimental design) and residence (i.e., urban versus rural regions) revealed single studies or large numbers of mixed samples, therefore preventing subgroup analyses of these variables.

Results

Search results

The electronic literature search yielded 112 conceptually relevant studies, and another 11 studies were found from hand searching. After applying our inclusion criteria, 47 studies remained. Among studies that met the inclusion criteria but were missing information, we attempted to contact the primary authors via email. Authors of four studies responded and provided the necessary information. Fourteen studies were excluded for the following three reasons: (a) sample sizes for ethnic/racial groups were not reported (Caplan et al., 1992; Casey et al., 2001; Friedman et al., 1995; Goel et al., 2003; Lane et al., 2000; O’Malley et al., 1997; Regan et al., 1999; Stoddard et al., 1998; Yood et al., 1999); (b) data on the white non-Hispanic comparison group were not reported (Wampler et al., 2006); and (c) combined screening and diagnostic mammograms (Henderson and Schenck, 2001; Parker et al., 1998; Preston et al., 1997; Sabogal et al., 2001).

The 33 studies identified compared white non-Hispanic women to African American women, Hispanic women, and Asian/Pacific Islander women. A number of studies reported multiple outcomes, such as assessments of more than one ethnic minority group compared to white non-Hispanics. Separate pooled analyses were conducted for each ethnic group comparison for screening mammography within the past 2 years: African American (meta-analysis 1: 28 outcomes), Hispanic (meta-analysis 2: 18 outcomes), and Asian/Pacific Islander (meta-analysis 3: 10 outcomes).

The studies were published between 1991 and 2006, and the majority (84.8%) used a non-experimental research design. Among the studies that reported it, five studies collected data between 1980 and 1989, 18 between 1990 and 1996, and six between 1997 and 2005. All studies were conducted in the United States; no studies from Canada met our inclusion criteria. Based on the census regions and divisions of the United States (U. S. Census Bureau 2005), 12 studies were conducted in the West (mostly in California), eight in the South (mostly in North Carolina), four in the Northeast (mostly in New York), and one study in the Midwest (Indiana). There were nine population-based studies of which six were nationwide (five National Health Interview Surveys, one Behavioral Risk Factor Surveillance Survey) and three were statewide. Most studies collected data from urban/metropolitan regions (62.5%). Over half of the studies (66.7%) used random sampling. Data were generally collected through self-report methods, with eight studies using self-administered/mail surveys, 12 using telephone interviews, and 10 using in-person interviews. The remaining three studies used archival data (e.g., medical files).

Meta-analysis

African American versus white non-Hispanics (meta-analysis 1)

Using the random effects model, the pooled OR was 0.87 (95% CI 0.75, 1.00) based on an aggregate sample of 76,338 women, including 14,298 African Americans (Table 1). Although African Americans appear to have been screened at a lower rate than white non-Hispanics, the difference was only marginally significant (p = .06). However, individual effect sizes revealed that 20 of the 28 OR point-estimates were in the expected direction of higher mammography use among white non-Hispanics, and six of them were statistically significant in the expected direction (p<.05). Furthermore, the fixed effects model indicated that the difference was significant (OR 0.77, 95% CI 0.74, 0.81) and the heterogeneity statistic was significant, Q (27, N = 76,338) = 192.62, p<.01. This discrepancy justified examining moderators to account for this variation.

Table 1.

Primary study and aggregate study outcomes by race: Non-Hispanic White (W) versus African American (AA) on obtaining a mammogram in the past 2 years

Citation Sample size
 Year data were collected Location Sampling method Data collection Effect size
W AA OR 95% CI
Auguston et al. (2003) 144 718 2000 West Random TI 1.33 0.89 1.96
Burack et al. (1998) 1521 221 1992 Nationwide Random IPI 0.80 0.60 1.08
Caplan et al. (1992) 19,646 3896 1987–1988 Nationwide Random SAS 0.44a 0.39 0.49
Cummings et al. (2002) 438 405 1997 South Random IPI 0.82 0.62 1.08
Fox and Stein (1991) 344 77 1988 West Random TI 1.11 0.64 1.91
Fox et al. (1998) 650 469 1995–1996 West Convenience TI 0.88 0.68 1.13
Frazier et al. (1996) 14,315 1424 1990 Nationwide Random TI 0.88a 0.78 0.98
Glanz et al. (1996) 531 111 1989–1992 Northeast Convenience TI 1.38 0.87 2.23
Goldberg and Lessard (2002) 363 1278 1996 West Convenience SAS 1.27 0.98 1.67
Haas et al. (2002) 4577 844 1996 Nationwide Random SAS 0.87 0.73 1.03
Hiatt et al. (1996) 605 602 1993–1996 West Random SAS 0.90 0.69 1.17
Johnson and Murata (1988) 316 210 1984–1985 West Convenience Arch 0.64 0.38 1.06
Lee and Vogel (1995) 5646 213 1986–1991 South Convenience SAS 0.25a 0.11 0.48
Magai et al. (2004) 154 295 nr nr Random IPI 0.96 0.63 1.46
Makuc et al. (1999) 3022 529 1993–1994 Nationwide Random IPI 1.27 1.04 1.56
Mayne and Earp (2003) 419 411 1993–1994 South Random IPI 0.62a 0.46 0.82
Mayo et al. (2001) 81 130 nr South Convenience IPI 1.26 0.65 2.43
Miller and Champion (1997) 807 242 nr Midwest Convenience SAS 0.74 0.50 1.09
Otero-Sabogal et al. (2004) 273 54 1999 West Random TI 0.83 0.44 1.57
Raucher et al. (2005) (group 1) 80 136 1993–1994 South Random SAS 0.43a 0.20 0.89
Raucher et al. (2005) (group 2) 139 124 1993–1994 South Random SAS 1.29 0.75 2.23
Rimer et al. (1996) 91 378 nr South Random TI 1.10 0.68 1.78
Sambamoorthi and McAlpine (2001) 1065 200 1996 Nationwide Random IPI 0.97 0.68 1.41
Song and Fletcher (1998) 1137 337 1994 West Convenience Arch 0.97 0.73 1.28
Taylor et al. (1998) 159 122 1995 West Convenience SAS 0.91 0.52 1.58
Taylor et al. (1999) 40 35 1995–1996 West Random Arch 0.86 0.24 3.01
Valdini and Cargill (1997) 1597 119 1999 Northeast Convenience IPI 0.44a 0.39 0.49
Wee et al. (2004) 3880 718 1998 Nationwide Random IPI 0.96 0.65 1.42
Meta-statistics
All study outcomes 62,040 14,298 0.87 0.75 1.00
SES-adjusted
outcomes		 25,170 4541 1.05 0.71 1.76
Open in a new tab

TI = telephone interview; IPI = in-person interview, SAS = self-administered survey; Arch = archival data; OR = odds ratio; CI = confidence intervals; and nr = data not reported.

a

p<.05.

Effect of socioeconomic factors

Seven studies reported ORs adjusted for socioeconomic status, typically by income, education and/or insurance status. According to the aggregate mean of these adjusted ORs, the difference in having a mammography in the past 2 years was no longer significant (OR 1.05, 95% CI 0.71, 1.76). For only meta-analysis 1, five of these seven studies provided enough data on education level and three of these seven studies provided enough data on insurance status to examine how each of these factors impact mammography screening. Income level was inconsistently reported preventing us from creating meaningful income categories, hence it was not examined. For education, the pooled meta-analytic data showed no significant difference in mammography screening for African Americans compared to white non-Hispanics. However, we explored within-group differences for each ethnic group and found that African Americans with high school education or less were less likely to have a screening mammography than those with education beyond high school (pooled 42.7% versus 56.3%), χ2 (1, N = 2411) = 30.69, p<.0001. Likewise, white non-Hispanics with high school education or less were less likely to receive a screening mammogram than those with education beyond high school (pooled 45.4% versus 58.7%), χ2 (1, N = 21,574) = 285.09, p<.0001. For insurance status, the pooled data showed no significant difference in mammography screening for African Americans compared to white non-Hispanics. However, within-group differences showed that African Americans without insurance were less likely to receive a mammogram than those with some form of insurance (pooled 44.4% versus 63.5%), χ2 (1, N = 432) = 15.43, p<.0001. Similarly, white non-Hispanics without insurance were less likely to receive a mammogram (pooled 43.8% versus 54.9%), χ2 (1, N = 1,435) = 17.72, p<.0001.

Effect of contextual variables and methodological factors

The only marginally significant moderator was geographical region (Table 2). Because there was only one study conducted in the Midwest and the Northeast, subgroup analysis focused on studies from the West and South. The difference between women in the West was not significant (p = .26). The difference between women in the South was marginally significant (p = .09), and this difference was significant for the fixed effects model (OR 0.72, 95% CI 0.62, 0.84, p <.01), suggesting that African Americans were screened less than white non-Hispanics.

Table 2.

Subgroup analysis for African American, Hispanic, and Asian/Pacific Islander women compared to white non-Hispanic women

Socioeconomic and contextual variables
 Methodological variables
Subgroup Race No. of studies OR 95% CI N total Subgroup Race No. of studies OR 95% CI N total
Geographical region Data collection method
 Northeast AA 2 1.28 0.79, 1.16 2358  Self-
administered
survey		 AA 8 0.81 0.63, 1.05 15,937
His 4 0.92 0.77, 1.09 4102 His 5 0.74 0.46, 1.18 17,152
As/PI 0 – – – As/PI 5 0.62 0.23, 1.36 8881
 Midwest AA 1 0.74 0.51, 1.08 1049  Telephone
interview		 AA 7 0.99 0.85, 1.15 19,579
His 0 – – – His 8 0.63 0.38, 1.03 19,807
As/PI 0 – – – As/PI 3 0.61 0.42, 0.87 1892
 South AA 7 0.74 0.52, 1.05 8691  In-person
interview		 AA 10 0.84 0.63, 1.11 38,748
His 2 0.53a 0.40, 0.70 6518 His 3 0.61 0.31, 1.21 24,453
As/PI 1 1.45 0.99, 2.05 1099 As/PI 0 – – –
 West AA 10 0.98 0.86, 1.12 7933  Archival data AA 3 0.87 0.67, 1.12 2075
His 8 0.56a 0.33, 0.95 9735 His 2 0.61a 0.46, 0.81 1836
As/PI 8 0.51a 0.32,.083 7264 As/PI 2 0.89 0.74, 1.09 2321
Year data collected Sampling method
 1980–1990 AA 5 0.65 0.38, 1.12 30,991  Random AA 18 0.86 0.72, 1.04 61,821
His 5 0.86 0.68, 1.08 22,821 His 11 0.70a 0.54, 0.90 49,165
As/PI 1 0.54 0.19, 1.51 350 As/PI 7 0.62 0.34, 1.11 10,352
 1991–1996 AA 14 0.92 0.82, 1.03 34,824  Convenience AA 10 0.89 0.72, 1.11 14,518
His 11 0.60a 0.42, 0.87 52,578 His 7 0.58 0.30, 1.11 14,083
As/PI 4 0.90 0.73, 1.11 6630 As/PI 3 0.82 0.61, 1.09 2742
 1997–2006 AA 14 0.92 0.82, 1.03 34,824
His 2 0.66 0.39, 1.08 2621
As/PI 4 0.90 0.73, 1.11 6630
Open in a new tab

AA = African American; His = Hispanic; As/PI = Asian/Pacific Islander; OR = Odds ratio; and CI = Confidence intervals.

a

p<.05.

Hispanic versus white non-Hispanics (meta-analysis 2)

Sixteen of the 18 OR point-estimates were in the expected direction of higher mammography use among white non-Hispanics, and eight of them were statistically significant (Table 3). The pooled OR of 0.65 (95% CI 0.50, 0.85) based on an aggregate sample of 63,247 women including 8522 Hispanics confidently infers that Hispanics were less likely to have received a mammography within the past 2 years compared to white non-Hispanics. The pooled summary statistic seems resistant to publication bias, as the Fail-safe N was 244. As the heterogeneity statistic was significant, Q (17, N = 63,247) = 261.97, p <.01, we explored possible moderators.

Table 3.

Primary study and aggregate study outcomes by race: Non-Hispanic White (W) versus Hispanic (H) on obtaining a mammogram in the past 2 years

Citation Sample size
 Year data were collected Location Sampling method Data collection Effect size
W H OR 95% CI
Bush and Langer (1998) 1996 457 1993–1994 West Convenience SAS 0.44a 0.33 0.60
Caplan et al. (1992) 19,646 1708 1987–1988 Nationwide Random SAS 0.34a 0.28 0.40
Fox and Stein (1991) 344 50 1988 West Random TI 0.73 0.35 1.46
Fox et al. (1998) 650 318 1995–1996 West Convenience TI 0.15a 0.11 0.20
Frazier et al. (1996) 14,315 457 1990 Nationwide Random TI 0.94 0.78 1.14
Fredman et al. (1999) 764 182 1995 Northeast Random TI 0.79 0.54 1.18
Goldberg and Lessard (2002) 363 702 1996 West Convenience SAS 1.96 1.48 2.61
Haas et al. (2002) 4577 898 1996 Nationwide Random SAS 0.85 0.72 1.00
Hiatt et al. (1996) 605 1601 1993–1996 West Random SAS 0.53a 0.43 0.66
Johnson and Murata (1988) 316 37 1984–1985 West Convenience Arch 0.63 0.18 1.72
Lee and Vogel (1995) 5646 307 1986–1991 South Convenience SAS 0.54a 0.35 0.81
Otero-Sabogal et al. (2004) 273 540 1999 West Random TI 0.51a 0.37 0.69
Polednak et al. (1993) (group 1) 568 121 1988 Northeast Random TI 1.23 0.78 1.90
Polednak et al. (1993) (group 2) 531 128 1989 Northeast Random TI 0.97 0.64 1.46
Sambamoorthi and McAlpine (2001) 1065 226 1996 Nationwide Random IPI 0.82 0.59 1.15
Song and Fletcher (1998) 1137 346 1994 West Convenience Arch 0.61a 0.45 0.82
Tortolero-Luna et al. (1995) 332 233 1992 South Random TI 0.52a 0.35 0.77
Valdini and Cargill (1997) 1597 211 1999 Northeast Convenience IPI 0.85 0.63 1.14
Meta-statistics
All study outcomes 54,725 8522 0.65a 0.50 0.85
SES-adjusted
outcomes		 23,090 3708 1.08 0.64 1.93
Open in a new tab

TI = telephone interview; IPI = in-person interview, SAS = self-administered survey; Arch = archival data; OR = odds ratio; CI = confidence intervals; and nr = data not reported.

a

p<.05.

Influence of socioeconomic factors

Seven studies adjusted for socioeconomic status, mostly by income and/or education (Table 3). According to the aggregate mean of these adjusted ORs, the difference for having a mammography in the past 2 years was no longer significant (OR 1.08, 95% CI 0.64, 1.93).

Effect of contextual and methodological factors

For the time period in which the data were collected, the pooled OR estimate was only significant for Time 2 and was in the expected direction (p<.05), revealing that Hispanics were screened less often than white non-Hispanics in 1991 to 1996 (Table 2). For geographical region, data were grouped into the following categories: Northeast, South and West. The results showed significant differences between Hispanics and white non-Hispanics in the South and West, such that Hispanics in the South and West were significantly screened less than white non-Hispanics (p<.05).

Eleven studies used random sampling and showed a significant difference between Hispanics and white non-Hispanics (p <.01), with Hispanics reporting to be screened less. For data collection method, subgroup analysis focused on three groupings: self-administered surveys, telephone interviews, and archival data. The results showed that mammography screening rates was significant for archival data, whereby Hispanics were less likely to have had a screening mammography compared to white non-Hispanics (p<.01). As some research suggest that data obtained through archival data sources may be more accurate than self-report methods (Fiscella et al., 2006), we removed the two studies that used archival data sources and re-ran all analyses. The overall pooled OR remained statistically significant (OR 0.66, 95% CI 0.49, 0.88), and the subgroup analyses showed one difference, such that data collected in the West was now only marginally significant, OR of 0.54 (95% CI 0.28, 1.06), p = .07, based on a sample of 7900 women.

Asian/Pacific Islander versus white non-Hispanics (meta-analysis 3)

Eight of the 10 OR point-estimates were in the expected direction of higher mammography use among white non-Hispanics (pooled 66.4% versus 45.2%), and 3 of them were statistically significant (Table 4). The pooled OR of 0.63 (95% CI 0.39, 0.99), based on an aggregate sample of 13,094 women including 2963 Asian/Pacific Islanders, was significant and suggests that Asian/Pacific Islanders were less likely to have received a mammography compared to white non-Hispanics. The pooled summary statistic seems resistant to publication bias, as the fail safe N was 34. The heterogeneity statistic was significant, Q (10, N = 13,094) = 146.24, p<.01, so possible moderators were examined.

Table 4.

Primary study and aggregate study outcomes by race: Non-Hispanic White (W) versus Asian/Pacific Islander (AS/PI) on obtaining a mammogram in the past 2 years

Citation Sample size
 Year data were collected Location Sampling method Data collection Effect size
W AS/PI OR 95% CI
Chen et al. (2004) 1081 108 1999–2000 West Random TI 0.70 0.44 1.16
Goldberg and Lessard (2002) 363 58 1996 West Convenience SAS 0.56 0.24 1.19
Haas et al. (2002) 4577 154 1996 Nationwide Random SAS 1.35 0.89 2.10
Hiatt et al. (1996) (Chinese) 605 775 1993–1996 West Random SAS 0.43a 0.34 0.55
Hiatt et al. (1996) (Vietnamese) 605 645 1993–1996 West Random SAS 0.19a 0.15 0.25
Johnson and Murata (1988) 316 34 1984–1985 West Convenience Arch 0.54 0.13 1.60
Lipkus et al. (2000) 901 198 1995 South Random SAS 1.43 0.98 2.11
Otero-Sabogal et al. (2004) (Chinese) 273 76 1999 West Random TI 0.41a 0.23 0.71
Otero-Sabogal et al. (2004) (Filipino) 273 81 1999 West Random TI 0.75 0.44 1.28
Song and Fletcher (1998) 1137 834 1994 West Convenience Arch 0.91 0.74 1.12
Meta-statistics
All study outcomes 10,131 2963 0.63a 0.40 0.99
SES-adjusted
outcomes		 2654 1586 0.57 0.31 1.27
Open in a new tab

TI = telephone interview; IPI = in-person interview, SAS = self-administered survey; Arch = archival data; OR = odds ratio; CI = confidence intervals; and nr = data not reported.

a

p<.05.

Effect of socioeconomic factors

Four studies adjusted for socioeconomic status, mostly by income and/or education (Table 4). Based on the aggregate means of these adjusted ORs, the difference for having had a mammogram in the past 2 years was no longer significant (OR 0.57, 95% CI 0.31, 1.27).

Effect of contextual and methodological factors

Only one study collected data during Time 1, therefore subgroup analysis focused on data collected during Time 2 and Time 3 (Table 2). Though the pooled OR estimates shows that Asian/Pacific Islanders reported being screened less than white non-Hispanics, this difference was not significant at Time 2 (p = .20) and marginally significant at Time 3 (p = .054). Nine of the 10 studies collected data from the West (e.g., mostly California), therefore subgroup analysis was not undertaken. The majority of these studies aggregated the Asian subpopulations, however two studies examined mammography use separately for Asian subgroups and found some differences. Hiatt et al. (1996) reported that Chinese women were screened less than Vietnamese women, (36.2% versus 55.8%), χ2 (1, N = 1380) = 51.17, p<.001. Similarly, Otero-Sabogal et al. (2004) reported that Chinese women were screened less than Filipino women, (38.7% versus 53.2%), χ2 (1, N = 1250) = 184.34, p<.001.

For data collection strategy, data from the 10 studies were divided into three groupings: self-administered surveys, telephone interviews, and archival data. The only significant difference in mammography screening were observed for data collected via telephone interviews (p <.01) and showed that Asian/Pacific Islanders reported having a screening mammography at a lower rate than white non-Hispanics.

Discussion

Commonly reported estimates of mammography screening suggest that American women are highly screened (Blackman et al., 1999; Breen et al., 2001; Smith et al., 2007), and recent research suggests that substantial differences by ethnicity in screening no longer exist. However, after systematically integrating studies of mammography screening, this study found evidence to suggest ethnic minority-mammography screening differences may persist.

Our first research question asked whether ethnic minority women were at risk of receiving screening mammograms within the past 2 years at a lower rate than white non-Hispanic women. The support for this research question is neither direct nor obvious for all ethnic minority-mammography comparisons. For example, the view that screening rates would be lower for African Americans given their higher breast cancer mortality rate is not entirely supported. Systematically integrating 28 studies showed that African Americans were screened less than white non-Hispanics, although using a conservative statistical approach this difference was only marginally significant. The pattern of results involving comparisons of Hispanics and Asians/Pacific Islanders with white non-Hispanics revealed larger and significant disparities. The results showed that both Hispanics and Asian/Pacific Islanders were screened less than white non-Hispanics.

Our second research question asked whether socioeconomic status significantly impacted reports of screening mammography. Among studies that controlled for socioeconomic status, significant differences in screening mammography for each ethnic minority group compared to white non-Hispanics no longer existed. This finding seems consistent with the hypothesis that there are not direct pathways from ethnicity to mammography screening adherence behavior. According to Meyerowitz et al. (1998), ethnic minority status is related to adherence behavior through socioeconomic factors (e.g., education, income), access to care (e.g., insurance, regular healthcare provider), and health-and cancer-related cognitions (e.g., fear of radiation, efficacy of procedure). Moreover, Rajaram and Rashidi (1998) contend that the sociocultural context such as cultural beliefs and values, and personal life experiences effect breast cancer screening behavior. Few studies in our meta-analysis provided adequate demographic or social variables such as age, education, income level, or attitudinal variables required to test such relationships. However, our preliminary results suggest a possible link between socioeconomic status and screening adherence. Although based on a small proportion of studies, when we explored within-group differences, we found that African Americans and white non-Hispanics with lower levels of education and no insurance coverage were less likely to report having a screening mammography within the past 2 years compared to those women with higher levels of education and some form of insurance coverage. We acknowledge ethnic differences in immunological and endocrinological functioning, but research that compares race per se is unlikely to provide conceptually rich or clinically useful information. Future research needs to integrate measurement of socioeconomic status, health beliefs, and sociocultural context in order to identify the factors underlying screening differences.

We also explored the impact of contextual and methodological variables. Subgroup analysis showed that African Americans and Hispanics living in the South reported the lowest levels of screening mammography and were screened less than white non-Hispanics. Also, studies using random sampling reported that Hispanics – and African Americans to a marginal level – were less likely to have a mammogram compared to white non-Hispanics; studies using convenience sampling showed no significant differences. Furthermore, two studies that used archival data showed that Hispanics were screened less than white non-Hispanics, whereas data collected through self-reports showed no such difference. This finding is in contrast to what was found for Asian/Pacific Islanders. Specifically, Asian/Pacific Islanders were screened less according to data collected via telephone interviews; data collected via archival data were in the expected direction but were not significant. In trying to reconcile these differences, we cannot determine whether self-report or an archival data is a more accurate reflection of mammograms received. It is possible that Asian/Pacific Islanders underestimated the time interval since their last mammogram, and thus tended to “over-report” during telephone interviews. It should be noted, however, that the finding of Asian/Pacific Islanders being screened less according to data collected via telephone interviews is based on three outcomes from only two relatively small studies (N = 1892, including 265 Asian/Pacific Islanders). Despite this apparent contradiction, the findings are generally consistent with recent research (e.g., Fiscella et al., 2006; Kagay et al., 2006) which reports that, compared with data collected through archival sources such as medical files and insurance claims, studies using convenience sampling and self-reports may overestimate incidence of mammography screening and present an optimist view that ethnic disparities cease to exist.

Study limitations and strengths

In addition to subgroup analyses, there are other possible reasons for the lack of clear and direct findings. First, research in this area is often confounded by methodological problems, many of which are common in multicultural research in general. For example, no culturally sensitive translations exist for widely used assessments despite evidence that language can impact the results (Angel and Guarnaccia, 1989). Although one of the strengths of the present study was examining various ethnic groups, identifying discrete and meaningful ethnic groups proved difficult as few authors provided information about how race or ethnicity was defined. For instance, Asian/Pacific Islanders are not a homogenous group and include subgroups such as Filipino, Chinese, and Vietnamese women. Even the definition of white non-Hispanic is debatable and varied across studies. The development of adequate tools for defining ethnicity is needed to advance our understanding of the reasons underlying ethnic disparities in cancer screening.

Heterogeneity was evident from the onset, both in the context (e.g., year data collected) and methodology (e.g., data collection strategy). Creating subgroups was one attempt to shed light on the factors influencing the relationship between ethnic minority status and mammography. Furthermore, we carefully developed our inclusion criteria to include only studies that reported screening mammography. As approximately 10% of all mammograms are done for diagnostic purposes (Breen et al., 2001), including studies that combined mammography for screening and diagnostic purposes would have likely inflated rates of mammography use. But perhaps more importantly, the four studies that combined mammography for screening and diagnostic purposes compared mammography use for African Americans with non-Hispanic whites only, and including them would have potentially led to invalid comparisons across other ethnic groups. In addition, this meta-analysis used only US data, thus the results may not generalize to other parts of the world. As the ethnic diversity in Canada increases, future research needs to examine ethnic minority status and mammography screening. Therefore, the results from this study should be considered tentative and exploratory until confirmed by additional research studies.

Conclusion

This meta-analysis found mammography screening rates among ethnic minority women over 50 years to be lower than their white non-Hispanic counterparts, and these relationships were significantly and consistently affected by socioeconomic status. As ethnic diversity within the United States continues to increase and the high cancer rates among some ethnic groups persist, research that examines the link between ethnicity and screening mammography as well as the possible factors that impact this relationship is required.

Acknowledgments

This research was supported in part by a grant from the Social Sciences and Humanities Research Council of Canada and a Canadian Institutes of Health Research investigator award. The authors would like to thank Dr. Kathryn Lafreniere for her considered feedback on our study and Simone Arbour for her assistance in coding the primary research studies. The authors would also like to thank the three anonymous reviewers for their insightful feedback.

References

  1. American Cancer Society. Breast Cancer Facts and Figures 2005–2006. American Cancer Society, Inc; Atlanta, GA: 2005. [Google Scholar]
  2. Angel R, Guarnacci PJ. Mind, body, and culture: somatization among Hispanics. Soc Sci Med. 1989;28:1229–1238. doi: 10.1016/0277-9536(89)90341-9. [DOI] [PubMed] [Google Scholar]
  3. Ashing-Giwa KT, Padilla GV, Tejero JS, Kim J. Breast cancer survivorship in a multiethnic sample: challenges in recruitment and measurement. Cancer. 2004;101:450–465. doi: 10.1002/cncr.20370. [DOI] [PubMed] [Google Scholar]
  4. Auguston EM, Vadaparampil ST, Paltoo DN, Kidd LR, O’Malley AS. Association between CBE, FOBT, and pap smear adherence and mammography adherence among older low-income women. Prev Med. 2003;36:734–739. doi: 10.1016/s0091-7435(03)00050-1. [DOI] [PubMed] [Google Scholar]
  5. Austin LT, McNally M, Stewart DE. Breast and cervical screening in Hispanic women: A literature review using the health belief model. Women’s Health Issues. 2002;12:122–128. doi: 10.1016/s1049-3867(02)00132-9. [DOI] [PubMed] [Google Scholar]
  6. Bastani R, Kaplan C, Maxwell C, Nisenbaum R, Pearce J, Marcus A. Initial and repeat mammography screening in a low income multi-ethnic population in Los Angeles. Cancer Epidemiol Biom and Prev. 1995;4:161–167. [PubMed] [Google Scholar]
  7. Berry DA, Cronin KA, Plevritis SK, Fryback DG, Clarke L, Zelen M, Mandelblatt JS, Yakovlev AY, Habbema JD, Feuer EJ Cancer Intervention and Surveillance Modeling Network (CISNET) Collaborators. Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J of Med. 2005;353:1784–1792. doi: 10.1056/NEJMoa050518. [DOI] [PubMed] [Google Scholar]
  8. Blackman DK, Bennett EM, Miller DS. Trends in self-reported use of mammograms (1989–1997) and Papanicolaou tests (1991–1997), behavioral risk factor surveillance system. Morb Mortal Wkly Rep. 1999;48:1–22. [PubMed] [Google Scholar]
  9. Breen N, Wagner DK, Brown ML, Davis WW, Ballard-Barbash R. Progress in cancer screening over a decade: results of cancer screening from the 1987, 1992, and 1998 National Health Interview Surveys. J Natl Cancer Inst. 2001;93:1704–1713. doi: 10.1093/jnci/93.22.1704. [DOI] [PubMed] [Google Scholar]
  10. Burack RC, Gurney JG, McDaniel AM. Health status and mammography use among older women. J Gen Int Med. 1998;13:366–372. doi: 10.1046/j.1525-1497.1998.00116.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Bush RA, Langer RD. The effects of insurance coverage and ethnicity on mammography utilization in a postmenopausal population. Western J Med. 1998;168:236–240. [PMC free article] [PubMed] [Google Scholar]
  12. Canadian Cancer Institute. Early detection and screening for breast cancer. 2007. [accessed September 2, 2007]. [internet]. Available at: http://www.cancer.ca/ccs/internet/standard/0,3182,3172_10175_74544430_langId-en,00.html.
  13. Caplan LS, Wells BL, Haynes S. Breast cancer screening among older racial/ethnic minorities and whites: barriers to early detection. J of Gerontology. 1992;47:101–110. [PubMed] [Google Scholar]
  14. Carson KP, Schriesheim CA, Kinicki A. The usefulness of the “fail-safe” statistic (N) in meta-analysis. Ed Psych Measurement. 1990;50:233–243. [Google Scholar]
  15. Casey MM, Call KT, Klingner JM. Are rural residents less likely to obtain recommended preventive healthcare services? Am J Prev Med. 2001;21:182–188. doi: 10.1016/s0749-3797(01)00349-x. [DOI] [PubMed] [Google Scholar]
  16. Chen JY, Diamant AL, Kagawa-Singer M, Pourat N, Wold C. Disaggregating data on Asian and Pacific islander women to assess cancer screening. Am J Prev Med. 2004;27:139–145. doi: 10.1016/j.amepre.2004.03.013. [DOI] [PubMed] [Google Scholar]
  17. Consedine NS, Magai C, Krivoshekova YS, Ryzewicz L, Neugut AI. Fear, anxiety, worry, and breast cancer screening behavior: a critical review. Cancer Epidemiol Bio & Prev. 2004;13:501–510. [PubMed] [Google Scholar]
  18. Cooper H. Synthesizing Research. 3. Sage; London: 1998. [Google Scholar]
  19. Cummings DM, Whetstone LM, Earp JA, Mayne L. Disparities in mammography screening in rural areas: analysis of county differences in North Carolina. J of Rural Health. 2002;18:77–83. doi: 10.1111/j.1748-0361.2002.tb00879.x. [DOI] [PubMed] [Google Scholar]
  20. Fiscella K, Holt K, Meldrum S, Franks P. Disparities in preventive procedures: comparisons of self-report and Medicare claims data. BMC Health Serv Res. 2006;6:122–130. doi: 10.1186/1472-6963-6-122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Fleiss JL. Statistical Methods for Rates and Properties. Wiley; London: 1981. [Google Scholar]
  22. Fox SA, Stein JA. The effect of physician–patient communication on mammography utilization by different ethnic groups. Med Care. 1991;29:1065–1082. doi: 10.1097/00005650-199111000-00001. [DOI] [PubMed] [Google Scholar]
  23. Fox SA, Pitkin K, Paul C, Carson S, Duan N. Breast cancer screening adherence: does church attendance matter? Health Educ & Behav. 1998;25:742–758. doi: 10.1177/109019819802500605. [DOI] [PubMed] [Google Scholar]
  24. Frazier EL, Jiles RB, Mayberry R. Use of screening mammography and clinical breast examinations among black, Hispanic, and white women. Prev Med. 1996;25:118–125. doi: 10.1006/pmed.1996.0037. [DOI] [PubMed] [Google Scholar]
  25. Fredman L, Sexton M, Cui Y, Althuis M, Wehren L, Hornback P, Kanarek N. Cigarette smoking, alcohol consumption, and screening mammography among women ages 50 and older. Prev Med. 1999;28:407–417. doi: 10.1006/pmed.1998.0445. [DOI] [PubMed] [Google Scholar]
  26. Friedman LC, Woodruff A, Lane M, Weinberg AD, Cooper HP, Webb JA. Breast cancer screening behaviors and intentions among asymptomatic women 50 years of age and older. Am J Prev Med. 1995;11:218–223. [PubMed] [Google Scholar]
  27. Glanz K, Resch N, Lerman C, Rimer BK. Black–white differences in factors influencing mammography use among employed female Health Maintenance Organization members. Ethn and Health. 1996;1:207–220. doi: 10.1080/13557858.1996.9961789. [DOI] [PubMed] [Google Scholar]
  28. Goldberg R, Lessard D. Use of preventive services in a population of very low-income women. J of Health Care for Poor and Underserved. 2002;13:151–163. doi: 10.1353/hpu.2010.0552. [DOI] [PubMed] [Google Scholar]
  29. Goel MS, Wee CC, McCarthy EP, Davis RB, Ngo-Metzger Q, Phillips RS. Racial and ethnic disparities in cancer screening: the importance of foreign birth as a barrier to care. J Gen Int Med. 2003;18:1028–1035. doi: 10.1111/j.1525-1497.2003.20807.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Gotzsche PC, Olsen O. Is screening for breast cancer with mammography justifiable? Lancet. 2000;355:129–133. doi: 10.1016/S0140-6736(99)06065-1. [DOI] [PubMed] [Google Scholar]
  31. Greenland S. Quantitative methods in the review of epidemiological literature. Epidemiol Rev. 1987;9:1–30. doi: 10.1093/oxfordjournals.epirev.a036298. [DOI] [PubMed] [Google Scholar]
  32. Haas JS, Phillips KA, Sonneborn D, McCulloch CE, Liang S. Effect of managed care insurance of the use of preventive care for specific ethnic groups in the United States. Med Care. 2002;40:743–751. doi: 10.1097/00005650-200209000-00004. [DOI] [PubMed] [Google Scholar]
  33. Hedeen AN, White E. Ethnicity and birthplace in relation to tumor size and stage in Asian American women with breast cancer. Am J Public Health. 1999;89:1248–1252. doi: 10.2105/ajph.89.8.1248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Hedges LV, Orkin I. Statistical Methods for Meta-Analysis. Academic Press; Orlando, FL: 1985. [Google Scholar]
  35. Henderson LM, Schenck AP. Identifying and characterizing one-time and never users of mammography screening services among Medicare eligible women. Prev Med. 2001;32:529–533. doi: 10.1006/pmed.2001.0846. [DOI] [PubMed] [Google Scholar]
  36. Hiatt RA, Pasick R, Perez-Stable E, McPhee S, Englestad L, Lee M. Pathways to early cancer detection in the multiethnic population of the San Francisco Bay Area. Health Educ Q. 1996;23(Suppl):S10–S27. [Google Scholar]
  37. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Brit Med J. 2003;327:557–560. doi: 10.1136/bmj.327.7414.557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Hunter JE, Schmidt FL. Fixed effects vs. random effects meta-analysis models: implications for cumulative research knowledge. Intl J of Selection and Assessment. 2000;8:275–292. [Google Scholar]
  39. Johnson RA, Murata PJ. Demographic, clinical, and financial factors relating to the completion rate of screening mammography. Cancer Detection and Prev. 1988;11:259–266. [PubMed] [Google Scholar]
  40. Lane DS, Zapka J, Breen N, Messina CR, Fotheringham DJ. A systems model of clinical preventive care: the case of breast cancer screening among older women. Prev Med. 2000;31:481–493. doi: 10.1006/pmed.2000.0747. [DOI] [PubMed] [Google Scholar]
  41. Lee JR, Vogel VG. Who uses screening mammography regularly? Cancer Epidemiol Biom & Prev. 1995;4:901–906. [PubMed] [Google Scholar]
  42. Lipkus IM, Rimer BK, Halabi S, Strigo TS. Can tailored interventions increase mammography use among HMO women? Am J Prev Med. 2000;18:1–10. doi: 10.1016/s0749-3797(99)00106-3. [DOI] [PubMed] [Google Scholar]
  43. Kagay CR, Quale C, Smith-Bindman R. Screening mammography in the American elderly. Am J Prev Med. 2006;31:142–149. doi: 10.1016/j.amepre.2006.03.029. [DOI] [PubMed] [Google Scholar]
  44. Katapodi MC, Lee KA, Facione NC, Dodd MJ. Predictors of perceived breast cancer risk and the relation between perceived risk and breast cancer screening: a meta-analytic review. Prev Med. 2004;38:388–402. doi: 10.1016/j.ypmed.2003.11.012. [DOI] [PubMed] [Google Scholar]
  45. Lannin DR, Matthews HF, Mitchell J, Swanson MS, Swanson FH, Edwards MS. Influence of socioeconomic and cultural factors on racial differences in late-stage presentation of breast cancer. JAMA. 1998;279:1801–1807. doi: 10.1001/jama.279.22.1801. [DOI] [PubMed] [Google Scholar]
  46. Magai C, Consedine N, Conway F, Neugut A, Culver C. Diversity matters: unique populations of women and breast cancer screening. Cancer. 2004;100:2300–2307. doi: 10.1002/cncr.20278. [DOI] [PubMed] [Google Scholar]
  47. Makuc DM, Breen N, Freid V. Low income, race, and the use of mammography. Health Serv Res. 1999;34:229–239. [PMC free article] [PubMed] [Google Scholar]
  48. Mayne L, Earp J. Initial and repeat mammography screening: different behaviors/different predictors. J Rural Health. 2003;19:63–71. doi: 10.1111/j.1748-0361.2003.tb00543.x. [DOI] [PubMed] [Google Scholar]
  49. Mayo RM, Ureda JR, Parker VG. Importance of fatalism in understanding mammography screening in rural elderly women. J Women & Aging. 2001;13:57–72. doi: 10.1300/J074v13n01_05. [DOI] [PubMed] [Google Scholar]
  50. Meyerowitz BE, Richardson J, Leedham B. Ethnicity and cancer outcomes: behavioral and psychosocial considerations. Psych Bull. 1998;123:47–70. doi: 10.1037/0033-2909.123.1.47. [DOI] [PubMed] [Google Scholar]
  51. Miller AM, Champion VL. Attitudes about breast cancer and mammography: racial, income, and educational differences. Women & Health. 1997;26:41–63. doi: 10.1300/J013v26n01_04. [DOI] [PubMed] [Google Scholar]
  52. National Cancer Institute. Screening mammograms. 2007. [accessed October 14, 2007]. [internet]. Available at: http://www.cancer.gov/cancertopics/factsheet/Detection/screening-mammograms.
  53. O’Malley MS, Earp J, Harris RP. Race and mammography use in two North Carolina counties. Am J Pub Health. 1997;87:782–786. doi: 10.2105/ajph.87.5.782. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Otero-Sabogal R, Owens D, Canchola J, Golding JM, Tabnak F, Fox P. Mammography rescreening among women of diverse ethnicities: patient, provider, and health care system factors. J Health Care for Poor and Underserved. 2004;15:390–412. doi: 10.1353/hpu.2004.0048. [DOI] [PubMed] [Google Scholar]
  55. Parker J, Gebretsadik T, Sabogal F, Newman J, Lawson HW. Mammography screening among California Medicare beneficiaries: 1993–1994. Am J Prev Med. 1998;15:198–205. doi: 10.1016/s0749-3797(98)00045-2. [DOI] [PubMed] [Google Scholar]
  56. Pearlman DN, Rakowski W, Ehrich B, Clark MA. Breast cancer screening practices among black, Hispanic, and white women: reassessing differences. Am J Prev Med. 1996;12:327–337. [PubMed] [Google Scholar]
  57. Peek ME, Han JH. Disparities in screening mammography: current status, interventions, and implications. J Gen Int Med. 2004;19:184–194. doi: 10.1111/j.1525-1497.2004.30254.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Polednak AP, Lane DS, Burg MA. Mammography use in Hispanic and Anglo visits to community health centers. Health Values. 1993;17:42–48. [Google Scholar]
  59. Polite BN, Olopade OI. Breast Cancer and Race: a rising tide does not lift all boats equally. Perspectives in Biol and Med. 2005;48 (Suppl):S166–S175. [PubMed] [Google Scholar]
  60. Preston JA, Scinto JD, Ni W, Wang Y, Galusha D, Schulz A, Petrillo MK. Mammography underutilization among older women in Connecticut. J Am Geriatrics Soc. 1997;45:1310–1314. doi: 10.1111/j.1532-5415.1997.tb02929.x. [DOI] [PubMed] [Google Scholar]
  61. Raja-Jones H. Breast screening and ethnic minority women: a literature review. Brit J Nursing. 1999;8:1284–1288. doi: 10.12968/bjon.1999.8.19.1284. [DOI] [PubMed] [Google Scholar]
  62. Rajaram SS, Rashidi A. Minority women and breast cancer screening: the role of cultural explanatory models. Prev Med. 1998;27:757–764. doi: 10.1006/pmed.1998.0355. [DOI] [PubMed] [Google Scholar]
  63. Raucher GH, Tropman Hawley S, Earp JL. Baseline predictors of initiation vs. maintenance of regular mammography use among rural women. Prev Med. 2005;40:822–830. doi: 10.1016/j.ypmed.2004.09.029. [DOI] [PubMed] [Google Scholar]
  64. Randolph WM, Goodwin JS, Mahnken JD, Freeman JL. Regular mammography use is associated with elimination of age-related disparities in size and stage of breast cancer at diagnosis. Ann Int Med. 2002;137:783–790. doi: 10.7326/0003-4819-137-10-200211190-00006. [DOI] [PubMed] [Google Scholar]
  65. Regan J, Lefkowitz B, Gaston MH. Cancer screening among community health center women: eliminating the gaps. J Ambulance Care Management. 1999;22:45–52. doi: 10.1097/00004479-199910000-00008. [DOI] [PubMed] [Google Scholar]
  66. Rimer BK, Conaway MR, Lyna PR, Rakowski W, Woods-Powell CT, Tessaro I, Yarnell KSH, Barber LT. Cancer screening practices among women in a community health center population. Am J Prev Med. 1996;12:351–357. [PubMed] [Google Scholar]
  67. Rosenthal R. The file drawer problem and tolerance for null results. Psych Bull. 1979;86:638–641. [Google Scholar]
  68. Sabogal F, Merrill SS, Packel L. Mammography rescreening among older California women. Health Care Financing Rev. 2001;22:63–75. [PMC free article] [PubMed] [Google Scholar]
  69. Sambamoorthi U, McAlpine DD. Racial, ethnic, socioeconomic, and access disparities in the use of preventive services among women. Prev Med. 2001;37:475–484. doi: 10.1016/s0091-7435(03)00172-5. [DOI] [PubMed] [Google Scholar]
  70. Siegler IC, Costa PT. Personality and breast cancer screening behaviors. Ann Behav Med. 1994;16:347–351. [Google Scholar]
  71. Smith RA, Cokkinides V, Eyre HJ. Cancer screening in the United States: a review of current guidelines, practices, and prospects. CA Cancer J Clin. 2007;57:90–104. doi: 10.3322/canjclin.57.2.90. [DOI] [PubMed] [Google Scholar]
  72. Song L, Fletcher R. Breast cancer rescreening in low-income women. Am J Prev Med. 1998;15:128–133. doi: 10.1016/s0749-3797(98)00039-7. [DOI] [PubMed] [Google Scholar]
  73. Stoddard AM, Rimer BK, Lane D, Fox SA, Lipkus I, Luckman R, Avrunin JS, Sprachman S, Costanza M, Urban N. Underusers of mammogram screening: stage of adoption on five U.S. subpopulations. Prev Med. 1998;27:478–487. doi: 10.1006/pmed.1998.0310. [DOI] [PubMed] [Google Scholar]
  74. Taylor V, Thompson B, Montano DE, Mahloch J, Johnson K, Li S. Mammography use among women attending an inner-city clinic. J Cancer Educ. 1998;13:96–101. doi: 10.1080/08858199809528524. [DOI] [PubMed] [Google Scholar]
  75. Taylor V, Thompson B, Lessler D, Yasul Y, Montano D, Johnson KM, Mahloch J, Mullen M, Li S, Bassett G, Goldberg HI. A clinic-based mammography intervention targeting inner-city women. J Gen Int Med. 1999;14:104–111. doi: 10.1046/j.1525-1497.1999.00295.x. [DOI] [PubMed] [Google Scholar]
  76. Tortolero-Luna G, Glober GA, Villarreal R, Palos G, Linares A. Screening practices and knowledge, attitudes, and beliefs and cancer among Hispanic and non-Hispanic white women 35 years old or older in Nueces County, Texas. J Natl Cancer Inst Monographs. 1995;18:49–56. [PubMed] [Google Scholar]
  77. U. S. Census Bureau. Census regions and divisions of the United States [online] 2005. (Updated Oct 14 2004). Available at http://www.census.gov/geo/www/us_regdiv.pdf. [cited May 11 2007]
  78. U.S. Preventive Services Task Force. Screening for breast cancer. 2002. [accessed October 7, 2007]. [internet]. Available at: http://www.ahrq.gov/clinic/uspstf/uspsbrca.htm.
  79. Valdini A, Cargill LC. Access and barriers to mammography in New England community health centers. J Family Pract. 1997;45:243–249. [PubMed] [Google Scholar]
  80. Vernon SW, Laville EA, Jackson GL. Participation in breast screening programs: a review. Soc Sci Med. 1990;30:1107–1118. doi: 10.1016/0277-9536(90)90297-6. [DOI] [PubMed] [Google Scholar]
  81. Wampler NS, Saba L, Rahman SM, Dignan M, Voeks JH, Strzelczyk J. Factors associated with adherence to recommendations for screening mammography among American Indian women in Colorado. Eth & Dis. 2006;16:808–815. [PubMed] [Google Scholar]
  82. Wee CC, McCarthy EP, Davis RB, Phillips RS. Obesity and breast cancer screening: the influence of race, illness burden, and other factors. J Gen Int Med. 2004;19:324–331. doi: 10.1111/j.1525-1497.2004.30354.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Weir HK, Thun MJ, Hankey BF, Ries LAG, Howe HL, Wingo PA, Jemal A, Ward E, Anderson RN, Edwards BK. Annual report to the Nation on the Status of Cancer, 1975–2000, featuring the uses of surveillance data for cancer prevention and control. J Natl Cancer Inst. 2003;95:1276–1299. doi: 10.1093/jnci/djg040. [DOI] [PubMed] [Google Scholar]
  84. Wells K, Roetzheim R. Health disparities in receipt of screening mammography in Latinas: a critical review of recent literature. Cancer Control. 2007;14:369–379. doi: 10.1177/107327480701400407. [DOI] [PubMed] [Google Scholar]
  85. Wu T, Bancroft J, Guthrie B. An integrative review on breast cancer screening practice and correlates among Chinese, Korean, Filipino, and Asian Indian American women. Health Care for Women Intl. 2004;26:225–246. doi: 10.1080/07399330590917780. [DOI] [PubMed] [Google Scholar]
  86. Yood MU, McCarthy BD, Lee NC, Jacobsen G, Johnson CC. Patterns and characteristics of repeat mammography among women 50 years and older. Cancer Epidemiol Biom and Prev. 1999;8:595–599. [PubMed] [Google Scholar]

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