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Reported breast cancer screening among American Indian women is consistently below that of White women. The last claims-based trends were from 1991 to 2001. This study updates mammography trends for American Indian women and examines the impact of race, urbanicity, and income on long-term mammography use.
Methods
This was a multi-year (2005−2019), retrospective study of women aged 40−89 years using a 5% sample of Medicare fee-for-service beneficiaries residing in Arizona, California, New Mexico, Oklahoma, and Washington. This study used multivariable logistic regression to examine the impact of urbanicity and income on receiving mammography for American Indian women compared with that for White women. Analyses were conducted in 2022.
Results
Overall, annual age-adjusted mammography use declined from 205 per 1,000 in 2005 to 165 per 1,000 in 2019. The slope of these declines was significantly steeper (difference = −2.41, p<0.001) for White women (−3.06) than for American Indian women (−0.65). Mammography-use odds across all urbanicity categories were less for American Indian women than for White women compared with those of their respective metropolitan counterparts (e.g., rural: 0.96, 95% CI=0.77, 1.20 for American Indian women and 1.47, 99% CI=1.39, 1.57 for White women). Although residing in higher-income communities was not associated with mammography use for American Indian women, it was 31% higher for White women (OR=1.31, 99% CI=1.28, 1.34).
Conclusions
The disparity in annual age-adjusted mammography use between American Indian and White women narrowed between 2005 and 2019. However, the association of urbanicity and community income on mammography use differs substantially between American Indian and White women. Policies to reduce disparities need to consider these differences.
INTRODUCTION
Among women, breast cancer is the most common cancer, with mortality second only to that of lung cancer.
regular breast cancer screening use among American Indian (AI) and Alaska Native (AN) women are consistently lower than that among women of other race/ethnicity (referred to as race in the remaining part of this article), resulting in less timely access to treatment.
National Center for Health Statistics. Use of mammography among women aged 40 and over, by selected characteristics: United States, selected years 1987–2018. https://www.cdc.gov/nchs/hus/contents2019.htm#Table-033. Published 2019.Accessed October 15, 2022.
The reasons for mammography disparities may include differences in premammography health, age, socioeconomic factors, health insurance providers or lack of insurance, out-of-pocket costs, access/proximity to providers, and sources of care.
Considering these observations, this study had 2 objectives. The first is to provide an update on mammography trends for AI women during the 2005–2019 period using Medicare fee-for-service claims data. The last trend study using claims data that included AI women was for the 1991–2001 period.
The Centers for Disease Control and Prevention most recently provided mammography trends by race from 1987 through 2018 on the basis of the National Health Interview Survey
National Center for Health Statistics. Use of mammography among women aged 40 and over, by selected characteristics: United States, selected years 1987–2018. https://www.cdc.gov/nchs/hus/contents2019.htm#Table-033. Published 2019.Accessed October 15, 2022.
The second objective was to examine disparities in mammography use between AI and White women across the spectrum of urbanicity (metropolitan to rural) and income. This analysis focused on the 5 states with the largest AI populations with Medicare fee-for-service insurance. These states cover the spectrum from large cities to rural areas with substantial differences in community income.
METHODS
Study Population
This study did not meet the standards of human subject research and was deemed exempt by the Advarra IRB. This was a retrospective study (2005–2019) of mammography use using a nationally representative 5% sample of Medicare fee-for-service beneficiaries generated by the Centers for Medicare and Medicaid Services. Data for 2020 were not included owing to the impact of coronavirus disease 2019 (COVID-19) on mammography use. Women aged 40–89 years with known race residing in Arizona, California, New Mexico, Oklahoma, and Washington were included in this study. Although women aged <40 and >89 years undergo mammography, those aged 40–89 years age align with various screening guidelines. These states were selected given the objective to examine disparities in mammography use for AI women compared with those for White women or women of other races (Asian, Black, Hispanic, other). In the Medicare data, these 5 states had the most AI women with Medicare fee-for-service insurance. Furthermore, Medicare data group AI and AN into a single category. Hence, by focusing on these 5 states, those classified by Medicare as AI/AN are likely AI rather than AN women.
DeVoe JF, Darling-Churchill KE, Snyder TD. Status and trends in the education of American Indians and Alaska natives: 2008. https://nces.ed.gov/pubs2008/2008084.pdf. Published September 2008. Accessed October 15, 2022.
The analysis focused on AI women given the cultural factors that influence AI mammography use and differences in federal legal history between AIs and ANs (such as the history of AI mistreatment before Alaska was purchased).
Given that Medicare data do not explicitly include income and urbanicity, average community income per capita and urbanicity of each woman's community were derived from publicly available data sets. Using each woman's ZIP code, community income was assigned using Internal Revenue Service data. Urbanicity (metropolitan, micropolitan, small town, rural) was also assigned by ZIP code on the basis of rural−urban commuting areas. Women with missing urbanicity were excluded from this study. For each year, beneficiaries who had screening mammography were identified using the following Current Procedural Terminology/Healthcare Common Procedure Coding System codes: 76,092; 77,057; 77,063; 77,067; and G0202.
Measures
The primary outcome of interest was the receipt of any screening mammography during the study period. The primary exposure of interest was the beneficiary's race (White, AI, or other race). Medicare data do not separately track race and ethnicity but include them in a single variable using administrative data to identify race. Race was stratified as White, AI, and other race (Asian, Black, Hispanic, other) given the race categories that Medicare provides. Other covariates included age group (40–49, 50–64, 65–74, 75–89 years), Charlson comorbidity index
(CCI) (0, 1, ≥2), urbanicity (metropolitan, 50,000+ population; micropolitan, 10,000−49,999; small town, 2,500−9,999; rural, <2,500), community per capita income (dichotomized as above or below 2019 average per capita income, $34,103), state, and first year in the study.
United States Census Bureau. S1902 Mean income in the past 12 months (in 2019 inflation-adjusted dollars), 2019&tid=ACSST5Y2019.S1902. American Community Survey 5-Year Estimates.https://data.census.gov/table?q=per+capita+income&tid=ACSST5Y2019.S1902. Published 2019. Accessed October 15, 2022.
This study employed multivariable logistic regression analysis to assess the beneficiary-level impact of race, urbanicity, and community income on the odds of a beneficiary having mammography at any point during the study period. Hence, the regression analysis examined beneficiary-level mammography use over an extended period rather than an annual cross-sectional analysis. This analysis was repeated for subsamples by race and state. For analyses of subsamples ≥10,000, statistical significance was assigned at α=0.01 owing to the large sample size. For AI women, α=0.05 was used for statistical significance because this subsample was <10,000.
This study tested for significant linear trends in annual age-adjusted mammogram use by race (further stratified by urbanicity and state) across the 15-year study using linear regression. Age-adjusted use was computed on the basis of age categories defined previously. Beta estimates, R2-values, and p-values are presented on each graph to show the directionality, goodness of fit, and statistical significance for each calculated linear regression. For this trend analysis, the value for each year represents the average annual age-adjusted mammography use for that year by race and urbanicity.
RESULTS
The study included 457,476 women with a mean (SD) age of 68.4 (9.2) years at their first instance in the study. Of these, 78.2% were White, 1.5% were AI, and 20.3% were of other races. AI women were younger than White women or women of other races (mean age [SD]=65.6 [10.2], 68.8 [9.2], and 67.1 [9.2], respectively). The average number of years of Medicare fee-for-service coverage over this 15-year study period was 7.7 (7.7 for White, 8.1 for AI, and 7.6 for other women). Most women (61.8%) had no mammogram at any time during the study period. Annually, the average percentage of women with mammography was 18.2% (19.5% for White, 15.9% for AI, and 13.4% for other women). By state, Oklahoma, New Mexico, and Arizona had the highest proportions of AI women in their populations: 7.5%, 5.6%, and 2.3%, respectively.
Over the study period, age-adjusted mammogram use declined from 205 per 1,000 in 2005 to 165 per 1,000 in 2019. Mammography use declined among metropolitan populations, regardless of race (Figure 1). From 2005 to 2019, annual age-adjusted mammography use per 1,000 declined significantly for White women (β= −3.06, p<0.001) and women of other races (β= −3.28, p<0.001) with no significant change for AI women (β= −0.65, p=0.202) (Figure 1); differences in the slope of these declines compared with those for AI women were significant (difference= −2.41, p<0.001 for White women; difference= −2.64, p<0.001 for women of other race). In micropolitan areas, White women (β= −3.71, p<0.001) and women of other races (β= −4.35, p<0.001) saw reduced mammography use over time, whereas AI women saw no statistically significant change (β= −1.31, p=0.200). In rural areas, annual mammogram use dropped among White women (β= −2.30, p<0.001) and rose among AI women (β=2.76, p<0.001).
Figure 1Annual age-adjusted mammography use, by race and urbanicity, 2005–2019.
The state-level analyses showed a decline in annual age-adjusted mammography use per 1,000 for White women in all states (Appendix Figure 1, available online). Among AI women, age-adjusted mammogram use rose significantly for residents of Arizona (β=3.57, p<0.001), dropped for residents of California (β= −3.37, p=0.008) and Washington (β= −4.40, p=0.011), and showed no statistically significant change for residents of New Mexico (β=1.35, p=0.115) and Oklahoma (β= −0.17, p=0.855).
Controlling for covariates, the multivariable analysis found that AI women (OR=0.87; 99% CI=0.81, 0.93) and women of other races (OR=0.86; 99% CI=0.84, 0.87) had lower odds of undergoing mammography than White women (Table 2). Women residing in Arizona (OR=1.25; 99% CI=1.22, 1.29), New Mexico (OR=1.16; 99% CI=1.11, 1.21), Oklahoma (OR=1.46; 99% CI=1.42, 1.51), and Washington (OR=1.41; 99% CI=1.37, 1.44) had greater odds of undergoing mammography than women in California. Women in micropolitan (OR=1.63; 99% CI=1.58, 1.69), small towns (OR=1.43; 99% CI=1.36, 1.50), and rural areas (OR=1.40; 99% CI=1.33, 1.49) had greater odds of mammography than women residing in metropolitan areas. Those in communities with an above-average per capita income had 27% greater odds of mammography than women in communities with below-average income (OR=1.27; 99% CI=1.25, 1.30). Women with at least 1 comorbidity had over twice the odds of undergoing mammography as those with no comorbidities (CCI=0), w (CCI=1, OR=3.04 [99% CI=2.96, 3.12]; CCI≥2, OR=2.36 [99% CI=2.30, 2.41]).
The results of the multivariable subanalyses by race group differed from the results for the entire sample (Table 3). For example, White women and women of other races in micropolitan areas compared with those in metropolitan areas had 66% (OR=1.66; 99% CI=1.60, 1.71) and 67% (OR=1.67; 99% CI=1.49, 1.87) greater odds, respectively, of undergoing mammography. The odd for similar women of the AI race was 23% (OR=1.23; 95% CI=1.02, 1.48). Contrastingly, there was no evidence that AI women in small towns (OR=1.06; 95% CI=0.87, 1.28) or rural areas (OR=0.96; 95% CI=0.77, 1.20) had odds of mammography different from those of metropolitan AI women, whereas White women in small towns (OR=1.48; 99% CI=1.41, 1.56) and rural areas (OR=1.47; 99% CI=1.39, 1.57) had significantly greater odds of mammography than their metropolitan counterparts. As for community income, both White women (OR=1.31; 99% CI=1.28, 1.34) and women of other race (OR=1.16; 99% CI=1.11, 1.20) residing in above-average income communities had greater odds of mammography than similar women in below-average income communities. In contrast, there was no evidence that income impacted mammography use for AI women (OR=0.95; 95% CI=0.78, 1.16). Mammography-use odds for those with multiple comorbidities (CCI≥2) was less for AI than for White women or women of other races compared with those of their respective counterparts without comorbidities (CCI=0): 1.33 (95% CI=1.12, 1.59) for AI women, 2.28 (99% CI=2.22, 2.35) for White women, and 2.79 (99% CI=2.65, 2.94) for women of other race.
The multivariable subanalyses by state (Appendix Table 1, available online) show that AI women had lower odds of undergoing mammography in Arizona than White women (OR=0.77; 99% CI=0.66, 0.91). There were no differences between AI and White women in the other states. The impact of urbanicity varied across states. Specifically, those in rural areas had greater odds of mammography in California (OR=1.89; 99% CI=1.72, 2.08), New Mexico (OR=1.54; 99% CI=1.32, 1.80), Washington (OR=1.28; 99% CI=1.12, 1.46), and Oklahoma (OR=1.12; 99% CI=1.00, 1.26) than women in metropolitan areas. There was no difference in Arizona (OR=0.94; 99% CI=0.78, 1.13). Women living in above-average income communities had greater odds of receiving mammography in Arizona (OR=1.47; 99% CI=1.40, 1.55), California (OR=1.29; 99% CI=1.26, 1.32), New Mexico (OR=1.14; 99% CI=1.04, 1.26), and Oklahoma (OR=1.26; 99% CI=1.18, 1.35) than women living in communities with below-average income. There was no difference in Washington (OR=1.01; 99% CI=0.96, 1.07).
Table 1Descriptive Statistics for the Study Population, Overall and by Race Group
Parameter
Overall
White
American Indian
Other race
n (%)
457,476
357,527 (78.2)
6,851 (1.5)
93,098 (20.3)
First year of coverage in data set
2005
206,797
168,113 (81.3)
3,074 (1.5)
35,610 (17.2)
2006
14,534
11,150 (76.7)
247 (1.7)
3,137 (21.6)
2007
14,851
11,609 (78.2)
219 (1.5)
3,023 (20.4)
2008
16,650
12,938 (77.7)
270 (1.6)
3,442 (20.7)
2009
15,566
12,032 (77.3)
258 (1.7)
3,276 (21.0)
2010
15,243
11,743 (77.0)
240 (1.6)
3,260 (21.4)
2011
17,430
13,432 (77.1)
293 (1.7)
3,705 (21.3)
2012
18,368
14,105 (76.8)
274 (1.5)
3,989 (21.7)
2013
19,925
14,764 (75.4)
320 (1.6)
4,511 (23.0)
2014
19,110
14,411 (75.4)
309 (1.6)
4,390 (23.0)
2015
19,772
14,692 (74.3)
343 (1.7)
4,737 (24.0)
2016
19,148
14,224 (74.3)
308 (1.6)
4,616 (24.1)
2017
20,083
14,633 (72.9)
315 (1.6)
5,135 (25.6)
2018
20,285
15,032 (74.1)
218 (1.1)
5,035 (24.8)
2019
20,014
14,619 (73.0)
163 (0.8)
5,232 (26.1)
State
Arizona
59,747
53,004 (88.7)
1,376 (2.3)
5,367 (9.0)
California
281,740
203,555 (72.2)
1,065 (0.4)
77,120 (27.4)
New Mexico
18,824
15,784 (83.8)
1,052 (5.6)
1,988 (10.6)
Oklahoma
36,612
30,996 (84.7)
2,747 (7.5)
2,869 (7.8)
Washington
60,553
54,188 (89.5)
611 (1.0)
5,754 (9.5)
Age, years
40–49
1,960
13,637 (69.5)
641 (3.3)
5,352 (27.3)
50–64
40,107
28,969 (72.2)
1,101 (2.8)
10,037 (25.0)
65–74
286,990
223,872 (78.0)
3,898 (1.4)
59,220 (20.6)
75–89
110,749
91,049 (82.2)
1,211 (1.1)
18,489 (16.7)
CCI
0
324,816
257,607 (79.3)
4,179 (1.3)
63,030 (19.4)
1
50,260
38,541 (76.7)
1,109 (2.2)
10,610 (21.1)
2+
61,318
45,990 (75.0)
1,390 (2.3)
13,938 (22.7)
Unknown
21,082
15,389 (73.0)
173 (0.8)
5,520 (26.2)
Urbanicity
Metropolitan
396,338
303,862 (76.7)
3,208 (0.8)
89,268 (22.5)
Micropolitan
36,925
32,754 (88.7)
1,453 (3.9)
2,718 (7.4)
Small town
14,195
12,163 (85.7)
1,302 (9.2)
730 (5.1)
Rural
10,018
8,748 (87.3)
888 (8.9)
382 (3.8)
Community income
Same as or lower than the national mean
206,132
150,802 (73.2)
5,613 (2.7)
49,717 (24.1)
Higher than national mean
251,344
206,725 (82.2)
1,238 (0.5)
43,381 (17.3)
Any screening mammogram during the study period
Yes
174,658
140,786 (80.6)
3,050 (1.7)
30,822 (17.6)
No
282,818
216,741 (76.6)
3,801 (1.3)
62,276 (22.0)
Mean person-years of enrollment (SD)
7.7 (4.6)
7.7 (4.6)
8.1 (4.5)
7.6 (4.6)
Mean mammograms per beneficiary (SD)
1.4 (2.6)
1.5 (2.7)
1.3 (2.2)
1.0 (2.1)
Median mammograms per beneficiary (IQR)
0 (0–2)
0 (0–2)
0 (0–2)
0 (0–1)
Notes: Year is an indicator variable for the first year the woman was in the data. The age category represents the age of the beneficiary at the end of the first year she was in our data. Community income represents per capita income by ZIP code. All variables were significantly different across race groups (p<0.001). Women of other races include Asian, Black, Hispanic, and other women.
Table 2Multivariable Logistic Regression for Adjusted Odds of Receiving a Mammogram, All Races
Parameter
OR (99% CI)
First year present in the study (Ref: 2005)
2006
0.878 (0.837, 0.921)
2007
0.858 (0.819, 0.900)
2008
0.812 (0.776, 0.849)
2009
0.758 (0.724, 0.795)
2010
0.724 (0.691, 0.759)
2011
0.763 (0.730, 0.797)
2012
0.743 (0.711, 0.775)
2013
0.752 (0.721, 0.784)
2014
0.784 (0.752, 0.818)
2015
0.756 (0.725, 0.789)
2016
0.736 (0.704, 0.769)
2017
0.664 (0.634, 0.695)
2018
0.528 (0.503, 0.554)
2019
0.496 (0.449, 0.549)
Expected mammograms
1.249 (1.240, 1.258)
State (Ref: California)
Arizona
1.252 (1.221, 1.285)
New Mexico
1.158 (1.109, 1.209)
Oklahoma
1.463 (1.415, 1.511)
Washington
1.407 (1.372, 1.443)
Race (Ref: White)
American Indian
0.866 (0.809, 0.928)
Other race
0.855 (0.837, 0.874)
Age, years (Ref: 40–49)
50–64
0.704 (0.668, 0.741)
65–74
1.037 (0.994, 1.082)
75–89
0.807 (0.769, 0.847)
CCI (Ref: 0)
1
3.039 (2.959, 3.121)
2+
2.355 (2.299, 2.414)
Unknown
0.445 (0.402, 0.492)
Urbanicity (Ref: metropolitan)
Micropolitan
1.634 (1.583, 1.686)
Small
1.425 (1.358, 1.496)
Rural
1.404 (1.327, 1.486)
Community income > national mean (Ref: ≤ national mean)
1.273 (1.250, 1.296)
Notes: N=457,476. Year is an indicator variable for the first year the woman was in the data. Expected mammograms are calculated according to USPSTF biennial screening guidelines for women aged 50–74 years. The age category represents the age of the beneficiary at the end of the first year she was in our data. Community income represents per capita income by ZIP code.
CCI, Charlson comorbidity index; USPSTF, U.S. Preventive Services Task Force.
Table 3Multivariable Logistic Regressions for Adjusted Odds of Receiving a Mammogram, by Race
White
American Indian
Other race
(n=357,527)
(n=6,851)
(n=93,098)
Parameter
OR (99% CI)
OR (99% CI)
OR (99% CI)
First year present in the study (Ref: 2005)
2006
0.840 (0.795, 0.887)
0.647 (0.448, 0.936)
1.052 (0.948, 1.168)
2007
0.825 (0.782, 0.870)
0.867 (0.586, 1.283)
0.993 (0.893, 1.104)
2008
0.792 (0.752, 0.833)
1.005 (0.707–1.429)
0.876 (0.792, 0.969)
2009
0.734 (0.696–0.774)
0.899 (0.625, 1.292)
0.840 (0.758, 0.931)
2010
0.706 (0.669, 0.745)
0.842 (0.581, 1.218)
0.788 (0.710, 0.873)
2011
0.755 (0.718, 0.793)
0.812 (0.581, 1.136)
0.782 (0.709, 0.862)
2012
0.735 (0.700, 0.771)
0.743 (0.527, 1.045)
0.767 (0.697, 0.843)
2013
0.750 (0.715, 0.786)
0.897 (0.652, 1.234)
0.747 (0.682, 0.818)
2014
0.797 (0.759, 0.836)
0.940 (0.680, 1.300)
0.728 (0.663, 0.800)
2015
0.755 (0.719, 0.792)
0.831 (0.605, 1.140)
0.752 (0.686, 0.825)
2016
0.746 (0.709, 0.785)
0.830 (0.591, 1.165)
0.693 (0.628, 0.764)
2017
0.690 (0.655, 0.727)
0.554 (0.383, 0.801)
0.588 (0.532, 0.650)
2018
0.555 (0.525, 0.586)
0.483 (0.305, 0.766)
0.437 (0.391, 0.489)
2019
0.515 (0.461, 0.575)
0.611 (0.259, 1.441)
0.411 (0.320, 0.529)
Expected mammograms*
1.253 (1.243, 1.264)
1.343 (1.272, 1.418)
1.231 (1.211, 1.251)
State (Ref: California)
Arizona
1.291 (1.256, 1.326)
0.667 (0.525, 0.848)
1.037 (0.953, 1.128)
New Mexico
1.145 (1.092, 1.199)
0.995 (0.776, 1.275)
1.252 (1.093, 1.433)
Oklahoma
1.477 (1.425, 1.530)
1.144 (0.928, 1.410)
1.344 (1.204, 1.501)
Washington
1.414 (1.376, 1.452)
1.074 (0.811, 1.423)
1.388 (1.283, 1.502)
Age, years (Ref: 40–49)
50–64
0.692 (0.651, 0.736)
0.656 (0.485, 0.886)
0.770 (0.695, 0.853)
65–74
1.151 (1.095, 1.211)
0.705 (0.551, 0.902)
0.816 (0.751, 0.888)
75–89
0.920 (0.869, 0.974)
0.598 (0.438, 0.815)
0.545 (0.493, 0.603)
CCI (Ref: 0)
1
3.003 (2.913, 3.095)
1.715 (1.423, 2.068)
3.424 (3.230, 3.630)
2+
2.282 (2.220, 2.347)
1.332 (1.120, 1.586)
2.790 (2.646, 2.943)
Unknown
0.447 (0.399, 0.500)
0.465 (0.195, 1.110)
0.452 (0.352, 0.579)
Urbanicity (Ref: metropolitan)
Micropolitan
1.655 (1.601, 1.711)
1.230 (1.022, 1.481)
1.670 (1.490, 1.872)
Small
1.480 (1.405, 1.559)
1.056 (0.869, 1.283)
1.359 (1.096, 1.685)
Rural
1.473 (1.387, 1.565)
0.963 (0.774, 1.198)
1.052 (0.777, 1.423)
Community income > national mean (Ref: ≤ national mean)
1.309 (1.282, 1.335)
0.951 (0.779, 1.160)
1.156 (1.111, 1.203)
Note: Year is an indicator variable for the first year the woman was in the data. Expected mammograms are calculated according to USPSTF biennial screening guidelines for women aged 50–74 years. The age category represents the age of the beneficiary at the end of the first year she was in our data. Community income represents per capita income by ZIP code.
CCI, Charlson comorbidity index; USPSTF, U.S. Preventive Services Task Force.
This study found that for women with Medicare fee-for-service insurance residing in the 5 states with the highest proportions of AI women, the annual age-adjusted mammography use declined from 205 per 1,000 in 2005 to 165 per 1,000 in 2019. Over this period, the annual White−AI screening gap narrowed because the annual mammography use decline was concentrated in White women. Despite this narrowing gap, screening use for AI women remained lower than for White women.
The annual mammography use in this study was lower than previously published estimates for Medicare fee-for-service beneficiaries, which range from 21% for AI women to 35% for White women in 2021.
That study found substantial geographic variation in mammography use; the 5 states included in this analysis all showed relatively lower use than the national average. Data from the 2005 to 2018 National Health Interview Surveys report biennial mammography use at approximately 67%
National Center for Health Statistics. Use of mammography among women aged 40 and over, by selected characteristics: United States, selected years 1987–2018. https://www.cdc.gov/nchs/hus/contents2019.htm#Table-033. Published 2019.Accessed October 15, 2022.
Furthermore, over the 15-year study period, 61.8% of women did not have any mammograms. Hence, annual screenings were concentrated in a minority of beneficiaries, indicating a need to broaden the reach of screening to more women rather than just increasing the frequency of those currently being screened, particularly among AI women.
Public health goals are broader than eliminating racial disparities. These goals would include eliminating any disparity such as those owing to urbanicity and income. Concerningly, the effect of urbanicity and income on mammography use varied by race. Hence, in addition to mitigating racial disparities generally, racial gradients in urbanicity and income disparities also need mitigation. In aggregate, women in micropolitan, small towns, or rural areas were more likely to undergo mammography at some point during the study than women residing in metropolitan areas. However, this effect is primarily driven by an increased likelihood of White women undergoing screening even as the urban−rural gradient increased. This may be due in part to longer travel times for AI women.
Effects of Medicare reimbursement stagnation may be felt more acutely by rural providers, suggesting that increasing rurality should be associated with a decline in service receipt, contrary to this study's findings. Although encouraging, these unmeasured drivers of increased mammography receipt among White women do not appear to extend to those of other races as rurality increased, indicating persistent disparity in service delivery.
Similarly, women residing in communities with above-average income were more likely to undergo mammography. However, just as with urbanicity, this aggregate association was not uniform across race categories. White women and women of other races in communities with above-average income were more likely to have mammography than their counterparts in communities with below-average income. For AI women, community income had no impact on mammography use. There may be unique cultural or structural access barriers to care among AI women that community income cannot overcome. Attention to these barriers is necessary to improve the provision of breast screening services among this population.
Studies show that cultural factors among AI women, such as comfort with discussing mammography, the degree of their connection to their Nativeness, fatalism, and reliance on traditional healers, are associated with lower mammography use.
In addition, other unmeasured social determinants of health, such as food or housing insecurity and transportation, may also contribute to differences in the influence of community income on screening use.
There have been numerous programs and initiatives to improve mammography use among AI women.
Lantz PM, Orians CE, Liebow E, et al. Implementing women's cancer screening programs in American Indian and Alaska Native populations. Health Care Women Int. 2003;24(8):674–696. https://doi.org/10.1080/07399330390227481.
Such programs are typically local, are resource intensive, and may be funded through targeted grants; they are not reimbursed, with the exception of mobile mammography. Providers may choose to engage in such work, but such efforts are hindered by a lack of reimbursement.
If the observed disparities for AI women are to be mitigated, health policy and reimbursement must support it. For example, the Indian Health Services (IHS) has been chronically underfunded relative to needs.
That poses challenges because higher mammography use is positively associated with receiving other care from an IHS facility and having comorbidities, whereas it is negatively associated with patient distance from IHS facilities.
Several studies have shown that those with public insurance had lower screening use than those with private insurance and that those without insurance were lower still.
A strength of this study was the elimination of screening differences owing to varying insurance types, which is associated with racial disparities in late-stage breast cancer.
The Medicare reimbursement rate for digital mammography increased only by 2.3% between 2005 and 2019 ($135.29−$138.39), and accounting for inflation, it declined by 21.9%. Even without this insurance confounder, Medicare reimbursement cannot be considered in isolation because the private−public payer mix also influences access. For example, the private-pay-to-Medicare reimbursement ratio for hospital care increased from 2.36 in 2015 to 2.41 in 2017.
Such a trend incentivizes prioritizing mammography capability in areas where the economics are more favorable.
Limitations
This study has limitations. First, Medicare race data group AI/AN women into a single category. Because this study specifically focused on AI women, it only included women from 5 western states for which these women were most likely AI women. Second, because this study was focused on 5 states, it may not be generalizable to AI women residing in other states. Third, this study's results were based on the Medicare fee-for-service population and may not be representative of women with other insurance types, including Medicare Advantage. The role of provider recommendations for screening mammography in women on the basis of age cannot be ascertained from claims data. Finally, this study used community income as an instrument for actual income and may not be representative of actual income.
CONCLUSIONS
Although health policy should strive to eliminate all disparities, this paper has focused on disparities specific to AI women and discussed reimbursement and cultural factors associated with these disparities. Specifically, this study found racial gradients in mammography use for both urbanicity and income disparities. Future research should examine these racial gradients for AI women more fully to inform policy. Such information may inform policies to mitigate disparities. Similarly, this study found that community income had no impact on mammography use for AI women. This may indicate that although income is generally a protective factor in cancer screening, there are other factors, likely systemic, that may dominate screening decisions in the AI communities. Policies that may address these cultural barriers to screening are needed.
ACKNOWLEDGMENTS
There was no funding for this study.
No financial disclosures were reported by the authors of this paper.
CRediT AUTHOR STATEMENT
Eric W. Christensen: Conceptualization, Methodology, Validation, Writing – original draft. Casey E. Pelzl: Methodology, Formal analysis, Writing – original draft, Visualization. Bhavika K. Patel: Conceptualization, Writing – review and editing. Ruth C. Carlos: Writing – review and editing. Elizabeth Y. Rula: Supervision, Conceptualization, Writing – review and editing.
National Center for Health Statistics. Use of mammography among women aged 40 and over, by selected characteristics: United States, selected years 1987–2018. https://www.cdc.gov/nchs/hus/contents2019.htm#Table-033. Published 2019.Accessed October 15, 2022.
DeVoe JF, Darling-Churchill KE, Snyder TD. Status and trends in the education of American Indians and Alaska natives: 2008. https://nces.ed.gov/pubs2008/2008084.pdf. Published September 2008. Accessed October 15, 2022.
United States Census Bureau. S1902 Mean income in the past 12 months (in 2019 inflation-adjusted dollars), 2019&tid=ACSST5Y2019.S1902. American Community Survey 5-Year Estimates.https://data.census.gov/table?q=per+capita+income&tid=ACSST5Y2019.S1902. Published 2019. Accessed October 15, 2022.
Lantz PM, Orians CE, Liebow E, et al. Implementing women's cancer screening programs in American Indian and Alaska Native populations. Health Care Women Int. 2003;24(8):674–696. https://doi.org/10.1080/07399330390227481.
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