Abstract
Background:
Varying mammography guidelines can serve as a barrier to care. It is unknown how financial hardship, insurance, and rurality are associated with mammography receipt.
Methods:
This cross-sectional study used 2023 National Health Interview Survey data. Outcomes included receipt of an American Cancer Society (ACS) or US Preventive Services Task Force (USPSTF) guideline-concordant mammogram. Exposures included report of healthcare-related financial hardship (HRFH), insurance status, and rurality. We estimated ORs and 95% confidence intervals (CI) to assess the interaction between (1) HRFH and insurance status and (2) HRFH and rurality on receipt of an ACS or USPSTF mammogram.
Results:
A total of 11,138 women were included who were eligible to receive a mammogram based on USPSTF guidelines. The mean age was 51 years, 61% were non-Hispanic White, 10% lived in poverty, and 19% had not received a USPSTF guideline–concordant mammogram. We found that among insured individuals, those who did vs did not report HRFH had lower odds of receipt of a USPSTF guideline–concordant mammogram (OR, 0.68; 95% CI, 0.58–0.80). Additionally, we found that among urban residents, those who did vs did not report HRFH had lower odds of receipt of an USPSTF guideline–concordant mammogram (OR, 0.68; 95% CI, 0.58–0.79). The results were similar assessing the receipt of an ACS guideline–concordant mammogram.
Conclusions:
Financial hardship is a barrier to mammography regardless of insurance status and rural residence.
Impact:
Programs that address financial barriers to cancer screening beyond insurance and rural access are needed.
Introduction
Mammography screening is important for early detection of breast cancer (1). There are many different health or cancer care organizations that offer guidelines or recommendations to women for when and how often they should be receiving mammography services (2–5). Often the recommendations have slight differences, such as starting age, upper age limit, and changes in frequency with increasing age that could confuse individuals about when they should receive their mammogram (6). For example, the United States Preventative Services Task Force (USPSTF) and American Cancer Society (ACS) are two of the major organizations that offer mammography guidelines; depending on the year of recommendation, the lower and upper age limits differ, as do the time between screening (3, 7–9). Whereas the ACS guidelines include a wider range of ages, insurance companies often use USPSTF guidelines for reimbursement purposes (10), even though USPSTF guidelines are more conservative. Furthermore, with the increase of early-onset breast cancer cases in women under the age of 50 years (11), these differences can be important to catching treatable breast cancer cases early.
In 2019, using National Health Interview Survey (NHIS) data, it was estimated that 76% of women ages 50 to 74 years received mammography screening services within the past 2 years (12). Furthermore, in a study using NHIS data from 2018, 2019, and 2021, it was estimated that about 74% of women were up to date on their mammograms (13). Not only do the differences in guidelines serve as barrier to care but also patient location (i.e., rural and urban) and insurance coverage affect mammography access (14–16). Whereas Centers for Disease Control and Prevention (CDC) programs assist individuals with low incomes and inadequate insurance coverage in finding free or low-cost mammography (17), it is currently unknown how many individuals are not receiving guideline-concordant screening due to financial hardship, insurance issues, and geographic location. Prior research has found that rural versus urban women have reduced access to mammograms and lower mammogram utilization (18, 19). Furthermore, a study by Jones and colleagues found that women experiencing financial hardship (which includes material conditions, psychologic responses, and coping behaviors) (20) were less adherent to mammogram screening, including women who were most at risk for breast cancer (21). Currently it is unknown how both insurance status and rurality interact with financial hardship on screening utilization. The objective of our study was to explore the relationship between self-reported healthcare-related financial hardship (HRFH), insurance status, and residential rurality and how these factors affect women receiving guideline-concordant mammography services.
Materials and Methods
Study design and participants
This cross-sectional study used secondary data from the 2023 NHIS (22). The NHIS is housed in the CDC and has been conducted since 1957. The NHIS collects data from face-to-face interviews that provide information on and track the health of the US noninstitutionalized population; the NHIS uses sampling techniques that will provide a nationally representative sample. Inclusion criteria for our study included women ages >18 years who reported ever having had or not had a mammogram, known status of insurance coverage, reported family history of breast cancer, and complete information on HRFH. Of the individuals who reported having had a mammogram, we included individuals who reported the last time they received a mammogram. Furthermore, we included women who reported a prior breast cancer diagnosis because it is recommended that women with prior breast cancer should still receive a mammography if they still have breast tissue (5). Unfortunately, there are no questions in the NHIS that asks about prior double mastectomy. As NHIS data are publicly available, this study was exempt from Institutional Review Board approval.
Outcome: receiving guideline-concordant mammography
USPSTF guideline–concordant mammography
The USPSTF guidelines for mammography from 2016 to April 2024 were used for this analysis. These guidelines state that women should start mammography screening services at age 50 years and receive a mammography every other year until the age of 74 years, and individual decisions can be made for women under the age of 50 years. Individuals between the ages of 50 and 74 years who stated they received a mammogram within the past year (anytime less than 12 months ago) or within the past 2 years (1 year but less than 2 years ago) were considered as having received USPSTF guideline–concordant mammography. Women less than 50 who did and did not receive a mammography were considered concordant (Supplementary Methods).
ACS guideline–concordant mammography
The ACS guidelines from 2015 to present were used for this analysis (as of October 2024, these guidelines are still recommended). Women ages 40 to 44 years can start annual mammography if they choose, ages 45 to 54 years should receive mammography yearly, and ages 55 years and older should receive mammography every 2 years but can choose to do so yearly. We considered the following groupings concordant in our analysis: women ages 40 to 44 who had received or had not received a mammogram anytime within the last five years, individuals ages 45 to 54 years who had received a mammogram within the last year, and women >55 years of age who had received a mammogram within the last year or the past 2 years.
Exposures: health insurance and HRFH
Health insurance
Individuals were grouped into either uninsured vs insured. Individuals who were insured reported having at least one type of insurance coverage: private health insurance, Medicare, Medicaid, a state-sponsored health plan, other government programs, or military health plan.
HRFH
Individuals were considered to have experienced financial hardship if they reported “yes” to at least one of the following questions: (1) In the past 12 months, did you or anyone in your family have problems paying or were unable to pay any medical bills? (2) During the past 12 months, have you delayed getting medical care because of the cost? (3) During the past 12 months, was there any time you needed medical care but did not get it because of the cost? Additionally, the number of financial hardships individuals responded yes to was included (range, 0–3).
Additional variables of interest
We also included the following potential confounder variables in our analysis: age at the time of survey completion (in years), race and ethnicity [Hispanic or Latino(a) origin or descent, non-Hispanic (NH) Black or African American, NH White, and other], sexual orientation [sexual minority (gay, lesbian, bisexual, or something else), straight, or unknown/refused], county-level rural–urban classification scheme (large central metropolitan, large fringe metropolitan, medium and small metropolitan, and nonmetropolitan), income to poverty ratio (0–0.99, 1.00–1.99, 2.00–2.99, 3.00–3.99, 4.00–4.99, and 5.00+), personal history of breast cancer (yes or no), and family history of breast cancer (yes or no).
Statistical analysis
Descriptive statistics were calculated using frequencies and weighted percentages for categorical variables and means and SDs for continuous variables. Due to the use of survey strata, cluster, and weight variables for NHIS data, we used Rao–Scott χ2P values for categorical variables and t test P values using a Bonferroni correction for continuous variables.
As this analysis was exploratory in nature, we ran multiple models to understand how HRFH, insurance status, and rurality affected receipt of guideline-concordant mammography. For each analysis, we estimated ORs and predicted probabilities and 95% confidence intervals (CI) using logistic regression models (specifically, PROC SURVEYLOGISTIC using the survey strata, cluster, and weight variables). For the first model, we assessed the individual effects of HRFH, insurance status, and county-level rural–urban classification scheme on mammography receipt. We also ran two sensitivity analyses of this first model in which we (1) further categorized insurance status and (2) the number of financial hardships reported. Then, we assessed the interaction between HRFH and insurance status on mammography. Lastly, we assessed the interaction between HRFH and rurality on mammography; for this model, we grouped any metropolitan value as not rural and nonmetropolitan as rural. Each model contained the following: HRFH, uninsured status, county-level rural–urban classification scheme, age at survey, prior breast cancer diagnosis, family history of breast cancer, poverty to income ratio, race, and ethnicity, and quarter survey was completed. Finally, we ran each model by receipt of a guideline-concordant mammogram according to USPSTF or ACS (sensitivity analyses, only) guidelines. The statistical significance level was set to 0.05. We tested the interaction models using F-tests and corresponding P values. Analyses were performed using SAS software, version 9.4 (SAS Institute).
Results
Sample characteristics
A total of 13,529 women were included for both the USPSTF and ACS analyses; we excluded 233 because of missing data. Those excluded were similar compared with individuals included; however, those included more often had a history of breast cancer and were NH White (Supplementary Table S1). Among individuals who were eligible to receive a mammogram according to USPSTF guidelines (N = 11,138), the mean (SD) age was 51 years (0.14), 61% were NH White, 4% were in a sexual minority group, and about a third had an income to poverty ratio of 5.00 or greater (Table 1). Additionally, 14% were rural residents, 7% were uninsured, and 10% reported at least one HRFH. Overall, 81% of eligible women received a USPSTF guideline–concordant mammogram.
Table 1.
Descriptive and clinical characteristics of total sample (N = 11,138), individuals who did not receive USPSTF guideline–concordant mammography (n = 2,083) versus those who did (n = 9,055).
| | Total | Not USPSTF guideline–concordant | USPSTF guideline–concordant | P value |
|---|---|---|---|---|
| N = 11,138 | n = 2,083 | n = 9,055 | ||
| n (weighted %) | ||||
| Age (years) at time of survey completion, mean (SD) | 51 (0.14) | 51 (0.32) | 51 (0.16) | 0.1269 |
| Race and ethnicity | | | | 0.3390 |
| Hispanic or Latino(a) origin or descent | 1,676 (17.4) | 333 (18.6) | 1,343 (17.1) | |
| NH Black or African American | 1,353 (12.6) | 216 (11.5) | 1,137 (12.9) | |
| NH White | 7,166 (60.9) | 1,363 (61.2) | 5,803 (60.9) | |
| Other | 943 (9.1) | 171 (8.7) | 772 (9.1) | |
| Rural–urban classification scheme, county-level | | | | 0.0011 |
| Large central metropolitan | 3,298 (30.0) | 552 (26.4) | 2,746 (30.8) | |
| Large fringe metropolitan | 2,666 (26.2) | 493 (25.9) | 2,173 (26.3) | |
| Medium and small metropolitan | 3,453 (29.7) | 653 (31.2) | 2,800 (29.3) | |
| Nonmetropolitan | 1,721 (14.1) | 385 (16.5) | 1,336 (13.6) | |
| Income to poverty ratio | | | | <0.0001 |
| 0–0.99 | 1,238 (10.4) | 336 (14.5) | 902 (9.5) | |
| 1.00–1.99 | 2,022 (18.3) | 476 (21.9) | 1,546 (17.5) | |
| 2.00–2.99 | 1,817 (16.2) | 349 (17.2) | 1,468 (16.0) | |
| 3.00–3.99 | 1,377 (12.2) | 226 (11.4) | 1,151 (12.3) | |
| 4.00–4.99 | 1,116 (10.0) | 172 (8.7) | 944 (10.3) | |
| 5.00+ | 3,568 (32.9) | 524 (23.3) | 3,044 (34.4) | |
| Sexual orientation | | | | 0.2182 |
| Sexual minority group | 478 (4.1) | 101 (4.9) | 377 (4.0) | |
| Straight | 10,274 (92.2) | 1,906 (91.2) | 8,368 (92.4) | |
| Unknown/missing | 386 (3.7) | 76 (3.9) | 310 (3.6) | |
| Uninsured status | | | | <0.0001 |
| Yes | 687 (7.2) | 220 (11.4) | 467 (6.3) | |
| No | 10,451 (92.8) | 1,863 (88.6) | 8,588 (93.7) | |
| Insurance status | | | | <0.0001 |
| Medicaid | 1,591 (15.2) | 374 (18.3) | 1,217 (14.5) | |
| Medicare | 1,391 (9.6) | 258 (9.4) | 1,133 (9.6) | |
| None | 687 (7.2) | 220 (11.4) | 467 (6.3) | |
| Other | 472 (3.8) | 383 (4.1) | 383 (3.7) | |
| Private | 6,997 (64.2) | 1,142 (56.9) | 5,855 (65.8) | |
| HRFH | | | | <0.0001 |
| Yes | 1,920 (18.2) | 475 (24.3) | 1,445 (16.8) | |
| No | 9,218 (81.8) | 1,608 (75.7) | 7,610 (83.2) | |
| Count of HRFH | | | | <0.0001 |
| 0 | 9,218 (81.8) | 1,608 (75.7) | 7,610 (83.2) | |
| 1 | 1,110 (10.4) | 236 (912.3) | 874 (9.9) | |
| 2 | 473 (4.7) | 136 (7.2) | 337 (4.1) | |
| 3 | 337 (3.1) | 103 (4.8) | 234 (2.8) | |
| History of breast cancer | | | | 0.6176 |
| Yes | 429 (3.3) | 87 (3.5) | 342 (3.3) | |
| No | 10,709 (96.7) | 1,996 (96.5) | 8,713 (96.7) | |
| Family history of breast cancer | | | | 0.1775 |
| Yes | 1,775 (15.1) | 288 (14.0) | 1,487 (15.4) | |
| No | 9,363 (84.9) | 1,795 (86.0) | 7,568 (84.6) | |
| Received ACS guideline–concordant mammography | | | | <0.0001 |
| Yes | 6,696 (58.0) | 63 (3.0) | 6,633 (70.3) | |
| No | 1,976 (17.7) | 1,400 (62.5) | 576 (7.7) | |
| Not applicable | 2,466 (24.3) | 620 (34.5) | 1,846 (22.0) | |
Abbreviations: ACS, American Cancer Society; NH, non-Hispanic or Latino(a) origin or descent; USPSTF, US Preventative Services Task Force.
NOTE: Sexual minority group includes gay, lesbian, bisexual, and something else. Individuals were considered to have experienced financial hardship if they reported “yes” to at least one of the following questions: (1) In the past 12 months, did you or anyone in your family have problems paying or were unable to pay any medical bills? (2) During the past 12 months, have you delayed getting medical care because of the cost? (3) During the past 12 months, was there any time you needed medical care but did not get it because of the cost? Due to the use of survey strata, cluster, and weight variables for NHIS data, we used Rao–Scott χ2P values for categorical variables and t test P values using a Bonferroni correction for continuous variables. Does not equal the total due to missing data. Bolded P values are significant at the α level of 0.05.
Compared with women who did not have a USPSTF guideline–concordant mammogram, we observed that women who did receive mammography were more often wealthier (income to poverty ratio of 5.00+: 34% vs. 23%; P value: <0.0001), privately insured (66% vs. 57%; <0.0001), and had no HRFHs (83% vs. 76%, <0.0001). When comparing women who did not vs did have an ACS guideline–concordant mammogram, the results were similar to USPSTF results (Supplementary Table S2).
USPSTF guideline–concordant mammography model results
In our first model exploring HRFH, uninsured status, and rurality individually in our model, both individuals with HRFH and who were uninsured had lower odds of receiving a USPSTF guideline–concordant mammogram (OR, 0.73; 95% CI, 0.63–0.84 and OR, 0.64; 95% CI, 0.52–0.79, respectively; Table 2). We found that individuals in large central metropolitan counties had 28% higher odds of receiving a USPSTF guideline–concordant mammogram when compared with nonmetro (or rural) counties. Furthermore, in the model with the individual insurance groups, we found that all other insurance groups had higher odds of receiving a USPSTF guideline–concordant screening mammogram when compared with those without insurance. Women with private insurance and Medicare beneficiaries versus none had higher odds of receipt of a USPSTF guideline–concordant mammogram (OR, 1.66; 95% CI, 1.33–2.07 and OR, 1.63; 95% CI, 1.22–2.18, respectively). Finally, as the number of financial hardships increased, the odds of receiving a mammogram decreased.
Table 2.
Model-estimated ORs, predicted probabilities, and 95% CIs evaluating the association between HRFH, insurance status, rurality, and receipt of USPSTF guideline–concordant mammography (N = 11,138).
| Model 1. HRFH, uninsured status, and rurality in model (N = 11,138). | ||
| | ORs (95% CI) | Predicted probabilities (95% CI) |
| HRFH | | |
| Yes | 0.73 (0.63–0.84) | 0.78 (0.76–0.80) |
| No | Reference | 0.83 (0.82–0.84) |
| Uninsured status | | |
| Yes | 0.64 (0.52–0.79) | 0.75 (0.71–0.79) |
| No | Reference | 0.83 (0.82–0.84) |
| Rural–urban classification scheme, county-level | | |
| Large central metropolitan | 1.28 (1.06–1.55) | 0.84 (0.83–0.86) |
| Large fringe metropolitan | 1.07 (0.89–1.29) | 0.82 (0.80–0.83) |
| Medium and small metropolitan | 1.07 (0.89–1.28) | 0.82 (0.80–0.83) |
| Nonmetropolitan | Reference | 0.81 (0.78–0.83) |
| Model 2. Sensitivity analysis of model 1 with insurance groups further categorized (N = 11,138). | ||
| | ORs (95% CI) | Predicted probabilities (95% CI) |
| HRFH | | |
| Yes | 0.72 (0.63–0.84) | 0.78 (0.76–0.80) |
| No | Reference | 0.83 (0.82–0.84) |
| Insurance status | | |
| Medicaid | 1.42 (1.12–1.82) | 0.81 (0.78–0.83) |
| Medicare | 1.63 (1.22–2.18) | 0.83 (0.80–0.85) |
| Other | 1.40 (0.98–1.99) | 0.81 (0.76–0.85) |
| Private | 1.66 (1.33–2.07) | 0.83 (0.82–0.84) |
| None | Reference | 0.75 (0.71–0.79) |
| Rural–urban classification scheme, county-level | | |
| Large central metropolitan | 1.28 (1.06–1.55) | 0.84 (0.83–0.86) |
| Large fringe metropolitan | 1.07 (0.89–1.30) | 0.82 (0.80–0.83) |
| Medium and small metropolitan | 1.07 (0.90–1.28) | 0.82 (0.80–0.83) |
| Nonmetropolitan | Reference | 0.81 (0.78–0.83) |
| Model 3. Sensitivity analysis of model 1 with HRFH as counts (N = 11,138). | ||
| | ORs (95% CI) | Predicted probabilities (95% CI) |
| Count of HRFHs | | |
| 1 | 0.81 (0.67–0.97) | 0.80 (0.77–0.82) |
| 2 | 0.64 (0.50–0.82) | 0.76 (0.71–080) |
| 3 | 0.64 (0.48–0.87) | 0.76 (0.70–0.81) |
| 0 | Reference | 0.83 (0.82–0.84) |
| Uninsured status | | |
| Yes | 0.66 (0.53–0.81) | 0.76 (0.72–0.79) |
| No | Reference | 0.83 (0.82–0.84) |
| Rural–urban classification scheme, county-level | | |
| Large central metropolitan | 1.29 (1.06–1.55) | 0.84 (0.83–0.86) |
| Large fringe metropolitan | 1.07 (0.89–1.29) | 0.82 (0.80–0.83) |
| Medium and small metropolitan | 1.08 (0.90–1.28) | 0.82 (0.80–0.83) |
| Nonmetropolitan | Reference | 0.81 (0.78–0.83) |
| Model 4. Interaction between HRFH and uninsured status (N = 11,138). P value: 0.0263 | ||
| | ORs (95% CI) | Predicted probabilities (95% CI) |
| Reported HRFH | | |
| Uninsured | 0.84 (0.61–1.16) | 0.75 (0.69–0.80) |
| Insured | Reference | 0.78 (0.75–0.80) |
| No reported HRFH | | |
| Uninsured | 0.53 (0.40–0.69) | 0.73 (0.68–0.78) |
| Insured | Reference | 0.84 (0.83–0.85) |
| Uninsured | | |
| Reported HRFH | 1.09 (0.75–1.58) | 0.75 (0.69–0.80) |
| No reported HRFH | Reference | 0.73 (0.68–0.78) |
| Insured | | |
| Reported HRFH | 0.68 (0.58–0.80) | 0.78 (0.75–0.80) |
| No reported HRFH | Reference | 0.84 (0.83–0.85) |
| Model 5. Interaction between HRFH and rural–urban classification scheme (N = 11,138). P value: 0.3021 | ||
| | ORs (95% CI) | Predicted probabilities (95% CI) |
| Reported HRFH | | |
| Rural | 1.01 (0.73–1.42) | 0.78 (0.72–0.82) |
| Not rural | Reference | 0.77 (0.75–0.80) |
| No reported HRFH | | |
| Rural | 0.84 (0.71–0.99) | 0.81 (0.79–0.83) |
| Not rural | Reference | 0.84 (0.83–0.84) |
| Rural | | |
| Reported HRFH | 0.81 (0.59–1.12) | 0.78 (0.72–0.82) |
| No reported HRFH | Reference | 0.81 (0.79–0.83) |
| Not rural | | |
| Reported HRFH | 0.68 (0.58–0.79) | 0.77 (0.75–0.80) |
| No reported HRFH | Reference | 0.84 (0.83–0.84) |
NOTE: Rural counties are considered nonmetropolitan and not rural counties are metropolitan groupings according to the rural–urban classification scheme. Individuals were considered to have experienced financial hardship if they reported “yes” to at least one of the following questions: (1) In the past 12 months, did you or anyone in your family have problems paying or were unable to pay any medical bills? (2) During the past 12 months, have you delayed getting medical care because of the cost? (3) During the past 12 months, was there any time you needed medical care but did not get it because of the cost? Models contained the following variables: healthcare-related financial hardship, insurance status, county-level rural–urban classification scheme, age at survey, prior breast cancer diagnosis, family history of breast cancer, poverty to income ratio, race, and ethnicity, and quarter survey was completed. We tested the interaction models using F-tests and corresponding P values. Bolded values are significant at the α level of 0.05.
Abbreviations: CIs, confidence intervals; HRFH, healthcare-related financial hardship; ORs, odds ratios; USPSTF, United States Preventative Services Task Force.
In our model that interacted HRFH with noninsurance, we found that most of the possible categories had lower odds of a USPSTF guideline–concordant mammogram. However, among uninsured women, those who reported HRFH had similar odds compared with those who did not. Among insured individuals, those who reported financial hardship vs not had 68% lower odds (OR, 0.68; 95% CI, 0.58–0.80). Individuals who were insured and did not report financial hardship had an 84% probability of having received a USPSTF guideline–concordant mammogram (Fig. 1A).
Figure 1.
Model-estimated predicted probabilities and 95% CIs evaluating the association between HRFH interacted with (A) uninsured status; (B) county-level rurality and receipt of USPSTF guideline–concordant mammography (N = 11,138). Footnote: Individuals were considered to have experienced financial hardship if they reported “yes” to at least one of the following questions: (1) In the past 12 months, did you or anyone in your family have problems paying or were unable to pay any medical bills? (2) During the past 12 months, have you delayed getting medical care because of the cost? (3) During the past 12 months, was there any time you needed medical care but did not get it because of the cost? Models contained the following variables: HRFH, uninsured status, county-level rural–urban classification scheme, age at survey, prior breast cancer diagnosis, family history of breast cancer, poverty to income ratio, race, and ethnicity, and quarter survey was completed.
In the final model which interacted HRFH with rurality, we found that among women who did report financial hardship, rural vs nonrural residents had lower odds of receipt of mammogram (OR, 0.88; 95% CI, 0.71–0.99). Additionally, among those in nonrural counties, when compared with individuals who did not report HRFH, those who did had 0.68 (95% CI, 0.58–0.79) times the odds of having a USPSTF guideline–concordant mammogram. Among individuals in nonrural counties, those who reported HRFH had 77% probability whereas those who did not report HRFH had 84% probability of having received a USPSTF guideline–concordant mammogram (Fig. 1B).
Sensitivity analyses: ACS guideline–concordant mammography model results
Overall, model results assessing the receipt of an ACS guideline–concordant mammogram were similar to the USPSTF results. Both those with a HRFH and who were uninsured had lower odds of receiving an ACS guideline–concordant mammogram (OR, 0.65; 95% CI, 0.56–0.76 and OR, 0.36; 95% CI, 0.28–0.47, respectively; Supplementary Table S3). Additionally, uninsured individuals who reported HRFH had the lowest probability of having received an ACS guideline–concordant mammography at 46% (Supplementary Fig. S1A). Finally, among individuals in nonrural counties, those who reported HRFH had 64% probability whereas those who did not report HRFH had 75% probability of having received guideline-concordant mammography (Supplementary Fig. S1B).
Discussion
This study utilized the 2023 NHIS data to evaluate the associations between insurance status, HRFH, and rurality with receipt of guideline-concordant mammography. ACS and USPSTF mammography guidelines contain slight differences between the age and frequency limits. ACS guidelines do not have an upper age limit and mammograms can occur yearly, whereas USPSTF guidelines limit to age 74 years with mammograms occurring every 2 years. Despite these differences, the results between the ACS and USPSTF guidelines were similar. Regardless of the screening guidelines followed, we found that women who reported financial hardship, including inability to pay medical bills and cost-related delayed or foregone care, was associated with lower odds of receiving guideline-concordant mammography services. Furthermore, when we interacted financial hardship and insurance status, we found that individuals who were both uninsured or insured had lower odds of receiving guideline-concordant mammography if they also had self-reported financial hardship. By interacting ability to pay for healthcare and insurance status, we were able to determine that even individuals who have some form of health insurance coverage and difficulty paying for medical care had lower utilization of guideline-concordant mammograms.
Based on our findings, it seems that problems associated with financial challenges could serve as barriers to mammograms, such as competing health and other expenses, working multiple jobs, inability to take time off work, lack of transportation, and/or being a caregiver (23–25). This is important when determining how best we can provide screening for those in need based on limited resources. The CDC’s National Breast and Cervical Cancer Early Detection Program offers free or low-cost screenings through state health departments; however, individuals must meet insurance, income, and age guidelines (17). Typically, these eligibility criteria differ slightly between states, but overall inclusion criteria include no health insurance (some states include women who are underinsured) and income <250% of the federal poverty limit (though some states do not use the federal poverty limit). Furthermore, women who are between the ages of 40 to 64 years meet the initial age requirements; however, the CDC states that certain women who are older or younger may qualify. Although we did not directly assess whether expanding these social services to insured and/or underinsured women who have trouble accessing care, we think that this service could be beneficial to this group of women; however, more research is needed. This is especially true due to the lack of awareness of the program, as previous research found that 94% of individuals had not heard of the National Breast and Cervical Cancer Early Detection Program (26).
In theory, insured women regardless of financial hardship should not have issues accessing mammography services due to laws passed under the Affordable Care Act. This states that screening mammograms are covered by most private insurance providers, therefore eliminating the cost barrier to receiving a mammogram. However, this law does not apply to grandfathered plans (i.e., plans in place before March 2010, when the Affordable Care Act was passed) (27). Furthermore, individuals may receive a bill if a 3D mammogram or diagnostic mammogram was utilized, but this is dependent on insurance plan and state of residence (28). Even if a state insurance law requires coverage of 3D mammograms, diagnostic mammograms, or expanded breast imaging, the law does not apply to all policies, such as self-funded, out-of-state, and national plans (29). The myriad of exemptions to the laws can induce a cost to women receiving screening mammography and thus could be driving why insured women with financial hardship concerns are forgoing mammograms. Changes to insurance policies should be made to include coverage of screening mammograms that may not otherwise be covered. This should be done alongside addressing additional healthcare-related and nonhealthcare-related financial challenges that seem to drive decreased screening mammogram utilization.
Our results are consistent with a 2021 study found that rural and urban women were similarly adherent to USPSTF mammography guidelines (30). Another study using Behavioral Risk Factor Surveillance System survey data found very minor differences in screening between urban and rural women, with urban women having slightly higher mammography utilization (31). One reason for this could be due to mobile mammography units, as a study by Pelzl and colleagues found that rural geography is associated with mobile mammography use (32). However, the finding that urban individuals with financial hardship had lower receipt of a guideline-concordant mammogram points to the main issue that HRFH is a key barrier to receiving a guideline-concordant mammogram regardless of where one resides; therefore, programs and interventions that address financial hardship are needed.
This study should also be considered in light of several limitations. All responses were self-reported by respondents, and therefore there may be information or recall bias present. Respondents were asked about their recent mammogram and did not specify whether this was a screening or diagnostic mammogram. Furthermore, prior research found potential issues with validity of self-reported mammography utilization due to telescoping, overestimates among vulnerable populations, and the inability to distinguish from screening or diagnosis mammography (33). Also, as some individuals in this study would have been due for a mammogram during the COVID-19 pandemic, our estimates of noncompliance may be higher than normal as screening decreased during this time (34). Additionally, as this is a cross-sectional study, we cannot establish causality. However, the strength of our study is the use of NHIS data as we are able to examine demographic, geographic, and socioeconomic factors associated with mammography. Additionally, the dataset is designed to be representative of the US population (35).
Conclusion
In our study utilizing NHIS data, we found that individuals with financial hardship had lower odds of receiving either an ACS or USPSTF guideline–concordant mammogram. Programs and interventions that address HRFH are needed. Additionally, public programs that offer free mammograms should be extended to include individuals with health insurance, or individuals who are underinsured, as it seems that there are many exceptions to cost-sharing requirements for mammograms.
Supplementary Material
This file contains supplementary materials and methods describing how the outcome variable was created.
Supplementary Table S1 contains demographics on individuals included vs excluded in the manuscript.
Supplementary Table S2 contains demographics of individuals in the sensitivity analysis.
Supplementary Table S3 contains model results from the sensitivity analysis.
Supplementary Figure S1 shows predicted probabilities of the sensitivity analysis.
Acknowledgments
N.E. Caston is supported by the NCI’s National Research Service Award sponsored by the Lineberger Comprehensive Cancer Center at the University of North Carolina at Chapel Hill (T32 CA116339). S.B. Wheeler has received salary support paid to her institution for unrelated work from the Pfizer Foundation/National Comprehensive Cancer Network and AstraZeneca. L.P. Spees has received salary support paid to her institution for unrelated work from AstraZeneca.
Footnotes
Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).
Data Availability
Data from the NHIS are publicly available at https://www.cdc.gov/nchs/nhis/index.htm.
Authors’ Disclosures
S.B. Wheeler reports grants from AstraZeneca and the ACS/Pfizer Medical Foundation outside the submitted work. No disclosures were reported by the other authors.
Authors’ Contributions
N.E. Caston: Conceptualization, formal analysis, writing–original draft, writing–review and editing. L.P. Spees: Conceptualization, supervision, writing–review and editing. A.R. Waters: Conceptualization, writing–review and editing. S.B. Wheeler: Supervision, writing–review and editing.
References
- 1. Nyström L, Andersson I, Bjurstam N, Frisell J, Nordenskjöld B, Rutqvist LE. Long-term effects of mammography screening: updated overview of the Swedish randomised trials. Lancet 2002;359:909–19. [DOI] [PubMed] [Google Scholar]
- 2. Practice bulletin number 179: breast cancer risk assessment and screening in average-risk women. Obstet Gynecol 2017;130:e1–16. [DOI] [PubMed] [Google Scholar]
- 3. Oeffinger KC, Fontham ET, Etzioni R, Herzig A, Michaelson JS, Shih YC, et al. Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA 2015;314:1599–614. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Monticciolo DL, Malak SF, Friedewald SM, Eby PR, Newell MS, Moy L, et al. Breast cancer screening recommendations inclusive of all women at average risk: update from the ACR and Society of Breast Imaging. J Am Coll Radiol 2021;18:1280–8. [DOI] [PubMed] [Google Scholar]
- 5. The American Society of Breast Surgeons . Official statement: position statement on screening mammography. p. 1.20242019.
- 6. Shi W, Nagler RH, Fowler EF, Gollust SE. Predictors of women’s awareness of the benefits and harms of mammography screening and associations with confusion, ambivalence, and information seeking. Health Commun 2021;36:303–14. [DOI] [PubMed] [Google Scholar]
- 7. The American Cancer Society Medical and Editorial Content Team . American Cancer Society Guidelines for the Early Detection of Cancer. [cited 2024 Dec 5]. Available from:https://www.cancer.org/cancer/screening/american-cancer-society-guidelines-for-the-early-detection-of-cancer.html#:∼:text=Women%20ages%2040%20to%2044,best%20screening%20plan%20for%20you.
- 8. MacMillan C. New mammogram guidelines: what women should know. Yale Medicine; [cited 2024 Dec 5]. Available from:https://www.yalemedicine.org/news/new-mammogram-screening-guidelines-what-women-should-know. [Google Scholar]
- 9. Siu AL; U S Preventive Services Task Force . Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2016;164:279–96. [DOI] [PubMed] [Google Scholar]
- 10. American Cancer Society . Frequently asked questions about the American Cancer Society’s breast cancer screening guidelines. [cited 2025 Jun 5]. Available from:https://www.cancer.org/cancer/types/breast-cancer/frequently-asked-questions-about-the-american-cancer-society-new-breast-cancer-screening-guideline.html.
- 11. Ellington TD, Miller JW, Henley SJ, Wilson RJ, Wu M, Richardson LC. Trends in breast cancer incidence, by race, ethnicity, and age among women aged ≥20 years - United States, 1999-2018. MMWR Morb Mortal Wkly Rep 2022;71:43–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. U.S. Department of Health and Human Services. Office of Disease Prevention and Health Promotion . Healthy People 2030. Increase the proportion of females who get screened for breast cancer–C-05. Data Methodology and Measurement. [cited 2024 Dec 6]. Available from:https://odphp.health.gov/healthypeople/objectives-and-data/browse-objectives/cancer/increase-proportion-females-who-get-screened-breast-cancer-c-05/data-methodology. [Google Scholar]
- 13. Zhang H, Charlton BM, Schnarrs PW, Trentham-Dietz A, Kuehne F, Siebert U, et al. Mammography screening and risk factor prevalence by sexual identity: a comparison of two national surveys. Cancer 2025;131:e35852. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Castaldi M, Smiley A, Kechejian K, Butler J, Latifi R. Disparate access to breast cancer screening and treatment. BMC Women’s Health 2022;22:249. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Jewett PI, Gangnon RE, Elkin E, Hampton JM, Jacobs EA, Malecki K, et al. Geographic access to mammography facilities and frequency of mammography screening. Ann Epidemiol 2018;28:65–71.e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Miles RC, Flores EJ, Carlos RC, Boakye-Ansa NK, Brown C, Sohn Y-J, et al. Impact of health care–associated cost concerns on mammography utilization: cross-sectional survey results from the National Health Interview Survey. J Am Coll Radiol 2022;19:1081–7. [DOI] [PubMed] [Google Scholar]
- 17. US Centers for Disease Control and Prevention . About the national breast and cervical cancer early detection program. [cited 2024 Dec 6]. Available from:https://www.cdc.gov/breast-cervical-cancer-screening/about/index.html.
- 18. Çiçekli Taşdemir T, Seçginli S. Factors associated with mammography screening among women living in rural areas. Florence Nightingale J Nurs 2022;30:146–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Wiese D, Islami F, Henry KA. Changes in geographic accessibility to mammography by state and rural-urban status, United States, 2006-2022. J Natl Cancer Inst 2023;115:337–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Altice CK, Banegas MP, Tucker-Seeley RD, Yabroff KR. Financial hardships experienced by cancer survivors: a systematic review. J Natl Cancer Inst 2017;109:djw205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Jones SMW, Schuler TA, Padamsee TJ, Andersen MR. Financial anxiety is associated with cancer screening adherence in women at high risk of breast cancer. Ann Behav Med 2021;55:1241–5. [DOI] [PubMed] [Google Scholar]
- 22. National Center for Health Statistics . National Health Interview Survey, 2023. Public-use data file and documentation. 2024. Available from:https://www.cdc.gov/nchs/nhis/data-questionnaires-documentation.htm.
- 23. Bradley CJ, Kitchen S, Owsley KM. Working, low income, and cancer caregiving: financial and mental health impacts. J Clin Oncol 2023;41:2939–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Planey AM, Spees LP, Biddell CB, Waters A, Jones EP, Hecht HK, et al. The intersection of travel burdens and financial hardship in cancer care: a scoping review. JNCI Cancer Spectr 2024;8:pkae093. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Jiang C, Yabroff R, Deng L, Wang Q, Perimbeti S, Nipp RD, et al. Beyond transportation: financial hardship, food insecurity, and care access barriers among US cancer survivors with transportation barriers to care. J Clin Oncol 2023;41(suppl):6534. [Google Scholar]
- 26. Biddell CB, Spees LP, Smith JS, Brewer NT, Des Marais AC, Sanusi BO, et al. Perceived financial barriers to cervical cancer screening and associated cost burden among low-income, under-screened women. J Womens Health (Larchmt) 2021;30:1243–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Health Resources & Services Administration . Women’s preventive services guidelines. [cited 2025 Apr 14]. Available from:https://www.hrsa.gov/womens-guidelines.
- 28. DenseBreast-info . Does insurance cover my mammogram and any additional tests?. [cited 2025 Apr 14]. Available from:https://densebreast-info.org/patient-faqs/does-insurance-cover-my-mammogram-and-any-additional-tests/.
- 29. DenseBreast-info . State law insurance map. [cited 2025 Apr 14]. Available from:https://densebreast-info.org/legislative-information/state-law-insurance-map/.
- 30. Shete S, Deng Y, Shannon J, Faseru B, Middleton D, Iachan R, et al. Differences in breast and colorectal cancer screening adherence among women residing in urban and rural communities in the United States. JAMA Netw Open 2021;4:e2128000. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Tran L, Tran P. US urban-rural disparities in breast cancer-screening practices at the national, regional, and state level, 2012-2016. Cancer Causes Control 2019;30:1045–55. [DOI] [PubMed] [Google Scholar]
- 32. Pelzl CE, Drake A, Patel BK, Rula EY, Christensen EW. Mobile mammography utilization and breast cancer screening adherence among Medicare fee-for-service beneficiaries. Clin Breast Cancer 2025;25:e288–300.e1. [DOI] [PubMed] [Google Scholar]
- 33. Levine RS, Kilbourne BJ, Sanderson M, Fadden MK, Pisu M, Salemi JL, et al. Lack of validity of self-reported mammography data. Fam Med Community Health 2019;7:e000096. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Richman I, Tessier-Sherman B, Galusha D, Oladele CR, Wang K. Breast cancer screening during the COVID-19 pandemic: moving from disparities to health equity. J Natl Cancer Inst 2023;115:139–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. National Center for Health Statistics . National health interview survey. About NHIS. [cited 2024 Jan 10]. Available from:https://www.cdc.gov/nchs/nhis/about/?CDC_AAref_Val=https://www.cdc.gov/nchs/nhis/about_nhis.htm.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
This file contains supplementary materials and methods describing how the outcome variable was created.
Supplementary Table S1 contains demographics on individuals included vs excluded in the manuscript.
Supplementary Table S2 contains demographics of individuals in the sensitivity analysis.
Supplementary Table S3 contains model results from the sensitivity analysis.
Supplementary Figure S1 shows predicted probabilities of the sensitivity analysis.
Data Availability Statement
Data from the NHIS are publicly available at https://www.cdc.gov/nchs/nhis/index.htm.