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Abstract

Background:

Health-related quality of life (HRQoL) measures may help incorporate patient preferences and deliver individualized care for older breast cancer survivors. However, few studies have used clinically meaningful thresholds to evaluate the various characteristics associated with HRQoL in older women.

Objectives:

To examine sociodemographic and clinical characteristics associated with HRQoL and minimal clinically important differences (MCIDs) among female breast cancer survivors aged ⩾65 years.

Design:

A cross-sectional study.

Methods:

We used 2006–2015 Surveillance, Epidemiology, and End Results data linked to the Medicare Health Outcomes Survey (SEER-MHOS) for U.S. women aged ⩾65 years diagnosed with stages I–III invasive breast cancer. Descriptive statistics were used to summarize data. Generalized linear regression models were fitted to identify characteristics associated with the HRQoL physical component summary (PCS) and mental component summary (MCS) scores from the Veterans RAND 12-Item Health Survey scale. Missing values were imputed using chained equations. A threshold of two points was used to identify clinically meaningful between-group differences.

Results:

The median age at diagnosis was 72 years (interquartile range: 68–76) among 3218 breast cancer survivors. Obesity (mean difference (MD): −2.42; 95% confidence interval (CI): −3.34, −1.49), older age (⩾80 years; MD: −2.05; 95% CI: −3.15, −0.96), difficulty with ⩾1 activities of daily living (ADL; MD: −11.70; 95% CI: −12.42, −10.99), and cardiovascular (MD: −2.27; 95% CI: −3.02, −1.53) and musculoskeletal disease (MD: −3.88; 95% CI: −4.67, −3.09) were associated with clinically meaningful lower PCS scores. Less than high school education (MD: −3.43; 95% CI: −4.53, −2.33), annual household income ≤$19,999 (MD: −4.08; 95% CI: −6.00, −2.16) and $20,000–$39,999 (MD: −2.71; 95% CI: −4.63, −0.79), no surgery (MD: −3.03; 95% CI: −5.33, −0.72), difficulty with ≥1 ADL (MD: −6.71; 95% CI: −7.52, −5.89), and obesity (MD:2.06; 95% CI: 1.00, 3.12) were associated with MCIDs in MCS scores.

Conclusion:

Clinically meaningful differences in HRQoL were observed across sociodemographic and clinical characteristics in older female breast cancer survivors. These results may help identify women in need of interventions to improve HRQoL post-diagnosis.

Plain language summary

What is the quality of life in older women after a breast cancer diagnosis?

We studied more than 3,000 women in the U.S. who were breast cancer survivors aged 65 and above. This study was conducted to learn various clinical (e.g., stage at diagnosis, weight status) and personal (e.g., race and/or ethnicity, age) factors that may determine a breast cancer survivor’s quality of life after a breast cancer diagnosis. We found that increased weight, older age, difficulty with normal day-to-day activities, and having conditions related to heart disease and arthritis were associated with lower physical quality of life. Lower socioeconomic status (e.g., income), no surgery, and difficulty with normal day-to-day activities were associated with reduced mental quality of life. These findings show that personal and clinical factors could meaningfully change the quality of life of older breast cancer survivors. Our findings may help identify women who are in need of interventions to improve physical and mental quality of life after breast cancer diagnosis.

Keywords

quality of life measurement aging comorbidities functional status survivorship Medicare minimal clinically important difference patient reported outcome measures social determinants of health

Introduction

In the past three decades, the 5-year relative survival rate for breast cancer has increased from 57.3% to 92.0% in women aged 65 years and older.^1,2 Despite these improvements in survival, older breast cancer survivors continue to report lower health-related quality of life (HRQoL)^3,4 due to higher comorbidities, symptom burden, and decreased cognitive function.^5–7 Moreover, financial strain associated with increasing cancer care costs could reduce both mental and physical quality of life among older breast cancer survivors.^8,9

Currently, there are several HRQoL assessment tools available to describe the physical and mental health of women diagnosed with breast cancer.^10–14 For example, the Functional Assessment of Cancer Therapy–Breast (FACT-B) instrument measures five domains of HRQoL (physical, functional, social/family, emotional, and breast cancer-related well-being).¹⁰ The FACT-B instrument has been used to recommend therapeutic and home-based multi-dimensional interventions for breast cancer survivors.^15,16 Moreover, studies of HRQoL often report a minimal clinically important difference (MCID) identifying the smallest difference in HRQoL scores perceived as beneficial (or harmful) by patients and/or clinicians which could warrant a change in patient management.^17,18 Accordingly, even a small difference in HRQoL scores may represent clinically meaningful variations in the functional ability and/or mental well-being of individual patients.^19,20 While several studies have identified individual characteristics associated with HRQoL among older breast cancer survivors, to our knowledge none have comprehensively evaluated these characteristics in relation to MCIDs.^21,22

Studies on individual sociodemographic (e.g., age, race, ethnicity) and clinical (e.g., weight) characteristics associated with breast cancer outcomes could inform the development of individualized interventions for breast cancer prevention, screening, treatment, and survivorship.^23–25 For example, current clinical guidelines recommend cessation of mammography screening in women aged ≥ 75 years due to increased risk of competing mortality and breast cancer over-detection with increasing age.^26–29 However, currently over 50% of community-dwelling older women (aged ≥ 75) report screening within 2 years.³⁰ Therefore, clinicians have developed clinical decision tools to increase knowledge on the benefits and harms of mammography screening in older women.^25,31 Furthermore, studies show that Black and Latina women are less likely to receive genetic counseling and testing services for hereditary breast and ovarian cancer in the United States.³² These services are important as Black and Latina women show higher breast cancer mortality rates compared to non-Hispanic White women.²⁶ Therefore, culturally tailored interventions are currently being developed and tested to increase uptake of genetic counseling and testing among Black and Latina women in the United States.^33,34 Similarly, clinical characteristics may also help inform the development of individualized clinical decision tools to support breast cancer survivorship care.²³ For example, decades of studies have linked physical inactivity and obesity to poorer survival among breast cancer survivors.^35–37 This data has informed the development of individualized interventions to improve physical activity and health outcomes among breast cancer survivors.^38–41

Similarly, information on the individual characteristics associated with HRQoL and MCIDs may help identify older breast cancer survivors in need of individualized interventions to improve HRQoL.²¹ For example, lower HRQoL in women with multiple comorbidities, low income, education, or obesity could highlight the need to develop targeted interventions to improve HRQoL among those women.^42–44 However, there is limited data on MCIDs in older breast cancer survivors. Therefore, the overarching goal of this study was to evaluate sociodemographic and clinical characteristics associated with HRQoL and MCIDs in older women (⩾65 years) diagnosed with breast cancer in the United States.

Methods

Data source and study population

Data was obtained from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) cancer registry data linked with the Centers for Medicare and Medicaid Services’ Medicare Health Outcomes Survey (MHOS) from 1998 to 2017. This is collectively known as the SEER-MHOS linked data resource.⁴⁵ SEER collects cancer incidence and survival data, as well as population-level demographics and tumor characteristics, from cancer registries representing approximately 48% of the U.S. population.^45,46 MHOS is a randomly sampled longitudinal survey of Medicare Advantage enrollees to assess health plan effectiveness and quality of life.^45,47 The analytic period was restricted to 2006 to 2015 as this was the most recent period of complete linked SEER-MHOS data including all the variables considered in our analysis. Consistent with prior research, we selected women with a diagnosis of invasive breast cancer from SEER who had completed at least one MHOS survey.^22,48–50 Exclusion was made for those who were aged <65 years at the time of diagnosis, diagnosed with distant or in situ breast cancer,^42,51–53 institutionalized, had end-stage renal disease, died within 6 months of the survey, or had missing data for physical component summary (PCS) score, mental component summary (MCS) score, or difficulties with activities of daily living (ADLs; Figure 1). The final sample included 3218 female breast cancer survivors aged ⩾65 years diagnosed with localized and regional breast cancer.

Figure 1.

Inclusion and exclusion criteria.

Outcome variables

The outcome variables of interest were the t-standardized PCS and MCS scores from each individual’s first survey post breast cancer diagnosis. Consistent with previous studies, use of first survey after diagnosis ensures comparability across patients.^22,49,50 These scores were provided from the Veterans RAND 12-Item Health Survey (VR-12) scale.^54,55 The VR-12 uses eight scales measuring mental health, social functioning, bodily pain, vitality, general health perceptions, physical functioning, and role limitations due to emotional or physical health, to generate the component scores.⁵⁵ Higher scores reflect greater levels of physical and mental well-being.⁵⁶ This scale has been validated for use in older adults.^57,58

Covariates

Sociodemographic characteristics

Sociodemographic characteristics included self-reported marital status (married, never married, divorced/separated/widowed), race and/or ethnicity (non-Hispanic American Indian/Alaskan Native, non-Hispanic Asian/Pacific Islander, non-Hispanic Black, Hispanic, other or multiracial, non-Hispanic White), education level (some college or more, high school, less than high school), annual household income ($0–$19,999, $20,000–$39,999, $40,000–$79,999, $80,000 or more), and smoking status (not at all, some days, every day). Non-Hispanic American Indian/Alaskan Native, Asian/Pacific Islander, and individuals reporting other or multiple races were used to create a single “other” category due to small sample sizes within each group. While these populations are demographically and culturally distinct, aggregation allowed for retention of participants and model convergence.

Clinical characteristics

Clinical characteristics included age at diagnosis (65–69, 70–74, 75–79, ⩾80 years), SEER historic stage (localized, regional), estrogen receptor (ER) status (negative, positive), receipt of surgery (none, lumpectomy, mastectomy, excision of biopsy site), year of diagnosis, and years from diagnosis to survey. We also included weight status measured using body mass index (underweight (<18.5 kg/m²), normal (18.5–24.9 kg/m²), overweight (25–29.9 kg/m²), obese (⩾30 kg/m²)),⁵⁹ difficulty with at least one ADL (yes/no), and comorbidities, including pulmonary disease, cardiovascular disease, hypertension, diabetes, and musculoskeletal disease (yes/no). Musculoskeletal disease was defined as having arthritis, sciatica, or osteoporosis.

Statistical analyses

Distributions of sociodemographic and clinical characteristics were summarized using descriptive statistics. Univariate analyses were conducted on PCS and MCS for sociodemographic and clinical characteristics. There were missing data for 12 of 17 variables (Table 1). Missing data were imputed using multiple imputation by chained equations (MICE), assuming missing at random. MICE generated missing values in 20 imputed datasets and provided pooled regression estimates. A least absolute shrinkage and selection operator (LASSO) was also used to assess model fit, overall accuracy, and variable selection for the final models.^60,61 Covariates for final multivariable models were selected using previous literature and expert opinion.^62,63 Generalized linear models, with an identity link function and normal distribution, for PCS and MCS scores included education level, race/ethnicity, annual household income, marital status, age at diagnosis, weight status, ER status, receipt of surgery, and difficulty with ⩾1 ADL, year of diagnosis, historical stage, time from diagnosis to survey, smoking status, and comorbidities. Pooled fit statistics were created for the PCS score (adjusted R² = 0.40, root mean square error (RMSE) = 9.28, Akaike information criterion (AIC) = 7.31) and MCS score (adjusted R² = 0.15, RMSE = 10.61, AIC = 7.58) imputation models.

Table 1.

Sample characteristics (n = 3218).

Sociodemographic characteristics	N	%
Race and/or ethnicity
Asian/PI	284	8.8
AI/AN	—^a	—^a
Black	305	9.5
Hispanic	291	9.0
Other or multiracial	—^a	—^a
White	2316	56.6
Marital status
Married	1203	37.4
Never married	116	3.6
Divorced/separated/widowed	1820	56.6
Missing	79	2.5
Education level
Some college or more	1321	41.1
High school	1102	34.2
Less than high school	696	21.6
Missing	99	3.1
Annual household income
⩾ $80,000	132	4.1
$40,000–$79,999	442	13.7
$20,000–$39,999	782	24.3
$0–$19,999	1104	34.3
Missing	758	23.6
Clinical characteristics
Age at diagnosis (years)	72 (median)	69–76 (IQR)
Time from diagnosis to survey (years)	5.9 (median)	2.5–9.4 (IQR)
Weight status^b
Normal	985	30.6
Underweight	214	6.7
Overweight	1016	31.6
Obese	841	26.1
Missing	162	5.0
Smoking status
Not at all	2969	92.3
Some days/every day	194	6.0
Missing	55	1.7
SEER historic stage
Localized	1897	59.0
Regional	1321	41.1
Estrogen receptor status
Positive	2447	76.0
Negative	445	13.8
Missing	326	10.1
Surgery
Excision of biopsy site	380	11.8
Lumpectomy	990	30.8
Mastectomy	1711	53.2
None	93	2.9
Missing	44	1.4
Difficulty with ⩾1 activities of daily living
No	1741	45.9
Yes	1477	54.1
Pulmonary disease
No	2664	82.8
Yes	492	15.3
Missing	62	1.9
Cardiovascular disease
No	2116	65.8
Yes	915	28.4
Missing	187	5.8
Hypertension
No	892	27.7
Yes	2287	71.1
Missing	39	1.2
Diabetes
No	2330	72.4
Yes	845	26.3
Missing	43	1.3
Musculoskeletal disease
No	807	25.1
Yes	2205	68.5
Missing	206	6.4

PI: Pacific Islander; AI: American Indian; AN: Alaska Native; SEER: Surveillance, Epidemiology, and End Results; IQR: interquartile range.

Categories were suppressed when the number of cases or events in any cell was less than 10 to reduce the likelihood of a breach of confidentiality.

Weight status represented by body mass index categories: normal (18.5–24.9 kg/m²), underweight (<18.5 kg/m²), overweight (25–29.9 kg/m²), obese (⩾30 kg/m²).

MCIDs were used to determine the smallest meaningful change in HRQoL useful for determining prognosis and symptom management.⁶⁴ Consistent with previous literature, a two-point difference in MCS or PCS scores was considered a minimally important difference in breast cancer survivors.^65–67

Sensitivity analyses

Estimates from multiple imputations were compared to a complete case analysis. For the complete case analysis, individuals with missing data for covariates in the multivariable models (i.e., annual household income, marital status, education, smoking status, ER status, surgery type, weight status, cardiovascular disease, diabetes, pulmonary disease, hypertension, musculoskeletal disease) were excluded.

All analyses were conducted in Stata, version 18.0.⁶⁸ The National Institutes of Health Institutional Review Board approved this work, which was deemed exempt research due to use of de-identified pre-existing data. This research followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines (Supplemental Table 1).⁶⁹

Results

The final sample included 3218 older breast cancer survivors (Figure 1). The sample had a median age of 72 years (interquartile range (IQR): 68–76) and a median time from breast cancer diagnosis to first MHOS survey of 5.9 years (IQR: 2.5–9.4; Table 1). Most women were non-Hispanic White (56.6%) and were divorced, separated, or widowed (56.6%). A higher proportion of survivors had some college education or more (41.1%) and an annual household income between $0 and $19,999 (34.3%). Most women reported not smoking at all (92.3%) and under half of women reported no difficulty with ⩾1 ADL (45.9%; Table 1).

The mean PCS and MCS scores across the sample were 36.4 (standard deviation (SD): 12.0), and 51.2 (SD: 11.5), respectively. Univariate analyses showed MCS and PCS scores were associated with race, education, income, age at diagnosis, weight status, difficulty with ⩾1 ADL, and cardiovascular and musculoskeletal disease (Supplemental Tables 2 and 3). In the univariate analyses, surgery type was only associated with MCS, while marital status was only associated with PCS.

Breast cancer survivors with older age (⩾80 years; mean difference (MD): −2.05; 95% confidence interval (CI): −3.15, −0.96), obesity (MD: −2.42.; 95% CI: −3.34, −1.49), difficulty with ⩾1 ADL (MD: −11.70; 95% CI: −12.42, −10.99), cardiovascular (MD: −2.27; 95% CI: −3.02, −1.53), and musculoskeletal disease (MD: −3.88; 95% CI: −4.67, −3.09) had a clinically meaningful lower PCS score compared to those with younger age (65–69 years), normal weight status, no difficulties with ADL, no cardiovascular, and no musculoskeletal disease (Figure 2). Statistically significant lower PCS scores were associated with older age (70–74, 75–79 years); however, these differences were not clinically meaningful.

Figure 2.

Sociodemographic and clinical characteristics associated with the physical component summary score for older women (⩾65 years) diagnosed with localized and regional breast cancer.

Breast cancer survivors who were married (MD: −2.25; 95% CI: −4.33, −0.17), or who had less than high school education (MD: −3.43; 95% CI: −4.53, −2.33), annual household income between $0 and $19,999 (MD: −4.08; 95% CI: −6.00, −2.16) or $20,000–$39,999 (MD: −2.71; 95% CI: −4.63, −0.79), no surgery (MD: −3.03; 95% CI: −5.33, −0.72), or difficulty with ⩾1 ADL (MD: −6.71; 95% CI: −7.52, −5.89) had a clinically meaningful lower MCS score compared to survivors who were never married, had some college education, income ⩾ $80,000, a lumpectomy, and no difficulties with ADL (Figure 3). Additional analysis showed that women who did not receive surgery were more likely to be aged ⩾80 years and/or report poorer general health compared to those who underwent surgery (Supplemental Table 4). Obesity (MD: 2.06; 95% CI: 1.00, 3.12) was associated with a clinically meaningful higher MCS score compared to normal weight status. Statistically significant lower MCS scores were associated with high school education and cardiovascular disease; however, the differences did not meet the clinically meaningful threshold.

Figure 3.

Sociodemographic and clinical characteristics associated with the mental component summary score for older women (⩾65 years) diagnosed with localized and regional breast cancer.

Sensitivity analyses

The results for the PCS and MCS scores were similar across the multiple imputation and complete case analyses (Supplemental Tables 5 and 6).

Discussion

This is one of the first studies to evaluate sociodemographic and clinical characteristics associated with clinically meaningful differences in HRQoL in a nationally representative sample of older non-metastatic breast cancer survivors. Our results show obesity, older age at diagnosis, being married, having difficulty performing ADL, no surgery, cardiovascular and musculoskeletal disease, lower household income, and education were associated with clinically meaningful differences in HRQoL among older breast cancer survivors. Our findings are consistent with previous studies indicating that weight and age are associated with statistically significantly lower physical HRQoL.^70,71 However, our findings extend prior studies in evaluating the individual characteristics associated with MCIDs in HRQoL in older women.

Our results show that older women (≥ 65 years) with obesity, difficulty engaging in at least one activity of daily living, cardiovascular, and musculoskeletal disease showed lower clinically meaningful physical components of HRQoL. Studies suggest that tailored exercise interventions for older women could help increase HRQoL among these women.⁷² Furthermore, our results showed that women with lower income and education had clinically meaningful lower mental components of HRQoL. Previous studies have shown that worry about cost of healthcare, insurance coverage, and affordability of basic needs could increase stress, depression, and anxiety among cancer patients which could be reflected in the low MCS scores in our study.^73,74 While further research is needed to better understand various factors contributing to lower MCS scores in older breast cancer survivors with low income and education, previous studies also support the development of interventions to mitigate financial toxicity in this population.^75,76

Interestingly, we found that not receiving surgery was associated with a clinically meaningful lower mental HRQoL in comparison to those who received lumpectomy. These findings differ from previous studies showing that surgery is associated with lower HRQoL, potentially mediated through body image disturbance.⁷⁷ Upon additional analysis, we found that women who did not receive surgery were more likely to be aged ⩾80 years and/or report poorer general health. These differences could partially explain lower mental HRQoL in this group reflecting disease severity or terminal disease associated with older age and/or poor general health. Further research is warranted to understand the underlying characteristics contributing to higher HRQoL observed among older breast cancer survivors who received surgery.

Consistent with prior literature, obesity, cardiovascular disease, and musculoskeletal disease were statistically significantly associated with physical HRQoL among breast cancer survivors.^78,79 Furthermore, in this study, we found these differences met the minimal clinically important threshold. Chronic inflammation, disability, and low perception of physical health associated with obesity may explain the lower HRQoL scores associated with obesity.^80,81 Moreover, comorbidities such as cardiovascular and musculoskeletal disease may affect HRQoL through poor functional status, bodily pain, and/or fatigue.^82–84 There are several tools that consider the relationship between comorbidities, weight status, and survival to support clinical care.^85,86 However, currently there are no tools that incorporate the effects of clinical characteristics on HRQoL to guide clinical decisions for breast cancer survivors. Development of these tools may help integrate HRQoL measures into clinical care to meaningfully improve HRQoL among older breast cancer survivors.

Household income was associated with clinically meaningful differences in both mental and physical HRQoL. These results indicate that it is important for healthcare systems to address increased costs associated with cancer treatment. Survivors who experience a high financial strain may forego medical care, be unable to afford basic needs, and/or work longer hours, which can contribute to worsened HRQoL.^87,88 We also found that less than high school education was associated with worse mental HRQoL compared to those who completed some college education. Educational attainment may lead to desirable occupational opportunities, career growth, increased social support and capital, and fewer reported symptoms of emotional distress.^89,90 Therefore, consideration of patients’ education level, together with their economic status, may help clinicians account for the social and economic realities influencing HRQoL. In this regard, clinicians may consider tools like the COST (comprehensive score for financial toxicity) measure to assess and support early interventions related to financial burden.^91,92

There are several strengths in this study. First, we conducted one of the first studies evaluating individual characteristics associated with clinically meaningful differences in HRQoL scores among older breast cancer survivors using a population-based dataset. Second, SEER-MHOS provided data from across the U.S. representing older (⩾65 years) breast cancer survivors seen in real-world settings, which may increase the generalizability of our findings to older breast cancer survivors enrolled in Medicare Advantage plans. Third, since SEER-MHOS includes data on other cancers (e.g., lung, prostate, colorectal), our findings may be replicated for different cancer sites. Finally, our study provides information for an understudied population of older breast cancer survivors (≥ 75) who are often excluded from clinical trials.^93,94

Limitations

Our study presents several limitations. The number of women for non-Hispanic American Indian/Alaska Native women, other or multiracial women, and those living in rural areas were too small to provide quality of life estimates for these groups.²² Future studies should consider using data sources that have larger sampling of these individuals. Additionally, this study focused on women aged ⩾65 years who were enrolled in Medicare Advantage programs, limiting generalizability of results to women with traditional Medicare fee-for-service coverage, younger breast cancer survivors, or women living in regions not covered by SEER. Although our analysis included localized treatment variables such as surgery and radiation, the absence of data on systemic therapies such as hormonal therapy, targeted therapy, or immunotherapy limited us from evaluating the full spectrum of treatments.

Conclusions

HRQoL and MCIDs could vary based on individual sociodemographic and clinical characteristics among older breast cancer survivors. These findings may help develop novel individualized interventions for older breast cancer survivors with lower socioeconomic characteristics, obesity, lower functional status, and comorbidities such as cardiovascular and musculoskeletal disease. However, further research is needed to inform the development and implementation of these interventions in clinical settings.

Supplemental Material

sj-docx-1-whe-10.1177_17455057261432623 – Supplemental material for Clinical and sociodemographic characteristics associated with health-related quality of life among older breast cancer survivors

Supplemental material, sj-docx-1-whe-10.1177_17455057261432623 for Clinical and sociodemographic characteristics associated with health-related quality of life among older breast cancer survivors by Jacob Schneider, Kaitlyn M. Wojcik, Emma Tian, Oliver W. A. Wilson and Jinani Jayasekera in Women's Health

Supplemental Material

sj-docx-2-whe-10.1177_17455057261432623 – Supplemental material for Clinical and sociodemographic characteristics associated with health-related quality of life among older breast cancer survivors

Supplemental material, sj-docx-2-whe-10.1177_17455057261432623 for Clinical and sociodemographic characteristics associated with health-related quality of life among older breast cancer survivors by Jacob Schneider, Kaitlyn M. Wojcik, Emma Tian, Oliver W. A. Wilson and Jinani Jayasekera in Women's Health

Footnotes

Acknowledgements

We thank Dr. Roxxane Jensen for her support in providing methodological and design consultation on the use of the SEER-MHOS linked dataset.

ORCID iDs

Jacob Schneider

Kaitlyn M. Wojcik

Emma Tian

Oliver W. A. Wilson

Jinani Jayasekera

Ethical considerations

This study was approved by the National Institutes of Health Institutional Review Board and was considered exempt research based on use of de-identified pre-existing data.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Author contributions

Jacob Schneider: Conceptualization; Investigation; Writing – original draft; Methodology; Visualization; Writing – review & editing; Validation.

Kaitlyn M. Wojcik: Conceptualization; Investigation; Writing – original draft; Visualization; Methodology; Validation.

Emma Tian: Writing – review & editing; Conceptualization.

Oliver W.A. Wilson: Writing – review & editing; Conceptualization; Supervision.

Jinani Jayasekera: Funding acquisition; Investigation; Conceptualization; Writing – review & editing; Methodology; Supervision.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Division of Intramural Research at the National Institute on Minority Health and Health Disparities of the National Institutes of Health (ZIA MD000022, PI Jayasekera). The contributions of the NIH authors were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements, and are considered Works of the United States Government. However, the findings and conclusions presented in this article are those of the authors and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services. This study used data from the SEER-MHOS linked data resource. The interpretation and reporting of these data are the sole responsibility of the authors. The authors acknowledge the efforts of the National Cancer Institute; the Centers for Medicare & Medicaid Services; Information Management Services (IMS), Inc.; and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER-MHOS database.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The SEER-MHOS datasets analyzed during the current study are not publicly available due to the risk of re-identification. The data are available to investigators for research purposes, but approval is required to obtain the data.

Supplemental material

Supplemental material for this article is available online.

References

Zheng

, et al. Trends in survival for cancer patients aged 65 years or over from 1995 to 2014 in the United States: a population-based study. Cancer Med 2023; 12: 6283–6293. https://doi.org/10.1002/cam4.5398.

Surveillance Research Program, National Cancer Institute. SEER*Explorer: an interactive website for SEER cancer statistics, https://seer.cancer.gov/statistics-network/explorer/ (2025, accessed 21 April 2025).

Park

Lee

, et al. Quality of life differences between younger and older breast cancer patients. J Breast Cancer 2011; 14: 112–118. https://doi.org/10.4048/jbc.2011.14.2.112.

Ganz

Guadagnoli

Landrum

, et al. Breast cancer in older women: quality of life and psychosocial adjustment in the 15 months after diagnosis. J Clin Oncol 2003; 21: 4027–4033. https://doi.org/10.1200/jco.2003.08.097.

Mandelblatt

Small

Luta

, et al. Cancer-related cognitive outcomes among older breast cancer survivors in the thinking and living with cancer study. J Clin Oncol 2018; 36: JCO1800140. https://doi.org/10.1200/JCO.18.00140.

Mandelblatt

Zhai

Ahn

, et al. Symptom burden among older breast cancer survivors: the thinking and living with cancer (TLC) study. Cancer 2020; 126: 1183–1192. https://doi.org/10.1002/cncr.32663.

Mandelblatt

Stern

Luta

, et al. Cognitive impairment in older patients with breast cancer before systemic therapy: is there an interaction between cancer and comorbidity? J Clin Oncol 2014; 32: 1909–1918. https://doi.org/10.1200/jco.2013.54.2050.

Stein

Syrjala

Andrykowski

MA.

Physical and psychological long-term and late effects of cancer. Cancer 2008; 112: 2577–2592. https://doi.org/10.1002/cncr.23448.

Perry

Hoerger

Seibert

, et al. Financial strain and physical and emotional quality of life in breast cancer. J Pain Symptom Manage 2019; 58: 454–459. https://doi.org/10.1016/j.jpainsymman.2019.05.011.

10.

Brady

Cella

, et al. Reliability and validity of the Functional Assessment of Cancer Therapy-Breast quality-of-life instrument. J Clin Oncol 1997; 15: 974–986. https://doi.org/10.1200/jco.1997.15.3.974.

11.

Kreimeier

Åström

Burström

, et al. EQ-5D-Y-5L: developing a revised EQ-5D-Y with increased response categories. Qual Life Res 2019; 28: 1951–1961. https://doi.org/10.1007/s11136-019-02115-x.

12.

Kaasa

Bjordal

Aaronson

, et al. The EORTC core quality of life questionnaire (QLQ-C30): validity and reliability when analysed with patients treated with palliative radiotherapy. Eur J Cancer 1995; 31a: 2260–2263. https://doi.org/10.1016/0959-8049(95)00296-0.

13.

Cella

Tulsky

, et al. The Functional Assessment of Cancer Therapy scale: development and validation of the general measure. J Clin Oncol 1993; 11: 570–579.

14.

Health-Related Quality of Life (HRQOL), https://archive.cdc.gov/www_cdc_gov/hrqol/index.htm (2022, accessed 5 November 2024).

15.

Kim

Lee

Kim

, et al. Effectiveness of therapeutic inflatable ball self-exercises for improving shoulder function and quality of life in breast cancer survivors after sentinel lymph node dissection. Support Care Cancer 2019; 27: 2349–2360. https://doi.org/10.1007/s00520-019-4656-0.

16.

Cheng

KKF

Lim

YTE

Koh

, et al. Home-based multidimensional survivorship programmes for breast cancer survivors. Cochrane Database Syst Rev 2017; 8: Cd011152. https://doi.org/10.1002/14651858.CD011152.pub2.

17.

Jaeschke

Singer

Guyatt

GH.

Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin Trials 1989; 10: 407–415. https://doi.org/10.1016/0197-2456(89)90005-6.

18.

Draak

THP

de Greef

BTA

Faber

, et al. The minimum clinically important difference: which direction to take. Eur J Neurol 2019; 26: 850–855. https://doi.org/10.1111/ene.13941.

19.

Hays

Morales

LS.

The RAND-36 measure of health-related quality of life. Ann Med 2001; 33: 350–357. https://doi.org/10.3109/07853890109002089.

20.

Samsa

Edelman

Rothman

, et al. Determining clinically important differences in health status measures. Pharmacoeconomics 1999; 15: 141–155. https://doi.org/10.2165/00019053-199915020-00003.

21.

Ferrier

Filipovic

Wasan

, et al. A targeted literature review on the impact of tailored interventions on patient outcomes in oncology. Oncogene 2025; 44: 1439–1451. https://doi.org/10.1038/s41388-025-03424-x.

22.

Karimi-Mostowfi

Chu

F-I

, et al. Trends in racial and ethnic disparities in the health-related quality of life of older adults with breast cancer: a SEER-MHOS national database study. Health Qual Life Outcomes 2025; 23: 28. https://doi.org/10.1186/s12955-025-02359-x.

23.

Wojcik

Kamil

Zhang

, et al. A scoping review of web-based, interactive, personalized decision-making tools available to support breast cancer treatment and survivorship care. J Cancer Surviv 2025; 19: 1496–1524. https://doi.org/10.1007/s11764-024-01567-6.

24.

Kamil

Wojcik

Smith

, et al. A scoping review of personalized, interactive, web-based clinical decision tools available for breast cancer prevention and screening in the United States. MDM Policy Pract 2024; 9: 23814683241236511. https://doi.org/10.1177/23814683241236511.

25.

Schonberg

Stout

Stein

, et al. Creating a mammography conversation aid for shared decision-making between clinicians and women aged 75 and older. J Am Geriatr Soc 2025; 10: 10.1111/jgs.19466. https://doi.org/10.1111/jgs.19466.

26.

U.S. Preventive Services Task Force. Screening for breast cancer: US Preventive Services Task Force recommendation statement. JAMA 2024; 331: 1918–1930. https://doi.org/10.1001/jama.2024.5534.

27.

Oeffinger

Fontham

Etzioni

, et al. Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA 2015; 314: 1599–1614. https://doi.org/10.1001/jama.2015.12783.

28.

Davidson

Mangione

Barry

, et al. Collaboration and shared decision-making between patients and clinicians in preventive health care decisions and US Preventive Services Task Force recommendations. JAMA 2022; 327: 1171–1176. https://doi.org/10.1001/jama.2022.3267.

29.

Yourman

Bergstrom

Bryant

, et al. Variation in receipt of cancer screening and immunizations by 10-year life expectancy among U.S. adults aged 65 or older in 2019. J Gen Intern Med 2024; 39: 440–449. https://doi.org/10.1007/s11606-023-08439-2.

30.

Austin

Shelton

Lee Argov

, et al. Older women’s perspectives driving mammography screening use and overuse: a narrative review of mixed-methods studies. Curr Epidemiol Rep 2020; 7: 274–289. https://doi.org/10.1007/s40471-020-00244-3.

31.

Jayasekera

Stein

Wilson

OWA

, et al. Benefits and harms of mammography screening in 75 + women to inform shared decision-making: a simulation modeling study. J Gen Intern Med 2024; 39: 428–439. https://doi.org/10.1007/s11606-023-08518-4.

32.

Cragun

Weidner

Lewis

, et al. Racial disparities in BRCA testing and cancer risk management across a population-based sample of young breast cancer survivors. Cancer 2017; 123: 2497–2505. https://doi.org/10.1002/cncr.30621.

33.

Sheppard

Sutton

Hurtado-De-Mendoza

, et al. Addressing disparities in the uptake of genetic counseling and testing in African American women; rationale, design and methods. Contemp Clin Trials 2023; 130: 107210. https://doi.org/https://doi.org/10.1016/j.cct.2023.107210.

34.

Conley

Castro-Figueroa

Moreno

, et al. A pilot randomized trial of an educational intervention to increase genetic counseling and genetic testing among Latina breast cancer survivors. J Genet Couns 2021; 30: 394–405. https://doi.org/10.1002/jgc 4.1324.

35.

Colditz

GA.

Epidemiology of breast cancer. findings from the nurses’ health study. Cancer 1993; 71: 1480–1489. https://doi.org/10.1002/cncr.2820710413.

36.

McTiernan

Kooperberg

White

, et al. Recreational physical activity and the risk of breast cancer in postmenopausal women: the Women’s Health Initiative Cohort Study. JAMA 2003; 290: 1331–1336. https://doi.org/10.1001/jama.290.10.1331.

37.

Petrelli

Cortellini

Indini

, et al. Association of obesity with survival outcomes in patients with cancer: a systematic review and meta-analysis. JAMA Netw Open 2021; 4: e213520. https://doi.org/10.1001/jamanetworkopen.2021.3520.

38.

Gnagnarella

Dragà

Raja

, et al. Physical activity and/or dietary intervention in overweight or obese breast cancer survivors: results of the InForma randomized trial. J Cancer Surviv 2024; 18: 1732–1746. https://doi.org/10.1007/s11764-023-01415-z.

39.

Yang

Liu

Zhang

Exercise interventions on body composition and quality of life of overweight/obese breast cancer survivors: a meta-analysis. BMC Womens Health 2023; 23: 484. https://doi.org/10.1186/s12905-023-02627-2.

40.

Jayasekera

Wilson

OWA

Schechter

, et al. Development and validation of a simulation model–based tool to support individualized physical activity discussions and prescriptions for breast cancer survivors. JCO Clin Cancer Inform 2025: e2500151. https://doi.org/10.1200/CCI-25-00151.

41.

Jayasekera

Wilson

OWA

Wojcik

, et al. Healthcare provider perspectives on a clinical decision tool to support individualized exercise prescriptions and discussions for breast cancer survivors. J Cancer Surviv. Epub ahead of print 12 March 2025. https://doi.org/10.1007/s11764-025-01750-3.

42.

Crouch

Champion

Von

Ah D

. Comorbidity, cognitive dysfunction, physical functioning, and quality of life in older breast cancer survivors. Support Care Cancer 2022; 30: 359–366. https://doi.org/10.1007/s00520-021-06427-y.

43.

Wang

, et al. Disparities in quality of life among patients with breast cancer based on surgical methods: a cross-sectional prospective study. Sci Rep 2024; 14: 11364. https://doi.org/10.1038/s41598-024-62105-z.

44.

Brown

Sarwer

Troxel

, et al. A randomized trial of exercise and diet on health-related quality of life in survivors of breast cancer with overweight or obesity. Cancer 2021; 127: 3856–3864. https://doi.org/10.1002/cncr.33752.

45.

Ambs

Warren

Bellizzi

, et al. Overview of the SEER–Medicare health outcomes survey linked dataset. Health Care Financ Rev 2008; 29: 5–21.

46.

Surveillance Research Program, National Cancer Institute. Overview of the SEER Program, https://seer.cancer.gov/about/overview.html (accessed 23 October 2024).

47.

Centers for Medicare & Medicaid Services. Medicare Health Outcomes Survey (HOS) website, https://www.hosonline.org/ (2023, accessed 23 October 2024).

48.

Byrne

Leiser

Mitchell

, et al. Trajectories of fatigue in a population-based sample of older adult breast, prostate, and colorectal cancer survivors: an analysis using the SEER-MHOS data resource. Support Care Cancer 2021; 29: 7393–7402. https://doi.org/10.1007/s00520-021-06267-w.

49.

Huang

Blackwood

Godoshian

, et al. Predictors of falls in older survivors of breast and prostate cancer: a retrospective cohort study of surveillance, epidemiology and end results—Medicare health outcomes survey linkage. J Geriatr Oncol 2019; 10: 89–97. https://doi.org/10.1016/j.jgo.2018.04.009.

50.

Siembida

Kent

Bellizzi

, et al. Healthcare providers’ discussions of physical activity with older survivors of cancer: potential missed opportunities for health promotion. J Geriatr Oncol 2020; 11: 437–443. https://doi.org/10.1016/j.jgo.2019.05.0071.

51.

Dialla

Chu

W-O

Roignot

, et al. Impact of age-related socio-economic and clinical determinants of quality of life among long-term breast cancer survivors. Maturitas 2015; 81: 362–370. https://doi.org/10.1016/j.maturitas.2015.03.025.

52.

George

Alfano

Wilder Smith

, et al. Sedentary behavior, health-related quality of life, and fatigue among breast cancer survivors. J Phys Act Health 2013; 10: 350–358. https://doi.org/10.1123/jpah.10.3.350.

53.

Todd

Feuerstein

Gehrke

, et al. Identifying the unmet needs of breast cancer patients post-primary treatment: the Cancer Survivor Profile (CSPro). J Cancer Surviv 2015; 9: 137–160. https://doi.org/10.1007/s11764-015-0428-0.

54.

Bridging SF-36 and VR-12, https://healthcaredelivery.cancer.gov/seer-mhos/program/recode.html (2024, accessed 23 October 2024).

55.

Kazis

Rogers

Building Bridges between the VR-12 and SF-36® Patient Reported Outcomes in the SEER-MHOS Data Resource, https://healthcaredelivery.cancer.gov/seer-mhos/program/_1.7%20NCI%20Webinar%20Boston%20Univ%20%203-22-16-3.pdf (2016, accessed 23 October 2024).

56.

Healthcare Delivery Research Program, National Cancer Institute. SEER-MHOS Data Documentation, https://healthcaredelivery.cancer.gov/seer-mhos/aboutdata/documentation.html (2024, accessed 23 October 2024).

57.

Wilson

Cuthbertson

Sasaki

, et al. Validation of an adapted version of the Veterans RAND 12-Item Health Survey for older adults living in long-term care homes. Gerontologist 2023; 63: 1467–1477. https://doi.org/10.1093/geront/gnad021.

58.

Walters

Munro

Brazier

JE.

Using the SF-36 with older adults: a cross-sectional community-based survey. Age Ageing 2001; 30: 337–343. https://doi.org/10.1093/ageing/30.4.337.

59.

Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults—the evidence report. National Institutes of Health. Obes Res 1998; 6(Suppl 2): 51S–209S.

60.

Least Absolute Shrinkage and Selection Operator (LASSO), https://www.publichealth.columbia.edu/research/population-health-methods/least-absolute-shrinkage-and-selection-operator-lasso (2024, accessed 18 March 2024).

61.

Tibshirani

Regression shrinkage and selection via the Lasso. J R Stat Soc Series B Stat Methodol 1996; 58: 267–288.

62.

Rincon

Smith

, et al. Trends in racial/ethnic disparity of health-related quality of life in older adults with and without cancer (1998–2012). Cancer Epidemiol Biomarkers Prev 2020; 29: 1188–1195. https://doi.org/10.1158/1055-9965.Epi-19-0819.

63.

Jackson

Rowan

Padhye

, et al. Racial/ethnic differences in health-related quality of life among female breast cancer survivors: cross-sectional findings from the Medical Expenditure Panel Survey. Public Health 2021; 196: 74–81. https://doi.org/10.1016/j.puhe.2021.05.021.

64.

Mouelhi

Jouve

Castelli

, et al. How is the minimal clinically important difference established in health-related quality of life instruments? Review of anchors and methods. Health Qual Life Outcomes 2020; 18: 136. https://doi.org/10.1186/s12955-020-01344-w.

65.

Avis

Levine

Goyal

, et al. Health-related quality of life among breast cancer survivors and noncancer controls over 10 years: Pink SWAN. Cancer 2020; 126: 2296–2304. https://doi.org/10.1002/cncr.32757.

66.

Kent

Ambs

Mitchell

, et al. Health-related quality of life in older adult survivors of selected cancers: data from the SEER-MHOS linkage. Cancer 2015; 121: 758–765. https://doi.org/10.1002/cncr.29119.

67.

Hays

Farivar

Liu

Approaches and recommendations for estimating minimally important differences for health-related quality of life measures. COPD 2005; 2: 63–67. https://doi.org/10.1081/copd-200050663.

68.

StataCorp. Stata Statistical Software: Release 18. StataCorp LLC, 2023.

69.

von Elm

Altman

Egger

, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet 2007; 370: 1453–1457. https://doi.org/10.1016/S0140-6736(07)61602-X.

70.

Reeve

Potosky

Smith

, et al. Impact of cancer on health-related quality of life of older Americans. J Natl Cancer Inst 2009; 101: 860–868. https://doi.org/10.1093/jnci/djp123.

71.

Apple

Samuels

Fonnesbeck

, et al. Body mass index and health-related quality of life. Obes Sci Pract 2018; 4: 417–426. https://doi.org/10.1002/osp4.292.

72.

Tian

Wilson

OWA

Wojcik

, et al. Recreational physical activity and health-related quality of life among breast cancer survivors: a systematic review. Qual Life Res 2025; 34: 2513–2529. https://doi.org/10.1007/s11136-025-03992-1.

73.

Altice

Banegas

Tucker-Seeley

, et al. Financial hardships experienced by cancer survivors: a systematic review. J Natl Cancer Inst 2017; 109: djw205. https://doi.org/10.1093/jnci/djw205.

74.

Ehsan

Minasian

, et al. Financial toxicity among patients with breast cancer worldwide: a systematic review and meta-analysis. JAMA Netw Open 2023; 6: e2255388. https://doi.org/10.1001/jamanetworkopen.2022.55388.

75.

Ping

Andrade

Housten

, et al. A scoping review of interventions to address financial toxicity in pediatric and adult patients and survivors of cancer. Cancer Med 2025; 14: e70879. https://doi.org/10.1002/cam4.70879.

76.

Ehsan

Minasian

, et al. Evaluation of financial interventions in breast cancer care worldwide: a systematic review. Plast Reconstr Surg Glob Open 2024; 12: e5683.

77.

Zhou

Wang

, et al. Body image mediates the relationship between post-surgery needs and health-related quality of life among women with breast cancer: a cross-sectional study. Health Qual Life Outcomes 2020; 18: 163. https://doi.org/10.1186/s12955-020-01400-5.

78.

Deshpande

Sefko

Jeffe

, et al. The association between chronic disease burden and quality of life among breast cancer survivors in Missouri. Breast Cancer Res Treat 2011; 129: 877–886. https://doi.org/10.1007/s10549-011-1525-z.

79.

Eheman

Henley

Ballard-Barbash

, et al. Annual Report to the Nation on the status of cancer, 1975-2008, featuring cancers associated with excess weight and lack of sufficient physical activity. Cancer 2012; 118: 2338–2366. https://doi.org/10.1002/cncr.27514.

80.

Imayama

Alfano

Neuhouser

, et al. Weight, inflammation, cancer-related symptoms and health-related quality of life among breast cancer survivors. Breast Cancer Res Treat 2013; 140: 159–176. https://doi.org/10.1007/s10549-013-2594-y.

81.

Peytremann-Bridevaux

Santos-Eggimann

Health correlates of overweight and obesity in adults aged 50 years and over: results from the Survey of Health, Ageing and Retirement in Europe (SHARE). Obesity and health in Europeans aged > or = 50 years. Swiss Med Wkly 2008; 138: 261–266. https://doi.org/10.4414/smw.2008.12067.

82.

H-S

Davis

Chen

Impact of comorbidity on symptoms and quality of life among patients being treated for breast cancer. Cancer Nurs 2019; 42: 381–387. https://doi.org/10.1097/ncc.0000000000000623.

83.

Smith

Reeve

Bellizzi

, et al. Cancer, comorbidities, and health-related quality of life of older adults. Health Care Financ Rev 2008; 29: 41–56.

84.

Axelrod

Guth

, et al. Comorbidities and quality of life among breast cancer survivors: a prospective study. J Pers Med 2015; 5: 229–242. https://doi.org/10.3390/jpm5030229.

85.

Wang

Abujarad

Chen

, et al. “Radiotherapy for older women (ROW)”: a risk calculator for women with early-stage breast cancer. J Geriatr Oncol 2020; 11: 850–859. https://doi.org/10.1016/j.jgo.2019.12.010.

86.

Meretoja

Andersen

Bruce

, et al. Clinical prediction model and tool for assessing risk of persistent pain after breast cancer surgery. J Clin Oncol 2017; 35: 1660–1667. https://doi.org/10.1200/jco.2016.70.3413.

87.

Zafar

Peppercorn

Schrag

, et al. The financial toxicity of cancer treatment: a pilot study assessing out-of-pocket expenses and the insured cancer patient’s experience. Oncologist 2013; 18: 381–390. https://doi.org/10.1634/theoncologist.2012-0279.

88.

Kent

Forsythe

Yabroff

, et al. Are survivors who report cancer-related financial problems more likely to forgo or delay medical care? Cancer 2013; 119: 3710–3717. https://doi.org/10.1002/cncr.28262.

89.

Ross

Van Willigen

Education and the subjective quality of life. J Health Soc Behav 1997; 38: 275–297.

90.

Ross

CL.

The links between education and health. Am Sociol Rev 1995; 60: 719–745. https://doi.org/10.2307/2096319.

91.

de Souza

Yap

Hlubocky

, et al. The development of a financial toxicity patient-reported outcome in cancer: the COST measure. Cancer 2014; 120: 3245–3253. https://doi.org/10.1002/cncr.28814.

92.

Smith

Nicolla

Zafar

SY.

Bridging the gap between financial distress and available resources for patients with cancer: a qualitative study. J Oncol Pract 2014; 10: e368–e372. https://doi.org/10.1200/jop.2013.001342.

93.

Trimble

Carter

Cain

, et al. Representation of older patients in cancer treatment trials. Cancer 1994; 74: 2208–2214. https://doi.org/10.1002/1097-0142(19941001)74:7+<2208::aid-cncr2820741737>3.0.co;2-#.

94.

Hutchins

Unger

Crowley

, et al. Underrepresentation of patients 65 years of age or older in cancer-treatment trials. N Engl J Med 1999; 341: 2061–2067. https://doi.org/10.1056/nejm199912303412706.

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