The impact of age on health utility values for older women with early-stage breast cancer: a systematic review and meta-regression

Wang, Yubo; Gavan, Sean P; Steinke, Douglas; Cheung, Kwok-Leung; Chen, Li‐Chia

doi:10.1186/s12955-022-02067-w

Cited by 6 publications

(7 citation statements)

References 54 publications

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“…Several other reviews including utilities in breast cancer exist, each having different eligibility criteria, exposures of interest, and methodological approaches [116][117][118][119][120][121][122]. The largest one that also included screening health states had fairly similar ndings when comparing exposures of interest for our review [118].…”

Section: Comparison With Other Existing Evidencementioning

confidence: 99%

Patient preferences for breast cancer screening: a systematic review update to inform recommendations by the Canadian Task Force on Preventive Health Care

Pillay,

Guitard,

Rahman

et al. 2024

Preprint

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Background Different guideline panels, and individuals, may make different decisions based in part by their preferences. This systematic review update examined the relative importance placed by patients aged ≥ 35 years on the potential outcomes of breast-cancer screening. Methods We updated our searches to June 19, 2023 in MEDLINE, PsycINFO, and CINAHL. We screened grey literature, submissions by stakeholders, and reference lists. We sought three types of preferences, directly through i) utilities of screening and curative treatment health states (measuring the impact of the outcome on one’s health-related quality of life), and ii) other preference-based data, such as outcome trade-offs, and indirectly through iii) the relative importance of benefits versus harms inferred from attitudes, intentions, and behaviors towards screening among informed patients. For screening we used machine learning as one of the reviewers after at least 50% of studies had been reviewed in duplicate by humans; full-text selection used independent review by two humans. Data extraction and risk of bias assessments used a single reviewer with verification. Our main analysis for utilities used data from utility-based health-related quality of life tools (e.g., EQ-5D) in patients. When suitable, we pooled utilities and explored heterogeneity. Disutilities were calculated for screening health states and between different treatment states. Non-utility data were grouped into categories and synthesized with creation of summary statements. Certainty assessments followed GRADE guidance. Findings Eighty-two studies (38 on utilities) were included. The estimated disutilities were 0.07 for a positive screening result (moderate certainty), 0.03-0.04 for a false positive (FP; “additional testing” resolved as negative for cancer) (low certainty), and 0.08 for untreated screen-detected cancer (moderate certainty) or (low certainty) an interval cancer. At ≤12 months, disutilities of mastectomy (vs. breast-conserving therapy), chemotherapy (vs. none) (low certainty), and radiation therapy (vs. none) (moderate certainty) were 0.02-0.03, 0.02-0.04, and little-to-none, respectively. Over the longer term, there was moderate certainty for little-to-no disutility from mastectomy versus breast-conserving surgery/lumpectomy with radiation and from radiation. There was moderate certainty that a majority (>50%) and possibly large majority (>75%) of women probably accept up to six cases of overdiagnosis to prevent one breast-cancer death.Low certainty evidence suggested that a large majority may accept that screening may reduce breast-cancer but not all-cause mortality, at least when presented with relatively high rates of breast-cancer mortality reductions (n=2; 2 and 5 fewer per 1000 screened), and at least a majority accept that to prevent one breast-cancer death at least a few hundred patients will receive a FP result and 10-15 will have a FP resolved through biopsy. When using data from studies assessing attitudes, intentions, and screening behaviors, across all age groups but most evident for women in their 40s, preferences reduced as the net benefit presented by study authors decreased in magnitude. In a relatively low net-benefit scenario, a majority of patients in their 40s may not weigh the benefits as greater than the harms from screening (low certainty evidence). A large majority of patients aged 70-71 years probably think the benefits outweigh the harms for continuing to screen. A majority of women in their mid-70s to early 80s may prefer to continue screening. Conclusions Evidence across a range of data sources on how informed patients value the potential outcomes from breast-cancer screening will be useful during decision-making for recommendations. Further, the evidence supports providing easily understandable information on possible magnitudes of effects to enable informed decision-making. Systematic review registration: Protocol available at Open Science Framework https://osf.io/xngsu/

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Section: Comparison With Other Existing Evidencementioning

confidence: 99%

Patient preferences for breast cancer screening: a systematic review update to inform recommendations by the Canadian Task Force on Preventive Health Care

Pillay,

Guitard,

Rahman

et al. 2024

Preprint

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“…However, utility scores on comparable health states have been found to vary between studies. [13][14][15][16][17] The differences between these estimates can largely be explained by three issues.…”

Section: Introductionmentioning

confidence: 99%

“…Utility scores have been reported for a range of health states, diseases and populations. However, utility scores on comparable health states have been found to vary between studies 13–17 . The differences between these estimates can largely be explained by three issues.…”

Section: Introductionmentioning

confidence: 99%

“…For breast cancer health states a variety of utility values are available. However, there are uncertainties which values are best to use 13,14,17 . These uncertainties regard utilities of the normative population, disutilities of breast cancer states (ie, the decrease in utility value due to a specific health state), and disutilities concerning screening and follow‐up.…”

Section: Introductionmentioning

confidence: 99%

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Health utility values of breast cancer treatments and the impact of varying quality of life assumptions on cost‐effectiveness

Kregting,

Vrancken Peeters,

Clarijs

et al. 2024

Intl Journal of Cancer

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In breast cancer research, utility assumptions are outdated and inconsistent which may affect the results of quality adjusted life year (QALY) calculations and thereby cost‐effectiveness analyses (CEAs). Four hundred sixty four female patients with breast cancer treated at Erasmus MC, the Netherlands, completed EQ‐5D‐5L questionnaires from diagnosis throughout their treatment. Average utilities were calculated stratified by age and treatment. These utilities were applied in CEAs analysing 920 breast cancer screening policies differing in eligible ages and screening interval simulated by the MISCAN‐Breast microsimulation model, using a willingness‐to‐pay threshold of €20,000. The CEAs included varying sets on normative, breast cancer treatment and screening and follow‐up utilities. Efficiency frontiers were compared to assess the impact of the utility sets. The calculated average patient utilities were reduced at breast cancer diagnosis and 6 months after surgery and increased toward normative utilities 12 months after surgery. When using normative utility values of 1 in CEAs, QALYs were overestimated compared to using average gender and age‐specific values. Only small differences in QALYs gained were seen when varying treatment utilities in CEAs. The CEAs varying screening and follow‐up utilities showed only small changes in QALYs gained and the efficiency frontier. Throughout all variations in utility sets, the optimal strategy remained robust; biennial for ages 40–76 years and occasionally biennial 40–74 years. In sum, we recommend to use gender and age stratified normative utilities in CEAs, and patient‐based breast cancer utilities stratified by age and treatment or disease stage. Furthermore, despite varying utilities, the optimal screening scenario seems very robust.

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“…According to the Taiwanese cancer registry in 2014, breast cancer was the most commonly diagnosed cancer in women, with an agestandardized incidence rate of 70.7/100,000 people [3], a gure which continues to increase with an annual increase of 3.5/100,000 people. Therefore, how to evaluate and determine the prognosis or recurrence rate of breast cancer after the diagnosis is important as this may in uence the treatment plan and the patient's quality of life [4].…”

Section: Introductionmentioning

confidence: 99%

Prognostic factors of disease-free survival in patients with breast cancer

Chen

Tang

et al. 2023

Preprint

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Background Breast cancer is the second most common malignancy globally and a leading cause of cancer death in women. Analysis of factors related to disease-free survival (DFS) has improved understanding of the disease and characteristics related to recurrence. The aim of this study was to investigate the predictors of DFS in patients with breast cancer to enable the identification of patients at high risk who may benefit from prevention interventions.Methods We retrospectively analyzed 559 women with breast cancer who underwent treatment between 2004 and 2022. The study endpoint was DFS (including local recurrence, regional recurrence, secondary breast cancer, or death from any cause). Baseline tumor-related characteristics, treatment-related characteristics, sociodemographic and biochemical data were analyzed using Cox proportional hazards analysis and receiving operating characteristic curves. The median DFS was 45 months (range, 2 to 225 months).Results Breast cancer recurred in 86 patients (15.4%). Regression analysis showed that age, body mass index, surgery type, T3-T4 clinical tumor stage, tumor grade 3, American Joint Committee on Cancer (AJCC) stage III, Ki67 ≥ 14%, estrogen receptor-negative, progesterone receptor-negative, chemotherapy, carcinoembryonic antigen, CA153, red cell distribution width (RDW)-standard deviation (SD), aspartate aminotransferase, estimated glomerular filtration rate, albumin, hemoglobin, RDW-coefficient of variation, and platelet, white blood cell, neutrophil and monocyte count were associated with DFS. Multivariate Cox regression analysis showed that AJCC stage III, Ki67 ≥ 14%, albumin, platelet, and RDW-SD were independently associated with DFS. Furthermore, Kaplan-Meier analysis showed that albumin ≤ 4.1 g/dl, platelet ≥ 253 10^3/µl, RDW-SD ≥ 44 fL were significantly reduced DFS in patients with breast cancer.Conclusions In addition to the traditional prognostic factors such as AJCC stage III and Ki67 ≥ 14%, preoperative a cutoff albumin value of ≤ 4.1 g/dl, a cutoff platelet value of ≥ 253 10^3/µl, and a cutoff RDW-SD value of ≥ 44 fL could significantly predict DFS in patients with breast cancer.

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The impact of age on health utility values for older women with early-stage breast cancer: a systematic review and meta-regression

Cited by 6 publications

References 54 publications

Patient preferences for breast cancer screening: a systematic review update to inform recommendations by the Canadian Task Force on Preventive Health Care

Patient preferences for breast cancer screening: a systematic review update to inform recommendations by the Canadian Task Force on Preventive Health Care

Health utility values of breast cancer treatments and the impact of varying quality of life assumptions on cost‐effectiveness

Prognostic factors of disease-free survival in patients with breast cancer

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