An Individualized Approach to Cancer Screening Decisions in Older Adults: A Multilevel Framework

Breslau, Erica S.; Gorin, Sherri Sheinfeld; Edwards, Heather M.; Schonberg, Mara A.; Saiontz, Nicole Gottlieb; Walter, Louise C.

doi:10.1007/s11606-016-3629-y

Cited by 54 publications

(56 citation statements)

References 51 publications

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“…High quality medical treatment is always relative to what is required and what is adequate for that particular patient. It requires an individualized, person-centered approach to care (Breslau et al 2016), and is in keeping with the anti-ageist ideology inherent in the principles of modern geriatric practice (Coupland and Coupland 1994). It supports the potential for the field of geriatric medicine not only to treat health conditions and reduce suffering in older persons, but also to "engage in work either to endorse or to reconstruct patients' [ageist] conceptions of their own ageing and health" (Coupland and Coupland 1994).…”

Section: Conclusion and Recommendationsmentioning

confidence: 84%

“…Multiple patient-level and institutional-level factors impact clinical decision-making about screening, diagnostic tests, and treatment (e.g., Breslau et al 2016). Clinical decision-making for older patients is often a highly complex task which is not made easier with the dearth of clinical evidence regarding appropriate treatments for this population.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

See 1 more Smart Citation

Ageism in the Health Care System: Providers, Patients, and Systems

Wyman

Shiovitz-Ezra

Bengel

2018

International Perspectives on Aging

190

117

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Section: Conclusion and Recommendationsmentioning

confidence: 84%

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Ageism in the Health Care System: Providers, Patients, and Systems

Wyman

Shiovitz-Ezra

Bengel

2018

International Perspectives on Aging

190

117

View full text Add to dashboard Cite

“…1–12 Research and clinical practice guidelines recommend incorporating life expectancy to inform cancer screening. 1–12 Specifically, several Choosing Wisely recommendations mention that clinicians should not routinely screen for cancer among patients with limited life expectancy.…”

mentioning

confidence: 99%

Older Adults’ Views and Communication Preferences About Cancer Screening Cessation

et al. 2017

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IMPORTANCE Older adults with limited life expectancy are frequently screened for cancer even though it exposes them to risks of screening with minimal benefit. Patient preferences may be an important contributor to continued screening.OBJECTIVE To examine older adults' views on the decision to stop cancer screening when life expectancy is limited and to identify older adults' preferences for how clinicians should communicate recommendations to cease cancer screening. DESIGN, SETTING, AND PARTICIPANTSIn this semistructured interview study, we interviewed 40 community-dwelling older adults (Ն 65 years) recruited at 4 clinical programs affiliated with an urban academic medical center. MAIN OUTCOMES AND MEASUREWe transcribed the audio recorded discussions and analyzed the transcripts using standard techniques of qualitative content analysis to identify major themes and subthemes. RESULTSThe participants' average age was 75.7 years. Twenty-three participants (57.5%) were female; 25 (62.5%) were white. Estimated life expectancy was less than 10 years for 19 participants (47.5%). We identified 3 key themes. First, participants were amenable to stopping cancer screening, especially in the context of a trusting relationship with their clinician. Second, although many participants supported using age and health status to individualize the screening decision, they did not often understand the role of life expectancy. All except 2 participants objected to a Choosing Wisely statement about not recommending cancer screening in those with limited life expectancy, often believing that clinicians cannot accurately predict life expectancy. Third, participants preferred that clinicians explain a recommendation to stop screening by incorporating individual health status but were divided on whether life expectancy should be mentioned. Specific wording of life expectancy was important; many felt the language of "you may not live long enough to benefit from this test" was unnecessarily harsh compared with the more positive messaging of "this test would not help you live longer." CONCLUSIONS AND RELEVANCEAlthough research and clinical practice guidelines recommend using life expectancy to inform cancer screening, older adults may not consider life expectancy important in screening and may not prefer to hear about life expectancy when discussing screening. The described communication preferences can help inform future screening discussions. Better delineating patient-centered approaches to discuss screening cessation is an important step toward optimizing cancer screening in older adults.

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“…The performance of our claims‐based model opens the door to future opportunities to improve clinical care using EHR data. We and others have proposed individualized decision making in clinical practice for older adults to maximize benefits and minimize harms from health care interventions . Unfortunately, the uptake of these prognostic models and decision support tools has been limited .…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a 5‐year mortality prediction model using regularized regression and Medicare data

Lund

Kuo

Brookhart

et al. 2019

Pharmacoepidemiology and Drug

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Purpose De‐implementation of low‐value services among patients with limited life expectancy is challenging. Robust mortality prediction models using routinely collected health care data can enhance health care stakeholders' ability to identify populations with limited life expectancy. We developed and validated a claims‐based prediction model for 5‐year mortality using regularized regression methods. Methods Medicare beneficiaries age 66 or older with an office visit and at least 12 months of pre‐visit continuous Medicare A/B enrollment were identified in 2008. Five‐year mortality was assessed through 2013. Secondary outcomes included 30‐, 90‐, and 180‐day and 1‐year mortality. Claims‐based predictors, including comorbidities and indicators of disability, frailty, and functional impairment, were selected using regularized logistic regression, applying the least absolute shrinkage and selection operator (LASSO) in a random 80% training sample. Model performance was assessed and compared with the Gagne comorbidity score in the 20% validation sample. Results Overall, 183 204 (24%) individuals died. In addition to demographics, 161 indicators of comorbidity and function were included in the final model. In the validation sample, the c‐statistic was 0.825 (0.823‐0.828). Median‐predicted probability of 5‐year mortality was 14%; almost 4% of the cohort had a predicted probability greater than 80%. Compared with the Gagne score, the LASSO model led to improved 5‐year mortality classification (net reclassification index = 9.9%; integrated discrimination index = 5.2%). Conclusions Our claims‐based model predicting 5‐year mortality showed excellent discrimination and calibration, similar to the Gagne score model, but resulted in improved mortality classification. Regularized regression is a feasible approach for developing prediction tools that could enhance health care research and evaluation of care quality.

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An Individualized Approach to Cancer Screening Decisions in Older Adults: A Multilevel Framework

Cited by 54 publications

References 51 publications

Ageism in the Health Care System: Providers, Patients, and Systems

Ageism in the Health Care System: Providers, Patients, and Systems

Older Adults’ Views and Communication Preferences About Cancer Screening Cessation

Development and validation of a 5‐year mortality prediction model using regularized regression and Medicare data

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