Exploring Cancer Survivor Needs and Preferences for Communicating Personalized Cancer Statistics From Registry Data: Qualitative Multimethod Study

"R" you getting this? Factors contributing to the public's understanding, evaluation, and use of basic reproduction numbers for infectious diseases

BACKGROUND

We (1) examined the effects of evaluative labels and visual aids on people's understanding, evaluation, and use of the COVID-19 reproduction number (or "r-number"), (2) examined whether people's perceived susceptibility and (intended) adherence to preventive measures changed after being exposed to the r-number, and (3) explored whether these effects and changes depended on people's numeracy skills.

METHODS

In an online experiment, participants from a large Dutch representative sample (N = 1,168) received information about the COVID-19 r-number displayed on the corona dashboard of the Dutch Ministry of Health, Welfare and Sport. The r-number was either presented with or without a categorical line display (i.e., evaluative label) and with or without an icon-based tree diagram (i.e., visual aid) explaining how the number works. Regarding people's use of the statistic, we measured perceived susceptibility to COVID-19 and adherence (intention) to five preventive measures before and after exposure to the r-number. After exposure, we also measured participants' understanding, perceived usefulness, affective and cognitive evaluation, and objective numeracy.

RESULTS

About 56% of participants correctly interpreted the r-number, with highly numerate people having better understanding than less numerate people. Information about the r-number was perceived as more useful when presented with a visual aid. There were no differences across experimental conditions in people's understanding, affective, and cognitive evaluations. Finally, independent of experimental conditions, intention to adhere to preventive measures was higher after seeing the r-number, but only among highly numerate people.

CONCLUSIONS

Although evaluative labels and visual aids did not facilitate people's understanding and evaluation of the r-number, our results show that the statistic is perceived as useful and may be used to stimulate adherence to preventive measures. Policy makers and public health communicators are advised to clearly explain why they are giving these numbers to - especially - the less numerate people, but also how people could use them for behavior change to combat the spread of virus during a pandemic.

Information and communication priorities of patients and healthcare professionals in shared decision making regarding adjuvant systemic breast cancer treatment: A survey study

PURPOSE

To assess information and communication priorities of patients and healthcare professionals in Shared Decision Making about adjuvant systemic treatment of primary breast cancer and identify key decision-relevant information accordingly.

METHODS

Patients (N = 122) and professionals working with breast cancer patients (N = 118), of whom 38 were nurse practitioners and 32 nurses, were recruited using convenience sampling, and surveyed about information/communication aspects key to decision-making, using ranking assignments. We further posed a simple open question, questions about receiving population-based statistics versus personalized statistics concerning treatment outcomes, and their attitude and experience concerning Shared Decision Making. Data were analyzed using descriptive analysis and a qualitative analysis.

RESULTS

Both patients and professionals prioritized information about treatment outcomes (i.e., survival, recurrence) as key decision-relevant information for patients. Patients prioritized information about relatively severe treatment side-effects and late effects (e.g., blood clot, stroke), whilst professionals prioritized information about effects that occur relatively often (e.g., hair loss, fatigue). Patients specifically wanted to know if the benefit of treatment is worth the negative impact. Both groups prioritized personalized statistics over population-based statistics.

CONCLUSIONS

Some differences between patients and professionals were found in information and communication priorities, specifically related to the different side-effects. It seems worthwhile to precisely address these side-effects in Shared Decision Making concerning adjuvant systemic treatment. Furthermore, it seems important to deliberate together on the question if expected benefit of treatment is worth the potential negative impact for the individual patient.

Use of Persuasive Language in Communication of Risk during Prostate Cancer Treatment Consultations

BACKGROUND

Physician treatment preference may influence how risks are communicated in prostate cancer consultations. We identified persuasive language used when describing cancer prognosis, life expectancy, and side effects in relation to a physician's recommendation for aggressive (surgery/radiation) or nonaggressive (active surveillance/watchful waiting) treatment.

METHODS

A qualitative analysis was performed on transcribed treatment consultations of 40 men with low- and intermediate-risk prostate cancer across 10 multidisciplinary providers. Quotes pertaining to cancer prognosis, life expectancy, and side effects were randomized. Coders predicted physician treatment recommendations from isolated blinded quotes. Testing characteristics of consensus predictions against the physician's treatment recommendation were reported. Coders then identified persuasive strategies favoring aggressive/nonaggressive treatment for each quote. Frequencies of persuasive strategies favoring aggressive/nonaggressive treatment were reported. Logistic regression quantified associations between persuasive strategies and physician treatment recommendations.

RESULTS

A total of 496 quotes about cancer prognosis (n = 127), life expectancy (n = 51), and side effects (n = 318) were identified. The accuracy of predicting treatment recommendation based on individual quotes containing persuasive language (n = 256/496, 52%) was 91%. When favoring aggressive treatment, persuasive language downplayed side effect risks and amplified cancer risk (recurrence, progression, or mortality). Significant predictors (P < 0.05) of aggressive treatment recommendation included favorable side effect interpretation, downplaying side effects, and long time horizon for cancer risk due to longevity. When favoring nonaggressive treatment, persuasive language amplified side effect risks and downplayed cancer risk. Significant predictors of nonaggressive treatment recommendation included unfavorable side effect interpretation, favorable interpretation of cancer risk, and short time horizon for cancer risk due to longevity.

CONCLUSIONS

Physicians use persuasive language favoring their preferred treatment, regardless of whether their recommendation is appropriate.

IMPLICATIONS

Clinicians should quantify risk so patients can judge potential harm without solely relying on persuasive language.

HIGHLIGHTS

Physicians use persuasive language favoring their treatment recommendation when communicating risks of prostate cancer treatment, which may influence a patient's treatment choice.Coders predicted physician treatment recommendations based on isolated, randomized quotes about cancer prognosis, life expectancy, and side effects with 91% accuracy.Qualitative analysis revealed that when favoring nonaggressive treatment, physicians used persuasive language that amplified side effect risks and downplayed cancer risk. When favoring aggressive treatment, physicians did the opposite.Providers should be cognizant of using persuasive strategies and aim to provide quantified assessments of risk that are jointly interpreted with the patient so that patients can make evidence-based conclusions regarding risks without solely relying on persuasive language.

Communicating the results of risk-based breast cancer screening through visualizations of risk: a participatory design approach

BACKGROUND

Risk-based breast cancer (BC) screening raises new questions regarding information provision and risk communication. This study aimed to: 1) investigate women's beliefs and knowledge (i.e., mental models) regarding BC risk and (risk-based) BC screening in view of implications for information development; 2) develop novel informational materials to communicate the screening result in risk-based BC screening, including risk visualizations of both quantitative and qualitative information, from a Human-Centered Design perspective.

METHODS

Phase 1: Interviews were conducted (n = 15, 40-50 years, 5 lower health literate) on women's beliefs about BC risk and (risk-based) BC screening. Phase 2: In three participatory design sessions, women (n = 4-6 across sessions, 40-50 years, 2-3 lower health literate) made assignments and created and evaluated visualizations of risk information central to the screening result. Prototypes were evaluated in two additional sessions (n = 2, 54-62 years, 0-1 lower health literate). Phase 3: Experts (n = 5) and women (n = 9, 40-74 years) evaluated the resulting materials. Two other experts were consulted throughout the development process to ensure that the content of the information materials was accurate. Interviews were transcribed literally and analysed using qualitative thematic analysis, focusing on implications for information development. Notes, assignments and materials from the participatory design sessions were summarized and main themes were identified.

RESULTS

Women in both interviews and design sessions were positive about risk-based BC screening, especially because personal risk factors would be taken into account. However, they emphasized that the rationale of risk-based screening and classification into a risk category should be clearly stated and visualized, especially for higher- and lower-risk categories (which may cause anxiety or feelings of unfairness due to a lower screening frequency). Women wanted to know their personal risk, preferably visualized in an icon array, and wanted advice on risk reduction and breast self-examination. However, most risk factors were considered modifiable by women, and the risk factor breast density was not known, implying that information should emphasize that BC risk depends on multiple factors, including breast density.

CONCLUSIONS

The information materials, including risk visualizations of both quantitative and qualitative information, developed from a Human-Centered Design perspective and a mental model approach, were positively evaluated by the target group.

The use of outcome data in patient consultations from the healthcare professionals' and patients' perspectives: A mixed methods study

OBJECTIVES

To gain insight into healthcare professionals' (HCPs') perspectives on the use of outcome data in consultations and to understand which aggregated outcomes patients find important.

METHODS

This study had a mixed-methods design and consisted of two steps: RESULTS: HCPs indicated that aggregated outcome data are not routinely used in consultations. They pointed out various barriers to using outcome data, e.g., low response rates of PROMs, and suggested actions to address these barriers, including training of HCPs in outcome data usage. Patients rated the majority of aggregated outcomes as important, although preferences differed between the studied health conditions.

CONCLUSION

Both HCPs and patients underscored the importance of discussing outcome data in consultations. Nevertheless, HCPs encountered several barriers to using outcome data. Furthermore, patients with different health conditions have somewhat different information needs.

PRACTICE IMPLICATIONS

The study identified several actionable steps to enhance the collection and application of outcome data in consultations.

Methods for Inclusive Underwriting of Breast Cancer Risk with Machine Learning and Innovative Algorithms

INTRODUCTION

-Due to early detection and improved therapies, the prevalence of long-term breast cancer survivors is increasing. This has increased the need for more inclusive underwriting in individuals with a history of breast cancer. Herein, we developed a method using algorithm aiming facilitating the underwriting of multiple parameters in breast cancer survivors.

METHODS

-Variables and data were extracted from the SEER database and analyzed using 4 different machine learning based algorithms (Logistic Regression, GA2M, Random Forest, and XGBoost) that were compared with Kaplan Meier survival estimates. The performances of these algorithms have been compared with multiple metrics (Log Loss, AUC, and SMR). In situ (non-invasive) and metastatic breast cancer were excluded from this analysis.

RESULTS

-Parameters included the pathological subtype, pTNM staging (T: tumor size, N; number of nodes; M presence or absence of metastases), Scarff-Bloom-Richardson grading, the expression of estrogen and progesterone hormone receptors were selected to predict the individual outcome at any time point from diagnosis. While all models had identical performance in terms of statistical metrics (AUC, Log Loss, and SMR), the logistic regression was the one and only model that respects all business constraints and was intelligible for medical and underwriting users.

CONCLUSION

-This study provides insight to develop algorithms to set underwriter-friendly calculators for more accurate risk estimations that can be used to rationalize insurance pricing for breast cancer survivors. This study supports the development of a more inclusive underwriting based on models that can encompass the heterogeneity of several malignancies such as breast cancer.

Understanding quality data correctly: a randomized comparison of presentation formats among cancer patients and relatives

Background: Patient representatives are increasingly engaged in quality in health care, and even though quality data are publicly available, correct interpretation may be challenging. We designed a randomized study with the primary aim to examine the association between preferred data presentation format and the interpretation of quality data among cancer patients and relatives.Material and methods: Surveys were distributed to the Danish Cancer Society Citizens' Panel between 31 March and 14 April 2019 and 55% completed the survey (N = 464) including six storyboards that presented authentic quality data in table format, league table and point estimates. The storyboards were randomized to expose participants to the data in the three different formats and in varying presentation order. Logistic regression models were used to calculate Odds Ratios (ORs) and 95% confidence intervals (CIs) for the association between preferred presentation format, health literacy, education and cohabitation status as exposures and interpretation of quality data as outcome.Results: The majority of participants (97%) had high literacy and 57% had a medium or long higher education. A total of 60% found the questions difficult or very difficult and 33% were not able to correctly interpret at least one format. Correct interpretation was associated with preferred league table (OR = 1.62; 95% CI = 1.04-5.52) and if the data was presented in the preferred format. Medium and long education were associated with correct interpretation of at least one format (OR = 1.93; 95% CI = 1.16-3.21 and OR = 3.89; 95% CI = 1.90-7.95, respectively) while health literacy and cohabitation status were not.Conclusions: More than one third of the participants were not able to correctly interpret the data and the understanding of quality data improved with longer education and if the data was presented in the preferred format. Decision-makers should carefully consider displaying quality data according to preferred presentation format and to guide interpretation for individuals with short education.

Need for numbers: assessing cancer survivors' needs for personalized and generic statistical information

Background

Statistical information (e.g., on long-term survival or side effects) may be valuable for healthcare providers to share with their patients to facilitate shared decision making on treatment options. In this pre-registered study, we assessed cancer survivors’ need for generic (population-based) versus personalized (tailored towards patient/tumor characteristics) statistical information after their diagnosis. We examined how information coping style, subjective numeracy, and anxiety levels of survivors relate to these needs and identified statistical need profiles. Additionally, we qualitatively explored survivors’ considerations for (not) wanting statistical information.

Methods

Cancer survivors’ need for statistics regarding incidence, survival, recurrence, side effects and quality of life were assessed with an online questionnaire. For each of these topics, survivors were asked to think back to their first cancer diagnosis and to indicate their need for generic and personalized statistics on a 4-point scale (‘not at all’- ‘very much’). Associations between information coping style, subjective numeracy, and anxiety with need for generic and personalized statistics were examined with Pearson’s correlations. Statistical need profiles were identified using latent class analysis. Considerations for (not) wanting statistics were analyzed qualitatively.

Results

Overall, cancer survivors (n = 174) had a higher need for personalized than for generic statistics (p < .001, d = 0.74). Need for personalized statistics was associated with higher subjective numeracy (r = .29) and an information-seeking coping style (r = .41). Three statistical need profiles were identified (1) a strong need for both generic and personalized statistics (34%), (2) a stronger need for personalized than for generic statistics (55%), and (3) a little need for both generic and personalized statistics (11%). Considerations for wanting personalized cancer statistics ranged from feelings of being in control to making better informed decisions about treatment. Considerations for not wanting statistics related to negative experience with statistics and to the unpredictability of future events for individual patients.

Conclusions

In light of the increased possibilities for using personalized statistics in clinical practice and decision aids, it appears that most cancer survivors want personalized statistical information during treatment decision-making. Subjective numeracy and information coping style seem important factors influencing this need. We encourage further development and implementation of data-driven personalized decision support technologies in oncological care to support patients in treatment decision making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-02005-2.

Communication, perception, and use of personalized side-effect risks in prostate cancer treatment-decision making: An observational and interview study

OBJECTIVE

We investigated how healthcare professionals (HPs) communicate personalized risks of treatment side-effects to patients with localized prostate cancer during consultations, and explored how these patients perceive and use such risks during treatment decision-making.

METHODS

Patient consultations with nurse practitioners and urologists discussing personalized risks of urinary incontinence after prostatectomy were audiotaped, transcribed, and coded. Patients (n = 27) were then interviewed to explore their perceptions and use of personalized side-effect risks.

RESULTS

HPs explained personalized risks by discussing risk factors, which was appreciated and recalled by patients. Personalized risks were typically communicated both numerically and verbally (70%). When using numbers, HPs always used percentages, but rarely used natural frequencies (14%). Uncertainty was disclosed in only 34% of consultations. One-third of patients used personalized risks in their treatment decision-making by either switching to another treatment or sticking to their initial preference.

CONCLUSIONS

Patients value and use personalized side-effect risks during treatment decision-making. Clearly explaining the relationship between risk factors and personalized risk estimates may help patients understand and recall those. Practice implications HPs should not only give patients specific and precise numerical risk information, but should also put effort in explaining how the personalized side-effect risks are determined.

Conditional estimates for uterine serous cancer: Tools for survivorship counseling and planning

OBJECTIVES

Develop conditional survival and risk-assessment estimates for uterine serous carcinoma (USC) overall and stratified by stage as tools for annual survivorship counseling and care planning.

METHODS

Patients in the National Cancer Data Base diagnosed between 2004 and 2014 with stage I-IV USC were eligible. Individuals missing stage or survival data or with multiple malignancies were excluded. Five-year conditional survival was estimated using the stage-stratified Kaplan-Meier method annually during follow-up. A standardized mortality ratio (SMR) estimated the proportion of observed to expected deaths in the U.S. adjusted for year, age, and race. The relationships between prognostic factors and survival were studied using multivariate Cox modeling at diagnosis and conditioned on surviving 5-years.

RESULTS

There were 14,575 participants, including 43% with stage I, 8% with stage II, 29% with stage III, and 20% with stage IV USC. Five-year survival at diagnosis vs. after surviving 5-years was 52% vs. 75% overall, 77% vs. 81% for stage I, 57% vs. 72% for stage II, 40% vs. 66% for stage III, and 17% vs. 60% for stage IV USC, respectively (P < 0.0001). Incremental improvements in 5-year conditional survival and reductions in SMR tracked with annual follow-up and higher stage. The adjusted risk of death at diagnosis vs. after surviving 5-years was 1.15 vs. 1.40 per 5-year increase of age, 1.26 vs. 1.68 for Medicaid insurance, 3.92 vs. 2.48 for stage III disease, and 6.65 vs. 2.79 for stage IV disease, respectively (P < 0.0001).

CONCLUSION

In USC, the evolution of conditional survival permits annual reassessments of prognosis to tailor survivorship counseling and care planning.

What are the late effects of older gastric cancer survivors?: A scoping review

Objective

Methods

Results

Conclusions