Rachel Benz

Quality of Life in Cancer Survivorship: 20 Years Later

OBJECTIVES To discuss the unique quality-of-life (QOL) issues for cancer survivors and provide guidance for the selection of measures to assess them. DATA SOURCES Literature review, PubMed search, electronic data, websites. CONCLUSION QOL is a vital outcome for cancer survivors. QOL measurement of the unique needs of cancer survivors has increased over the past 20 years. IMPLICATIONS FOR NURSING PRACTICE Oncology nurses can move QOL science in cancer survivorship forward by using research findings to implement evidence-based practice.

An application of longitudinal analysis with skewed outcomes.

Background: Longitudinal designs are indispensable to the study of change in outcomes over time and have an important role in health, social, and behavioral sciences. However, these designs present statistical challenges particularly related to accounting for the variance and covariance of the repeated measurements on the same participants and to modeling outcomes that are not normally distributed. Objectives: The purpose of this study was to introduce a general methodology for longitudinal designs to address these statistical challenges and to present an example of an analysis conducted with data collected in a randomized clinical trial. In this example, the outcome of interest-monthly health-related out-of-pocket expenses incurred by breast cancer survivors-had a skewed distribution. Methods: Common statistical approaches are for longitudinal analysis using linear and generalized linear mixed models are reviewed, and the discussed methods are applied to analyze monthly health-related out-of-pocket expenses. Discussion: Although standard statistical software is available to conduct longitudinal analyses, training is necessary to understand and to take advantage of the various options available for model fitting. However, knowledge of the basics of the methodology allows assimilation and incorporation into practice of evidence from the numerous studies that use these designs.

Strategies to retain rural breast cancer survivors in longitudinal research

AIMS To describe the retention of rural women in the Rural Breast Cancer Survivors (RBCS) Intervention. BACKGROUND Few studies describe strategies and procedures for retention of participants enrolled in cancer research. Fewer studies focus on underserved rural cancer survivors. METHODS A descriptive design was used. A conceptual model of retention based on three factors: researcher, participant, and context with primary, secondary, and tertiary strategies was used to unify the data. RESULTS 432 women enrolled in the RBCS study, of which 332 (77%) were retained and completed the 12 month study. Favorable retention strategies included: run-in period, persistent attempts to re-contact hard to reach, recruitment and enrollment tracking database, and a trusting and supportive relationship with the research nurse. CONCLUSION A conceptual model of retention with differential strategies can maintain participant retention in a longitudinal research study.

Out-of-pocket costs and burden among rural breast cancer survivors

Little is known about out‐of‐pocket (OOP) costs incurred for medical and health needs by rural breast cancer survivors and what factors may be associated with higher OOP costs and the associated economic burden. Data were examined for 432 survivors participating in the Rural Breast Cancer Survivor Intervention trial. OOP costs were collected using the Work and Finances Inventory survey at baseline and four assessments every 3 months. Mean and median OOP costs and burden (percent of monthly income spent on OOP costs) were reported and factors associated with OOP costs and burden identified with generalized linear models fitted with over‐dispersed gamma distributions and logarithmic links (OOP costs) and with beta distributions with logit link (OOP burden). OOP costs per month since the end of treatment were on average

Cognitive remediation therapies for older adults: implications for nursing practice and research.

232.7 (median

Predictors of attrition among rural breast cancer survivors.

95.6), declined at the next assessment point to

Telehealth intervention for Latina breast cancer survivors: A pilot:

186.5 (median

Development and evaluation of home-based speed-of-processing training for older adults.

89.1), and thereafter remained at that level. Mean OOP burden was 9% at baseline and between 7% and 8% at the next assessments. Factors suggestive of contributing to higher OOP costs and OOP burden were the following: younger age, lower income, time in survivorship from diagnosis, and use of supportive services. OOP costs burden rural breast cancer survivors, particularly those who are younger and low income. Research should investigate the impact of OOP costs and interventions to reduce economic burden.

The out of pocket cost of breast cancer survivors: a review

&NA; Among older adults, 20%‐56% report having cognitive problems, and such cognitive complaints frequently correspond to actual neuropsychological impairment. The loss of cognitive abilities can be frustrating and frightening and can have a negative impact on instrumental activities of daily living and quality of life. Cognitive remediation interventions have been shown to be successful in improving mental function in older adults in many situations and may increase the number of everyday activities they are able to carry out. Nurses, given their direct contact with older adult patients, are able to inquire about or observe cognitive loss, make appropriate referrals, and emphasize steps such as cognitive remediation and other interventions that promote successful cognitive aging.

Multimorbidity and Breast Cancer

Attrition can jeopardize both internal and external validity. The goal of this secondary analysis was to examine predictors of attrition using baseline data of 432 participants in the Rural Breast Cancer Survivors study. Attrition predictors were conceptualized based on demographic, social, cancer treatment, physical health, and mental health characteristics. Baseline measures were selected using this conceptualization. Bivariate tests of association, discrete-time Cox regression models and recursive partitioning techniques were used in analysis. Results showed that 100 participants (23%) dropped out by Month 12. Non-linear tree analyses showed that poor mental health and lack of health insurance were significant predictors of attrition. Findings contribute to future research efforts to reduce research attrition among rural underserved populations.