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      Quality of Life of Caregivers of Older Patients with Advanced Cancer

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          Abstract

          To evaluate the relationships between aging-related domains captured by geriatric assessment (GA) for older patients with advanced cancer and caregivers’ emotional health and quality of life (QoL). In this analysis of baseline data from a nationwide investigation of older patients and their caregivers, patients completed a GA that included validated tests to evaluate eight domains of health (e.g., function, cognition). Enrolled patients were aged 70+, had ≥1 GA domain impaired, and had an incurable solid tumor malignancy or lymphoma; each could choose one caregiver to enroll. Caregivers completed the Generalized Anxiety Disorder-7, Distress Thermometer, Patient Health Questionnaire-2 (depression), and Short Form Health Survey-12 (SF-12 for QoL). Separate multivariate linear or logistic regression models were used to examine the association of the number and type of patient GA impairments with caregiver outcomes, controlling for patient and caregiver covariates. In total, 541 patients were enrolled, 414 with a caregiver. Almost half (43.5%) of caregivers screened positive for distress, 24.4% for anxiety, and 18.9% for depression. Higher numbers of patient GA domain impairments were associated with caregiver depression [Adjusted Odds Ratio (AOR)=1.29, p <0.001], caregiver physical health on SF-12 (regression coefficient (β)=−1.24, p <0.001), and overall caregiver QoL (β=−1.14, p <0.01). Impaired patient function was associated with lower caregiver QoL (β=−4.11, p <0.001). Impaired patient nutrition was associated with caregiver depression (AOR=2.08, p<0.01). Lower caregiver age, caregiver comorbidity, and patient distress were also associated with worse caregiver outcomes. Patient GA impairments were associated with poorer emotional health and lower QoL of caregivers.

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          Most cited references38

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          Do multiple outcome measures require p-value adjustment?

          Background Readers may question the interpretation of findings in clinical trials when multiple outcome measures are used without adjustment of the p-value. This question arises because of the increased risk of Type I errors (findings of false "significance") when multiple simultaneous hypotheses are tested at set p-values. The primary aim of this study was to estimate the need to make appropriate p-value adjustments in clinical trials to compensate for a possible increased risk in committing Type I errors when multiple outcome measures are used. Discussion The classicists believe that the chance of finding at least one test statistically significant due to chance and incorrectly declaring a difference increases as the number of comparisons increases. The rationalists have the following objections to that theory: 1) P-value adjustments are calculated based on how many tests are to be considered, and that number has been defined arbitrarily and variably; 2) P-value adjustments reduce the chance of making type I errors, but they increase the chance of making type II errors or needing to increase the sample size. Summary Readers should balance a study's statistical significance with the magnitude of effect, the quality of the study and with findings from other studies. Researchers facing multiple outcome measures might want to either select a primary outcome measure or use a global assessment measure, rather than adjusting the p-value.
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            Hidden morbidity in cancer: spouse caregivers.

            This study assesses psychological distress among advanced cancer patients and their spouse caregivers, while examining the relative contribution of caregiving burden and relational variables (attachment orientation and marital satisfaction) to depressive symptoms in the spouse caregivers. A total of 101 patients with advanced GI or lung cancer and their spouse caregivers were recruited for the study. Measures included Beck Depression Inventory-II (BDI-II), Caregiving Burden scale, Experiences in Close Relationships scale, and ENRICH Marital Satisfaction scale. A total of 38.9% of the caregivers reported significant symptoms of depression (BDI-II > or = 15) compared with 23.0% of their ill spouses (P < .0001). In a hierarchical regression predicting caregiver's depression, spouse caregiver's age and patient's cancer site were entered in the first step, objective caregiving burden was entered in the second step, subjective caregiving burden was entered in the third step, caregiver's attachment scores were entered in the fourth step, and caregiver's marital satisfaction score was entered in the fifth step. The final model accounted for 37% of the variance of caregiver depression, with subjective caregiving burden (beta = .38; P < .01), caregiver's anxious attachment (beta = .21; P < .05), caregiver's avoidant attachment (beta = .20; P < .05), and caregiver's marital satisfaction (beta = -.18; P < .05) making significant contributions to the model. Spouse caregivers of patients with advanced cancer are a high-risk population for depression. Subjective caregiving burden and relational variables, such as caregivers' attachment orientations and marital dissatisfaction, are important predictors of caregiver depression.
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              Geriatric Assessment-Guided Care Processes for Older Adults: A Delphi Consensus of Geriatric Oncology Experts.

              Structured care processes that provide a framework for how oncologists can incorporate geriatric assessment (GA) into clinical practice could improve outcomes for vulnerable older adults with cancer, a growing population at high risk of toxicity from cancer treatment. We sought to obtain consensus from an expert panel on the use of GA in clinical practice and to develop algorithms of GA-guided care processes.
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                Author and article information

                Journal
                Journal of the American Geriatrics Society
                J Am Geriatr Soc
                Wiley
                0002-8614
                1532-5415
                March 29 2019
                March 29 2019
                Affiliations
                [1 ]URCC NCORP Research BaseUniversity of Rochester Medical Center Rochester New York
                [2 ]Department of Public Health SciencesUniversity of Rochester School of Medicine Rochester New York
                [3 ]Rutgers School of Public Health New Brunswick New Jersey
                [4 ]SCOREboard Advisory GroupUniversity of Rochester Medical Center Rochester New York
                [5 ]City of Hope National Medical Center Duarte California
                [6 ]University of North Carolina Chapel Hill North Carolina
                [7 ]Tulane University New Orleans Louisiana
                [8 ]Southeast Clinical Oncology Research (SCOR)Consortium NCI Community Oncology Research Program (NCORP), Winston Salem, North Carolina; Novant Health‐GWSM
                [9 ]Heartland NCORP Decatur Illinois
                [10 ]Metro‐Minnesota NCORP St. Louis Park Minnesota
                Article
                10.1111/jgs.15862
                7818364
                30924548
                246d228a-27db-4581-9249-7b5a8356a414
                © 2019

                http://doi.wiley.com/10.1002/tdm_license_1.1

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