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Patients’ supportive care needs beyond the end of treatment: A prospective, longitudinal study. Chief Investigators:
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Patients’ supportive care needs beyond the end of treatment: A prospective, longitudinal study
Chief Investigators: Alison Richardson- Professor of Cancer and Palliative Nursing Care, King’s College LondonMaggie Crowe– Consultant Nurse Cancer Care and Lead Cancer Nurse, Royal United Hospital Bath NHS Trust Project Management Group: Jo Armes - Research Fellow, King’s College London Lynne Colbourne – Nurse Practitioner, Gloucestershire Hospitals NHS Foundation Trust Helen Morgan – Assistant Director of Nursing, United Bristol Healthcare NHS Trust Catherine Oakley – Macmillan Lead Cancer Nurse, St George’s Healthcare NHS Trust Nigel Palmer – NCRI Consumer Liaison and Psychosocial Oncology Clinical Studies Group Emma Ream - Senior Lecturer, King’s College London Annie Young – Director of Nursing, Three Counties Cancer Network Katie Booth – Macmillan Cancer Support
Acknowledgements • This project was supported with funds from: • Macmillan Cancer Support • King’s College London • Collaborators • NCRN research staff • All health care professionals who took part
Study aims • Identify prevalence of unmet supportive care needs of cancer patients at the end of treatment and six months later • Identify factors at the end of treatment that predict those patients with high unmet supportive care needs six months later
Study overview (1) • Design • Prospective, longitudinal observational study • Potential subjects • Breast cancer • Colorectal cancer • Gynaecological cancers • Prostate cancer • Non-Hodgkin's lymphoma
Study Overview (2): Eligibility Criteria • Aware that he/she has cancer • Greater than 18 years of age • Able to read and understand English • Clinician caring for them agreed to their participation • Patients receiving chemotherapy and/or radiotherapy given with curative intent and the person had not relapsed during treatment • Patients receiving the last cycle/episode of planned course of treatment (not including ‘maintenance’ therapy) • Patients on phase 3 clinical trials were recruited.
Study overview (3) • Sample size • Estimated sample size of 1000 at T0 • 50-100 patients from each diagnostic group at T1 Response rate • T0 was 79%, n=1425/1850 • T1 was 82%, n=1152/1410 • Timing of assessments • T0: End of planned course of treatment • T1: 6 months following T0
Study overview (4): Measures • Supportive Care Needs Survey (SCNS) and Access to Ancillary Support Services module • Hospital Anxiety and Depression Scale (HADS) • Positive Affectivity and Negative Affectivity Scale (PANAS) • Health Concerns Questionnaire (HCQ) • Demographic and medical data
Supportive Care Needs Survey Domains • Sexuality needs • Health system and information needs • Patient care and support needs • Psychological needs • Physical and daily living needs • Total needs
Study variables of interest • Primary variable of interest • All SCNS dimensions and unmet multiple needs • Secondary variables of interest • Fear of recurrence • Anxiety and depression • Positive and negative affect • Personal characteristics • Clinical characteristics
Mean age: 61 years Sex: male 31% Female 69% Employment status: Retired 49% Working (FT/PT) 28% Domestic status: Married 69% Living with partner: 6% Widowed 10% Divorced/separated 8% Single 6% Participant Characteristics (1)
Diagnosis: Breast 56% Prostate 23% Bowel 9% Gynae 6% Lymphoma 5% Last treatment: Radiotherapy 80% Chemotherapy 19% Hormone therapy: No 68% Yes 32% Comorbid disease: No 56% Yes 42% Participant characteristics (2)
Analysis • Descriptive analysis of data to assess the prevalence of unmet needs for each SCNS domain at both time points • Logistic regression used to identify baseline factors that would predict those patients with high need six months later for: • each domain of SCNS • multiple unmet need
Logistic regression • Analysis attempts to predict which of two categories a person belongs on the basis of other information about them (e.g. age, sex, treatment) • Main outcome variable split into 2 outcomes (no or low need vs. moderate or severe unmet need)
Predictors of SCNS physical and daily living unmet needs • High moderate or severe physical unmet needs at the end of treatment (p=0.000) • High moderate or severe unmet health service and information needs at the end of treatment (p=0.028) • High level of negative affect at the end of treatment (p=0.001) • Having a co-morbid disorder (p=0.007) • Taking hormone therapy (p=0.010) • Being educated to GCSE/’A’ Level standard (p=0.017) • Having experienced a significant event after treatment finished (p = 0.018)
Predictors of SCNS psychological unmet needs • High moderate or severe psychological unmet needs at the end of treatment (p=0.000) • High moderate or severe unmet physical needs at the end of treatment (p=0.001) • High level of negative affect at the end of treatment (p=0.009) • High level of depression (0.004) • High level of fear of recurrence (p=0.001) • Being 60-67 years old (p=0.019) • Having experienced a significant event after treatment finished (p = 0.000)
Predictors of SCNS health system & information unmet needs • High moderate or severe unmet health service and information needs at the end of treatment (p=0.000) • High moderate or severe unmet patient care needs at the end of treatment (p=0.037) • High moderate or severe unmet sexuality needs at the end of treatment (p=0.049) • High level of anxiety at the end of treatment (p=0.002) • Taking hormone therapy (p=0.001) • Having experienced a significant event after treatment finished (p = 0.019)
Predictors of SCNS total unmet needs • High moderate or severe unmet total needs at the end of treatment (p=0.000) • High level of negative affect at the end of treatment (p=0.001) • High level of depression at the end of treatment (p=0.000) • Taking hormone therapy (p=0.027) • Having experienced a significant event after treatment finished (p = 0.001)
Study limitations • Most had a diagnosis of breast or prostate cancer • Considerable variation in our sample in terms of diagnosis and treatment histories • Patients whose only cancer treatment was surgery were excluded • Clinical information was provided by participants rather than being collected from patient records
Summary of main results • Most patients express few or no unmet need for support • Significant minority report multiple unmet needs • Number of baseline factors identified that predict multiple moderate or severe unmet needs: • Depression • Negative mood • Receiving hormone therapy • Younger age • Experiencing a significant event post treatment
Implications & Considerations • An important minority have needs not currently being met. How might we identify these patients in practice? • What are the most effective models of care for helping patients manage unmet needs following treatment? • Consider how to enhance self-management in order to better prepare patients for the transition from cancer patient in receipt of acute care to survivor.
To obtain a copy of the final report • visit: www.kcl.ac.uk/schools/nursing/research/disease/supportivecareneeds