Chief Investigators:
Alison Richardson -
Professor of Cancer and Palliative Nursing Care, King’s College
London
Maggie 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

• 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
3

Study collaborators
4

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
5

Study overview (1)
Design
•
Prospective, longitudinal observational study
Potential subjects
•
Breast cancer
•
Colorectal cancer
•
Gynaecological cancers
•
Prostate cancer
•
Non-Hodgkin's lymphoma
6

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.
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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
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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
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Supportive Care Needs Survey Domains
1. Sexuality needs
2. Health system and information needs
3. Patient care and support needs
4. Psychological needs
5. Physical and daily living needs
Total needs
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SCNS scoring
NO
NEED
HIGH
NEED
1 Not applicable – This was not a problem for me as a result of having cancer.
2 Satisfied - I did need help with this, but my need for help was satisfied at the time.
3 Low need - This item caused me only a little concern or discomfort. I had only a little need for additional help.
4 Moderate need – This item caused me some concern or discomfort. I had some need for additional help.
5 High need - This item caused me a lot of concern or discomfort. I had a strong need for additional help.
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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
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Participant Characteristics (1)
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%
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Participant characteristics (2)
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%
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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
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Prevalence of unmet need by SCNS dimension
Sexuality needs
Patient care needs
Psychological needs
Physical needs
Information needs
0
T0 (n=1425)
T1 (n=1152)
5 10 15 20 25
Percentage
30 35 40 45
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Prevalence of SCNS physical and daily living unmet needs
T0 (n=1425)
T1 (n=1152)
P ain
Feeling unwell a lo t
Wo rk aro und ho me
Unable to do things used to
Tiredness
0 5 10
P ercentage
15 20 25
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Prevalence of SCNS psychological unmet needs
T0 (n=1425)
T1 (n=1152)
Feelings re death & dying
Learning to feel in co ntro l o f situatio n
Feeling sad
Depressio n
A nxiety
Wo rry that treatment results beyo nd yo ur co ntro l
Uncertainty abo ut future
Co ncerns re family wo rries
Fear o f cancer spreading
0 5 10 15 20
P ercentage
25 30 35
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Prevalence of SCNS sexuality unmet needs
Information on sexual relationships
T0 (n=1425)
T1 (n=1152)
Changes in sexual relationships
Changes in sexual feelings
0 5 10
Percentage
15 20
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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)
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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)
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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)
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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)
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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)
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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
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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
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•
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.
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www.kcl.ac.uk/schools/nursing/research/disease/supportivecareneeds
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