1. Exploring reasons for clients’ non-attendance of appointments within a community-based alcohol service: clients' perspectives.
Faisal Mahmood
Faisal Mahmood

2. Faisal Mahmood BACP Accredited & Registered Counsellor
Counselling Theoretical Approach: Gestalt Therapy Theory & Integrative
Senior Lecturer in Counselling – Newman University, Birmingham
Undertaking a PhD at MMU
20 Years of Experience in Addiction
Research Supervisors: Dr Sarah Galvani, Dr Lucy Webb & Dr Andy Guppy
Faisal Mahmood

3. Literature review
12 million GP appointments are missed each year in the UK, costing in excess of £162 million per year. A further 6.9 million outpatient hospital appointments are missed each year in the UK, costing an average of £108 per appointment in 2012/13 (NHS, 2015).
Coulson et al (2009) found four separate categories for DNA in substance misuse treatment programmes. These consisted of extraneous factors (e.g. work commitments, illness, social and logistical issues), perceived service shortcomings, no further need for service, and motivational ambivalence.
Faisal Mahmood

4. Literature review
Ethnic minority communities are significantly less likely to seek treatment and advice for drinking problems, keeping them hidden from there strict culture (Hurcombe, Bayley and Goodman, 2010).
Orford et al (2009) looked more closely at the attributes of behaviour change that facilitate whether the client continues treatment or not. Involving significant others in a client’s treatment signified a change in behaviour and retention in the treatment programme.
Faisal Mahmood

5. Aim, Methodology & Data
Aim: To gain a deeper understanding of the reasons for non-attendance of appointments within alcohol misuse community services.
Methodology: Mixed Methods
Data (Quantitative Study)
Secondary dataset analysis
West Midlands based substance misuse agency – data from 10 sites in different regions.
Appointments’ attendance history of four years (Jan 2010 – Dec 2013).
Faisal Mahmood

6. Demographics Unique service users: 22,405
Total sessions’ data: 194,679
Ethnicity: White British: 68%; Blank: 14%
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7. Demographics
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8. Attended 62% Cxd by Client 13% Cxd by Service 2% DNA 23%
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9. Quantitative Study Secondary analysis of existing datasets
Key research questions:
To what extent do socio-demographic factors of clients such as age, gender, employment status, accommodation needs, parental status, and number of children living with client determine non-attendance?
To what extent do clinical factors of clients such as risk levels, smoking status, pregnancy, dual diagnosis, and overall discharge reasons determine non-attendance?
To what extent do the receipts of text messages (appointment reminders) determine non-attendance?
To what extent do the session times determine non-attendance?
Faisal Mahmood

10. CHI-SQUARED TEST
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11. Age DNA Percentage 18-24y 37.80% 25-34y 32.50% 35-44y 27.50% 45-54y
24.10%
55-64y
19.00%
65-74y
14.00%
75+
14.30%
DNA Percentage
X2(6) = , p<.001
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12. Gender Male 28.60% Female 24.10% X2(1) = 389.078, p<.001
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13. Ethnic Origin Bangladeshi 37.60% Pakistani 36.70% African 34.60%
Other Black
34.20%
Caribbean
31.50%
Other Mixed
31.30%
White and Black Caribbean
30.80%
Other Asian
30.40%
White and Black African
28.50%
White Irish
28.20%
Indian
27.10%
White and Asian
26.80%
Other White
26.10%
White British
24.40%
Chinese
6.50%
X2(14) = , p<.001
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14. Smoking Current smoker 23.70% Ex smoker 17.80% Never smoked 16.40%
X2(2) = , p<.001
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15. Types of session DNA Percentage Arrest Referral 60.90% Assessment
40.90%
Individual Session
24.00%
Care Plan Review
15.30%
Healthcare Review Meeting
Family Session
5.40%
DNA Percentage
X2 (5) = , p<0.001
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16. Employment Not in education or training or employment 62.50%
Unemployed
28.00%
Job Seekers Allowance
27.00%
Work Programme or Training
19.80%
Regular Employment
18.30%
Unpaid voluntary work
15.70%
Retired from work
15.40%
Homemaker
14.80%
Retired from paid work
10.80%
X2(18) = , p<0.001
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17. Locations Site 1 32.70% Site 2 29.60% Site 3 27.80% Site 4 26.70%
26.00%
Site 6
21.80%
Site 7
21.00%
Site 8
16.00%
Site 9
13.20%
Site 10
9.30%
X2(9) = , p<0.001
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18. LOGISTIC REGRESSION
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19. Predictors of Non-Attendance
Significant Predictors
Not Significant Predictors
Age
Current Employment Status
Accommodation Needs
Parental Status
Number of Children Living with Client
Overall Discharge Reason
Clinical Risk Levels
Event Time
Gender
Smoking Status
Pregnancy Status
Dual Diagnosis
Text Messages* (Only 12% sent sms)
(p<.001)
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20. LOGISTIC REGRESSION
Accommodation: Young Persons in settled accommodation were over five times more likely to not attend.
Parental status: Clients who live with ‘some of their children’, clients where ‘none of the children with them’ and the clients who are not parent are more likely to not attend as compared to clients who have all the children living with them.
Faisal Mahmood

21. LOGISTIC REGRESSION
Age: and 75+ years old are more likely to not attend as compared to other age groups.
Employment: Clients on employment support allowance, economically inactive due to mental health and clients who are long term sick or disabled are more likely to not attend.
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22. LOGISTIC REGRESSION
Overall discharge reason: Clients are more likely to complete their treatment in a successful manner if they attend their treatment appointments.
Faisal Mahmood

23. LOGISTIC REGRESSION
Health and wellbeing risk levels: Clients are more likely to not attend if they have high risk levels.
Appointment times: Two appointment time slots and increase the likelihood of non-attendance.
Faisal Mahmood

24. Appointment Times – High Attendance
am event time reduced DNA by 74.5%
am event time reduced DNA by 38.5%
Out of hours (after 5pm) event times reduced DNA by 28.1%.
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25. Qualitative Study Method: Semi Structured Interviews
Analysis: Framework Analysis
Key research questions:
What is the client’s subjective experience of not attending their appointment?
What time was the decision taken by the client that the session could not be attended? For example, last minute, a day before, when the session was booked?
What are the main reasons for clients’ non-attendance?
Faisal Mahmood

26. Feedback: f.mahmood@newman.ac.uk
Thank You
Faisal Mahmood