Applying Behavioural Science to increase numbers in specialist Drug and Alcohol treatment

Spring / summer 2024

Using behavioural science principles to increase the numbers of people undertaking drug and alcohol treatment in a local authority setting.

Dr Liz Sheils – Behavioural Science Practitioner, Public Health, Somerset Council

In 2020, Dame Carol Black published the first of a two-part review providing evidence on drug supply and demand, the relationship between drugs and serious violence, the increase of drug use and harms, and the underfunding of treatment services (Black, 2020). The second part of the review examined treatment, recovery, and prevention of drug use and provided recommendations (Black, 2021). The seminal review gave evidence of the widespread benefits to society by investing in high-quality treatment and recovery. In response to the review, the Government published an ambitious strategy entitled 'From harm to hope: A 10-year drugs plan to cut crime and save lives' (HM Government, 2021). To support the implementation, the government allocated funding to Local Authorities to devise strategies based on local needs and resources. This article aims to explore how behavioural science has been applied in one local authority to increase numbers of adults into specialist drug and alcohol treatment. The initial project focused on exploring attrition to waiting points for treatment.

Understanding the problem

The project plan was demarcated into four stages. Stage 1 involved information gathering to understand the problem. Data from a modelled estimate from Office of National Statistics for 2019/20 suggested there were 2,294 opiate and/or crack users in Somerset with 57.5% unmet need and 5,230 alcohol dependent people with 81% unmet need. These data indicate a significant challenge. Although the local drug and alcohol service received 3,214 referrals in January 2023-24 only 40.3% started treatment.

The client journey was mapped from when a person first encountered the local drug and alcohol service up until they began treatment (Figure 1). The investigation revealed three different waiting points. Waiting Point 1 was the time between a person self-referring or being referred to treatment and an initial assessment involving a phone call to collect brief demographic/substance/risk information. Waiting Point 2 was the time after the initial brief assessment and before the specialist drug and alcohol service called to conduct a more comprehensive assessment with the client. After this the client was at Waiting Point 3 for treatment to begin.

Figure 1. The waiting points from initial point of contact to treatment.

The data showed attrition at each stage, particularly at Waiting Point 1 and 2. It was necessary to choose one waiting point to focus on. Since the initial assessment was conducted by a national provider, any changes for Waiting Point 1 would have been challenging to implement. There was a greater level of potential influence for Waiting Point 2 where local services were involved. Therefore, a detailed map was created of the pathway and points of contact from the initial assessment to the more comprehensive assessment. Data revealed around 40% of clients did not attend this comprehensive assessment. Therefore, the target behaviour of this project was for clients to attend their assessment appointment.

Understanding the barriers and facilitators

Stage 2 involved looking at literature and speaking to Peer Mentors (people who had previously attended treatment and engaged with a mentoring training programme) to understand the barriers and facilitators to attending this appointment. The barriers were mapped to the Capability Opportunity Motivation – Behaviour model (COM-B, Michie, van Stralen, and West, 2011; Table 1). The barriers covered the three domains of the COM-B model. One could assume people are not interested in attending their appointment (motivation), but a person may forget (capability) or be unable to take time off work for the appointment (opportunity). Therefore, the COM-B model helped to identify the broad range of barriers in attempt to step away from a common sense understanding of behaviour. The proposed barriers were presented to Peer Mentors, who agreed that the list adequately summarised the barriers they encountered whilst waiting for appointments. 

Table 1. Barriers and facilitators mapped to COM-B model.

Identifying intervention options

Having identified some of the barriers to the waiting points, Stage 3 involved exploring potential intervention options. There is little evidence to suggest decreasing waiting times would increase retention, since barriers can exist at any length of wait. Redko et al., (2006) found participants waiting for treatment were surprised to wait and anticipated ‘treatment on demand’. However, due to limited resources ‘treatment on demand’ is not a feasible solution. Previous interventions have involved regular phone contact and re-framing the waiting period to indicate treatment has started (Guitar, 2017). Additionally, text message reminders have been found an effective way of improving appointment attendance (Boksmati et al., 2016).

Stage 3 involved developing intervention components from the literature search and Peer Mentor feedback. These components were mapped to the barriers identified in Stage 2 (Table 2). The intervention components were considered with the APEASE (Acceptability, Practicability, Effectiveness, Affordability, Side-effects, Equity) criteria, which indicated areas that needed to be considered before potential implementation.

Table 2. Proposed intervention components, mapped to COM-B, with peer feedback.

Implementation

Stage 4 involved discussing which intervention component to implement. The treatment service implemented SMS reminders to prompt clients of their upcoming comprehensive assessment. This intervention was selected after considering the APEASE criteria: text reminders were deemed acceptable by peer mentors, easy to implement, potentially effective as indicated by previous research, and affordable with no anticipated spillover effects. Regarding equity, messages could not be sent to those without a mobile however the comprehensive assessment is conducted via telephone. Amendments and recommendations were made to a 7-day letter that the service was sending to ‘high risk’ people who missed their appointment. The letter was re-framed as an ‘invitation to re-book’ letter, using guidance from the EAST framework (Service et al., 2012), targeted motivation, Behaviour Change Techniques (Michie et al., 2013), and health literacy guidelines. It was recommended to send this letter to all clients who missed their appointment regardless of previous identified risk.  Recommendations were made to improve the content of the website to target some of the motivational barriers and a resource toolkit is being reviewed to support recovery goals. Preliminary results indicated an increase in attendance to the comprehensive assessment, although Christmas and Dry January may have had an impact in terms of clients missing appointments over Christmas and a general trend of increased referrals for January. Furthermore, it is hard to evaluate which techniques were effective in the intervention due to co-occurring changes being implemented. However, within ‘real-life’ settings it is important for treatment services to make changes when and where possible.

Behavioural science has provided a toolkit to understand attrition. Whilst further data on appointment attendance is being explored, the same behavioural science principles are being used to explore Alcohol Treatment Requirements as part of Community Orders. The work to date has involved understanding the problem and identifying the barriers/facilitators for the different stakeholders (probation, court, treatment services, and the offender), ahead of identifying intervention options.

References

Black, C. (2020). Review of drugs: executive summary.

Black, C. (2021). Review of drugs part two: prevention, treatment and recovery: annexesDepartment of Health & Social Care.

Boksmati, N., Butler-Henderson, K., Anderson, K., & Sahama, T. (2016). The effectiveness of SMS reminders on appointment attendance: a meta-analysis. Journal of medical systems, 40, 1-10.

Guitar, N. A. (2017). Why can’t patients last the wait? Decreasing substance abuse treatment waiting list attrition. University of Western Ontario Medical Journal, 86(2), 40-41.

HM Government (2021). From harm to hope - A 10-year drugs plan to cut crime and save lives.

Michie, S., Van Stralen, M. M., & West, R. (2011). The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implementation science, 6(1), 1-12.

Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., Eccles, M. P., Cane, J., & Wood, C. E. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine, 46(1), 81-95. doi: 10.1007/s12160-013-9486-6.

Redko, C., Rapp, R. C., & Carlson, R. G. (2006). Waiting time as a barrier to treatment entry: perceptions of substance users. Journal of Drug Issues, 36(4), 831-852.

Service, O., Hallsworth, M., Halpern, D., Algate, F., Gallagher, R., Nguyen, S., Ruda, S., Sanders, M., Pelenur, M., Gyani, A., Harper, H., Reinhard, J., & Kirkman, E. (2012). EAST: Four simple ways to apply behavioural insights.