STOPP/START interventions to improve medicines management for people aged 65 years and over: a realist synthesis

Abstract

Background Drug-related problems and potentially inappropriate prescribing impose a huge burden on patients and the health-care system. The most widely used tools for appropriate prescription in older adults in England and in other European countries are the Screening Tool of Older People’s Prescriptions (STOPP)/Screening Tool to Alert to the Right Treatment (START) tools. STOPP/START tools support medicines optimisation for older adults. Objectives To identify, test and refine the programme theories underlying how interventions based on the STOPP/START tools are intended to work, for whom, in what circumstances and why, as well as the resource use and cost requirements or impacts. Design A realist synthesis. Setting Primary care, hospital care and nursing homes. Patients Patients aged ≥ 65 years. Interventions Any intervention based on the use of the STOPP/START tools. Review methods Database and web-searching was carried out to retrieve relevant evidence to identify and test programme theories about how interventions based on the use of the STOPP/START tools work. A project reference group made up of health-care professionals, NHS decision-makers, older people, carers and members of the public was set up. In phase 1 we identified programme theories about STOPP/START interventions on how, for whom, in what contexts and why they are intended to work. We searched the peer-reviewed and grey literature to identify documents relevant to the research questions. We interviewed experts in the field in our reference group to gain input on our list of candidate context–mechanism–outcome configurations, to identify additional context–mechanism–outcome configurations and to identify additional literature and/or relevant concepts. In phase 2 we reviewed and synthesised relevant published and unpublished empirical evidence and tested the programme theories using evidence from a larger set of empirical studies. Results We developed a single logic model structured around three key mechanisms: (1) personalisation, (2) systematisation and (3) evidence implementation. Personalisation: STOPP/START-based interventions are based on shared decision-making, taking into account patient preferences, experiences and expectations (mechanisms), leading to increased patient awareness, adherence, satisfaction, empowerment and quality of life (outcomes). Systematisation: STOPP/START tools provide a standardised/systematic approach for medication reviews (mechanisms), leading to changes in professional and organisational culture and burden/costs (outcomes). Evidence implementation: delivery of STOPP/START-based interventions is based on the implementation of best evidence (mechanisms), reducing adverse outcomes through appropriate prescribing/deprescribing (outcomes). For theory testing, we identified 40 studies of the impact of STOPP/START-based interventions in hospital settings, nursing homes, primary care and community pharmacies. Most of the interventions used multiple mechanisms. We found support for the impact of the personalisation and evidence implementation mechanisms on selected outcome variables, but similar impact was achieved by interventions not relying on these mechanisms. We also observed that the impact of interventions was linked to the proximity of the selected outcomes to the intervention in the logic model, resulting in a clearer benefit for appropriateness of prescribing, adverse drug events and prescription costs. Limitations None of the available studies had been explicitly designed for evaluating underlying causal mechanisms, and qualitative information was sparse. Conclusions No particular configuration of the interventions is associated with a greater likelihood of improved outcomes in given settings. Study registration This study is registered as PROSPERO CRD42018110795. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 9, No. 23. See the NIHR Journals Library website for further project information.

Advisory groups in realist reviews: Systematically mapping current research and recommendations for practice

“…Generally, minimal information was given on how advisory group members were recruited. For example, Wade et al [29] explained their youth advisory group was formed by “advertising through batyr, a for‐purpose preventative mental health organization in Australia.” Those that did give information described sending invitations to existing PPI groups [26, 45] or using a pre‐established advisory group panel from a wider study, supplemented with additional stakeholders [57]. In contrast, one review [27] reported actively trying to recruit outside of established PPI groups to engage with new points of view.…”

“…Figure 1 provides a flowchart of the screening process and documents reasons for exclusion. 40% ( n = 37) of the realist reviews cited use of an advisory group at some stage (as per the working definition for this study, notably other terminology was often reported) [14–17, 25–61]. 60% ( n = 56) did not mention the use of an advisory group [62–117].…”

“…Fernandez Turienzo et al [61] x x Fowokan et al [38] x x Gangannagaripalli et al [26] x x x x Gorchs-Font et al [49] x Grunwald et al [57] x x x…”

“…While no clear preferred term emerged, some commonalities were seen across the reviews. For example, "advisory group" or versions of this were reported by nine reviews [15,17,[25][26][27][28][29][30]130], while "stakeholder group" or versions of this were reported by eight reviews [25,27,[31][32][33][34][35][36]. Additionally, variations of "expert panel" were reported by five reviews [37][38][39][40][41].…”

Factors Influencing Self-Reported Medication Use in the Survey of Health Aging and Retirement in Europe (SHARE) Dataset

“…The influence of polypharmacy in particular is highly relevant, as polypharmacy poses a risk for patients since not all prescribed medications are necessarily appropriate. Several methods have been developed to detect such inappropriate medication; however, it is essential to keep in mind the influence of the number of medications alone on self-reported medication use [ 42 , 43 , 44 ], to detect potentially harmful medication regimes.…”