Headroom analysis as a method to estimate the potential for a cost-effective implementation of self-management interventions

The COMPAR-EU project aims to rank the most (cost-)effective interventions for self-management. To estimate the cost-effectiveness of self-management interventions (SMIs) health economic models were used to predict the lifetime health benefits and (healthcare) costs for a scenario assuming one-time implementation of a SMI in comparison with a scenario assuming care as usual.

One of the important components of a cost-effectiveness analysis is the cost of the intervention. However, in some cases these costs are not readily available. As an alternative to standard cost-effectiveness analysis a headroom analysis can then be performed. In a headroom analysis (1), a headroom is estimated indicating how much an intervention or treatment may maximally cost to be considered cost-effective given the health benefits associated to the intervention and a threshold for the cost associated to these health benefits. Health benefits are often expressed in quality-adjusted life-years (QALYs). A threshold for the cost per QALY reflects the maximum cost society is willing to pay to gain one additional QALY.

In the COMPAR-EU project data on the characteristics and health benefits of SMIs were obtained from published data. The cost of SMIs is determined by factors such as the type of healthcare provider involved, the time spend per patient and the mode of delivery. The majority of published studies on self-management did not provide enough detailed information on these factors to be able to estimate the cost of SMIs. Therefore, a headroom analysis was conducted to estimate what SMIs may maximally cost to be considered cost-effective given its health benefits and a certain threshold for cost-effectiveness. Headrooms were estimated for two different threshold values: €20,000 per QALY gained, as this is a figure that is often used in the context of preventive interventions, and €50,000 per QALY gained, as this value is more often used for curative interventions in for instance COPD and heart failure patients. Overall, headrooms for SMIs varied across diseases and countries and were estimated to range from €0 to €2,400 and from €200 to €8,000 at a threshold of €20,000 and €50,000, respectively.

A lower headroom for a particular SMI implies that the SMI needs to be delivered at lower cost in order to achieve cost-effectiveness. As such, headroom estimates are relevant for policymakers and health care providers as they give guidance to when (and when not) to consider SMIs a tool to gain health at reasonable costs, and in what disease areas and patient groups it might be more efficient to invest in SMIs.

Hoogendoorn-Lips_Martine

Martine Hoogendoorn

Martine Hoogendoorn is a Senior Researcher at iMTA with more than 15 years experience in modelling the disease COPD. She holds a Master´s degree in Human Nutrition from Wageningen University and a PhD in Health Economics from the Erasmus University Rotterdam. She has extensive experience in disease modelling using different types of models (e.g. cohort, patient-level, Markov, DES).

de_Groot_ Saskia

Saskia de Groot

Saskia is a Medior Researcher at iMTA. She holds a Master´s degree in Health Economics, Policy & Law from the Erasmus University Rotterdam, a Master´s degree in Clinical Epidemiology from the Netherlands Institute for Health Science of the Erasmus Medical Center and a PhD in Health Economics from the Erasmus University Rotterdam.

Reference

[1] Girling A, Lilford R, Cole A, Young T. Headroom approach to device development: current and future directions. Int J Technol Assess Health Care. 2015 Jan;31(5):331-8.

Qualitative interviews to facilitate a smooth transition from the evidence of self-management interventions to practice

During digitization in the healthcare system and the continuously advancing process toward patient-centred care, the responsibility of patients for their own health is becoming increasingly important. Particularly in the case of chronic diseases, it is important that patients are enabled to actively participate in the management of the disease [1]. This is where self-management interventions (SMIs) play a significant role. SMIs aim to equip patients and informal caregivers if appropriate to actively participate in the management of their disease [2].

SMIs have good evidence and are very well researched. We are developing the COMPAR-EU interactive platform with different types of decision-making tools that summarize this evidence about SMI in a structured and transparent way. The decision tools can support decision-making on SMIs and so integrating the evidence into practice for different end users such as patients, clinicians, policymakers or guideline developers.

Many well evidenced and exhaustively developed decision-making tools and interventions do not deliver their potential impact because their use requires changes in clinical workflows as well as organizational structure. In addition to this there is insufficient support for these changes [3]. This leads to the question how decision-making tools can facilitate and disseminate the use of the most effective SMIs into the real life? How can it be made visible at the right time and what incentives are there for healthcare professionals and managers to use decision-making tools and the other way around SMIs?

Interviews with managers and clinicians

These are the questions we want to explore with qualitative implementation interviews with managers and health care professionals at the organisational level in COMPAR-EU countries. We will use their insights to build and refine business plans for the various provider and health system context. This means that a manager or clinician working in any relevant provider can look at the business plan and identify any key actions they should take which would help themselves and their colleagues make use of the decision tools produced by COMPAR-EU.

Collecting information on different health systems represented in COMPAR-EU countries illustrates that Germany and Spain provide a good representation of health system feature, in particular due to the differences in organisational enablers in insurance-based system than purely public system. Therefore, we have conducted a total of 40x qualitative interviews with managers and clinicians in Spain and Germany. The participants were recruited from different settings (primary care practice, hospital or special care practice) so we can obtain the implementation factors of innovations from different perspectives. We developed a semi-structured interview guide with reference to the TICD framework and a realist review by Joseph-Williams et. al (2020).

Figure 1 The overall process of qualitative implementation study

Directed qualitative content analysis

Currently, the interviews need to be analysed. We will use directed qualitative content analysis (QCA). This approach allows combining the development of deductive as well as inductive codes, and so allowing the use of existing evidence. A guide for analysis will be provided, based on the work of Hsieh and Shannon [4], Hamilton [5], and Gale et al. [6]. A codebook will be developed by OptiMedis based on the TICD framework [7] (inductive) and interviews conducted (deductive).

Challenges of the cross-national research

As the translation of the interview guide in different languages is considered to be challenging task [8], we had several meetings to adjust and translate the interview guide to ensure the cultural meaning of the questions and the shared understanding between both teams, as the interviewers from different countries may have different views and experience.

Additionally, we agreed to conduct the interviews in the local language of the respective country to avoid any language difficulties for both the participants and the interviewers. After pretesting of the developed coding system, we will discuss and add possible additional themes if needed. Similar as in other studies [9] [10], each country team will analyse the data in the native language as long as possible and not translate all interview data into one language. This form of rapid qualitative analysis yields similar results compared to traditional qualitative analysis and is therefore a useful tool for this analysis [11].

Business plans with key actions for implementation

In summary, the interviews aim to identify drivers and barriers for the use of decision tools on the COMPAR-EU platform capturing the evidence of SMIs in practice. These results will serve to develop business plans for organisations who will actually be making use of the decision-making tools. These business plans will focus on how to implement evidence based decision aids about SMI at the organizational level into the healthcare system.

Nina Sofie Krah_Quadrat

Nina Sofie Krah

Nina has a background in Health Economics & Health Care Management as well as Ethics in Health Care and works at OptiMedis as a working student. With a keen interest in the use of evidence in the health care system, her master’s thesis explores the conditions under which self-management interventions can be meaningfully implemented into clinical workflow.

Porträt_Zietzsch_Paula

Paula Zietzsch

Paula has a background in Health Economics and works at OptiMedis in different national and EU projects with a particular interest in shared decision-making. As a Research & Innovation Manager, she focuses on implementing evi-dence-based interventions in routine clinical care.

References

[1] Aujoulat I, Marcolongo R, Bonadiman L, Deccache A. Reconsidering patient empowerment in chronic illness: a critique of models of self- efficacy and bodily control. Soc Sci Med. 2008;66(5):1228-1239. https://doi.org/10.1016/j.socscimed.2007.11.034.

[2] Tattersall RL. TThe expert patient: a new approach to chronic disease management for the twenty-first century. Clin Med (Lond). 2002;2(3):227-229. https://doi.org/10.7861/clinmedicine.2-3-227.

[3] Orrego C, Ballester M, Heymans M, et al; the COMPAR-EU Group. Talking the same language on patient empowerment: Development and content validation of a taxonomy of self-management interventions for chronic conditions. Health Expect. 2021;00:1–13. https://doi.org/10.1111/hex.13303.

[4] Hsieh H-F, Shannon SE. Three Approaches to Qualitative Content Analysis . Qual Health Res 2005;15:1277–88.

[5] Hamilton AB, Finley EP. Qualitative methods in implementation research: An introduction. Psychiatry Res 2019;280. https://doi.org/10.1016/j.psychres.2019.112516.

[6] Gale RC, Wu J, Erhardt T, Bounthavong M, Reardon CM, Damschroder LJ, et al. Comparison of rapid vs in-depth qualitative analytic methods from a process evaluation of academic detailing in the Veterans Health Administration. Implement Sci 2019;14:1–12. https://doi.org/10.1186/s13012-019-0853-y.

[7] Flottorp SA, Oxman AD, Krause J, Musila NR, Wensing M, Godycki-Cwirko M, et al. A checklist for identifying determinants of practice: A systematic review and synthesis of frameworks and taxonomies of factors that prevent or enable improvements in healthcare professional practice. Implement Sci 2013;8:1–11. https://doi.org/10.1186/1748-5908-8-35.

[8] McGreevy J, Orrevall Y, Belqaid K, Bernhardson BM. Reflections on the process of translation and cultural adaptation of an instrument to investigate taste and smell changes in adults with cancer. Scand J Caring Sci. 2014 Mar;28(1):204-11. DOI: 10.1111/scs.12026.

[9] Woolhead G, Tadd W, Boix-Ferrer JA, Krajcik S, Schmid-Pfahler B, Spjuth B, Stratton D, Dieppe P; Dignity, and Older Europeans (DOE) project. “Tu” or “Vous?” A European qualitative study of dignity and communication with older people in health and social care settings. Patient Educ Couns. 2006 Jun;61(3):363-71. doi: https://doi.org/10.1016/j.pec.2005.04.014. Epub 2005 Jun 20. PMID: 15970421.

[10] Knutsen, I., Foss, C., Todorova, E., Roukova, P., Kennedy, A., Portillo, M., . . . Rogers, A. (2015). Negotiating diet in networks: A cross-European study of the experiences of managing Type 2 diabetes. Qualitative Health Research. 1-12. https://doi.org/10.1177/1049732315610318.

[11] Nevedal, A.L., Reardon, C.M., Opra Widerquist, M.A. et al. Rapid versus traditional qualitative analysis using the Consolidated Framework for Implementation Research (CFIR). Implementation Sci. 2021; 16(67).

Self-Management Europe has great ambitions!

In this way we want to create a network of researchers, health care professionals, developers, industry and other stakeholders whose common goal is to improve patient self-management and empowerment of patients with chronic diseases in Europe. SME is a spin-off of COMPAR-EU and initiated by four COMPAR-EU partners that all have a broad experience on self-management, patient empowerment and personalized health care: FAD, Nivel, OptiMedis and EPF.

SME aims to raise public, professional and political awareness of the critical role patients play in living with chronic disease by providing the most updated and innovative multidisciplinary knowledge about self-management interventions and empowerment and how to address these topics in Europe in policy and practice; SME wants to provide practical tools to encourage and support health care professionals to adopt SMI in their real life contexts; work with organizations and industry to develop approaches to incorporate SMI in practice; will organize training courses on implementing approaches which support patients’ self-management and empowerment, and make the expertise of experienced programs and interventions available to others.

During the last year we worked on a website and on a mission paper on SME (in progress). While we are looking for some structural funding to carry out the ambitious activities outlined above, we are giving small steps on some areas that, in our opinion, complement the work done in COMPAR-EU. The first dissemination activity of SME were two “ALERTS” for healthcare professionals, managers and other stakeholders looking for practical recommendations to implement practices that enhance self-management and patient empowerment. The first alert was on empowerment of patient to take an active role in health care, the second alert on health literacy.

Click here to read more about Self-Management Europe.

Monique

Monique Heijmans

Monique Heijmans is a Health Psychologist and works as a Senior Researcher at Nivel since 1998. She is an expert in the area of (determinants of) self-management and chronic illness and has extensive research experience in the area of psychosocial factors and their interaction with health behavior and health.

Available tools and challenges encountered when interpreting results from sparse networks of multicomponent interventions

Multi-component SMIs

SMIs are multicomponent interventions and they are determined by their characteristics (support techniques) and the context under which they are applied (e.g. type of encounter, mode of delivery, type of provider, intensity). It is our goal in COMPAR-EU to

  1. locate those components that work (or do not work) and
  2. explore how these components interact with each other.

To this end, we employed network meta-analyses models. The main benefits of network meta-analysis is that it synthesizes both direct and indirect evidence and results in more precise effect estimates compared to those of pairwise meta-analyses.

Sparse networks

SMIs are very heterogeneous interventions. They typically form sparse networks with each combination of components observed only in a few studies. An example of a network of SMIs depicting a network of 461 randomized clinical trials comparing a total of 97 distinct SMIs for the reduction in glycated haemoglobin (HbA1c) is shown in the Figure below. Nodes represent interventions (combination of components in this case) and edges represent direct evidence (trials directly comparing the interventions shown in the connecting nodes). Size of nodes and thickness of edges is proportional to the information the network provides for the respective intervention and comparison. Information flows across the network and each trial informs the entire network. Out of these 461 trials, 386 (84%) compare an SMI to usual care. This is why we note a large node for “usual care”. The remaining nodes are poorly connected to each other. A relative effect for a pair of SMIs (a line in the Figure) is informed mainly by those studies (usually one or two) that compare this pair. As a result, this NMA estimate depends heavily on what is observed on these trials and as this is the case for most pairs of SMIs, we infer that NMA results are heavily confounded with study characteristics. This compromises the main assumption in NMA; the distribution of effect modifiers is similar across treatment comparisons.

Consider there is a small trial comparing a SMI (e.g. comprising education and shared decision making techniques, done remotely by a non-professional) to “usual care”. This study has a large effect maybe because its population is severely ill or the population is a certain minority group or the duration and intensity of the SMI is very large or even for reasons such as fraud or poor methodology. The NMA effect for that intervention would be very large just like what we observed in the trial just because the rest of the network will not provide much information (if any at all) for this NMA effect. This result would be misleading as the remaining studies in the network have other characteristics. But what drives the effect of this SMI are the study characteristics and not the SMI per se.

Evaluating components’ effectiveness

The classical interpretation of NMA effects won’t be of much help in sparse networks of multi-component SMIs such as those we deal with in COMPAR-EU. Not only efficacy is confounded with study characteristics but we also get very imprecise NMA effects as there is little flow of information.

Component network meta-analysis

Alternative evidence synthesis models have been developed that aim to estimate the effect of each component (component NMA -CNMA). Each component is included in many trials and all these trials will inform its relative effect. Hence, we get very precise effects. Unfortunately, problems do not just vanish into thin air

  1. We don’t know how components interact with each other (mathematically we assume that components have an additive effect, an assumption that is hard to test and/or defend). Even if we identify the “perfect” SMIs by combining those components with large effects, we do not know how these will perform in practice.
  2. The context and conditions under which a SMI is applied is of paramount importance. Hence, confounding may still be an issue, especially if a component is included in a few trials. Most probably the contextual factors would reveal themselves in large statistical heterogeneity compromising the validity of results and making interpretation difficult.

Visual inspection of NMA results.

A visual inspection of the NMA results may reveal important information regarding which components work and interact well with each other. For example, if most of the SMIs with large effects are applied face-to-face whereas those with small effects are applied remotely, this is an indication that face-to-face is working. This is not an easy task and we have developed a series of results and graphical methods to disentangle those components which are associated with large effects from those that are not.

When I realized that in COMPAR-EU we will be working with networks of hundreds of studies I felt I hit the jackpot. In practice, with so many known and unknown effect modifiers, components and nodes in the network, it is easy to downplay uncertainty and hard to separate signal from noise. A famous aphorism in statistics, attributed to British statistician George Box, is “All models are wrong, but some are useful”. The aphorism recognizes that we cannot perfectly model the complex systems of reality but we can still get some useful information. Our results, from many NMAs on many outcomes, include a wealth of information!

Dimitris

Dimitris Mavridis

Dimitris Mavridis is an Assistant Professor in statistics for the social sciences at the Department of Primary School Education at the University of Ioannina. He has published more than 40 papers relevant to network meta-analysis (NMA). His works include both methodological papers and applications of NMA in various fields.

Development and user-testing of a web-based patient decision aid for self-management interventions

Towards the end of the COMPAR-EU project – after completing network meta-analysis, assessing the certainty of the evidence, selecting the most promising interventions for the four different chronic conditions, and formulating recommendations on these interventions – we are now in the process of developing interactive web-based decision aids tools including all of the above information.

COMPAR-EU decision-aids will assist patients to decide on which self-management intervention best suits them. They will help them to compare different intervention options (e.g., how important are the potential desirable and undesirable effects), and prepare them to participate with their health professional in making an optimally informed decision.

We relied on the work conducted by leaders in the field of shared decision-making, including the Ker Unit at Mayo Clinic, and other emerging groups like the MAGIC consortium to identify the main aspects that need to be included and considered in their development. We also reviewed research synthesis in this field, and existing decision aids tools. Finally, after an in-depth brainstorming, we developed a first prototype.

To ensure the usability and understandability, we gathered feedback from a Task Force, a multidisciplinary group including patients, clinicians, methodologists, and other relevant stakeholders. We also assembled an external group of patients who have been involved in previous steps of the COMPAR-EU project. With the Task Force, we held regular meetings in which we presented the tools and their progress, whereas with the patients’ group we conducted a workshop in which we presented the prototype. Finally, we conducted semi-structured interviews with patients. At the moment, we are currently working on making all the necessary changes in the decision aid web-based tools and have an optimal version ready to conduct – in collaboration with OptiMedis – user-testing with patients and health professionals.

This user-testing will allow us to identify errors and areas of improvement by asking both, patients and health professionals, about their experience navigating the web-based decision aid tool. With their feedback, we will finalize the web-based decision aid tools, translate them into six different languages (English, French, German, Spanish, Dutch and Greek) and make them available in the COMPAR-EU platform during 2022.

Caludia_Valli

Claudia Valli

Claudia Valli is a Researcher at the Biomedical Research Institute (Hospital Sant Pau) and a PhD Candidate in the Methodology of Biomedical Research and Public Health Programme (Univeristat Autonoma of Barcelona). Her work focuses on conducting clinical and nutritional systematic reviews and synthetising research evidence to support informed decision-making and guideline development.

Managing my Type 2 Diabetes: personal patient story of a successful journey

It´s time to change!

The COVID-19 pandemic scared me into action. Social media was invaded by posters and articles highlighting the increase in obesity during lockdown as people increasingly turned to food for comfort. I wondered what complications I was likely to get if my weight kept increasing. During the first two months of the pandemic and lockdown, I taught myself to make no-knead bread after following a YouTube video. Thus, I found myself endlessly making brown bread rolls, just in case we ran out. And I ate and ate.

I am becoming hugely interested in learning more about patient-driven self-management tools and processes.

My husband, on the other hand, started a diet and was soon shedding weight. He encouraged me to change my relationship to food. I thought about this and asked myself: “What do I most love to eat?” The instant answer was “Indian Food”. Because of the pandemic, we could no longer have our occasional meal in an Indian restaurant. So, I decided that, as I had learnt to bake bread daily, I could also learn to cook the Indian dishes that I most yearned for. And that’s what I started to do, from September 2020 to the present. YouTube is the school that helped me to become a chef.

As I am a vegetarian, I bought a steamer to cook 5 different types of fresh vegetables daily, adding spices according to Indian recipes that I have learnt to make – my food is extremely well balanced now. If I hanker after something sweet, I’ve found a recipe using chia seeds, raw cacao, and skimmed milk. After a few hours in the fridge, the mixture turns into a mousse – only a few calories and sugar-free. And the hankering is satisfied!

I now avoid the temptation of eating processed carbs and stick to low calory fresh veg which I steam and mix with herbs, spices and lentils to die for. I have learned to toast and grind my own spices. With advice from a dietitian, I was overjoyed to be encouraged to continue reducing my insulin intake. I used to take a total of 82 units of a mixed dose of Insulatard and Actrapid daily. I am now down to a combined dose of 8 and 6 in the morning and sometimes have none in the evening if I am on intermittent fasting. I check my blood sugar 4 times a day, and avoid bread, pasta and rice. The HbA1c blood test was recently down to 6. A huge difference! Hope I manage to continue on this promising pathway.

Through the European Patients’ Forum and COMPAR-EU, I am becoming hugely interested in learning more about patient-driven self-management tools and processes, exchanging good practices and knowledge with the other members of the Panel. The activities that the Panel is part of are vital to the success of COMPAR-EU project and empowerment patients. Finally, I am also interested in how patients who are managing their condition well can become mentors of other patients who are affected similarly. There are some plans for building a Toolkit for Mentors with useful tips and recommendations within the project.

Nora_compar

Nora Macelli

Nora Macelli is CEO of the St Jeanne Antide Foundation (SJAF) in Malta, a registered social purpose NGO that provides a range of support services
for very vulnerable families at both community and national levels. Nora studied social work in India (MSW), specialized in community development and was a full-time volunteer community development worker there for six years and a volunteer with the United Nations Volunteers (UNV) for two years. With a colleague, she has edited two books in Maltese for family caregivers of mentally ill persons. She is especially interested in volunteer mentoring with persons with complex needs and peer mentoring by persons with the lived experience of domestic violence and family caregiving in the field of mental health.

Applying incentives to adopting shared decision-making with patient decision tools

Shared decision-making (SDM) is a well-known approach where clinicians and patients share evidence-based information about medical interventions and their risks and benefits, while taking into account the patient’s concerns and preferences. Tools such as patients’ decision aids (PDAs) have been developed to support SDM. Patients using PDAs improve their understanding of the treatment options, are more likely to participate in the decision-making process, and can as a result avoid unwanted treatment [1]. Despite the reported positive effects, decision-making tools are not often implemented in routine clinical care [2]. There is a clear need for sustainable incentives for both individuals [3] and organisations to engage in the adoption and use of a PDAs.

In the last several years, there has been growing interest by academics and policy-makers in advancing SDM in routine healthcare. Many countries are attempting to commit to an SDM approach and its inclusion in a number of clinical practice procedures by developing specific health policies [4]. Despite these efforts, the uptake in the real-world application stays poor[1].

There has been less focus on the characteristics of the healthcare system in which SDM with its decision aids are embedded

There are various factors influencing the implementation of SDM which have been conceptualized into the several models and frameworks. For instance, such factors could be categorized by the level of impact into four main groups: individual-level (patient, clinicians), interactional-level (patient-clinician), organizational-level and system-level factors [1]. Many efforts have already been made to study the interaction between clinicians and patients, but there has been less focus on the characteristics of the healthcare system in which the SDM and PDAs are embedded and which guide the work of the healthcare organizations [5]. To shed more light on this topic we focus on one of the system-level factors – incentives. Incentives are defined as external stimuli that serve as a motive for implementation [6]. We can differentiate between financial and non-financial incentives [7].

Providing financial and non-financial incentives

Financial incentives used for implementation of SDM and PDAs can be, for instance, payment models (i.e. bonuses, saving contracts, payment for activities etc.). These could be especially attractive to healthcare organizations. In practice, we can see that the payment models can influence the “amount of time a HCP has for a patient’s visit” and in turn the use of the PDAs tool itself [1]. In case of individuals like clinic staff working within the healthcare system, the research shows that monetary factors can discourage or ‘‘cheapen’’ desired behaviors that may be linked to more intrinsic motivations such as altruism or self-determination [3]. Furthermore, it has been shown that the financial incentives to promote an adoption of new approaches have a short-term positive effect, but they do not lead to sustained use when rewards are withdrawn [8].

Non-financial incentives, on the other hand, can be, for instance, the issuing of accreditation/certification criteria when applying SDM. The policy-makers could include the degree to which SDM with PDAs are included in the HCP´s workflow as a criterion for the accreditation [1]. This would make it possible for the organizations to differ from each other and be more motivated to adopt the SDM approach and its PDAs. In case of individuals, non-financial incentives are based as emphasized above on the intrinsic motivation of the individuals and other factors like “social/professional role and identity” or “belief about consequences” [6]. Possible examples of such incentives are: the clinic staff seeing the real-life and immediate positive effects on patient’s care [3] or continuing in educational activities by receiving education credits [7].

All in all, the implementation of SDM is frequently included in the healthcare policies of individual countries and seems to generally be encouraged. However, the level of utilization of the SDM approach and its tools in the routine practice stays poor. As the literature indicates, incentives are an important strategy at the system-level to facilitate the implementation of SDM with its PDAs. Nevertheless, it should be recognized that even successful behavioral changes can diminish once incentives are removed. There is therefore a need for such incentives for individuals and organizations to be sustainable.

My Post (6)

Paula Zietzsch

Paula has a background in Health Economics and works at OptiMedis in different national and EU projects with a particular interest in shared decision-making. As a Research & Innovation Manager, she focuses on implementing evidence-based interventions in routine clinical care.

  1. Scholl, Isabelle, Allison LaRussa, Pola Hahlweg, Sarah Kobrin, und Glyn Elwyn. „Organizational- and System-Level Characteristics That Influence Implementation of Shared Decision-Making and Strategies to Address Them — a Scoping Review“. Implementation Science 13, Nr. 1 (Dezember 2018): 40. https://doi.org/10.1186/s13012-018-0731-z.
  2. Gayer, Christopher C, Matthew J Crowley, William F Lawrence, Jennifer M Gierisch, Bridget Gaglio, John W Williams, Evan R Myers, Amy Kendrick, Jean Slutsky, und Gillian D Sanders. „An Overview and Discussion of the Patient-Centered Outcomes Research Institute’s Decision Aid Portfolio“. Journal of Comparative Effectiveness Research 5, Nr. 4 (Juli 2016): 407–15. https://doi.org/10.2217/cer-2016-0002.
  3. Kostick, Kristin M., Meredith Trejo, Robert J. Volk, Jerry D. Estep, und J.S. Blumenthal-Barby. „Using Nudges to Enhance Clinicians’ Implementation of Shared Decision Making With Patient Decision Aids“. MDM Policy & Practice 5, Nr. 1 (Januar 2020): 238146832091590. https://doi.org/10.1177/2381468320915906.
  4. Härter, Martin, Nora Moumjid, Jacques Cornuz, Glyn Elwyn, und Trudy van der Weijden. „International accomplishments in policy, research and implementation.“ ZEFQ Z Evidenz Fortbild Qual G., Nr. 123–124 (o. J.): 1–5. https://doi.org/10.1016/j.zefq.2017.05.024.
  5. Elwyn, Glyn, Dominick L. Frosch, und Sarah Kobrin. „Implementing Shared Decision-Making: Consider All the Consequences“. Implementation Science 11, Nr. 1 (Dezember 2015): 114. https://doi.org/10.1186/s13012-016-0480-9.
  6. Munro, Sarah, Ruth Manski, Kyla Z. Donnelly, Daniela Agusti, Gabrielle Stevens, Michelle Banach, Maureen B. Boardman, u. a. „Investigation of Factors Influencing the Implementation of Two Shared Decision-Making Interventions in Contraceptive Care: A Qualitative Interview Study among Clinical and Administrative Staff“. Implementation Science 14, Nr. 1 (Dezember 2019): 95. https://doi.org/10.1186/s13012-019-0941-z.
  7. Flottorp, Signe A, Andrew D Oxman, Jane Krause, Nyokabi R Musila, Michel Wensing, Maciek Godycki-Cwirko, Richard Baker, und Martin P Eccles. „A Checklist for Identifying Determinants of Practice: A Systematic Review and Synthesis of Frameworks and Taxonomies of Factors That Prevent or Enable Improvements in Healthcare Professional Practice“. Implementation Science 8, Nr. 1 (Dezember 2013): 35. https://doi.org/10.1186/1748-5908-8-35.
  8. Uy, Visith, Suepattra G. May, Caroline Tietbohl, und Dominick L. Frosch. „Barriers and Facilitators to Routine Distribution of Patient Decision Support Interventions: A Preliminary Study in Community-Based Primary Care Settings: Distribution of Patient Decision Support“. Health Expectations 17, Nr. 3 (Juni 2014): 353–64. https://doi.org/10.1111/j.1369-7625.2011.00760.x.

Longevity gains and postponed informal care with self-management interventions?

A societal perspective includes the impact of a disease on informal care, that is care given by people other than healthcare professionals. Informal care includes, but is not limited to, care and support given by family and friends. Especially chronically ill older adults need day-to-day help with personal care, such as dressing and eating, practical household help, such as shopping, and many other activities essential to their health and quality of life.

We are familiar with the costs associated with a doctor, a nurse, or other healthcare workers providing professional care to older adults.

However, the costs when older adults are cared for by family members or friends often receive less attention. While the provision of informal care is a burden on the informal caregiver in terms of time (that the caregiver could otherwise use to perform paid work or spend on leisure), these costs are often ignored in economic evaluations.

In part, that’s because information on informal care use is often sparse if not missing at all, thus in order to predict these costs we need to study its relationship with known predictors of health care use.

Different studies evaluated the relationship between proximity to death and health care expenditures. These studies found that proximity to death is a much better predictor of health care expenditures than age.[1] This is supported by the finding that health losses are more pronounced in the last years of life [2], and similarly severe disability is centered in the final phase of life. [3] Within COMPAR-EU, we therefore aimed to predict informal care use based on age and proximity to death.

We used data from the Survey of Health, Ageing and Retirement in Europe (SHARE) release 7.0.0. [4] SHARE is a longitudinal, multidisciplinary, and cross-national survey, which aims to collect data on health, socio-economic status along with social and family networks of non-institutionalized people aged over 50 in 21 European countries and Israel. With these data we predicted informal care use based on age, gender and proximity to death. Our findings show that the weekly use of informal care increased with proximity to death from 19% to 53% among those who died in the same year of the interview. Also, the number of hours of informal care per day increased from 2.0 to 4.7 in the last year of life.

In an aging population, where interventions potentially prolong life, severe disability is rather postponed to the last years of life. Therefore, proximity to death could be considered as a proxy of disability, which is an important determinant of informal care use. The overall aim of COMPAR-EU is to identify, compare and rank the most cost-effective self-management interventions for adults suffering from type 2 diabetes, obesity, chronic obstructive pulmonary disease, and heart failure. By using proximity to death to predict informal care use, we are able to take into account the impact that self-management interventions might have on the costs of informal care when they accomplish to prolong life.

Photo_Irene Santi

Irene Santi

Irene Santi, PhD is senior researcher at the institute for Medical Technology Assessment (iMTA) at Erasmus University. She holds an MS in Biology from the University of Genoa, Italy, a post-graduate specialization in Medical Statistics and Epidemiology from the University of Pavia, Italy and a PhD (Dr. Sc. Hum.) from the Medical Faculty of Heidelberg, Germany.

de_Groot_ Saskia

Saskia de Groot

Saskia de Groot is a Medior Researcher at iMTA. She holds a Master´s degree in Health Economics, Policy & Law from the Erasmus University Rotterdam, a Master´s degree in Clinical Epidemiology from the Netherlands Institute for Health Science of the Erasmus Medical Center and a PhD in Health Economics from the Erasmus University Rotterdam.

References

  1. Seshamani M, Gray A. The impact of ageing on expenditures in the National Health Service.
  2. Gheorghe M, Picavet S, Verschuren M, et al. Health losses at the end of life: a Bayesian mixed beta regression approach. J R Stat Soc Ser A (Statistics Soc 2017;180:723–49. doi:10.1111/RSSA.12230
  3. De Meijer C, Koopmanschap M, d’Uva TB, et al. Determinants of long-term care spending: Age, time to death or disability? J Health Econ 2011;30:425–38. doi:10.1016/J.JHEALECO.2010.12.010
  4. Börsch-Supan A, Team on behalf of the SCC, Brandt M, et al. Data Resource Profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). Int J Epidemiol 2013;42:992–1001. doi:10.1093/IJE/DYT088

Online self-management enhancing interventions; lessons learned to bear in mind

The COVID-19 pandemic has accelerated various processes in healthcare which previously proceeded slowly. At the one hand, the cancellation or postponement of medical visits and medical treatments forced many patients with various types of diseases to take care of their conditions themselves. Together with a heightened consciousness of the importance of staying healthy, this forced self-care boosted people’s self-management skills.

At the other hand, physical medical appointments were replaced by digital ones, forcing patients to get acquainted with web-based applications that facilitate video consultations and other online services. These services ranged from ordering repeat medication through the Internet to sending pictures of skin rash through a secured app. This paradigm shift took place in an incredibly fast speed and seemingly happened overnight.

Seemingly indeed, because already before we were confronted with the current pandemic, numerous online self-management enhancing interventions were developed and evaluated and, sometimes, implemented successfully. We learned a lot about how to develop such interventions together with the end-users in iterative processes. Still, the actual usage (uptake) and implementation of our thoroughly designed interventions, remained disappointingly low. I will illustrate this with a few examples of self-management enhancing PhD-projects in which I participated as supervisor. For patients with Rheumatoid Arthritis, we developed the program Vascular View (Puijk et al, 2017). Vascular View is a comprehensive, multi-component, tailored, web-based self-management support program for patients with cardiovascular disease (CVD).

Vascular View includes 6 modules, all identified through a thorough needs assessment among patients:

(1) Coping with CVD and its consequences;
(2) Setting boundaries in daily life;
(3) Lifestyle (general and tobacco and harmful alcohol use);
(4) Healthy nutrition;
(5) Being physically active in a healthy way; and
(6) Interaction with health professionals.

These modules were based on behavioral change techniques which were incorporated in the courses through general written information, quotes from and videos of patients with CVD, personalized feedback, diaries, and exercises. Unfortunately, our carefully conducted explorative RCT showed that, overall, the uptake of the program was low; 38% of the patients did not use the program or used it only once (Engelen et al, 2020). Similar results were found in another study in which we developed and tested an online tailored self-management enhancing program for patients with Rheumatoid Arthritis (RA) (Zuidema et al, 2015).

Program usage was low although we used several implementation strategies to increase the uptake:

(1) patients received a written instruction manual for the program,
(2) reminders to (re)visit the program were sent twice weekly via email, and (3) nurses brought the program to the attention of the intervention group participants during their consultation.

In our study we even noticed that patients in the intervention group dropped out more than patients in the control group.

We learned a lot (Zuidema et al, 2019) from these and our other studies (e.g., Sieben et al, 2019, 2020; du Pon et al, 2019). But the most important lesson to me is that chronic diseases like CVD, RA and especially diabetes type 1, already place a high burden on self-management; having to watch what you eat, to check your health outcomes throughout the day and take measures to remain within a safe range, requires a person to be aware of one’s bodily signs and symptoms 24/7, a full-time job for many people. When, on top of that, they are asked to also use an online self-management enhancing program with all kinds of tasks to accomplish, this may be too much. It does not leave any room to live beyond your disease.

We therefore need to think carefully about how much extra self-management people can endure; some might be able to integrate extra self-management efforts in their daily live, others may not (Sieben et al, 2020).

Therefore, the burden of a disease for an individual should be kept in mind when offering additional self-management interventions.

sandra

Sandra van Dulmen

Research coordinator at Nivel (Netherlands institute for health services research); Professor of Communication in healthcare at Radboud university medical center.

References

Engelen MM, Dulmen S van, Puijk-Hekman S, Vermeulen H, Bredie BJH, Nijhuis-van der Sanden MWG, Gaal BGI. Evaluating the web-based support program vascular view: Results from an explorative randomized controlled trial. JMIR 2020 Jul 24;22(7):e17422

Pon E du, Kleefstra N, Cleveringa F, Dooren A van, Heerdink ER, Dulmen S van. Effects of the Proactive Interdisciplinary Self-Management (PRISMA) program on self-reported and clinical outcomes in type 2 diabetes: A randomized controlled trial. BMC Endocrine Disorders 2019 Dec 11;19(1):139

Puijk-Hekman S, van Gaal BG, Bredie SJ, Nijhuis-van der Sanden MW, van Dulmen S. Self-management support program for patients with cardiovascular diseases: User-centered development of the tailored, web-based program Vascular View. JMIR research protocols 2017 Feb 08;6(2):e18

Sieben A, Onzenoort HAW van, Dulmen AM van, Laarhoven K van, Bredie SJH. A nurse-based intervention for improving medication adherence in cardiovascular patients: an evaluation of a randomized controlled trial. An integrated process and outcome evaluation of the MIRROR trial. Pat Pref Adh 2019:13 837–852

Sieben A, Onzenoort HAW van, Bredie SJH, Laarhoven CJHM van, Dulmen S van. Identification of cardiovascular patient groups at risk for poor medication adherence, a cluster analysis. The Journal of Cardiovascular Nursing 2020 (in press)

Zuidema RM, Gaal BGI van, Dulmen S van, Repping-Wuts H, Schoonhoven L. Development of an online tailored self-management program for patients with Rheumatoid Arthritis. JMIR ResProtoc 2015 Dec 25;4(4):e140

Zuidema R, Dulmen S van, Nijhuis- van der Sanden M, Meek I, Ende E van den, Fransen J, Gaal B van. Efficacy of an online self-management enhancing programme for patients with rheumatoid arthritis: an explorative RCT. J Med Internet Res 2019 Apr 30;21(4):e12463

Zuidema R, Dulmen S van, Gaal B van, Nijhuis-van der Sanden M, Fransen J. Lessons learned from patients with access to an online self-management enhancing program for RA patients: qualitative analysis of interviews alongside a randomized clinical trial. Patient Educ Couns 2019; 102: 1170-1177

COMPAR-EU Patient Panel Activities – 2021 Mid-Term Review

EPF leads the work of COMPAR-EU on eliciting patients’ priorities and preferences. In this role, EPF ensures that patient’s views, experience, gender, and socio-economic dimensions are accounted for and also guarantees meaningful patient involvement across various project outputs, tasks and activities. EPF is closely working together with all COMPAR-EU partners in order to embed and promote what matters to patients the most. To inform this work, EPF has set up a dedicated Patient Panel back in 2019. Since then, the Patient Panel regularly meets and through its work advises with first-hand experience and expertise on various project outputs. Furthermore, EPF representatives are part of the COMPAR-EU IT Platform Task Force and in constant coordination with other project partners in order to bring the patient perspective. Finally, in order to make the results of the project more accessible to non-expert audiences, EPF produces lay language summaries of key project documents and these will be translated in various European languages towards mid-2022.

Activities, Achievements and Ambitions of the Patient Panel in 2021

This year so far, members of the COMPAR-EU Patient Panel, together with EPF representatives, joined forces and focused their efforts on four key aspects related to patient involvement, co-design and patient engagement:

  • Four webinars – regular online meetings where members of the Patient Panel provide their input onto project’s activities
  • COMPAR-EU IT Platform Task Force – two members of the Patient Panel became a vital part of the Task Force dedicated to the development, design, implementation and of the Platform
  • Work on various project outputs and their translation into lay language continued
  • Kicked-off the process of translating the lay summaries into 8 European languages

A Quote from the Members of the Patient Panel

With self-management interventions patients are not only the “people being treated”, but they also become the person performing the treatments on themselves. Therefore, our role and importance as stakeholders are at least doubled in projects like COMPAR-EU.

Where to now – what is ahead for the Patient Panel in 2021?

Looking ahead, EPF is planning a range of activities focused on involving and empowering patients in 2021. EPF will continue to hold its monthly webinars with the Patient Panel to validate project materials and consult on planned activities. As with previous years, EPF will continue to adapt key project materials into lay language, but this year EPF will be coordinating the translation of these lay materials into the working languages of the project. Together with the Patient Panel, we will be producing one-page infographics and short white board videos to support the dissemination and communication of these materials. Lastly, as part of its work to ensure accessibility and sustainability, EPF will continue to contribute to the COMPAR-EU IT Platform testing, validation and review. In June, during the first Patient Panel Workshop for 2021, there will be a dedicated session to discuss a key output of the project – the Decision Aid Tool. During the session, survey results (done earlier in June) will be presented, followed by conducting semi-structured interviews with some members of the Patient Panel.

Stay tuned for more news in the fall of 2021.

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Lyudmil Ninov

Lyudmil is EPF´s Senior Programme Officer. He oversees project development, planning and costs monitoring for the following EPF projects: COMPAR-EU, H2O, DigitalHealthEurope and PERMIT. Prior to joining EPF, Lyudmil Ninov has spent most of his professional time working in the health care sector for the International Diabetes Federation’s head office in Brussels, managing various diabetes-related international projects.