Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next

Keywords Social care, social care data, economic evidence, decision modelling ALL Metrics - Views Downloads How to cite this article Huo D, Hinde S, Bojke L et al. Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.13789.3) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente Select a format first ▬ ✚ Review Revised Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] Dacheng Huo https://orcid.org/0009-0009-4204-5323 1, Sebastian Hinde https://orcid.org/0000-0002-7117-4142 1, Laura Bojke1, Annette Bauer https://orcid.org/0000-0001-5156-1631 2, Helen Weatherly https://orcid.org/0000-0002-9117-6452 1Dacheng Huo https://orcid.org/0009-0009-4204-5323 1, Sebastian Hinde https://orcid.org/0000-0002-7117-4142 1, [...] Laura Bojke1, Annette Bauer https://orcid.org/0000-0001-5156-1631 2, Helen Weatherly https://orcid.org/0000-0002-9117-6452 1 PUBLISHED 23 Oct 2025 Author details Author details 1 University of York Centre for Health Economics, York, England, UK 2 The London School of Economic and Political Science Care Policy and Evaluation Centre, London, England, UK 2 The London School of Economic and Political Science Care Policy and Evaluation Centre, London, England, UK Dacheng Huo Roles: Investigation, Methodology, Software, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Investigation, Methodology, Software, Writing – Original Draft Preparation, Writing – Review & Editing Sebastian Hinde Roles: Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Laura Bojke Roles: Methodology, Project Administration, Supervision, Writing – Review & Editing Roles: Methodology, Project Administration, Supervision, Writing – Review & Editing Annette Bauer Roles: Methodology, Writing – Review & Editing Roles: Methodology, Writing – Review & Editing Helen Weatherly Roles: Methodology, Writing – Review & Editing Roles: Methodology, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS In social care research economic evaluation has had limited impact, in contrast to other health related areas. However, increasing research funding and policy interest is occurring, including regarding the role of cost-effectiveness decision modelling. We explore why cost-effectiveness decision modelling is informative in a social care setting, how it can and has previously been implemented, and what next steps are needed to ensure consistent, robust, and informative models are produced to inform social care decisions. This paper consists of an overview of the theoretical added value of cost-effectiveness decision modelling in a social care setting, alongside a literature search summarising the key features of decision models in the current published and grey literature. Cost-effectiveness decision modelling in social care is relatively undeveloped with only a few examples identified and minimal methodological research in the area. These studies varied greatly in the approaches taken but demonstrate the practicality and value of decision modelling. The pragmatic approach to the literature review may have missed some existing decision models but we consider the findings to be appropriate. Cost-effectiveness decision modelling has the potential to play an important role in informing effective, consistent, and transparent decision-making processes in social care. However, methodological developments are needed to standardise the approaches taken. This article focuses on a method called "cost-effectiveness decision modelling," which can help improve decision-making in social care. Social care provides essential services to people who need help with daily activities due to age, illness, or disability. Even though economic evaluation is common in other areas of health, it hasn’t been widely used in social care, where the need for thoughtful use of limited resources is just as important. Recently, however, there’s been more interest in using cost-effectiveness models to inform decisions about which services provide the best value for the resources spent. In this article, we explore why decision modelling is important for social care, how it has been used in the past, and what needs to happen to make it a more reliable and useful tool. Decision modelling brings together information from different sources, like research studies and expert opinions, to help predict the costs and benefits of different services. By comparing different care options, decision modelling can show which services deliver the best outcomes for individuals and the community. Our review of existing research found that decision modelling in social care is still quite limited, with only a small number of examples available. The studies we found used different approaches, which makes it difficult to compare their results. However, these models show the potential of decision modelling to improve how resources are allocated in social care. To make this tool more effective, further research is needed to standardize the methods used and ensure that models are robust and reliable. With these improvements, decision modelling could play an important role in helping policymakers and practitioners make better, more informed decisions about social care services. Social care, social care data, economic evidence, decision modelling Corresponding Author(s) Dacheng Huo ([email protected]) Grant information: This project is funded by the National Institute for Health and Care Research (NIHR) under its Applied Research Collaboration (ARC) Yorkshire and Humber (Grant Reference Number NIHR200166). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The funder(s) and sponsor had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Huo D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Huo D, Hinde S, Bojke L et al. Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.13789.3) First published: 27 Jan 2025, 5:9 (https://doi.org/10.3310/nihropenres.13789.1) Latest published: 23 Oct 2025, 5:9 (https://doi.org/10.3310/nihropenres.13789.3) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This revised version improves the clarity and practical relevance of the paper. We have expanded the introduction to better explain why evidence-based and modelling approaches are less commonly used in social care, referring to factors such as fragmented funding, broader outcome measures, limited routine data, and the gap between national and local priorities. The Discussion now sets out specific ways in which decision modelling can contribute to social care research and practice, including supporting more efficient resource allocation, incorporating equity through distributional cost-effectiveness analysis, and improving comparability by suggesting a “minimum reporting set” for models. The conclusion has been restructured to present a clearer pathway for developing modelling in social care—focusing on standardising methods, reflecting social care outcomes, and considering equity—rather than ending with a general call for more research. We have also revised the Plain English Summary to make it clearer and more accessible This revised version improves the clarity and practical relevance of the paper. We have expanded the introduction to better explain why evidence-based and modelling approaches are less commonly used in social care, referring to factors such as fragmented funding, broader outcome measures, limited routine data, and the gap between national and local priorities. The Discussion now sets out specific ways in which decision modelling can contribute to social care research and practice, including supporting more efficient resource allocation, incorporating equity through distributional cost-effectiveness analysis, and improving comparability by suggesting a “minimum reporting set” for models. The conclusion has been restructured to present a clearer pathway for developing modelling in social care—focusing on standardising methods, reflecting social care outcomes, and considering equity—rather than ending with a general call for more research. We have also revised the Plain English Summary to make it clearer and more accessible We have expanded the introduction to better explain why evidence-based and modelling approaches are less commonly used in social care, referring to factors such as fragmented funding, broader outcome measures, limited routine data, and the gap between national and local priorities. The Discussion now sets out specific ways in which decision modelling can contribute to social care research and practice, including supporting more efficient resource allocation, incorporating equity through distributional cost-effectiveness analysis, and improving comparability by suggesting a “minimum reporting set” for models. The conclusion has been restructured to present a clearer pathway for developing modelling in social care—focusing on standardising methods, reflecting social care outcomes, and considering equity—rather than ending with a general call for more research. We have also revised the Plain English Summary to make it clearer and more accessible See the authors' detailed response to the review by Elizabeth Goodwin See the authors' detailed response to the review by Ian Litchfield and Melyda Melyda See the authors' detailed response to the review by Willem Kuyken Social care, also known as ‘long term care’ outside of the UK, provides social work, personal care, protection and social support services to children and adults who are deemed to be in need or at risk due to illness, disability, frailty, or poverty. As well as supporting service users or clients, it can support informal unpaid carers, such as family members of the service user. In the UK, social care receives funding through the statutory (public) sector, however supply is limited and need for care is routinely means tested, resulting in many users self-funding. The voluntary, community, and social enterprise as well as the private sector are important partners for statutory health and social care agencies in providing social care, meaning that public, private, and third sectors are all part of the delivery of social care. The objectives of adult social care in England, and responsibilities of local authorities in relation to social care, are set out in the Health and Social Care Act (Glover-Thomas, 2013). Key objectives of social care include supporting individuals to remain independent in the community, supporting their dignity, personal choice, and control over their daily lives, as well as supporting social-care related quality of life and safety. More broadly, social care supports the health care system with its role in prevention and reducing health care service use. Alternative outcome measures relevant to social care, such as the Adult Social Care Outcomes Toolkit (ASCOT), ICEpop CAPability measure of older people ICECAP-O, and ICECAP-A (for adults), provide valuable metrics for capturing the broader impacts of social care interventions beyond traditional health outcomes. Despite this potential and the well-established use of economic evaluation in health care, we believe its application within social care research and practice has been limited. The primary purpose of this paper is to identify further research needs in the field of economic evaluation within social care and to provide comprehensive guidance for researchers and practitioners interested in the methodologies of social care interventions. This paper highlights the potential value of modelling in the evaluation of social care service. Through a purposeful review of existing modelling studies, we aim to offer a clearer understanding of the current landscape, methodological strengths and gaps, and encourage the development and application of more rigorous, transparent, and context-appropriate modelling approaches in social care settings. Cost-effectiveness analysis, the summation of the costs and effects of alternative courses of action, is considered an informative component in the development of National Institute for Health and Care Excellence’s (NICE) guidance related to best social care provision in England (NICE, 2014). By seeking to estimate the value gained from resources used to implement an intervention or policy, cost-effectiveness analysis aims to determine the best alternative use of limited resources, and the benefits that would have been gained from taking the alternative course of action. The challenges of conducting robust cost-effectiveness analysis in social care, in contrast to the other areas such as health technology assessment, have previously been defined and discussed (Bauer et al., 2021; El-Banna et al., 2021; Suh & Holmes, 2022; Weatherly et al., 2017). The challenges include: defining the objectives of the intervention and services, and relatedly, the complexity of outcomes of interest, the range of stakeholders in the public, private, and third sectors, complexity of interventions, and availability of research and data. These challenges are especially evident when compared to the evaluation of new pharmaceuticals, for which the methodological approaches to conduct such evaluations were originally developed and for which the consideration of health maximisation subject to costs borne by the public healthcare system are considered sufficient (Drummond et al., 2015). Furthermore, reviews of cost-effectiveness analyses of social care interventions (Bauer et al., 2021; Weatherly et al., 2017) reveal that an increasing number are being undertaken in the social care field but with heterogeneity in methodological approaches used, raising the question of limited standardisation of evaluative methods. In addition to these methodological challenges, there are systemic reasons why evidence-based and modelling approaches have historically been less common in social care compared with healthcare. Social care provision is fragmented across public, private, and voluntary sectors, making it harder to generate and apply standardised evidence (Bauer et al., 2021; Weatherly et al., 2017). Outcomes of interest often extend beyond health gains to dimensions such as independence, dignity, and wellbeing, which are less amenable to conventional trial-based evidence (Gould & Kendall, 2007; Stuttard et al., 2021). Moreover, routine and longitudinal data collection in social care has traditionally been limited (Tinelli et al., 2020), and local authority commissioners often prioritise short-term affordability within constrained budgets rather than long-term efficiency gains (Hinde, 2024). These contextual features partly explain why evidence-based decision-making has been less embedded in social care practice, despite clear potential benefits. Establishing the cost-effectiveness of an intervention, broadly employs one of two approaches with which to generate findings: empirical data analysis from a single study and decision modelling (Drummond et al., 2015). The use of empirical data relies on evidence generated from sources such as clinical trials and observational and routinely collected data. In contrast, in decision modelling the pathways of care are simulated, and multiple data sources used to impute costs and benefits as they are assumed to occur at different decision-making points or key events (Briggs et al., 2006). This method allows extrapolating costs and effects beyond the time-period of an experimental study. This paper explores why cost-effectiveness decision modelling has the potential to be informative in a social care setting, how it can and has previously been implemented, and what next steps are needed to ensure consistent, robust, and informative models are produced to inform social care decisions. To do so, first we introduce decision modelling as an approach, exploring its distinctive features and aims and its conceptual value to inform decision making in social care. Through a purposeful review of existing published models, we consider how decision modelling has been operationalised in social care to date and finally seek to define a route forward to increase the usefulness and application of decision modelling in social care. To evaluate the effectiveness and cost-effectiveness of a commissioning decision it is necessary to understand what the aims and objectives are of all relevant stakeholders and consider how the cost and outcomes of interest to each are impacted by different available commissioning decisions (Drummond et al., 2015) Given the potential for social care interventions to have multiple decision makers, sectors, objectives, and budgets, this can be challenging. Decision modelling can address many of the conceptual and logistical challenges of conducting cost-effectiveness analyses of complex, real-world interventions. Decision, or ‘economic’ modelling, is a broad term to describe the use of mathematical analysis to simplify the complexity of real-world situations. In health economics decision modelling has been defined as the use of such analyses to estimate a series of possible health outcomes and resource use implications which would result from alternative interventions (Briggs et al., 2006). In practice, decision models are created by conceptualising and simplifying a complex reality in which interventions exist, focussing on the areas where a demonstrable difference exists between the alternative course of action. This simplification focuses on a limited number of definable states in which the individual can exist. These may be health states, e.g. pre-frail, frail, and dead, or service states, e.g. living at home, admitted to care home, admitted to hospital. Movement between these states, and the impact of each mode of care being evaluated on these transitions is then estimated, with the outcomes of interest and resource use implications of each state reported. The model can then be ‘run’ over the time-period considered relevant, and the sum of the outcomes and resource use implications estimated and compared. The approach is commonly implemented to inform the deliberations of a national commissioner of health services where the quality adjusted life years (QALYs) of the treated individual are the primary outcome of interest (Briggs et al., 2006). While a healthcare QALY based model may not be of primary interest in social care commissioning deliberations, a social care QALY based model might do (Stuttard et al., 2021). For instance, employing an outcome instrument like the ASCOT, which better reflects social care outcomes, permits assessment based on impacts to social care-related quality of life. This means evaluating services on improvements to independence, social connection, or control, rather than just clinical health metrics. Decision modelling as an approach is flexible so long as it is possible to conceptualise and estimate the relevant states individuals may occupy, the costs and outcomes associated with their membership, how they transition between them, and the impact of competing modes of care. Decision modelling approaches can be argued to have five key benefits in informing deliberations in any setting (Harris et al., 2015): 1) Bringing information together from multiple sources 2) Extrapolating over the longer term, and more specifically into the future 3) Making comparisons across relevant interventions – making more efficient use of data 4) Characterising and evidencing uncertainty 5) Informing multiple stakeholder perspectives In this section we explore the role of each of these elements related to social care decision making. A substantial challenge of conducting cost-effectiveness analysis in social care is the limited availability of evidence which can be considered robust in isolation, although this is improving (Tinelli et al., 2020) Moreover, in social care, evidence is often fragmented across different agencies and sectors, with data collected independently and for varying purposes. Decision models allow the integration of these disparate sources, including qualitative data, expert judgment, and observational studies, into a unified evaluative framework. This is particularly crucial in cases where traditional health-related metrics (e.g., QALYs) are insufficient to capture the full breadth of social care outcomes. However, decision modelling provides a framework to both synthesise evidence from a disparate range of sources but also to test the sensitivity of the conclusion to changes in the informative data. Such data may include trial data but could also include routinely collected data, expert opinion, or exploratory scenario analysis where little is currently known about the value of a parameter. Data suitable to inform cost-effectiveness analysis are currently less common in social care, with evidence on best practice having developed in unstructured ways that are experience-based rather than experiment-based (Gould & Kendall, 2007; Shields & Elvidge, 2020; Suh & Holmes, 2022). Furthermore, some have argued that as a field of research social care is not sufficiently developed to meaningfully develop what could be considered unbiased estimates through approaches such as RCTs, necessitating the use of alternative sources of evidence (Rosten, 2020). However, the social care economic evidence base is expanding (Tinelli et al., 2020) as seen in the ESSENCE compendium (described below). Additionally, more administrative data is available, and methods such as expert elicitation have been developed to inform the necessary structures and parameters of decision models which can be applied to a social care setting (Bojke et al., 2021). Many of the reasons for providing a service, be it social care or health care, are to improve longer-term outcomes. This may be to strengthen an individual’s activities of daily living over the next month, or to improve their chance of being robust to frailty in a decade’s time. Decision modelling allows this bridge between intervention today and outcomes over the longer-term to be made explicit, and for any necessary conditions for outcomes to be achieved, made clear (Knight et al., 2016). While in social care many of the interventions are designed to deal with immediate care needs, such as personal care, areas of intervention such as home adaptation and reablement services entail intervention now, to support living independently within the community and to improve potential outcomes in the future. While their aim may be to reduce the risk of catastrophic deterioration (requiring hospitalisation or institutional care) rather than any curative intent as is often applied in a health care setting, consideration of effect beyond the initial intervention period represents an important element in considering the value of a service. Social care can also delay access to more intensive services such as residential homes and may facilitate earlier discharge from hospital. Cost-effectiveness analyses which only include a limited set of the full range of competing modes of care risk drawing inefficient or erroneous conclusions by potentially recommending one option as cost-effective when better options may be available but were excluded from the analysis (NICE, 2013) for example in a two-armed trial which omits other relevant comparators. Decision modelling provides a framework to overcome these challenges through its ability to draw from multiple sources of evidence directly or use methodological approaches such as scenario analysis and meta-analysis. While not unique to the area, one of the challenges of social care is that it represents a complex set of modes of care, especially compared to pharmaceutical interventions which can easily be characterised into the medication and doses provided. Relevant to this, the NICE Social Care Guidance (NICE, 2013) recommends that all modes of care ‘routinely delivered by the public and non-public social care sector’ should be incorporated in any economic evaluation. Coupled with the challenges of conducting trial analysis in this setting such a requirement typically would necessitate the use of decision modelling. An important strength of cost-effectiveness analysis is its ability to inform decision makers about the level of uncertainty in conclusions drawn. Decision modelling facilitates this through probabilistic and deterministic sensitivity analyses. These approaches draw on information beyond the expected average costs and benefits of the relevant interventions to demonstrate the uncertainty around the conclusion of the evaluation. This uncertainty can be expressed in several ways including the probability of the decision being incorrect, the impact of such a wrong decision, and whether the cost and delay of additional research can be justified to reduce our uncertainty. Evidence to inform such analyses can be drawn from the primary source, for example a trial, wider evidence, or exploratory scenarios. The comparatively limited level of research evidence and level of complexity regarding causal pathways of care evident in social care implies the consideration of uncertainty is of importance. To be useful in the decision-making process, cost-effectiveness analysis must reflect the needs of the key stakeholders (Williams & Calnan, 1991). However, defining who the key stakeholders of an evaluation are in social care can be difficult, potentially spanning all sectors of the economy. Social care-related services are delivered by a range of providers, including various public sector agencies, commercial providers, the voluntary and community sector, and unpaid carers. Furthermore, services may be provided in-house or externally contracted, for example with the commissioning of vision rehabilitation services by local authorities (Longo et al., 2020). The costs are also borne by various public sector agencies, service users and their families. As a result, the payer-provider matrix is complex and differs across the range of social care services available; some social care is funded by the local authority, others by service users or a mixture of both, and some social care is provided by carers or volunteers. This is further complicated by often strong correlations between health and social care activities, for example the lack of social care provision may delay discharge of individuals from hospitals. By explicitly conceptualising the movement of an individual through a finite number of states and estimating the payoffs as these transitions occur, decision modelling allows the analysis to be re-run from different perspectives. For example, costs borne from a health care perspective can be recorded separately from a social care or a private individual payer’s perspective. In England, since 1999, NICE has provided national guidance on the promotion of good health and the prevention and treatment of ill health. Under the Health and Social Care Act 2012 NICE was given additional responsibility to develop guidance and quality standards for social care, making it the first health technology agency internationally to do so. As part of its remit NICE developed a reference case for the economic evaluation of interventions with a social care focus, a generalisable specification of the methods considered appropriate (NICE, 2014). The reference case has been summarised elsewhere, a key finding of which is that compared to HTA guidance, economic evaluation methods guidance for use in social care is much less prescriptive and the range of suggested methods much broader (Bauer et al., 2021; Weatherly et al., 2017). Published systematic reviews have shown that over recent years more economic evaluations of social care interventions have been undertaken, but that the scale is still limited (Tinelli et al., 2020; Weatherly et al., 2017). The latest general NICE reference case provides some guidance regarding the appropriate approach to conducting decision modelling to inform cost effectiveness analysis (NICE, 2014) and there are existing good practice guides when conducting decision modelling informed cost-effectiveness analysis which have focussed on health technology assessment (Briggs et al., 2006). However, the extent to which these are generalisable to social care of implemented is unclear. On the other hand, although several reviews have examined economic evaluations in the field of social care, they tend to concentrate on cost-effectiveness results within specific population groups or service areas, without providing a systematic analysis of the modelling methodologies employed. For example, Suh and Holmes (2022) critically reviewed existing cost-effectiveness research in children's social care, highlighting key methodological and policy challenges. El-Banna et al. (2021) conducted a systematic review of economic evaluations of children’s social care interventions, synthesising evidence across a range of programme types. Rizzo and Rowe (2016) focused on social work services for ageing populations, providing an updated review of their cost-effectiveness. As a result, there remains a lack of clarity regarding how modelling is conducted in the broader context of social care. Economics of Social Care Compendium (ESSENCE) Toolkit, funded by the NIHR School for Social Care Research (SSCR), is a comprehensive repository of economic evaluations and decision models in the adult social care sector (Tinelli et al., 2020). The project is designed to support evidence-based decision-making within England's social care system. It systematically gathered and built economic evidence that is relevant to the social care practice in England, with a strong emphasis on economic evaluations. The economic evidence included in the ESSENCE Toolkit was drawn from relevant databases identified through an iterative selection process guided by the project’s advisory group. A detailed account of the methodology—covering the glossary of terms and concepts employed, data sources, composition of the expert team, and criteria for assessing the type and robustness of the included evidence—is provided in Tinelli et al. (2020). A key strength of the ESSENCE Toolkit lies in its broad definition of adult social care, which is taken from a framework developed by National Audit Office (NAO, 2018). It includes economic evidence on a diverse set of interventions across various service user groups, settings, and caregiver contexts. Although the toolkit’s primary focus was adult social care, now it includes economic evidence on children's social care interventions too. Another advantage of the ESSENCE Toolkit is that it goes beyond simply summarizing existing evidence. It addresses gaps in the literature by generating new findings through modelling techniques, providing insights where evidence is lacking. To explore the scale and scope of decision modelling studies in social care, we conducted a purposeful review of published studies listed in the ESSENCE Toolkit. It provided a readily available evidence base, allowing for a targeted and efficient review aligned with our research objectives, in contrast to a broader, less focused search with uncertain additional value. The literature selected in this study are those that met the following criteria: 1) Conducted economic evaluations of social care interventions, using the NHS England definition of social care as practical support provided because of illness or disability (NHS, 2021). The broader term of ‘economic evaluation’ was used to ensure any relevant models which conducted similar analyses such as cost-benefit or cost-consequence were also included; and 2) employed decision modelling methods such as decision trees, Markov decision processes, or simulated modelling to estimate the performance of the programme. We are aware that there is also a literature describing the use of agent-based modelling in social care to, for example, predict social care demand (Gostoli & Silverman, 2019; Gostoli & Silverman, 2022) however these do not extend to cost-effectiveness analyses, nor do they evaluate specific interventions and thus for brevity were excluded from this review. However, they are informative when considering available methodological approaches to decision modelling in this setting. Future research should explore integrating agent-based models to capture the multifaceted nature of social care interventions. We conducted a quality assessment of the included studies using the Philip’s checklist criteria to evaluate the robustness of their decision modelling approaches. Despite the purposeful nature of this review, incorporating a quality assessment helps mitigate potential biases and provides a more comprehensive understanding of the existing literature, covering aspects such as model structure, data inputs, consistency checks, and uncertainty analysis (Philips et al., 2004). The reason the Philips' checklist was chosen over the Drummond checklist is that it provides a more specialized focus on the modelling evaluations (Min et al., 2021). This approach identified 55 studies from an initial search of the titles by criteria 1), with eight being considered relevant to the aims of this paper (Bauer et al., 2010; Bauer et al., 2017; Dixon et al., 2014; Mavranezouli et al., 2014; McDaid et al., 2017; Public Health England, 2018; Tong et al., 2017), one of which contained six independent models (McDaid et al., 2017). The purposeful nature of this review implied pragmatic inclusion and exclusion criteria but broadly the aim was to identify economic evaluations of social care interventions which incorporated some element of a clearly described decision model. We extracted data on key aspects such as the type of decision model used, perspective adopted, time horizon, data sources, measures of effect, uncertainty analysis, headline results, and identified challenges. This systematic extraction process ensured consistency and comprehensiveness in capturing relevant information from each study. The majority of the 55 studies identified as economic evaluations did not incorporate a decision modelling element, consisting of estimates of cost and benefits from primary data sources, for example trials. While not a complete description of the published decision models in social care we consider these eight studies to be an indication of the state of play in the discipline sufficient for the aims of this paper. A summary of the models produced is available in Supplementary Table 1 (Refer extended data), and the result of the quality check is presented in Supplementary Table 2 (Refer extended data). There is a wide range of approaches taken to modelling in social care, despite the relatively small number of available studies (see Supplementary Table 1). Approaches range from relatively simple decision trees exploring outcomes over a short time horizon (Bauer et al., 2010) to individual/client level simulations over the lifetime of the individual (Tong et al., 2017). In general, many of the decision models considered a short time horizon, for example a year, focussing on the period of intervention, with most limiting their horizon to five years or less. This was consistent with the model design and nature of the intervention being evaluated, with the case made that the costs and benefits would only occur over the short term, e.g. support at home services (Dixon et al., 2014) and falls prevention (Public Health England, 2018). The models included evidence from a wide range of sources, primarily the published literature but also evidence from trials (Cottrell et al., 2018; Mavranezouli et al., 2014), observational data (Dixon et al., 2014), and hypothetical values (Dixon et al., 2014) were used. Most model parameters were drawn from published literature or relevant trial data. While the more complex models reviewed included extensive uncertainty analysis, including probabilistic and scenario-based sensitivity analyses (Bauer et al., 2017; Cottrell et al., 2018; Tong et al., 2017) the majority of the models focussed on a single base-case analysis. Where it was presented, uncertainty was only explored in terms of impact of the headline results of the analysis with no exploration of advanced modelling approaches such as value of information methodologies or distributional cost-effectiveness analysis, methods which seek to estimate the value of investing in additional research and the differential impact of interventions by socio-economic factors respectively (Weatherly et al., 2017). As the Philips checklist shows, most evaluations clearly state perspectives, model structures, and justify data inputs. However, common limitations persist, particularly in addressing uncertainty, heterogeneity, and extrapolation methods. Few studies justify the exclusion of options or incorporate alternative structural assumptions, highlighting the need for more transparent handling of uncertainty and broader methodological rigour in social care economic modelling. Overall, the published decision models demonstrate the lack of availability of a single appropriate reference case to apply when deciding the optimal decision modelling approach, with a wide range of approaches taken to conduct economic evaluation but importantly no clear logic as to why the different approaches were selected. While reference was made to the complexity of social care decision making in some of the studies (e.g. McDaid et al. (2017) and Public Health England (2018)), few attempts were made to reflect this complexity in the decision model, with the focus most often on costs falling on the public sector, specifically NHS and personal social services, and benefits to the intervention recipients. In some cases, a broader perspective was additionally defined as a secondary analysis (Tong et al., 2017), however, the lack of any widely agreed approach to incorporating the wider implications of social care beyond the public sector cost and health implications makes the extension of any decision model challenging. However, incorporating broader economic evaluation methods such as cost-benefit analysis or cost-consequence analysis could provide a more comprehensive understanding of the multifaceted impacts of social care interventions. Economic evaluation is increasingly being applied in social care settings to provide valuable insights to inform the effective and cost-effective use of limited resources. Decision modelling represents a valuable tool to synthesise and analyse data for economic evaluation as it facilitates the incorporation of evidence from multiple sources, the extrapolation of evidence over a longer period than may be directly available, comparisons across all relevant interventions, and the characterisation of uncertainty. Modelling has been employed in social care programs with examples of highly complex models (Tong et al., 2017) and attempts to incorporate the complex set of stakeholders and relevant outcomes evident with social care delivery in England (McDaid et al., 2017). However, as demonstrated by our review, the overall scale of such models is limited, with only eight separate studies being identified as having implemented decision models relevant to a social care setting. There are many potential reasons for this lack of scale, but key challenges facing decision modelling in this setting can be considered in three elements: contextual, methodological, and evidential (Squires et al., 2016). In addition to the challenges facing economic evaluation of social care more broadly, and discussed elsewhere (Bauer et al., 2021; Weatherly et al., 2017), the contextualisation of the decision problem and care pathway represents a key challenge for decision modelling in this area. The wide variation in the form of intervention and real-world service provision makes summarising the decision problem in a robust way in the form of a decision model much more challenging in social care, with the risk being the issue is over simplified. While a key strength of decision modelling techniques is the ability to draw information from a range of sources including trial, observational, and expert elicited data, the complexity of the evidence requirement and relatively limited history of data collection in social care settings, limits the opportunity to conduct meaningful decision modelling in this setting. Efforts are, however, being made to better report and make available routine data in social care (e.g. The Catalogue of Social Care Data (CPEC, 2023)) and to increase the level of funding of research for example by NIHR in the UK (NIHR, 2022). In recent years there has been significant progress made in methodological developments related to conducting economic evaluation in more complex areas of health and social care provision, for example Skivington et al. (2021) provide a framework for developing and evaluating complex interventions and economic considerations are considered core in determining the comparative resource and outcome consequences of the interventions for those people and organisations affected. They suggest that broad economic approaches to evaluation such as cost-consequence analysis or cost-benefit analysis might be relevant to capture the full range of non-health as well as health costs and benefits across different sectors, and NICE (2013) (2014) also supports use of these economic evaluation approaches of complex intervention. Squires et al. (2016) have produced a similar framework when conducting decision modelling applied to public health. However, there are still numerous areas still requiring methodological development including the modelling of impacts across different sectors and spillover effects (e.g. accounting for the network of support including family and friends), the incorporation of social care specific outcome measures into decision modelling, and the relevant opportunity cost threshold in social care. While these are all issues that have contributed to the limited application of decision modelling in social care it is evident that it is not the fundamental nature of decision modelling to which they apply, rather to the broader challenges of conducting robust evaluations in a social care setting. As the models identified in the pragmatic review demonstrate, the use of decision modelling in social care is both possible and informative to the setting. Modelling is well used in these studies to highlight the potential costs and benefits of interventions and importantly identify where data is lacking (Dixon et al., 2014). In terms of ‘what next for decision modelling in social care’, our review has demonstrated that a range of economic evaluation methods and modelling approaches are being used, applied to social care. The range of methods used is consistent with NICE methods guidance in social care. As a tool for undertaking cost-effectiveness analysis, models offer a flexible and somewhat creative approach to undertake rigorous analysis, using available data to best serve the needs of the relevant stakeholders. However, while the current flexibility in approaches has ensured relevance of each decision model to the setting in which it is applied, care is needed that this is not to the detriment of comparison across the findings of each model. Furthermore, understanding how local authorities interpret the results of cost-effectiveness analyses is crucial. The national versus local perspective disconnect, articulated by Hinde et al. (2020). Local authorities often focus on cost savings and resource allocation, which may influence the prioritization of interventions. National CEA frameworks prioritising long-term QALY maximisation using fixed discount rates and specific thresholds often fail to address local concerns about short-term budgets, affordability risk, and broader social objectives like equity (Hinde, 2024). This necessitates specific adaptations in how models are built and reported for local audiences (e.g., scenario analyses on time horizons and explicit budget impact reporting) and requires training to focus on assessing the local applicability of any economic evidence presented. Providing clear guidelines on interpreting CEA results can also help mitigate potential misinterpretations and ensure informed decision-making. In the context of social care, relying solely on conventional outcome measures such as QALYs may not fully capture the primary benefits of many interventions. As such, alternative outcome measures with a better conceptual fit should be considered. Measures such as the ASCOT, SCRQoL and ICECAP, which reflects broader notions of capability wellbeing, may offer more appropriate frameworks for evaluating the outcomes of social care interventions (Stuttard et al., 2021). Beyond methodological advances, decision modelling can provide immediate practical value by identifying where scarce resources deliver the greatest long-term benefits. Services such as reablement, falls prevention, or home adaptations are relatively low-cost but can substantially reduce downstream health and care expenditure while enabling people to remain independent for longer. Embedding these examples into modelling not only highlights the relevance of cost-effectiveness analysis to commissioners but also demonstrates how modelling can guide smarter allocation of resources in practice. At the same time, substantial heterogeneity in current modelling approaches makes it difficult to compare findings across studies (Bauer et al., 2021; Weatherly et al., 2017). To address this, we propose that moving towards a “minimum reporting set”—including base-case cost-effectiveness results, local budget impact assessments, both short- and long-horizon scenarios, and distributional outcomes—could enhance transparency and comparability, while also making evidence more directly actionable for decision-makers. Another area ripe for future development is the inclusion of equity and distributional considerations within decision models. Current approaches largely focus on average outcomes, but there is increasing recognition that social care interventions often benefit different population subgroups in varying ways. Distributional cost-effectiveness analysis (DCEA) offers a structured way to examine how benefits and costs are distributed across groups defined by age, disability, or income, thereby helping policymakers to prioritise not only efficiency but also equity in resource allocation (Cookson et al., 2021). Incorporating DCEA distributional cost-effectiveness analysis into future models would allow for more nuanced conclusions about how benefits and costs are distributed across different socio-economic groups, ensuring that decision-makers can account for equity when prioritising resources. Some hybrid modelling approaches that have been widely used in the cost-effectiveness analysis of other areas such as vaccines and infectious diseases may also be useful for evaluating social care interventions. For example, although the studies by Gostoli and Silverman (2019; 2022) did not carry out a cost-effectiveness analysis, their use of ABM to predict social care demand shows that these models can be applied in this area. It might also be worthy to explore the value of some integrated models which incorporate the strength of other models such as discrete event simulation (DES), system dynamics (SD), and ABM etc. These models are helpful for evaluating complex interventions, especially those involving different groups of people and complicated service processes. Using such models in social care can make economic evaluations more realistic and useful for decision-makers. Social care systems are characterised by long-term trajectories, complex interactions, high heterogeneity, and intricate systems. Although this purposeful review didn’t capture the hybrid models value of alternative modelling methods may also be explored such as microsimulation models, discrete event simulation (DES), system dynamics (SD), and ABM. Microsimulation captures individual-level heterogeneity and long-term outcomes; DES focuses on resource flows and service processes; SD highlights systemic feedback loops and delayed effects; and ABM models micro-level behavioural interactions that shape macro-level system patterns. These models allow for a more nuanced simulation of interventions, particularly those with high heterogeneity or involve complex service delivery pathways. This review adopted a pragmatic approach, which may have resulted in some relevant decision models being overlooked. However, we believe the findings are still appropriate, as the ESSENCE Toolkit provided a strong foundation for evidence gathering. Another limitation is related to the definition of social care used in this study. Due to the lack of a consistent definition in the literature, we followed the broad definition adopted by ESSENCE. While this allowed us to include a wide range of relevant studies and modelling techniques, it also means that some of the interventions reviewed may not fall within the narrower, working definition of social care—typically limited to services commissioned or delivered by local authority social services departments. Although there may be some overlap with other sectors, this broader scope also provides a useful overview of the current landscape of modelling in social care. George Box’s eponymous statement that ‘all models are wrong; some are useful’ could be argued to never have been as fitting as in the setting of decision modelling in social care, a setting in which complexity and immaturity of data and research combine to limit the ability to create robust decision models. However, as has been shown through the models that do exist in this setting, there is simultaneously significant potential for decision modelling approaches to provide clarity to evaluations in this setting. As increasing research funding (NIHR, 2022), policy interest, and routine data collection develops, decision modelling will represent an important approach to undertaking economic evaluation in the field. To strengthen its role, we propose a pathway with three priorities: (i) standardisation of methods and reporting to improve transparency and comparability; (ii) explicit incorporation of outcomes that capture the distinctive aims of social care; and (iii) systematic use of distributional analyses to reflect equity considerations. These steps would enhance the reliability and policy relevance of decision modelling, supporting more consistent and evidence-informed decision-making in social care. No data associated with this article. Figshare: Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next, DOI: 10.6084/m9.figshare.27612558 The project contains the following extended data: Literature Table - Summary of existing social care decision models Quality Assessment – Philip Checklist Results Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Faculty Opinions recommendedReferences - Bauer A, Fernández JL, Knapp M, et al.: Economic evaluation of an expert by experience model in the Basildon district. 2010. Reference Source - Bauer A, Knapp M, Wistow G, et al.: Costs and economic consequences of a help-at-home scheme for older people in England. Health Soc Care Community. 2017; 25(2): 780–789. 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PubMed Abstract | Publisher Full Text Author details Author details 1 University of York Centre for Health Economics, York, England, UK 2 The London School of Economic and Political Science Care Policy and Evaluation Centre, London, England, UK 2 The London School of Economic and Political Science Care Policy and Evaluation Centre, London, England, UK Dacheng Huo Roles: Investigation, Methodology, Software, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Investigation, Methodology, Software, Writing – Original Draft Preparation, Writing – Review & Editing Sebastian Hinde Roles: Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Laura Bojke Roles: Methodology, Project Administration, Supervision, Writing – Review & Editing Roles: Methodology, Project Administration, Supervision, Writing – Review & Editing Annette Bauer Roles: Methodology, Writing – Review & Editing Roles: Methodology, Writing – Review & Editing Helen Weatherly Roles: Methodology, Writing – Review & Editing Roles: Methodology, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This project is funded by the National Institute for Health and Care Research (NIHR) under its Applied Research Collaboration (ARC) Yorkshire and Humber (Grant Reference Number NIHR200166). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The funder(s) and sponsor had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (3) Copyright © 2025 Huo D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. metrics VIEWS $counts.viewCount downloads Citations CITE how to cite this article Huo D, Hinde S, Bojke L et al. Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.13789.3) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. track receive updates on this article Track an article to receive email alerts on any updates to this article. Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions Version 3 VERSION 3 PUBLISHED 23 Oct 2025 Revised Views 0 How to cite this report: Manthorpe J. Reviewer Report For: Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.15380.r39041) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/5-9/v3#referee-response-39041 https://openresearch.nihr.ac.uk/articles/5-9/v3#referee-response-39041 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 10 Jan 2026 Approved VIEWS 0 Thank you for the opportunity to read this interesting paper. It makes a strong case for increased and more impactful economic evaluation of social care services and options. The summary of studies will be helpful to others, and the supplementary ... Continue reading I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close Thank you for the opportunity to read this interesting paper. It makes a strong case for increased and more impactful economic evaluation of social care services and options. The summary of studies will be helpful to others, and the supplementary material useful in summarising the current evidence. There are few comments to make, but perhaps the authors might consider their language so that it chimes with social care perspectives of inclusion and thus makes their arguments more meaningful. For example, people are not 'admitted' to care homes and references to this as being 'institutional care' undermine efforts to be person-centred and the exercise of choice. For economists, residential care may be 'intensive' but this is not generally experienced as such, expensive maybe ... There are a few remaining typos in the text eg in the paragraph starting 'The latest general NICE ...' the end of this paragraph would seem to need OR not OF. Programmes are generally used in UK social care not programs. The term 'personal social services' is very dated now and only used in rarefied documents or data. Elsewhere social care is used appropriately, so if PPS is being used to mean LA funded services that could be said, not forgetting of course that many people pay for these or have state benefits reduced if in receipt. The discussion centres round three helpful themes. There might be a case for economists to be more social care 'aware' through professional development and early education/training. This may be encouraged by the investment in social care research but earlier equipping of economists with social care awareness might be helpful and of course more attention in social care skills development to economics would likely be mutually helpful. There are a few remaining typos in the text eg in the paragraph starting 'The latest general NICE ...' the end of this paragraph would seem to need OR not OF. Programmes are generally used in UK social care not programs. The term 'personal social services' is very dated now and only used in rarefied documents or data. Elsewhere social care is used appropriately, so if PPS is being used to mean LA funded services that could be said, not forgetting of course that many people pay for these or have state benefits reduced if in receipt. The discussion centres round three helpful themes. There might be a case for economists to be more social care 'aware' through professional development and early education/training. This may be encouraged by the investment in social care research but earlier equipping of economists with social care awareness might be helpful and of course more attention in social care skills development to economics would likely be mutually helpful. - Is the topic of the review discussed comprehensively in the context of the current literature? Yes - Are all factual statements correct and adequately supported by citations? Yes - Is the review written in accessible language? Yes - Are the conclusions drawn appropriate in the context of the current research literature? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: social care CITE HOW TO CITE THIS REPORT Manthorpe J. Reviewer Report For: Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.15380.r39041) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/5-9/v3#referee-response-39041 https://openresearch.nihr.ac.uk/articles/5-9/v3#referee-response-39041 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. Version 2 VERSION 2 PUBLISHED 17 Jul 2025 Revised Views 0 How to cite this report: Kuyken W. Reviewer Report For: Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.15245.r36694) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/5-9/v2#referee-response-36694 https://openresearch.nihr.ac.uk/articles/5-9/v2#referee-response-36694 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 08 Sep 2025 Not Approved VIEWS 0 This report reviews how cost-effectiveness decision modelling could improve decision-making in social care, where resources are scarce and this approach is rare. Only a small number of social care studies have used such models, and they vary so much in method ... Continue reading I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close This report reviews how cost-effectiveness decision modelling could improve decision-making in social care, where resources are scarce and this approach is rare. Only a small number of social care studies have used such models, and they vary so much in method that results can’t easily be compared, though they do show the approach’s potential value. The authors argue that standardized methods and stronger evidence are needed to make decision modelling a reliable tool for guiding policy and practice in social care. I found the "evidence is limited, more research needed" rather disappointing. The exploration of why social care seems to use evidence-based approaches much less also seemed a glaring omission, as without that deeper exploration a conclusion "more research is needed" is very unlikely to have any impact. Given NIHR's brief to use research impact health and social care this seems a missed opportunity. While the review is robust, the discussion could do more to explore promising frameworks, approaches that are likely to yield benefits in social care, nothing of course that this needs further research. Obvious low hanging fruit are: Smarter allocation of scarce resources – Decision modelling can reveal which services (like reablement, falls prevention, or home adaptations) give the best long-term outcomes for the money. That means fewer wasted resources and more people receiving interventions that keep them independent longer. Fairness and equity – Newer modelling approaches (e.g., distributional cost-effectiveness analysis) can show how benefits are spread across different groups (e.g., by age, disability, or income). This allows decision-makers to prioritize not just efficiency but also fairness in service provision. The plain English summary could be much improved if a good PPI group were involved. I used OpenAI 5.0 and it generated this: "This article looks at a way of making choices in social care called “cost-effectiveness decision modelling.” Social care is about helping people who need support with daily life because of age, illness, or disability. Money and resources are limited, so it’s important to spend them wisely. While this kind of analysis is already common in healthcare, it hasn’t been used much in social care until recently. The article explains: To make it more reliable, more research is needed. The methods need to be standardised (so everyone uses the same approach) and tested to make sure they give trustworthy results. If that happens, decision modelling could become an important tool to guide better choices in social care." I found the "evidence is limited, more research needed" rather disappointing. The exploration of why social care seems to use evidence-based approaches much less also seemed a glaring omission, as without that deeper exploration a conclusion "more research is needed" is very unlikely to have any impact. Given NIHR's brief to use research impact health and social care this seems a missed opportunity. While the review is robust, the discussion could do more to explore promising frameworks, approaches that are likely to yield benefits in social care, nothing of course that this needs further research. Obvious low hanging fruit are: Smarter allocation of scarce resources – Decision modelling can reveal which services (like reablement, falls prevention, or home adaptations) give the best long-term outcomes for the money. That means fewer wasted resources and more people receiving interventions that keep them independent longer. Fairness and equity – Newer modelling approaches (e.g., distributional cost-effectiveness analysis) can show how benefits are spread across different groups (e.g., by age, disability, or income). This allows decision-makers to prioritize not just efficiency but also fairness in service provision. The plain English summary could be much improved if a good PPI group were involved. I used OpenAI 5.0 and it generated this: "This article looks at a way of making choices in social care called “cost-effectiveness decision modelling.” Social care is about helping people who need support with daily life because of age, illness, or disability. Money and resources are limited, so it’s important to spend them wisely. While this kind of analysis is already common in healthcare, it hasn’t been used much in social care until recently. The article explains: - Why this method matters: It can help decide which services give the best results for the money spent. - How it works: It combines information from studies, expert opinions, and other sources to predict the costs and benefits of different services. - What it can show: By comparing options, it can highlight which services give the best outcomes for both individuals and the wider community. To make it more reliable, more research is needed. The methods need to be standardised (so everyone uses the same approach) and tested to make sure they give trustworthy results. If that happens, decision modelling could become an important tool to guide better choices in social care." - Is the topic of the review discussed comprehensively in the context of the current literature? Yes - Are all factual statements correct and adequately supported by citations? Yes - Is the review written in accessible language? Partly - Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Translational mental health research and impact CITE HOW TO CITE THIS REPORT Kuyken W. Reviewer Report For: Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.15245.r36694) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/5-9/v2#referee-response-36694 https://openresearch.nihr.ac.uk/articles/5-9/v2#referee-response-36694 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. - Author Response 23 Oct 2025Dacheng Huo, University of York Centre for Health Economics, York, UK23 Oct 2025Author ResponseDear Reviewer, Thank you very much for your thoughtful and constructive comments. We found your feedback extremely valuable in strengthening both the argument and the practical relevance of our ... Continue reading Dear Reviewer, Thank you very much for your thoughtful and constructive comments. We found your feedback extremely valuable in strengthening both the argument and the practical relevance of our paper. Please see our responses below: On why social care seems to use evidence-based approaches much less We fully agree that our initial manuscript did not adequately explore this. We have now expanded the introduction to discuss several reasons: the fragmentation of funding and provision across public, private, and voluntary sectors; the broader outcomes of interest (e.g., independence, dignity, wellbeing) which are less amenable to standardised measurement; limited availability of longitudinal or routine data; and the differences between national policy goals and local authority budget priorities. We also highlight how these contextual barriers have shaped the relative lack of decision modelling in social care to date. On exploring promising frameworks and approaches We agree this is a valuable direction. In the revised Discussion we now go beyond “more research needed” and identify concrete ways in which modelling can add value: On the robustness of the review but depth of discussion Smarter allocation of scarce resources – Decision modelling can demonstrate which services (such as reablement, falls prevention, or home adaptations) provide the best long-term outcomes for money spent, thereby reducing waste and extending independence. Fairness and equity – We emphasise the potential of newer approaches such as distributional cost-effectiveness analysis (DCEA) to highlight how benefits and costs are distributed across subgroups defined by age, disability, or income, helping policymakers prioritise both efficiency and equity. Improved comparability – We suggest a “minimum reporting set” for models (including base-case analysis, local budget impact, short/long horizon scenarios, and distributional results), which could make findings more actionable for decision-makers. On the conclusion We have restructured the conclusion, so it does not end with a generic call for “more research needed.” Instead, we propose a pathway: standardisation of methods, explicit incorporation of social care outcomes, and inclusion of distributional analyses. This reframing emphasises that decision modelling can become a practical tool for guiding better social care decisions once these steps are taken. On the Plain English Summary We appreciate your feedback and have substantially revised the Plain English Summary. The revised version now uses clearer language, includes concrete examples (reablement, falls prevention, home adaptations), and highlights the importance of fairness in resource allocation. We also note that we will seek input from people with lived experience and practitioners (PPI) to further improve accessibility and resonance. We are very grateful for your guidance, which has enabled us to make the paper stronger, more concrete, and more useful for both academic and policy audiences. Sincerely, Dacheng HuoDear Reviewer,Competing Interests: No competing interests were disclosed. Close Thank you very much for your thoughtful and constructive comments. We found your feedback extremely valuable in strengthening both the argument and the practical relevance of our paper. Please see our responses below: On why social care seems to use evidence-based approaches much less We fully agree that our initial manuscript did not adequately explore this. We have now expanded the introduction to discuss several reasons: the fragmentation of funding and provision across public, private, and voluntary sectors; the broader outcomes of interest (e.g., independence, dignity, wellbeing) which are less amenable to standardised measurement; limited availability of longitudinal or routine data; and the differences between national policy goals and local authority budget priorities. We also highlight how these contextual barriers have shaped the relative lack of decision modelling in social care to date. On exploring promising frameworks and approaches We agree this is a valuable direction. In the revised Discussion we now go beyond “more research needed” and identify concrete ways in which modelling can add value: On the robustness of the review but depth of discussion Smarter allocation of scarce resources – Decision modelling can demonstrate which services (such as reablement, falls prevention, or home adaptations) provide the best long-term outcomes for money spent, thereby reducing waste and extending independence. Fairness and equity – We emphasise the potential of newer approaches such as distributional cost-effectiveness analysis (DCEA) to highlight how benefits and costs are distributed across subgroups defined by age, disability, or income, helping policymakers prioritise both efficiency and equity. Improved comparability – We suggest a “minimum reporting set” for models (including base-case analysis, local budget impact, short/long horizon scenarios, and distributional results), which could make findings more actionable for decision-makers. On the conclusion We have restructured the conclusion, so it does not end with a generic call for “more research needed.” Instead, we propose a pathway: standardisation of methods, explicit incorporation of social care outcomes, and inclusion of distributional analyses. This reframing emphasises that decision modelling can become a practical tool for guiding better social care decisions once these steps are taken. On the Plain English Summary We appreciate your feedback and have substantially revised the Plain English Summary. The revised version now uses clearer language, includes concrete examples (reablement, falls prevention, home adaptations), and highlights the importance of fairness in resource allocation. We also note that we will seek input from people with lived experience and practitioners (PPI) to further improve accessibility and resonance. We are very grateful for your guidance, which has enabled us to make the paper stronger, more concrete, and more useful for both academic and policy audiences. Sincerely, Dacheng Huo COMMENTS ON THIS REPORT - Author Response 23 Oct 2025Dacheng Huo, University of York Centre for Health Economics, York, UK23 Oct 2025Author ResponseDear Reviewer, Thank you very much for your thoughtful and constructive comments. We found your feedback extremely valuable in strengthening both the argument and the practical relevance of our ... Continue reading Dear Reviewer, Thank you very much for your thoughtful and constructive comments. We found your feedback extremely valuable in strengthening both the argument and the practical relevance of our paper. Please see our responses below: On why social care seems to use evidence-based approaches much less We fully agree that our initial manuscript did not adequately explore this. We have now expanded the introduction to discuss several reasons: the fragmentation of funding and provision across public, private, and voluntary sectors; the broader outcomes of interest (e.g., independence, dignity, wellbeing) which are less amenable to standardised measurement; limited availability of longitudinal or routine data; and the differences between national policy goals and local authority budget priorities. We also highlight how these contextual barriers have shaped the relative lack of decision modelling in social care to date. On exploring promising frameworks and approaches We agree this is a valuable direction. In the revised Discussion we now go beyond “more research needed” and identify concrete ways in which modelling can add value: On the robustness of the review but depth of discussion Smarter allocation of scarce resources – Decision modelling can demonstrate which services (such as reablement, falls prevention, or home adaptations) provide the best long-term outcomes for money spent, thereby reducing waste and extending independence. Fairness and equity – We emphasise the potential of newer approaches such as distributional cost-effectiveness analysis (DCEA) to highlight how benefits and costs are distributed across subgroups defined by age, disability, or income, helping policymakers prioritise both efficiency and equity. Improved comparability – We suggest a “minimum reporting set” for models (including base-case analysis, local budget impact, short/long horizon scenarios, and distributional results), which could make findings more actionable for decision-makers. On the conclusion We have restructured the conclusion, so it does not end with a generic call for “more research needed.” Instead, we propose a pathway: standardisation of methods, explicit incorporation of social care outcomes, and inclusion of distributional analyses. This reframing emphasises that decision modelling can become a practical tool for guiding better social care decisions once these steps are taken. On the Plain English Summary We appreciate your feedback and have substantially revised the Plain English Summary. The revised version now uses clearer language, includes concrete examples (reablement, falls prevention, home adaptations), and highlights the importance of fairness in resource allocation. We also note that we will seek input from people with lived experience and practitioners (PPI) to further improve accessibility and resonance. We are very grateful for your guidance, which has enabled us to make the paper stronger, more concrete, and more useful for both academic and policy audiences. Sincerely, Dacheng HuoDear Reviewer,Competing Interests: No competing interests were disclosed. Close Thank you very much for your thoughtful and constructive comments. We found your feedback extremely valuable in strengthening both the argument and the practical relevance of our paper. Please see our responses below: On why social care seems to use evidence-based approaches much less We fully agree that our initial manuscript did not adequately explore this. We have now expanded the introduction to discuss several reasons: the fragmentation of funding and provision across public, private, and voluntary sectors; the broader outcomes of interest (e.g., independence, dignity, wellbeing) which are less amenable to standardised measurement; limited availability of longitudinal or routine data; and the differences between national policy goals and local authority budget priorities. We also highlight how these contextual barriers have shaped the relative lack of decision modelling in social care to date. On exploring promising frameworks and approaches We agree this is a valuable direction. In the revised Discussion we now go beyond “more research needed” and identify concrete ways in which modelling can add value: On the robustness of the review but depth of discussion Smarter allocation of scarce resources – Decision modelling can demonstrate which services (such as reablement, falls prevention, or home adaptations) provide the best long-term outcomes for money spent, thereby reducing waste and extending independence. Fairness and equity – We emphasise the potential of newer approaches such as distributional cost-effectiveness analysis (DCEA) to highlight how benefits and costs are distributed across subgroups defined by age, disability, or income, helping policymakers prioritise both efficiency and equity. Improved comparability – We suggest a “minimum reporting set” for models (including base-case analysis, local budget impact, short/long horizon scenarios, and distributional results), which could make findings more actionable for decision-makers. On the conclusion We have restructured the conclusion, so it does not end with a generic call for “more research needed.” Instead, we propose a pathway: standardisation of methods, explicit incorporation of social care outcomes, and inclusion of distributional analyses. This reframing emphasises that decision modelling can become a practical tool for guiding better social care decisions once these steps are taken. On the Plain English Summary We appreciate your feedback and have substantially revised the Plain English Summary. The revised version now uses clearer language, includes concrete examples (reablement, falls prevention, home adaptations), and highlights the importance of fairness in resource allocation. We also note that we will seek input from people with lived experience and practitioners (PPI) to further improve accessibility and resonance. We are very grateful for your guidance, which has enabled us to make the paper stronger, more concrete, and more useful for both academic and policy audiences. Sincerely, Dacheng Huo Views 0 How to cite this report: Goodwin E. Reviewer Report For: Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.15245.r36321) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/5-9/v2#referee-response-36321 https://openresearch.nihr.ac.uk/articles/5-9/v2#referee-response-36321 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 27 Aug 2025 Approved VIEWS 0 No ... Continue reading I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close No further comments Competing Interests: No competing interests were disclosed. Reviewer Expertise: Health economics; economic evaluation of complex interventions; measurement of health and wellbeing outcomes. Prior to my academic career, I worked in planning, performance and project management for a local authority social services department. CITE HOW TO CITE THIS REPORT Goodwin E. Reviewer Report For: Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.15245.r36321) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/5-9/v2#referee-response-36321 https://openresearch.nihr.ac.uk/articles/5-9/v2#referee-response-36321 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. Version 1 VERSION 1 PUBLISHED 27 Jan 2025 Views 0 How to cite this report: Goodwin E. Reviewer Report For: Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.14977.r34705) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/5-9/v1#referee-response-34705 https://openresearch.nihr.ac.uk/articles/5-9/v1#referee-response-34705 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 26 Feb 2025 Approved with Reservations VIEWS 0 Thank you for the opportunity to review this interesting paper on the use of decision modelling in the context of social care. This is a relevant and timely discussion, given the increasing interest in how economic evaluation methods can be ... Continue reading I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close Thank you for the opportunity to review this interesting paper on the use of decision modelling in the context of social care. This is a relevant and timely discussion, given the increasing interest in how economic evaluation methods can be applied to complex interventions that may sit partially or wholly outside the traditional “health” arena, and the extension of the remit of NICE to encompass adult and children’s social care. The paper explores the potential role of decision modelling in the evaluation of social care interventions before presenting a review that aims to illustrate the current state of play. This forms the basis for a discussion of key challenges and areas for future research and methodological development. This is a clear and well-written paper, which offers a useful contribution to the growing literature in this area, however I do have some suggestions for improvement. My primary concern is that there appears to be a mismatch between the content of the review and the rest of the paper; this is an issue that must be addressed to make the article scientifically sound. While the paper is framed throughout as focusing on social care interventions, this focus is not reflected in the studies included in the review. As a result of this, I have unfortunately concluded that the topic of the review has only partly been discussed in the context of the literature, and the conclusions drawn are only partly appropriate. The inclusion criteria for the review state that social care was defined as “practical support provided because of illness or disability”. In the UK, however, the working definition of social care is much more specific, and is typically limited to the types of interventions that may be commissioned or delivered by local authority social services departments. A number of the interventions included in this review, as described in Table 1 and in the original publications, do not meet this definition. For example, the six interventions reported by McDaid et al (2017) are public health interventions, the cognitive screening tests (Tong et al, 2017) are conducted by GPs in a primary care setting, and supported employment (Mavranezouli et al, 2014) is more likely to fall under the remit of the Department for Work and Pensions, eg via Job Centres. This is an important distinction, given that there are fundamental differences between social care and these other sectors in terms of their statutory responsibilities, primary intended outcomes, funding arrangements and evaluation guidelines. As such, it seems to me that this review covers the broader use of cost-effectiveness decision modelling in the evaluation of complex interventions that sit either partially or wholly outside the health sector (although under this definition it remains difficult to justify the inclusion of the cognitive screening tests), rather than its more specific use in the evaluation of social care interventions. I suggest either reframing the stated aims and focus of the paper along these lines, or limiting the included studies to those evaluating interventions that fall within the generally accepted definition and remit of social care. Other suggestions: My primary concern is that there appears to be a mismatch between the content of the review and the rest of the paper; this is an issue that must be addressed to make the article scientifically sound. While the paper is framed throughout as focusing on social care interventions, this focus is not reflected in the studies included in the review. As a result of this, I have unfortunately concluded that the topic of the review has only partly been discussed in the context of the literature, and the conclusions drawn are only partly appropriate. The inclusion criteria for the review state that social care was defined as “practical support provided because of illness or disability”. In the UK, however, the working definition of social care is much more specific, and is typically limited to the types of interventions that may be commissioned or delivered by local authority social services departments. A number of the interventions included in this review, as described in Table 1 and in the original publications, do not meet this definition. For example, the six interventions reported by McDaid et al (2017) are public health interventions, the cognitive screening tests (Tong et al, 2017) are conducted by GPs in a primary care setting, and supported employment (Mavranezouli et al, 2014) is more likely to fall under the remit of the Department for Work and Pensions, eg via Job Centres. This is an important distinction, given that there are fundamental differences between social care and these other sectors in terms of their statutory responsibilities, primary intended outcomes, funding arrangements and evaluation guidelines. As such, it seems to me that this review covers the broader use of cost-effectiveness decision modelling in the evaluation of complex interventions that sit either partially or wholly outside the health sector (although under this definition it remains difficult to justify the inclusion of the cognitive screening tests), rather than its more specific use in the evaluation of social care interventions. I suggest either reframing the stated aims and focus of the paper along these lines, or limiting the included studies to those evaluating interventions that fall within the generally accepted definition and remit of social care. Other suggestions: - The methods employed to identify papers for inclusion in the review could be reported in greater detail. More information on search terms and strategies, and on the inclusion/exclusion criteria and how these were applied, would strengthen the paper. This could be provided as Supplementary Material. - The authors mention using the Drummond Checklist to assess the quality of the included studies, however the findings are not reported. A summary of this in the text would be informative, perhaps supported by an additional Supplementary Table. - There are several factual statements throughout the paper that are not supported by citations; please can these be added. - Is the topic of the review discussed comprehensively in the context of the current literature? Partly - Are all factual statements correct and adequately supported by citations? Partly - Is the review written in accessible language? Yes - Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Health economics; economic evaluation of complex interventions; measurement of health and wellbeing outcomes. Prior to my academic career, I worked in planning, performance and project management for a local authority social services department. CITE HOW TO CITE THIS REPORT Goodwin E. Reviewer Report For: Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.14977.r34705) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/5-9/v1#referee-response-34705 https://openresearch.nihr.ac.uk/articles/5-9/v1#referee-response-34705 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. - Author Response 11 Sep 2025Dacheng Huo, University of York Centre for Health Economics, York, UK11 Sep 2025Author ResponseThank you for your constructive review and for acknowledging the relevance and contribution of our paper. Your comments have been very helpful in refining the clarity, scope, and positioning of ... Continue reading Thank you for your constructive review and for acknowledging the relevance and contribution of our paper. Your comments have been very helpful in refining the clarity, scope, and positioning of our work. Below, we provide responses to each of your suggestions. Scope of the review and definition of social care Thank you for raising this important point. We agree that conceptual clarity around what constitutes “social care” is essential, especially given the boundaries between health, public health, and social care in the UK context. In our review, we deliberately based the inclusion criteria on the scope of the ESSENCE Toolkit, a resource developed by the NIHR School for Social Care Research to support evidence-informed decision-making in adult social care. The Toolkit uses a broad but policy-relevant definition of social care derived from the National Audit Office (2018) framework, encompassing a range of services that provide practical support due to illness, disability, or age-related needs. This includes not only traditional local authority-provided care services, but also interventions related to housing, support, and community-based services that contribute to social care outcomes and resource use. We chose to adopt this framework for two reasons:- First, the ESSENCE Toolkit is a curated, peer-reviewed source specifically intended to inform adult social care policy and commissioning decisions in England, making it directly relevant to our research aims. - Second, the definition used in ESSENCE reflects the operational reality of social care delivery, where services are often delivered in partnership with or overlap with other sectors (e.g. health, employment, housing). These interventions, while not always under local authority remit, are frequently commissioned to improve outcomes for social care service users. We believe that this approach is methodologically appropriate and well aligned with the aims of our paper—to understand how decision modelling is being used (or underused) in the broader social care context, and to identify where future methodological work is most needed. Response to Other suggestions:- Methods: search process and inclusion criteria - Quality assessment reporting - Supporting citations We are pleased to hear that you appreciated the inclusion of the George Box quotation. We have retained it in the revised version, as we agree it effectively captures the role of modelling in complex systems such as social care. We hope the revisions and clarifications made in response to your comments are satisfactory. Your feedback has been instrumental in improving the quality, clarity, and focus of our manuscript, and we are sincerely grateful for your contribution.Thank you for your constructive review and for acknowledging the relevance and contribution of our paper. Your comments have been very helpful in refining the clarity, scope, and positioning of our work. Below, we provide responses to each of your suggestions.Competing Interests: No competing interests were disclosed. Close Scope of the review and definition of social care Thank you for raising this important point. We agree that conceptual clarity around what constitutes “social care” is essential, especially given the boundaries between health, public health, and social care in the UK context. In our review, we deliberately based the inclusion criteria on the scope of the ESSENCE Toolkit, a resource developed by the NIHR School for Social Care Research to support evidence-informed decision-making in adult social care. The Toolkit uses a broad but policy-relevant definition of social care derived from the National Audit Office (2018) framework, encompassing a range of services that provide practical support due to illness, disability, or age-related needs. This includes not only traditional local authority-provided care services, but also interventions related to housing, support, and community-based services that contribute to social care outcomes and resource use. We chose to adopt this framework for two reasons:- First, the ESSENCE Toolkit is a curated, peer-reviewed source specifically intended to inform adult social care policy and commissioning decisions in England, making it directly relevant to our research aims. - Second, the definition used in ESSENCE reflects the operational reality of social care delivery, where services are often delivered in partnership with or overlap with other sectors (e.g. health, employment, housing). These interventions, while not always under local authority remit, are frequently commissioned to improve outcomes for social care service users. We believe that this approach is methodologically appropriate and well aligned with the aims of our paper—to understand how decision modelling is being used (or underused) in the broader social care context, and to identify where future methodological work is most needed. Response to Other suggestions:- Methods: search process and inclusion criteria - Quality assessment reporting - Supporting citations We are pleased to hear that you appreciated the inclusion of the George Box quotation. We have retained it in the revised version, as we agree it effectively captures the role of modelling in complex systems such as social care. We hope the revisions and clarifications made in response to your comments are satisfactory. Your feedback has been instrumental in improving the quality, clarity, and focus of our manuscript, and we are sincerely grateful for your contribution. COMMENTS ON THIS REPORT - Author Response 11 Sep 2025Dacheng Huo, University of York Centre for Health Economics, York, UK11 Sep 2025Author ResponseThank you for your constructive review and for acknowledging the relevance and contribution of our paper. Your comments have been very helpful in refining the clarity, scope, and positioning of ... Continue reading Thank you for your constructive review and for acknowledging the relevance and contribution of our paper. Your comments have been very helpful in refining the clarity, scope, and positioning of our work. Below, we provide responses to each of your suggestions. Scope of the review and definition of social care Thank you for raising this important point. We agree that conceptual clarity around what constitutes “social care” is essential, especially given the boundaries between health, public health, and social care in the UK context. In our review, we deliberately based the inclusion criteria on the scope of the ESSENCE Toolkit, a resource developed by the NIHR School for Social Care Research to support evidence-informed decision-making in adult social care. The Toolkit uses a broad but policy-relevant definition of social care derived from the National Audit Office (2018) framework, encompassing a range of services that provide practical support due to illness, disability, or age-related needs. This includes not only traditional local authority-provided care services, but also interventions related to housing, support, and community-based services that contribute to social care outcomes and resource use. We chose to adopt this framework for two reasons:- First, the ESSENCE Toolkit is a curated, peer-reviewed source specifically intended to inform adult social care policy and commissioning decisions in England, making it directly relevant to our research aims. - Second, the definition used in ESSENCE reflects the operational reality of social care delivery, where services are often delivered in partnership with or overlap with other sectors (e.g. health, employment, housing). These interventions, while not always under local authority remit, are frequently commissioned to improve outcomes for social care service users. We believe that this approach is methodologically appropriate and well aligned with the aims of our paper—to understand how decision modelling is being used (or underused) in the broader social care context, and to identify where future methodological work is most needed. Response to Other suggestions:- Methods: search process and inclusion criteria - Quality assessment reporting - Supporting citations We are pleased to hear that you appreciated the inclusion of the George Box quotation. We have retained it in the revised version, as we agree it effectively captures the role of modelling in complex systems such as social care. We hope the revisions and clarifications made in response to your comments are satisfactory. Your feedback has been instrumental in improving the quality, clarity, and focus of our manuscript, and we are sincerely grateful for your contribution.Thank you for your constructive review and for acknowledging the relevance and contribution of our paper. Your comments have been very helpful in refining the clarity, scope, and positioning of our work. Below, we provide responses to each of your suggestions.Competing Interests: No competing interests were disclosed. Close Scope of the review and definition of social care Thank you for raising this important point. We agree that conceptual clarity around what constitutes “social care” is essential, especially given the boundaries between health, public health, and social care in the UK context. In our review, we deliberately based the inclusion criteria on the scope of the ESSENCE Toolkit, a resource developed by the NIHR School for Social Care Research to support evidence-informed decision-making in adult social care. The Toolkit uses a broad but policy-relevant definition of social care derived from the National Audit Office (2018) framework, encompassing a range of services that provide practical support due to illness, disability, or age-related needs. This includes not only traditional local authority-provided care services, but also interventions related to housing, support, and community-based services that contribute to social care outcomes and resource use. We chose to adopt this framework for two reasons:- First, the ESSENCE Toolkit is a curated, peer-reviewed source specifically intended to inform adult social care policy and commissioning decisions in England, making it directly relevant to our research aims. - Second, the definition used in ESSENCE reflects the operational reality of social care delivery, where services are often delivered in partnership with or overlap with other sectors (e.g. health, employment, housing). These interventions, while not always under local authority remit, are frequently commissioned to improve outcomes for social care service users. We believe that this approach is methodologically appropriate and well aligned with the aims of our paper—to understand how decision modelling is being used (or underused) in the broader social care context, and to identify where future methodological work is most needed. Response to Other suggestions:- Methods: search process and inclusion criteria - Quality assessment reporting - Supporting citations We are pleased to hear that you appreciated the inclusion of the George Box quotation. We have retained it in the revised version, as we agree it effectively captures the role of modelling in complex systems such as social care. We hope the revisions and clarifications made in response to your comments are satisfactory. Your feedback has been instrumental in improving the quality, clarity, and focus of our manuscript, and we are sincerely grateful for your contribution. Views 0 How to cite this report: Litchfield I and Melyda M. Reviewer Report For: Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.14977.r34630) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/5-9/v1#referee-response-34630 https://openresearch.nihr.ac.uk/articles/5-9/v1#referee-response-34630 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 08 Feb 2025 Ian Litchfield, University of Birmingham, Birmingham, England, UK Melyda Melyda, Department of Health Sciences, University of Birmingham (Ringgold ID: 152874), Birmingham, England, UK Approved with Reservations VIEWS 0 Thank you for the opportunity to review this paper on decision modelling in social care. The topic is particularly relevant given the increasing recognition of the need for robust economic evaluations in social care settings. The paper is well-structured and ... Continue reading We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Close Thank you for the opportunity to review this paper on decision modelling in social care. The topic is particularly relevant given the increasing recognition of the need for robust economic evaluations in social care settings. The paper is well-structured and provides an overview of the role of decision modelling in economic evaluation for social care interventions. It highlights the methodological challenges, the current state of decision modelling in the field, and areas for future development. While the paper is generally well-written and informative, there are a few areas where it could be strengthened. On Making comparisons across relevant interventions - "Decision modelling provides a framework to overcome these challenges through its ability to draw from multiple sources of evidence directly or use methodological approaches such as scenario analysis and meta-analysis." (page 6) Providing supporting references for these statements would strengthen the paper. While the paper is generally well-written and informative, there are a few areas where it could be strengthened. - Introduction: The opening quote, while insightful, does not clearly tie into the discussion that follows. Also, while the authors clearly state their objective—to "….bridge existing gaps and enhance the robustness and applicability of decision modelling in social care settings"—the introduction does not clearly define what the current gap is. It would also be helpful to clarify how this review differs from existing reviews of cost-effectiveness analyses of social care interventions (Bauer et al., 2021; Weatherly et al., 2017) and what specific contribution it makes beyond these prior studies. - Page 5: The statement, “While a healthcare QALY-based model may not be of primary interest in social care commissioning deliberations, a social care QALY-based model might do (Stuttard et al., 2021),” would benefit from further elaboration. It is not entirely clear what the authors mean by a social care QALY-based model in this context and how it differs in application or relevance of the decision model. Would its use impact the decision model discussed in this section? - Several claims regarding the benefits of decision modelling require additional referencing. For instance: On Making comparisons across relevant interventions - "Decision modelling provides a framework to overcome these challenges through its ability to draw from multiple sources of evidence directly or use methodological approaches such as scenario analysis and meta-analysis." (page 6) Providing supporting references for these statements would strengthen the paper. - The criteria for selecting the reviewed studies are not entirely clear. It is not specified whether only UK studies were included, what years were considered, or what types of social care interventions were reviewed. The paper also does not specify where the review search was conducted (e.g., databases used, search terms applied). - The authors review decision models using the Drummond checklist, but I think the recommended approach for decision models is the Phillips checklist (https://www.nice.org.uk/process/pmg20/resources/appendix-h-appraisal-checklists-evidence-tables-grade-and-economic-profiles-pdf-8779777885). I also wonder about the quality of those eight papers that were found. - Page 7: The statement, “While reference was made to the complexity of social care decision-making in some of the studies (e.g. McDaid et al. (2017) and Public Health England (2018)), few attempts were made to reflect this complexity in the decision model, with the focus most often on costs falling on the public sector, specifically NHS and personal social services, and benefits to the intervention recipients,” could benefit from further clarification. It is unclear what the authors mean by the complexity of the model in this context. How is complexity being defined, and what criteria are being used to assess it? Additionally, it would be helpful to elaborate on how the authors determine whether a model sufficiently captures the complexity of social care decision-making. - The authors frequently discuss social care outcomes but do not elaborate on the findings of the eight referenced papers. Unlike healthcare, where outcomes focus on clinical effectiveness (e.g., survival, QALYs), social care outcomes are broader and more complex, as shown in these studies. This might highlight the need for standardised metrics (e.g., ASCOT and ICECAP) to improve comparability and maybe consistency in decision models use in social care. - The review of existing decision models highlights the lack of a standardised approach, but the discussion does not fully explore how a more unified framework could be developed. Given the variability in modelling techniques and perspectives, some discussion on how consensus might be achieved—perhaps through a core set of recommended methods or guidelines—would be valuable. - Is the topic of the review discussed comprehensively in the context of the current literature? Partly - Are all factual statements correct and adequately supported by citations? Partly - Is the review written in accessible language? Yes - Are the conclusions drawn appropriate in the context of the current research literature? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Health Economics, Health Service Delivery CITE HOW TO CITE THIS REPORT Litchfield I and Melyda M. Reviewer Report For: Cost-effectiveness decision modelling in social care: exploring the why, the how, and the what next [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved]. NIHR Open Res 2025, 5:9 (https://doi.org/10.3310/nihropenres.14977.r34630) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/5-9/v1#referee-response-34630 https://openresearch.nihr.ac.uk/articles/5-9/v1#referee-response-34630 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. - Author Response 11 Sep 2025Dacheng Huo, University of York Centre for Health Economics, York, UK11 Sep 2025Author ResponseThank you for your thoughtful and constructive review. We appreciate your recognition of the relevance and clarity of our manuscript. Your comments helped us refine the focus, improve the transparency ... Continue reading Thank you for your thoughtful and constructive review. We appreciate your recognition of the relevance and clarity of our manuscript. Your comments helped us refine the focus, improve the transparency of our methods, and strengthen the overall contribution of this review. Below we provide point-by-point responses. 1. Introduction and objective clarity We have revised the introduction to more clearly define the gap addressed in this paper—specifically, the limited methodological development and inconsistent application of decision modelling in social care economic evaluation. The opening quote is now removed. We also clarified how this paper differs from existing reviews (e.g. Bauer et al., 2021; Weatherly et al., 2017) by focusing on the use of decision modelling itself, the challenges observed in practice, and potential directions for future development. The stated objective has been revised to better reflect this specific focus. 2. Clarification of the “social care QALY-based model” We have revised this section to explain more clearly what is meant by a “social care QALY-based model.” Specifically, we now illustrate this using the example of the ASCOT tool, which captures domains such as independence, social connection, and control—outcomes that are highly relevant in social care contexts but not covered by conventional health-related quality of life measures. We also added a sentence explaining how decision modelling approaches can accommodate such outcome structures, provided that relevant states, transitions, and consequences can be appropriately conceptualized. 3. Study selection criteria and use of the ESSENCE Toolkit Our review solely relied on the ESSENCE Toolkit. In the revised manuscript, we now provide a fuller explanation of why this source was appropriate. ESSENCE offers a comprehensive, policy-relevant collection of economic evaluations in adult social care and is based on a broad yet systematic definition developed by the National Audit Office. We clarified that the review focused on UK-based studies and added further details on databases and search terms. 4. Quality assessment using the Phillips checklist Following your recommendation, we replaced the Drummond checklist with the Phillips checklist, which is more appropriate for assessing decision-analytic models. We updated the methods section accordingly and added a summary of the quality assessment results in both the main text and supplementary material. 5. Clarifying the notion of “complexity” in models We did not apply a formal criteria for complexity, as this was not the central focus of the review and may not be appropriate across such a diverse set of studies. However, we revised the discussion to more clearly describe the types of complexity relevant to social care modelling—such as cross-sector costs and benefits, long-term impacts, and the involvement of multiple stakeholders. These issues were considered when commenting on how models reflected the complexity of real-world decision-making. 6. Discussion on standardization and frameworks We agree that a more unified modelling framework would be valuable. However, proposing or outlining such a framework was beyond the scope of this review. Our aim was to map current practices and identify methodological gaps, rather than to offer prescriptive guidance. We now clarify this intention in the revised discussion and note that future methodological work could build on our findings to support greater standardization in the field. We hope that the revisions and clarifications we have made are in line with your suggestions. Your feedback has helped improve the clarity and focus of the manuscript, and we are sincerely grateful for your input.Thank you for your thoughtful and constructive review. We appreciate your recognition of the relevance and clarity of our manuscript. Your comments helped us refine the focus, improve the transparency of our methods, and strengthen the overall contribution of this review. Below we provide point-by-point responses.Competing Interests: No competing interests were disclosed. Close 1. Introduction and objective clarity We have revised the introduction to more clearly define the gap addressed in this paper—specifically, the limited methodological development and inconsistent application of decision modelling in social care economic evaluation. The opening quote is now removed. We also clarified how this paper differs from existing reviews (e.g. Bauer et al., 2021; Weatherly et al., 2017) by focusing on the use of decision modelling itself, the challenges observed in practice, and potential directions for future development. The stated objective has been revised to better reflect this specific focus. 2. Clarification of the “social care QALY-based model” We have revised this section to explain more clearly what is meant by a “social care QALY-based model.” Specifically, we now illustrate this using the example of the ASCOT tool, which captures domains such as independence, social connection, and control—outcomes that are highly relevant in social care contexts but not covered by conventional health-related quality of life measures. We also added a sentence explaining how decision modelling approaches can accommodate such outcome structures, provided that relevant states, transitions, and consequences can be appropriately conceptualized. 3. Study selection criteria and use of the ESSENCE Toolkit Our review solely relied on the ESSENCE Toolkit. In the revised manuscript, we now provide a fuller explanation of why this source was appropriate. ESSENCE offers a comprehensive, policy-relevant collection of economic evaluations in adult social care and is based on a broad yet systematic definition developed by the National Audit Office. We clarified that the review focused on UK-based studies and added further details on databases and search terms. 4. Quality assessment using the Phillips checklist Following your recommendation, we replaced the Drummond checklist with the Phillips checklist, which is more appropriate for assessing decision-analytic models. We updated the methods section accordingly and added a summary of the quality assessment results in both the main text and supplementary material. 5. Clarifying the notion of “complexity” in models We did not apply a formal criteria for complexity, as this was not the central focus of the review and may not be appropriate across such a diverse set of studies. However, we revised the discussion to more clearly describe the types of complexity relevant to social care modelling—such as cross-sector costs and benefits, long-term impacts, and the involvement of multiple stakeholders. These issues were considered when commenting on how models reflected the complexity of real-world decision-making. 6. Discussion on standardization and frameworks We agree that a more unified modelling framework would be valuable. However, proposing or outlining such a framework was beyond the scope of this review. Our aim was to map current practices and identify methodological gaps, rather than to offer prescriptive guidance. We now clarify this intention in the revised discussion and note that future methodological work could build on our findings to support greater standardization in the field. We hope that the revisions and clarifications we have made are in line with your suggestions. Your feedback has helped improve the clarity and focus of the manuscript, and we are sincerely grateful for your input. COMMENTS ON THIS REPORT - Author Response 11 Sep 2025Dacheng Huo, University of York Centre for Health Economics, York, UK11 Sep 2025Author ResponseThank you for your thoughtful and constructive review. We appreciate your recognition of the relevance and clarity of our manuscript. Your comments helped us refine the focus, improve the transparency ... Continue reading Thank you for your thoughtful and constructive review. We appreciate your recognition of the relevance and clarity of our manuscript. Your comments helped us refine the focus, improve the transparency of our methods, and strengthen the overall contribution of this review. Below we provide point-by-point responses. 1. Introduction and objective clarity We have revised the introduction to more clearly define the gap addressed in this paper—specifically, the limited methodological development and inconsistent application of decision modelling in social care economic evaluation. The opening quote is now removed. We also clarified how this paper differs from existing reviews (e.g. Bauer et al., 2021; Weatherly et al., 2017) by focusing on the use of decision modelling itself, the challenges observed in practice, and potential directions for future development. The stated objective has been revised to better reflect this specific focus. 2. Clarification of the “social care QALY-based model” We have revised this section to explain more clearly what is meant by a “social care QALY-based model.” Specifically, we now illustrate this using the example of the ASCOT tool, which captures domains such as independence, social connection, and control—outcomes that are highly relevant in social care contexts but not covered by conventional health-related quality of life measures. We also added a sentence explaining how decision modelling approaches can accommodate such outcome structures, provided that relevant states, transitions, and consequences can be appropriately conceptualized. 3. Study selection criteria and use of the ESSENCE Toolkit Our review solely relied on the ESSENCE Toolkit. In the revised manuscript, we now provide a fuller explanation of why this source was appropriate. ESSENCE offers a comprehensive, policy-relevant collection of economic evaluations in adult social care and is based on a broad yet systematic definition developed by the National Audit Office. We clarified that the review focused on UK-based studies and added further details on databases and search terms. 4. Quality assessment using the Phillips checklist Following your recommendation, we replaced the Drummond checklist with the Phillips checklist, which is more appropriate for assessing decision-analytic models. We updated the methods section accordingly and added a summary of the quality assessment results in both the main text and supplementary material. 5. Clarifying the notion of “complexity” in models We did not apply a formal criteria for complexity, as this was not the central focus of the review and may not be appropriate across such a diverse set of studies. However, we revised the discussion to more clearly describe the types of complexity relevant to social care modelling—such as cross-sector costs and benefits, long-term impacts, and the involvement of multiple stakeholders. These issues were considered when commenting on how models reflected the complexity of real-world decision-making. 6. Discussion on standardization and frameworks We agree that a more unified modelling framework would be valuable. However, proposing or outlining such a framework was beyond the scope of this review. Our aim was to map current practices and identify methodological gaps, rather than to offer prescriptive guidance. We now clarify this intention in the revised discussion and note that future methodological work could build on our findings to support greater standardization in the field. We hope that the revisions and clarifications we have made are in line with your suggestions. Your feedback has helped improve the clarity and focus of the manuscript, and we are sincerely grateful for your input.Thank you for your thoughtful and constructive review. We appreciate your recognition of the relevance and clarity of our manuscript. Your comments helped us refine the focus, improve the transparency of our methods, and strengthen the overall contribution of this review. Below we provide point-by-point responses.Competing Interests: No competing interests were disclosed. Close 1. Introduction and objective clarity We have revised the introduction to more clearly define the gap addressed in this paper—specifically, the limited methodological development and inconsistent application of decision modelling in social care economic evaluation. The opening quote is now removed. We also clarified how this paper differs from existing reviews (e.g. Bauer et al., 2021; Weatherly et al., 2017) by focusing on the use of decision modelling itself, the challenges observed in practice, and potential directions for future development. The stated objective has been revised to better reflect this specific focus. 2. Clarification of the “social care QALY-based model” We have revised this section to explain more clearly what is meant by a “social care QALY-based model.” Specifically, we now illustrate this using the example of the ASCOT tool, which captures domains such as independence, social connection, and control—outcomes that are highly relevant in social care contexts but not covered by conventional health-related quality of life measures. We also added a sentence explaining how decision modelling approaches can accommodate such outcome structures, provided that relevant states, transitions, and consequences can be appropriately conceptualized. 3. Study selection criteria and use of the ESSENCE Toolkit Our review solely relied on the ESSENCE Toolkit. In the revised manuscript, we now provide a fuller explanation of why this source was appropriate. ESSENCE offers a comprehensive, policy-relevant collection of economic evaluations in adult social care and is based on a broad yet systematic definition developed by the National Audit Office. We clarified that the review focused on UK-based studies and added further details on databases and search terms. 4. Quality assessment using the Phillips checklist Following your recommendation, we replaced the Drummond checklist with the Phillips checklist, which is more appropriate for assessing decision-analytic models. We updated the methods section accordingly and added a summary of the quality assessment results in both the main text and supplementary material. 5. Clarifying the notion of “complexity” in models We did not apply a formal criteria for complexity, as this was not the central focus of the review and may not be appropriate across such a diverse set of studies. However, we revised the discussion to more clearly describe the types of complexity relevant to social care modelling—such as cross-sector costs and benefits, long-term impacts, and the involvement of multiple stakeholders. These issues were considered when commenting on how models reflected the complexity of real-world decision-making. 6. Discussion on standardization and frameworks We agree that a more unified modelling framework would be valuable. However, proposing or outlining such a framework was beyond the scope of this review. Our aim was to map current practices and identify methodological gaps, rather than to offer prescriptive guidance. We now clarify this intention in the revised discussion and note that future methodological work could build on our findings to support greater standardization in the field. We hope that the revisions and clarifications we have made are in line with your suggestions. Your feedback has helped improve the clarity and focus of the manuscript, and we are sincerely grateful for your input. Alongside their report, reviewers assign a status to the article: - Approved - Approved with reservations - Not approved | Invited Reviewers | |||| |---|---|---|---|---| | 1 | 2 | 3 | 4 | | | Version 3 (revision) 23 Oct 25 | read | ||| | Version 2 (revision) 17 Jul 25 | read | read | || | Version 1 27 Jan 25 | read | read | Sign up for content alerts You are now signed up to receive this alert Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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