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Intervention Scalability Assessment Tool: A decision support tool for health policy makers and implementers.

ABSTRACT: BACKGROUND:Promising health interventions tested in pilot studies will only achieve population-wide impact if they are implemented at scale across communities and health systems. Scaling up effective health interventions is vital as not doing so denies the community the most effective services and programmes. However, there remains a paucity of practical tools to assess the suitability of health interventions for scale-up. The Intervention Scalability Assessment Tool (ISAT) was developed to support policy-makers and practitioners to make systematic assessments of the suitability of health interventions for scale-up. METHODS:The ISAT was developed over three stages; the first stage involved a literature review to identify similar tools and frameworks that could be used to guide scalability assessments, and expert input to develop draft ISAT content. In the second stage, the draft ISAT tool was tested with end users. The third stage involved revising and re-testing the ISAT with end users to further refine the language and structure of the final ISAT. RESULTS:A variety of information and sources of evidence should be used to complete the ISAT. The ISAT consists of three parts. Part A: 'setting the scene' requires consideration of the context in which the intervention is being considered for scale-up and consists of five domains, as follows: (1) the problem; (2) the intervention; (3) strategic/political context; (4) evidence of effectiveness; and (5) intervention costs and benefits. Part B asks users to assess the potential implementation and scale-up requirements within five domains, namely (1) fidelity and adaptation; (2) reach and acceptability; (3) delivery setting and workforce; (4) implementation infrastructure; and (5) sustainability. Part C generates a graphical representation of the strengths and weaknesses of the readiness of the proposed intervention for scale-up. Users are also prompted for a recommendation as to whether the intervention (1) is recommended for scale-up, (2) is promising but needs further information before scaling up, or (3) does not yet merit scale-up. CONCLUSION:The ISAT fills an important gap in applied scalability assessment and can become a critical decision support tool for policy-makers and practitioners when selecting health interventions for scale-up. Although the ISAT is designed to be a health policy and practitioner tool, it can also be used by researchers in the design of research to fill important evidence gaps.

SUBMITTER: Milat A

PROVIDER: S-EPMC6942323 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Intervention Scalability Assessment Tool: A decision support tool for health policy makers and implementers.

Milat Andrew A Lee Karen K Conte Kathleen K Grunseit Anne A Wolfenden Luke L van Nassau Femke F Orr Neil N Sreeram Padmaja P Bauman Adrian A

Health research policy and systems 20200103 1

<h4>Background</h4>Promising health interventions tested in pilot studies will only achieve population-wide impact if they are implemented at scale across communities and health systems. Scaling up effective health interventions is vital as not doing so denies the community the most effective services and programmes. However, there remains a paucity of practical tools to assess the suitability of health interventions for scale-up. The Intervention Scalability Assessment Tool (ISAT) was developed ...[more]

PMID: 31900230

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Project description:Background. Metamodels can simplify complex health policy models and yield instantaneous results to inform policy decisions. We investigated the predictive validity of linear regression metamodels used to support a real-time decision-making tool that compares infant HIV testing/screening strategies. Methods. We developed linear regression metamodels of the Cost-Effectiveness of Preventing AIDS Complications Pediatric (CEPAC-P) microsimulation model used to predict life expectancy and lifetime HIV-related costs/person of two infant HIV testing/screening programs in South Africa. Metamodel performance was assessed with cross-validation and Bland-Altman plots, showing between-method differences in predicted outcomes against their means. Predictive validity was determined by the percentage of simulations in which the metamodels accurately predicted the strategy with the greatest net health benefit (NHB) as projected by the CEPAC-P model. We introduced a zone of indifference and investigated the width needed to produce between-method agreement in 95% of the simulations. We also calculated NHB losses from "wrong" decisions by the metamodel. Results. In cross-validation, linear regression metamodels accurately approximated CEPAC-P-projected outcomes. For life expectancy, Bland-Altman plots showed good agreement between CEPAC-P and the metamodel (within 1.1 life-months difference). For costs, 95% of between-method differences were within $65/person. The metamodels predicted the same optimal strategy as the CEPAC-P model in 87.7% of simulations, increasing to 95% with a zone of indifference of 0.24 life-months ( ? 7 days). The losses in health benefits due to "wrong" choices by the metamodel were modest (range: 0.0002-1.1 life-months). Conclusions. For this policy question, linear regression metamodels offered sufficient predictive validity for the optimal testing strategy as compared with the CEPAC-P model. Metamodels can simulate different scenarios in real time, based on sets of input parameters that can be depicted in a widely accessible decision-support tool.

Project description:BackgroundSystems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Where end-users have been included, there has been limited examination of their experience of the participatory modelling process and their views about the utility of the findings. This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context.MethodsSemi-structured interviews were conducted with end-user participants from three participatory simulation modelling case studies in Australian real-world policy settings. Interviewees were employees of government agencies with jurisdiction over policy and program decisions and were purposively selected to include perspectives at different stages of model development.ResultsThe 'co-production' aspect of the participatory approach was highly valued. It was reported as an essential component of building understanding of the modelling process, and thus trust in the model and its outputs as a decision-support tool. The unique benefits of simulation modelling included its capacity to explore interactions of risk factors and combined interventions, and the impact of scaling up interventions. Participants also valued simulating new interventions prior to implementation in the real world, and the comprehensive mapping of evidence and its gaps to prioritise future research. The participatory aspect of simulation modelling was time and resource intensive and therefore most suited to high priority complex topics with contested options for intervening.ConclusionThese findings highlight the value of a participatory approach to dynamic simulation modelling to support its utility in applied health policy settings.

Project description:BackgroundSurrogate decision makers of stroke patients are often unprepared to make critical decisions on life-sustaining treatments. We describe the development process and key features for the Understanding Stroke web-based decision support tool.MethodsWe used multiple strategies to develop a patient-centered, tailored decision aid. We began by forming a Patient and Family Advisory Council to provide continuous input to our multidisciplinary team on the development of the tool. Additionally, focus groups consisting of nurses, therapists, social workers, physicians, stroke survivors, and family members reviewed key elements of the tool, including prognostic information, graphical displays, and values clarification exercise. To design the values clarification exercise, we asked focus groups to provide feedback on a list of important activities of daily living. An ordinal prognostic model was developed for ischemic stroke and intracerebral hemorrhage using data taken from the Virtual International Stroke Trials Archive Plus, and incorporated into the tool.ResultsFocus group participants recommended making numeric prognostic information optional due to possible emotional distress. Pie charts were generally favored by participants for graphical presentation of prognostic information, though a horizontal stacked bar chart was also added due to its prevalence in stroke literature. Plain language descriptions of the modified Rankin Scale were created to accompany the prognostic information. A values clarification exercise was developed consisting of a list of 13 situations that may make an individual consider comfort measures only. The final version of the web based tool (which can be viewed on tablets) included the following sections: general introduction to stroke, outcomes (prognostic information and recovery), in-hospital and life-sustaining treatments, decision making and values clarification, post-hospital care, tips for talking to the health care team, and a summary report. Preliminary usability testing received generally favorable feedback.ConclusionWe developed Understanding Stroke, a tailored decision support tool for surrogate decision makers of stroke patients. The tool was well received and will be formally pilot tested in a group of stroke surrogate decision makers.Trial registrationClinicalTrials.gov ( NCT03427645 ).

Project description:BACKGROUND:Health care planning for pandemic influenza is a challenging task which requires predictive models by which the impact of different response strategies can be evaluated. However, current preparedness plans and simulations exercises, as well as freely available simulation models previously made for policy makers, do not explicitly address the availability of health care resources or determine the impact of shortages on public health. Nevertheless, the feasibility of health systems to implement response measures or interventions described in plans and trained in exercises depends on the available resource capacity. As part of the AsiaFluCap project, we developed a comprehensive and flexible resource modelling tool to support public health officials in understanding and preparing for surges in resource demand during future pandemics. RESULTS:The AsiaFluCap Simulator is a combination of a resource model containing 28 health care resources and an epidemiological model. The tool was built in MS Excel© and contains a user-friendly interface which allows users to select mild or severe pandemic scenarios, change resource parameters and run simulations for one or multiple regions. Besides epidemiological estimations, the simulator provides indications on resource gaps or surpluses, and the impact of shortages on public health for each selected region. It allows for a comparative analysis of the effects of resource availability and consequences of different strategies of resource use, which can provide guidance on resource prioritising and/or mobilisation. Simulation results are displayed in various tables and graphs, and can also be easily exported to GIS software to create maps for geographical analysis of the distribution of resources. CONCLUSIONS:The AsiaFluCap Simulator is freely available software (http://www.cdprg.org) which can be used by policy makers, policy advisors, donors and other stakeholders involved in preparedness for providing evidence based and illustrative information on health care resource capacities during future pandemics. The tool can inform both preparedness plans and simulation exercises and can help increase the general understanding of dynamics in resource capacities during a pandemic. The combination of a mathematical model with multiple resources and the linkage to GIS for creating maps makes the tool unique compared to other available software.

Project description:There is increasing interest globally in the use of more rigorous processes to ensure that maternal, newborn, and child health (MNCH) care recommendations are informed by the best available research evidence use. The purpose of this study was to engage Nigerian MNCH policy-makers and other stakeholders to consider issues around research to policy and practice interface and to assess their existing knowledge and capacity on the use of research evidence for policy-making and practice.The study design is a cross-sectional evaluation of MNCH stakeholders' knowledge as it pertains different dimensions of research to practice. This was undertaken during a national MNCH stakeholders' engagement event convened under the auspices of the West African Health Organization (WAHO) and the Federal Ministry of Health (FMoH) in Abuja, Nigeria. A questionnaire was administered to participants, which was designed to assess participants' knowledge, capacity and organizational process of generation, synthesis and utilization of research evidence in policy-making regarding MNCH.A total of 40 participants signed the informed consent form and completed the questionnaire. The mean ratings (MNRs) of participants' knowledge of electronic databases and capacity to identify and obtain relevant research evidence from electronic databases ranged from 3.62-3.68 on the scale of 5. The MNRs of participants' level of understanding of a policy brief, a policy dialogue and the role of researchers in policy-making ranged from 3.50-3.86. The MNRs of participants' level of understanding of evidence in policy-making context, types and sources of evidence, capacity to identify, select, adapt, and transform relevant evidence into policy ranged from 3.63-4.08. The MNRs of the participants' organization's capacity to cover their geographical areas of operation were generally low ranging from 3.32-3.38 in terms of manpower, logistics, facilities, and external support. The lowest MNR of 2.66 was recorded in funding.The outcomes of this study suggest that a stakeholders' engagement event can serve as an important platform to assess policy-makers' knowledge and capacity for evidence-informed policy-making and for the promotion of evidence use in the policy process.

Project description:IntroductionEarly intervention (EI) endorses family-centred and participation-focused services, but there remain insufficient options for systematically enacting this service approach. The Young Children's Participation and Environment Measure electronic patient-reported outcome (YC-PEM e-PRO) is an evidence-based measure for caregivers that enables family-centred services in EI. The Parent-Reported Outcomes for Strengthening Partnership within the Early Intervention Care Team (PROSPECT) is a community-based pragmatic trial examining the effectiveness of implementing the YC-PEM e-PRO measure and decision support tool as an option for use within routine EI care, on service quality and child outcomes (aim 1). Following trial completion, we will characterise stakeholder perspectives of facilitators and barriers to its implementation across multiple EI programmes (aim 2).Methods and analysisThis study employs a hybrid type 1 effectiveness-implementation study design. For aim 1, we aim to enrol 223 caregivers of children with or at risk for developmental disabilities or delays aged 0-3 years old that have accessed EI services for three or more months from one EI programme in the Denver Metro catchment of Colorado. Participants will be invited to enrol for 12 months, beginning at the time of their child's annual evaluation of progress. Participants will be randomised using a cluster-randomised design at the EI service coordinator level. Both groups will complete baseline testing and follow-up assessment at 1, 6 and 12 months. A generalised linear mixed model will be fitted for each outcome of interest, with group, time and their interactions as primary fixed effects, and adjusting for child age and condition severity as secondary fixed effects. For aim 2, we will conduct focus groups with EI stakeholders (families in the intervention group, service coordinators and other service providers in the EI programme, and programme leadership) which will be analysed thematically to explain aim 1 results and identify supports and remaining barriers to its broader implementation in multiple EI programmes.Ethics and disseminationThis study has been approved by the institutional review boards at the University of Illinois at Chicago (2020-0555) and University of Colorado (20-2380). An active dissemination plan will ensure that findings have maximum reach for research and practice.Trial registration numberNCT04562038.

Project description:IntroductionAlthough shortcomings in clinician-family communication and decision making for incapacitated, critically ill patients are common, there are few rigorously tested interventions to improve outcomes. In this manuscript, we present our methodology for the Pairing Re-engineered Intensive Care Unit Teams with Nurse-Driven Emotional support and Relationship Building (PARTNER 2) trial, and discuss design challenges and their resolution.Methods and analysisThis is a pragmatic, stepped-wedge, cluster randomised controlled trial comparing the PARTNER 2 intervention to usual care among 690 incapacitated, critically ill patients and their surrogates in five ICUs in Pennsylvania. Eligible subjects will include critically ill patients at high risk of death and/or severe long-term functional impairment, their main surrogate decision-maker and their clinicians. The PARTNER intervention is delivered by the interprofessional ICU team and overseen by 4-6 nurses from each ICU. It involves: (1) advanced communication skills training for nurses to deliver support to surrogates throughout the ICU stay; (2) deploying a structured family support pathway; (3) enacting strategies to foster collaboration between ICU and palliative care services and (4) providing intensive implementation support to each ICU to incorporate the family support pathway into clinicians' workflow. The primary outcome is surrogates' ratings of the quality of communication during the ICU stay as assessed by telephone at 6-month follow-up. Prespecified secondary outcomes include surrogates' scores on the Hospital Anxiety and Depression Scale, the Impact of Event Scale, the modified Patient Perception of Patient Centredness scale, the Decision Regret Scale, nurses' scores on the Maslach Burnout Inventory, and length of stay during and costs of the index hospitalisation.We also discuss key methodological challenges, including determining the optimal level of randomisation, using existing staff to deploy the intervention and maximising long-term follow-up of participants.Ethics and disseminationWe obtained ethics approval through the University of Pittsburgh, Human Research Protection Office. The findings will be published in peer-reviewed journals.Trial registration numberNCT02445937.

Project description:BACKGROUND:While known efficacious preventive health interventions exist, the current capacity to scale up these interventions is limited. In recent years, much attention has focussed on developing frameworks and methods for scale-up yet, in practice, the pathway for scale-up is seldom linear and may be highly dependent on contextual circumstances. Few studies have examined the process of scaling up from decision to implementation nor examined the sustainability of scaled-up interventions. This study explores decision-makers' perceptions from real-world scaled-up case studies to examine how scale-up decisions were made and describe enablers of successful scale-up and sustainability. METHODS:This qualitative study included 29 interviews conducted with purposively sampled key Australian policy-makers, practitioners and researchers experienced in scale-up. Semi-structured interview questions obtained information regarding case studies of scaled-up interventions. The Framework Analysis method was used as the primary method of analysis of the interview data to inductively generate common and divergent themes within qualitative data across cases. RESULTS:A total of 31 case studies of public health interventions were described by interview respondents based on their experiences. According to the interviewees' perceptions, decisions to scale up commonly occurred either opportunistically, when funding became available, or when a deliberate decision was made and funding allocated. The latter scenario was more common when the intervention aligned with specific political or strategic goals. Decisions to scale up were driven by a variety of key actors such as politicians, senior policy-makers and practitioners in the health system. Drivers of a successful scale-up process included good governance, clear leadership, and adequate resourcing and expertise. Establishing accountability structures and appropriate engagement mechanisms to encourage the uptake of interventions were also key enablers. Sustainability was influenced by evidence of impact as well as good acceptability among the general or target population. CONCLUSIONS:Much like Kingdon's Multiple Streams Theory of 'policy windows', there is a conceptually similar 'window for scale-up', driven by a complex interplay of factors such as political need, strategic context, funding and key actors. Researchers and policy-makers need to consider scalability from the outset and prepare for when the window for scale-up opens. Decision-makers need to provide longer term funding for scale-up to facilitate longer term sustainability and build on the resources already invested for the scale-up process.

Dataset Information

Intervention Scalability Assessment Tool: A decision support tool for health policy makers and implementers.

Publications

Intervention Scalability Assessment Tool: A decision support tool for health policy makers and implementers.

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