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Holmström et al provide an interesting and thought-provoking contribution to a perennial problem: why, despite a vast number of applications of simulation modelling in healthcare over the past 70 years, there is still remarkably little evidence of successful implementation of model results. Their paper is a retrospective analysis of five case studies, all undertaken as consultancy, that used a blend of system dynamics (SD) modelling and action research (AR). This commentary assesses the effectiveness of this approach in achieving implementation, based on the evidence presented, and discusses some of the issues raised. These issues include a comparison of Holmström’s approach with group model building (GMB) in SD, the differences between healthcare modelling projects undertaken by (a) business consultants and (b) academics, and the challenges of undertaking ‘systematic’ reviews of the grey literature.

Dengue fever is a major public health problem in tropical/subtropical regions. Prior economic analyses have predominantly evaluated either vaccination or vector-control programmes in isolation and do not really consider the incremental benefits and cost-effectiveness of mixed strategies and combination control. We estimated the cost-effectiveness of single and combined approaches in Thailand. The impacts of different control interventions were analysed using a previously published mathematical model of dengue epidemiology and control incorporating seasonality, age structure, consecutive infection, cross protection, immune enhancement and combined vector-host transmission. An economic model was applied to simulation results to estimate the cost-effectiveness of 4 interventions and their various combinations (6 strategies): i) routine vaccination of 1-year olds; ii) chemical vector control strategies targeting adult and larval stages separately; iii) environmental management/ public health education and awareness [EM/ PHEA]). Payer and societal perspectives were considered. The health burden of dengue fever was assessed using disability-adjusted life-years (DALYs) lost. Costs and effects were assessed for 10 years. Costs were discounted at 3% annually and updated to 2013 United States Dollars. Incremental cost-effectiveness analysis was carried out after strategies were rank-ordered by cost, with results presented in a table of incremental analysis. Sensitivity and scenario analyses were undertaken; and the impact and cost-effectiveness of Wolbachia was evaluated in exploratory scenario analyses. From the payer and societal perspectives, 2 combination strategies were considered optimal, as all other control strategies were dominated. Vaccination plus adulticide plus EM/ PHEA was deemed cost-effective according to multiple cost-effectiveness criteria. From the societal perspective, incremental differences vs. adulticide and EM/ PHEA resulted in costs of $157.6 million and DALYs lost of 12,599, giving an expected ICER of $12,508 per DALY averted. Exploratory scenario analyses showed Wolbachia to be highly cost-effective ($343 per DALY averted) vs. other single control measures. Our model shows that individual interventions can be cost-effective, but that important epidemiological reductions and economic impacts are demonstrated when interventions are combined as part of an integrated approach to combating dengue fever. Exploratory scenario analyses demonstrated the potential epidemiological and cost-effective impact of Wolbachia when deployed at scale on a nationwide basis. Our findings were robust in the face of sensitivity analyses.

In recent years, there has been a growing debate, particularly in the UK and Europe, over the merits of using discrete-event simulation (DES) and system dynamics (SD); there are now instances where both methodologies were employed on the same problem. This book details each method, comparing each in terms of both theory and their application to various problem situations. It also provides a seamless treatment of various topics--theory, philosophy, detailed mechanics, practical implementation--providing a systematic treatment of the methodologies of DES and SD, which previously have been treated separately.        

BackgroundFrailty is a common condition in older adults which becomes more prevalent and more severe with age. Health and care services designed to meet the needs of older adults living with frailty are expanding in number and scope, but information on the workforce needed to deliver services both now and in the future is lacking.ObjectivesTo understand the service design and staffing configurations for frailty services through gathering data on the setting and purpose of services; target population; referral methods into the service; specific activities delivered; frailty assessment tools; key service and patient outcomes; staff involved; future service priorities and anticipated future workforce requirements.DesignNational survey, circulated electronically via national networks and organisations involved in the care of patients with frailty (n=26).SettingEngland health and care settings.ParticipantsHealth and care professionals delivering services for people living with frailty.ResultsThere were 93 survey responses from frailty services across England, of which 82 contained usable information. Respondents included clinicians and managers in a range of health and care sectors and the voluntary sector. Frailty services across settings commonly prioritised reducing frailty-associated risks but few focused on prevention. Staff teams included representation across professions, with specialists in older people’s care (eg, geriatricians, advanced practitioners) present in most teams, but non-specialist team members (eg, therapists, social workers and care co-ordinators) comprised a large proportion of the total workforce. All respondents identified similar priorities for their service in future, including reducing frailty progression, and specified needs for additional staff which broadly reflected the current team configurations. However, staff vacancies or unmet patient need due to low capacity was highlighted, and all respondents identified the need for additional staff in future.ConclusionsServices designed to identify and manage people with frailty are complex and require a workforce with specialist training to assess, plan and deliver care. Current services are understaffed with insufficient capability to prevent frailty onset or slow progression, thereby failing to address unmet need. Workforce planning and resourcing to address frailty-related needs is urgently required.

Background With the challenges that dengue fever (DF) presents to healthcare systems and societies, public health officials must determine where best to allocate scarce resources and restricted budgets. Constrained optimization (CO) helps to address some of the acknowledged limitations of conventional health economic analyses and has typically been used to identify the optimal allocation of resources across interventions subject to a variety of constraints. Methods A dynamic transmission model was developed to predict the number of dengue cases in Thailand at steady state. A CO was then applied to identify the optimal combination of interventions (release of Wolbachia -infected mosquitoes and paediatric vaccination) within the constraints of a fixed budget, set no higher than cost estimates of the current vector control programme, to minimize the number of dengue cases and disability-adjusted life years (DALYs) lost. Epidemiological, cost, and effectiveness data were informed by national data and the research literature. The time horizon was 10 years. Scenario analyses examined different disease management and intervention costs, budget constraints, vaccine efficacy, and optimization time horizon. Results Under base-case budget constraints, the optimal coverage of the two interventions to minimize dengue incidence was predicted to be nearly equal ( Wolbachia 50%; paediatric vaccination 49%) with corresponding coverages under lower bound ( Wolbachia 54%; paediatric vaccination 10%) and upper bound ( Wolbachia 67%; paediatric vaccination 100%) budget ceilings. Scenario analyses indicated that the most impactful situations related to the costs of Wolbachia and paediatric vaccination with decreases/ increases in costs of interventions demonstrating a direct correlation with coverage (increases/ decreases) of the respective control strategies under examination. Conclusions Determining the best investment strategy for dengue control requires the identification of the optimal mix of interventions to implement in order to maximize public health outcomes, often under fixed budget constraints. A CO model was developed with the objective of minimizing dengue cases (and DALYs lost) over a 10-year time horizon, within the constraints of the estimated budgets for vector control in the absence of vaccination and Wolbachia . The model provides a tool for developing estimates of optimal coverage of combined dengue control strategies that minimize dengue burden at the lowest budget.

Production planning in the blood supply chain is a challenging task. Many complex factors such as uncertain supply and demand, blood group proportions, shelf life constraints and different collection and production methods have to be taken into account, and thus advanced methodologies are required for decision making. This paper presents an integrated simulation-optimization model to support both strategic and operational decisions in production planning. Discrete-event simulation is used to represent the flows through the supply chain, incorporating collection, production, storing and distribution. On the other hand, an integer linear optimization model running over a rolling planning horizon is used to support daily decisions, such as the required number of donors, collection methods and production planning. This approach is evaluated using real data from a blood center in Colombia. The results show that, using the proposed model, key indicators such as shortages, outdated units, donors required and cost are improved.

This paper describes a model that has been used to inform government policy in Sri Lanka. The model has two components, representing supply and demand for dental-care services. The supply-side component (the dental workforce model) uses system dynamics to represent the career progression of dentists from recruitment and training at the University Dental School, through different career paths through to retirement. The demand-side model calculates a range of future demand scenarios for dental care, based on different assumptions about Sri Lanka's potential future economic development. The combined model was used by the Sri Lankan government to plan better provision of state-funded dental care and the future university intake of dental students. Data collection for both models was challenging, and required some innovative research. The model itself, and its successful application, raise a number of wider issues concerning model implementation and acceptability by clients and model users.

Background Despite being a preventable disease, cervical cancer continues to be a public health concern, affecting mainly lower and middle-income countries. Therefore, in Bogotá a home-visit based program was instituted to increase screening uptake. However, around 40% of the visited women fail to attend their Pap smear test appointments. Using this program as a case study, this paper presents a methodology that combines machine learning methods, using routinely collected administrative data, with Champion’s Health Belief Model to assess women’s beliefs about cervical cancer screening. The aim is to improve the cost-effectiveness of behavioural interventions aiming to increase attendance for screening. The results presented here relate specifically to the case study, but the methodology is generic and can be applied in all low-income settings. Methods This is a cross-sectional study using two different datasets from the same population and a sequential modelling approach. To assess beliefs, we used a 37-item questionnaire to measure the constructs of the CHBM towards cervical cancer screening. Data were collected through a face-to-face survey ( N  = 1699). We examined instrument reliability using Cronbach’s coefficient and performed a principal component analysis to assess construct validity. Then, Kruskal–Wallis and Dunn tests were conducted to analyse differences on the HBM scores, among patients with different poverty levels. Next, we used data retrieved from administrative health records ( N  = 23,370) to fit a LASSO regression model to predict individual no-show probabilities. Finally, we used the results of the CHBM in the LASSO model to improve its accuracy. Results Nine components were identified accounting for 57.7% of the variability of our data. Lower income patients were found to have a lower Health motivation score ( p -value < 0.001), a higher Severity score ( p -value < 0.001) and a higher Barriers score ( p -value < 0.001). Additionally, patients between 25 and 30 years old and with higher poverty levels are less likely to attend their appointments (O.R 0.93 (CI: 0.83–0.98) and 0.74 (CI: 0.66–0.85), respectively). We also found a relationship between the CHBM scores and the patient attendance probability. Average AUROC score for our prediction model is 0.9. Conclusion In the case of Bogotá, our results highlight the need to develop education campaigns to address misconceptions about the disease mortality and treatment (aiming at decreasing perceived severity), particularly among younger patients living in extreme poverty. Additionally, it is important to conduct an economic evaluation of screening options to strengthen the cervical cancer screening program (to reduce perceived barriers). More widely, our prediction approach has the potential to improve the cost-effectiveness of behavioural interventions to increase attendance for screening in developing countries where funding is limited.

Background Frailty is a common condition in older adults and has a major impact on patient outcomes and service use. Information on the prevalence in middle-aged adults and the patterns of progression of frailty at an individual and population level is scarce. To address this, a cohort was defined from a large primary care database in England to describe the epidemiology of frailty and understand the dynamics of frailty within individuals and across the population. This article describes the structure of the dataset, cohort characteristics and planned analyses. Methods Retrospective cohort study using electronic health records. Participants were aged ≥50 years registered in practices contributing to the Oxford Royal College of General Practitioners Research and Surveillance Centre between 2006 to 2017. Data include GP practice details, patient sociodemographic and clinical characteristics, twice-yearly electronic Frailty Index (eFI), deaths, medication use and primary and secondary care health service use. Participants in each cohort year by age group, GP and patient characteristics at cohort entry are described. Results The cohort includes 2,177,656 patients, contributing 15,552,946 person-years, registered at 419 primary care practices in England. The mean age was 61 years, 52.1% of the cohort was female, and 77.6% lived in urban environments. Frailty increased with age, affecting 10% of adults aged 50–64 and 43.7% of adults aged ≥65. The prevalence of long-term conditions and specific frailty deficits increased with age, as did the eFI and the severity of frailty categories. Conclusion A comprehensive understanding of frailty dynamics will inform predictions of current and future care needs to facilitate timely planning of appropriate interventions, service configurations and workforce requirements. Analysis of this large, nationally representative cohort including participants aged ≥50 will capture earlier transitions to frailty and enable a detailed understanding of progression and impact. These results will inform novel simulation models which predict future health and service needs of older people living with frailty. Study registration Registered on www.clinicaltrials.gov October 25th 2019, NCT04139278 .

Background/objectives Visual Electro-Diagnostic Testing (EDTs) are a highly specialised service in the NHS. The high cost of tests and a paucity of trained visual electrophysiologists has resulted in very few services across the UK and, when combined with increasing patient backlogs, has caused significant travel burden and variable waiting times. Here, we study the potential for impact on patients and services by adding a screening step to traditional referral pathways using an Electroretinogram (ERG) test from a relatively inexpensive, portable, hand-held EDT device; the RETeval ® (LKC technologies, Gaithersburg, MD, USA). Subjects/methods We model a large regional-referral EDT service using Discrete Event Simulation (DES) modelling based on retrospective patient data and published best evidence for the device. We evaluate the potential impact that adding the screening step in referral pathways could have on patient waiting times should the device prove to be safe and useable in clinical practice. Results We demonstrate that should the RETeval ® ERG be safe and useable in real-world clinical practice, it has the potential to significantly reduce patient waiting times by avoiding lab-based EDT assessment for up to 45% of patients. We also show that the impact on services and patients is likely to be resilient to realistic changes in referral numbers, sensitivity/specificity of the device and changes in clinical capacity. Conclusions This work demonstrates that a RETeval ® ERG screening step, performed at the point of referral, has the potential to result in significantly reduced EDT waiting lists through fewer patients requiring lab-based EDT assessment and that DES modelling is a useful tool in making this assessment. However, many questions remain about using the device in the real-world setting for this purpose. Future studies are needed to assess its sensitivity/specificity, test/retest variability, changes in referral patterns due to the device, useability, acceptability to patients and importantly, the consequences of screening errors. Our work, using only retrospective data and a DES model, shows that using the device as an ERG screening tool warrants further investigation due to the potential impact on both patients and clinical services.