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Abstract System dynamics approaches are increasingly addressing the complexity of public health problems such as childhood overweight and obesity. These approaches often use system mapping methods, such as the construction of causal loop diagrams, to gain an understanding of the system of interest. However, there is limited practical guidance on how such a system understanding can inform the development of an action programme that can facilitate systems changes. The Lifestyle Innovations Based on Youth Knowledge and Experience (LIKE) programme combines system dynamics and participatory action research to improve obesity-related behaviours, including diet, physical activity, sleep and sedentary behaviour, in 10–14-year-old adolescents in Amsterdam, the Netherlands. This paper illustrates how we used a previously obtained understanding of the system of obesity-related behaviours in adolescents to develop an action programme to facilitate systems changes. A team of evaluation researchers guided interdisciplinary action-groups throughout the process of identifying mechanisms, applying the Intervention Level Framework to identify leverage points and arriving at action ideas with aligning theories of change. The LIKE action programme consisted of 8 mechanisms, 9 leverage points and 14 action ideas which targeted the system’s structure and function within multiple subsystems. This illustrates the feasibility of developing actions targeting higher system levels within the confines of a research project timeframe when sufficient and dedicated effort in this process is invested. Furthermore, the system dynamics action programme presented in this study contributes towards the development and implementation of public health programmes that aim to facilitate systems changes in practice.

PurposeChildhood overweight is considered a complex problem influenced by a range of factors, including energy balance-related behaviours (EBRBs) and interacting drivers of these behaviours. There is growing support that applying a systems approach is required to tackle complex problems resulting in actions that attempt to change the system’s dynamics. Additionally, a participatory approach is advocated to include the lived experience of the population of interest both in the understanding of the system as well as the development, implementation and evaluation of relevant actions. We therefore combined Intervention Mapping, Participatory Action Research (PAR) and system dynamics in the development, implementation and evaluation of actions contributing to healthy EBRBs together with adolescents.MethodsFour PAR groups comprising of 6–8 adolescent co-researchers (10–14 years) and 1–2 adult facilitators met weekly during 3–4 years. The structured Intervention Mapping protocol guided the process of the systematic development, implementation and evaluation of actions. System dynamics tools were included for the creation of Causal Loop Diagrams and development of systemic actions.ResultsOur approach comprised six steps that were executed by the PAR groups: (1) build Causal Loop Diagrams for each EBRB through peer research and identify overarching mechanisms, (2) determine leverage points using the Intervention Level Framework, (3) develop action ideas, (4) develop detailed actions including an implementation plan, (5) implement and, (6) evaluate the actions. PAR ensured that the actions fitted the lived experience of the adolescents, whilst system dynamics promoted actions at different levels of the system. The Intervention Mapping protocol ensured that the actions were theory-based. The main challenge involved integrating system dynamics within our practise in cooperation with adolescent co-researchers.ConclusionWe experienced that combining Intervention Mapping, PAR and system dynamics worked well in developing, implementing and evaluating actions that target different levels of the system that drive adolescents’ EBRBs. This study serves as an example to other studies aimed at developing, implementing and evaluating actions using a participatory and systems approach.

Introduction and MethodsTo develop an understanding of the dynamics driving obesity-related behaviours in adolescents, we conducted systems-based analysis on a causal loop diagram (CLD) created from a multi-actor perspective, including academic researchers, adolescents and local stakeholders.ResultsThe CLD contained 121 factors and 31 feedback loops. We identified six subsystems with their goals: (1) interaction between adolescents and the food environment, with profit maximisation as goal, (2) interaction between adolescents and the physical activity environment, with utility maximisation of outdoor spaces as goal, (3) interaction between adolescents and the online environment, with profit maximisation from technology use as goal, (4) interaction between adolescents, parenting and the wider socioeconomic environment, with a goal focused on individual parental responsibility, (5) interaction between healthcare professionals and families, with the goal resulting in treating obesity as an isolated problem, and (6) transition from childhood to adolescence, with the goal centring around adolescents’ susceptibility to an environment that stimulates obesity-related behaviours.DiscussionAnalysis showed that inclusion of the researchers’ and stakeholders’ perspectives contributed to an understanding of how the system structure of an environment works. Integration of the adolescents’ perspective enriched insights on how adolescents interact with that environment. The analysis further showed that the dynamics driving obesity-related behaviours are geared towards further reinforcing such behaviours.

Abstract Background Systems thinking embraces the complexity of public health problems, including childhood overweight and obesity. It aids in understanding how factors are interrelated, and it can be targeted to produce favourable changes in a system. There is a growing call for systems approaches in public health research, yet limited practical guidance is available on how to evaluate public health programmes within complex adaptive systems. The aim of this paper is to present an evaluation framework that supports researchers in designing systems evaluations in a comprehensive and practical way. Methods We searched the literature for existing public health systems evaluation studies. Key characteristics on how to conduct a systems evaluation were extracted and compared across studies. Next, we overlaid the identified characteristics to the context of the Lifestyle Innovations Based on Youth Knowledge and Experience (LIKE) programme evaluation and analyzed which characteristics were essential to carry out the LIKE evaluation. This resulted in the Evaluation of Programmes in Complex Adaptive Systems (ENCOMPASS) framework. Results The ENCOMPASS framework includes five iterative stages: (1) adopting a system dynamics perspective on the overall evaluation design; (2) defining the system boundaries; (3) understanding the pre-existing system to inform system changes; (4) monitoring dynamic programme output at different system levels; and (5) measuring programme outcome and impact in terms of system changes. Conclusions The value of ENCOMPASS lies in the integration of key characteristics from existing systems evaluation studies, as well as in its practical, applied focus. It can be employed in evaluating public health programmes in complex adaptive systems. Furthermore, ENCOMPASS provides guidance for the entire evaluation process, all the way from understanding the system to developing actions to change it and to measuring system changes. By the nature of systems thinking, the ENCOMPASS framework will likely evolve further over time, as the field expands with more completed studies.

Abstract Background While influenza-like-illness (ILI) surveillance is well-organized at primary care level in Europe, few data are available on more severe cases. With retrospective data from intensive care units (ICU) we aim to fill this current knowledge gap. Using multiple parameters proposed by the World Health Organization we estimate the burden of severe acute respiratory infections (SARI) in the ICU and how this varies between influenza epidemics. Methods We analyzed weekly ICU admissions in the Netherlands (2007–2016) from the National Intensive Care Evaluation (NICE) quality registry (100% coverage of adult ICUs in 2016; population size 14 million) to calculate SARI incidence, SARI peak levels, ICU SARI mortality, SARI mean Acute Physiology and Chronic Health Evaluation (APACHE) IV score, and the ICU SARI/ILI ratio. These parameters were calculated both yearly and per separate influenza epidemic (defined epidemic weeks). A SARI syndrome was defined as admission diagnosis being any of six pneumonia or pulmonary sepsis codes in the APACHE IV prognostic model. Influenza epidemic periods were retrieved from primary care sentinel influenza surveillance data. Results Annually, an average of 13% of medical admissions to adult ICUs were for a SARI but varied widely between weeks (minimum 5% to maximum 25% per week). Admissions for bacterial pneumonia (59%) and pulmonary sepsis (25%) contributed most to ICU SARI. Between the eight different influenza epidemics under study, the value of each of the severity parameters varied. Per parameter the minimum and maximum of those eight values were as follows: ICU SARI incidence 558–2400 cumulated admissions nationwide, rate 0.40–1.71/10,000 inhabitants; average APACHE score 71–78; ICU SARI mortality 13–20%; ICU SARI/ILI ratio 8–17 cases per 1000 expected medically attended ILI in primary care); peak-incidence 101–188 ICU SARI admissions in highest-incidence week, rate 0.07–0.13/10,000 population). Conclusions In the ICU there is great variation between the yearly influenza epidemic periods in terms of different influenza severity parameters. The parameters also complement each other by reflecting different aspects of severity. Prospective syndromic ICU SARI surveillance, as proposed by the World Health Organization, thereby would provide insight into the severity of ongoing influenza epidemics, which differ from season to season.

Summary: This paper reports how we applied systems dynamics methods to gain insight into the complexity of obesity‐related behaviors in youth, including diet, physical activity, sedentary behavior, and sleep, by integrating a literature review into causal loop diagrams (CLDs). Results showed that the CLDs consisted of multiple subsystems and three types of dynamics appeared, including (1) feedback loops, (2) connections between feedback loops and subsystems, and (3) mechanisms. We observed clear similarities in the dynamics for the four behaviors in that they relate to "traditional" subsystems, such as home and school environments, as well as to newly added subsystems, including macroeconomics, social welfare, and urban systems. The CLDs provided insights that can support the development of intervention strategies, including (1) the confirmation that a range of mechanisms cover and connect multiple levels and settings, meaning that there is no silver bullet to address obesity; (2) understanding of how interventions in one particular setting, such as school, might be influenced by the interactions with other settings, such as urban systems; and (3) a comprehensive view of (un)intended consequences. This way of framing the problem will assist moving towards public health interventions that respond to and operate in the complexity of the real world. [ABSTRACT FROM AUTHOR]

Background: The burden of severe influenza virus infections is poorly known, for which surveillance of severe acute respiratory infection (SARI) is encouraged. Hospitalized SARI patients are however not always tested for influenza virus infection. Thus, to estimate the impact of influenza circulation we studied how influenza in primary care relates to intensive care unit (ICU) admissions using a modelling approach., Methods: We used time-series regression modelling to estimate a) the number of SARI admissions to ICU associated with medically attended influenza infections in primary care; b) how this varies by season; and c) the time lag between SARI and influenza time series. We analysed weekly adult ICU admissions (registry data) and adult influenza incidence (primary care surveillance data) from July 2007 through June 2016., Results: Depending on the year, 0% to 12% of annual SARI admissions were associated with influenza (0-554 in absolute numbers; population rate: 0/10 000-0.39/10 000 inhabitants), up to 27% during influenza epidemics. The average optimal fitting lag was +1 week (SARI trend preceding influenza by 1 week), varying between seasons (-1 to +4) with most seasons showing positive lags., Conclusion: Up to 12% of yearly SARI admissions to adult ICU are associated with influenza, but with large year-to-year variation and higher during influenza epidemics. In most years, SARI increases earlier than medically attended influenza infections in the general population. SARI surveillance could thus complement influenza-like illness surveillance by providing an indication of the season-specific burden of severe influenza infections and potential early warning of influenza activity and severity., (© 2020 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)