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      Dynamics of non-communicable disease prevention, diagnosis and control in Lebanon, a fragile setting

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          Abstract

          Background

          Non-communicable diseases (NCD) present an increasing global health challenge, particularly for settings affected by fragility where access to care may be disrupted, and where high-quality continuous care delivery is difficult to achieve. This study documents the complex dynamics of NCD prevention and management in the fragile setting of rural Beqaa, Lebanon.

          Methods

          Participatory system dynamics methods were used, including 30 semi-structured interviews and three Group Model Building (GMB) workshops. Participants included health care providers offering NCD care, and Lebanese host- and Syrian refugees community members affected by NCDs.

          Results

          Participants across all groups articulated a shared complex understanding of both the structural and direct determinants behind NCD onset. Lebanese and Syrian community members further identified several barriers to health seeking, including restrictions in health coverage, limited availability of services in the Beqaa and perceptions of poor-quality care. Health providers and community members described a health system overtly focused on disease control and overwhelmed by delivery of care to people living with NCD across both communities.

          Conclusion

          Participants across all groups agreed on the need for health promotion and primary prevention activities and identified priority interventions in these areas.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13031-020-00337-2.

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          Most cited references29

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          Social modeling of eating: a review of when and why social influence affects food intake and choice.

          A major determinant of human eating behavior is social modeling, whereby people use others' eating as a guide for what and how much to eat. We review the experimental studies that have independently manipulated the eating behavior of a social referent (either through a live confederate or remotely) and measured either food choice or intake. Sixty-nine eligible experiments (with over 5800 participants) were identified that were published between 1974 and 2014. Speaking to the robustness of the modeling phenomenon, 64 of these studies have found a statistically significant modeling effect, despite substantial diversity in methodology, food type, social context and participant demographics. In reviewing the key findings from these studies, we conclude that there is limited evidence for a moderating effect of hunger, personality, age, weight or the presence of others (i.e., where the confederate is live vs. remote). There is inconclusive evidence for whether sex, attention, impulsivity and eating goals moderate modeling, and for whether modeling of food choice is as strong as modeling of food intake. Effects with substantial evidence were: modeling is increased when individuals desire to affiliate with the model, or perceive themselves to be similar to the model; modeling is attenuated (but still significant) for healthy-snack foods and meals such as breakfast and lunch, and modeling is at least partially mediated through behavioral mimicry, which occurs without conscious awareness. We discuss evidence suggesting that modeling is motivated by goals of both affiliation and uncertainty-reduction, and outline how these might be theoretically integrated. Finally, we argue for the importance of taking modeling beyond the laboratory and bringing it to bear on the important societal challenges of obesity and disordered eating.
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            System Dynamics Modeling: Tools for Learning in a Complex World

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              System dynamics modeling for public health: background and opportunities.

              The systems modeling methodology of system dynamics is well suited to address the dynamic complexity that characterizes many public health issues. The system dynamics approach involves the development of computer simulation models that portray processes of accumulation and feedback and that may be tested systematically to find effective policies for overcoming policy resistance. System dynamics modeling of chronic disease prevention should seek to incorporate all the basic elements of a modern ecological approach, including disease outcomes, health and risk behaviors, environmental factors, and health-related resources and delivery systems. System dynamics shows promise as a means of modeling multiple interacting diseases and risks, the interaction of delivery systems and diseased populations, and matters of national and state policy.
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                Author and article information

                Contributors
                KDiaconu@qmu.ac.uk
                Journal
                Confl Health
                Confl Health
                Conflict and Health
                BioMed Central (London )
                1752-1505
                11 January 2021
                11 January 2021
                2021
                : 15
                : 4
                Affiliations
                [1 ]NIHR Global Health Research Unit on Health in Situations of Fragility, Musselburgh, UK
                [2 ]GRID grid.22903.3a, ISNI 0000 0004 1936 9801, Global Health Institute, , American University of Beirut, ; Beirut, Lebanon
                [3 ]GRID grid.104846.f, Institute for Global Health and Development, , Queen Margaret University, ; Edinburgh, UK
                [4 ]GRID grid.22903.3a, ISNI 0000 0004 1936 9801, Nutrition and Food Sciences Department, Faculty of Agriculture and Food Sciences, , American University of Beirut, ; Beirut, Lebanon
                Author information
                http://orcid.org/0000-0002-5810-9725
                Article
                337
                10.1186/s13031-020-00337-2
                7802297
                33430916
                e66935ce-e4fb-4f74-87f0-1921850c3e9a
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 25 March 2020
                : 22 December 2020
                Funding
                Funded by: National Institute for Health Research
                Award ID: 16/136/100
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Health & Social care
                fragility,non-communicable diseases,prevention,system dynamics
                Health & Social care
                fragility, non-communicable diseases, prevention, system dynamics

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