Abstract
More than half of the population in the world lives in cities and urban populations are still rapidly expanding. Increasing population growth in cities inevitably brings about the intensification of urban health problems. The multidimensional nature of factors associated with health together with the dynamic, interconnected environment of cities moderates the effects of policies and interventions that are designed to improve population health. With the emergence of the “Internet of Things” and the availability of “Big Data,” policymakers and practitioners are in need of a new set of analytical tools to comprehensively understand the social, behavioral, and environmental factors that shape population health in cities. Systems science, an interdisciplinary field that draws concepts, theories, and evidence from fields such as computer science, engineering, social planning, economics, psychology, and epidemiology, has shown promise in providing practical conceptual and analytical approaches that can be used to solve urban health problems. This chapter describes the level of complexity that characterizes urban health problems and provides an overview of systems science features and methods that have shown great promise to address urban health challenges. We provide two specific examples to showcase systems science thinking: one using a system dynamics model to prioritize interventions that involve multiple social determinants of health in Toronto, Canada, and the other using an agent-based model to evaluate the impact of different food policies on dietary behaviors in NewYork City. These examples suggest that systems science has the potential to foster collaboration among researchers, practitioners, and policymakers from different disciplines to evaluate interconnected data and address challenging urban health problems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
Batty, M.: The New Science of Cities. MIT Press (2013). Retrievedfrom https://books.google.com/books?hl=en&lr=&id=yX-YAQAAQBAJ&oi=fnd&pg=PR7&dq=the+new+science+of+cities &ots=2jOm4VDely&sig=aPS7_YRLt-HT4QvYsV5pnhz6nvM
Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. 99(suppl 3), 7280–7287 (2002)
Braveman, P.A., Egerter, S.A., Mockenhaupt, R.E.: Broadening the focus: the need to address the social determinants of health. Am. J. Prev. Med. 40(1), S4–S18 (2011)
Capewell, S., Graham, H.: Will cardiovascular disease prevention widen health inequalities? PLoS Med. 7(8), e1000320 (2010)
Corburn, J.: Confronting the challenges in reconnecting urban planning and public health. Am. J. Public Health. 94(4), 541–546 (2004)
Diez Roux, A.V.: Complex systems thinking and current impasses in health disparities research. Am. J. Public Health. 101(9), 1627–1634 (2011)
Diez Roux, A.V.: Health in cities: is a systems approach needed? Cad. Saude Publica. 31, 9–13 (2015)
Epstein, J.M.: Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University Press, Princeton (2006)
Eubank, S., Guclu, H., Kumar, V.A., Marathe, M.V., Srinivasan, A., Toroczkai, Z., Wang, N.: Modelling disease outbreaks in realistic urban social networks. Nature. 429(6988), 180–184 (2004)
Fabian, M.P., Stout, N.K., Adamkiewicz, G., Geggel, A., Ren, C., Sandel, M., Levy, J.I.: The effects of indoor environmental exposures on pediatric asthma: a discrete event simulation model. Environ. Health. 11(1), 66 (2012)
Fawcett, S., Schultz, J., Watson-Thompson, J., Fox, M., Bremby, R., et al.: Building multisectoral partnerships for population health and health equity. Prev. Chronic Dis. 7(6), A118 (2010)
Fong, W.-K., Matsumoto, H., Lun, Y.-F.: Application of system dynamics model as decision making tool in urban planning process toward stabilizing carbon dioxide emissions from cities. Build. Environ. 44(7), 1528–1537 (2009)
Forrester, J.W., Forrester, J.W.: Urban Dynamics, vol. 114. MIT Press, Cambridge (1969). Retrieved from http://search.proquest.com/openview/111accf0f58d4e948aa8aa8e8e44530a/1.pdf?pq-origsite=gscholar&cbl=35192
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gen. Comput. Syst. 29(7), 1645–1660 (2013)
Handy, S.L., Boarnet, M.G., Ewing, R., Killingsworth, R.E.: How the built environment affects physical activity: views from urban planning. Am. J. Prev. Med. 23(2), 64–73 (2002)
Homer, J.B., Hirsch, G.B.: System dynamics modeling for public health: background and opportunities. Am. J. Public Health. 96(3), 452–458 (2006)
Jack, D., Neckerman, K., Schwartz-Soicher, O., Lovasi, G.S., Quinn, J., Richards, C.: Socio-economic status, neighborhood food environments and consumption of fruits and vegetables in New York City. Public Health Nutr. 16(7), 1197–1205 (2013)
Kumar, S., Grefenstette, J.J., Galloway, D., Albert, S.M., Burke, D.S.: Policies to reduce influenza in the workplace: impact assessments using an agent-based model. Am. J. Public Health. 103(8), 1406–1411 (2013)
Kypridemos, C., Allen, K., Hickey, G.L., Guzman-Castillo, M., Bandosz, P., Buchan, I., Capewell, S., O’Flaherty, M.: Cardiovascular screening to reduce the burden from cardiovascular disease: microsimulation study to quantify policy options. BMJ. 353, i2793 (2016)
Li, Y., Lawley, M. A., Siscovick, D. S., Zhang, D., Pagán, J.A.: Agent-based modeling of chronic diseases: a narrative review and future research directions. Prev. Chronic Dis. 13, 150561 (2016a). Retrieved from http://origin.glb.cdc.gov/pcd/issues/2016/15_0561.htm
Li, Y., Zhang, D., Pagán, J.A.: Social norms and the consumption of fruits and vegetables across New York City neighborhoods. J. Urban Health. 93(2), 244–255 (2016b)
Luke, D.A., Stamatakis, K.A.: Systems science methods in public health: dynamics, networks, and agents. Annu. Rev. Public Health. 33, 357–376 (2012)
Mabry, P.L., Milstein, B., Abraido-Lanza, A.F., Livingood, W.C., Allegrante, J.P.: Opening a window on systems science research in health promotion and public health. Health Educ. Behav. 40(1 suppl), 5S–8S (2013)
Macal, C.M., North, M.J.: Tutorial on agent-based modelling and simulation. J. Simul. 4(3), 151–162 (2010)
Mahamoud, A., Roche, B., Homer, J.: Modelling the social determinants of health and simulating short-term and long-term intervention impacts for the city of Toronto, Canada. Soc. Sci. Med. 93, 247–255 (2013)
Marmot, M., Wilkinson, R.: Social Determinants of Health. OUP, Oxford (2005). Retrieved from https://books.google.com/books?hl=en&lr=&id=AmwiS8HZeRIC&oi=fnd&pg=PA17&dq= social+determinants+of+health+urban+health&ots=y_IBd2UvD1&sig=qAMeWsnozsUDTiJ9 H7Gf8VGBoPE
Milstein, B., Jones, A., Homer, J.B., Murphy, D., Essien, J., Seville, D.: Charting plausible futures for diabetes prevalence in the United States: a role for system dynamics simulation modeling. Prev. Chronic Dis. 4(3), A52 (2007). Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1955415/
Milstein, B., Homer, J., Hirsch, G.: Analyzing national health reform strategies with a dynamic simulation model. Am. J. Public Health. 100(5), 811–819 (2010)
Nianogo, R.A., Arah, O.A.: Agent-based modeling of noncommunicable diseases: a systematic review. Am. J. Public Health. 105(3), e20–e31 (2015)
Sawyer, R.K.: Social Emergence: Societies as Complex Systems. Cambridge University Press, Cambridge (2005). Retrieved from https://books.google.com/books?hl=en&lr=& id=Hgs007Rd_moC&oi=fnd&pg=PP11&dq=,+Social+Emergence:++Societies+as+Complex+ Systems&ots=IagDx3YbrC&sig=GTkC_be-BbKlMHggnYD1XXDtCtA
Thomas, Y.F., Boufford, J.I., Talukder, S.H.: Focusing on health to advance sustainable urban transitions. J. Urban Health. 93(1), 1–5 (2016)
Tozan, Y., Ompad, D.C.: Complexity and dynamism from an urban health perspective: a rationale for a system dynamics approach. J. Urban Health. 92(3), 490–501 (2015)
Vlahov, D., Freudenberg, N., Proietti, F., Ompad, D., Quinn, A., Nandi, V., Galea, S.: Urban as a determinant of health. J. Urban Health. 84(1), 16–26 (2007)
Vlahov, D., Boufford, J.I., Pearson, C.E., Norris, L.: Urban Health: Global Perspectives, vol. 18. Wiley (2011). Retrieved from https://books.google.com/books?hl=en&lr=&id=Br5oCwA AQBAJ&oi=fnd&pg=PR11&dq=%22urban%22+JI+boufford&ots=UBWSZmVkOW&sig=y HrpLX6NW9SIZ_v1DwlH0O-_qQI
Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature. 393(6684), 440–442 (1998)
World Health Organization: Global Report on Urban Health: Equitable, Healthier Cities for Sustainable Development. UN Habitat (2016). Retrieved from http://www.who.int/kobe_centre/measuring/urban-global-report/ugr_full_report.pdf?ua=1
Zhang, D., Giabbanelli, P.J., Arah, O.A., Zimmerman, F.J.: Impact of different policies on unhealthy dietary behaviors in an urban adult population: an agent-based simulation model. Am. J. Public Health 104(7), 1217–1222 (2014). http://doi.org/10.2105/AJPH.2014.301934
Zimmerman, F.J.: Habit, custom, and power: a multi-level theory of population health. Soc. Sci. Med. 80, 47–56 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Li, Y., Boufford, J.I., Pagán, J.A. (2017). Systems Science Simulation Modeling to Inform Urban Health Policy and Planning. In: Rassia, S., Pardalos, P. (eds) Smart City Networks. Springer Optimization and Its Applications, vol 125. Springer, Cham. https://doi.org/10.1007/978-3-319-61313-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-61313-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61312-3
Online ISBN: 978-3-319-61313-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)