Systems Modeling and Big Data for Non-Communicable Disease Prevention
- LAST MODIFIED: 22 February 2018
- DOI: 10.1093/obo/9780199756797-0176
- LAST MODIFIED: 22 February 2018
- DOI: 10.1093/obo/9780199756797-0176
Non-communicable diseases (NCDs) are a complex problem and are the leading cause of death globally. Numerous factors contribute to the development of NCDs including environmental, social, physical, cultural, socio-economic, behavioral, and biological determinants. These factors are interrelated and change over time, making them challenging to understand and address effectively. Quantitative systems science methods, such as dynamic simulation modeling, have been used for many years in engineering and other disciplines. Dynamic simulation modeling methods are increasingly being employed in the health sector to navigate the complex causes of NCDs and help formulate efficient and effective responses to address them. Agent-based modeling, system dynamics, and discrete event simulation are methods collectively termed “dynamic simulation modeling.” Dynamic simulation modeling is demonstrating increasing promise for enabling the collaborative development of “what if” tools that can inform policy and practice, and help build consensus for action. Parallel technological developments in data generation and collection have led to the creation of extremely large, high density and diverse data sets, commonly known as “big data.” Increasingly, attention is being paid to the potential for the fields (and analytic outputs) of dynamic simulation modeling and big data to be cross-leveraged, with each being informed, refined, and increased in predictive power by the other. This bibliography provides key resources and publications that highlight the value of dynamic simulation modeling and the use of big data for informing actions for improved prevention of NCDs. A brief overview of the challenges of policy making for complex health problems is presented. Next, a description of dynamic simulation modeling methods is provided, including an overview of the application of these methods in NCD prevention specifically, and public health more broadly. This is followed by commentary on big data and the use of big data in NCD prevention specifically and public health more broadly. An overview of the value and more recent advances and applications of combining dynamic simulation modeling methods with big data for public health is then included. Finally, a number of useful systems resources are listed as they offer relevant guidance. In this article, the term “non-communicable diseases” refers to the health conditions sometimes described to as “chronic diseases,” and “modeling” has been used for brevity in section titles as an abbreviation for “dynamic simulation modeling.” We dedicate this work to our colleague, mentor, and above-all friend, Associate Professor Sonia Wutzke (1970–2017). The public health community is richer for having had you as one of its most passionate advocates.
Introductory Works: Health Policy Challenges and Systems Science
The World Health Organization 2011 has released a number of reports on the burden of NCDs as well an international strategy for strengthening prevention efforts from World Health Organization 2013. The challenges experienced in public health and the value of systems science techniques to address these challenges are explored by Green 2006. Ip, et al. 2013 elaborates on how these challenges can be addressed by systems science techniques. The authors claim that while statistical and systems science may differ in strategies and language, these differences can be navigated and resolved. An Oxford Bibliography by Finegood, et al. 2012 presents a broader review of useful resources for understanding complexity and systems theory. Reponses to the considerable burden of NCDs are partly determined by the effectiveness of service delivery systems that can facilitate these solutions. International context on health systems is provided by the World Health Organization 2007, with its Framework for Action identifying six building blocks of health system features. In their review of systems science research, Carey, et al. 2015 proposes a framework for health systems that includes guidance on where public health could engage more fully with systems methodologies, including modeling.
Carey, G., E. Malbon, N. Carey, A. Joyce, B. Crammond, and A. Carey. 2015. Systems science and systems thinking for public health: A systematic review of the field. BMJ Open 5.12: e009002.
In this systematic review, the authors provide a useful overview of systems science research in public health. The future potential and limitations in dynamic simulation modeling taking place in this field are also explored.
Finegood, D., L. Johnston, P. Giabbanelli, P. Deck, S. Frood, L. Burgos-Liz, and A. Best. 2012. Complexity and systems theory. Oxford Bibliographies.
The authors provide a bibliography of works covering the principles of systems thinking and complexity science. Also included are articles and resources on the application of these approaches in public health. The application to individual behavior change is included, which is of particular relevance to the prevention of lifestyle related chronic disease.
Green, L. 2006. Public health asks of systems science: To advance our evidence-based practice, can you help us get more practice-based evidence? American Journal of Public Health 96.3: 406–409.
This commentary proposes how systems science techniques can help in the improvement of public health. The author describes how the public health community seek a more evidence-based public health practice, while most evidence comes from artificially controlled research that does not reflect practice.
Ip, E. H., H. Rahmandad, D. A. Shoham, R. Hammond, T. T. -K. Huang, Y. Wang, and P. L. Mabry. 2013. Reconciling statistical and systems science approaches to public health. Health Education & Behavior 40.1 Suppl.: 123S–131S.
This article describes how statistical and systems science approaches, while having some conflicts, can be reconciled, and together can progress solutions to complex challenges. The authors present different forms of models as representing various compromises among the four requirements of generality, realism, fit, and precision.
World Health Organization. 2007. Everybody’s business: Strengthening health systems to improve health outcomes. Framework for Action.
This is the WHO Framework for Action. This report articulates international health service issues and challenges, aiming to “clarify and strengthen” the WHO’s role in health systems. A framework containing six building blocks is provided, to allow a definition of required features of health systems, express the WHO’s priorities, and identify gaps in WHO support. The building blocks include service delivery, health workforce, information, medical products, vaccines and technologies, and financing and stewardship.
World Health Organization. 2011. Global status report on noncommunicable diseases 2010.
This report highlights the extent of the burden of disease from NCDs. It includes commentary on the impact this has on development, particularly among populations in lower social and economic positions. Suggested actions focus on improving data collection, encouraging population-wide interventions, and system level solutions, such as taxation, advertising bans on tobacco, product reformulation, and restricting access to alcohol sales.
World Health Organization. 2013. Global action plan for the prevention and control of noncommunicable diseases 2013–2020.
This action plan emphasizes how the multitude of premature deaths internationally from NCDs could have largely been prevented. It provides a suite of policy options for member states, the World Health Organization (WHO), other United Nations organizations and intergovernmental organizations, nongovernmental organizations, and the private sector for the attainment of voluntary global targets, including that of a 25 percent relative reduction in premature mortality from NCDs by 2025.
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