Noncommunicable disease modeling and simulation as means of understanding childhood obesity and intervention effectiveness

Meghana Aruru; Saumyadipta Pyne

doi:10.4103/bjhs.bjhs_8_18

REVIEW ARTICLE

Year : 2018 | Volume : 3 | Issue : 1 | Page : 3-8

Noncommunicable disease modeling and simulation as means of understanding childhood obesity and intervention effectiveness

Meghana Aruru¹, Saumyadipta Pyne²
¹ Program Evaluation and Research Unit (PERU), Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, USA
² Department of Biostatistics, Public Health Dynamics Laboratory, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, USA; ICMR National Institute of Medical Statistics, New Delhi, India

Date of Submission	27-Mar-2018
Date of Acceptance	17-Apr-2018
Date of Web Publication	19-Jun-2018

Correspondence Address:
Prof. Saumyadipta Pyne
Department of Biostatistics, Public Health Dynamics Laboratory, Graduate School of Public Health, University of Pittsburgh, Pittsburgh

Source of Support: None, Conflict of Interest: None

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DOI: 10.4103/bjhs.bjhs_8_18

Abstract

Lifestyle and dietary changes have led to rise in noncommunicable diseases such as diabetes, obesity, and cardiovascular disorders, accounting for increased mortality and morbidity in many parts of the world including developing countries. Obesity has doubled since 1980s and continues to be a growing problem of our times. Public health policies to address obesity are evolving in connection with dynamically changing human behaviors and complex interactions with the environment. However, designing and testing of new interventions are expensive and time-consuming. Computational simulations to model interventions offer useful tools to compare the effectiveness of potential interventions. In this article, we discuss a popular computational approach, agent-based modeling (ABM), to address the global challenge of childhood obesity through modeling of different interventions as described in the literature.

Keywords: Agent-based modeling, childhood obesity, public health intervention, simulation

How to cite this article:
Aruru M, Pyne S. Noncommunicable disease modeling and simulation as means of understanding childhood obesity and intervention effectiveness. BLDE Univ J Health Sci 2018;3:3-8

How to cite this URL:
Aruru M, Pyne S. Noncommunicable disease modeling and simulation as means of understanding childhood obesity and intervention effectiveness. BLDE Univ J Health Sci [serial online] 2018 [cited 2020 Nov 25];3:3-8. Available from: https://www.bldeujournalhs.in/text.asp?2018/3/1/3/234650

According to the World Health Organization (WHO), obesity and overweight are defined as abnormal or excessive fat accumulations that may impair health.^[1] Obesity is a risk factor for multiple noncommunicable diseases including cardiovascular problems, diabetes, musculoskeletal disorders, and certain cancers. It has a variety of adverse effects on blood pressure, cholesterol, triglycerides, and insulin resistance. Risks of coronary heart disease, ischemic stroke, and type 2 diabetes mellitus increase with corresponding increases in body mass index (BMI). Higher BMI increases the risks for certain cancers: breast, colon/rectum, endometrium, kidney, esophagus, and pancreas.^[2],[3]

Obesity and overweight are measured using the BMI, a simple index given by weight-by-height-squared. BMI values are classified by the WHO as follows:^[4]

Underweight: BMI <18.5
Normal: BMI 18.5–24.99
Overweight: BMI ≥25 and
Obese: BMI ≥ 30.

In 2014, globally, 39% of adults (age >18 years) were overweight and 13% obese. The 2010 WHO report “Global Status Report on Noncommunicable Diseases” stated that 2.8 million people die each year as a result of being overweight or obese.^[3] An estimated 35.8 million “Disability-Adjusted Life Years” are attributed to overweight or obesity.^[5] By 2030, the projected population will include 2.16 billion overweight and 1.12 billion obese individuals [Figure 1]a and [Figure 1]b.^[6]

Figure 1: (a) Prevalence of overweight adult males (WHO data, 2014) (b) Prevalence of overweight adult females (WHO data, 2014)

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Industrialization and agricultural advances have led to a rapidly changing society in the 21^st century. There is a rise in sedentary lifestyles aside calorie-rich diets, reduced strenuous physical activity, and decreased energy expenditures. In the past decade between 2007–2008 and 2015–2016, rise in obesity in American adults persisted, while there were no significant trends observed in American youth.^[7]

Concurrent with their rising economic growth, low- and middle-income countries such as India, Brazil, China, and others are seeing increase in obesity among their populations.^{[8],[9],[10],[11],[12],[13]} Reduction of obesity is now an important public health goal in many countries and also included among the WHO global nutrition targets for 2025. In fact, the United Nations Sustainable Development goals include obesity reduction as part of the nutrition relevant goals for 2030.^[14]

Several nations are beginning to address overweight and obesity through different interventions including education, taxation on high-calorie foods, investments in preventive care, improved nutrition supply chains, marketing regulations, and food labeling.^{[13],[15],[16]} Such steps may lead to corresponding declines in the prevalence of type 2 diabetes and other comorbidities. A particular challenge in public health nutrition pertains to childhood obesity that often continues into adulthood, with increased risks for type 2 diabetes, cardiovascular disease, hypertension, and polycystic ovarian syndrome.^[17]

Overweight and obesity in children aged between 5 and 19 years are defined as follows:

Overweight: BMI-for-age >1 standard deviation above the WHO growth reference median
Obesity: BMI-for-age >2 standard deviations above the WHO growth reference median.

Other measures to determine overweight/obesity include bioelectric impedance analysis cutoff, triceps skinfold cutoff, waist circumference, and waist-to-hip ratio cutoff.

According to the WHO, 41 million children (<5 years) worldwide were found to be obese/overweight in 2016.^[18] The concurrent rise of obesity across the globe appears to be driven by changes in the global food system, taking place in an obesogenic environment. The advent of developed transportation systems, food manufacturing and processing plants, and rapid urbanization has led to an increased availability of low-cost, high-calorie, and nutrient-poor foods with high sugar, salt, and fat over the last four decades.^[10]

In addition to physical health, obesity in childhood can also have significant psychological impact including recurrent depression.^[19] Studies of obese adolescents have indicated bullying and victimization by peers leading to social isolation, withdrawal, and depression.^{[20],[21],[22]} Most adolescents in the absence of good familial/social relationships may develop eating disorders perpetuating the cycle of poor diets and unhealthy lifestyles [Figure 2].

Figure 2: Progression from childhood obesity to adult obesity^[36]

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Strategies to prevent childhood obesity and subsequent spillover effects into adulthood involve multimodal interventions at various settings – home, school, and community. In Singapore, for example, an 8-year school-based campaign with government support was successful in reducing the prevalence of obesity from 16.6% in early 1990's to less than 14.6% in 1988.^[23] Developing countries such as India are experiencing rapid socioeconomic growth and increase in disposable incomes along with a concurrent rise in obesity among their children. In 2010, pooled estimates from 52 studies conducted in 16 Indian states indicated a combined prevalence of 19.3% childhood overweight and obesity compared to 16.3% reported in 2001–2005.^[24] Cases such as the one described below are becoming increasingly common, particularly in urban areas.^[25]

Targeted interventions to reduce obesity are usually difficult, time-consuming, and expensive and may have little effectiveness over a larger population. This is because of individual-level heterogeneity in terms of one's diet and lifestyle choices. Targeted interventions can be effective when combined with alternate strategies to combat resistance to the proposed intervention and a laggard response from the population.

Such concerns underscore the importance of harnessing computer simulation models to test an intervention in virtual but demographically realistic populations. Computational modeling is commonly used in finance and engineering. In healthcare, computational modeling is only now beginning to be applied to solve large-scale complex problems such as non-communicable diseases.

Testing Interventions With Agent-Based Modeling

Advances in computational capacity and mathematical algorithms have led to the development of simulation models that can test interventions and compare different interventions to demonstrate cost-effectiveness. Agent-based modeling (ABM) is a computational modeling approach where system-level phenomena can be observed by modeling individual behaviors and their interactions with each other and their environment.^[26],[27]

An agent is an individual and may represent a human, an organism, a business, or any entity that pursues a unique goal. Agents can vary in terms of their characteristics such as size, location, resources, and history.^[28] Studies in social sciences and epidemiology define agents as individuals who live and interact in a virtual society. Deterministic or probabilisticrules determine how agents interact with themselves as well as the environment under specified conditions.

In an ABM, a researcher builds a simulated environment that represents a simplified version of the real-world processes of interest and observes the consequences of manipulation of key variables on attitudes and behaviors of individual agents.^[29] For example, each agent may be a student who goes to school in the morning and returns home in the evening. Simulations enable researchers to test interventions such as riding a bicycle to and from the school and observe outcomes. Changes such as alterations in food supply, introduction of new market players, and regulatory policymaking can be built into the model to study their effects. For instance, costs of products in a simulated environment could be varied to observe changes in agent purchasing behaviors that may negatively impact the intervention. A model is built through a series of steps [Table 1] that include parameter inputs to test various hypotheses or scenarios.^[29] An ABM is often calibrated with real-world data.

Table 1: Steps in agent-based modeling^[28]

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Open source platforms are available to build and implement ABMs. Repast (repast.github.io/index.html), Anylogic (anylogic. com), and NetLogo (ccl.northwestern. edu/NetLogo) are commonly used. Choice of software may be determined by cost, ease of programming, power, and preference.^[29] Another platform is the Framework for Reconstructing Epidemiological Dynamics (FRED) developed by the Public Health Dynamics Laboratory at the University of Pittsburgh (fred.publichealth.pitt.edu/).^[30] FRED is used by researchers to test public health interventions such as school closures and effect of vaccinations [Table 1].^[30]

Synthetic populations are created to mimic real populations in terms of their demographics, socioeconomic patterns, and behavioral characteristics. An advantage of using synthetic populations is that real-life individuals are not at risk of being identified, thereby making a broad range of intervention studies more ethically acceptable. This allows a researcher to simulate and study disease spreads, illness trajectories, cost-effectiveness of interventions, population outcomes for real-time decisionmaking, and so on.

Indeed, some ABM studies have focused on obesity even though obesity is a highly complex phenomenon. An early use of ABM in childhood obesity is SimObesity,^[31] in which researchers developed a spatial microsimulation model of obesogenic environments for children in Leeds, UK. In this study, individual-level microdata were drawn from two national surveys and, the UK 2001 census to create a synthetic population. Additional variables such as health, environment, and socioeconomic status were added to create microlevel estimates of the obesogenic environments. Results indicated that social capital and poverty were strongly associated with childhood obesity.^[31] Additional studies addressing childhood obesity are described in [Table 2].

Table 2: Some case studies on agent-based modeling and obesity

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Discussion

With the advent of rapid urbanization and upward mobilization, the upwardly mobile middle class in many developing countries is now exposed to Western diets that are higher in caloric content than traditional diets. Increases in income may have large effects on calorie intake. Rapid urbanization has reduced energy expenditures through reduced physical activity, increased sedentary lifestyles, and an increased uptake of alcohol and tobacco.^[32],[33]

There are significant costs and logistical difficulties with planning, developing, and implementing interventions with real populations. Testing different hypotheses through ABM simulations may provide useful insights about efficacy (and costs) of interventions and appropriate allocation of resources to maximize outcomes. Researchers could track synthetic populations longitudinally using ABM to demonstrate long-term impacts of prospective interventions while evaluating them and making necessary corrections along the course.

ABM has the potential to address public health challenges such as noncommunicable diseases in general and its manifestations like childhood obesity in particular. In addition to social influences, incorporating health behaviors including population genetics, diets, lifestyles, environments (e.g., suitable public spaces such as walking paths and occupational exposures), and factors such as food supply chain would serve to further enhance the simulation models. The available datasets in India include the National Family Health Survey,^[38] National Nutrition Monitoring Board,^[39] Annual Health Survey,^[40] District Level Health Survey,^[41] and Census data ^[42]. The flexibility afforded by ABM in modeling interactions between individual agents and their environments is particularly useful for comparing interventions across space and time.

Toward this end, building capacity in disease modeling is necessary for developing a network of health professionals who can provide actionable inputs including modeled scenarios to policymakers and health systems within their respective spheres of influence. Focused workshops ^[43] can prepare researchers to use ABM and other computational techniques as means to understand and address complex public health problems for informed and timely policymaking.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

References

1.	Obesity and Overweight. World Health Organization. Available from: http://www.who.int/mediacentre/factsheets/fs311/en/. [Last accessed on 2017 Dec 15].
2.	17 Million Obese Children in India by 2025. Hindustan Times. Available from: http://www.hindustantimes.com/mumbai-news/17-million-obese-children -in-india-by- 2025-international-journal/story-vTSZnYhFIWTjgKwSg5J2AL.html. [Last accessed on 2017 Dec 15].
3.	World Health Report 2002. Reducing Risks, Promoting Healthy Life. World Health Organization; 2002. Available from: http://www.who.int/whr/2002/en/. [Last accessed on 2018 Jan 02].
4.	BMI Index. Global Database on Body Mass Index. World Health Organization. Available from: http://www.apps.who.int/bmi/index.jsp?introPage=intro_3.html. [Last accessed on 2018 Jan 02].
5.	Global Health Risks Mortality and Burden of Disease Attributable to Selected Major Risks. Vol. 1. World Health Organization; 2009. Available from: http://www.who.int/healthinfo/global_burden_disease/GlobalHealthRisks_report_full.pdf. [Last accessed on 2018 Jan 02].
6.	Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes 2008;32:1431-7.
7.	Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in Obesity and Severe Obesity Prevalence in US Youth and Adults by Sex and Age, 2007-2008 to 2015-2016. J Am Med Assoc 2018;319:1723-5.
8.	Mendez MA, Monteiro CA, Popkin BM. Overweight exceeds underweight among women in most developing countries. Am J Clin Nutr 2005;81:714-21. [PUBMED]
9.	Monteiro CA, Moura EC, Conde WL, Popkin BM. Socioeconomic status and obesity in adult populations of developing countries: A review. Bull World Health Organ 2004;82:940-6. [PUBMED]
10.	Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. The global obesity pandemic: Shaped by global drivers and local environments. Lancet 2011;378:804-14. [PUBMED]
11.	Arbex AK, Rocha DR, Aizenberg M, Ciruzzi MS. Obesity epidemic in Brazil and Argentina: A public health concern. J Health Popul Nutr 2014;32:327-34. [PUBMED]
12.	Abrha S, Shiferaw S, Ahmed KY. Overweight and obesity and its socio-demographic correlates among urban Ethiopian women: Evidence from the 2011 EDHS. BMC Public Health 2016;16:636. [PUBMED]
13.	Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev 2012;70:3-21. [PUBMED]
14.	Framework to Monitor and Evaluate Implementation of the Global Strategy on Diet, Physical Activity and Health. World Health Organization; 2014. Available from: http://www.who.int/dietphysicalactivity/DPASindicators/en/. [Last accessed on 2018 Jan 04].
15.	Vir SC. Public Health Nutrition in Developing Countries. Part II. New Delhi, India: Woodhead Publishing India Pvt. Ltd.; 2012. p. 1030-64, 1133-53.
16.	Vir SC. Public Health Nutrition in Developing Countries. Part I. New Delhi, India: Woodhead Publishing India Pvt. Ltd.; 2011. p. 50-104.
17.	Preventing Chronic Diseases: A Vital Investment. World Health Organization; 2015. Available from: http://www.who.int/chp/chronic_disease_report/en/. [Last accessed on 2017 Dec 04].
18.	Obesity: Preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 2000;894:i-xii, 1-253. [PUBMED]
19.	Daniels SR, Arnett DK, Eckel RH, Gidding SS, Hayman LL, Kumanyika S, et al. Overweight in children and adolescents: Pathophysiology, consequences, prevention, and treatment. Circulation 2005;111:1999-2012. [PUBMED]
20.	Griffiths LJ, Parsons TJ, Hill AJ. Self-esteem and quality of life in obese children and adolescents: A systematic review. Int J Pediatr Obes 2010;5:282-304. [PUBMED]
21.	van Geel M, Vedder P, Tanilon J. Are overweight and obese youths more often bullied by their peers? A meta-analysis on the correlation between weight status and bullying. Int J Obes 2014;38:1263-7.
22.	Accelerating Progess in Obesity Prevention: Solving the Weight of the Nation. Health and Medicine Division. Institute of Medicine. Available from: http://www.nationalacademies.org/hmd/Reports/2012/Accelerating-Progress-in-Obesity-Prevention.aspx. [Last accessed on 2018 Jan 09].
23.	Toh CM, Cutter J, Chew SK. School based intervention has reduced obesity in Singapore. British Med J 2002;324:427.
24.	Ranjani H, Mehreen TS, Pradeepa R, Anjana RM, Garg R, Anand K, et al. Epidemiology of childhood overweight & obesity in India: A systematic review. Indian J Med Res 2016;143:160-74. [PUBMED] [Full text]
25.	Kamala T. India's Quiet Tide of Childhood Obesity. IndiaSpend. Available from: http://www.indiaspend.com/cover-story/indias-quiet-tide-of-childhood-obesity-10854. [Last accessed on 2018 Feb 18].
26.	Epstein JM. Agent-based computational models and generative social science. Complexity 1999;4:41-60.
27.	Li Y, Lawley MA, Siscovick DS, Zhang D, Pagán JA. Agent-based modeling of chronic diseases: A narrative review and future research directions. Prev Chronic Dis 2016;13:E69.
28.	Railsback SF, Grimm V. Agent-Based and Individual-Based Modeling: A Practical Introduction. New Jersey, USA: Princeton University Press; 2011.
29.	Nianogo RA, Arah OA. Agent-based modeling of noncommunicable diseases: A systematic review. Am J Public Health 2015;105:e20-31.
30.	Grefenstette JJ, Brown ST, Rosenfeld R, DePasse J, Stone NT, Cooley PC, et al. FRED (a framework for reconstructing epidemic dynamics): An open-source software system for modeling infectious diseases and control strategies using census-based populations. BMC Public Health 2013;13:940. [PUBMED]
31.	Edwards KL, Clarke GP. The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity. Soc Sci Med 2009;69:1127-34. [PUBMED]
32.	Tripathy JP, Thakur JS, Jeet G, Chawla S, Jain S, Prasad R, et al. Urban rural differences in diet, physical activity and obesity in India: Are we witnessing the great Indian equalisation? Results from a cross-sectional STEPS survey. BMC Public Health 2016;16:816.
33.	Biro FM, Wien M. Childhood obesity and adult morbidities. Am J Clin Nutr 2010;91:1499S-505S. [PUBMED]
34.	Hennessy E, Ornstein JT, Economos CD, Herzog JB, Lynskey V, Coffield E, et al. Designing an agent-based model for childhood obesity interventions: A case study of ChildObesity180. Prev Chronic Dis 2016;13:E04. [PUBMED]
35.	Ramirez-Nafarrate A, Gutierrez-Garcia JO. An Agent Based Simulation Framework to Analyze the Prevalence of Child Obesity. Proceedings of the 2013 Winter Simulation Conference: Making Decisions in a Complex World; 2013. p. 2330-9.
36.	Zhang J, Tong L, Lamberson PJ, Durazo-Arvizu RA, Luke A, Shoham DA, et al. Leveraging social influence to address overweight and obesity using agent-based models: The role of adolescent social networks. Soc Sci Med 2015;125:203-13.
37.	El-Sayed AM, Scarborough P, Seemann L, Galea S. Social network analysis and agent-based modeling in social epidemiology. Epidemiol Perspect Innov 2012;9:1. [PUBMED]
38.	National Family Health Survey, India. Available from: http://www.rchiips.org/NFHS/nfhs4.shtml. [Last accessed on 2018 Jan 10].
39.	National Nutrition Monitoring Board (NNMB). Available from: http://www.nnmbindia.org/aboutus.html. [Last accessed on 2018 Jan 10].
40.	Annual Health Survey. Office of the Registrar General & Census Commissioner India. Available from: http://www.censusindia.gov.in/2011-Common/AHSurvey.html. [Last accessed on 2018 Jan 10].
41.	District Level Household and Facility Survey (DLHS). Available from: http://www.rchiips.org/. [Last accessed on 2018 Jan 10].
42.	Census of India. Office of the Registrar General & Census Commissioner India. Available from: http://www.censusindia.gov.in/. [Last accessed on 2018 Jan 10].
43.	International Symposium on Health Analytics and Disease Modeling (HADM 2018), New Delhi; 2018. Available from: http://www.healthanalytics.net/HADM2018/. [Last accessed on 2018 Mar 01].