Modeling the obesity epidemic: social contagion and its implications for control
© Ejima et al.; licensee BioMed Central Ltd. 2013
Received: 21 January 2013
Accepted: 2 March 2013
Published: 9 March 2013
As an obesity epidemic has grown worldwide, a variety of intervention programs have been considered, but a scientific approach to comparatively assessing the control programs has still to be considered. The present study aims to describe an obesity epidemic by employing a simple mathematical model that accounts for both social contagion and non-contagious hazards of obesity, thereby comparing the effectiveness of different types of interventions.
An epidemiological model is devised to describe the time- and age-dependent risk of obesity, the hazard of which is dealt with as both dependent on and independent of obesity prevalence, and parameterizing the model using empirically observed data. The equilibrium prevalence is investigated as our epidemiological outcome, assessing its sensitivity to different parameters that regulate the impact of intervention programs and qualitatively comparing the effectiveness. We compare the effectiveness of different types of interventions, including those directed to never-obese individuals (i.e. primary prevention) and toward obese and ex-obese individuals (i.e. secondary prevention).
The optimal choice of intervention programs considerably varies with the transmission coefficient of obesity, and a limited transmissibility led us to favour preventing weight gain among never-obese individuals. An abrupt decline in the prevalence is expected when the hazards of obesity through contagious and non-contagious routes fall into a particular parameter space, with a high sensitivity to the transmission potential of obesity from person to person. When a combination of two control strategies can be selected, primary and secondary preventions yielded similar population impacts and the superiority of the effectiveness depends on the strength of the interventions at an individual level.
The optimality of intervention programs depends on the contagiousness of obesity. Filling associated data gaps of obesity transmission would help systematically understand the epidemiological dynamics and consider required control programs.
Obesity has become more and more widespread, increasingly recognized as one of the biggest global health problems. According to the estimate of the World Health Organization (WHO), the prevalence of obese individuals across the world was estimated at 9.8% in 2005, and with a subsequent increase, an urgent preventive action has been deemed essential. The public health need for obesity control is evident, because obesity serves as one of the most important risk factors of various chronic diseases, including acute coronary heart disease and other circulatory diseases, diabetes and several types of cancer (e.g. colon cancer). Following the WHO’s declaration of the global epidemic of obesity in 1997, the World Health Assembly endorsed the Global Strategy on Diet, Physical Activity and Health (DPAS) in 2004 aiming to improve the situation by intervening diet and physical activity. Accordingly, the member states of the WHO and other international partners have faced a need to construct and carry out obesity control programs. As part of the control effort, various epidemiological studies have been conducted to assess the effectiveness of each control program (i.e. through individual nutritional or physical exercise programs). However, there have been little attempt to qualitatively and quantitatively compare the effectiveness of different types of control programs and optimize obesity control program as a whole. In addition, very little epidemiological effort has been made to understand the entire epidemiological dynamics of obesity and its control using mathematical and theoretical approaches.
While actual interventions of dietary behaviours (e.g. avoiding excessive calorie intake) and those against insufficient physical activities are implemented, Christakis and Fowler scientifically demonstrated that obesity can spread from person to person via a social contact network. The epoch-making finding of the spread of non-infectious disease through a social contact network was not only limited to obesity but also other health-related issues such as smoking. Statistical review of social network analysis took place elsewhere, because the estimation problem of social network effects, including the use of dynamic models and statistical control of confounders, has been discussed[7, 8]. The underlying biological and social mechanisms of obesity epidemics have fascinated a broad range of scientific audience.
Provided that non-negligible fraction of obesity is caused by person-to-person transmission, the effectiveness of essential control programs against obesity epidemic would be characterized by nonlinear dynamics with a correlated risk structure. That is, estimating the risk of obesity involves the issue of dependence in which the risk of obesity in a single individual is determined not only by that particular individual but also by other individuals in the same population unit (i.e. the so-called “dependent happening”). In a positive sense, the dependence implies that one could expect herd effect (or herd immunity) by implementing public health interventions, which has been commonly seen in the epidemiology of infectious diseases. However, it also implies that the contagious effect could lead to social problems including potential need to intervene friendship network and social discrimination.
The present study aims to describe an obesity epidemic by employing a simple mathematical model that accounts for both social contagion and non-contagious hazards of obesity, thereby comparing the effectiveness of different types of interventions. Using a simplistic model with randomly mixing assumption, we intend to explore the most effective intervention in a qualitative manner and identify epidemiological data gaps that have prevented us from explicitly evaluating and comparing the effectiveness of various obesity control programs.
A model for the social contagion of obesity
where N represents the total population size, assumed to be a constant over time for the sake of our exposition of epidemiological data gaps, that is, N = S(t) + I(t) + R(t) for any t, μ is the birth and death rate of human host, β is the transmission coefficient, ε is the hazard of obesity due to non-contagious reasons, γ is the natural recovery rate, and σ is the relative risk of weight regain among ex-obese individuals which typically takes a value greater than 1 due to high risk of coming back to the obese state. It should be noted that the system (1) assumes that ex-obese is not contagious. All three equations describe the background birth and death of the host using the rate, μ. Otherwise all terms are associated with acquirement of or recovery from obesity. Among never-obese individuals, λ(t) = βI(t) + ε is the hazard rate of obesity on a whole (or, is frequently referred to as the “force of infection” in infectious disease epidemiology) at which they experience obesity for the first time. Among ex-obese individuals, the hazard is σ times greater than that among never-obese individuals. The natural recovery of obesity occurs at the rate, γ.
It should be noted that the force of infection, λ(t) is modelled in an additive manner, i.e., expressed as a sum of two hazards, one through the contagious route λ1 = βI(t) and the other via the non-contagious route λ2 = ε, the latter of which is determined by many factors including genetics and lifestyle including dietary habit. For simplicity, we consider a situation in which λ2 is constant. By employing the additive model for the force of infection, it is assumed that the contagious and non-contagious risks are independent from each other. However, considering that the social contagion should eventually influence dietary behaviour and physical activity to achieve a “transmission of obesity” in real life, it should be more natural to account for the dependence between β and ε (see Discussion). When we numerically solve the system (1), we consider an initial condition with S(0) = N. Solving equations, d(S, I, R)/dt = 0 and analysing the linearized equations, we find an asymptotically stable equilibrium point, (S*, I*, R*) to which all the trajectories of the system converge so that the parameter sensitivity and the age-specific risk in the equilibrium can be examined.
Lifetime risk of obesity: age-dependence
Although the present study focuses on temporal dynamics of obesity epidemic, here we consider the age-dependent dynamics rather than time-evolution, ignoring time-dependency and measuring only the age-specific risk of obesity in an endemic equilibrium. The age-dependency is specifically considered here, because (i) the most typical epidemiological measurement of obesity at an individual level may be the risk of obesity or associated disease by a certain age (or throughout the course of life), and (ii) we intend to understand the fundamental epidemiological dynamics of obesity using the model (1) as it has direct implication for age-dependent risk of obesity.
As a → ∞, q(a) takes λ * /(λ * + μ). This indicates that, the larger the prevalence, the larger the life-time risk to experience obesity at least once during the course of life. Accordingly, hereafter we use the equilibrium prevalence, calculated from time-dependent system (1), as an epidemiological outcome measure to assess and compare the effectiveness of different interventions.
Parameter values for the transmission model to describe an obesity epidemic
Average life expectancy at birth
Transmission coefficient of obesity
2.96 × 10-7 (per year)
Non-contagious hazard of obesity
0.012 (per year)
Relative hazard of obesity among ex-obese
Average duration of obesity
First, we solve the system (1) numerically to understand the time-dependent dynamics of never-obese, obese and ex-obese individuals. Second, we explore the impact of hazard parameters (i.e. hazards for contagious and non-contagious routes) and recovery parameters on the equilibrium prevalence of obesity. Third, to assess and compare the effectiveness of different control programs of obesity, we investigate the sensitivity of the equilibrium prevalence on the shift of parameters that determine the effectiveness of each program. When exploring the effectiveness of interventions, we use two different types of classification of control programs: (i) we consider varying only one parameter for each sensitivity analysis, and (ii) we consider varying a combination of parameters. For the latter, varying a combination of parameters that influence the risk of obesity among never-obese individuals is hereafter referred to as the primary prevention, and varying the other combination of interventions that influence the risk of obesity among obese and ex-obese individuals is referred to as the secondary prevention. It should be noted that the term “secondary prevention” is used here to represent the intervention that happens after experiencing illness (i.e. obesity) at least once. We measure the effectiveness of control programs by examining the impact of relative change in either (i) or (ii) on the equilibrium prevalence value.
Baseline dynamics of obesity
Hazard of and recovery from obesity
There are two types of interventions that belong to the secondary prevention, i.e. the dietary control program among obese individuals and the follow-up program of ex-obese individuals, each influencing on the duration of obesity 1/γ and the relative hazard among ex-obese σ, respectively. The dietary restriction in this context is targeted on obese individuals only, and the follow-up program is to encourage ex-obese individuals not to be overweight again; ex-obese individuals are known to be more prone to obesity than never-obese individuals. Figure 2B shows the role of 1/γ and σ in regulating the prevalence of obesity. Overall, the shorter the duration of obesity 1/γ is, the lower the equilibrium prevalence would be. Unlike Figure 2A, the prevalence does not abruptly vary with σ. Varying σ to lower or greater values led the prevalence of obesity to be less sensitive to 1/γ.
Comparison of intervention effectiveness
It should be noted that the follow-up program reduced the overall hazard of re-infection (including those arising from social contagion and lifestyle), because the follow-up program does not specify the way of regaining weight among ex-obese individuals and is primarily intended to reduce susceptibility of ex-obese individuals toward re-infection.
However, when the transmission coefficient is set to be very high so that the transmission of obesity can be maintained through social contagion, preventing weight gain among never-obese individuals, ε, appears to be the least effective. Rather, promoting the dietary restriction (1/γ) and implementing the follow-up program (σ) would be more effective in reducing the prevalence of obesity. In a certain range, intervening β is the most influential parameter in reducing the prevalence, while in reality it might be difficult to directly reduce obesity contagion by a control program. When a combination of two control strategies can be selected, the primary and secondary preventions yielded similar population impacts and the superiority of the effectiveness depends on the strength of the interventions at an individual level.
In the present study, we investigated epidemiological models that describe the obesity epidemic, spreading via social contact and acquired due to non-contagious reasons. We assessed and compared the effectiveness of different types of intervention programs which aim to reduce the risk of obesity. As the most important practical finding, we identified that the optimal choice of intervention programs considerably varies with the transmission coefficient of obesity, β. When β is small, the transmission cannot be maintained by social contagion alone. In such an instance, our model has suggested that preventing weight gain among never-obese individuals would be the most effective option, although it should be remembered that our approach adopted marginal independence between β and ε, and the effectiveness of primary prevention might have been overestimated. When β is large enough to sustain the transmission of obesity through the person-to-person route, dietary restriction among obese individuals could potentially be the most effective. In other words, depending on the transmissibility of obesity, the effectiveness of reducing obesity hazards would greatly vary, and thus, the population impact of each program would be dependent on the transmission dynamics of obesity. When a combination of interventions can be selected, the primary prevention is likely more influential than the secondary prevention for a small effect size, but on the whole primary and secondary preventions yielded similar population impacts. Despite the dependence of optimal interventions on β, it should be noted that the transmission potential of obesity in community setting has yet to be explicitly estimated.
Since WHO has addressed DPAS, emphasizing the importance of diet and physical activity as two main factors that determine the risk of obesity, the worldwide effort of obesity control has started, conducting and evaluating various programs. As we have shown using a simplistic model, the social contagion of obesity must be a key concern for public health for decision-making, because the design of effective control programs requires us to capture and understand the population dynamics of obesity in an explicit manner, and moreover, empirically quantify the transmissibility of obesity. As the most important data gap, we have identified that the transmission potential of obesity contagion has to be estimated, as it drastically varies the optimal choice of interventions. To estimate the contagious hazard of obesity, household-based prospective cohort study of susceptible and recovered individuals is desirable, because not only the transmissibility within households but also the relevance of the transmission potential to the natural history of obesity can be measurable. Nevertheless, it should be noted that the threshold property using R0 is unlikely to be useful in the obesity model due to non-contagious risk and re-infection.
Whereas we have shown that primary and secondary preventions yielded similar reductions in the equilibrium prevalence of obesity in a certain parameter space, it should be remembered that the primary and secondary preventions require different types and amounts of effort, not sharing an identical effect size. Considering that the length of obese period could influence the risk of later health outcomes (e.g. diabetes), the primary prevention may better be more advantageous in reducing the devastating outcomes. Addressing the associated life-course issues including an assessment of economic impact is the subject for future studies.
Despite our key finding in identifying the transmissibility as the most influential component to determine the optimal interventions, there are five issues that are regarded as limitation or should be cautiously interpreted. First, while obesity contagion on a social contact network has been empirically studied in literature[22–24], we have employed a homogeneously mixing assumption for mathematical convenience and to identify key parameter of obesity dynamics without ambiguity. Of course, using empirically observed network data would permit us to describe more realistic situations. Considering that the threshold level of obesity epidemic likely differs in heterogeneous contact networks, future studies should quantify the transmissibility of obesity on an explicit contact network and identify the corresponding appropriate way of public health control. Second, the natural history of obesity, including the duration of obesity and frequency of recurrence, is largely unknown. Due to shortage of information, we have had to ignore age-dependent heterogeneity, e.g. differential calorie consumptions by age. Third, an equilibrium prevalence of our model is calculated as high as 60.8%, which is greater than currently observed prevalence. However, the prevalence in the present day has yet to reach the stable level, and has been in increasing trend, and thus, we believe that our exercise has not been far from reality even by using a simplistic model. Fourth, we did not take into account the cost to be compared across different intervention programs. Identification of optimal programs would require an explicit analysis of cost-benefit and cost-effectiveness aspects. Lastly, on the technical side, further work could explore the use of alternative modelling approaches, e.g. conditional risk model with stochastic dependence structure between risks with different routes of obesity, which could avoid overestimating the effectiveness of primary prevention.
although the use of 1-x(a) is subject to discussion (e.g. rather, one may prefer to use individual history of being obese). Such modelling exercise can potentially enable us to describe the long-term and secondary impact of obesity control in reducing closely associated diseases or deaths at a population level, while explicitly accounting for nonlinearity in the spread and control of obesity. Third, a little more complex natural history may better be incorporated into the model. For instance, non-contagious hazard was assumed as a fixed value in the present study, but in reality the hazard may depend on age which reflects not only the physiological age-dependence but also the history of escape from obesity. Not only obesity but also other behavioural contagion can be analysed using similar modelling approaches. Despite numerous future tasks, we believe that we have successfully simplified the population dynamics of obesity, identifying the importance of quantifying the transmission potential to determine public health control programs in the future.
The optimal choice of interventions against obesity varies by the transmission potential of obesity from person to person. To attain appropriate assessment and comparison of different types of public health control programs of obesity, it is critical that the epidemiological dynamics of obesity, especially the transmission potential, is quantified in advance.
HN received funding support from the Japan Science and Technology Agency (JST) PRESTO program. KE received scholarship support from the Japan Society for Promotion of Science (JSPS). KA received funding support from the Aihara Project, the FIRST program from JSPS, initiated by CSTP. This work also received financial support from the Harvard Center for Communicable Disease Dynamics from the National Institute of General Medical Sciences (grant no. U54 GM088558). The funding bodies were not involved in the collection, analysis and interpretation of data, the writing of the manuscript or the decision to submit for publication.
- World Health Organization (WHO): Global Health Observatory Data Repository. 2011, Geneva, Switzerland: WHO, Available from: http://apps.who.int/ghodata/#) (last accessed on: 17 September 2012 Available from: ) (last accessed on: 17 September 2012
- World Health Organization: Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation. WHO Technical Report Series no. 894. 2000, Geneva: WHO, available from: http://www.who.int/nutrition/publications/obesity/WHO_TRS_894/en/index.html) (Last accessed on: 17 September 2012 available from: ) (Last accessed on: 17 September 2012
- World Health Organization: The Global Strategy on Diet, Physical Activity and Health (DPAS). 2004, Geneva: WHO, (available from: http://www.who.int/nmh/wha/59/dpas/en/) (Last accessed on: 17 September 2012) (available from: ) (Last accessed on: 17 September 2012)
- Christakis NA, Fowler JH: The spread of obesity in a large social network over 32 years. N Engl J Med. 2007, 357: 370-379. 10.1056/NEJMsa066082.View ArticlePubMed
- Christakis NA, Fowler JH: The collective dynamics of smoking in a large social network. N Engl J Med. 2008, 358: 2249-2258. 10.1056/NEJMsa0706154.PubMed CentralView ArticlePubMed
- Christakis NA, Fowler JH: Social contagion theory: examining dynamic social networks and human behavior. Stat Med. 2012, 10.1002/sim.5408. in press
- Cohen-Cole E, Fletcher JM: Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic. J Health Econ. 2008, 27: 1382-1387. 10.1016/j.jhealeco.2008.04.005.View ArticlePubMed
- Cohen-Cole E, Fletcher JM: Detecting implausible social network effects in acne, height, and headaches: longitudinal analysis. BMJ. 2008, 337: a2533-10.1136/bmj.a2533.PubMed CentralView ArticlePubMed
- Blok DJ, VAN Empelen P, VAN Lenthe FJ, Richardus JH, DE Vlas SJ: Unhealthy behaviour is contagious: an invitation to exploit models for infectious diseases. Epidemiol Infect. 2013, 10.1017/S0950268812000891. in press
- Kermack WO, McKendrick AG: A contribution to the mathematical theory of epidemics. Proc Roy Soc A. 1927, 115: 700-721. 10.1098/rspa.1927.0118. reprinted in Bull. Math Biol. 1991, 53:57–87View Article
- Anderson RM, May RM: Infectious Diseases of Humans: Dynamics and Control. 1991, Oxford, The United Kingdom: Oxford University Press
- Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH: Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med. 1997, 337: 869-873. 10.1056/NEJM199709253371301.View ArticlePubMed
- Hill AL, Rand DG, Nowak MA, Christakis NA: Infectious Disease Modeling of Social Contagion in Networks. PLoS Comput Biol. 2010, 6: e1000968-10.1371/journal.pcbi.1000968.PubMed CentralView ArticlePubMed
- Velthuis AG, Bouma A, Katsma WE, Nodelijk G, De Jong MC: Design and analysis of small-scale transmission experiments with animals. Epidemiol Infect. 2007, 135: 202-217. 10.1017/S095026880600673X.PubMed CentralView ArticlePubMed
- Nishiura H, Yen HL, Cowling BJ: Sample size considerations for one-to-one animal transmission studies of the influenza A viruses. PLoS ONE. 2013, 8: e55358-10.1371/journal.pone.0055358.PubMed CentralView ArticlePubMed
- Diekmann O, Heesterbeek H, Britton T: Mathematical tools for understanding infectious disease dynamics. 2012, New York: Princeton University PressView Article
- Klinkenberg D, Fraser C, Heesterbeek H: The effectiveness of contact tracing in emerging epidemics. PLoS One. 2006, 1: e12-10.1371/journal.pone.0000012.PubMed CentralView ArticlePubMed
- Mizumoto K, Nishiura H, Yamamoto T: Effectiveness of antiviral prophylaxis coupled with contact tracing in reducing the transmission of the influenza A (H1N1-2009): a systematic review. Theor Biol Med Model. 2013, 10: 4-10.1186/1742-4682-10-4.PubMed CentralView ArticlePubMed
- Reilly JJ, Kelly L, Montgomery C, Williamson A, Fisher A, McColl JH, Lo Conte R, Paton JY, Grant S: Physical activity to prevent obesity in young children: cluster randomised controlled trial. Br Med J. 2006, 333: 1041-1043. 10.1136/bmj.38979.623773.55.View Article
- Merino J, Megias-Rangil I, Ferré R, Plana N, Girona J, Rabasa A, Aragonés G, Cabré A, Bonada A, Heras M, Masana L: Body weight loss by very-low-calorie diet program improves small artery reactive hyperemia in severely obese patients. Obes Surg. 2013, 23: 17-23. 10.1007/s11695-012-0729-6.View ArticlePubMed
- Taylor RW, McAuley KA, Barbezat W, Farmer VL, Williams SM, Mann JI, APPLE project: Two-year follow-up of an obesity prevention initiative in children: the APPLE project. Am J Clin Nutr. 2008, 88: 1371-1377.PubMed
- El-Sayed AM, Scarborough P, Seemann L, Galea S: Social network analysis and agent-based modeling in social epidemiology. Epidemiol Perspect Innov. 2012, 9: 1-10.1186/1742-5573-9-1.PubMed CentralView ArticlePubMed
- Gonzalez-Parra G, Acedo L, Villanueva Mico RJ, Arenas AJ: Modeling the social obesity epidemic with stochastic networks. Physica A. 2010, 389: 3692-3701. 10.1016/j.physa.2010.04.024.View Article
- Jodar L, Santonja FJ, Gonzalez-Parra G: Modeling dynamics of infant obesity in the region of Valencia, Spain. Comp Math Appl. 2008, 56: 679-689. 10.1016/j.camwa.2008.01.011.View Article
- Omori R, Cowling BJ, Nishiura H: How is vaccine effectiveness scaled by the transmission dynamics of interacting pathogen strains with cross-protective immunity?. PLoS ONE. 2012, 7: e50751-10.1371/journal.pone.0050751.PubMed CentralView ArticlePubMed
- Preston SH, Mehta NK, Stokes A: Modeling obesity histories in cohort analyses of health and mortality. Epidemiology. 2013, 24: 158-166. 10.1097/EDE.0b013e3182770217.View ArticlePubMed
- Mizumoto K, Yamamoto T, Nishiura H: Age-dependent estimates of the epidemiological impact of pandemic influenza (H1N1-2009) in Japan. Comp Math Methods Med. 2013, 2013: 637064-
- Gonzalez-Parra G, Jodar L, Santonja F, Villanueva R: An age-structured model for childhood obesity. Math Popul Stu. 2010, 17: 1-11. 10.1080/07481180903467218.View Article
- Mizumoto K, Ejima K, Yamamoto T, Nishiura H: Vaccination and clinical severity: Is the effectiveness of contact tracing and case isolation hampered by past vaccination?. Int J Environ Res Public Health. 2013, 10: 816-829. 10.3390/ijerph10030816.PubMed CentralView ArticlePubMed
- Hill AL, Rand DG, Nowak MA, Christakis NA: Emotions as infectious diseases in a large social network: the SISa model. Proc R Soc Lond Ser B. 2010, 277: 3827-3835. 10.1098/rspb.2010.1217.View Article
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