Improving Adult Immunization Rates through use of Social Network Theory
Introduction
Immunization is a great success story of preventive medicine. Regulatory mandates for schools and daycares have vastly improved childhood immunization rates; however, adult immunization rates are lagging behind. Although 90% of the children aged 19-35 months receive the recommended vaccines, only 66% of adults aged 65 or older received influenza vaccine and about 62% received pneumococcal vaccine in 2002 (Orenstein, Douglas, Rodewald, & Hinman, 2005) .
The Advisory Committee on Immunization Practices (ACIP) recommends immunizations against the following diseases/pathogens for adolescents and adults age 19 and older: tetanus, diphtheria, pertussis, pneumococcus, Herpes zoster, meningococcus, Human papillomavirus and Hepatitis A and B for certain high risk people (CDC, ). In January 2000, the Department of Health and Human Services launched a nationwide health promotion and disease prevention program called Healthy People 2010. It contains 467 objectives organized in 28 focus areas. One of the objectives is to improve Hepatitis A and B, and Influenza and Pneumococcal vaccinations among high risk adults (U.S Department of Health and Human Services, ). The goal for 2010 is 90% coverage for annual influenza vaccine for adults aged 65 and over and 90% coverage for one dose of pneumococcal vaccine (Poland et al., 2003).
Most of the current interventions for vaccine acceptance are based on the Health Belief Model (HBM) (DiClemente, Crosby, & Kegler, 2002)and on the assumption that mixing of individuals in society is homogeneous (Chen & Yuan, 2009). The current immunization strategies are designed based on these beliefs. However, the current strategies have not been effective as mentioned above. In recent years powerful computing has facilitated the study of social networks. Based on this tool the social network theory can help augment the immunization rates not only for endemic diseases but also help in epidemic and pandemic preparedness.
Traditional vaccination Strategies
The Health Belief Model (HBM) has been extensively utilized in health care programs including immunizations (DiClemente et al., 2002)(Painter, Borba, Hynes, Mays, & Glanz, 2008). The HBM was first proposed by Social Psychologists Hochbaum, Kegels and Rosenstock in the 1950?s to explain and predict preventive health behavior. Hochbaum initiated the research on HBM when the turnout for free chest x-rays for early detection of tuberculosis was low. The originators were concerned with the widespread failure of people to engage in preventive health behaviors. The HBM is an individual based theory which posits that a person?s health behavior depends on some key factors: perceived susceptibility to a condition, perceived seriousness of a condition, perceived benefits of taking action toward prevention and barriers to taking action. The perceived susceptibility provides the force to act, and the difference between benefits and barriers provide the path of action. An individual may also require an internal or external cue for action. Other theories also used for designing health interventions are the theory of reasoned action and the theory of planned behavior (Dutta-Bergman, 2005). The Boston Public Health Commission has well developed educational programs for Seasonal Influenza and H1N1 Flu in the form of posters in public places, brochures, videos, websites, Twitter, Facebook, podcasts, and Youtube (BPHC, ). There are some education and outreach programs for hepatitis. However, for other diseases mentioned in the ACIP recommendations, there are only fact sheets providing information about the disease, its symptoms, treatment, and immunization guidelines. The Centers for Disease Control and Prevention (CDC) also offers educational materials on their website and in the form of brochures for physicians? offices (CDC, ). All these immunization efforts are based on educating people about a disease, risks for acquiring it, and vaccine availability. The fact sheets are expected to lead to immunizations. However the immunization strategies based on these theories have certain shortcomings. One shortcoming is that the focus of the efforts is on the individual. It is assumed that individuals make decisions based on calculations of their susceptibility and risks. It is also assumed that people make rational decisions. Another shortcoming of the traditional strategies is the assumption that individuals have homogeneous mixing in society and every person has the same risk of infection. I propose that immunization strategies should be based on social network theory to help overcome these issues.
1. Individual level, Susceptibility and risk based:
As noted above, various agencies have put forth fact sheets that provide information about diseases and immunization guidelines. The assumption is that if people are educated about a disease and about the availability of the vaccine to protect them against the disease, they will take advantage of it and get immunized. The efforts for immunization are focused on individuals because of the flawed logic of the theories on which these programs based. The programs do not take into account special influences, cultural factors, socioeconomic factors and previous experiences. Dutta-Bergman performed a critical review of HBM in health communications. He stated that one of the limitations was Individualistic bias which means that the focus is on the individual. However, the individual is part of a collective and the outcome of a particular behavior and barriers to action are located in the society. Health behaviors occur within the context of the complex social fabric. The social norms are very important. Another reason cited by Dutta-Bergman was Minimizing Context. This means that the theory does not take into account the social context in which a behavior is occurring (Dutta-Bergman, 2005). Immunization programs are based on the HBM and they rely on individual action without any attention to the social norms.
Another limitation of the traditional immunization programs is that it they are deeply rooted in an individual?s perceived risk for developing a condition. It is assumed that fact sheets about a disease will educate people about their risk of acquiring the disease and that will lead to immunization. However, a person?s perception of risk may not be accurate. People may have an unrealistic optimism causing them to perceive their personal outcome to be more positive than that of others? in a similar situation. Several studies exemplify the fact that risk perception is not accurate. Ann Bostrom, in her essay ?Future risk Communication? discusses that risk appears factual when communicated in terms of magnitude and probability of harm. However, risk is not a fact. It is a composite of values, specific context and future events. This is why some people feel that vaccine-preventable diseases are less risky than other diseases. (Bostrom, 2003).
The HBM was developed in the 1950?s and despite its limitations, it continues to be widely used and advocated in designing preventive services. However, with the advances in the field of psychology, it is clear that the individual level theories based on susceptibility and risk perception do not accurately depict human behavior. Hence the programs based on these theories have had limited success (Painter et al., 2008).
2. Behavior is rational:
The traditional vaccination strategies assume that human behavior is rational and therefore the individual is responsible for making rational decisions about his or her health (DiClemente et al., 2002). This too is a result of immunization programs being based on the HBM. Dutta-Bergman described a limitation of the HBM which he called Cognitive Orientation. This means that the HBM assumes that individuals are rational beings and conduct a cost-benefit analysis taking into account the susceptibility, severity, benefits and barriers to a health behavior before enacting that behavior. Decisions made at the spur of the moment or habitual choices are not taken into account (Dutta-Bergman, 2005). Traditional immunization strategies are based on the assumption that after people have been educated about a disease through fact sheets, they will perform a mental arithmetic of the benefits and barriers and reach a rational decision. This rational decision will result in appropriate behavior of receiving immunization. Any cultural factors, fears, or rumors about the vaccine or access issues are not taken into consideration.
Human behavior is not always rational. Emotions are powerful navigators of behavior. Chapman and Coups evaluated the role of emotions (worry about getting the flu, regret about not getting flu vaccine and perceived risk about getting flu) in influenza vaccination among 428 university employees. Their study showed that the emotions (worry and regret) are the ?more immediate precursors of decisions than are calculations of the risk probability and severity?. They feel that these results can also be applied to other preventive health behaviors and not just influenza vaccination (Chapman & Coups, 2006).
3. Uniform and homogenous mixing of population:
About 100 years ago, researchers started studying dynamics of infectious diseases. Early models assumed uniform mixing of people and ignored spatial patterning (Hartvigsen, Dresch, Zielinski, Macula, & Leary, 2007)(Eubank et al., 2004). The immunization guidelines are based only on the age and existing medical conditions of individuals. Social networks and patterns of clustering of people in society are not taken into consideration. Guidelines assume homogeneous mixing of people or that every individual has equal probability of contracting a particular disease. However, this is not accurate. Due to social networks, individuals have varying degrees of risk in acquiring an infection. Bascompte states that traditional models in epidemiology were developed in a homogeneous setting (where all individuals have the same number of interactions and the same probability of infecting each other). The concept of homogeneous population led to the belief that it is not necessary to vaccinate all individuals in a community. It has been assumed that after a certain fraction of the population is immunized, an eradication threshold is reached and the disease disappears. The fact is that the social networks are complex and heterogeneous. This means that the bulk of the nodes (individuals in a network) have a small number of connections, but a few nodes have a large number of connections, more than can be expected by chance. In addition, the probability is higher that new nodes will connect with the existing nodes with greater number of links. In these complex networks with heterogeneous distributions of links per node, there are always a few nodes that maintain the disease and therefore the disease never disappears (Bascompte, 2007). This means that a disease outbreak can occur even with a high immunization rate because the immunizations have been spread homogeneously in a heterogeneous population. Dallaire et al. reported a recent measles outbreak in Canada despite an immunity level estimated at 95%. The cases belonged to several unrelated networks of unvaccinated individuals. This outbreak underscores the fact that social networks are very important. Even a small change in the aggregation of individuals can lead to transmission of infection in unvaccinated individuals (Dallaire, De Serres, Tremblay, Markowski, & Tipples, 2009). Perisic et al. also noted that voluntary vaccination was able to contain the infection in models of small neighborhoods, but after a certain neighborhood size, voluntary vaccination failed to contain the epidemic despite a large number of people who were vaccinated. This occurs because in heterogeneous networks, disease is not localized anymore (Perisic & Bauch, 2009).
Summary of Critique
The rational cost-benefit Health Belief Model dominated the field of public health and psychology for several decades and led to the development public health interventions including immunization programs. As the field of psychology has matured, it has led to the development of conceptual models which take into account the contextual factors that lead to behaviors. The focus of theories has shifted from the individual to larger social networks including family, community and even global systems. In order to address the health issues of people in modern society, one must understand the lifestyle patterns and social context in which people live. It is imperative for new health programs to shift the focus from the individual to groups and take into account the contemporary way of life (Coreil, 2008). Newer computational models offer greater information about social networks and insight into spread of infectious agents within these networks. If we are to make improvements in adult immunization rates, cognitive theories need to be supplanted by group based theories which acknowledge that human behavior does not occur in isolation, nor is it planned and rational. Strategies should take into account the effects of heterogeneous societal structure and modern technology on individuals. Social network theory fulfils these criteria and can be utilized for developing new public health interventions.
What is Social network Theory?
There has been tremendous interest to develop novel models that address immunizations through use of social network theory. According to Dunn, social network theory is a set of assumptions and guidelines for ?development and appraisal of particular theories of knowledge creation, diffusion and utilization?. Social network theory makes four basic assumptions: (1) knowledge structures are constituted by relations among people or events or actions and not by attributes of individuals, (2) relations are structured, (3) structured relations have overt behavioral properties (such as frequency of direct contact) and cognitive properties (such as congruence of beliefs), and (4) behavioral and cognitive properties emerge from structured relationships (Dunn, 1983).
History and definition of networks
May has provided a historical account of networks. In the earliest model, introduced by Erdos-Renyi, n nodes (or vertices) are connected by links or edges. Nodes represent the individuals in the network. Degree distribution is the number of links per nodes. In the Erdos-Renyi model, the degree distribution is governed by a Poisson distribution where m is the average number of links. A uniform network is one where each node is linked to exactly m others (May, 2006). In 1967 Stanley Milgram, a Harvard professor, discussed the idea of real-world networks. His goal was to find the ?distance? between any two people in the United States. He found that the median number of intermediate persons was 5.5. This led to the famous concept of ?six degrees of separation? (Barabási, 2002), (May, 2006). This was followed by the work of Watts and Strogatz on ?small world? networks. These networks combine local clusters and occasional ?long hops? (May, 2006). Lastly, there are ?scale-free? networks (SF) which obey the power-law distribution where there is no specific number of links per node; a small number of nodes have a large number of links and a large number of nodes have a small number of links. Two important characteristics of networks are network ?diameter? and ?clustering coefficient?. Diameter is found by calculating the shortest path between each pair of nodes. The largest of these shortest paths is the diameter. Clustering coefficient is the average probability that two neighbors of a node are also neighbors of each other (May, 2006). Network dynamics are being used to study infectious diseases, epidemic preparedness, and immunization strategies. Networks can be interpersonal or web based.
Immunization strategies based on social network theory addresses the three limitations discussed previously. These interventions are group based, they do not assume rational behavior, and they address heterogeneous mixing of people.
1. Group based:
Social network based immunization strategies will target the entire network of people and not just individuals. Individuals in a network behave as a unit and exhibit health behaviors simultaneously. Introduction of appropriate immunization related information in the networks has the potential of affecting individual acceptance through establishing social norms and social acceptance. Interventions can be developed to target interpersonal networks or may be Internet-based. People are more likely to adopt certain behaviors through word of mouth in a trusted social group and if they see people in their social group adopting these behaviors.
The Internet is an example of an SF network. Social networking on the Internet leads to formation of virtual communities and to changes in behavior of groups instead of individuals. Social networks are rapidly emerging on the Internet. Khan et al. call them ?digital town squares? where people with common interests can interact (Khan & Shaikh, 2008). The current Web 2.0 offers a plethora of social networking technologies and software to connect healthcare organizations, clinicians, and laypeople. KamelBoulos and Wheelert feel that the educational potential of Web 2.0 applications, such as ?wikis, blogs and podcasts are just the tip of the social software iceberg?. Online interactions between users create a sense of community, promote learning through feedback and result in ?collective intelligence?. These social networking services may be used for linking people based on a medical issue (KamelBoulos & Wheeler, 2007). A number of disease-based blogs and websites provide a forum for individuals with these conditions to interact and share ideas. Example of such a websites and blogs are http://www.patientslikeme.com and http://diabetes.wikia.com/wiki/Diabetes_Wiki. Introduction of health behaviors in such networks has the potential for spreading through diffusion of innovations.
Gary Bennett, a professor at the Harvard School of Public Health, found that about 75% of adult Americans are regular Internet users and many access health related information. Bennett states that the Internet provides a high reach. Internet-based interventions are efficacious and offer an advantage because they allow the users to participate at their own convenience and allow anonymity (Bennett, ).
Interventions for boosting immunizations should be designed to reach the target social groups so that the dissemination of information can occur in a viral fashion. Future efforts to improve immunization rates should be focused on development of interactive Internet programs and development of programs to reach interpersonal groups of people in the society. This would provide greater impact in terms of behavior change in the target social groups.
2. Behavior is irrational:
Contrary to traditional belief, people do not always behave in a rational manner. Social norms, peer pressure, and herd mentality are important determinants of behavior. Individuals are embedded in their social networks and adoption of behaviors is not dependent on rational thinking. Various authors have suggested ways that behaviors can spread through social networks like contagions. The diffusion of innovations can spread desirable behaviors in social networks. These networks can be utilized to increase vaccine acceptance. Spread of behavior in this manner does not assume or expect rational behavior of people.
Numerous researchers have studied the spread of behaviors in various types of networks. It has been generally seen that new behaviors spread faster if clusters of people are linked by bridges. Granovetter calls this phenomenon the strength of weak ties. Weak ties are the links between acquaintances, and they bridge the connection between two close knit groups (Granovetter, 1983). Immunization interventions should be developed to target the networks and weak ties in a strategic manner. Computer modeling can assist in locating clusters and bridges of these clusters in social networks.
Other studies also favor the idea of bridges or weak ties between clusters. Choi et al. studied the diffusion of innovation in computational network models. They found that diffusion patterns are dependent on the number of initial adopters. The larger the number of initial adopters, the faster is the diffusion. As the group centrality increases (a large number of bridges and lower degree of cliquish sub-networks) diffusion is faster but is more likely to fail. When the number of bridges is low and the level of cliquish sub-networks is high, the diffusion is slower but has greater penetration. Diffusion of innovation is successful in cliquish networks with few bridges. Too many bridges lead to a rapid early stage but then the diffusion fails because it does not gain enough momentum to go forward due to insufficient build up of benefits (Choi, Kim, & Lee, ).
An individual is more likely to engage in a behavior if a large number of people in the network are already engaging in that behavior. One?s adoption of a new collective behavior depends on the behavior of the group (Valente, 1996). All these studies show that ideas spread in social networks based on network structure and irrational human behavior. Immunization programs should take advantage of this phenomenon and strategize the efforts to weak ties and early adopters.
3. Heterogeneous mixing:
As noted in the previous section, the traditional models have assumed that the population is homogeneous and has uniform mixing. However, the population is quite diverse and heterogeneous. It can be divided into subpopulations based on disease related factors or other factors that are social, cultural, economic, demographic or geographic. Population can also be subdivided based on spatial factors such as schools, neighborhoods, cities, states etc. Modeling based on Social networks takes into account that people move around and do not have homogeneous mixing. New immunization interventions should take into account this heterogeneous nature of population and develop heterogeneous strategies that superimpose the network. This would provide a more optimal coverage of immunizations. As noted previously, even high immunization rates in a community were unable to contain an epidemic because they were based on the assumption of homogeneous mixing. The epidemiological information about a disease and the topology of social networks should guide immunization strategies because social networks are complex structures and different people have different probability of getting infected. A study performed by Eubank et al. shows that infectious disease outbreaks can be contained by targeted vaccinations instead of mass vaccinations. They suggest that vaccinating people who visit a popular and most visited location would yield a better outcome than vaccinating the most gregarious people who meet the same group of people regularly (Eubank et al., 2004).
Hartvigsen et al. discuss strategies to improve influenza immunization through utilization of network structures. Increased computational capability had led to development of spatial models that better represent social networks. These networks mimic the spread of some infectious agents in a community. They evaluated various vaccination strategies in the network models and vaccination rates ranging from 0% to 90% for spread of an epidemic. They found that vaccinating people at the hubs of the networks or people with the highest degree (largest number of neighbor contacts e.g. health care providers) provided the greatest protection. The hub strategy also led to the shortest epidemic duration. Even vaccinating low clustering coefficient models and models with longest distance between nodes were better than random vaccinations. Vaccinating people with a high clustering coefficient (the proportion of the host?s neighbors that are connected to each other relative to the number of possible connections i.e. individuals whose contacts know each other e.g. residents of group homes or members of the same family) showed the lowest amount of protection and longest epidemic duration (Hartvigsen et al., 2007). Such information on network based targeted immunization can have significant bearing on routine vaccines and epidemic preparedness.
Miller et al. compared the effects of various vaccination strategies in preventing the spread of infectious diseases in realistic social networks. They found that PageRank (network with complete information, also the algorithm behind Google web ranking) outperformed all other models, namely Degree vaccination (vaccinates nodes by descending degree), Degree vaccination with Dynamic Reranking (recalculates the degree and vaccinates the node with most unvaccinated neighbors), Acquaintance vaccination (selects a node randomly and vaccinates one of its neighbors) and Random vaccination (vaccinates a fraction of the population randomly). However, obtaining complete information about a social network is not practical. Therefore the authors suggest vaccinating people who visit the most locations because this is easier to ascertain and yielded comparable results to Acquaintance vaccination and better results than Random vaccination in their study. They noted that vaccination efficacy was low on vaccinating individuals whose contacts also had many contacts. Essentially, strategy based vaccination was found to be better than random vaccination (Miller & Hyman, 2007).
Various researchers have proposed different strategies based on network topologies, and they have all yielded better results than current random strategies. Chen and Yuan propose a novel immunization strategy on scale-free networks. It is called the random walk immunization strategy. They performed a theoretical analysis and found this model to be very effective when compared to other strategies (Chen & Yuan, 2009). Guo et al. propose a targeted immunization strategy called Local region immunization using a novel Euclidean Distance Preferred (EDP) model. They generated EDP models in a small world network and found that there exists a critical immunization radius for a susceptible-infective pair of nodes to effectively suppress the epidemic prevalence (Guo, Li, & Wang, 2007). Khan et al. propose algebraic computation for immunization of social contacts of a person who has contracted an illness (Khan & Shaikh, 2008). Kretzschmar also recommends mathematical modeling for effective vaccination strategies in public health. These models will help determine best ages for various vaccines, target groups and other strategies for eliminating infectious diseases (Kretzschmar, 2008). All these studies highlight the fact that strategic heterogeneous immunization of population based on topology of networks is likely to provide improved control of diseases and therefore should be incorporated in future programs.
In conclusion, the adult immunization rates have improved over the past few years, however, there is still work needed to minimize the incidence of vaccine-preventable morbidity and mortality. We need to address this problem with strategies that provide widespread reach to people, especially in high risk categories. The strategies should conform to the modern societal structure and lifestyles. This will require a multi-prong approach and social network theory appears to offer a promising potential.
References
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Immunization is a great success story of preventive medicine. Regulatory mandates for schools and daycares have vastly improved childhood immunization rates; however, adult immunization rates are lagging behind. Although 90% of the children aged 19-35 months receive the recommended vaccines, only 66% of adults aged 65 or older received influenza vaccine and about 62% received pneumococcal vaccine in 2002 (Orenstein, Douglas, Rodewald, & Hinman, 2005) .
The Advisory Committee on Immunization Practices (ACIP) recommends immunizations against the following diseases/pathogens for adolescents and adults age 19 and older: tetanus, diphtheria, pertussis, pneumococcus, Herpes zoster, meningococcus, Human papillomavirus and Hepatitis A and B for certain high risk people (CDC, ). In January 2000, the Department of Health and Human Services launched a nationwide health promotion and disease prevention program called Healthy People 2010. It contains 467 objectives organized in 28 focus areas. One of the objectives is to improve Hepatitis A and B, and Influenza and Pneumococcal vaccinations among high risk adults (U.S Department of Health and Human Services, ). The goal for 2010 is 90% coverage for annual influenza vaccine for adults aged 65 and over and 90% coverage for one dose of pneumococcal vaccine (Poland et al., 2003).
Most of the current interventions for vaccine acceptance are based on the Health Belief Model (HBM) (DiClemente, Crosby, & Kegler, 2002)and on the assumption that mixing of individuals in society is homogeneous (Chen & Yuan, 2009). The current immunization strategies are designed based on these beliefs. However, the current strategies have not been effective as mentioned above. In recent years powerful computing has facilitated the study of social networks. Based on this tool the social network theory can help augment the immunization rates not only for endemic diseases but also help in epidemic and pandemic preparedness.
Traditional vaccination Strategies
The Health Belief Model (HBM) has been extensively utilized in health care programs including immunizations (DiClemente et al., 2002)(Painter, Borba, Hynes, Mays, & Glanz, 2008). The HBM was first proposed by Social Psychologists Hochbaum, Kegels and Rosenstock in the 1950?s to explain and predict preventive health behavior. Hochbaum initiated the research on HBM when the turnout for free chest x-rays for early detection of tuberculosis was low. The originators were concerned with the widespread failure of people to engage in preventive health behaviors. The HBM is an individual based theory which posits that a person?s health behavior depends on some key factors: perceived susceptibility to a condition, perceived seriousness of a condition, perceived benefits of taking action toward prevention and barriers to taking action. The perceived susceptibility provides the force to act, and the difference between benefits and barriers provide the path of action. An individual may also require an internal or external cue for action. Other theories also used for designing health interventions are the theory of reasoned action and the theory of planned behavior (Dutta-Bergman, 2005). The Boston Public Health Commission has well developed educational programs for Seasonal Influenza and H1N1 Flu in the form of posters in public places, brochures, videos, websites, Twitter, Facebook, podcasts, and Youtube (BPHC, ). There are some education and outreach programs for hepatitis. However, for other diseases mentioned in the ACIP recommendations, there are only fact sheets providing information about the disease, its symptoms, treatment, and immunization guidelines. The Centers for Disease Control and Prevention (CDC) also offers educational materials on their website and in the form of brochures for physicians? offices (CDC, ). All these immunization efforts are based on educating people about a disease, risks for acquiring it, and vaccine availability. The fact sheets are expected to lead to immunizations. However the immunization strategies based on these theories have certain shortcomings. One shortcoming is that the focus of the efforts is on the individual. It is assumed that individuals make decisions based on calculations of their susceptibility and risks. It is also assumed that people make rational decisions. Another shortcoming of the traditional strategies is the assumption that individuals have homogeneous mixing in society and every person has the same risk of infection. I propose that immunization strategies should be based on social network theory to help overcome these issues.
1. Individual level, Susceptibility and risk based:
As noted above, various agencies have put forth fact sheets that provide information about diseases and immunization guidelines. The assumption is that if people are educated about a disease and about the availability of the vaccine to protect them against the disease, they will take advantage of it and get immunized. The efforts for immunization are focused on individuals because of the flawed logic of the theories on which these programs based. The programs do not take into account special influences, cultural factors, socioeconomic factors and previous experiences. Dutta-Bergman performed a critical review of HBM in health communications. He stated that one of the limitations was Individualistic bias which means that the focus is on the individual. However, the individual is part of a collective and the outcome of a particular behavior and barriers to action are located in the society. Health behaviors occur within the context of the complex social fabric. The social norms are very important. Another reason cited by Dutta-Bergman was Minimizing Context. This means that the theory does not take into account the social context in which a behavior is occurring (Dutta-Bergman, 2005). Immunization programs are based on the HBM and they rely on individual action without any attention to the social norms.
Another limitation of the traditional immunization programs is that it they are deeply rooted in an individual?s perceived risk for developing a condition. It is assumed that fact sheets about a disease will educate people about their risk of acquiring the disease and that will lead to immunization. However, a person?s perception of risk may not be accurate. People may have an unrealistic optimism causing them to perceive their personal outcome to be more positive than that of others? in a similar situation. Several studies exemplify the fact that risk perception is not accurate. Ann Bostrom, in her essay ?Future risk Communication? discusses that risk appears factual when communicated in terms of magnitude and probability of harm. However, risk is not a fact. It is a composite of values, specific context and future events. This is why some people feel that vaccine-preventable diseases are less risky than other diseases. (Bostrom, 2003).
The HBM was developed in the 1950?s and despite its limitations, it continues to be widely used and advocated in designing preventive services. However, with the advances in the field of psychology, it is clear that the individual level theories based on susceptibility and risk perception do not accurately depict human behavior. Hence the programs based on these theories have had limited success (Painter et al., 2008).
2. Behavior is rational:
The traditional vaccination strategies assume that human behavior is rational and therefore the individual is responsible for making rational decisions about his or her health (DiClemente et al., 2002). This too is a result of immunization programs being based on the HBM. Dutta-Bergman described a limitation of the HBM which he called Cognitive Orientation. This means that the HBM assumes that individuals are rational beings and conduct a cost-benefit analysis taking into account the susceptibility, severity, benefits and barriers to a health behavior before enacting that behavior. Decisions made at the spur of the moment or habitual choices are not taken into account (Dutta-Bergman, 2005). Traditional immunization strategies are based on the assumption that after people have been educated about a disease through fact sheets, they will perform a mental arithmetic of the benefits and barriers and reach a rational decision. This rational decision will result in appropriate behavior of receiving immunization. Any cultural factors, fears, or rumors about the vaccine or access issues are not taken into consideration.
Human behavior is not always rational. Emotions are powerful navigators of behavior. Chapman and Coups evaluated the role of emotions (worry about getting the flu, regret about not getting flu vaccine and perceived risk about getting flu) in influenza vaccination among 428 university employees. Their study showed that the emotions (worry and regret) are the ?more immediate precursors of decisions than are calculations of the risk probability and severity?. They feel that these results can also be applied to other preventive health behaviors and not just influenza vaccination (Chapman & Coups, 2006).
3. Uniform and homogenous mixing of population:
About 100 years ago, researchers started studying dynamics of infectious diseases. Early models assumed uniform mixing of people and ignored spatial patterning (Hartvigsen, Dresch, Zielinski, Macula, & Leary, 2007)(Eubank et al., 2004). The immunization guidelines are based only on the age and existing medical conditions of individuals. Social networks and patterns of clustering of people in society are not taken into consideration. Guidelines assume homogeneous mixing of people or that every individual has equal probability of contracting a particular disease. However, this is not accurate. Due to social networks, individuals have varying degrees of risk in acquiring an infection. Bascompte states that traditional models in epidemiology were developed in a homogeneous setting (where all individuals have the same number of interactions and the same probability of infecting each other). The concept of homogeneous population led to the belief that it is not necessary to vaccinate all individuals in a community. It has been assumed that after a certain fraction of the population is immunized, an eradication threshold is reached and the disease disappears. The fact is that the social networks are complex and heterogeneous. This means that the bulk of the nodes (individuals in a network) have a small number of connections, but a few nodes have a large number of connections, more than can be expected by chance. In addition, the probability is higher that new nodes will connect with the existing nodes with greater number of links. In these complex networks with heterogeneous distributions of links per node, there are always a few nodes that maintain the disease and therefore the disease never disappears (Bascompte, 2007). This means that a disease outbreak can occur even with a high immunization rate because the immunizations have been spread homogeneously in a heterogeneous population. Dallaire et al. reported a recent measles outbreak in Canada despite an immunity level estimated at 95%. The cases belonged to several unrelated networks of unvaccinated individuals. This outbreak underscores the fact that social networks are very important. Even a small change in the aggregation of individuals can lead to transmission of infection in unvaccinated individuals (Dallaire, De Serres, Tremblay, Markowski, & Tipples, 2009). Perisic et al. also noted that voluntary vaccination was able to contain the infection in models of small neighborhoods, but after a certain neighborhood size, voluntary vaccination failed to contain the epidemic despite a large number of people who were vaccinated. This occurs because in heterogeneous networks, disease is not localized anymore (Perisic & Bauch, 2009).
Summary of Critique
The rational cost-benefit Health Belief Model dominated the field of public health and psychology for several decades and led to the development public health interventions including immunization programs. As the field of psychology has matured, it has led to the development of conceptual models which take into account the contextual factors that lead to behaviors. The focus of theories has shifted from the individual to larger social networks including family, community and even global systems. In order to address the health issues of people in modern society, one must understand the lifestyle patterns and social context in which people live. It is imperative for new health programs to shift the focus from the individual to groups and take into account the contemporary way of life (Coreil, 2008). Newer computational models offer greater information about social networks and insight into spread of infectious agents within these networks. If we are to make improvements in adult immunization rates, cognitive theories need to be supplanted by group based theories which acknowledge that human behavior does not occur in isolation, nor is it planned and rational. Strategies should take into account the effects of heterogeneous societal structure and modern technology on individuals. Social network theory fulfils these criteria and can be utilized for developing new public health interventions.
What is Social network Theory?
There has been tremendous interest to develop novel models that address immunizations through use of social network theory. According to Dunn, social network theory is a set of assumptions and guidelines for ?development and appraisal of particular theories of knowledge creation, diffusion and utilization?. Social network theory makes four basic assumptions: (1) knowledge structures are constituted by relations among people or events or actions and not by attributes of individuals, (2) relations are structured, (3) structured relations have overt behavioral properties (such as frequency of direct contact) and cognitive properties (such as congruence of beliefs), and (4) behavioral and cognitive properties emerge from structured relationships (Dunn, 1983).
History and definition of networks
May has provided a historical account of networks. In the earliest model, introduced by Erdos-Renyi, n nodes (or vertices) are connected by links or edges. Nodes represent the individuals in the network. Degree distribution is the number of links per nodes. In the Erdos-Renyi model, the degree distribution is governed by a Poisson distribution where m is the average number of links. A uniform network is one where each node is linked to exactly m others (May, 2006). In 1967 Stanley Milgram, a Harvard professor, discussed the idea of real-world networks. His goal was to find the ?distance? between any two people in the United States. He found that the median number of intermediate persons was 5.5. This led to the famous concept of ?six degrees of separation? (Barabási, 2002), (May, 2006). This was followed by the work of Watts and Strogatz on ?small world? networks. These networks combine local clusters and occasional ?long hops? (May, 2006). Lastly, there are ?scale-free? networks (SF) which obey the power-law distribution where there is no specific number of links per node; a small number of nodes have a large number of links and a large number of nodes have a small number of links. Two important characteristics of networks are network ?diameter? and ?clustering coefficient?. Diameter is found by calculating the shortest path between each pair of nodes. The largest of these shortest paths is the diameter. Clustering coefficient is the average probability that two neighbors of a node are also neighbors of each other (May, 2006). Network dynamics are being used to study infectious diseases, epidemic preparedness, and immunization strategies. Networks can be interpersonal or web based.
Immunization strategies based on social network theory addresses the three limitations discussed previously. These interventions are group based, they do not assume rational behavior, and they address heterogeneous mixing of people.
1. Group based:
Social network based immunization strategies will target the entire network of people and not just individuals. Individuals in a network behave as a unit and exhibit health behaviors simultaneously. Introduction of appropriate immunization related information in the networks has the potential of affecting individual acceptance through establishing social norms and social acceptance. Interventions can be developed to target interpersonal networks or may be Internet-based. People are more likely to adopt certain behaviors through word of mouth in a trusted social group and if they see people in their social group adopting these behaviors.
The Internet is an example of an SF network. Social networking on the Internet leads to formation of virtual communities and to changes in behavior of groups instead of individuals. Social networks are rapidly emerging on the Internet. Khan et al. call them ?digital town squares? where people with common interests can interact (Khan & Shaikh, 2008). The current Web 2.0 offers a plethora of social networking technologies and software to connect healthcare organizations, clinicians, and laypeople. KamelBoulos and Wheelert feel that the educational potential of Web 2.0 applications, such as ?wikis, blogs and podcasts are just the tip of the social software iceberg?. Online interactions between users create a sense of community, promote learning through feedback and result in ?collective intelligence?. These social networking services may be used for linking people based on a medical issue (KamelBoulos & Wheeler, 2007). A number of disease-based blogs and websites provide a forum for individuals with these conditions to interact and share ideas. Example of such a websites and blogs are http://www.patientslikeme.com and http://diabetes.wikia.com/wiki/Diabetes_Wiki. Introduction of health behaviors in such networks has the potential for spreading through diffusion of innovations.
Gary Bennett, a professor at the Harvard School of Public Health, found that about 75% of adult Americans are regular Internet users and many access health related information. Bennett states that the Internet provides a high reach. Internet-based interventions are efficacious and offer an advantage because they allow the users to participate at their own convenience and allow anonymity (Bennett, ).
Interventions for boosting immunizations should be designed to reach the target social groups so that the dissemination of information can occur in a viral fashion. Future efforts to improve immunization rates should be focused on development of interactive Internet programs and development of programs to reach interpersonal groups of people in the society. This would provide greater impact in terms of behavior change in the target social groups.
2. Behavior is irrational:
Contrary to traditional belief, people do not always behave in a rational manner. Social norms, peer pressure, and herd mentality are important determinants of behavior. Individuals are embedded in their social networks and adoption of behaviors is not dependent on rational thinking. Various authors have suggested ways that behaviors can spread through social networks like contagions. The diffusion of innovations can spread desirable behaviors in social networks. These networks can be utilized to increase vaccine acceptance. Spread of behavior in this manner does not assume or expect rational behavior of people.
Numerous researchers have studied the spread of behaviors in various types of networks. It has been generally seen that new behaviors spread faster if clusters of people are linked by bridges. Granovetter calls this phenomenon the strength of weak ties. Weak ties are the links between acquaintances, and they bridge the connection between two close knit groups (Granovetter, 1983). Immunization interventions should be developed to target the networks and weak ties in a strategic manner. Computer modeling can assist in locating clusters and bridges of these clusters in social networks.
Other studies also favor the idea of bridges or weak ties between clusters. Choi et al. studied the diffusion of innovation in computational network models. They found that diffusion patterns are dependent on the number of initial adopters. The larger the number of initial adopters, the faster is the diffusion. As the group centrality increases (a large number of bridges and lower degree of cliquish sub-networks) diffusion is faster but is more likely to fail. When the number of bridges is low and the level of cliquish sub-networks is high, the diffusion is slower but has greater penetration. Diffusion of innovation is successful in cliquish networks with few bridges. Too many bridges lead to a rapid early stage but then the diffusion fails because it does not gain enough momentum to go forward due to insufficient build up of benefits (Choi, Kim, & Lee, ).
An individual is more likely to engage in a behavior if a large number of people in the network are already engaging in that behavior. One?s adoption of a new collective behavior depends on the behavior of the group (Valente, 1996). All these studies show that ideas spread in social networks based on network structure and irrational human behavior. Immunization programs should take advantage of this phenomenon and strategize the efforts to weak ties and early adopters.
3. Heterogeneous mixing:
As noted in the previous section, the traditional models have assumed that the population is homogeneous and has uniform mixing. However, the population is quite diverse and heterogeneous. It can be divided into subpopulations based on disease related factors or other factors that are social, cultural, economic, demographic or geographic. Population can also be subdivided based on spatial factors such as schools, neighborhoods, cities, states etc. Modeling based on Social networks takes into account that people move around and do not have homogeneous mixing. New immunization interventions should take into account this heterogeneous nature of population and develop heterogeneous strategies that superimpose the network. This would provide a more optimal coverage of immunizations. As noted previously, even high immunization rates in a community were unable to contain an epidemic because they were based on the assumption of homogeneous mixing. The epidemiological information about a disease and the topology of social networks should guide immunization strategies because social networks are complex structures and different people have different probability of getting infected. A study performed by Eubank et al. shows that infectious disease outbreaks can be contained by targeted vaccinations instead of mass vaccinations. They suggest that vaccinating people who visit a popular and most visited location would yield a better outcome than vaccinating the most gregarious people who meet the same group of people regularly (Eubank et al., 2004).
Hartvigsen et al. discuss strategies to improve influenza immunization through utilization of network structures. Increased computational capability had led to development of spatial models that better represent social networks. These networks mimic the spread of some infectious agents in a community. They evaluated various vaccination strategies in the network models and vaccination rates ranging from 0% to 90% for spread of an epidemic. They found that vaccinating people at the hubs of the networks or people with the highest degree (largest number of neighbor contacts e.g. health care providers) provided the greatest protection. The hub strategy also led to the shortest epidemic duration. Even vaccinating low clustering coefficient models and models with longest distance between nodes were better than random vaccinations. Vaccinating people with a high clustering coefficient (the proportion of the host?s neighbors that are connected to each other relative to the number of possible connections i.e. individuals whose contacts know each other e.g. residents of group homes or members of the same family) showed the lowest amount of protection and longest epidemic duration (Hartvigsen et al., 2007). Such information on network based targeted immunization can have significant bearing on routine vaccines and epidemic preparedness.
Miller et al. compared the effects of various vaccination strategies in preventing the spread of infectious diseases in realistic social networks. They found that PageRank (network with complete information, also the algorithm behind Google web ranking) outperformed all other models, namely Degree vaccination (vaccinates nodes by descending degree), Degree vaccination with Dynamic Reranking (recalculates the degree and vaccinates the node with most unvaccinated neighbors), Acquaintance vaccination (selects a node randomly and vaccinates one of its neighbors) and Random vaccination (vaccinates a fraction of the population randomly). However, obtaining complete information about a social network is not practical. Therefore the authors suggest vaccinating people who visit the most locations because this is easier to ascertain and yielded comparable results to Acquaintance vaccination and better results than Random vaccination in their study. They noted that vaccination efficacy was low on vaccinating individuals whose contacts also had many contacts. Essentially, strategy based vaccination was found to be better than random vaccination (Miller & Hyman, 2007).
Various researchers have proposed different strategies based on network topologies, and they have all yielded better results than current random strategies. Chen and Yuan propose a novel immunization strategy on scale-free networks. It is called the random walk immunization strategy. They performed a theoretical analysis and found this model to be very effective when compared to other strategies (Chen & Yuan, 2009). Guo et al. propose a targeted immunization strategy called Local region immunization using a novel Euclidean Distance Preferred (EDP) model. They generated EDP models in a small world network and found that there exists a critical immunization radius for a susceptible-infective pair of nodes to effectively suppress the epidemic prevalence (Guo, Li, & Wang, 2007). Khan et al. propose algebraic computation for immunization of social contacts of a person who has contracted an illness (Khan & Shaikh, 2008). Kretzschmar also recommends mathematical modeling for effective vaccination strategies in public health. These models will help determine best ages for various vaccines, target groups and other strategies for eliminating infectious diseases (Kretzschmar, 2008). All these studies highlight the fact that strategic heterogeneous immunization of population based on topology of networks is likely to provide improved control of diseases and therefore should be incorporated in future programs.
In conclusion, the adult immunization rates have improved over the past few years, however, there is still work needed to minimize the incidence of vaccine-preventable morbidity and mortality. We need to address this problem with strategies that provide widespread reach to people, especially in high risk categories. The strategies should conform to the modern societal structure and lifestyles. This will require a multi-prong approach and social network theory appears to offer a promising potential.
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