Introduction
Antibiotics were developed and refined at the start of World War II to supply the government with a means of treating wounded soldiers with increasingly dangerous and previously untreatable illnesses (1). As production streamlined, public access increased and thus the first signs of treatment failures and resistances started to appear (2). Antimicrobial resistance is the natural evolution of microorganisms to gain methods of rendering current, effective antimicrobial agents ineffective for the treatment of illness (3). As standard treatments for infection lose effectiveness, diseases are able to spread more widely and aggressively, limiting the variety of safe and effective antimicrobial agents. Importantly, improper use of antimicrobial agents speeds the natural evolution of resistance, spreading it throughout human populations.
Due to the noticeable emergence of resistant microorganisms, the Centers for Disease Control and Prevention (CDC), the National Institutes of Health (NIH), the World Health Organization (WHO), the Institute of Medicine (IOM), and the medical c0mmunity as a whole, began monitoring the type and quantity of ever-increasing resistance bacteria, viruses, fungi and other microbes that humans interact with (3–7). The medical and public health community have increasingly criticized the lack of direction and control around historical antimicrobial application, forcing discussions on responsibility of administering the drugs (8–11). The most vocal critics call the situation a ticking time-bomb (6,12,13).
In response to community outcry and the need for study and policies to slow the spread of resistance, the CDC, WHO, NIH and other governing medical bodies have created programs and initiatives to address the spread of antimicrobial resistance; The CDC created the “Get Smart” Campaign (14), The NIH’s NIAID (National Institute of Allergy and Infectious Diseases) group created the “Antibacterial Resistance Program” (15), The WHO started its Antimicrobial Resistance informative webpage (3). These organizations provide education materials, resource links, surveillance data, and other resources, covering a range of audiences from the general public to governing health officials. A public health plan has also been created by an interagency task force headed by the CDC, FDA and NIH (16,17). This incorporates each agency’s individual measures and provides communication between them for improved reporting. In addition to education initiatives, non-medical applications of antimicrobials have come under increased scrutiny. The US Food and Drug Administration (FDA) recently increased their involvement in controlling the spread of resistance through newly proposed guidelines for farming and agriculture (18).
The overall goal of public health initiatives is to prevent spreading antimicrobial resistance by lessening unnecessary selective pressure on microorganisms. These efforts will extend the utility of current treatments and allow time for new developments in fighting diseases (3,7). Current efforts are necessary and well intentioned, but significant progress will not be made unless improvements are made in the approach of the medical community’s fight against rapid development of antimicrobial resistance. These arguments are laid out in the remainder of this commentary.
The First Flaw with Current Initiatives
Multiple reports note that programs like the CDC’s Get Smart campaign and the NIH’s Antibacterial Resistance program place most of their focus on the hospital, professional, research, and medical education settings (16). Few resources address the public, and arguably the most important setting, the physician-patient interaction point. Teaching medical students in training, re-educating professionals with continuing education opportunities, and hosting medical society discussions have not done enough to face the growing resistance issue. The medical system is only a portion of the problem.
Historically, the medical system is proficient at changing protocols based on results of scientific studies, but with extremely varied momentum. This variability in time for policy adoption has many factors, some stemming from personal characteristics of healthcare personnel (19–21). These characteristics include individual behaviors and feelings, and their participation in creation of the new policies. While the intentions behind new protocols to face resistance are well understood, the management of medical personnel and patients in an abrupt and authoritative manner leads to psychological reactance (22). Psychological reactance is an individual’s response to perceived loss of freedom. For example, if a new policy that prevents over-prescription of a certain drug is put in effect, a physician who favors that agent for treatments may not agree with the policy and ignore it. Even if the physician understands why he or she is being forced to change prescription habits, the physician may feel some level of agitation and anger with the loss of a choice. This example demonstrates how intentions, even when fully understood, may not translate into directives being followed. Responsibility as a dictated policy creates the perfect environment for non-compliance of procedure.
Instituted stewardship may prevent irresponsible prescription and dispensing of agents, but does not address psychological reactance. Stewardship circumvents reactance and cannot replace guided collaboration. Reactance in professional settings, physician-physician relations or system-physician relations, is one area of concern, but the physician-patient interaction presents a larger reactance opportunity.
During care provider meetings, individuals ask for specific treatments and testing based on previous knowledge gained from commonplace talks, waiting room magazine ads, or Internet based searches. Without sufficient time and energy (23) going to the patient-physician interaction, the way information is relayed to patients changes. For example, quickly instructing or dispensing a sheet of instructions, on why and how to take medication leaves patients with no alternative to a “do as you are told” situation, instigating feelings of reactance. Patients have also been known to ‘doctor shop’ and look for a physician that agrees with their viewpoint and wishes, and provides them with what they ask. Such as situations where sufferers of the common-cold seek out antibiotics, even with known viral infections. This is not to be confused with comfortableness of care; this is the patient seeking out a treatment and not an understanding (24,25).
The ways in which the medical community is attempting to police itself and face the antimicrobial resistance issue most closely fit with social learning (social cognitive) theory (26). The Get Smart campaign promotes intra-professional action with resources for stewardship initiation and promotes hand-washing policies that fall under the control of hospital or system Infectious Diseases leadership. Social learning theory takes into account learning behavior and modeling one’s self after others, and also can include the effects of self-efficacy on outcomes. This model unfortunately still allows for irrational decisions and does not remove the individual as the main controller of outcomes (27,28). Reasoned and planned action does not preclude an outcome from being irrational and inconsistent when the individual is left in control.
The Second Flaw in Current Initiatives
A comparison of a transtheoretical model (explained in steps a-e below) to the medical community’s stewardship approach, as well as other initiatives, is readily visible and should not be ignored. A part of the transtheoritical model is the assessment of physician’s readiness to accept change. Transtheoretical modeling relies on individual patient facing physicians to follow rationally planned behaviors.
Core elements of the CDC’s listed stewardship example are Tracking and Monitoring, with measures that relate to cost and level of antibiotic use to show how well targets are met and what might need to be changed to meet them (29). The medical community and governing agencies follow transtheoretical modeling: with their pre-contemplation phase of (a) thinking about initiating action to change growing antibiotic resistance; contemplation phase of (b) meeting to discuss the issue and possible actions; preparation phase of (c) planning out possible actions, and making preparations to carry-out selected actions; action phases of (d) implementing policy and programs; and checking the maintenance needed with (e) efficiency and results of implemented actions (30). This cycle repeats often and has slowed forward movement with discrete steps. This model also provides some comfort to the medical community because of its likeness to the scientific method. There are stages of planning, measuring and experimentation to prove a hypothesis, with results adding to the knowledge base and allowing for a change in hypothesis and retesting(31). This comfort is unfounded though, because the scientific method does not need to account for human behavior and emotion like the transtheoretical model. Patient treating physicians may be unwilling to adhere to new prescription protocols due to emotional attachments and limited views on positive evaluations, or limitations of a policy that the transtheoretical model underestimates.
A Third Flaw with Current Initiatives
Planned behavior modeling in public education campaigns rely on people acting rationally, such as anti-smoking ads and healthy diet information campaigns (32–34). While it is helpful to think that providing relevant information to the public and explanations of the information is sufficient, individuals irrational behavior is not accounted for, which can lead to lower than theoretical efficiency. Planned behavior cannot account for how individuals act in “hot” mental states (35). A “cold” mental state would be the rational, planning state of mind individuals are in, but when they are “hot,” on the spot or agitated, plans are not followed. The rational situations patients encounter are ones where they learn information about viruses and illnesses in a receptive mindset. However, when a patient enters a hot state they cannot predict or always understand their behavior. Failure to implement or follow a planned cold state action is imminent; like ignoring explained information on drug effectiveness, time for action, or duration of treatment when a patient falls ill and suddenly starts demanding drugs and treatments. Mental perception and self-efficacy changes again when patients start to feel better and they stop taking medications they once agreed they needed to finish completely to recover fully. Stopping a course of antimicrobials before the treatment plan is finished leads to antimicrobial resistance by not allowing the body to clear the infectious agents completely, creating a reservoir of resistant, infectious microorganisms. Therefore, planned behavior model dispensing of information, with individual level targets, and without properly accounting for other individual factors and social influences, will not work to alter the future of antimicrobial resistance.
Proposed Changes to Make Effective Interventions
Overall, individual modeling and inadequate social approaches have been employed in an attempt to alter the course of a national and global problem. Correction of flaws discussed above can be achieved with different theories that apply to large-scale groups, like social networking theories, and alternative individual modeling, like labeling theories, that account for irrationality and end user variability. Proper attention and effective outcomes can be gained without losing the health information at the basis of the message by changing the modeling and campaign approaches.
Intervention Change of Flaw 1
By applying a social network approach to responsible use and dispensing of antimicrobial drugs, stewardship can become self-promoting and spread in the medical community. Granovetter (35) demonstrated that there are different tie strengths within a social network (strong, weak or absent). Strong ties consist of close friends or family members. However, weak-ties have the ability to spread information over a large social network (36). Targeting a few small networks can spread information faster and more easily than targeting important individuals with strong-ties. Since this interaction is also through peers, psychological reactance is minimized (37). Routine interactions by medical staff can reinforce new common policy and reasoning behind it once adoption begins and evolves.
A program based on social networking can employ branding and labeling strategies to gain more traction within the medical community. Creating a brand of antimicrobial responsibility allows individuals and networks alike to label themselves as something new and different (38). By labeling themselves and their networks, medical staff have a greater likelihood of living up to those labels and achieving cognizant, responsible used of antimicrobial drugs (39–41). Something as simple as gold pins and stickers in the shape of pills for self-labeling and a mantra like “I am a responsible physician, so I utilize precious antimicrobials responsibly,” for network labels would work.
Intervention Change of Flaw 2
By placing emphasis on the patient-physician interaction, initiatives with responsible use of antimicrobials will have better success by removing the physician as the sole, rationally dependent participant affecting the outcome. Advertising theories lend themselves well in this situation, and ownership possibilities provide opportunity for emotional and personal investment in care planning. Short hospital and network based advertisements can be created to emphasize cooperation of care and health management. Building of trust with patients can be shown by physician-patient interactions with rewarding exchanges of more than just medicine as a routine business. The more participation in their care plan design, the more ownership patients have of it. This translates to increased follow-through with care plans that may have been disliked because stronger, attached feelings created in cooperative development carried ownership of the individual care plan (35,42–46). Further, this allows for the opportunity of medically guided, responsible care when using antibiotics, while simultaneously preventing the use of antibiotics when it is medically contraindicated.
Intervention Change of Flaw 3
Public education campaigns need a greater focus from initiatives, like Get Smart. The end user of all antibiotics is the healthcare consumer. A lot of this consumption happens outside of the hospital setting, especially outside the United States. By increasing understanding with consumers and promoting correct treatment, general awareness can be raised about proper and improper activity around antimicrobial drugs. Whether it is sharing medications, incomplete medication consumption, improper environmental disposal, or irrational requests, consumers are beyond the medical community once they leave the physician office. Advertising theory is applicable again, but combined with personalized messages, elimination of unrealistic perception of life events is achieved and message integration reached. Individuals have an unrealistic perception of their inherent chances of negative and positive life events (35,44,47,48). Consumers will not chose to believe that they are susceptible to dangerous strains of infectious agents they may have encountered before, or know others who had infections caused by before, that were easily treated and cured. Individuals may admit the existence of such highly dangerous infectious agents as Methicillin Resistant Staph. Aureus (MRSA), but they are relying too much on personal beliefs that they have been OK up to now and “that kind of thing happens to other people.” Ignoring that others have the same chances for infection as they do, with the same helpful deterrents or susceptibility risks, general consumers use unrealistic optimism when faced with events they do not like. Personalized messages, like one from a mother with a son who had community acquired MRSA and fell significantly ill, or the neighbor who got sick with campylobacter food poisoning, will penetrate the unrealistic view consumers hold. Aristotle’s concepts of persuasion still hold true, by applying to one side of an individual, here emotional, you can gain attention and be received (49). This approach is similar to the anti-smoking ads once used depicting a father who explains how smoking caused his wife to live only half a life. The ads do not explicitly say smoking is bad for your health, but they demonstrate outcomes. Similarly, a neighbor with food poisoning can explain why they were sicker longer than expected and how they were not sure if they would get better, and how their family suffered as well. It is important to invoke emotion in this approach, but not fear. Safety is important to hold as a feature in a positive manner.
Conclusion
With the impending crisis of antimicrobial resistance, it is now more important than ever to start facing the issue and implement effective public health campaigns to stall the spread of deadly infectious diseases. Individual based modeling is no longer sufficient for dispensing health information on this topic with the hope that it will spur some level of activity. The medical community as a whole needs to make large-scale adjustments and initiatives that encompass public education and medical system flaws that led to slow adoption of what policies and information does exist. The change to alternative modeling and social networking theory from dated, traditional ideals like the transtheoretical model and health belief modeling can achieve the required level of attention and accomplishment for significant progress in reduction of antimicrobial progression. Without new approaches to this looming hardship, we may not retain our current ability to treat curable illnesses.
References
1. Lerner PI. Producing Penicillin. N Engl J Med. 2004;351(6):524–524.
2. Levy SB. The Antibiotic Paradox: How the Misuse of Antibiotics Destroys Their Curative Powers. Da Capo Press; 2002. 380 p.
3. WHO | Antimicrobial resistance [Internet]. WHO. [cited 2014 Apr 25]. Available from: http://www.who.int/drugresistance/en/
4. CDC - Antibiotic / Antimicrobial Resistance [Internet]. [cited 2014 Apr 25]. Available from: http://www.cdc.gov/drugresistance/
5. Antimicrobial (Drug) Resistance, NIAID, NIH [Internet]. [cited 2014 Apr 25]. Available from: http://www.niaid.nih.gov/topics/antimicrobialresistance/Pages/default.aspx
6. Painter K. WHO sounds alarm on widespread “superbug” infections [Internet]. USA Today. [cited 2014 Apr 30]. Available from: http://www.usatoday.com/story/news/nation/2014/04/30/who-alarm-superbug-infections/8502853/
7. Antimicrobial Resistance: Global Report on Surveillance [Internet]. WHO; 2014 p. 256. Report No.: 978 92 4 156474 8. Available from: http://apps.who.int/iris/bitstream/10665/112642/1/9789241564748_eng.pdf?ua=1
8. Carlet J, Collignon P, Goldmann D, Goossens H, Gyssens IC, Harbarth S, et al. Society’s failure to protect a precious resource: antibiotics. The Lancet. 2011;378(9788):369–71.
9. Heyman G, Cars O, Bejarano M-T, Peterson S. Access, excess, and ethics-towards a sustainable distribution model for antibiotics. Ups J Med Sci. 2014 Apr 15;
10. Carlet J, Jarlier V, Harbarth S, Voss A, Goossens H, Pittet D. Ready for a world without antibiotics? The Pensières Antibiotic Resistance Call to Action. Antimicrob Resist Infect Control. 2012 Dec 1;1(1):1–13.
11. Engberg J, Aarestrup FM, Taylor DE, Gerner-Smidt P, Nachamkin I. Quinolone and macrolide resistance in Campylobacter jejuni and C. coli: resistance mechanisms and trends in human isolates. Emerg Infect Dis. 2001 Feb;7(1):24.
12. Gottlieb T, Nimmo GR. Antibiotic resistance is an emerging threat to public health: an urgent call to action at the Antimicrobial Resistance Summit 2011. Med J Aust. 2011;194(6):281–3.
13. Whiley DM, Goire N, Lahra MM, Donovan B, Limnios AE, Nissen MD, et al. The ticking time bomb: escalating antibiotic resistance in Neisseria gonorrhoeae is a public health disaster in waiting. J Antimicrob Chemother. 2012 Sep 1;67(9):2059–61.
14. CDC - Get Smart: Homepage [Internet]. [cited 2014 Apr 25]. Available from: http://www.cdc.gov/getsmart/
15. The Antimicrobial Resistance Research Program of the National Institute of Allergy and Infectious Diseases - ARstrategicplan2014.pdf [Internet]. [cited 2014 Apr 25]. Available from: http://www.niaid.nih.gov/topics/antimicrobialResistance/Documents/ARstrategicplan2014.pdf
16. CDC - Interagency Task Force - Antimicrobial Resistance [Internet]. [cited 2014 Apr 25]. Available from: http://www.cdc.gov/drugresistance/actionplan/taskforce.html
17. A PUBLIC HEALTH ACTION PLAN TO COMBAT ANTIMICROBIAL RESISTANCE [Internet]. CDC, FDA, and NIH: Interagency Task Force on Antimicrobial Resistance; [cited 2014 Apr 25]. Available from: http://www.cdc.gov/drugresistance/pdf/public-health-action-plan-combat-antimicrobial-resistance.pdf
18. Medicine C for V. Guidance for Industry - FDA’s Strategy on Antimicrobial Resistance - Questions and Answers [Internet]. [cited 2014 Apr 25]. Available from: http://www.fda.gov/animalveterinary/guidancecomplianceenforcement/guidanceforindustry/ucm216939.htm
19. Greer AL. The State of the Art Versus the State of the Science: The Diffusion of New Medical Technologies into Practice. Int J Technol Assess Health Care. 1988;4(01):5–26.
20. Davis DA, Taylor-Vaisey A. Translating guidelines into practice: a systematic review of theoretic concepts, practical experience and research evidence in the adoption of clinical practice guidelines. Can Med Assoc J. 1997 Aug 15;157(4):408–16.
21. Kimberly JR, Evanisko MJ. Organizational Innovation: The Influence of Individual, Organizational, and Contextual Factors on Hospital Adoption of Technological and Administrative Innovations. Acad Manage J. 1981 Dec 1;24(4):689–713.
22. Burke WW, Lake DG, Paine JW. Organization Change: A Comprehensive Reader. John Wiley & Sons; 2008. 1219 p.
23. Gottschalk A, Flocke SA. Time Spent in Face-to-Face Patient Care and Work Outside the Examination Room. Ann Fam Med. 2005 Nov;3(6):488–93.
24. Kasteler J, Kane RL, Olsen DM, Thetford C. Issues Underlying Prevalence of “Doctor-Shopping” Behavior. J Health Soc Behav. 1976 Dec 1;17(4):328–39.
25. Worley J, Thomas SP. Women Who Doctor Shop for Prescription Drugs. West J Nurs Res. 2014 Apr;36(4):456–74.
26. Rosenstock IM, Strecher VJ, Becker MH. Social learning theory and the Health Belief Model. Health Educ Q. 1988;15(2):175–83.
27. Patel SJ, Saiman L, Duchon JM, Evans D, Ferng Y, Larson E. Development of an Antimicrobial Stewardship Intervention Using a Model of Actionable Feedback. Interdiscip Perspect Infect Dis [Internet]. 2012 Feb 21 [cited 2014 May 1];2012. Available from: http://www.hindawi.com/journals/ipid/2012/150367/abs/
28. Kluger AN, DeNisi A. The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychol Bull. 1996;119(2):254–84.
29. Core Elements - Implementation Resources - Get Smart for Healthcare - CDC [Internet]. [cited 2014 May 1]. Available from: http://www.cdc.gov/getsmart/healthcare/implementation/core-elements.html#_ENREF_102
30. Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997 Oct;12(1):38–48.
31. Cleland CE. Historical science, experimental science, and the scientific method. Geology. 2001 Nov 1;29(11):987–90.
32. Ajzen I. The Theory of Planned Behavior. Organ Behav Hum Decis Process. 1991 Dec;50(2):179–211.
33. Droomers M, Schrijvers CTM, Mackenbach JP. Educational differences in the intention to stop smoking Explanations based on the Theory of Planned Behaviour. Eur J Public Health. 2004 Jun 1;14(2):194–8.
34. Lien N, Lytle LA, Komro KA. Applying Theory of Planned Behavior to Fruit and Vegetable Consumption of Young Adolescents. Am J Health Promot. 2002 Mar 1;16(4):189–97.
35. Ariely D. Predictably Irrational, Revised and Expanded Edition: The Hidden Forces That Shape Our Decisions. 1 Exp Rev edition. New York: Harper Perennial; 2010. 384 p.
36. Granovetter MS. The Strength of Weak Ties. Am J Sociol. 1973 May;78(6):1360–80.
37. Silvia PJ. Deflecting Reactance: The Role of Similarity in Increasing Compliance and Reducing Resistance. Basic Appl Soc Psychol. 2005;27(3):277–84.
38. Evans WD, Hastings G. Public Health Branding: Applying Marketing for Social Change. 1 edition. Oxford ; New York: Oxford University Press; 2008. 316 p.
39. Link BG, Phelan JC. Conceptualizing stigma. Annu Rev Sociol. 2001;27:363–85.
40. Rosenfield S. Labeling mental illness: The effects of received services and perceived stigma on life satisfaction. Am Sociol Rev. 1997 Aug;62(4):660–72.
41. Goffman E. Stigma: Notes on the Management of Spoiled Identity. Reissue edition. New York: Touchstone; 1986. 168 p.
42. Strat Seminar 6.5 - Strategy.pdf [Internet]. [cited 2014 Apr 29]. Available from: http://www.adbuzz.com/OLD/Strategy.pdf
43. Bendinger B. Copy Workshop Workbook: Second Edition. Chicago, IL: Copy Workshop; 1993. 396 p.
44. Langer EJ. The illusion of control. J Pers Soc Psychol. 1975;32(2):311–28.
45. Kogler C, Kühberger A, Gilhofer R. Real and hypothetical endowment effects when exchanging lottery tickets: Is regret a better explanation than loss aversion? J Econ Psychol. 2013 Aug;37:42–53.
46. Kahneman D, Tversky A. Prospect Theory: An Analysis of Decision Under Risk. Econom Pre-1986 [Internet]. 1979 Mar [cited 2014 May 1];47(2). Available from: http://search.proquest.com.ezproxy.bu.edu/docview/214665840/abstract?accountid=9676
47. Weinstein ND. Unrealistic optimism about future life events. J Pers Soc Psychol. 1980 Nov;39(5):806–20.
48. Ayanian JZ, Cleary PD. PErceived risks of heart disease and cancer among cigarette smokers. JAMA. 1999 Mar 17;281(11):1019–21.
49. Hauser GA. Aristotle’s Example Revisited. Philos Rhetor. 1985 Jan 1;18(3):171–80.
No comments:
Post a Comment