Sexually Transmitted Diseases:
Rothenberg, Richard MD
From the Department of Family and Preventive Medicine, Emory University School of Medicine, Atlanta, Georgia
Reprint requests: Richard Rothenberg, MD, Department of Family and Preventive Medicine, Emory University School of Medicine, 69 Butler Street SE, Atlanta GA 30303. E‐mail: email@example.com
Received for publication December 21, 1999, and accepted December 28, 1999.
EXPLICITLY OR NOT, sexually transmitted disease (STD) interventions are based on some underlying theory of disease‐transmission dynamics. The traditional practice of contact tracing, for example, is a direct outgrowth of the random (or proportional) mixing model, wherein three groups or compartments (susceptibles, infecteds, removeds [SIR]) interact in accordance with a system of partial differential equations to produce a hypothetical epidemic curve.1 Under these circumstances, random mixing is proportional because the likelihood of encountering someone in a given compartment is proportional to the size of the compartment. The visual cognate of random mixing is the branching tree pattern (Figure 1), a chain of infection that has a chronology but no origin or terminus. Contact tracing (and its latter‐day incarnations, partner notification, and partner referral services) was based on the premise that testing and treating the sexual partners of infected persons will diminish disease transmission by “interrupting” such chains of transmission.
Few believe now, and doubtless believed back then, that sexual mixing is random.2 The earliest models soon gave way to more sophisticated attempts to model mixing patterns based on gender, sexual orientation, and level of risk. An early example in these pages was Yorke, Hethcote, and Nold's3 study of gonorrhea dynamics, whose formulation and more complete exposition gave rise to the concept of “core groups.”4 Contemporaneously, from a base in infectious disease ecology, Anderson, May and their colleagues5 developed the models of compartment interaction (Figure 2) that permitted a more detailed evaluation of nonrandom mixing (assortative and disassortative5,6) and the relative impact of group interactions. The concept that emerged was that not only is sexual interaction nonrandom, but may in fact be highly focal and driven by the interactions of a relatively small number of groups and of individuals. The addition of another set of theoretical and empirical constructs‐social network analysis7–10‐permitted examination of the effect of social structure and dynamics, in a context that transcended simple sexual relationships, on sexual transmission. The theory of social network influences posited that Figure 1 would probably be more like Figure 3 for groups actively involved in transmission. Here, we imagine that “B” and “D” were actually the same person, “BD,” and an additional five persons (the numbers) are actively involved in drug use with the original group. These current drug partners may share needles with members of the original group, or are potential if not current sex partners. Failure to identify such a group would be evidence of the unlikelihood of further transmission. Its identification, however, tells disease investigators that they are dealing with persons who are important in transmission.
Considerable empirical evidence, both for the STDs and HIV, now supports the theoretical base that has been developed.11–17 The work of Gunn and colleagues18 in the current issue of Sexually Transmitted Diseases is heartening evidence that some STD control programs may be moving away from the traditional contact tracing and toward a view that is more consonant with current concepts. These investigators demonstrate that, in their clinic, a previous history of gonorrhea or chlamydial infection is the strongest predictor of another infection in the next 12 months. They postulate that such persons may be core group members (or, as they say in the title, possible “core” transmitters) who, after treatment, return to an environment in which the STD is actively transmitted. They are, therefore, important in transmission dynamics and require special attention.
Whether the work of Gunn et al represents a step that others will follow remains to be seen. Priorities are fundamental to STD control program operations, but they are often based on administrative or clinical characteristics of cases, rather than on epidemiologic or behavioral features. In a control environment that is clinic‐based, and wherein case management has precedence over disease dynamics, adopting new approaches and techniques may be difficult. Gunn et al describe a trial of such newer activity in their report, and include a powerful exhortation to other programs to identify sexual networks that support transmission. Several other recent publications point to the growing interest in developing an empirical base to test newer theoretical findings and to translate such findings into program activity.19,20 A new set of Program Operations Guidelines from the STD Division at the Centers for Disease Control and Prevention (Atlanta, GA), contains, in draft form, a discussion of these issues and consideration of how program managers might incorporate these concepts into day‐to‐day activities (Wasserheit J, personal communication, 2000). Perhaps we can see in all this the stirrings of sea change in the way in which STD control programs approach the epidemiology of the STDs.
But changes in approaches to STD control have been proposed in the past and are often not accepted. The case in point is the work of Brooks, Darrow, et al21 who, in 1978, said: “Intensive follow‐up of the small number of high‐risk repeaters and their contacts could result in a major reduction in the number of reported cases of gonorrhea.” Thus, the importance of the repeater was emphasized over 20 years ago, but had little or no impact at the time. The parallel between reacquiring an STD, and rediscovering the importance of repeat infection is manifest. The diffusion of information and the transmission of disease have striking similarities‐an observation now currently exploited by investigators in the field. Perhaps if we could understand why the diffusion of innovation has been so slow in the disease control community, we would understand better how STDs diffuse in communities at risk.
Despite such grumblings, indications of a greater rapprochement between empirical and theoretical observations on the one hand, and programmatic approaches on the other, is encouraging. The suggestions of Gunn and his colleagues that control programs seek out sexual networks (or core groups, or core transmitters, or whatever we choose to call them, given the imprecision of overuse that these concepts have suffered) is a clarion call to reconsider the way we conduct STD control business. Such a reevaluation might be extended to the larger armamentarium of STD interventions. One such construct was developed in the Institute of Medicine's The Hidden Epidemic,22 wherein the authors categorized interventions by their effect on the variables in the reproductive number (R0): the number of new sex partners (c), the transmission probability (β), and the duration of infection (D). In this formulation, behavioral change (individual, community‐based, school‐based, and mass media), personal prophylaxis, contact tracing, and screening were viewed as affecting c and β; early diagnosis and effective treatment have an impact on D.
An alternative would be to make explicit the theoretical basis for transmission on which interventions are grounded (Table 1). The specifics are debatable, particularly regarding the finer distinctions between dealing with compartments (groups of similar people) and networks (groups of people who actually associate with each other). More important, the construct makes us examine the basis for what we do. In these terms, program mainstays such as traditional partner services and public education are structured by an approach that assumes random (or proportional) sexual mixing. The newer approaches, typified by those discussed by Gunn et al and others referenced previously, are connected to a more powerful concept of the underlying dynamics. The goal is to get beyond the recidivism of research and to act on these findings.
1. Bailey NTJ, ed. The Mathematical Theory of Infectious Diseases. 2nd ed. New York: Hafner Press, 1975.
2. Bailey NTJ. Introduction to the modeling of venereal disease. J Math Biol 1979; 8:301–322.
3. Yorke JA, Hethcote WHJ, Nold A. Dynamics and control of the transmission of gonorrhea. Sex Transm Dis 1978; 5:51–56.
4. Hethcote HW, Yorke JA. Gonorrhea transmission dynamics and control. In: Levin S, ed. Lecture Notes in Biomathematics. Berlin: Springer-Verlag, 1984; 56:1–105.
5. Anderson RM. The role of mathematical models in the study of HIV transmission and the epidemiology of AIDS. J Acquir Immun Defic Syndr Hum Retrovirol 1988; 88:241–256.
6. Gupta S, Anderson RM, May RM. Networks of sexual contacts: implications for the pattern of spread of HIV. AIDS 1989; 89:807–817.
7. Klovdahl AS. Social networks and the spread of infectious diseases: the AIDS example. Soc Sci Med 1985; 21:1203–1216.
8. Kretzchmar M, Reinking DP, Brouwers H, van Zessen G, Jager JC. Network models: from paradigm to mathematical tool. In: Kaplan EH, Brandeau ML, eds. Modeling the AIDS Epidemic: Planning, Policy, and Prediction. New York: Raven Press, 1994:561–583.
9. Morris M. Epidemiology and social networks: modeling structured diffusion. Soc Methods Res 1993; 22:99–126.
10. Morris M. Sexual networks and HIV. AIDS 1997; 11(suppl A):S209-S216.
11. Service SK, Blower SM. HIV transmission in sexual networks: an empirical analysis. Proc R Soc Lond B 1995; 260:237–244.
12. Friedman SR, Neaigus A, Jose B, et al. Sociometric risk networks and risk for HIV infection. Am J Public Health 1997; 87:1289–1296.
13. Neaigus A, Friedman SR, Curtis R, et al. The relevance of drug injectors' social networks and risk networks for understanding and preventing HIV infection. Soc Sci Med 1994; 38:67–78.
14. Potterat J, Zimmerman HP, Muth SQ, et al. Chlamydial transmission: concurrency, reproduction number and the epidemic trajectory. Am J Epidemiol, 1999; 115:1331–1339.
15. Rothenberg RB, Sterk C, Toomey KE, et al. Using social network and ethnographic tools to evaluate syphilis transmission. Sex Transm Dis 1998; 25:154–160.
16. Rothenberg RB, Potterat JJ, Woodhouse DE, Muth SQ, Darrow WW, Klovdahl AS. Social network dynamics and HIV transmission. AIDS 1998; 12:1529–1536.
17. Rothenberg RB, Kimbrough L, Lewis-Hardy R, et al. Social network methods for endemic foci of syphilis: a pilot project. Sex Transm Dis 2000; 27:12–18.
18. Gunn RA, Fitzgerald S, Aral SO. Sexually transmitted disease clinic clients at risk for subsequent gonorrhea and chlamydia infections: possible “core” transmitters. Sex Transm Dis 2000; 27:343–349.
19. Aral SO, Hughes JP, Stoner B, et al. Sexual mixing patterns in the spread of gonococcal and chlamydial infections. Am J Public Health 1999; 89:825–833.
20. Blanchard JF, Moses S, Greenaway C, Orr P, Hammond GW, Brunham RC. The evolving epidemiology of chlamydial and gonococcal infections in response to control programs in Winnipeg, Canada. Am J Public Health 1998; 88:1496–1502.
21. Brooks GF, Darrow WW, Day JA. Repeated gonorrhea: an analysis of importance and risk factors. J Infect Dis 1978; 137:161–169.
22. Eng TR, Butler WT, eds. The Hidden Epidemic: Confronting Sexually Transmitted Diseases. Washington DC: National Academy Press, 1997.
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