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Sex Transm Infect 78:i152-i158 doi:10.1136/sti.78.suppl_1.i152
  • Symposium

Sexual network structure as an indicator of epidemic phase

  1. J J Potterat1,
  2. S Q Muth1,
  3. R B Rothenberg2,
  4. H Zimmerman-Rogers1,
  5. D L Green1,
  6. J E Taylor3,
  7. M S Bonney1,
  8. H A White3
  1. 1STD/HIV Programs, El Paso County Department of Health and Environment, Colorado Springs, Colorado, USA
  2. 2Department of Family and Preventive Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
  3. 3Preventive Medicine Department, Evans Community Hospital, Fort Carson, Colorado, USA
  1. Correspondence to:
 John J Potterat, STD/HIV Programs, El Paso County Department of Health and Environment, 301 South Union Blvd, Colorado Springs, Colorado 80910, USA;
 jjpotterat{at}earthlink.net
  • Accepted 6 December 2001

Abstract

Ascertaining epidemic phase for a sexually transmitted disease (STD) has depended on secular trend data which often contain significant artefacts. The usefulness of sexual network structure as an indicator of STD epidemic phase is explored in an analysis of community wide genital chlamydia reports, with network analysis of interviewed cases and linked sexual partners, in Colorado Springs, USA, 1996 to 1999. In this period, the chlamydia case rate per 100 000 increased by 46%. Three quarters of cases (n= 4953) were interviewed, nominating 7365 partners; these, combined with index cases, made up the 9114 persons in the network. Epidemiologic analysis of cases suggests that secular trend increases are artefactual. Network analysis supports this view: overall network structure is fragmented and dendritic, notably lacking the cyclic (closed loops) structures associated with network cohesion and thus with efficient STD transmission. Comparison of network structure with that of an intense STD outbreak (characterised by numerous cyclic structures) suggests low level or declining endemic rather than epidemic chlamydia transmission during the study interval. These observations accord with intuitive and stochastic predictions.

Footnotes