title: Modelling the effectiveness of chlamydia screening in England.
abstract: BACKGROUND
  Several developed countries have initiated chlamydia screening programmes. Screening for a sexually transmitted infection has both direct individual and indirect population-wide effects. Mathematical models can incorporate these non-linear effects and estimate the likely impact of different screening programmes and identify areas where more data are needed.
  
  
  METHODS
  A stochastic, individual based dynamic network model, parameterised from UK screening studies and data on sexual behaviour and chlamydia epidemiology, was used to investigate the likely impact of opportunistic screening on chlamydia prevalence. Three main strategies were considered for <25 year olds: (1) annual offer to women; (2) annual offer to women or if changed partner within last 6 months; (3) annual offer to men and women. Sensitivity analyses were performed for key screening parameters including uptake rate, targeted age range, percentage of partners notified, and screening interval.
  
  
  RESULTS
  Under strategy 1, continuous opportunistic screening of women <25 years of age is expected to reduce the population prevalence by over 50% after 5 years. Prevalence is also expected to decrease in unscreened older women and in men. For all three strategies screening those aged over 25 results in small additional reductions in prevalence. Including men led to a faster and greater reduction in overall prevalence, but involved approximately twice as many tests as strategy 1 and 10% more than strategy 2. The frequency of attendance at healthcare sites limits the number of opportunities to screen and the effect of changing the screening interval.
  
  
  CONCLUSIONS
  The model suggests that continuous opportunistic screening at high uptake rates could significantly reduced chlamydia prevalence within a few years. Opportunistic programmes depend on regular attendance at healthcare providers, but there is a lack of high quality data on patterns of attendance. Inequalities in coverage may result in a less efficient and less equitable outcome.
authors.affiliation: Health Protection Agency, Centre for Infections, 61 Colindale Ave, Colindale, London NW9 5EQ, UK. katherine.turner@imperial.ac.uk
authors.forename: K M E
authors.initials: KM
authors.lastname: Turner
authors.forename: E J
authors.initials: EJ
authors.lastname: Adams
authors.forename: D S
authors.initials: DS
authors.lastname: Lamontagne
authors.forename: L
authors.initials: L
authors.lastname: Emmett
authors.forename: K
authors.initials: K
authors.lastname: Baster
authors.forename: W J
authors.initials: WJ
authors.lastname: Edmunds
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D002690:Chlamydia Infections / Q000175:diagnosis* / Q000453:epidemiology; D016358:Contact Tracing; D004739:England / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000592:standards*; D008875:Middle Aged; D010342:Patient Acceptance of Health Care; D015995:Prevalence; D012747:Sexual Partners; D012944:Social Support; D013997:Time Factors; D016896:Treatment Outcome