Filling in the gaps: estimating numbers of chlamydia tests and diagnoses by age group and sex before and during the implementation of the English National Screening Programme, 2000 to 2012

Abstract

To inform mathematical modelling of the impact of chlamydia screening in England since 2000, a complete picture of chlamydia testing is needed. Monitoring and surveillance systems evolved between 2000 and 2012. Since 2012, data on publicly funded chlamydia tests and diagnoses have been collected nationally. However, gaps exist for earlier years. We collated available data on chlamydia testing and diagnosis rates among 15–44-year-olds by sex and age group for 2000–2012. Where data were unavailable, we applied data- and evidence-based assumptions to construct plausible minimum and maximum estimates and set bounds on uncertainty. There was a large range between estimates in years when datasets were less comprehensive (2000–2008); smaller ranges were seen hereafter. In 15–19-year-old women in 2000, the estimated diagnosis rate ranged between 891 and 2,489 diagnoses per 100,000 persons. Testing and diagnosis rates increased between 2000 and 2012 in women and men across all age groups using minimum or maximum estimates, with greatest increases seen among 15–24-year-olds. Our dataset can be used to parameterise and validate mathematical models and serve as a reference dataset to which trends in chlamydia-related complications can be compared. Our analysis highlights the complexities of combining monitoring and surveillance datasets.

100 years of STIs in the UK: a review of national surveillance data

“…Testing was only readily available since the mid-1990s, and since then widespread testing, which in England was facilitated by the NCSP, led to a rapid increase in chlamydia diagnoses 17. The NCSP offers opportunistic screening of sexually active young people aged 15–24 years with the aim of increasing the detection of chlamydia and reducing the prevalence of associated sequelae.…”

Gini coefficients for measuring the distribution of sexually transmitted infections among individuals with different levels of sexual activity

“…We used the transmission model to examine how two different control interventions for CT affect the Gini coefficient and infection prevalence. First, we assumed that the increase in CT screening between the two survey periods of Natsal-2 (1999-2001) and Natsal-3 (2010-2012) through the National Chlamydia Screening Programme (NCSP) [29] has resulted in a reduction in the overall infectious duration. The transmission model suggests a concurrent drop in prevalence with a higher Gini coefficient ( figure 2B).…”

Discrepancies between observed data and predictions from mathematical modelling of the impact of screening interventions onChlamydia trachomatisprevalence

“…22 We used estimates for the numbers of chlamydia tests and diagnoses from 2000 to 2011 in England from Chandra and colleagues. 7 In this study, available data from several monitoring and surveillance systems in England, including NCSP, were collated to construct plausible minimum and maximum estimates for the numbers of tests and diagnoses each year for men and women in five-year age groups: 15-19, 20-24, 25-34 and 35-44 year olds. Chlamydia testing data did not distinguish between tests provided to people with symptoms suggestive of infection with C. trachomatis and screening tests amongst people without symptoms.…”

“…Testing coverage in young women increased from 4% in 2000 to 35% in 2012. 7 However, chlamydia prevalence, estimated in two cross-sectional population-based British National Surveys of Sexual Attitudes and Lifestyles (Natsal) was similar in adults aged 18 to 24 years; in 1999-2001 (Natsal-2), women 3.1% (95% confidence interval, CI 1.8-5.2) and men 2.9% (1.3-6.3) and in 2010-2012 (Natsal-3). Women 3.2% (2.2-4.6) and men 2.6% (1.7-4.0).…”

“…[11][12][13][14] In a simpler model, without detailed sexual behaviour, Lewis and White inferred changes in C. trachomatis prevalence and incidence using time-series data about chlamydia testing and diagnoses in England between 2000-2015. 7,15 Their model output proposed that prevalence had declined as chlamydia testing increased and increased as testing levels fell. An assumption in these models, irrespective of structure, is that amongst people without symptoms suggestive of infection, testing for chlamydia is not influenced by the underlying risk of infection in the screened population.…”