Estimating chlamydia prevalence: more difficult than modelling suggests – Authors' reply
Authors: Peter J White, P Joanna Lewis
Journal: The Lancet Public Health (2018)

Low and Smid's Comment1Low N Smid JH Changes in chlamydia prevalence over time: how to observe the unobserved.Lancet Public Health. 2018; 3: e260-e261Summary Full Text Full Text PDF PubMed Scopus (3) Google Scholar on our Article2Lewis J White PJ Changes in chlamydia prevalence and duration of infection inferred from testing and diagnosis rates in England: a model-based analysis using surveillance data, 2000–15.Lancet Public Health. 2018; 3: e271-e278Summary Full Text Full Text PDF PubMed Scopus (19) Google Scholar analysing chlamydia testing data from England stated: “The model assumes the same screening rate in uninfected and infected people, although people at higher risk of chlamydia infection are more likely to be tested.” The authors' use of “screening” interchangeably with “testing” is ambiguous. In our model,2Lewis J White PJ Changes in chlamydia prevalence and duration of infection inferred from testing and diagnosis rates in England: a model-based analysis using surveillance data, 2000–15.Lancet Public Health. 2018; 3: e271-e278Summary Full Text Full Text PDF PubMed Scopus (19) Google Scholar, 3Lewis J White PJ Estimating local chlamydia incidence and prevalence using surveillance data.Epidemiology. 2017; 28: 492-502Crossref PubMed Scopus (16) Google Scholar testing in the absence of symptoms (ie, screening) by those with asymptomatic infection and those who are uninfected is indeed at the same rate. However, the rate of (diagnostic) testing by those with symptomatic infection is much higher. Therefore, the overall rate of testing is higher for those who are infected than for those who are not (figure). Soldan and colleagues challenge our results2Lewis J White PJ Changes in chlamydia prevalence and duration of infection inferred from testing and diagnosis rates in England: a model-based analysis using surveillance data, 2000–15.Lancet Public Health. 2018; 3: e271-e278Summary Full Text Full Text PDF PubMed Scopus (19) Google Scholar by asserting that non-symptomatic individuals screened for chlamydia are more likely to be infected than those who are not. There is currently no evidence either for or against this assertion. Various studies have shown that some individual-level behavioural predictors of chlamydia infection also predict testing,4Riha J Mercer CH Soldan K French CE Macintosh M Who is being tested by the English National Chlamydia Screening Programme? A comparison with national probability survey data.Sex Transm Infect. 2011; 87: 306-311Crossref PubMed Scopus (32) Google Scholar, 5Woodhall SC Soldan K Sonnenberg P et al.Is chlamydia screening and testing in Britain reaching young adults at risk of infection? Findings from the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3).Sex Transm Infect. 2016; 92: 218-227Crossref PubMed Scopus (30) Google Scholar but there is currently no evidence to determine whether this relationship can (or cannot) be entirely explained by the mediating effect of symptoms—an effect that is accounted for in our model. Soldan and colleagues cite two references in support of their assertion. The first does not address screening specifically by non-symptomatic individuals.5Woodhall SC Soldan K Sonnenberg P et al.Is chlamydia screening and testing in Britain reaching young adults at risk of infection? Findings from the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3).Sex Transm Infect. 2016; 92: 218-227Crossref PubMed Scopus (30) Google Scholar The second asserts that variation in positivity in different settings is due to differing proportions of patients being symptomatic, making no mention of variable infection risk in non-symptomatic patients; furthermore, it states that reasons for variation in positivity by test setting are “assumed” and presents no data on proportions of patients with and without symptoms. Soldan and colleagues state that we ignored variability in the infection risk of people being tested. In fact, we have considered the matter in detail. We discussed it at length in the original description of the method,3Lewis J White PJ Estimating local chlamydia incidence and prevalence using surveillance data.Epidemiology. 2017; 28: 492-502Crossref PubMed Scopus (16) Google Scholar in which we presented a sensitivity analysis based on the paper they cite,5Woodhall SC Soldan K Sonnenberg P et al.Is chlamydia screening and testing in Britain reaching young adults at risk of infection? Findings from the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3).Sex Transm Infect. 2016; 92: 218-227Crossref PubMed Scopus (30) Google Scholar which found that our prevalence estimates are robust.2Lewis J White PJ Changes in chlamydia prevalence and duration of infection inferred from testing and diagnosis rates in England: a model-based analysis using surveillance data, 2000–15.Lancet Public Health. 2018; 3: e271-e278Summary Full Text Full Text PDF PubMed Scopus (19) Google Scholar, 3Lewis J White PJ Estimating local chlamydia incidence and prevalence using surveillance data.Epidemiology. 2017; 28: 492-502Crossref PubMed Scopus (16) Google Scholar In any case, and as we also reported, the risk behaviour of individuals tested is not recorded in the surveillance data so it is not possible to include it in an analysis. Our understanding of chlamydia epidemiology and control is currently limited by the lack of sufficiently detailed data.2Lewis J White PJ Changes in chlamydia prevalence and duration of infection inferred from testing and diagnosis rates in England: a model-based analysis using surveillance data, 2000–15.Lancet Public Health. 2018; 3: e271-e278Summary Full Text Full Text PDF PubMed Scopus (19) Google Scholar, 3Lewis J White PJ Estimating local chlamydia incidence and prevalence using surveillance data.Epidemiology. 2017; 28: 492-502Crossref PubMed Scopus (16) Google Scholar, 6Lewis J Price MJ Horner PJ White PJ Genital C. trachomatis infections clear more slowly in men than women, but are less likely to become established.J Infect Dis. 2017; 216: 237-244Crossref PubMed Scopus (13) Google Scholar Soldan and colleagues also state that in our model “the likelihood of being screened… is not variable by time or place except as a result of changing prevalence”. In fact, in our geographical analysis considering 1 year2Lewis J White PJ Changes in chlamydia prevalence and duration of infection inferred from testing and diagnosis rates in England: a model-based analysis using surveillance data, 2000–15.Lancet Public Health. 2018; 3: e271-e278Summary Full Text Full Text PDF PubMed Scopus (19) Google Scholar the probability of being screened varies by place and in our temporal analysis considering England as a whole3Lewis J White PJ Estimating local chlamydia incidence and prevalence using surveillance data.Epidemiology. 2017; 28: 492-502Crossref PubMed Scopus (16) Google Scholar it varies with time. We compared our prevalence-change estimates to changes in positivity for all age-sex groups (appendix). As Soldan and colleagues note, since 2008 the changes in estimated prevalence have largely mirrored changes in positivity. Before 2008, however, positivity fell in all four age-sex groups, while estimated prevalence increased (in men) or was stable (in women). Our estimated changes in prevalence are not simply due to changes in positivity: the scatterplots (appendix) show the absence of correlation. We propose two studies that could elucidate the controversial relationship between sexual risk, prevalent chlamydia infection, symptoms, and testing. First, data could be collected on symptoms, reason for testing, and risk behaviour in those tested and diagnosed. This approach would yield a highly detailed spatiotemporal dataset: there were 9·5 million recorded testing events (813 283 positive) in individuals aged in 15–24 years England in 2012–17. Second, mediation analysis could be used to understand whether the correlation observed between being infected with chlamydia and being tested can—or cannot—be explained entirely by the intermediary effect of symptoms. For the 2018 Health Protection Report by Public Health England see https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/713944/hpr2018_AA-STIs_v5.pdf For the 2018 Health Protection Report by Public Health England see https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/713944/hpr2018_AA-STIs_v5.pdf We declare no competing interests. The views expressed are those of the authors and not necessarily those of the Department of Health, Medical Research Council, NHS, NIHR, or Public Health England. Download .pdf (.46 MB) Help with pdf files Supplementary appendix Changes in chlamydia prevalence and duration of infection estimated from testing and diagnosis rates in England: a model-based analysis using surveillance data, 2000–15Our analysis provides the first evidence for a reduction in chlamydia prevalence in England concurrent with large-scale population testing. It also shows a consistent decline in the average duration of infections, which is a measure of screening effectiveness that is unaffected by behavioural changes. Full-Text PDF Open AccessChanges in chlamydia prevalence over time: how to observe the unobservedThe ways that widespread testing and treatment of sexually transmitted Chlamydia trachomatis infections affect the prevalence and incidence of infection at the population level are not easy to understand, and the difficulties are well documented.1 First, chlamydia is an infection, so the dynamics of transmission between infectious and susceptible individuals are given by a non-linear function. Second, chlamydia screening recommendations were introduced without randomised controlled trial evidence of the effects of screening on prevalence. Full-Text PDF Open AccessEstimating chlamydia prevalence: more difficult than modelling suggestsIn their Lancet Public Health Article (June 2018) Joanna Lewis and Peter White1 used national programme monitoring data2 to infer chlamydia prevalence over time. In a previous article they used the same method to infer prevalence by local authority using national data for 2012.3 The problem with using these data for these purposes is the inherent variability regarding who is being tested. Unfortunately Lewis and White haven't overcome this problem. They infer prevalence estimates using equations in which the number of diagnoses is a function of prevalence, rate of symptomatic infection presentation (and the rate of symptoms), and the amount of testing done. Full-Text PDF Open Access