A New Approach to Estimating Trends in Chlamydia Incidence
A New Approach to Estimating Trends in Chlamydia Incidence
This study provides a new approach to estimate trends in chlamydia incidence in the general population. It uses a simple model which translates routinely available surveillance data and a limited number of key assumptions into estimates of incidence. Since this method relies only on routinely available data, chlamydia incidence can now be estimated easily on an ongoing basis; non-routine data (eg, prevalence or incidence studies) can be used for model calibration or reserved for validation. A recent modelling study, from the UK, used two separate methods to estimate incidence of chlamydia. The first method used existing incidence estimates, while the other used prevalence estimates; neither of which is a routine data source, and hence cannot be used for routine incidence estimation.
Our model shows large increases in incidence. We believe such increases are plausible. First, there has been an increase in the number of notifications. Second, prevalence as notified by the ACCESS sentinel surveillance system at sexual health services across Australia showed increasing trends in young people aged 15–29 between 2006 and 2010—which when expanded to the whole time period will be more pronounced. Third, positivity among 15–24-year-old men, as calculated by notification-to-testing ratio has remained roughly constant. Simple mathematics (not presented here) suggest that (a) if incidence increases but testing stays constant, the positivity must increase; (b) if testing increases but incidence stays constant, positivity must decrease and (c) positivity can only remain constant, if both incidence and testing decrease or both increase or both remain unchanged. Since we know that testing rates have increased substantially and positivity rates are estimated to have remained steady, incidence must have increased for this age group. We note that other sex–age groups did not have constant positivity rates over time despite increased testing. Fourth, there is a relationship between changes in testing rate, prevalence and positivity rate. Online supplementary figure S2 http://sti.bmj.com/content/91/7/513/suppl/DC1 demonstrates this relationship. Here, we observe the 'positivity contour' association between prevalence and testing rates. It infers how prevalence has likely changed over time while maintaining a steady positivity rate and increasing testing rates for 15–24-year-olds. This figure also relates testing and positivity rates to prevalence, had there been other testing or positivity data for this age group. In addition, there has been an increase in reported sexual risk-taking behaviour in young people in Australia. The Australian sexual health surveys among secondary students show that condom use at the most recent sexual encounter decreased over the last 10 years; and the proportion of young people reporting three or more sexual partners increased over this period.
Like all Bayesian analyses, this study relies on both the validity of the modelling assumptions and the suitability of the prior distributions adopted. Perhaps the greatest limitation arising from the former is that the model does not take into account any re-infections or re-testing. High repeat positive test rates have been reported for chlamydia in young women in Australia (22.3 per 100 person-years). In our analyses, all repeat positive tests were considered incident infections since the end point of this model's pathway was notification of infection. Since a vast majority of infections are asymptomatic and undiagnosed (and hence only cured naturally or by background antibiotic use), the contribution of re-infections has been assumed to be small for this study. A study by Althaus et al reported that re-infections have little impact on the estimates of the average duration of infection.
Another key assumption is that the designated time-independent parameters of our model are indeed time independent. These can be divided into (A) those related to disease (the proportions of asymptomatic infections in men and women and the probability of naturally clearing the infection within a year); (B) those related to testing (true positive and false positive test rates in the context of the same underlying diagnostic technology since 1999/2002 when Australia adopted nucleic acid amplification testing (NAAT)) and (C) those related to behaviour and practices (probabilities of being tested for chlamydia, the rate of background antibiotic use and case reporting completeness). The parameters in group A are strongly expected to be truly time independent given no evidence for biological changes in the pathogen; likewise for the parameters in group B as NAATs have been used over the study period. However, the time independence of the parameters in group C is an assumption representing the simplest hypothesis in the absence of relevant data. Although there may be some minor differences, it was assumed that all these time-independent parameters (except for the proportions of those with symptoms) were the same for both sexes.
Our model suggests that incidence increased at a faster rate in men. Although incidence was higher in women than men aged 15–24 years until 2005, it was higher in men after 2005. Also, it was higher in men than women aged 25–34 and >34 years for all years. More chlamydia diagnoses occur among women, reflecting a higher rate of asymptomatic testing among women. However, as there is little known about the natural history of chlamydia infection in men, with most studies reporting on natural history conducted in women, it seemed reasonable to assume that the probability of natural clearance of infection over a year is the same for both sexes. It is also notable that our model does not differentiate between heterosexual versus homosexual men nor other sexual mixing patterns.
Through a careful sensitivity analysis, presented in the online supplementary material http://sti.bmj.com/content/91/7/513/suppl/DC1, we have confirmed the robustness of our results against moderate changes to our input priors. For four of our nine time-independent parameters, the data are highly informative (and our results are largely insensitive to our prior assumptions); these are the asymptomatic proportions in men and women, the probability of natural clearance over a year and the false positive rate of testing. The remaining five time-independent parameters for which our prior assumptions dominate are (a and b) the probabilities of attending and consequently testing for symptomatic infections, (c) the true positive rate of the diagnostic test, (d) the rate of background antibiotic use and (e) the probability of reporting a test. Of these, all except the second and third (the focus of our prior sensitivity analysis) have narrow prior ranges based on reliable references and thus would not be expected to substantially alter our overall quantitative findings.
Until now, only one prospective cohort study of chlamydia in the general population has ever been conducted in Australia: the chlamydia incidence and re-infection study (CIRIS). This study included only young women aged 16–25 years and reported a chlamydia prevalence of 4.9% (95% CI 3.7% to 6.4%) and an incidence of 4.4 per 100 person-years (3.3–5.9) in 2007–2008. The incidence reported by CIRIS is similar to the estimates produced by our model: 5.6% (5.2% to 6.1%) and 5.7% (5.3% to 6.3%) in 15–24-year-old women in 2007 and 2008, respectively. Multivariate analysis from CIRIS showed that younger women (16–20 years) were more likely to have an incident infection, consistent with our study. CIRIS also reported that recent use of antibiotics was protective against incident infection. We accounted for background antibiotic use as a model parameter to allow for self-cure when antibiotics were taken for any reason, although the assumed level in our analyses may differ from actual levels of use.
Although there is a dearth of studies reporting on chlamydia incidence in the general population internationally, a study from the USA estimated that there were about 2.86 million incident infections in the USA in 2008. The number of chlamydia notifications in 2008, in the USA, was 1.2 million, which gives an incidence-to-notification ratio of 2.4. This is less than the incidence-to-notification ratio of 4 for Australia based on our estimates. The difference in ratios between the USA and Australia could reflect differences in testing patterns or epidemiology between these settings and/or the methods used to calculate the ratios. It would be valuable to compare these factors in future studies.
Historically, Australia has based its chlamydia prevention strategies on the number of diagnoses notified. However, in 2013 the number of notifications among people aged 15 years and older (n=82 484) represents only 23% of all estimated incident infections (n=356 000 according to this study) in the year. Thus, the estimated incidence-to-notification ratio in Australia was 4.3 in 2013. This also implies that 77% of new chlamydia infections remain undiagnosed. Our findings also suggest that incident infections of chlamydia more than doubled between 2001 and 2013, from 160 000 to 356 000 infections. However, this relative change (120%) is substantially less than the increase (>300%) in numbers of chlamydia notifications reported during the same time. This clearly demonstrates that the increase in the scale of the infection as observed by the trends in notification numbers has been misleading and is somewhat an artefact of increased testing.
This study has reported a new approach to estimating chlamydia incidence in the general population using routine testing and notification data. Other countries that collate and report data on chlamydia diagnoses and testing numbers can also use this method to estimate chlamydia incidence.
Discussion
This study provides a new approach to estimate trends in chlamydia incidence in the general population. It uses a simple model which translates routinely available surveillance data and a limited number of key assumptions into estimates of incidence. Since this method relies only on routinely available data, chlamydia incidence can now be estimated easily on an ongoing basis; non-routine data (eg, prevalence or incidence studies) can be used for model calibration or reserved for validation. A recent modelling study, from the UK, used two separate methods to estimate incidence of chlamydia. The first method used existing incidence estimates, while the other used prevalence estimates; neither of which is a routine data source, and hence cannot be used for routine incidence estimation.
Our model shows large increases in incidence. We believe such increases are plausible. First, there has been an increase in the number of notifications. Second, prevalence as notified by the ACCESS sentinel surveillance system at sexual health services across Australia showed increasing trends in young people aged 15–29 between 2006 and 2010—which when expanded to the whole time period will be more pronounced. Third, positivity among 15–24-year-old men, as calculated by notification-to-testing ratio has remained roughly constant. Simple mathematics (not presented here) suggest that (a) if incidence increases but testing stays constant, the positivity must increase; (b) if testing increases but incidence stays constant, positivity must decrease and (c) positivity can only remain constant, if both incidence and testing decrease or both increase or both remain unchanged. Since we know that testing rates have increased substantially and positivity rates are estimated to have remained steady, incidence must have increased for this age group. We note that other sex–age groups did not have constant positivity rates over time despite increased testing. Fourth, there is a relationship between changes in testing rate, prevalence and positivity rate. Online supplementary figure S2 http://sti.bmj.com/content/91/7/513/suppl/DC1 demonstrates this relationship. Here, we observe the 'positivity contour' association between prevalence and testing rates. It infers how prevalence has likely changed over time while maintaining a steady positivity rate and increasing testing rates for 15–24-year-olds. This figure also relates testing and positivity rates to prevalence, had there been other testing or positivity data for this age group. In addition, there has been an increase in reported sexual risk-taking behaviour in young people in Australia. The Australian sexual health surveys among secondary students show that condom use at the most recent sexual encounter decreased over the last 10 years; and the proportion of young people reporting three or more sexual partners increased over this period.
Like all Bayesian analyses, this study relies on both the validity of the modelling assumptions and the suitability of the prior distributions adopted. Perhaps the greatest limitation arising from the former is that the model does not take into account any re-infections or re-testing. High repeat positive test rates have been reported for chlamydia in young women in Australia (22.3 per 100 person-years). In our analyses, all repeat positive tests were considered incident infections since the end point of this model's pathway was notification of infection. Since a vast majority of infections are asymptomatic and undiagnosed (and hence only cured naturally or by background antibiotic use), the contribution of re-infections has been assumed to be small for this study. A study by Althaus et al reported that re-infections have little impact on the estimates of the average duration of infection.
Another key assumption is that the designated time-independent parameters of our model are indeed time independent. These can be divided into (A) those related to disease (the proportions of asymptomatic infections in men and women and the probability of naturally clearing the infection within a year); (B) those related to testing (true positive and false positive test rates in the context of the same underlying diagnostic technology since 1999/2002 when Australia adopted nucleic acid amplification testing (NAAT)) and (C) those related to behaviour and practices (probabilities of being tested for chlamydia, the rate of background antibiotic use and case reporting completeness). The parameters in group A are strongly expected to be truly time independent given no evidence for biological changes in the pathogen; likewise for the parameters in group B as NAATs have been used over the study period. However, the time independence of the parameters in group C is an assumption representing the simplest hypothesis in the absence of relevant data. Although there may be some minor differences, it was assumed that all these time-independent parameters (except for the proportions of those with symptoms) were the same for both sexes.
Our model suggests that incidence increased at a faster rate in men. Although incidence was higher in women than men aged 15–24 years until 2005, it was higher in men after 2005. Also, it was higher in men than women aged 25–34 and >34 years for all years. More chlamydia diagnoses occur among women, reflecting a higher rate of asymptomatic testing among women. However, as there is little known about the natural history of chlamydia infection in men, with most studies reporting on natural history conducted in women, it seemed reasonable to assume that the probability of natural clearance of infection over a year is the same for both sexes. It is also notable that our model does not differentiate between heterosexual versus homosexual men nor other sexual mixing patterns.
Through a careful sensitivity analysis, presented in the online supplementary material http://sti.bmj.com/content/91/7/513/suppl/DC1, we have confirmed the robustness of our results against moderate changes to our input priors. For four of our nine time-independent parameters, the data are highly informative (and our results are largely insensitive to our prior assumptions); these are the asymptomatic proportions in men and women, the probability of natural clearance over a year and the false positive rate of testing. The remaining five time-independent parameters for which our prior assumptions dominate are (a and b) the probabilities of attending and consequently testing for symptomatic infections, (c) the true positive rate of the diagnostic test, (d) the rate of background antibiotic use and (e) the probability of reporting a test. Of these, all except the second and third (the focus of our prior sensitivity analysis) have narrow prior ranges based on reliable references and thus would not be expected to substantially alter our overall quantitative findings.
Until now, only one prospective cohort study of chlamydia in the general population has ever been conducted in Australia: the chlamydia incidence and re-infection study (CIRIS). This study included only young women aged 16–25 years and reported a chlamydia prevalence of 4.9% (95% CI 3.7% to 6.4%) and an incidence of 4.4 per 100 person-years (3.3–5.9) in 2007–2008. The incidence reported by CIRIS is similar to the estimates produced by our model: 5.6% (5.2% to 6.1%) and 5.7% (5.3% to 6.3%) in 15–24-year-old women in 2007 and 2008, respectively. Multivariate analysis from CIRIS showed that younger women (16–20 years) were more likely to have an incident infection, consistent with our study. CIRIS also reported that recent use of antibiotics was protective against incident infection. We accounted for background antibiotic use as a model parameter to allow for self-cure when antibiotics were taken for any reason, although the assumed level in our analyses may differ from actual levels of use.
Although there is a dearth of studies reporting on chlamydia incidence in the general population internationally, a study from the USA estimated that there were about 2.86 million incident infections in the USA in 2008. The number of chlamydia notifications in 2008, in the USA, was 1.2 million, which gives an incidence-to-notification ratio of 2.4. This is less than the incidence-to-notification ratio of 4 for Australia based on our estimates. The difference in ratios between the USA and Australia could reflect differences in testing patterns or epidemiology between these settings and/or the methods used to calculate the ratios. It would be valuable to compare these factors in future studies.
Historically, Australia has based its chlamydia prevention strategies on the number of diagnoses notified. However, in 2013 the number of notifications among people aged 15 years and older (n=82 484) represents only 23% of all estimated incident infections (n=356 000 according to this study) in the year. Thus, the estimated incidence-to-notification ratio in Australia was 4.3 in 2013. This also implies that 77% of new chlamydia infections remain undiagnosed. Our findings also suggest that incident infections of chlamydia more than doubled between 2001 and 2013, from 160 000 to 356 000 infections. However, this relative change (120%) is substantially less than the increase (>300%) in numbers of chlamydia notifications reported during the same time. This clearly demonstrates that the increase in the scale of the infection as observed by the trends in notification numbers has been misleading and is somewhat an artefact of increased testing.
This study has reported a new approach to estimating chlamydia incidence in the general population using routine testing and notification data. Other countries that collate and report data on chlamydia diagnoses and testing numbers can also use this method to estimate chlamydia incidence.
