Of the 40 unique models, 34 considered progression from chlamydia only to PID, 5 examined both gonorrhea and chlamydia,9,30,40,43,62 and 1 looked at gonorrhea only.22 All models that included both chlamydia and gonorrhea assumed the same values for progression from each infection to PID. In 39 models, the purpose of the model was to examine the cost-effectiveness of interventions for the diagnosis and management of chlamydia or gonorrhea; 1 model examined the effects of a chlamydia vaccine.34 Nine models incorporated progression from chlamydia or gonorrhea to PID dynamically.9,22,25,31,34,38,41,57,60 Of these, we categorized 4 as Markov models,9,22,38,60 3 as compartmental models,25,31,57 and 2 as individual-based network models.34,41 There were 28 static decision trees.21,23,24,27 – 30,32,35 – 37,40,42 – 44,47 – 53,58,59,61 – 64 This group includes 4 models that considered the transmission of chlamydia infection dynamically21,23,27,63 but used static decision trees for the progression to PID. In 3 publications, it was not possible to determine whether progression to PID was incorporated statically or dynamically.33,45,46 Further descriptive analyses were therefore restricted to the remaining 37 models.
For 4 of 9 dynamic models, explicit statements about the timing of progression from chlamydia or gonorrhea infection to PID were found with descriptions about how this was implemented in the model (Table 2); 2 of 4 Markov models,9,38,39 1 of 3 compartmental models,57 and 1 of 2 individual-based models.34 For Smith et al, the influence of the timing of progression on the cost-effectiveness of different screening intervals was the main research question.9 In their Markov model with a 1-month cycle time, the interval from initial infection to PID development time was varied between 1 and 12 months, with an assumption that PID development time was the same for chlamydial and gonococcal infections. Results showed that screening prevented more cases of PID when the PID development time was 12 months than 1 month. The authors did not reach a conclusion about the most likely interval for PID development because this was not a required outcome for the economic analysis. In another Markov model with a 6-month cycle, Hu et al stated explicit assumptions that 30% of women would develop PID within 6 months of initial chlamydia infection, and that the risk of PID continued as long as chlamydia infection persisted; the average duration of chlamydia infection was assumed to be 0.93 years.38
Among the compartmental models, Townshend and Turner made an explicit assumption that tubal damage occurs in the second half of chlamydia infection.57 Their compartmental model included different chlamydia infection stages: stage 1 “allows individuals to be screened very early … before any damage is done”; in stage 2, a proportion of women develop PID. The mean time to reaching the second stage was assumed to be half the mean natural duration of the infection.57 In an individual-based model, Gray et al stated their assumption that PID can occur uniformly during the infectious period.34
In the remaining dynamic models, the timing of progression to PID was not stated explicitly but could be inferred from the model structure. Buhaug et al published the first compartmental model on progression from chlamydia to PID in 1989.25 This model included a separate compartment to incorporate progression (with a probability of 20%) from chlamydia to a PID episode, which lasted 3 weeks, and after this period, a woman became susceptible. Gift et al also published a model with a separate compartment for PID to investigate the effects of chlamydia screening in men on prevention of sequelae in women as an outcome.31 Low et al used an individual-based model with a daily progression rate to PID calibrated to PID incidence rates from empirical estimates.41,65 Aledort et al and Walleser et al used Markov models with cycle times of 6 and 12 months, respectively.22,60 Walleser et al stated that there was no conclusive information about the timing or rate of progression from chlamydia to PID (Table 2).
In one study, it was explicitly stated that the timing of progression was unknown61 (Table 2). There were also some statements in introduction or discussion sections that implicitly assumed that that progression to PID does not happen immediately, for example: “Early diagnosis of [chlamydia] is important, not only to minimize disease spread, but also to prevent untreated infections from progressing to pelvic inflammatory disease …”24 Values in the decision trees for the percentage of women with chlamydia infection who progress to PID are shown in Figure 2. The average of the main value was 22.4% (range, 10%–35%, n = 25). For 16 of 28 models references to published articles were given for the stated ranges.
This systematic review found 40 unique mathematical models that considered the progression from chlamydia or gonorrhea infection to PID. Among these, 4 distinct possibilities for the timing of the development of PID were identified in studies that modeled the progression to PID dynamically. In most included studies, there was no explicit statement about progression or natural history. In static models, the average fraction of cases of chlamydia assumed to develop PID was 22%.
The strength of this study is the collation of publications for more than 20 years, which allowed an overview of conceptual frameworks in mathematical modeling studies about the natural history of chlamydia and gonorrhea infections and of the quality of reporting of dynamic models. This review expands the scope of a previous systematic review, which was limited to studies about C. trachomatis transmission to examine the cost-effectiveness of chlamydia screening.66 Among the modeling studies that incorporated both chlamydia and gonorrhea, all assumed the same characteristics for both infections. Although there might be differences, these probably did not affect study outcomes because chlamydia accounts for a much higher proportion of PID cases than gonorrhea. A weakness of the present review is that the literature search might have missed some relevant studies because there is no single MeSH term for mathematical modeling studies. We tried to overcome this by building a search string combining MeSH headings for relevant study designs and by searching for additional studies in the reference lists of included publications.
This review has identified 4 potential ways in which the timing of progression of chlamydia or gonorrhea infections to PID has been conceptualized by authors of mathematical modeling studies: uniformly throughout the duration of infection34; in the first half of the infectious period38; in the second half of the infectious period57; or that there is a most likely interval from the initial infection for the development, which varies between 1 to 12 months.9 These possibilities span the whole duration of the lower genital tract infection. It is not possible to compare the results of these studies directly to say which of the potential timings is most plausible because of the many other differences in model structure, parameter values, and presentation of findings. Model structures that incorporate a longer9,34,57 rather than a shorter38 interval for PID development would be expected to predict a strong impact of a chlamydia screening interventions, consistent with that observed in randomized controlled trials.12,13 This is not consistent with studies in mice, however, in which salpingitis was documented 24 hours after vaginal inoculation with a C. trachomatis mouse pneumonitis biovar.67 Empirical studies in humans cannot provide the information needed because it is not possible to measure the timings of the start of a chlamydia infection and the onset of upper genital tract infection accurately.
Assumptions about the probability of progression from chlamydia or gonorrhea infection to PID are known to have a strong influence on the predicted effectiveness of chlamydia screening.21,39 The average value used for the percentage of women developing chlamydia-associated PID in decision tree analyses was around 20%. This is higher than the probability of around 10% within 12 months estimated by Oakeshott et al in the control arm of their randomized controlled trial.13,69 Adams et al also compared the prediction from their model with empirical data about cases of PID diagnosed in primary care in England and concluded that “an estimate of around 10% progression to PID is the most likely.”21
Understanding the natural history of disease and the mechanisms of action of interventions are important components of the program science that is needed to understand the effects of prevention programs whose primary aim is to reduce morbidity.70 The main unanswered question identified by this review is that of PID development time. Smith et al suggest that further research is worthwhile from a cost-effectiveness standpoint in populations that are not at high risk of developing PID,9 which includes the young female populations targeted by current chlamydia screening recommendations.6,7 This review allows recommendations for future research and reporting practice. First, models that use a dynamic structure for the progression from lower genital tract infection to PID are needed to investigate uncertainty about mechanisms of PID development. Second, a single dynamic mathematical model that can incorporate the different possible mechanisms identified in this review would allow direct comparison of the incidence of PID predicted by each mechanism as well as comparison with the observed results from randomized controlled trials of chlamydia screening interventions, such as that done by Oakeshott et al.13 Third, reports of mathematical modeling studies should describe how the natural history of chlamydia infection is conceptualized and implemented in the model.14,15 The results of this review offer the opportunity to advance our understanding about the how chlamydia screening interventions work to prevent PID.
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