How Long Does Chlamydia Take To Cause Infertility

• Research article
• Open Access
• Published: 11 August 2012

Timing of progression from Chlamydia trachomatisinfection to pelvic inflammatory affliction: a mathematical modelling study

• Christian L Althaus¹,
• Janneke CM Heijne¹,
• Pippa Oakeshott²,
• Sally Kerry^iv,
• Phillip Hay^three &
• …
• Nicola Depression¹

BMC Infectious Diseases book 12, Commodity number:187 (2012) Cite this commodity

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Abstract

Groundwork

Pelvic inflammatory disease (PID) results from the ascending spread of microorganisms from the vagina and endocervix to the upper genital tract. PID can atomic number 82 to infertility, ectopic pregnancy and chronic pelvic hurting. The timing of development of PID after the sexually transmitted bacterial infection Chlamydia trachomatis (chlamydia) might affect the touch of screening interventions, but is currently unknown. This study investigates three hypothetical processes for the timing of progression: at the start, at the end, or throughout the elapsing of chlamydia infection.

Methods

Nosotros develop a compartmental model that describes the trial construction of a published randomised controlled trial (RCT) and allows each of the 3 processes to be examined using the same model structure. The RCT estimated the issue of a unmarried chlamydia screening exam on the cumulative incidence of PID up to one year later. The fraction of chlamydia infected women who progress to PID is obtained for each hypothetical process by the maximum likelihood method using the results of the RCT.

Results

The predicted cumulative incidence of PID cases from all causes after one yr depends on the fraction of chlamydia infected women that progresses to PID and on the type of progression. Progression at a abiding rate from a chlamydia infection to PID or at the end of the infection was compatible with the findings of the RCT. The respective estimated fraction of chlamydia infected women that develops PID is 10% (95% confidence interval 7-13%) in both processes.

Conclusions

The findings of this study suggest that clinical PID can occur throughout the form of a chlamydia infection, which will get out a window of opportunity for screening to prevent PID.

Peer Review reports

Background

Pelvic inflammatory illness (PID) is a clinical syndrome resulting from the ascending spread of microorganisms from the vagina and endocervix to the endometrium, fallopian tubes, and/or contiguous structures [1]. Damage to the fallopian tubes following PID is a predisposing cistron for ectopic pregnancy and infertility [2]. Chlamydia trachomatis (chlamydia) has been found in approximately 30% of all PID cases [2, three] and is the nigh common bacterial sexually transmitted infection in many developed countries [4]. Chlamydia infection is usually asymptomatic in women, merely can exist treated with antibiotics when diagnosed [v]. The estimated mean elapsing of untreated asymptomatic infection is more than one twelvemonth in women [6, 7].

Early on detection and treatment of chlamydia through screening has been proposed as a strategy to prevent PID and subsequent reproductive tract morbidity in sexually agile young women [8]. Three randomised controlled trials have investigated the efficacy of a single chlamydia screening exam on the incidence of clinically diagnosed PID with a follow-up period of one yr in young women [ix–11]. Uptake of screening ranged from 64 to 100% and all three trials constitute a reduction in the incidence of PID from any cause in the intervention group compared to the command group.

It is important to understand when in the grade of infection PID occurs and when screening and treatment should have place to maximise the potential of chlamydia screening to prevent PID, only this is currently unknown. The natural history of untreated chlamydia in humans cannot be direct observed for ethical and logistical reasons and randomised controlled trials do not provide this data because the time from the start of infection is unknown. It has been suggested that treatment is needed before long after infection, based on observations from an animal model [12]. Pal et al. isolated the C. trachomatis mouse pneumonitis biovar from the upper genital tract 24 hours after vaginal inoculation in mice [12].

Mathematical modelling studies are a valuable tool for investigating hypothetical processes of chlamydia transmission and ascending infection. Amongst the few mathematical modelling studies with explicit descriptions of progression from chlamydia infection to PID, information technology has been proposed that PID develops in the beginning half of a chlamydia infection, in the 2d half, or tin can occur at any time during a chlamydia infection [13]. The objectives of this study were: to investigate how differences in the timing of progression from chlamydia infection to PID touch the result of a chlamydia screening intervention; and to estimate the fraction of chlamydia infections that progresses to PID, using a mathematical model to simulate the results of a published randomised controlled trial.

Methods

Data

We used data from the Prevention Of Pelvic Infection (POPI) randomised controlled trial of chlamydia screening, which provides information about C. trachomatis infection status at baseline in both the intervention and the control groups and virtually incident clinically diagnosed PID up to one year later [11, fourteen]. In cursory, the study enrolled most 2500 sexually active women anile 16 to 24 years from colleges and universities in London. All women provided cocky-collected vaginal swabs at enrolment and were randomised to immediate testing for chlamydia infection and treatment if positive (intervention group), or the nerveless swabs were stored and tested later on one year (command group). The prevalence of chlamydia infection was 5.4% (68/1254) in the intervention group and v.9% (75/1265) in the control grouping, i.e. overall 5.7% (143/2519). Almost 22.ii% (527/2377) of the women in both groups reported being tested independently for chlamydia during the follow-up catamenia. The incidence of clinically diagnosed PID (by self-report, mostly backed up by test of medical records) afterward ane year was 1.iii% (xv/1191, 95% CI 0.7 to 2.ane%) in the intervention group and i.9% (23/1186, 95% CI one.2 to 2.9%) in the control group. The incidence rates of PID in women with chlamydia infection at baseline were 1.6% (1/63) in the intervention group and nine.5% (seven/74) in the control group. Amongst women in the control grouping who developed PID, 30.0% (7/23) were chlamydia positive at baseline.

Model

We adult a compartmental model that describes the trial construction using a Susceptible-Infected-Susceptible (SIS) framework (Figure i). We presume a closed population of susceptible (S) women who can become infected (I) at constant rate λ, i.eastward., the forcefulness of infection, and articulate the infection naturally at rate r. The infection is separated into two stages and then that we can distinguish between infected women without PID (I₁) and with PID (I₂). The transition from the first to the second stage happens at the progression charge per unit γ and allows united states to investigate different possibilities for the timing of progression in the same model. During follow-up a woman can receive a test and is successfully treated at rate α, which incorporates the percentage of women ( c) who reported beingness tested for chlamydia during the follow-up flow and the proportion with handling failure ( δ). This results in the following system of ordinary differential equations:

$$\begin{array}{l}\frac{dS\left(t\right)}{dt}=-\lambda S\left(t\right)+(r+\alpha )\left(I_1\right(t)+I_2(t\left)\right)\\ \frac{d{I}_{\mathrm{ane}}\left(t\right)}{dt}=\lambda \mathrm{Southward}\left(t\right)-\left(r+\alpha +\gamma \right)I_1\left(t\right)\\ \frac{d{I}_2\left(t\right)}{dt}=\gamma I_1\left(t\right)-\left(r+\alpha \right)I_2\left(t\right)\end{array}$$

Figure 1

Schematic overview of the model framework. The model has a susceptible-infected-susceptible (Sis) framework and allows iii hypothetical processes for the timing of progression from chlamydia to PID to be investigated. A adult female tin can become infected at rate λ (force of infection), can clear her infection naturally (rate r), or can be effectively screened and treated (charge per unit α). Numbers point when during the chlamydia infection progression to PID could happen: 1) immediate progression, 2) constant progression, and three) progression at the end of infection. For all three types of progression a certain fraction f of chlamydia-infected women will develop PID. For the constant progression model a adult female moves from being infected without PID (I₁) to existence infected with PID (I₂) at rate γ, which is ready to $\gamma =\frac{f}{1-f}r$. For firsthand progression and the progression at the end of infection we set $\gamma =0$ and $\text{I}={\text{I}}_{\mathrm{i}}+{\text{I}}_{\mathrm{ii}}$.

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The force of infection λ is assumed to exist constant over fourth dimension because the study population is small compared to the population in which the study took identify so changes in prevalence inside the study population are unlikely to touch the overall chlamydia prevalence. The force of infection λ is calibrated so that the steady state prevalence in the model is equal to the prevalence p at baseline in the absence of the trial ( α = 0). Nosotros assume the infection duration to be exponentially distributed [half-dozen] with a mean duration of one/r. This takes into business relationship the fact that that some women articulate the infection rapidly whereas others tin remain infected for substantially longer fourth dimension periods [vii].

At model initiation nosotros simulate the conditions in the two arms in the trial. In the control group, a percentage of women is infected, reflecting the observed baseline prevalence. In the intervention group all women have received treatment but a small percent remains infected owing to handling failure (encounter Additional file 1 for more than details).

Types of progression

We explored three hypothetical processes for the timing of progression from endocervical C. trachomatis infection to PID (Figure ane). For each type of progression it is assumed that, of all women infected, a sure fraction f will develop PID in the absence of an intervention. The first possibility is that PID develops at the kickoff of a chlamydia infection (immediate progression); the incidence of PID depends on the strength of infection and the number of susceptible women ( fλS), and so we set γ = 0 and I = I₁ + I_two. The 2d possibility is that PID can develop at a constant rate throughout the course of a chlamydia infection (constant progression); the incidence of PID depends on the progression charge per unit γ and the number of women in the infected compartment without PID ( γI_one). The progression rate is divers as $\gamma =\frac{f}{\mathrm{i}-f}r$ and the mean duration of the infection is consistent with the other two types of progression. Finally, progression to PID could happen at the finish of a chlamydia infection but before natural clearance (progression at the end). In this situation, PID incidence depends on the clearance charge per unit and the number of infected women (frI), where γ = 0 and I = I₁ + I₂. In the absence of the trial (α = 0), the incidence rates of PID are the aforementioned for all three types of progression. The cumulative incidence of PID cases caused by C. trachomatis is tracked for both groups and is set to zero at model initiation (Additional file i).

Proportion of PID cases caused by C. trachomatis

The observed numbers of PID cases in the intervention and control grouping are presumed to be a mixture of PID cases caused by C. trachomatis and by other microorganisms. Nosotros assume that a certain proportion x of PID cases in the command group is caused by chlamydia and that the amount caused past other microorganisms is the same in both groups. In the simulated trial it is assumed that the intervention only reduces the incidence of chlamydial PID.

The model estimates the cumulative incidence of chlamydial PID for the intervention grouping (g _I ) and for the control group (g _C ). We get the overall cumulative incidence of PID cases in the intervention grouping (due east _I ) and in the control group (e _C ) by using the proportion of PID cases caused by chlamydia (x), every bit follows:

$$\begin{array}{l}{e}_C=\frac{g_C}{\mathrm{10}}\\ e_I=g_I+(e_C-{\mathrm{1000}}_C)\end{array}$$

where (east _C -g _C ) is the contribution of PID caused past other microorganisms. Annotation that to obtain the overall cumulative incidence for PID cases it is required that 10 > 0.

Analysis

We compared the overall cumulative incidence of PID cases predicted by the model for each type of progression in intervention and control groups using the baseline values (Table one). First, we examined the predicted cumulative incidences of chlamydial PID later on one year when varying the fraction of chlamydia infection progressing to PID from 0 to 100%. 2d, nosotros used the maximum likelihood method to obtain the best fit estimate (and standard error) for the fraction progressing for each type of progression, using the observed cumulative incidences of PID cases in the trial. Third, we estimated the best fit (and standard error) for the fraction progressing to PID amidst women who were chlamydia positive at baseline, assuming that all PID cases were acquired past C. trachomatis. The best fits for the models for the three types of progression were compared based on Akaike's Information Criterion (AIC) (Additional file ane) [15]. Fourth, for each blazon of progression we used baseline values and the obtained maximum likelihood estimators to make up one's mind the fourth dimension point since first of infection until half of the expected PID cases occurred (see Additional file 1 for more details).

Tabular array 1 Parameter values describing the natural history of chlamydia infection, PID development and the screening intervention

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Sensitivity analysis

A univariable sensitivity analysis was done for all model parameters and the proportion of PID cases caused past chlamydia. The parameters were varied within the 95% conviction interval using the distributions in Table 1. We obtained the maximum likelihood estimates for the fraction of women progressing to PID. Second, we did a multivariable sensitivity analysis by sampling each model parameter and the proportion of PID cases acquired by chlamydia 1000 times from the distributions in Table ane. The maximum likelihood estimates for the fraction of women progressing to PID were determined and the quantiles (0.025 and 0.975) were obtained as 95% credibility intervals.

Third, we explored the effect of varying the mean time between start of infection and when progression to PID becomes possible. We practice this in a model framework similar to the constant progression scenario. An additional parameter $\left(\tilde{f}\right)$ is needed to specify the fraction of women who develop PID at the time point when PID becomes possible. This differs from the fraction f in that $\tilde{f}$ refers but to the women who remain infected at the fourth dimension point at which progression to PID becomes possible. We did non fit this model with the additional unknown parameter to the trial information equally nosotros accept only two data points.We derived maximum likelihood estimates for the fraction $\left(\tilde{f}\right)$ for a stock-still mean time between commencement of infection and progression to PID and report the corresponding fraction f (see Additional file 1 for more details).

Belittling results were derived in Mathematica and numerical solutions were obtained in R [xix, 20]. Code files can exist obtained from the authors on request.

Results

The predicted cumulative incidence of PID cases from chlamydia infection afterwards i year depends on the fraction of chlamydia infected women who progress to PID and on the type of progression (Figure 2). In the intervention groups, the immediate progression scenario results in the highest cumulative incidence of PID, progression at the finish the everyman, with intermediate values for the constant progression scenario (Figure 2A). In the control groups the predicted cumulative incidence of PID is like for all three types of progression (Effigy 2B).

Effigy 2

Predicted cumulative incidence of chlamydial PID for the three types of timing of progression. Panel A, results for intervention group; console B, results for control grouping. Immediate progression (dashed line); constant progression (solid line); progression at the terminate (dashed-dotted line). The fraction progressing from chlamydia to PID is varied from 0-100% using baseline values for all other model parameters.

Total size epitome

In the immediate progression scenario, the predicted cumulative incidence of PID in the intervention and control groups is very similar; for any value of the fraction progressing to PID, women in both groups develop PID immediately after infection so testing and treating does not prevent whatever PID cases (Effigy 2A, 2B). If the fraction progressing to PID is 100%, all women who go infected will progress to PID and the predicted cumulative incidence of PID after ane year is similar to the baseline prevalence of chlamydia, considering the mean duration of infection was assumed to be one year.

For scenarios of abiding progression to PID or progression at the end of chlamydia infection, the predicted cumulative incidences of PID are like if the fraction progressing to PID is depression because the formulae describing PID incidence are similar when this value approaches zero (Effigy 2A). For both of these scenarios, the incidence of PID depends on the number of infected women. The scenario with progression at the end always has a lower cumulative incidence than the other two, even when the fraction progressing to PID is 100%, because some of the infected women will have been finer tested and treated before they clear the infection naturally, which is when they are at risk of developing PID.

Table 2 shows the maximum likelihood estimator (MLE) and the respective 95% CI for the estimated fraction of chlamydia infected women who progress to PID, using the observed cumulative incidences from the trial. The corresponding cumulative incidences of PID cases in the intervention and control groups shown are the all-time plumbing equipment values. For all types of progression to PID, the all-time fitting values for the fraction of women progressing to PID are between 8 and 10%. The AIC values are like so the estimated fractions progressing to PID with all three types of progression are compatible with the information. Similar results were obtained considering merely the indicate estimates of the cumulative incidence of PID cases of women who were chlamydia positive at baseline (results not shown).

Table 2 Estimated fraction progressing from chlamydia infection to PID, using baseline values

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In the scenario of constant progression to PID, with a constant daily hazard of developing PID, information technology takes 228 days until half of the expected PID cases are observed and for the progression at the end it takes 253 days, using the MLE in Table 2 (encounter Additional file 1 Figure A1). In the immediate progression scenario, it takes 0 days which is an intuitive result of progression without a delay.

Sensitivity assay

In the univariable analysis the proportion of PID cases due to chlamydia in the command group is the nigh influential parameter affecting the best fit for the fraction progressing to PID (Figure three). Inside the range of values sampled, the fraction progressing to PID varies from 4 to 19% (Effigy 3A). The cumulative incidences of PID cases caused by chlamydia and other microorganisms afterward one yr (Effigy 3B-D) are also influenced for the scenarios of abiding progression or progression at the end of infection merely merely marginally for immediate progression. Varying the duration of chlamydia infection or the baseline prevalence influences the force of infection simply results in relatively small changes in the fraction progressing or the cumulative incidence of all-crusade PID. Irresolute the percentage with treatment failure or the uptake of testing during follow up has marginal influence (results not shown). In the multivariable sensitivity analysis the means over all parameter sets for the fraction progressing to PID were similar to those in the baseline assay (see Additional file 1 Effigy A2). In the additional model framework the corresponding best fitting values for the fraction of infected women developing PID (f) were in the same range as the main three types of progression (see Additional file one Figure A3).

Figure 3

Univariable sensitivity analysis, varying the proportion of PID cases due chlamydia in the control grouping. Panel A, fraction of progression needed in each type of timing of progression: firsthand progression (dashed line); constant progression (solid line); and progression at the end (dashed-dotted line). Panels B- D, cumulative incidences of PID cases acquired by chlamydia and other microorganisms after one year in the command group (dashed line) and in the intervention group (solid line) for the three types of timing of progression: firsthand progression (B); constant progression (C); and progression at the stop (D). The baseline value scenario is indicated with a blackness dot. Proportion of PID cases due chlamydia infection in the command group from thirteen-53% using baseline values for all other parameters. The observed cumulative incidences of PID after one twelvemonth (%) in the trial were: control grouping 1.9 (95% CI 1.2 to 2.9), intervention group 1.3 (95% CI 0.seven to 2.1).

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Discussion and decision

This written report used a mathematical model to simulate the results of a randomised controlled trial of a chlamydia screening intervention. The predicted cumulative incidence of PID was lower in the intervention than the command group if progression to PID occurred at a constant charge per unit or at the end of chlamydia infection. If progression to PID occurs immediately after chlamydia infection, screening and treatment do not reduce the cumulative incidence of PID. The model estimates, for constant progression and progression at the end, that x% (95% CI 7-13%) of chlamydia infections progress to PID.

A strength of this study was the use of a dynamic mathematical model to investigate the timing of progression from chlamydia infection to PID. At that place were, however, several simplifying assumptions. Showtime, it is non biologically plausible for chlamydia to ascend in the genital tract either immediately later endocervical infection or just earlier natural clearance. These extreme situations were called to correspond progression early on and late in the course of chlamydia infection. Other plausible possibilities virtually the timing of progression, e.k. bold a woman has to be infected for a sure fourth dimension menstruum earlier being at a constant daily risk of developing PID, were non investigated because we did not have enough information to fit models with more ane unknown parameter. 2nd, we counted the number of PID episodes rather than the number of women developing PID. The model structure assumed that PID could happen repeatedly in the same adult female but that a history of PID did non influence the course of chlamydia infection, susceptibility to chlamydia or time to come progression to PID. These assumptions might non be truthful but, since both the trial follow-up menstruum and baseline value for the mean duration of chlamydia infection were one yr, there was a negligibly small percentage of women with repeated chlamydia infections or PID episodes in the model. 3rd, it was assumed that antibiotic treatment was specific to C. trachomatis, which is not the case. Azithromycin is also agile against Mycoplasma genitalium just a causal association with PID is still debated so information technology was not possible to gauge the potential consequence of handling on other microorganisms [2, 21]. Finally, we considered a closed population; this was a reasonable assumption because very few women in the trial were lost to follow-upward.

The use of empirical information from a randomised controlled trial was too an advantage. The Prevention of Pelvic Infection study is the only trial with data about the baseline prevalence of chlamydia in the control grouping, which allowed us to investigate the incidence of PID amongst untreated women. There are too limitations to the trial. Although discussed previously [11], we restate limitations every bit they utilise to our study here. First, the point estimates of PID incidence were rather imprecise, owing to the lower than expected incidence of PID in the trial [11]. The relative reduction in PID incidence in the Prevention of Pelvic Infection study was consequent with, but smaller than in the other two randomised trials [9, 10], probably considering of a lower risk of methodological bias; another possibility is the high testing uptake during the follow-up flow in both groups [3, 11]. When using the maximum likelihood method to gauge the fraction progressing to PID, the best fit values for the cumulative PID incidence rates in the command and intervention groups were closer than observed in data. The value for the command group was, nevertheless, higher than for the intervention group for the model assuming a constant rate of progression. Second, nosotros only used the values for the 12-month incidence of PID to fit the model, rather than individual dates of PID diagnosis. These dates were collected retrospectively, past self-report backed by medical records, only were express to the date when the participant presented to a healthcare facility and was diagnosed with PID, and were not accurate enough to construct a survival curve. Third, only symptomatic PID cases were observed so the cumulative incidence of PID cases could have been underestimated. This would lead to an underestimation of the fraction of women becoming infected with chlamydia who volition develop PID.

There are very few mathematical modelling studies that consider explicitly how the timing of progression to PID might touch the outcome of chlamydia screening interventions [13]. Smith and colleagues examined dissimilar intervals for the development of PID post-obit a chlamydia infection using a Markov model [22]. They used data from a prospective cohort study of women at high take chances of PID [23, 24]. Our study addresses the proposition of Smith et al. to investigate PID evolution time in women at depression risk of chlamydia comparing information nearly PID rates from dissimilar screening strategies. Our findings also support those of Smith et al., with the well-nigh cases of PID averted with the longest development fourth dimension. Our study estimated that viii-10% of women with chlamydia infection develop PID, which corresponds to the guess of Adams and colleagues, based on data most clinical PID reports from primary care [25], merely lower than the estimated progression fraction causeless in many price-effectiveness studies [thirteen]. The baseline value of 30% (vii/23) for the proportion of PID cases due to chlamydia infection in the trial is in line with what has been reported in the literature [2, three].

A abiding rate of progression from chlamydia to clinically diagnosed PID or progression at the end of the grade of chlamydia was compatible with the findings of the Prevention of Pelvic Infection trial. The ii scenarios differ conceptually, however, regarding the window of opportunity for screening to prevent PID. In the scenario with progression at the end of infection, the time window for preventing PID is the whole infection menstruum. In the abiding progression scenario, the fourth dimension window might be shorter than the elapsing of infection. The constant rate assumes that the fourth dimension betwixt start of infection and developing PID follows an exponential distribution. This implies that some women will develop PID soon afterward infection whereas others will develop information technology very tardily in their infection. In practice, there would always be some unpreventable chlamydial PID equally the screening interval cannot be made short plenty to find each infected adult female earlier she progresses. Progression at the finish of the course of chlamydia infection is probably less biologically plausible than abiding progression. Progression early on in the course of chlamydia infection, represented in the model equally immediate progression, was the least likely. This differs from the findings from animal models in which progression in the mouse model happens by 24 hours [12] and in the guinea squealer model within the commencement week [26]. Information technology is possible that C. trachomatis ascends early in the course of infection in humans but that clinical PID is observed later. Even so, if most chlamydia infections in women progressed so early on in the class of infection, many clinical PID cases would be expected to have occurred earlier detection of prevalent infections through screening [27]. The development of PID symptoms and clinical diagnosis have to be able to happen over a longer fourth dimension course for screening to achieve reductions in the incidence of PID of 35% [11] or more than [9, ten], given that but thirty% of PID cases are acquired by chlamydia and that PID resulting from a new infection during the follow-up period cannot be prevented [28]. Well-nigh women with PID in the trial reported sexual intercourse with ii or more partners during the twelvemonth. Since bacterial vaginosis is thought to promote ascending C. trachomatis infection [23], it could be hypothesised that sex with a new partner alters the composition of vaginal flora and encourages progression of prevalent endocervical chlamydia to PID.

This study has implications for future research and exercise. The relatively depression estimated fraction of asymptomatic chlamydia progressing to clinical PID tin can be used to requite advice to women with chlamydia infection. The uptake of the screening interventions in randomised controlled trials was much college than uptake rates observed in practise [29, 30]. We plan to behave future modelling studies that investigate the impact of achievable levels of chlamydia screening on the pause of ascending chlamydia infections using a model that tin likewise examine the effect of differences in the timing of progression. The numbers of PID cases prevented could so exist compared to those prevented indirectly equally the event of reduced exposure to chlamydia. The findings of this study suggest that clinical PID tin occur throughout the course of a chlamydia infection, which leaves a window of opportunity for screening to foreclose PID.

Abbreviations

PID:

Pelvic inflammatory disease

chlamydia:

Chlamydia trachomatis infection

POPI:

Prevention Of Pelvic Infection

CI:

Conviction interval

AIC:

Akaike'south Information Criterion.

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Acknowledgements

This piece of work was supported past Swiss National Science Foundation (grants 320030_118424 and PDFMP3_124952 to JCMH and SAH.); U.k. National Found for Wellness Research (NIHR) Health Applied science Assessment programme (project 07/42/02 to CLA.). The POPI trial was funded by The BUPA Foundation.

The views and opinions in this commodity are those of the authors and practise not necessarily reflect those of the funders.

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Authors and Affiliations

1. Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, CH-3012, Switzerland

   Sereina A Herzog, Christian 50 Althaus, Janneke CM Heijne & Nicola Low

2. Partition of Population Wellness Sciences and Instruction, St George's, University of London, London, SW17 0RE, Great britain

   Pippa Oakeshott

3. Section of Genitourinary Medicine, St George's Hospital, London, SW17 0QT, Great britain

   Phillip Hay

4. Center for Primary Intendance and Public Health, Blizard Institute, Queen Mary University of London, London, E12AB, U.k.

   Sally Kerry

Corresponding author

Correspondence to Sereina A Herzog.

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Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

SAH, CLA, JCMH, and NL developed the idea for the study and analyzed the model. PO, SK, and PH provided detailed data and insights to the RCT. SAH programmed the model and wrote the get-go draft of the paper. All authors commented on the manuscript and approved the final version.

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Herzog, S.A., Althaus, C.Fifty., Heijne, J.C. et al. Timing of progression from Chlamydia trachomatisinfection to pelvic inflammatory disease: a mathematical modelling study. BMC Infect Dis 12, 187 (2012). https://doi.org/10.1186/1471-2334-12-187

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• Received: 01 February 2012

• Accepted: 25 July 2012

• Published: eleven August 2012

• DOI : https://doi.org/10.1186/1471-2334-12-187

Keywords

• Chlamydia infection
• Pelvic inflammatory disease
• Mathematical model
• Compartmental model
• Randomised controlled trials

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