An Economic Evaluation of Opportunistic Screening For Chlamydia Trachomatis using a Transmission Dynamic Model

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1 PHIAC 6.7 An Economic Evaluation of Opportunistic Screening For Chlamydia Trachomatis using a Transmission Dynamic Model Pelham Barton and Tracy Roberts, University of Birmingham 1

2 Status of this Document This document reports the results of a cost-effectiveness analysis commissioned to inform the decision making of the Public Health Interventions Advisory Committee of the UK National Institute for Health and Clinical Excellence. It is based on adapting the model produced for the Chlamydia Screening Studies (ClaSS). This document was written by Pelham Barton and Tracy Roberts alone, but we acknowledge input of all of our colleagues and advisors in the ClaSS project. Background The importance of using an appropriate model for the transmission of C. trachomatis to allow for interaction between individuals has been explained elsewhere (Roberts et al, STI In press). Of the available dynamic modelling approaches we chose to use discrete event simulation, an individualbased approach, because this was the only approach that would allow the effects of partner notification to be examined. System dynamics, which uses aggregated data would only permit the estimation of average effects whereas discrete event simulation allows records of partners attached to specific individuals to be kept. We have used the simulation model developed for the ClaSS Study to evaluate the relative cost effectiveness of opportunistic screening based on new data provided by a recent review of the clinical literature. In the model, a population is simulated over time, with individual characteristics changing as necessary on a daily basis. The model was parameterised wherever possible using empirical data collected in the ClaSS project, with nationally representative data from studies such as the National Survey of Sexual Attitudes and Lifestyles in 2000 and with new data from a review of the clinical literature. A substantial improvement of this dynamic model (and the ClaSS model) compared to others published to date, is the dynamic modelling of the incidence of long term complications associated with chlamydia. In the original economic evaluation by Welte et al, the occurrence of these sequelae was modelled statically because the most cited literature about the complications of chlamydial infection provides the data as a fixed probability (Stamm et al 1984). We used data from the Uppsala Women s Cohort Study, in which the cumulative incidences of ectopic pregnancy and infertility were estimated for a population of over 40,000 young women in Uppsala County, Sweden (Low et al 2005). 2

3 Methods The main features of the model are explained in detail elsewhere (Low et al forthcoming) and are referred to again only briefly here. The explanation is divided into the following sections: ageing and replacement; partnership formation and dissolution; chlamydia transmission and progression; testing and treatment; sequelae associated with pregnancy. The main difference for this report is the methods applied to the testing and treatment because the current focus is on opportunistic screening and not population screening as in the ClaSS study. The sequelae associated with chlamydia include inflammatory complications such as pelvic inflammatory disease in women and epididymitis in males, sequelae associated with pregnancy such as infertility, ectopic pregnancy and neonatal complications; conjunctivitis and pneumonia. In modelling terms, the inflammatory sequelae were modelled as part of the progression of chlamydia, while the sequelae associated with pregnancy including the neonatal complications, were treated as a separate part of the model. Ageing and replacement The initial population consists of a number of virtual individuals with ages drawn from a uniform distribution between lower and upper limits. As the model runs, individuals in the model die (from other causes ) in line with standard United Kingdom life tables and new individuals at the minimum age are added to the model. Partnership formation and dissolution The initial population does not contain any partnerships and as the model runs, new partnerships form and are dissolved. Properties of the partnership include frequency of unprotected sexual contact. Opposite-sex partnerships only are included because of the scarcity of other relavent data. Individuals were categorised into three sexual activity groups according to rates of sexual partner change. Chlamydia transmission and progression At any time in the model an individual s chlamydia status is one of the following: no chlamydia; latent chlamydia; asymptomatic chlamydia; symptomatic chlamydia; inflammation (pelvic inflammatory disease in women, epididymitis in men). Testing and treatment In the absence of a planned opportunistic screening programme, individuals may be treated either through presenting with symptoms or through background opportunistic screening. For simplicity, we describe first an opportunistic screening programme aimed only at women. Once the screening 3

4 programme is under way, any woman within a defined age range has a probability of being offered screening on any given day. She may or may not accept this offer. If so, she receives a screening result at a fixed delay after testing. Allowance is made for sensitivity and specificity of the test used to be below 100%. A woman who screens negative is not treated, but may be offered screening in the future. A woman testing positive will be treated, and asked to notify partners. If she complies with partner notification, she will inform current and former partners within a specified time interval (in the baseline scenario, for simplicity, it was assumed that notification was either zero or complete). Each partner individually may then comply with a request to attend for treatment. Any partner attending will be treated without waiting for a test result. As well as looking at screening women only, we also considered the possibility of planned opportunistic screening aimed at men and women. Compliance is an important issue for both screening methods. The term compliance is used here as a general term that refers to the uptake rate to the invitation for screening, treatment and partner notification. In the model compliance with treatment (single dose azithromycin) is assumed to be 100%. Sequelae associated with pregnancy Sequelae currently considered in the model are infertility, ectopic pregnancy and neonatal complications. Instances of infertility are recorded as occasions when a woman does not become pregnant, but would have done so had her fertility not been reduced by the effect of chlamydiarelated damage. Calibration of the model A common problem with modelling, which applies particularly to the ClaSS model, is that the data available often relate to model outputs rather than model inputs. For example, although the model requires some individuals to have chlamydia at the start of a model run (input), the steadystate prevalence of chlamydia in the model (output) without population screening is a function of the transmission dynamics, and the same steady-state prevalence is reached for different starting patterns of chlamydia. Another example is that the model inputs relate to propensity to form a new partnership on a given day. In contrast the pattern of partnerships formation which actually takes place is a model output to be compared with these data. Thus, instead of direct incorporation of data points as input parameters to the model, it is necessary to calibrate the model by adjusting the input parameters until a reasonable fit to the 4

5 available data is obtained. This raises the question of how closely it is desirable to fit the existing data. The purpose of the model is to estimate the effects over time of introducing a screening policy, in terms of the difference between the costs and outcomes under different policies (including a policy of no population screening). Given that sexual behaviour patterns vary over time, but that it is not feasible to reflect such variation in a model, there is limited value in producing an extremely close fit to current behaviour patterns. It is more important to test how robust policy decisions would be to variations in such patterns. It is also worth noting that it is possible to produce similar observed outputs from a variety of different patterns of input: again, it is important to test the robustness of conclusions to such variation. The determination of the input set used to produce the current set of results for the ClaSS model is described in detail in the following sections. This input set is determined by a process of calibration. The data shown in Tables 8 to 11 (presented later in this report) represent outcomes rather than model inputs, and so the model is calibrated to these. Basic population characteristics The initial population is evenly distributed in age between 12 and 62, and is assumed to be 50 per cent female, although this proportion can be altered as a model input. The population is allocated to activity groups according to sexual partner change in proportions shown in Table 1. Laumann and Youm use activity groups defined by the number of partners in the past 12 months, calling those with 0 or 1 partner the periphery, those with 2 or 3 the adjacent and those with 4 or more the core (Laumann and Youm 1999). The allocation of activity groups for the ClaSS model is designed so that the outputs of the model are broadly in line with the data from the lifestyle questionnaire collected in the ClaSS case-control study but also broadly in accordance with the Laumann and Youm groups. Thus, these data, presented Table 1 correspond approximately to the Laumann and Youm activity groups which are reported later. They have been adjusted so the model outputs fit the data. Table 1: Initial percentage allocation of sexual activity groups Male, % Female, % Group Group Group Group 1 least active, Group 3 most active In order to ensure that there is some chlamydia in the model, an initial arbitrary age-related prevalence of chlamydia is applied: zero at age 15, 7% at age 20, 7% at age 25, 2% at age 30 and zero at age 40. The values of these are unimportant as the steady-state prevalence in the model 5

6 after the warm-up period depends on the transmission dynamics within the model, not on the initial state. Each individual is given chlamydia or not, with probabilities appropriate to the starting age. Linear interpolation is used between the ages given: for example, the probability of a 26 year old starting the model with chlamydia is 6 per cent. Everyone outside the age range 15 to 40 starts chlamydia negative. As the model runs, new (chlamydia negative) 12-year-olds are added to the model population. Chlamydia transmission and progression The main inputs relating to chlamydia transmission and progression are summarised in Table 2. Except for progression to PID, these parameters were kept from the original models (Kretzschmar et al 2001, Welte et al 2000). To assess the rate of progression to PID, the model was run for a high-risk population to replicate the study by Scholes et al (1996). The value used gave a satisfactory match for the relative risk of PID over a 12 month period. Table 2: Inputs relating to chlamydia transmission and progression Parameter Value Source Probability of transmission male to female K Probability of transmission female to male K Incubation period male (days) 10 K Incubation period female (days) 12 K Probability asymptomatic female 0.7 K Probability asymptomatic male 0.25 K Recovery rate per day asymptomatic female K Recovery rate per day symptomatic female K Recovery rate per day asymptomatic male K Recovery rate per day symptomatic male 0.03 K Progression per day chlamydia to epididymitis W Progression per day chlamydia to PID a Calibration K parameter sustained from Kretzschmar et al. (2001); W estimated from proportion progressing to epididymitis in Welte R et al. (2000). a) The progression per day is calibrated to the relative risk reported by Scholes et al (1996). Testing and treatment Even in the no screening arm, some background screening is assumed. It is assumed that the highest activity group are more likely to receive background screening. The daily probability of background screening is age, sex and activity group dependent as shown in Table 3. This is subject to a minimum gap of 200 days since last screened. These inputs are largely arbitrary but are informed by the data from the Uppsala Women s Cohort Study (Table 10). Part of the 6

7 justification for the model inputs presented in Table 3 is that the Swedish data are applied to a Swedish population with opportunistic screening only, so differences between the never screened and screened groups are likely to depend on the level of baseline screening. Table 3: Assumed daily probability of background screening Age Groups 1 and 2 Group 3 Male Female Male Female Parameters relating to the screening programme are shown in Table 4. The daily probability that an individual within the appropriate age range is offered screening is calculated so that approximately 59% of individuals are offered screening in a 12-week period, to match the study by Senok et al (2005). Individuals are assumed to be consistent compliers or non-compliers. The minimum period between successive screening was set at 200 days. Table 4: Parameters for opportunistic screening Parameter Value Source Daily probability of being offered screening 0.01 Senok Compliance with screening 0.35 Senok Waiting time for result of screening (days) 30 Assumption Sensitivity of screening test (male) ClaSS Specificity of screening test (male) ClaSS Sensitivity of screening test (female) ClaSS Specificity of screening test (female) ClaSS Range in days for recent partner 120 Assumption Probability that a partner will attend for treatment 0.45 ClaSS Delay in days for partner to receive treatment 3 Assumption Screening tests are Cobas PCR on urine specimen for men and vulvo-vaginal swab for women Probability of partner attending for treatment applied independently to each partner Sequelae associated with pregnancy With the exception of neonatal complications, all inputs here have been calibrated to the best available data, using British Office for National Statistics (ONS) data for pregnancy rate and the Uppsala Women s Cohort Study for risk of sequelae. Table 5 shows the parameters relating to 7

8 pregnancy risk, defined as age-related risk of pregnancy per episode of unprotected intercourse. This is assumed to take into account variation in both fertility and use of non-barrier contraception. Table 6 shows the other parameters used. Explanation of the risk factors for ectopic pregnancy and infertility follows the table. Table 5: Age-related pregnancy risk Age, years Risk of pregnancy per day It is generally accepted that repeat infection increases risk of ectopic pregnancy and infertility but it is not clear whether this is due to separate number of episodes or simply cumulative effects of the time infected. The model allows either theory to be applied; figures above give a combination of the two. Consider, for example, a woman who has had two separate episodes of chlamydia infection, lasting a total of 50 days. With the input parameters above, her risk of ectopic pregnancy (if she becomes pregnant) is = For the same woman, her fertility is reduced by a factor = In the model, an instance of infertility is recorded when a woman who would become pregnant does not become so as a result of reduced fertility. Table 6: Other parameters relating to pregnancy Parameter Value Source Ectopic pregnancy risk factor (days) Calibration Ectopic pregnancy risk factor (episodes) Calibration Infertility risk factor (days) Calibration Infertility risk factor (episodes) Calibration Probability of neonatal complication a 0.45 W Duration (in days) of ectopic pregnancy 50 Advice Duration (in days) of normal pregnancy 280 Advice a) applied to women who are chlamydia positive at the time of giving birth W, parameter obtained from Welte R et al, Costs used in the model Unit costs used in the model are shown in Table 7. 8

9 Table 7: Unit costs in the model Resource use data required Unit cost, Source Screening tests Adams et al (2004) Treatment of index case including PN Adams et al (2004) Treatment of partners ClaSS, PSSRU Infertility a 428 NICE guidelines Ectopic pregnancy b 2319 HRG costs PID 2846 HRG costs Epididymitis 790 Welte c Neonatal complications 708 HRG costs a) NICE baseline costs for one cycle converted to 2003 costs b) NICE baseline costs converted to 2003 costs c) Weighted average of Welte R et al converted to 2003 UK costs Abbreviations: NICE, National Institute for Clinical Excellence; HRG, Health Resource Groups; PSSRU, Personal and Social Services Research Unit Calibration of model to existing data Table 8 shows the observed live births per 1000 women per year in a single run of the model using each input set, running for 5,000 days after a 5,000 day warm-up period. This is compared to ONS data for England and Wales in The fit is reasonable. Table 8: Live births per 1000 women per year Age range of mother Results from model ONS data 15 to to to to to to to 50 a ONS data reported as 45 and over Table 9 shows comparisons with ClaSS data applying the ClaSS prevalence survey to the model at the end of the run. Here the general patterns are approximately preserved. 9

10 Table 9: Comparisons between model outputs and ClaSS prevalence study data Results from model Mean age difference with partner, years ClaSS prevalence study Age range Male Female Male Female 16 to 19 y to 24 y Percentage reporting ever having had sex a Age range Male Female Male Female 16 to 19 y / / to 24 y / / 96.3 Mean length of reported partnership, months Age range Male Female Male Female 16 to 19 y to 24 y Laumann and Youm activity groups, percentages 16 to 24 y Male Female Male Female Periphery Adjacent Core to 39 y Male Female Male Female Periphery Adjacent Core a) Two sets of data figures are given for percentages reporting ever having had sex. In each case, the first figure is from the case-control study, the second from the prevalence study. For long-term sequelae in women, we used data from the Uppsala Women s Cohort Study. (Low et al 2005). Information form this was extracted into Kaplan-Meier curves, from which a failure function could be estimated by age. This represents the probability that an individual will have experienced a given event at least once by the age stated. This process was replicated by the model. Table 10 shows the results from the model and from the Swedish database allowing a comparison to be made. The overall level of sequelae is approximately the same. The good matching in the never screened groups and the fact that we have much higher results for at least one positive suggests that our model over estimates the relationship between Chlamydia and sequelae (as based on the Swedish data) and therefore is likely to lead to relatively more optimistic results. 10

11 Table 10: Comparisons of incidence of sequelae associated with chlamydia in ClaSS project model and Uppsala Women s Cohort Study Infertility ClaSS model Uppsala Age 25 Age 35 Age 25 Age 35 Never screened Always ve At least one +ve Ectopic pregnancy Never screened Always ve At least one +ve Results The model was run with inputs as described above. The model uses random numbers throughout and therefore it is necessary to re-run the model a sufficient number of times, with different random numbers, to ensure the effects of randomisation are reduced. The model was run 26 times, on a population of 50,000 individuals, with no screening (background screening only), for a total of 10,000 (simulated) days each time. Prevalence of chlamydia was recorded every 20 days by sex and 5-year age bands. The baseline results are shown in Figure 1. The vertical line is at 5,000 days and indicates the end of the warm-up period. The part of the graph to the left of the line is the model reaching a steady state of prevalence. Note that the steady state prevalence was determined by the transmission dynamics of the model, and not by the initial prevalence incorporated. It can be seen that the prevalence after the warm-up period remains constant over time. 11

12 Figure 1: Baseline results of the ClaSS model The mean prevalence after the warm-up period is shown in Table 11. The prevalence among females is underestimated compared to the ClaSS survey. Table 11: Prevalence from model compared with ClaSS survey Model results, % ClaSS survey, % (95% CI) Male Female Male Female 15 to 19 a (2.26, 5.15) 6.20 (4.80, 8.59) 20 to (5.22, 8.98) 6.15 (4.93, 8.35) 25 to (0.20, 1.86) 3.27 (2.01, 6.65) 30 to (0.06, 2.94) 0.32 (0.05, 2.34) a) ClaSS survey results for age group 16 to 19 years 12

13 Incorporating opportunistic screening The model was re-run, introducing opportunistic screening from ages 16 to 24 after the 5,000-day warm-up period. The results for screening women only and men and women are shown in Figure 2. Figure 2: Base-case results for screening women only and men and women Top panel women only, bottom panel men and women 13

14 As in Figure 1, there is a vertical line marking the end of the warm-up period. After the introduction of planned opportunistic screening, prevalence dropped to a new equilibrium value particularly in the younger age group where screening is likely to have more effect. The prevalence dropped slightly more in the case of screening both women and men (Table 12). Table 12: Steady-state prevalence for baseline run of the model Age, years No screening a Females only Males and females Male, % Female, % Male, % Female, % Male, % Female, % 15 to to to to a) Slight variations from Table 11 because of randomness in the model. Costs and major outcomes averted In a dynamic model such as the ClaSS model, the results are likely to depend on the time horizon used for calculations. We took all calculations from the end of the warm-up period, costs and outcomes being discounted to that point. In line with current guidelines, costs and outcomes were discounted at 3.5%. Figure 3 shows the cumulative difference in cost between the three strategies, taken over an arbitrarily chosen period of up to 12 years from the start of planned opportunistic screening. 14

15 Figure 3: Cumulative difference in costs F only v no scr M and F v no scr M and F v F only Incremental Costs Time in years All the figures resulting from the model give three comparisons: screening women versus no screening, screening men and women versus no screening and screening men and women versus screening women only. We report outputs from only the first two comparisons because the third is an artefact generated by the model. Figure 4 shows the difference in the aggregated major outcomes pelvic inflammatory disease, infertility, ectopic pregnancy and neonatal complications (given as major outcomes averted). Cumulative costs and outcomes refer only to those incurred up to the given time. Thus the time horizon is such that we do not consider anything beyond that point. The model is built on the assumption that a screening programme, once introduced, would remain in place indefinitely. 15

16 Figure 4: Cumulative aggregated major outcomes averted F only v no scr M and F v no scr M and F v F only Major Outcomes Averted Time in years Includes cases of pelvic inflammatory disease, ectopic pregnancy, infertility, and neonatal complications Cost-effectiveness analysis Table 13 shows the results of the runs of the ClaSS model, taken to an illustrative (arbitrarily chosen) period of 12 years after the introduction of planned screening. These calculations give the estimated results for the first 12 years of a screening programme intended to continue indefinitely. Because they do not include costs and effects beyond 12 years, they give a slightly conservative estimate of the costs and effects of a screening programme lasting 12 years only. Table 13: Summary of results for 12 years follow-up Results from running individual strategies Cost ( 000) Major outcomes No screening Women only Men and women females Comparison between strategies Difference in cost ( 000) Major outcomes averted ICER ( /MOA) F only vs. no screening M and F vs. no screening ICER, incremental cost-effectiveness ratio; MOA, major outcomes averted 16

17 These results are shown graphically in Figure 5. Here the screening options are shown in comparison to no screening. The incremental cost-effectiveness ratio (ICER) for screening males in addition to females is lower (more favourable) than that for screening females compared to no screening. That means that, under the assumptions included in this run of the model, if it is considered desirable to screen females, then it is more desirable to screen males as well as females. This phenomenon is known as weak or extended dominance: dominated means that the option is cheaper and more effective than the comparison. Weak dominance relates to a situation where two options are both more expensive and more effective than some third option, but the more effective of the two initial options has a lower ICER than the other two. The option of screening females only is said to be weakly dominated by the option of screening males as well as females. Figure 5: Results over 12 years on the cost-effectiveness plane ) Additional cost ( Women only Men and women Major outcomes averted Figure 6 shows the results for a range of time horizons from 6 years to 12 years. The gradual fall in the incremental cost-effectiveness ratio over time reflects the delay inherent in a screening programme in which there is a lag before the full effect of the major outcomes averted as a result of screening become apparent. The values of the incremental cost-effectiveness ratio were consistently high, suggesting that planned screening is unlikely to be cost-effective under the conditions built in to this version of the model. To compare with a threshold of 30,000 per QALY gained, the value for each major outcome averted would have to equal approximately 1 QALY. 17

18 Figure 6: Base case results for a range of time horizons F only v no scr M and F v no scr M and F v F only ICER ( /MOA) Time in years Other scenarios We considered two other cases. Firstly, we considered opportunistic screening for ages 16 to 29. Second, as a sensitivity analysis, we considered opportunistic screening for ages 16 to 24 assuming a higher risk of PID, equivalent to that used by Welte et al (2000). Welte et al. used a probability of 0.25 that an asymptomatically infected woman would develop pelvic inflammatory disease, and a further conditional probability of 0.4 that this would be symptomatic. Our definition of pelvic inflammatory disease only included symptomatic cases, this converts to a probability of 0.1 that an asymptomatic woman would develop pelvic inflammatory disease. For the ClaSS model this converts to a daily probability of progression of from asymptomatic chlamydia to pelvic inflammatory disease. In this analysis we used the same probability for progression from symptomatic chlamydia to pelvic inflammatory disease. Not surprisingly, the much higher incidence of pelvic inflammatory disease among chlamydia-infected women led to an increase in the number of major outcomes averted and lower incremental cost-effectiveness ratios. The results of these sensitivity analysis are summarised in Table 14 and Figures 7 and 8. 18

19 Table 14: Summary of incremental cost-effectiveness ratios over time under different conditions Scenario Incremental cost-effectiveness ratios, /MOA F vs. none 8 years 12 years M&F vs. none F vs. none M&F vs. none Screening ages 16 to 24 25,700 32,200 27,700 22,400 Screening ages 16 to 29 35,300 22,700 23,400 21,000 Incidence of PID equivalent to Welte (screening ages 16 to 24) 4,300 5,100 4,400 4,800 Abbreviations: F, screening women only, M&F, screening men and women, none, no planned screening; MOA, major outcome averted; ICER, incremental cost-effectiveness ratio Figure 7: Screening ages 16 to F only v no scr M and F v no scr M and F v F only ICER ( /MOA) Time in years 19

20 Figure 8: Results assuming incidence of PID equivalent to Welte et al F only v no scr M and F v no scr M and F v F only 7000 ICER ( /MOA) Time in years Discussion The results of the this revised ClaSS model suggest that opportunistic screening at estimated uptake levels of 35%, and assuming a low incidence of chlamydia-associated complications was not cost-effective. This uptake rate is based on a review of the literature and applied to women (Senok et al 2005) which, in the absence of other data, was also applied to men for the purpose of the current modelling exercise. The model also shows that, provided that the response rate in men is not much lower than in women, screening men and women is preferred to screening females only. If the incidence of complications is assumed to be high as per Welte et al (2000), screening appears relatively more cost-effective. The strengths of this study are that we used an individual level dynamic mathematical model that gave the closest approximation to real sexual behaviour in the population together with empirical data for as many parameters as possible. This model, based on the ClaSS model, is the first to attempt to incorporate some concept of tubal damage (prevention) caused by repeated or persistent infection. There are a number of limitations to these results. First, these results are based on a single input set. The input set used is determined through a process of calibration. It is possible to determine further input sets based on different assumptions of partner mixing and background screening rates, which would necessitate a different calibration process. 20

21 Second, due to the long running time of the current model, use of these additional input sets was beyond the remit of the current study. In terms of the level of uncertainty surrounding the illustrative cost effectiveness results presented in Table 14, confidence intervals were not presented. The most appropriate method for exploring uncertainty in these figures is by re-running the model with another input set. Thus the model needs to be re-run with different input sets to test the robustness of the conclusions. The model was run 26 times, on a population of 10,000 simulated individuals, with no screening (background screening only), for a total of 10,000 (simulated) days each time. The results of calibrating the model were generally consistent with the empirical data for men but there was a discrepancy between the prevalence expected in the model under opportunistic screening and that observed in the ClaSS prevalence data for females. Third, the current calibration, based on new opportunistic screening figures of uptake and relative risk of PID was difficult for a number of reasons: a compromise had to be found between calibrating the model both to the prevalence data, sexual activity data and new PID figures. The calibration process is in itself a lengthy process and producing new results once calibration is complete takes at least five working days in terms of the running of the model a sufficient number of times to produce new results.. The incidence of long term sequelae used in this model were approximated through the calibration process to be comparable with the results of the Uppsala Women s Cohort Study. These data showed a lower incidence than those used in virtually all other cost-effectiveness analyses found in the literature or which have been used in other studies. In the sensitivity analysis we show that varying the incidence of PID to concur with the rate typically used in other published studies produces a lower cost effectiveness ratios. The difference in the results of the sensitivity analysis, Figure 8, which used an incidence for PID equivalent to that used by Welte et al (2000) compared to the results of the base case analysis serve to underline the importance of more accurate data on long term sequelae. Given the variation in the model, the pattern presented in the graphs are informative but the exact numbers presented in the summary table should be interpreted with some degree of caution. In this and the ClaSS model, unlike the analysis by Welte and colleagues, the sequelae are incorporated into the stochastic model which leads to an increased variance within the model. This necessitates a proportionally larger overall number of patient runs. The definitions of sequelae may not match those used elsewhere, making it inappropriate to use data relating to alternative definitions. In particular, cases of infertility need to be defined very clearly. For the rarer sequelae such as infertility, the problem is one of definition and to compare with other data, the definition used within the model is acceptable but not easy to measure in practice. For both infertility and ectopic pregnancy, it is clinically not clear how these are related to repeat infections. 21

22 References Adams EJ, La Montagne DS, Johnston AR, Pimenta JM, Fenton KA, Edmunds WJ (2004) Modelling the healthcare costs of an opportunistic chlamydia screening programme. Sexually Transmitted Infections 80: Kretzschmar M, Welte R, van Den HA, Postma MJ (2001) Comparative Model-based Analysis of Screening Programs for Chlamydia trachomatis Infections. Am.J.Epidemiol. 153: Laumann EO, Youm Y (1999) Racial/ethnic group differences in the prevalence of sexually transmitted diseases in the United States: a network explanation. Sex Transm Dis 26: Low N, Harbord RM, Egger M, Sterne JA, Herrmann B. (2005) Screening for chlamydia. Lancet 365:1539. Low N et al (Forthcoming) Epidemiological, social, diagnostic, and economic evaluation of population screening for genital chlamydial infection: the Chlamydia Screnning Studies project. Roberts TE, Robinson S, Barton P, Bryan S, Low N for the Chlamydia Screening Studies (ClaSS) Group. (in press) Screening for Chlamydia trachomatis: a systematic review of the economic evaluations and modelling. Sexually Transmitted Infections Scholes D, Stergachis A, Heidrich FE, Andrilla H, Holmes KK, Stamm WE. (1996) Prevention of pelvic inflammatory disease by screening for cervical chlamydial infection. New England Journal of Medicine 334: Senok A et al. (2005) Can we evaluate population screening strategies in UK general practice? A pilot randomised controlled trial comparing postal and opportunistic screening for genital chlamydial infection. Journal of Epidemiology & Community Health 59: Stamm WE, Guinan ME, Johnson C, Starcher T, Holmes KK, McCormack WM. (1984) Effect of treatment regimens for Neisseria gonorrhoeae on simultaneous infection with Chlamydia trachomatis. New England Journal of Medicine 310: Welte R, Kretzschmar M, Leidl R, van Den HA, Jager JC, Postma MJ. (2000) Cost-effectiveness of screening programs for Chlamydia trachomatis: a population-based dynamic approach. Sex Transm Dis. 27:

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