In the dynamic model, increasing the number of partners leads to a significantly higher CT prevalence and incidence, whereas decreasing the number of partners decreases these measures. On the contrary, changing the number of partners does not affect the prevalence in the static model. As a result, the qualitative effect of a higher or lower number of partners on the net costs and MOAs is predictable in the static model, whereas this effect has to be simulated for each number of partners in the dynamic model. In the latter, fewer partners do not necessarily mean a lower cost-effectiveness ratio as a result of the complex interaction of the various model parameters. Still, both partner number scenarios rendered cost-savings in the dynamic model.
When we change the number of partners as well as the prevalence of the static model so that the latter is identical to the respective prescreening prevalences of the dynamic model, the net costs decrease by 74% or increase by 41%, whereas the number of MOAs increases by 68% or decreases by 28%, respectively (not shown in Fig. 4). These results of the static model diverge not only quantitatively, but also qualitatively from the results gained when changing only the number of partners of the static model (Fig. 4).
Obviously, a higher test sensitivity will result in a more cost-effective screening program. This improvement is stronger in the static than in the dynamic model. The static model assumes the same high CT prevalence for each year and thus the same high number of tested individuals. On the other hand, the dynamic model takes into account the decreasing CT prevalence in response to the successful screening program, and thus fewer infected persons are detected by screening.
Parameter changes that do not affect the force of infection in the dynamic model have a similar level of influence on some outcomes of the 2 models. Decreasing the PID risk resulting from untreated CT infections by 50% leads to an identical decrease (48%) in the number of MOAs in both models. The MOA decrease is less than 50% because the risk for PID does not affect the risk of neonatal pneumonia, a CT complication also considered an MOA. However, the lower PID risk leads to different net costs increases in the 2 models. This is caused by a difference in the percent of averted costs attributable to prevented cases of PID or its sequelae under the dynamic (approximately 70%) or static model (95%) (Fig. 3). Using a different discount rate influences the results of the dynamic model more than those of the static model because the dynamic model estimates more net savings and MOAs in the later years of the screening program than in the starting years. Increasing the discount rate to 4% (as requested by the Dutch pharmacoeconomic guidelines)17 has a very limited effect on the results of both models (Fig. 4).
We have shown that our dynamic model leads to very different results than our static model for the investigated GP-based opportunistic screening program for women. As expected, the static model estimated the prevention of fewer negative health outcomes and associated averted costs resulting from screening. Moreover, it identified the incorrect optimal age group. The screening program limited to women aged 15 to 24 years is the most cost-effective option according to the static model. However, in the dynamic model, this screening option is dominated by those including women aged 15 to 29 years and women aged 15 to 34 years. Thus, the assumption that neglecting the influence of an effective screening program on the force of infection may only lead to conservative results is wrong; it may also result in screening the wrong target group. In addition, although the static model indicates that the duration of the screening program has no impact on the cost-effectiveness ratio, the dynamic model shows exactly the opposite. As a result, dynamic and not static models seem appropriate for the economic evaluation of CT screening programs that might affect the force of infection. However, static models have frequently been applied in the past, although many of them have not included the risk of reinfection resulting from failed partner referral.6
Table 3 summarizes the advantages and disadvantages of the 2 models. In general, the dynamic model should be first choice because it treats CT as an infectious and transmittable disease, whereas the static model accounts for this only in a very limited and not appropriate way. The only reasons speaking against using our dynamic stochastic network simulation model are its higher complexity, data demand, time and monetary costs, and need of mathematical modeling expertise. On the other hand, static models might be the preferred option for estimating the cost-effectiveness of screening programs that have no impact on the force of infection. An example of such programs might be screening in developed countries that is limited to pregnant women. Such a program 1) targets only a small group within a population, and 2) the selected group is not likely to consist of a higher-than-average number of core group women.
The problem of (partially) neglecting the infectious character of infectious diseases in economic evaluation is not limited to STDs. When assessing the cost-effectiveness of prevention measures against infectious diseases, researchers often ignore the fact that finding and treating an infected person or the active vaccination of a healthy person might decrease the force of infection and thus result in indirect protection effects. For estimation of the pathogen spread and the impact of intervention, mathematical models of the transmission of infectious pathogens are valuable tools because they enable the integration of biologic, medical, and epidemiologic data. Dynamic models of the SIR-type (susceptible, infected [and infectious], or removed [immune or dead])19,24,25 have been frequently used for the economic evaluation of vaccination programs against many infectious pathogens. In these models, the population consists of individuals who are susceptible, infected, or removed and the model describes the transition between these states. The model assumes that the population mixes homogenously (analogously to the law of mass action) and thus that the probability of a contact between any 2 members of the population is always the same. However, it is possible to relax the condition of the SIR model about the population mixing by dividing the population further into subgroups such as persons with high or low sexual activity. SIR models also assume that contacts between individuals take no time. These assumptions seem reasonable for highly infectious diseases such as measles, but not for STDs like CT because partnerships often persist for a long time. To account for partnership duration, pair formation models have been developed. Their main disadvantage is the assumption that individuals can have at most 1 partner at a time. Relaxing this condition leads to unmanageable models. Recently, Eames and Keeling have presented a monogamous network model, which combines features of pair formation and network models. Each person is constrained to, at most, 1 sexually active relationship. After the partnership breaks up, a new partnership may subsequently form within a fixed set of potential partnerships, which are represented by a mixing network.26 Stochastic individual-based network models such as the dynamic model presented are capable of considering partnership duration and concurrent partnerships.27
As already mentioned, the concept of indirect protection is well known in the vaccination field, and most definitions only refer to the fact that in a population, a high percentage of immune persons decreases the infectious risk for susceptible persons.19,21,28 However, the results of our dynamic model demonstrate that indirect protection effects also occur for STD screening programs; cured persons cannot infect their partners any more, who in turn will not infect their partner, and so on. Nevertheless, cured persons can get reinfected and thus are not immune to the STD. As a result, considering the transmission dynamics is even more important for screening programs of STDs like CT and Neisseria gonorrhoeae than for most vaccination programs because a cured person remains—unlike a vaccinated person—still susceptible.
We would like to emphasize that the presented results, about the cost-effectiveness of screening different age classes of women, cannot be simply transferred to other settings and countries. It is necessary to perform a detailed transferability check after which the required adjustments of the results are identified and made.29 For example, populations with different sexual behavior would require parameter changes of the dynamic model. In addition to population characteristics, also healthcare system and methodologic characteristics might need to be corrected. Our dynamic model has recently been adjusted for Denmark, where it has been successfully used for the economic evaluation of a systematic home-based screening program.30 The evaluation rendered a prescreening CT prevalence that corresponds well with the results of Danish screening studies supporting the validity of our model. At this time, 2 research groups in the United Kingdom and in The Netherlands are creating new dynamic models for simulating the cost-effectiveness of CT screening programs. Members of the Chlamydia trachomatis Screening Studies (CLASS) have recently proposed that “all future economic evaluations of chlamydia screening should use a dynamic modeling approach.”31 Thus, it seems that the transmittable character of CT is increasingly being recognized in economic evaluations. However, one should keep in mind that there is no perfect model and that dynamic models also have their drawbacks and limitations.
Large-scale screening programs have the potential to decrease the chlamydial incidence and prevent severe sequelae as demonstrated with our dynamic model and observed in some countries. Such programs will also shorten the average duration of infection, which in turn might decrease the development of infection-induced immunity,32 resulting in a higher population susceptibility. Another potential danger is the development of antibiotic resistance,33 because even a successful screening program will lead to an increase of antibiotic use.
Progression of Disease
Figure 6 shows the progression of disease. We distinguished between symptomatically and asymptomatically infected persons. Symptomatically infected women and men were defined as all infected persons that visit a healthcare provider for treatment of their disease. We assumed that all of them got effectively treated without serious side effects and that no progression of disease took place. We also assumed that the treatment of asymptomatic persons with azithromycin led to no serious side effects requiring the use of medical resources.
We assumed that PID, epididymitis, and complications in newborns take place in the year of CT infection and that chronic pelvic pain occurs within 5 years of PID onset. The age-specific probabilities of giving birth were used for calculating the age-specific vertical transmission probabilities. The probability of an ectopic pregnancy or infertility assessment depends on the age-specific active interest of having a child. Diagnosis and treatment of infertility was assumed to occur 2 years after the first unsuccessful attempt to conceive.
Table 4 presents the used unit costs. Only direct medical costs were considered. The resource utilization and valuation of the different CT-associated complications have been described in detail in a previous article.9 They correspond to the latest STD treatment guidelines of the Centers for Disease Control and Prevention.34 Costs were converted from Dutch guilders to US dollars by using gross domestic product purchasing-power parities from the OECD.35
Variation of Partner Number
For altering the partner number in the sensitivity analysis, we changed the parameters ρ and f in the dynamic model.7 The parameter ρ determines the rate of formation of new partnerships, i.e., it determines the number of new partnerships formed per time unit, whereas the parameter f denotes the probability that a partnership is steady (i.e., not casual). For increasing the number of partners, we changed ρ from 0.006 to 0.1 and f from 0.2 to 0.1. For decreasing the partner number, we used 0.005 instead of 0.006 for ρ. The derived higher/lower number of partners was also used in the static model to get comparable results. Cited Here...
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