The screening program (baseline) significantly reduces the CT prevalence in the model population.19 In particular, the total CT prevalence in the 15 to 44 years age classes, which is crucial for calculating CERs, decreases by 67% (from 6.3% to 2.1%). A similar effect can also be observed for CT incidence, as shown in Figure 2.
The cumulative net total costs of the CT screening program decrease the longer the CT screening program runs (Figure 3). This decrease is related to the decreasing CT incidence in the population, which is an effect of the screening program (Figure 2). Comparisons of screening with no screening show that the number of MOAs per year and the averted total costs per year significantly increase with screening duration (Figure 3: increasing slope of the line of cumulative number of MOAs and decreasing slope of the line of cumulative averted total costs). In contrast, the yearly total investment costs of the screening program change only slightly (Figure 3: approximately constant slopes of the cumulative total screening and total treatment cost lines). Because of the decreasing net total costs and the increasing number of MOAs, the CER also decreases with time (data not shown).
The overall CER of the screening program for the considered screening time frame of 10 years is −US $492 or −US $1,086 per MOA based on exclusion or inclusion of indirect costs, respectively. The screening program averts 127 major outcomes and reaches the point of saving after 5.0 or 3.3 years, excluding or including indirect costs, respectively. Therefore, the investigated screening program has to run at least 5.0 (3.3) years to render any cost savings. The point of saving is defined as the moment at which the averted costs due to the screening program equal its investment costs. Averted costs in the future were attributed to the year in which they were prevented through screening and subsequent treatment, and were accordingly discounted. This means that the point of saving is reached earlier than the break-even point. In our model population, the screening program required investments of US $175,800 (direct costs) and US $180,100 (total costs) and led to averted costs of −US $238,400 (direct costs) and −US $318,200 (total costs). The net direct costs are −US $62,600 and the net total costs are −US $138,100.
The highly infectious character of CT, the lack of an appropriate vaccine, and the failure of the immune system to build up resistance to reinfection makes a complex dynamic approach necessary to determine the CER of any CT screening program. Until now, a decision analysis-based static approach has been the standard for determining the CER of CT screening programs.8-16 However, only a dynamic approach adequately accounts for the CT transmission dynamics and the impact of CT prevention measures on CT incidence at the population level during longer intervals. These are essential points because the bacteriologic eradication of CT in one person decreases the average CT infection risk in the population. Furthermore, the risk of becoming reinfected by CT-infected members of the population, especially via failed partner referral or a new CT-positive partner, can be adequately modeled only by using a dynamic approach. Finally, the static approach can roughly account only for the effects of the first year of screening and is not suitable for analyzing longer intervals, whereas the presented dynamic model can be used to determine the effects of screening programs over the long term. As shown in Figure 3, the CER in the first year of screening does not represent the CER of any long-term screening program. Evaluations based on 1 year in the investigated screening program will show that program to be far from cost saving. However, the picture changes dramatically with each additional year of screening. We conclude that cost-effectiveness of CT screening programs seems best to be determined with dynamic modeling on a population basis.
Our results indicate that the investigated CT screening program would eventually save costs for the society. However, in the early stages costs will outweigh savings, and it will take several years to reach the point of saving and longer to reach the break-even point. This period is primarily caused by the dynamic spread of CT, especially by the rapid reinfection of effectively treated persons because of failed partner referral. However, the longer the screening program runs, the more CT is eradicated in the population, resulting in a decreased risk of infection or reinfection and a greater aversion of related sequelae and costs compared with no screening. This suggests that a CT screening program should run a specific minimum period to achieve full effectiveness on a population basis (i.e., to significantly decrease the CT incidence). However, the cost-effectiveness of each successive year of screening becomes less favorable with increasing screening time because less CT infections are avoided by screening and treatment than in the preceding screening year. We did not separately evaluate the cost-effectiveness of single years of screening because this would have substantially enlarged the model's complexity. Further studies will be necessary to answer the question of which screening strategy should be chosen after reducing the CT incidence to a low level.
Because of improvements in the field of diagnostic and increasing returns to scale in large-scale programs, greater savings may be realized. In two recent studies, it was shown that pooling of urine specimens for LCR or polymerase chain reaction can be a cost-saving strategy.51,52 For screening costs of US $15, net direct savings would almost double. One of the primary determinants of success in CT screening programs is the rate of partner referral. Decreasing partner referral rates produces higher percentage decreases in net direct and net total savings in the baseline program. Therefore, effective partner referral is an essential part of cost-effective CT screening programs. Furthermore, the prescreening CT prevalence has a major impact on the CER of the investigated program. The prescreening prevalence of asymptomatic CT infections in men and women 15 to 39 years in the model population is similar to the one recently found in the 1996 and 1997 Amsterdam study.17,19 However, even with a total prescreening CT prevalence of 1.8% in the total model population, the investigated CT screening program generates savings of US $194 per MOA when considering total costs.
The scenario analysis showed that different screening strategies (inclusion of older persons or exclusion of men) may be dominant, depending on budget restrictions (the program's investment costs are positively correlated with the number of persons eligible for the program) and on whether maximization of direct or total savings or of MOAs is preferred (Table 5).
Our CERs may be conservative for several reasons. First, not all adverse effects of CT infections were taken into account. For example, the higher risk of HIV infection among CT-infected persons was not considered. Furthermore, study findings indicate that CT may be associated with postpartum endometritis, prematurity, and low birth weight.4 Moreover, genital chlamydial infection may increase the risk for chlamydial conjunctivitis in adults.53 Second, the effects of the screening program on CT prevalence or incidence at the end of the program were not included. If the screening program is cancelled after 10 years, it will take a couple of years before the CT prevalence or incidence reaches the level of prescreening.19 Third, the indirect costs of care given to CT-infected persons (e.g., from the mother to her CT-infected child) and direct non-medical costs were not considered.
There are several limitations of our study. Our model is, like other dynamic models, sensitive to changes in specific parameters (sensitivity analysis); therefore, results are profoundly impacted by the precision of the according parameter estimates. There is limited empirical knowledge about sexual behavior (e.g., size of the highly sexually active core group and its distribution over the age classes) and disease-specific parameters (e.g., probability of CT transmission per sexual contact and the fraction of asymptomatically CT-infected persons) that influence the prevalences and incidences generated by the model.19 Assumptions that the CT incidence would remain stable over time without CT screening may be not true. Public health measures aimed at other STDs can also decrease the CT incidence. Our model assumes a closed population, and no migration effects are taken into account. The model population has a uniform age and sex distribution. Emerging antibiotic resistance of CT could negatively impact the success of a screening program. However, the apparent inability of CT to acquire antibiotic resistance54 makes this problem rather theoretical. Technology and relative prices in the medical field and productivity per capita may change during the period considered in our analysis. Gains in quality of life due to the screening were not measured. The determination of these gains seems not to be relevant because our results indicate cost savings for society. Because of the model's complexity, the number of assumptions inherent in the model is high. To avoid substantially increasing the model's complexity further and to accommodate a deficit of necessary data, we did not take into account that the susceptibility for CT infection decreases with age55 and the risk for PID and its sequelae increases with each asymptomatic CT infection in women. Instead, we used conservative average estimates for the CT-related risks.
Despite these limitations, we think that our dynamic approach leads to more realistic CERs of CT screening programs than the commonly used static models because it overcomes their most important limitation: the failure to account for dynamic processes such as chlamydial transmission and the impact of screening on CT incidence in the population. As with other cost-effectiveness analyses, there may be problems directly transferring our CERs to other countries because of possible differences in CT prevalence or incidence, sexual behavior, resource use, and resource valuation. However, the presented methodology can be applied worldwide.
Discounting and Time Frame
It was assumed that PID, epididymitis, and complications in newborns occur in the year of CT infection and that chronic pelvic pain takes place within 5 years of PID onset.14 The probabilities of having an ectopic pregnancy or of being identified as infertile depend on active interest in having a child. Hence, CT-induced damage of a young woman's fallopian tubes is commonly discovered later on, and the costs have to be discounted accordingly. Diagnosis of infertility and subsequent in-vitro fertilization treatment is assumed to occur within 2 years after the first unsuccessful attempt to conceive. The average age of the youngest age group is 17.5 years (range, 15-19 years), and the average age of the oldest childbearing age group is 47.5 years (range, 44-49 years). Subtracting 17.5 from 47.5 and adding 2 years for the diagnosis of infertility and subsequent in-vitro fertilization treatment renders a time horizon of up to 32 years for healthcare use after a CT infection. With an 10-year investigation of CT screening, the time horizon of our analysis is 42 years.
This model has been described in detail elsewhere.56 The parameters of the model were based on the results of a survey about sexual behavior in a representative sample of the Dutch general population performed in 1989.20 To account for recent study findings, some minor adjustments of parameter values were made.19 The types of partnership are distinguished by their average duration (steady partnerships, 6.9 years; casual partnerships, 10 days) and frequency of sexual intercourse (steady partnerships, 0.25/day; casual partnerships, 1/day). The probability of having a partner who is in an older or younger age group was considered. Because the model is stochastic, 100 simulation runs were performed for every set of parameter values, and averages were used to compute prevalence and incidence of asymptomatic and symptomatic infections for the different age classes (Monte Carlo simulation).19 Total prescreening CT prevalence derived from the model in the 15 to 64 years age group is 4.3% for women and 3.9% for men. Prescreening prevalences of asymptomatic CT infections for women and men were 9.9% and 5.9% in the 15 to 19 years age group, 9.3% and 6.2% in the 20 to 24 years age group, 7.8% and 7.0% in the 24 to 29 years age group, 7.2% and 6.7% in the 30 to 34 years age group, and 3.7% and 4.1% in the 35 to 39 years age group, respectively.
The average Dutch labor costs by age and sex were calculated on the basis of the average Dutch gross income by age and sex57 and an analysis of labor costs in The Netherlands.58 Labor costs were multiplied with the employed labor-force participation rate by age and sex,57 yielding the yearly net product per capita due to paid work. The age-specific and sex-specific averages of time spent on unpaid work were derived from a Dutch time budget study59 for calculations of the net product per capita due to unpaid work. Because of the lack of specific Dutch data, estimates of the loss of productivity time due to CT infections and their sequelae were taken from a literature review.9,10,14,44 The most conservative published estimates were used in estimating the loss of productivity time due to PID, chronic pelvic pain, ectopic pregnancy, and epididymitis (Table 3).
Screened and Treated Persons
The eligible CT screening population equals the number of individuals in age-specific and sex-specific categories multiplied by the probabilities that they are sexually active (Table 1). Multiplying the eligible CT screening population by age and sex with the respective probabilities of visiting a GP during 1 year and the sex-specific probability of accepting the test (Table 1) yielded the number of persons per year that were tested because of the screening program. In the baseline analysis, this resulted in 510 tested women and 337 tested men for each year of the program. The calculation of the number of tested women was as follows: EQUATION
- where, N1 is the number of women 15-19 years;
- P1s is the sexual activity of women 15-19 years;
- P1v is the GP visits per year of women 15-19 years;
- N2 is the number of women 20-24 years;
- P2s is the sexual activity of women 20-24 years;
- P2v is the GP visits per year of women 20-24 years;
- P3 is the test acceptance of women.
The number of infected persons found by the screening program and subsequently treated was derived from our epidemiologic model and varies by year. Dividing this number (by age, sex, and screening year) by the respective positive predictive value of the screening test rendered the total number of persons who received a positive test result and were treated via the screening program. The positive predictive values were computed by using the sensitivity and specificity of the screening test (Table 1) and the prevalence of asymptomatic CT infections (epidemiologic model) in the screening eligible population by age, sex, and screening year. Our epidemiologic model also supplied the screening year-specific number of infected persons found via partner referral. On the basis of the different evaluation or treatment options for referred partners, the number of persons that were tested or treated via partner referral was computed per year. Referral of healthy partners was included by assuming that 32% of partners of CT-infected persons were not infected.27
Based on the outcomes of the pilot study, three evaluation or treatment alternatives for referred partners were considered: (1) test-based treatment, which includes a GP visit, an LCR test, and if necessary a single-dose prescription of azithromycin; (2) epidemiologic treatment 1, which involves an azithromycin prescription plus a GP visit; and (3) epidemiologic treatment 2, which includes only azithromycin treatment. On the basis of the pilot study results, it was assumed that of referred partners, 22% received test-based treatment, 37% received epidemiologic treatment 1, and 41% received epidemiologic treatment 2. The compliance with single-dose azithromycin was assumed to be 98% for test-based treatment, 95% for epidemiologic treatment 1, and 85% for epidemiologic treatment 2. Taking into account the compliance, test sensitivity, and azithromycin effectiveness, we calculated that 83% of the referred infected partners were cured.
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