Although a number of studies have examined the feasibility and cost-effectiveness of screening for Chlamydia in military cohorts,4,9,23,33 ours is the first study to use a calibrated Markov model to investigate the potential healthcare payer cost savings from averted female complications of Chlamydia infection through notification of screened male military partners. Examining only female direct costs, our analyses suggest that male Chlamydia screening programs lead to higher short-term healthcare costs among notified females, but that these costs are exceeded by the expected discounted long-term savings from later averted sequelae of untreated Chlamydia infection. Screening of male recruits is effective at preventing a significant number of adverse outcomes among female partners, and this benefit is achieved at reasonable incremental cost-effectiveness under both selective and universal screening policies. Our results suggest the principal direct costs of these policies are the costs of the male NAAT screening. With continued improvements in the convenience, availability and marginal costs of NAAT,11 the incremental cost-effectiveness of either policy will decrease.
Costs of female community screening were not modeled in our analysis because the incremental difference in expected healthcare payer costs of screening would not differ substantially across male screening policies. In sensitivity analysis, we found that increasing community screening in concert with an increased probability of notification decreased the ICER of male screening policies, but increasing community screening in isolation significantly increased the ICER. Increased detection of asymptomatic infection through female community screening would lead to fewer cases detected solely as a result of notification from male screening, hence increasing the relative cost-effectiveness of screening policies targeted solely at males. For this reason, programs that either increase opportunistic female community screening, such as in urban emergency departments,34 or that increase systematic female community screening in other healthcare settings22,31,35 may be a more cost-effective allocation of healthcare funding than programs targeted specifically at males. Research has demonstrated rates of screening in emergency departments are relatively low as compared with other healthcare settings.36 Others have argued for caution in increasing female screening at the expense of male screening, arguing that gender inequalities in screening carry hidden emotion costs.37
Our model has a number of important limitations. Our Markov model may misestimate the expected rate of transition from Chlamydia infection and PID to CP, thereby misestimating the potential costs, effectiveness, and cost-effectiveness of male recruit screening policies. In our model, significant uncertainty exists in the parameters used to model transitions between sequelae health states SP, OP, prechronic pain, and CP. Our Markov model was calibrated using data from one observational study, but other observational studies have noted fewer cases of PID developing than predicted by our calibration model.18 We noted moderate sensitivity of our effectiveness and cost-effectiveness measures to uncertainty in these parameters.
We did not model the health states of subsequent female partners of infected males, limiting our model to prior female partners at the time of screening. Our model incorporated assumptions about the behavior of these female partners, which permitted no subsequent opportunities for infection during the modeled period. These assumptions permitted static modeling. Although these assumptions are unrealistic, assuming both a decrease in the number of infections among subsequent female partners, and a nondifferential distribution of subsequent infections and adverse outcomes among prior female partners from unmodeled sources would likely preserve or improve the qualitative cost-savings and cost-effectiveness differences between screening policies consistent with the results of our sensitivity analysis. Dynamic modeling,14–16 although superior, is challenging to implement, and we believe static modeling will continue to play a role in addressing issues involving mass tandem screening.
Our findings are in consonance with previous studies employing static modeling. A study examining school-based Chlamydia screening among both males and female found an ICER of $1.5K per case of PID prevented38 above a policy of community screening only. Another study39 found that the universal population-based use of NAAT among males would result in 346 cases of PID per 100,000 males screened, at an ICER of $21.7K over a policy of no screening. Importantly, our methods differ substantially from those used in both models yet arrive at similar conclusions.
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The following notes apply to the parameters listed in Table 1:
Note 1: (c_op × (1 − p_ip)) + (c_ip × p_ip).
Note 2: Among women aged 20 to 29 from Sutton et al.21
Note 3: Period costs discounted at 3% and assuming 10 year time horizon for lifetime costs from Smith et al.19
Note 4: (p_ctu25 × p_u25) + (p_cto25 × (1 − p_u25)).
Note 5: Among recruits aged 18 to 24 from Sutton et al.23
Note 6: Range ± 10% of point estimate percentage.
Note 7: Using parameters for 2 months before screening from Sutton et al.23
Note 8: Range ± 5% parameter estimate.
Note 9: Based on notification of 927 of 1744 local contacts in Zimmerman-Rogers et al.24
Note 10: Range obtained probabilities from 1 sex partner versus multiple sex partners in Tao et al.26; adjusted to a 6 month period.
Note 11: Point estimate from women aged 15 to 19 in Joesoef et al.27
Note 12: Upper limit of range is approximate from Scholes et al.29
Note 13: To calculate the proportion of nonclearing infections transitioning to PID, we used data from Scholes et al.29 that showed 9 of 44 treated infections transitioned to OP over 12 months. Assuming 19% spontaneous clearing in 12 months (Yeh et al.28) of the 36 treated cases that were nonspontaneously clearing, 9 (25%) transitioned to OP. Ranges calculated from binomial distribution were used in model calibration to select parameter estimate.
Note 14: Point estimate and ranges calculated using the formula 1 − √(1 − p_opidy).
Note 15: Point estimate calculated using the formula p_opid /p_io. Range calculated from range of point estimate for p_opid.
Note 16: This is also the proportion of uncured symptomatic cases remaining symptomatic.
Note 17: Calculated using the formula 1 − √(1 − p_cpy).