Optimizing strategies for population-based chlamydia infection screening among young women: an age-structured system dynamics approach

Abstract

Background: Chlamydia infection (CT) is one of the most commonly reported sexually transmitted diseases. It is often referred to as a "silent" disease with the majority of infected people having no symptoms. Without early detection, it can progress to serious reproductive and other health problems. Economical identification of asymptomatically infected is a key public health challenge. Increasing evidence suggests that CT infection risk varies over the range of adolescence. Hence, age-dependent screening strategies with more frequent testing for certain age groups of higher risk may be cost-saving in controlling the disease.

Methods: We study the optimization of age-dependent screening strategies for population-based chlamydia infection screening among young women. We develop an age-structured compartment model for CT natural progress, screening, and treatment. We apply parameter optimization on the resultant PDE-based system dynamical models with the objective of minimizing the total care spending, including screening and treatment costs during the program period and anticipated costs of treating the sequelae afterwards). For ease of practical implementation, we also search for the best screening initiation age for strategies with a constant screening frequency.

Results: The optimal age-dependent strategies identified outperform the current CDC recommendations both in terms of total care spending and disease prevalence at the termination of the program. For example, the age-dependent strategy that allows monthly screening rate changes can save about 5% of the total spending. Our results suggest early initiation of CT screening is likely beneficial to the cost saving and prevalence reduction. Finally, our results imply that the strategy design may not be sensitive to accurate quantification of the age-specific CT infection risk if screening initiation age and screening rate are the only decisions to make.

Conclusions: Our research demonstrates the potential economic benefit of age-dependent screening strategy design for population-based screening programs. It also showcases the applicability of age-structured system dynamical modeling to infectious disease control with increasing evidence on the age differences in infection risk. The research can be further improved with consideration of the difference between first-time infection and reinfection, as well as population heterogeneity in sexual partnership.

Fig. 1

An age-dependent SIER Model for CT transmission and screening. Each box (compartment) represents a particular state that the total population is stratified into. For instance, S standards for the susceptible population subgroup. The solid lines indicate transitions due to natural disease progression and standard therapeutic intervention; and the dashed lines indicate additional transitions due to screening. With the system dynamics, each subpopulation size may fluctuate over time. Note that this is an age-structured model, which implies that the fluctuation of each subpopulation size is also age dependent, i.e.,many transition rates are age-dependent such as β

Fig. 2

An age-independent SIER Model with two constant CT infection rates over the periods before and after screening initiation. This is a 10-compartment model with two portions. The upper portion captures the disease progression without screening from age 0 to the age determined to start screening. The lower portion captures the disease progression with screening from the age determined to start screening to age A. The solid lines and dashed lines are used in the same way as in Fig. 1 to indicate the dynamics. The dotted lines indicate the necessary vital dynamics with population aging

Fig. 3

Initial condition for model ODE_1

Fig. 4

Optimal age-dependent screening strategy (S1)

Fig. 5

Screening rate, per-capita cumulative cost, and terminal prevalence of strategy S2 for each possible screening initiation age

Fig. 6

Screening rate, per-capita cumulative cost, and terminal prevalence of strategy S3 for each possible screening initiation age