Exploring How Epidemic Context Influences Syphilis Screening Impact: A Mathematical Modeling Study

Abstract

Background: The current syphilis epidemic in the United States is concentrated in gay, bisexual, and other men who have sex with men (MSM), but substantial heterosexual transmission is reported in some parts of the country. Using the US states of Louisiana and Massachusetts as case studies, we investigated how epidemic context influences the impact of population screening approaches for syphilis control.

Methods: We constructed a compartmental metapopulation model parameterized to describe observed patterns of syphilis transmission. We estimated the impact of different approaches to screening, including perfect adherence to current US screening guidelines in MSM.

Results: In Louisiana, where syphilis cases are more evenly distributed among MSM and heterosexual populations, we projected that screening according to guidelines would contribute to no change or an increase in syphilis burden, compared with burden with current estimated screening coverage. In Massachusetts, which has a more MSM-focused outbreak, we projected that screening according to guidelines would be as or more effective than current screening coverage in most population groups.

Conclusions: Men who have sex with men-focused approaches to screening may be insufficient for control when there is substantial transmission in heterosexual populations. Epidemic characteristics may be useful when identifying at-risk groups for syphilis screening.

Figure 1

Overview of syphilis transmission model. A, Syphilis natural history is described by the following states: not sexually active (A), susceptible (S, SR), exposed (E, ER), primary syphilis (I₁, IR₁), secondary syphilis (I₂, IR₂), early latent syphilis (L₁, LR₁), and late latent syphilis (L₂, LR₂). The states followed by “R” indicate a separate set of compartments for those with a prior treated infection. T₁–T₃ are treated states during which an individual is protected from reinfection with time spent in these states dependent on infection stage at treatment. B, Mixing within and between subpopulations is dependent on age group (young: 20–44 years: 45–64 years old), sex, and sexual activity level (based on annual rate of partner change). Lines representing partnerships are illustrative only and do not represent all possible combinations of sexual partnerships. Additional details, including model equations, are provided in the Technical Appendix, http://links.lww.com/OLQ/A529.

Figure 2

Reported early syphilis in Louisiana and Massachusetts, 2012 to 2016. Data are shown separately for Louisiana (A, C, E, F) and Massachusetts (B, D, G, H). A and B, Reported early syphilis cases per 100,000 population, by age group (20–44 and 45–64 years) and sex (female and male). C and D, Reported cases per 100,000 population by age, sex, and race/ethnicity. Note that the y axes are different for women and men. E and G, Proportion of early syphilis cases in men that are reported in MSM. F and H, Proportion of early syphilis cases in MSM occurring in men with HIV coinfection. Note that HIV coinfection data from Louisiana are only available for the years 2014 to 2016.

Figure 3

Comparison of model fits to reported early syphilis case data. Model-projected reported cases are shown for Louisiana (A) and Massachusetts (B) for the years 2012 to 2016. Modeled outputs are based on 1000 best-fit parameter sets. Median values are shown in black. Note that different y-axis ranges are used for the data from the 2 states. Early syphilis includes primary, secondary, and early latent syphilis cases.

Figure 4

Comparison of base case (best-fit) model (solid line) to a counterfactual model with screening uptake in MSM at rates recommended by US syphilis screening guidelines (dashed line). Results are shown for different measures of syphilis burden in (A, C, and E) Louisiana and (B, D, and F) Massachusetts and are stratified by age group and sex. Reported early syphilis cases (A and B) represent primary, secondary, and early latent cases that are tested, treated, and reported to public health. Incident cases (C and D) include all new infections. Prevalent cases (E and F) include all cases with untreated primary, secondary, or early latent infection. Median and 95% credible intervals are shown for 1000 simulations for each intervention. Note that, because of large differences in outcome values, the y axes have different scales.

Figure 5

Comparison of impact of alternate screening approaches to best-fit model projections. The difference in total reported (A and B), incident (C and D), and prevalent (E and F) syphilis infections with each scenario and the base case model was calculated for the 5-year modeled period. Negative values indicate a reduction in the outcome compared with the base case, whereas positive values indicated an increase in the outcome with intervention, relative to the base case. The lower, middle, and upper hinges of the boxes correspond to the 25th, 50th, and 75th percentiles, with the whiskers extending to the largest and smallest values up to 1.5 times the interquartile range. Details of each scenario are provided in Methods.