The potential population-level impact of different gonorrhea screening strategies in Baltimore and San Francisco: an exploratory mathematical modeling analysis

2019 
BACKGROUND: Baltimore and San Francisco represent high burden areas for gonorrhea in the United States. We explored different gonorrhea screening strategies and their comparative impact in the 2 cities. METHODS: We used a compartmental transmission model of gonorrhea stratified by sex, sexual orientation, age, and race/ethnicity, calibrated to city-level surveillance data for 2010 to 2017. We analyzed the benefits of 5-year interventions which improved retention in care cascade or increased screening from current levels. We also examined a 1-year outreach screening intervention of high-activity populations. RESULTS: In Baltimore, annual screening of population aged 15 to 24 years was the most efficient of the 5-year interventions with 17.9 additional screening tests (95% credible interval [CrI], 11.8-31.4) needed per infection averted while twice annual screening of the same population averted the most infections (5.4%; 95% CrI, 3.1-8.2%) overall with 25.3 (95% CrI, 19.4-33.4) tests per infection averted. In San Francisco, quarter-annual screening of all men who have sex with men was the most efficient with 16.2 additional (95% CrI, 12.5-44.5) tests needed per infection averted, and it also averted the most infections (10.8%; 95% CrI, 1.2-17.8%). Interventions that reduce loss to follow-up after diagnosis improved outcomes. Depending on the ability of a short-term outreach screening to screen populations at higher acquisition risk, such interventions can offer efficient ways to expand screening coverage. CONCLUSIONS: Data on gonorrhea prevalence distribution and time trends locally would improve the analyses. More focused intervention strategies could increase the impact and efficiency of screening interventions.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    15
    References
    4
    Citations
    NaN
    KQI
    []