Fig 1.
People who inject drugs population size estimates, data inputs [12] and weighted averages*.
*Because inverse probability weighting is sensitive to bias in the contributing estimates, from all estimates included in Chen, et al. 2016,[12] we excluded: 1) a multiplier estimate based on access of STD testing, which appeared biased by underreporting of injection drug use, and 2) the sequential method, which was strongly influenced by the assumptions of the model, rather than population-based data.
Fig 2.
People who inject drugs HCV seroprevalence estimates, data inputs and weighted averages.
Table 1.
Estimated population size, seroprevalence and number Anti-HCV antibody and HCV RNA positive, San Francisco, 2017.
Table 2.
Summary of estimated HCV burden by subpopulation.
This table demonstrates the percentage of total infections borne by each subpopulation; this helps to illustrate HCV health disparities among subpopulations. For example, 66.4% of all HCV seropositives in San Francisco are PWID, though only an estimated 2.8% of San Francisco residents overall are PWID.
Table 3.
Sensitivity analysis of key point estimates used in final calculations.
This table highlights a series of point estimates used to calculate the results in Table 1, along with two variations for estimate, demonstrating the impact that different assumptions (see “description”) would have had on the final calculations (see “Total # viremics”).