Volume 11, Number 8—August 2005
Optimizing Treatment of Antimicrobial-resistant Neisseria gonorrhoeae
|Prevalence (%) gonorrhea‡||Strategy§||0.1%
|$/case treated¶||% cases with no PID¶||$/case treated||% cases with no PID||$/case treated||% cases with no PID|
*PID, pelvic inflammatory disease, which can cause sequelae such as chronic pelvic pain, infertility, and ectopic pregnancy.
†Baseline values given in Tables 2 and 3.
‡When gonorrhea prevalence is 1% and prevalence of ciprofloxacin-resistant Neisseria gonorrhoeae is 0.1%, PID would not develop in 98.4% of patients treated. In the absence of any treatment, PID would not develop in 74% (range 60%–90%) of gonorrhea-infected women.
§Strategies modeled were ST1: ciprofloxacin + culture-based tests + ciprofloxacin-susceptibility tests; ST2: ciprofloxacin + nonculture-based tests; ST3: ceftriaxone + culture-based tests + ceftriaxone-susceptibility tests; ST4: ceftriaxone + nonculture-based tests. See Table 1 and text for further details.
¶Cost per patient treated and percentage of patients treated refer to all women who come to the public health clinic and undergo therapy as per 1 of the 4 strategies, regardless of actual infection.
1In 2000, only 18% of gonorrhea tests performed by public health laboratories in the United States were culture-based tests.
2Monte Carlo simulation involves specifying a probability distribution of values for model inputs. A computer algorithm then runs the model for several iterations. During each iteration, the computer algorithm selects input values from the probability distributions, and calculates the output (e.g., cost per patient successfully treated). After the final run, the model provides results such as the mean, median, and 5th and 95th percentiles for each specified output.