Volume 11, Number 8—August 2005
Research
Optimizing Treatment of Antimicrobial-resistant Neisseria gonorrhoeae
, Susan A. Wang*, and Martin I. Meltzer*
Table A2
Tool kit for decision-making
| Prevalence of gonorrhea, % | Prevalence of ciprofloxacin resistance, % | Optimal strategy*,†,‡ |
|---|---|---|
| 0–1 | 0–20 | ST1: ciprofloxacin + culture |
| 2–3 | 0–5 | ST1 |
| 2–3 | >5 | ST3: ceftriaxone + culture |
| 3–10 | 0–20 | ST3 |
| 10–13 | 0–3 | ST2: ciprofloxacin + nonculture |
| 10–13 | >3 | ST3 |
| 13–15 | 0–3 | ST2 |
| 13–15 | >3 | ST4: ceftriaxone + nonculture |
*Optimal strategy is the one that yields the lowest cost per case successfully treated for given combinations of prevalence of gonorrhea and prevalence of ciprofloxacin-resistant Neisseria gonorrhoeae.
†Since the alternative strategies are similar in effectiveness, cost-effectiveness analysis may not offer a practical decision-making tool. Instead, cost minimization which selects as optimal a strategy that costs the least while achieving the same level of effectiveness (i.e., per case of successful treatment) may serve as a more practical and intuitive toolkit for decision-making.
‡The above table shows the choice of an optimal strategy (lowest cost per case successfully treated) on varying the prevalence of gonorrhea and prevalence of ciprofloxacin resistance across several geographic settings. All other variables are assumed to have baseline values.
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.