GOAL: The goal was to determine the optimal strategy for screening coverage, test selection, and treatment for infection in asymptomatic women for a given family-planning-program budget. STUDY DESIGN: We developed a resource allocation model to determine the optimal strategy using data from 5078 visits by women universally screened for infection in a publicly funded family planning clinic system in Philadelphia. We maximized the number of infected women cured from the clinic perspective and maximized the cost-savings from the healthcare system perspective. The model incorporated the following age distributions: <20 years (27%), 20 to 24 years (30%), and >24 years (43%), with prevalences of 10.6%, 6.9%, and 2.3%, respectively. We modeled two screening test assays (DNA probe and ligase chain reaction [LCR] for cervical specimens) and two treatments (doxycycline and azithromycin). The model allowed for different test and treatment choices by age group. RESULTS: At the baseline annual budget of $6 per visit, the strategy that maximized both the number of infected women cured and cost savings would be to screen all women with DNA probe and to treat all women with positive tests with azithromycin. This strategy would result in 183 women cured at a cost-savings of $140,176. Sensitivity analysis showed that the total budget had a great impact on the optimal strategy, incorporating screening coverage, test selection, and treatment. CONCLUSIONS: Using resource allocation models enables clinic managers operating with a fixed budget to identify a strategy that maximizes the number of asymptomatic women cured and cost savings when the clinic age distribution and age-specific prevalences are known.
GOAL: The goal was to determine the optimal strategy for screening coverage, test selection, and treatment for infection in asymptomatic women for a given family-planning-program budget. STUDY DESIGN: We developed a resource allocation model to determine the optimal strategy using data from 5078 visits by women universally screened for infection in a publicly funded family planning clinic system in Philadelphia. We maximized the number of infected women cured from the clinic perspective and maximized the cost-savings from the healthcare system perspective. The model incorporated the following age distributions: <20 years (27%), 20 to 24 years (30%), and >24 years (43%), with prevalences of 10.6%, 6.9%, and 2.3%, respectively. We modeled two screening test assays (DNA probe and ligase chain reaction [LCR] for cervical specimens) and two treatments (doxycycline and azithromycin). The model allowed for different test and treatment choices by age group. RESULTS: At the baseline annual budget of $6 per visit, the strategy that maximized both the number of infected women cured and cost savings would be to screen all women with DNA probe and to treat all women with positive tests with azithromycin. This strategy would result in 183 women cured at a cost-savings of $140,176. Sensitivity analysis showed that the total budget had a great impact on the optimal strategy, incorporating screening coverage, test selection, and treatment. CONCLUSIONS: Using resource allocation models enables clinic managers operating with a fixed budget to identify a strategy that maximizes the number of asymptomatic women cured and cost savings when the clinic age distribution and age-specific prevalences are known.