Introduction:HIV epidemics in hard-to-reach high-risk subpopulations are often discovered years after epidemic emergence in settings with poor surveillance infrastructure.Using hypothesis-generation modeling,we aimed ...Introduction:HIV epidemics in hard-to-reach high-risk subpopulations are often discovered years after epidemic emergence in settings with poor surveillance infrastructure.Using hypothesis-generation modeling,we aimed to investigate and demonstrate the concept of using routine HIV testing data to identify and characterize hidden epidemics in high-risk subpopulations.We also compared this approach to surveillance based on AIDS case notifications.Methods:A deterministic mathematical model was developed to simulate an emerging HIV epidemic in a high-risk subpopulation.A stochastic Monte Carlo simulation was implemented on the total population to simulate the sampling process of generating routine HIV testing data.Epidemiological measures were estimated on the simulated epidemic and on the generated testing sample.Sensitivity analyses were conducted on the results.Results:In the simulated epidemic,HIV prevalence saturated at 32%in the high-risk subpopulation and at 0.33%in the total population.The epidemic started its emergingepidemic phase 28 years after infection introduction,and saturated 67 years after infection introduction.In the simulated HIV testing sample,a significant time trend in prevalence was identified,and the generated metrics of epidemic emergence and saturation were similar to those of the simulated epidemic.The epidemic was identified 4.0(95%CI 3.4e4.6)years after epidemic emergence using routine HIV testing,but 29.7(95%CI 15.8 e52.1)years after emergence using AIDS case notifications.In the sensitivity analyses,none of the sampling biases affected the conclusion of an emerging epidemic,but some affected the estimated epidemic growth rate.Conclusions:Routine HIV testing data provides a tool to identify and characterize hidden and emerging epidemics in high-risk subpopulations.This approach can be specially useful in resource-limited settings,and can be applied alone,or along with other complementary data,to provide a meaningful characterization of emerging but hidden epidemics.展开更多
基金The Qatar National Research Fund(NPRP 9-040-3-008).
文摘Introduction:HIV epidemics in hard-to-reach high-risk subpopulations are often discovered years after epidemic emergence in settings with poor surveillance infrastructure.Using hypothesis-generation modeling,we aimed to investigate and demonstrate the concept of using routine HIV testing data to identify and characterize hidden epidemics in high-risk subpopulations.We also compared this approach to surveillance based on AIDS case notifications.Methods:A deterministic mathematical model was developed to simulate an emerging HIV epidemic in a high-risk subpopulation.A stochastic Monte Carlo simulation was implemented on the total population to simulate the sampling process of generating routine HIV testing data.Epidemiological measures were estimated on the simulated epidemic and on the generated testing sample.Sensitivity analyses were conducted on the results.Results:In the simulated epidemic,HIV prevalence saturated at 32%in the high-risk subpopulation and at 0.33%in the total population.The epidemic started its emergingepidemic phase 28 years after infection introduction,and saturated 67 years after infection introduction.In the simulated HIV testing sample,a significant time trend in prevalence was identified,and the generated metrics of epidemic emergence and saturation were similar to those of the simulated epidemic.The epidemic was identified 4.0(95%CI 3.4e4.6)years after epidemic emergence using routine HIV testing,but 29.7(95%CI 15.8 e52.1)years after emergence using AIDS case notifications.In the sensitivity analyses,none of the sampling biases affected the conclusion of an emerging epidemic,but some affected the estimated epidemic growth rate.Conclusions:Routine HIV testing data provides a tool to identify and characterize hidden and emerging epidemics in high-risk subpopulations.This approach can be specially useful in resource-limited settings,and can be applied alone,or along with other complementary data,to provide a meaningful characterization of emerging but hidden epidemics.
